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Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-foodborne-outbreaks/", "url": "https://ai-analytics.org/writing/cdc-foodborne-outbreaks/", "title": "CDC Foodborne Outbreak Database: The Federal Record Behind 25,000 Annual Illness Clusters", "date_published": "2027-01-25T00:00:00.000Z", "tags": [ "CDC", "Foodborne Illness", "Outbreaks", "Food Safety", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/osha-300a-injury-data/", "url": "https://ai-analytics.org/writing/osha-300a-injury-data/", "title": "OSHA 300A Injury Data: The Federal Database Behind Establishment-Level Workplace Injury Rates", "date_published": "2027-01-24T00:00:00.000Z", "tags": [ "OSHA", "Workplace Safety", "Injury Data", "Labor", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/doj-civil-rights/", "url": "https://ai-analytics.org/writing/doj-civil-rights/", "title": "DOJ Civil Rights Division: The Federal Database Behind Police Reform Consent Decrees and Civil Rights Enforcement", "date_published": "2027-01-23T00:00:00.000Z", "tags": [ "DOJ", "Civil Rights", "Police Reform", "Enforcement", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usda-ers-food-economics/", "url": "https://ai-analytics.org/writing/usda-ers-food-economics/", "title": "USDA ERS Food Economics: The Federal Database Behind Farm Income, Food Prices, and Rural America", "date_published": "2027-01-22T00:00:00.000Z", "tags": [ "USDA", "ERS", "Food Economics", "Farm Income", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-part-d-prescribers/", "url": "https://ai-analytics.org/writing/cms-part-d-prescribers/", "title": "CMS Medicare Part D Prescriber Data: The Federal Database Behind Drug Spending for 1 Million Providers", "date_published": "2027-01-21T00:00:00.000Z", "tags": [ "CMS", "Medicare", "Part D", "Drug Prescribing", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dea-registrant-enforcement/", "url": "https://ai-analytics.org/writing/dea-registrant-enforcement/", "title": "DEA Registrant Enforcement: The Federal Database Behind Controlled Substance License Revocations", "date_published": "2027-01-20T00:00:00.000Z", "tags": [ "DEA", "Controlled Substances", "Registrant", "Opioids", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nrc-reactor-oversight/", "url": "https://ai-analytics.org/writing/nrc-reactor-oversight/", "title": "NRC Reactor Oversight Process: The Federal Database Behind Nuclear Plant Safety Ratings", "date_published": "2027-01-19T00:00:00.000Z", "tags": [ "NRC", "Nuclear Safety", "Reactor", "Energy", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cftc-enforcement-actions/", "url": "https://ai-analytics.org/writing/cftc-enforcement-actions/", "title": "CFTC Enforcement Actions: The Federal Database Behind Commodity Market Fraud Penalties", "date_published": "2027-01-18T00:00:00.000Z", "tags": [ "CFTC", "Commodities", "Derivatives", "Enforcement", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hmda-mortgage-lending/", "url": "https://ai-analytics.org/writing/hmda-mortgage-lending/", "title": "HMDA Mortgage Lending Data: The Federal Database Behind 15 Million Annual Mortgage Applications", "date_published": "2027-01-17T00:00:00.000Z", "tags": [ "HMDA", "Mortgages", "Fair Lending", "Housing Finance", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-hospital-cost-reports/", "url": "https://ai-analytics.org/writing/cms-hospital-cost-reports/", "title": "CMS Hospital Cost Reports: The Federal Database Behind Hospital Financial Data for 6,000 US Facilities", "date_published": "2027-01-16T00:00:00.000Z", "tags": [ "CMS", "Hospital Cost Reports", "Healthcare Finance", "Medicare", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fec-enforcement-murs/", "url": "https://ai-analytics.org/writing/fec-enforcement-murs/", "title": "FEC Campaign Finance Enforcement: The Federal Database Behind Matters Under Review", "date_published": "2027-01-15T00:00:00.000Z", "tags": [ "FEC", "Campaign Finance", "Enforcement", "Political Money", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/irs-criminal-investigation/", "url": "https://ai-analytics.org/writing/irs-criminal-investigation/", "title": "IRS Criminal Investigation: The Federal Database Behind Tax Fraud and Financial Crime Prosecutions", "date_published": "2027-01-14T00:00:00.000Z", "tags": [ "IRS", "Criminal Investigation", "Tax Fraud", "Financial Crime", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-nndss-notifiable-diseases/", "url": "https://ai-analytics.org/writing/cdc-nndss-notifiable-diseases/", "title": "CDC NNDSS: The Federal Database Behind Reportable Disease Surveillance in the United States", "date_published": "2027-01-13T00:00:00.000Z", "tags": [ "CDC", "NNDSS", "Infectious Disease", "Public Health", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/osha-violations-data/", "url": "https://ai-analytics.org/writing/osha-violations-data/", "title": "OSHA Violations Database: The Federal Record of 200,000 Annual Workplace Safety Citations", "date_published": "2027-01-12T00:00:00.000Z", "tags": [ "OSHA", "Workplace Safety", "Violations", "Labor", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/gao-reports-database/", "url": "https://ai-analytics.org/writing/gao-reports-database/", "title": "GAO Reports Database: The Congressional Watchdog Behind 900 Annual Federal Audits", "date_published": "2027-01-11T00:00:00.000Z", "tags": [ "GAO", "Federal Audits", "Congress", "Oversight", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fcc-universal-licensing/", "url": "https://ai-analytics.org/writing/fcc-universal-licensing/", "title": "FCC Universal Licensing System: The Federal Database Behind Every US Radio License", "date_published": "2027-01-10T00:00:00.000Z", "tags": [ "FCC", "Spectrum", "Radio", "Wireless", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/uflpa-entity-list/", "url": "https://ai-analytics.org/writing/uflpa-entity-list/", "title": "UFLPA Entity List: The Federal Database Behind Uyghur Forced Labor Supply Chain Enforcement", "date_published": "2027-01-09T00:00:00.000Z", "tags": [ "UFLPA", "Forced Labor", "Supply Chain", "CBP", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fincen-bsa-enforcement/", "url": "https://ai-analytics.org/writing/fincen-bsa-enforcement/", "title": "FinCEN BSA Enforcement: The Federal Database Behind Anti-Money Laundering Civil Penalties", "date_published": "2027-01-08T00:00:00.000Z", "tags": [ "FinCEN", "BSA", "AML", "Money Laundering", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sam-gov-exclusions/", "url": "https://ai-analytics.org/writing/sam-gov-exclusions/", "title": "SAM.gov Exclusions: The Federal Database Behind Government Contractor Debarments", "date_published": "2027-01-07T00:00:00.000Z", "tags": [ "SAM.gov", "Debarments", "Procurement", "Contractor Exclusions", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fhwa-national-bridge-inventory/", "url": "https://ai-analytics.org/writing/fhwa-national-bridge-inventory/", "title": "FHWA National Bridge Inventory: The Federal Database Behind 620,000 US Bridge Inspections", "date_published": "2027-01-06T00:00:00.000Z", "tags": [ "FHWA", "Bridges", "Infrastructure", "Transportation", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nih-research-grants/", "url": "https://ai-analytics.org/writing/nih-research-grants/", "title": "NIH Research Portfolio: The Federal Database Behind $50 Billion in Annual Biomedical Grants", "date_published": "2027-01-05T00:00:00.000Z", "tags": [ "NIH", "Research Grants", "Biomedical", "Science Funding", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usda-snap-program/", "url": "https://ai-analytics.org/writing/usda-snap-program/", "title": "USDA SNAP Program Data: The Federal Database Behind $100 Billion in Food Assistance", "date_published": "2027-01-04T00:00:00.000Z", "tags": [ "USDA", "SNAP", "Food Stamps", "Nutrition", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fema-disaster-declarations/", "url": "https://ai-analytics.org/writing/fema-disaster-declarations/", "title": "FEMA Disaster Declarations: The Federal Database Behind 70 Years of US Natural Disasters", "date_published": "2027-01-03T00:00:00.000Z", "tags": [ "FEMA", "Disasters", "Emergency Management", "Natural Disasters", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-hospital-compare/", "url": "https://ai-analytics.org/writing/cms-hospital-compare/", "title": "CMS Hospital Compare: The Federal Database Behind Quality Ratings for 5,000 US Hospitals", "date_published": "2027-01-02T00:00:00.000Z", "tags": [ "CMS", "Hospital Compare", "Healthcare Quality", "Medicare", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-oflc-h1b/", "url": "https://ai-analytics.org/writing/dol-oflc-h1b/", "title": "DOL OFLC Visa Disclosures: The Federal Database Behind H-1B, H-2A, and H-2B Wage Records", "date_published": "2027-01-01T00:00:00.000Z", "tags": [ "DOL", "OFLC", "H-1B", "Visa", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/pacer-federal-courts/", "url": "https://ai-analytics.org/writing/pacer-federal-courts/", "title": "PACER Federal Courts: The Database Behind 1 Billion Federal Court Documents", "date_published": "2026-12-31T00:00:00.000Z", "tags": [ "PACER", "Federal Courts", "Judiciary", "Legal Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bis-export-enforcement/", "url": "https://ai-analytics.org/writing/bis-export-enforcement/", "title": "BIS Export Enforcement: The Federal Database Behind US Export Control Violations", "date_published": "2026-12-30T00:00:00.000Z", "tags": [ "BIS", "Export Controls", "Commerce", "Sanctions", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/treasury-dts/", "url": "https://ai-analytics.org/writing/treasury-dts/", "title": "Treasury Daily Treasury Statement: The Federal Database Behind the US Government Daily Cash Position", "date_published": "2026-12-29T00:00:00.000Z", "tags": [ "Treasury", "DTS", "Federal Finance", "Budget", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-saipe/", "url": "https://ai-analytics.org/writing/census-saipe/", "title": "Census SAIPE: The Federal Database Behind County-Level Poverty and Income Estimates", "date_published": "2026-12-28T00:00:00.000Z", "tags": [ "Census", "SAIPE", "Poverty", "Income", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/eeoc-charges/", "url": "https://ai-analytics.org/writing/eeoc-charges/", "title": "EEOC Discrimination Charges: The Federal Database Behind 80,000 Annual Workplace Bias Claims", "date_published": "2026-12-27T00:00:00.000Z", "tags": [ "EEOC", "Discrimination", "Employment Law", "Civil Rights", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-adverse-events/", "url": "https://ai-analytics.org/writing/fda-adverse-events/", "title": "FDA FAERS: The Federal Adverse Event Reporting Database Behind Drug Safety Surveillance", "date_published": "2026-12-26T00:00:00.000Z", "tags": [ "FDA", "FAERS", "Drug Safety", "Adverse Events", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dot-fars/", "url": "https://ai-analytics.org/writing/dot-fars/", "title": "NHTSA FARS: The Federal Database Behind Every US Traffic Fatality Since 1975", "date_published": "2026-12-25T00:00:00.000Z", "tags": [ "NHTSA", "FARS", "Traffic Safety", "Crash Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cpsc-recalls/", "url": "https://ai-analytics.org/writing/cpsc-recalls/", "title": "CPSC Recalls: The Federal Database Behind 50 Years of Consumer Product Safety Recalls", "content_text": "CPSC recalls database: ~9,800 recalls since 1973. CPSIA 2008 third-party testing, NEISS injury surveillance, SaferProducts.gov. recalls.gov API with hazard/category/units fields. Python recalls analysis.", "summary": "CPSC recalls database: ~9,800 recalls since 1973. CPSIA 2008 third-party testing, NEISS injury surveillance, SaferProducts.gov. recalls.gov API with hazard/category/units fields. Python recalls analysis.", "date_published": "2026-12-24T00:00:00.000Z", "tags": [ "CPSC", "Recalls", "Consumer Safety", "Product Safety", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/clinical-trials/", "url": "https://ai-analytics.org/writing/clinical-trials/", "title": "ClinicalTrials.gov: The Federal Database Behind 500,000 Clinical Trials and Drug Approval Research", "content_text": "ClinicalTrials.gov: 500,000+ studies, FDAAA 801 mandatory registration, Phase 0-4 structure. API v2 at clinicaltrials.gov/api/v2/studies. Publication bias, AllTrials campaign. Python Phase 3 oncology trial analysis.", "summary": "ClinicalTrials.gov: 500,000+ studies, FDAAA 801 mandatory registration, Phase 0-4 structure. API v2 at clinicaltrials.gov/api/v2/studies. Publication bias, AllTrials campaign. Python Phase 3 oncology trial analysis.", "date_published": "2026-12-23T00:00:00.000Z", "tags": [ "NIH", "Clinical Trials", "Drug Approval", "Research", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-cps/", "url": "https://ai-analytics.org/writing/census-cps/", "title": "Census Current Population Survey: The Federal Database Behind the Official US Poverty and Unemployment Rates", "content_text": "Census CPS: 60,000 households/month, U-1 through U-6 unemployment measures, ASEC supplement for official poverty rate (Orshansky thresholds) and SPM. IPUMS-CPS, FRED UNRATE/U6RATE. Python state unemployment/poverty comparison.", "summary": "Census CPS: 60,000 households/month, U-1 through U-6 unemployment measures, ASEC supplement for official poverty rate (Orshansky thresholds) and SPM. IPUMS-CPS, FRED UNRATE/U6RATE. Python state unemployment/poverty comparison.", "date_published": "2026-12-22T00:00:00.000Z", "tags": [ "Census", "CPS", "Poverty", "Unemployment", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/arcos-opioid-distribution/", "url": "https://ai-analytics.org/writing/arcos-opioid-distribution/", "title": "DEA ARCOS: The Federal Opioid Distribution Database Behind 380 Million Pill Shipment Transactions", "content_text": "DEA ARCOS: 380M opioid shipment records 2006-2014, released via MDL 2804 litigation. 76B pills shipped, WV 780/person/year. Big Three distributors 44% market share. Washington Post bulk download. Python pills-per-capita county analysis.", "summary": "DEA ARCOS: 380M opioid shipment records 2006-2014, released via MDL 2804 litigation. 76B pills shipped, WV 780/person/year. Big Three distributors 44% market share. Washington Post bulk download. Python pills-per-capita county analysis.", "date_published": "2026-12-21T00:00:00.000Z", "tags": [ "DEA", "ARCOS", "Opioids", "Drug Distribution", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-ui-claims/", "url": "https://ai-analytics.org/writing/dol-ui-claims/", "title": "DOL UI Claims: The Federal Database Behind Weekly US Unemployment Statistics Since 1967", "content_text": "DOL ETA weekly UI claims: initial claims SA (ICSA) + continuing claims SA (CCSA). COVID peak 6.9M initial claims April 2020. 53 jurisdictions. FRED ICSA/CCSA/CC4WSA. Python FRED API analysis 2019-present.", "summary": "DOL ETA weekly UI claims: initial claims SA (ICSA) + continuing claims SA (CCSA). COVID peak 6.9M initial claims April 2020. 53 jurisdictions. FRED ICSA/CCSA/CC4WSA. Python FRED API analysis 2019-present.", "date_published": "2026-12-20T00:00:00.000Z", "tags": [ "DOL", "UI Claims", "Unemployment", "Economic Indicators", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-nursing-homes/", "url": "https://ai-analytics.org/writing/cms-nursing-homes/", "title": "CMS Nursing Home Compare: The Federal Database Behind Quality Ratings for 14,700 US Nursing Homes", "content_text": "CMS Five-Star Quality Rating: ~15,000 certified nursing homes. Health inspections F-tag system, PBJ staffing data, MDS quality measures, SFF list. data.cms.gov datasets. Python star-rating distribution and SFF analysis.", "summary": "CMS Five-Star Quality Rating: ~15,000 certified nursing homes. Health inspections F-tag system, PBJ staffing data, MDS quality measures, SFF list. data.cms.gov datasets. Python star-rating distribution and SFF analysis.", "date_published": "2026-12-19T00:00:00.000Z", "tags": [ "CMS", "Nursing Homes", "Elder Care", "Healthcare Quality", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-qcew/", "url": "https://ai-analytics.org/writing/bls-qcew/", "title": "BLS QCEW: The Federal Database Behind US Payroll Data for Every Industry and County", "content_text": "BLS QCEW: 11M establishment records/quarter from UI administrative records, 95% of civilian employment. area_fips/industry_code/own_code/avg_weekly_wage fields. 5-month lag vs. CES 1-month. QCEW drives CES March benchmark revision. Python QCEW API analysis.", "summary": "BLS QCEW: 11M establishment records/quarter from UI administrative records, 95% of civilian employment. area_fips/industry_code/own_code/avg_weekly_wage fields. 5-month lag vs. CES 1-month. QCEW drives CES March benchmark revision. Python QCEW API analysis.", "date_published": "2026-12-18T00:00:00.000Z", "tags": [ "BLS", "QCEW", "Payroll Data", "Employment", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-ces/", "url": "https://ai-analytics.org/writing/bls-ces/", "title": "BLS Current Employment Statistics: The Federal Database Behind the Monthly Jobs Report", "content_text": "BLS CES monthly jobs report: 140,000 businesses, 440,000 worksites. CEU series IDs. April 2020 -20.5M jobs. March benchmark revision from QCEW. AHE ~$35/hr. Python BLS API 20-series fetch and supersector analysis.", "summary": "BLS CES monthly jobs report: 140,000 businesses, 440,000 worksites. CEU series IDs. April 2020 -20.5M jobs. March benchmark revision from QCEW. AHE ~$35/hr. Python BLS API 20-series fetch and supersector analysis.", "date_published": "2026-12-17T00:00:00.000Z", "tags": [ "BLS", "CES", "Jobs Report", "Employment", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bop-federal-prison-population/", "url": "https://ai-analytics.org/writing/bop-federal-prison-population/", "title": "BOP Federal Prison Population: The Federal Database Behind 148,000 US Federal Inmates", "content_text": "BOP: 148,000+ federal inmates, 122 institutions. Offenses: drug ~44%, weapons ~20%, sex offenses ~8%, immigration ~6%. Mandatory minimums: 21 USC 841(b) 10-yr minimum at drug weight thresholds. FSA 2010: 18:1 crack/powder. Booker 2005: Guidelines advisory. First Step Act 2018: FSA retroactivity, safety valve, earned-time credits, PATTERN. Demographics: 93% male, 37% Black, 23% non-US citizens. ADX Florence supermax. Biden EO 14006 private prison non-renewal; Trump 2025 re-expansion. USSC datafiles for sentencing data.", "summary": "BOP: 148,000+ federal inmates, 122 institutions. Offenses: drug ~44%, weapons ~20%, sex offenses ~8%, immigration ~6%. Mandatory minimums: 21 USC 841(b) 10-yr minimum at drug weight thresholds. FSA 2010: 18:1 crack/powder. Booker 2005: Guidelines advisory. First Step Act 2018: FSA retroactivity, safety valve, earned-time credits, PATTERN. Demographics: 93% male, 37% Black, 23% non-US citizens. ADX Florence supermax. Biden EO 14006 private prison non-renewal; Trump 2025 re-expansion. USSC datafiles for sentencing data.", "date_published": "2026-12-16T00:00:00.000Z", "tags": [ "BOP", "Federal Prison", "Incarceration", "Criminal Justice", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-drug-overdose-mortality/", "url": "https://ai-analytics.org/writing/cdc-drug-overdose-mortality/", "title": "CDC Drug Overdose Mortality: The Federal Database Behind the US Opioid Crisis", "content_text": "107,543 overdose deaths in 2023; first 100k+ year 2021. Three waves: prescription opioids (OxyContin 1996), heroin (2010-2013), synthetic opioids (fentanyl 2013-present, ~75k synthetic deaths 2022). VSRR Socrata API (data.cdc.gov, monthly rolling 12); WONDER (county-level ICD-10 queries); state drug category flat file. ICD-10: T40.4 = synthetic opioids (fentanyl key field). Fentanyl: Mexico (Sinaloa/CJNG), counterfeit M30 pills, xylazine (tranq). WV ~80/100k. Purdue $8.34B 2022; $55B+ total opioid settlements. MOUD: buprenorphine, methadone, naltrexone. Python VSRR API synthetic opioid rate by state.", "summary": "107,543 overdose deaths in 2023; first 100k+ year 2021. Three waves: prescription opioids (OxyContin 1996), heroin (2010-2013), synthetic opioids (fentanyl 2013-present, ~75k synthetic deaths 2022). VSRR Socrata API (data.cdc.gov, monthly rolling 12); WONDER (county-level ICD-10 queries); state drug category flat file. ICD-10: T40.4 = synthetic opioids (fentanyl key field). Fentanyl: Mexico (Sinaloa/CJNG), counterfeit M30 pills, xylazine (tranq). WV ~80/100k. Purdue $8.34B 2022; $55B+ total opioid settlements. MOUD: buprenorphine, methadone, naltrexone. Python VSRR API synthetic opioid rate by state.", "date_published": "2026-12-15T00:00:00.000Z", "tags": [ "CDC", "Drug Overdose", "Opioid Crisis", "Mortality", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-form-5500-pension-data/", "url": "https://ai-analytics.org/writing/dol-form-5500-pension-data/", "title": "DOL Form 5500: The Federal Database Behind Every US Pension and Benefit Plan", "content_text": "Form 5500: ~217k filings/year, $30T+ assets. DB plans (27M->13M participants); DC 401k ($23k employee deferral 2024, $69k total). Schedule C: service provider fees, basis for ERISA fee litigation (Boeing/Intel/MIT settled). PBGC: $80k/yr guarantee; ARP 2021 $86B SPAP for multiemployer plans. Large plan audit: 100-participant threshold; 2015 OIG 39% deficient. EFAST2 public record. Python: top-50 401k plans by assets + fee rates by asset tier.", "summary": "Form 5500: ~217k filings/year, $30T+ assets. DB plans (27M->13M participants); DC 401k ($23k employee deferral 2024, $69k total). Schedule C: service provider fees, basis for ERISA fee litigation (Boeing/Intel/MIT settled). PBGC: $80k/yr guarantee; ARP 2021 $86B SPAP for multiemployer plans. Large plan audit: 100-participant threshold; 2015 OIG 39% deficient. EFAST2 public record. Python: top-50 401k plans by assets + fee rates by asset tier.", "date_published": "2026-12-14T00:00:00.000Z", "tags": [ "DOL", "Form 5500", "Pensions", "Retirement Benefits", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-oews-wage-statistics/", "url": "https://ai-analytics.org/writing/bls-oews-wage-statistics/", "title": "BLS OEWS: The Federal Database Behind Wage Statistics for 830 Occupations Across the US Economy", "content_text": "BLS OEWS: 1.1M establishments surveyed, 830 occupations, 590+ areas. Mean/median/percentile wages + employment. SOC 6-digit codes: anesthesiologists ~$331k top; Software developers national mean $124k, San Jose MSA $176k. Data: area_type/occ_code/o_group/emp/h_mean/a_median/h_pct10/h_pct90; * = above $208k. Bulk zip at bls.gov/oes/tables.htm. NEM projections 2022-2032. Python: national zip download, Computer & Math occupation ranking, healthcare vs. tech percentile spread.", "summary": "BLS OEWS: 1.1M establishments surveyed, 830 occupations, 590+ areas. Mean/median/percentile wages + employment. SOC 6-digit codes: anesthesiologists ~$331k top; Software developers national mean $124k, San Jose MSA $176k. Data: area_type/occ_code/o_group/emp/h_mean/a_median/h_pct10/h_pct90; * = above $208k. Bulk zip at bls.gov/oes/tables.htm. NEM projections 2022-2032. Python: national zip download, Computer & Math occupation ranking, healthcare vs. tech percentile spread.", "date_published": "2026-12-13T00:00:00.000Z", "tags": [ "BLS", "OEWS", "Wages", "Occupations", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fra-railroad-accidents/", "url": "https://ai-analytics.org/writing/fra-railroad-accidents/", "title": "FRA Railroad Accident Data: The Federal Database Behind Every US Rail Incident Since 1975", "content_text": "FRA accident reporting (49 CFR Part 225): ~224k records since 1975. Forms 54/57/55 (train accidents/grade crossing/employee injuries). Cause codes 5-character. Threshold: $11,200 damage or death/injury/hazmat. East Palestine OH 2023: Norfolk Southern 32N derailment; vinyl chloride; NTSB 37 recommendations; FRA Emergency Order. Grade crossing: ~2,000-2,200 collisions/yr, ~270-290 deaths, 128k public crossings, Operation Lifesaver. PTC fully implemented 2020 after Chatsworth 2008. FRA: 140k inspections/yr, 28k violations, $27,904 max penalty. CRISI grants $1B+ (IIJA 2021). FRA Safety Data API + bulk CSV downloads. Python: derailments by state + hazmat releases by commodity.", "summary": "FRA accident reporting (49 CFR Part 225): ~224k records since 1975. Forms 54/57/55 (train accidents/grade crossing/employee injuries). Cause codes 5-character. Threshold: $11,200 damage or death/injury/hazmat. East Palestine OH 2023: Norfolk Southern 32N derailment; vinyl chloride; NTSB 37 recommendations; FRA Emergency Order. Grade crossing: ~2,000-2,200 collisions/yr, ~270-290 deaths, 128k public crossings, Operation Lifesaver. PTC fully implemented 2020 after Chatsworth 2008. FRA: 140k inspections/yr, 28k violations, $27,904 max penalty. CRISI grants $1B+ (IIJA 2021). FRA Safety Data API + bulk CSV downloads. Python: derailments by state + hazmat releases by commodity.", "date_published": "2026-12-12T00:00:00.000Z", "tags": [ "FRA", "Railroad", "Rail Safety", "Transportation Safety", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/opm-federal-workforce/", "url": "https://ai-analytics.org/writing/opm-federal-workforce/", "title": "OPM FedScope: The Federal Database Behind 2.1 Million US Government Workers", "content_text": "OPM CPDF/FedScope quarterly: ~2.1-2.3M federal civilians. DOD ~750k, VA ~400k, DHS ~250k. GS-1 Step 1 $22,270 to GS-15 Step 10 $163,964 + locality pay; 34 areas, DC +33.26%; SES ~9,000, $155k-$235k. FedScope: agency, occupation, location, pay plan, grade, education, age, race, gender, veterans (27% federal vs 6% private). FERS: 1.1%/yr x high-3 x years + TSP 5% match; CSRS pre-1984. DOGE 2025: 75k deferred resignations; USAID 10k terminated; HHS 20k; union lawsuits. fedscope.opm.gov cube + opm.gov bulk CSV. Python GS grade distribution + SES density.", "summary": "OPM CPDF/FedScope quarterly: ~2.1-2.3M federal civilians. DOD ~750k, VA ~400k, DHS ~250k. GS-1 Step 1 $22,270 to GS-15 Step 10 $163,964 + locality pay; 34 areas, DC +33.26%; SES ~9,000, $155k-$235k. FedScope: agency, occupation, location, pay plan, grade, education, age, race, gender, veterans (27% federal vs 6% private). FERS: 1.1%/yr x high-3 x years + TSP 5% match; CSRS pre-1984. DOGE 2025: 75k deferred resignations; USAID 10k terminated; HHS 20k; union lawsuits. fedscope.opm.gov cube + opm.gov bulk CSV. Python GS grade distribution + SES density.", "date_published": "2026-12-11T00:00:00.000Z", "tags": [ "OPM", "Federal Workforce", "FedScope", "Civil Service", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nifc-wildfire-data/", "url": "https://ai-analytics.org/writing/nifc-wildfire-data/", "title": "NIFC Wildfire Data: The Federal Database Behind a Century of US Fire Statistics", "content_text": "NIFC wildfire stats since 1926; 2023: 56,580 fires, 2.7M acres (10-yr avg ~7M). Record: 2015 (10.1M), 2020 (10.1M). Smokey Bear 1944+ fuel accumulation paradox. USFS FOD: 2.3M fires 1992-present (SQLite; size class A-G; cause human/lightning/unknown; lat-lon; county). MTBS: Landsat dNBR burn severity >=1,000 acres at mtbs.gov. ICS-209 at famweb.nwcg.gov. WUI: 43M homes (Radeloff 2018); Camp Fire 2018 (153k acres, 85 dead, Paradise CA); Lahaina 2023 (2,200 structures, 100+ deaths). Active fire: NIFC ArcGIS GeoJSON; NASA FIRMS MODIS/VIIRS. Climate: Westerling 2006 Science + Williams 2019 PNAS VPD. Python decade averages + active fire GeoJSON query.", "summary": "NIFC wildfire stats since 1926; 2023: 56,580 fires, 2.7M acres (10-yr avg ~7M). Record: 2015 (10.1M), 2020 (10.1M). Smokey Bear 1944+ fuel accumulation paradox. USFS FOD: 2.3M fires 1992-present (SQLite; size class A-G; cause human/lightning/unknown; lat-lon; county). MTBS: Landsat dNBR burn severity >=1,000 acres at mtbs.gov. ICS-209 at famweb.nwcg.gov. WUI: 43M homes (Radeloff 2018); Camp Fire 2018 (153k acres, 85 dead, Paradise CA); Lahaina 2023 (2,200 structures, 100+ deaths). Active fire: NIFC ArcGIS GeoJSON; NASA FIRMS MODIS/VIIRS. Climate: Westerling 2006 Science + Williams 2019 PNAS VPD. Python decade averages + active fire GeoJSON query.", "date_published": "2026-12-10T00:00:00.000Z", "tags": [ "NIFC", "Wildfire", "Forest Service", "Climate Risk", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cfpb-consumer-complaints/", "url": "https://ai-analytics.org/writing/cfpb-consumer-complaints/", "title": "CFPB Consumer Complaint Database: The Federal Record of 7 Million Financial Complaints", "content_text": "CFPB complaint database (March 2012): 7M+ complaints; credit reporting ~60%, debt collection ~10%, mortgage ~7%. Equifax/Experian/TransUnion = 50%+ of all. Fields: complaint_id, product, sub_product, issue, consumer_complaint_narrative (~20% consent, PII-scrubbed), company response (monetary/non-monetary/explanation), timely, consumer_disputed, state, zip (3-digit). COVID forbearance surge; Biden loan forgiveness 2-3x student loan complaints. Navient $1.85B 2022; Wells Fargo $3.7B 2022 (largest-ever CFPB). API: api.consumerfinance.gov/data-research/consumer-complaints/search (no key, 10k max). Bulk ~1.5GB+. Python mortgage analysis by company and response type.", "summary": "CFPB complaint database (March 2012): 7M+ complaints; credit reporting ~60%, debt collection ~10%, mortgage ~7%. Equifax/Experian/TransUnion = 50%+ of all. Fields: complaint_id, product, sub_product, issue, consumer_complaint_narrative (~20% consent, PII-scrubbed), company response (monetary/non-monetary/explanation), timely, consumer_disputed, state, zip (3-digit). COVID forbearance surge; Biden loan forgiveness 2-3x student loan complaints. Navient $1.85B 2022; Wells Fargo $3.7B 2022 (largest-ever CFPB). API: api.consumerfinance.gov/data-research/consumer-complaints/search (no key, 10k max). Bulk ~1.5GB+. Python mortgage analysis by company and response type.", "date_published": "2026-12-09T00:00:00.000Z", "tags": [ "CFPB", "Consumer Complaints", "Financial Services", "Consumer Protection", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/noaa-storm-events-database/", "url": "https://ai-analytics.org/writing/noaa-storm-events-database/", "title": "NOAA Storm Events: The Federal Database Behind 50 Years of US Weather Disaster Data", "content_text": "NOAA NCEI Storm Events Database: ~2.1M events since 1950, 48 standardized event types (tornado, hurricane, flash flood, hail, winter storm, wildfire), county-level property/crop damage, injuries, fatalities. Bulk download: ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/ (annual gzipped CSVs). DAMAGE_PROPERTY uses K/M/B suffix encoding. Billion-Dollar Disasters tracker: 376 events since 1980, $2.6T CPI-adjusted; 2023: 28 events (record). Tornado: EF0-EF5, 2011 Super Outbreak 362/3 days, Dixie Alley shift. Hurricane: Harvey $125B, Ian $112B county-level. Flood: deadliest type most years (~88 fatalities/yr avg). NCEI CDO API at www.ncei.noaa.gov/cdo-web/api/v2/ with free key. Python DAMAGE_PROPERTY parsing + top-10 states by storm damage.", "summary": "NOAA NCEI Storm Events Database: ~2.1M events since 1950, 48 standardized event types (tornado, hurricane, flash flood, hail, winter storm, wildfire), county-level property/crop damage, injuries, fatalities. Bulk download: ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/ (annual gzipped CSVs). DAMAGE_PROPERTY uses K/M/B suffix encoding. Billion-Dollar Disasters tracker: 376 events since 1980, $2.6T CPI-adjusted; 2023: 28 events (record). Tornado: EF0-EF5, 2011 Super Outbreak 362/3 days, Dixie Alley shift. Hurricane: Harvey $125B, Ian $112B county-level. Flood: deadliest type most years (~88 fatalities/yr avg). NCEI CDO API at www.ncei.noaa.gov/cdo-web/api/v2/ with free key. Python DAMAGE_PROPERTY parsing + top-10 states by storm damage.", "date_published": "2026-12-08T00:00:00.000Z", "tags": [ "NOAA", "Storm Events", "Weather Disasters", "Climate Risk", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fbi-nibrs-crime-data/", "url": "https://ai-analytics.org/writing/fbi-nibrs-crime-data/", "title": "FBI NIBRS: The National Crime Database Behind Incident-Level Crime Statistics", "content_text": "FBI NIBRS replaced summary UCR in 2021 with incident-level records from 15,000+ agencies covering ~79% of US population. 52 Group A offense categories + 11 Group B citation-only. Segments: Incident, Offense, Victim (age/sex/race/victim-offender relationship), Offender, Arrestee, Property. Hate crime: 88 bias motivation codes. Crime Data Explorer API (cde.ucr.cjis.gov): /api/nibrs/{offense}/offense/agencies, /api/nibrs/{offense}/victim/count — free API key, 1,000 req/day. Bulk annual downloads: incident/offense/victim/offender/arrestee/property files. NYPD (8M people) began NIBRS 2023. SHR since 1976: victim-offender-weapon-circumstance, 40-50% unsolved cases unknown offender. NIBRS covers only ~43% of violent crime (vs. NCVS). TRAC for coverage gap analysis. Python CDE API violent crime rate by state.", "summary": "FBI NIBRS replaced summary UCR in 2021 with incident-level records from 15,000+ agencies covering ~79% of US population. 52 Group A offense categories + 11 Group B citation-only. Segments: Incident, Offense, Victim (age/sex/race/victim-offender relationship), Offender, Arrestee, Property. Hate crime: 88 bias motivation codes. Crime Data Explorer API (cde.ucr.cjis.gov): /api/nibrs/{offense}/offense/agencies, /api/nibrs/{offense}/victim/count — free API key, 1,000 req/day. Bulk annual downloads: incident/offense/victim/offender/arrestee/property files. NYPD (8M people) began NIBRS 2023. SHR since 1976: victim-offender-weapon-circumstance, 40-50% unsolved cases unknown offender. NIBRS covers only ~43% of violent crime (vs. NCVS). TRAC for coverage gap analysis. Python CDE API violent crime rate by state.", "date_published": "2026-12-07T00:00:00.000Z", "tags": [ "FBI", "NIBRS", "Crime Data", "Law Enforcement", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ssa-social-security-oasdi/", "url": "https://ai-analytics.org/writing/ssa-social-security-oasdi/", "title": "SSA Social Security: The Federal Database Behind $1.4 Trillion in Annual OASDI Benefits", "content_text": "SSA OASDI: ~$1.4T/yr to ~70M beneficiaries. OASI (~58M, ~$1.2T), DI (~8.8M, ~$160B), SSI (~7.5M, ~$65B means-tested). FICA: 6.2%+6.2%, $168,600 taxable max 2024. Trust funds: OASI ~$2.75T in special-issue Treasuries; 2034 projected depletion (77% payable). AIME/PIA formula: 35-year indexed earnings average; bend points $1,174/$7,078 (2024); 90%/32%/15% brackets. FRA 67 (born 1960+); early at 62 ~-30%; DRCs +8%/yr to 70. SSA data: data.ssa.gov — Monthly Snapshot, Annual Statistical Supplement (Tables 5.A/4.B/6.C), state/county CSV. FRED: SSASSHDI, SSARECEIPTSDISABILITY. DI sequential eval: SGA → Blue Book → RFC → vocational grids; ALJ backlog ~1M. WEP/GPO eliminated Jan 2025 (Social Security Fairness Act, 3.2M workers). Python retired-worker benefit penetration by state.", "summary": "SSA OASDI: ~$1.4T/yr to ~70M beneficiaries. OASI (~58M, ~$1.2T), DI (~8.8M, ~$160B), SSI (~7.5M, ~$65B means-tested). FICA: 6.2%+6.2%, $168,600 taxable max 2024. Trust funds: OASI ~$2.75T in special-issue Treasuries; 2034 projected depletion (77% payable). AIME/PIA formula: 35-year indexed earnings average; bend points $1,174/$7,078 (2024); 90%/32%/15% brackets. FRA 67 (born 1960+); early at 62 ~-30%; DRCs +8%/yr to 70. SSA data: data.ssa.gov — Monthly Snapshot, Annual Statistical Supplement (Tables 5.A/4.B/6.C), state/county CSV. FRED: SSASSHDI, SSARECEIPTSDISABILITY. DI sequential eval: SGA → Blue Book → RFC → vocational grids; ALJ backlog ~1M. WEP/GPO eliminated Jan 2025 (Social Security Fairness Act, 3.2M workers). Python retired-worker benefit penetration by state.", "date_published": "2026-12-06T00:00:00.000Z", "tags": [ "SSA", "Social Security", "OASDI", "Retirement", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/irs-990-nonprofit-data/", "url": "https://ai-analytics.org/writing/irs-990-nonprofit-data/", "title": "IRS Exempt Organizations: The Federal Database Behind 1.26 Million US Nonprofits", "content_text": "IRS EO BMF: 1.26M active tax-exempt orgs — 501(c)(3) charities/private foundations (~1M), 501(c)(4) social welfare (~80k), 501(c)(6) trade assoc, 527 political orgs. $2.8T annual revenues, ~12M employees. BMF fields: EIN, subsection code, NTEE code (26 major categories A-Z), ruling date, deductibility/foundation codes, asset/income ranges. Monthly download at IRS.gov (tab-delimited). Form 990 e-file JSON at AWS S3 s3://irs-form-990/ since 2013 (index JSON + per-filing XML). Key schedules: Part VII compensation (top 5 officers), Schedule A (public support test), Schedule B (donors, confidential), Schedule C (political activity), 990-PF (private foundations: 1.39% NII excise, 5% min distribution, IRC 4941-4945). Citizens United + 501(c)(4) dark money: Form 8976 required. ProPublica Nonprofit Explorer API: api.propublica.org/nonprofits/v2/organizations/{ein}.json. Church filing exemption = largest data gap. Python NTEE subsector analysis by asset tier.", "summary": "IRS EO BMF: 1.26M active tax-exempt orgs — 501(c)(3) charities/private foundations (~1M), 501(c)(4) social welfare (~80k), 501(c)(6) trade assoc, 527 political orgs. $2.8T annual revenues, ~12M employees. BMF fields: EIN, subsection code, NTEE code (26 major categories A-Z), ruling date, deductibility/foundation codes, asset/income ranges. Monthly download at IRS.gov (tab-delimited). Form 990 e-file JSON at AWS S3 s3://irs-form-990/ since 2013 (index JSON + per-filing XML). Key schedules: Part VII compensation (top 5 officers), Schedule A (public support test), Schedule B (donors, confidential), Schedule C (political activity), 990-PF (private foundations: 1.39% NII excise, 5% min distribution, IRC 4941-4945). Citizens United + 501(c)(4) dark money: Form 8976 required. ProPublica Nonprofit Explorer API: api.propublica.org/nonprofits/v2/organizations/{ein}.json. Church filing exemption = largest data gap. Python NTEE subsector analysis by asset tier.", "date_published": "2026-12-05T00:00:00.000Z", "tags": [ "IRS", "Nonprofits", "501(c)(3)", "Tax-Exempt", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usaid-foreign-aid-data/", "url": "https://ai-analytics.org/writing/usaid-foreign-aid-data/", "title": "USAID Foreign Aid Data: The Federal Database Behind $40 Billion in Annual US Development Assistance", "content_text": "USAID ~$40B/yr in 100+ countries. ForeignAssistance.gov (IATI): whole-of-government data by agency/country/sector/partner. Award types: contracts (Chemonics ~$1-2B/yr, DAI, AECOM), grants (Save the Children, CARE, IRC), cooperative agreements. PEPFAR: $110B+ since 2003, 20M+ on ARVs, Country Operational Plans at pepfar.gov. DATA Act: USAID on USASpending.gov. IATI XML at iatiregistry.org. Top FY2022 recipients: Ukraine, Ethiopia, DR Congo, Nigeria, South Africa. SSA ~35%, Near East ~20%. API: foreignassistance.gov/api/v1/resources.json. Python analysis by country and sector.", "summary": "USAID ~$40B/yr in 100+ countries. ForeignAssistance.gov (IATI): whole-of-government data by agency/country/sector/partner. Award types: contracts (Chemonics ~$1-2B/yr, DAI, AECOM), grants (Save the Children, CARE, IRC), cooperative agreements. PEPFAR: $110B+ since 2003, 20M+ on ARVs, Country Operational Plans at pepfar.gov. DATA Act: USAID on USASpending.gov. IATI XML at iatiregistry.org. Top FY2022 recipients: Ukraine, Ethiopia, DR Congo, Nigeria, South Africa. SSA ~35%, Near East ~20%. API: foreignassistance.gov/api/v1/resources.json. Python analysis by country and sector.", "date_published": "2026-12-04T00:00:00.000Z", "tags": [ "USAID", "Foreign Aid", "International Development", "ForeignAssistance.gov", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/pcaob-auditor-inspections/", "url": "https://ai-analytics.org/writing/pcaob-auditor-inspections/", "title": "PCAOB Auditor Inspections: The Federal Database Behind Public Company Audit Oversight and Accounting Firm Deficiencies", "content_text": "PCAOB (Sarbanes-Oxley 2002): ~1,700 registered audit firms globally. Annual inspections (>100 issuer clients) or triennial (≤100). Part I deficiencies (public immediately): insufficient evidence, ICFR failures, revenue recognition. Big Four 2022 deficiency rates: 31-44%. HFCAA: August 2022 China access agreement; first KPMG Huazhen/PwC Zhong Tian inspections. Enforcement: $15M/$750k penalties; KPMG 2019 $50M for stealing inspection data. CAMs required since 2019. All reports at pcaobus.org/inspections. Python deficiency rate trend analysis 2004-2024.", "summary": "PCAOB (Sarbanes-Oxley 2002): ~1,700 registered audit firms globally. Annual inspections (>100 issuer clients) or triennial (≤100). Part I deficiencies (public immediately): insufficient evidence, ICFR failures, revenue recognition. Big Four 2022 deficiency rates: 31-44%. HFCAA: August 2022 China access agreement; first KPMG Huazhen/PwC Zhong Tian inspections. Enforcement: $15M/$750k penalties; KPMG 2019 $50M for stealing inspection data. CAMs required since 2019. All reports at pcaobus.org/inspections. Python deficiency rate trend analysis 2004-2024.", "date_published": "2026-12-03T00:00:00.000Z", "tags": [ "PCAOB", "Auditing", "Accounting", "Securities", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/medicare-part-d-drug-spending/", "url": "https://ai-analytics.org/writing/medicare-part-d-drug-spending/", "title": "Medicare Part D Drug Spending Data: The Federal Database Behind $225 Billion in Annual Prescription Drug Costs", "content_text": "Medicare Part D (MMA 2003): ~50M beneficiaries, ~$225B annual drug spending. CMS Prescriber Data: NPI/specialty/drug/claims/cost. Top drugs: Eliquis ~$14B, Humira ~$6B pre-biosimilar, Keytruda ~$5B, GLP-1s rising. PBM formulary tiers 1-5; manufacturer rebates >80% on insulin. IRA 2022: drug price negotiation (first 10 drugs 2026), $2,000 OOP cap 2025. Humira 2023: 7 biosimilars simultaneously. LIS/Extra Help: ~13M beneficiaries. CMS data at data.cms.gov. Python opioid prescriber outlier analysis.", "summary": "Medicare Part D (MMA 2003): ~50M beneficiaries, ~$225B annual drug spending. CMS Prescriber Data: NPI/specialty/drug/claims/cost. Top drugs: Eliquis ~$14B, Humira ~$6B pre-biosimilar, Keytruda ~$5B, GLP-1s rising. PBM formulary tiers 1-5; manufacturer rebates >80% on insulin. IRA 2022: drug price negotiation (first 10 drugs 2026), $2,000 OOP cap 2025. Humira 2023: 7 biosimilars simultaneously. LIS/Extra Help: ~13M beneficiaries. CMS data at data.cms.gov. Python opioid prescriber outlier analysis.", "date_published": "2026-12-02T00:00:00.000Z", "tags": [ "Medicare", "Part D", "Drug Spending", "CMS", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nfip-flood-insurance/", "url": "https://ai-analytics.org/writing/nfip-flood-insurance/", "title": "NFIP Flood Insurance Data: The Federal Program Behind $20 Billion in Flood Claims and the National Flood Hazard Layer", "content_text": "NFIP (1968): ~5M policies, ~$1.3T coverage in force, 22,000+ communities. SFHAs (Zone A/AE/V) require coverage for federally-backed mortgages. Limits: $250k/$100k residential. Katrina $16B; Harvey $8.9B; Ian $3.6B. NFIP $20B+ in debt to Treasury. Risk Rating 2.0 (Oct 2021): property-specific pricing; 18% annual cap; 1.2M policies canceled. NFHL at msc.fema.gov; WFS API at hazards.fema.gov. OpenFEMA: FimaNfipClaims/FimaNfipPolicies. Repetitive loss: 25k properties = 25-30% of claims. Python Harvey claims by county.", "summary": "NFIP (1968): ~5M policies, ~$1.3T coverage in force, 22,000+ communities. SFHAs (Zone A/AE/V) require coverage for federally-backed mortgages. Limits: $250k/$100k residential. Katrina $16B; Harvey $8.9B; Ian $3.6B. NFIP $20B+ in debt to Treasury. Risk Rating 2.0 (Oct 2021): property-specific pricing; 18% annual cap; 1.2M policies canceled. NFHL at msc.fema.gov; WFS API at hazards.fema.gov. OpenFEMA: FimaNfipClaims/FimaNfipPolicies. Repetitive loss: 25k properties = 25-30% of claims. Python Harvey claims by county.", "date_published": "2026-12-01T00:00:00.000Z", "tags": [ "NFIP", "Flood Insurance", "FEMA", "Climate Risk", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fara-foreign-agents/", "url": "https://ai-analytics.org/writing/fara-foreign-agents/", "title": "FARA: The Foreign Agents Registration Act Database Behind Lobbying Disclosure for Foreign Governments", "content_text": "FARA (22 U.S.C. §§ 611-621, 1938): agents of foreign governments/political parties register with DOJ NSD. ~500-600 active registrations. Form RA-1 (initial, 10-day deadline) + NSD-3 (semi-annual); discloses principal identity, activities, compensation, disbursements, political contacts. LDA §613(h) exemption gap. Mueller-era: Manafort conviction, Flynn retroactive, Barrack acquittal. Saudi Arabia $14M+/yr, $450M+ since 2016. CGTN/Xinhua 2019 China registrations. Criminal: 22 U.S.C. § 618 felony up to 5 years. eFARA bulk CSV at efile.fara.gov/bulk/. Python FARA disbursements by country.", "summary": "FARA (22 U.S.C. §§ 611-621, 1938): agents of foreign governments/political parties register with DOJ NSD. ~500-600 active registrations. Form RA-1 (initial, 10-day deadline) + NSD-3 (semi-annual); discloses principal identity, activities, compensation, disbursements, political contacts. LDA §613(h) exemption gap. Mueller-era: Manafort conviction, Flynn retroactive, Barrack acquittal. Saudi Arabia $14M+/yr, $450M+ since 2016. CGTN/Xinhua 2019 China registrations. Criminal: 22 U.S.C. § 618 felony up to 5 years. eFARA bulk CSV at efile.fara.gov/bulk/. Python FARA disbursements by country.", "date_published": "2026-11-30T00:00:00.000Z", "tags": [ "FARA", "Foreign Agents", "Lobbying", "National Security", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-open-payments/", "url": "https://ai-analytics.org/writing/cms-open-payments/", "title": "CMS Open Payments: The Federal Database Behind $12 Billion in Annual Pharma and Device Payments to Physicians", "content_text": "Sunshine Act (ACA 2010): manufacturers report all payments ≥$10 to physicians/teaching hospitals/PAs/NPs. 2022: $12.7B total (research $4B, general $2.5B, ownership $6.2B). Three CMS datasets at openpaymentsdata.cms.gov: GP/RP/OI. Fields: NPI, total_amount, nature_of_payment (consulting/speaking/food/royalty/research), drug/device name. NPI links to NPPES specialty/location. Research: Carey 2021 meal-to-prescribing association; DeJong 2016. Socrata API at data.cms.gov. Python analysis by specialty, manufacturer, payment type.", "summary": "Sunshine Act (ACA 2010): manufacturers report all payments ≥$10 to physicians/teaching hospitals/PAs/NPs. 2022: $12.7B total (research $4B, general $2.5B, ownership $6.2B). Three CMS datasets at openpaymentsdata.cms.gov: GP/RP/OI. Fields: NPI, total_amount, nature_of_payment (consulting/speaking/food/royalty/research), drug/device name. NPI links to NPPES specialty/location. Research: Carey 2021 meal-to-prescribing association; DeJong 2016. Socrata API at data.cms.gov. Python analysis by specialty, manufacturer, payment type.", "date_published": "2026-11-29T00:00:00.000Z", "tags": [ "CMS", "Open Payments", "Pharma", "Healthcare", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nlrb-union-elections/", "url": "https://ai-analytics.org/writing/nlrb-union-elections/", "title": "NLRB Union Elections and Unfair Labor Practice Data: The Federal Database Behind US Labor Organizing", "content_text": "NLRB: ~2,500-3,000 election cases + ~15,000-20,000 ULP charges/year. RC/RM/RD petition types; majority of valid votes cast. 2014 Ambush Election rule (23-day timeline); 2023 Biden rule restoration. Union win rate ~65-70% (2022). Amazon LDJ5 (2022): 2,654-2,131; Starbucks 400+ stores. ULP: 8(a)(1)/8(a)(3)/8(a)(5) most common; Gissel bargaining orders; McLaren Macomb 2023. BLS 2023: 10.0% total density, 6.0% private, 33.1% public. Election results CSV at nlrb.gov. Python win rate analysis by industry.", "summary": "NLRB: ~2,500-3,000 election cases + ~15,000-20,000 ULP charges/year. RC/RM/RD petition types; majority of valid votes cast. 2014 Ambush Election rule (23-day timeline); 2023 Biden rule restoration. Union win rate ~65-70% (2022). Amazon LDJ5 (2022): 2,654-2,131; Starbucks 400+ stores. ULP: 8(a)(1)/8(a)(3)/8(a)(5) most common; Gissel bargaining orders; McLaren Macomb 2023. BLS 2023: 10.0% total density, 6.0% private, 33.1% public. Election results CSV at nlrb.gov. Python win rate analysis by industry.", "date_published": "2026-11-28T00:00:00.000Z", "tags": [ "NLRB", "Labor", "Union Elections", "Collective Bargaining", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/atf-firearm-trace-data/", "url": "https://ai-analytics.org/writing/atf-firearm-trace-data/", "title": "ATF Firearm Trace Data: The Federal Database Behind 350,000 Annual Crime Gun Traces", "content_text": "ATF eTrace: ~350,000-400,000 annual crime gun traces. Trace chain: law enforcement → ATF → manufacturer/importer → FFL of first sale → first retail purchaser. TTC 7-8 years average; <3 years flags trafficking. Tiahrt Amendments (2003): no public release, no civil litigation use. Annual state-level tables at atf.gov. Iron Pipeline: GA/SC/VA/FL to NY/NJ/MD. NIBIN 300+ sites, 7,300 leads/week. 130,000 active FFLs; 920M+ OBRC records. Python TTC distribution by state.", "summary": "ATF eTrace: ~350,000-400,000 annual crime gun traces. Trace chain: law enforcement → ATF → manufacturer/importer → FFL of first sale → first retail purchaser. TTC 7-8 years average; <3 years flags trafficking. Tiahrt Amendments (2003): no public release, no civil litigation use. Annual state-level tables at atf.gov. Iron Pipeline: GA/SC/VA/FL to NY/NJ/MD. NIBIN 300+ sites, 7,300 leads/week. 130,000 active FFLs; 920M+ OBRC records. Python TTC distribution by state.", "date_published": "2026-11-27T00:00:00.000Z", "tags": [ "ATF", "Firearms", "Crime Guns", "Gun Trace", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fdic-institution-database/", "url": "https://ai-analytics.org/writing/fdic-institution-database/", "title": "FDIC Institution Database: The Federal Profile of Every FDIC-Insured Bank and Thrift", "content_text": "FDIC BankFind Suite (banks.data.fdic.gov/api): ~4,600 active insured institutions + 10,000+ historical back to 1934. Charter types N/SM/NM/SA/SB (national bank OCC, state member Fed, state nonmember FDIC, savings assoc/bank). Dual banking: state vs. federal charter regulatory competition. Consolidation: 14,000+ (1984) to ~4,600 (2024), 67% decline from S&L crisis, Riegle-Neal 1994, GLBA 1999, GFC failures. Summary of Deposits annual branch data enables banking desert analysis. CRA exam ratings (Outstanding/Satisfactory/Needs to Improve/Substantial Noncompliance). BankFind API: CERT/ACTIVE/ASSET/CLASSP/STALP fields, no key required. Python institution analysis by state and asset tier.", "summary": "FDIC BankFind Suite (banks.data.fdic.gov/api): ~4,600 active insured institutions + 10,000+ historical back to 1934. Charter types N/SM/NM/SA/SB (national bank OCC, state member Fed, state nonmember FDIC, savings assoc/bank). Dual banking: state vs. federal charter regulatory competition. Consolidation: 14,000+ (1984) to ~4,600 (2024), 67% decline from S&L crisis, Riegle-Neal 1994, GLBA 1999, GFC failures. Summary of Deposits annual branch data enables banking desert analysis. CRA exam ratings (Outstanding/Satisfactory/Needs to Improve/Substantial Noncompliance). BankFind API: CERT/ACTIVE/ASSET/CLASSP/STALP fields, no key required. Python institution analysis by state and asset tier.", "date_published": "2026-11-26T00:00:00.000Z", "tags": [ "FDIC", "Banking", "Bank Data", "Financial Institutions", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fmcsa-crash-data/", "url": "https://ai-analytics.org/writing/fmcsa-crash-data/", "title": "FMCSA Crash Data: The Federal Database Behind 5,000 Annual Large Truck Fatalities", "content_text": "FMCSA MCMIS: ~500,000 reportable CMV crashes/year. 5,837 large truck fatalities 2022 (highest since 2005); 80%+ are passenger vehicle occupants. LTCCS 963-crash study: driver critical reason 55%, 87% driver error (decision/recognition/performance). HOS 49 CFR Part 395: 11-hr drive, 14-hr window, 30-min break, 60/70-hr weekly, ELD mandate Dec 2017. CSA SMS 7 BASICs. Roadside inspections 3.5M/year; vehicle OOS 20%, driver OOS 5%. ATA v. FMCSA 2019. SAFER, A&I, FMCSA public API, NHTSA FARS. Industry: 3.5M drivers, 750,000 carriers, 350,000 owner-operators. Here is state VMT-normalized fatality rates, time-of-day and road-type breakdowns, critical reason attribution, and carrier-level Python crash lookup.", "summary": "FMCSA MCMIS: ~500,000 reportable CMV crashes/year. 5,837 large truck fatalities 2022 (highest since 2005); 80%+ are passenger vehicle occupants. LTCCS 963-crash study: driver critical reason 55%, 87% driver error (decision/recognition/performance). HOS 49 CFR Part 395: 11-hr drive, 14-hr window, 30-min break, 60/70-hr weekly, ELD mandate Dec 2017. CSA SMS 7 BASICs. Roadside inspections 3.5M/year; vehicle OOS 20%, driver OOS 5%. ATA v. FMCSA 2019. SAFER, A&I, FMCSA public API, NHTSA FARS. Industry: 3.5M drivers, 750,000 carriers, 350,000 owner-operators. Here is state VMT-normalized fatality rates, time-of-day and road-type breakdowns, critical reason attribution, and carrier-level Python crash lookup.", "date_published": "2026-11-25T00:00:00.000Z", "tags": [ "FMCSA", "Trucking", "Transportation Safety", "Crash Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/crs-congressional-reports/", "url": "https://ai-analytics.org/writing/crs-congressional-reports/", "title": "CRS Reports: The Congressional Research Service Database Behind US Policy Analysis", "content_text": "CRS (Congressional Research Service): nonpartisan Library of Congress research arm, 700 analysts, 6 product types (Reports, Insight, In Focus, Legal Sidebar, Report Updates, Testimony). 9,000+ public reports since 2018 Consolidated Appropriations Act at crsreports.congress.gov. 25 policy areas. 2012 Coburn tax rate analysis blocked then released -- landmark public access case. EveryCRSReport.com API (everycrsreport.com/reports.json): id/title/topics/date/versions fields. Differs from GAO (audit focus) and CBO (budget only). Python publication trend and update frequency analysis by policy area.", "summary": "CRS (Congressional Research Service): nonpartisan Library of Congress research arm, 700 analysts, 6 product types (Reports, Insight, In Focus, Legal Sidebar, Report Updates, Testimony). 9,000+ public reports since 2018 Consolidated Appropriations Act at crsreports.congress.gov. 25 policy areas. 2012 Coburn tax rate analysis blocked then released -- landmark public access case. EveryCRSReport.com API (everycrsreport.com/reports.json): id/title/topics/date/versions fields. Differs from GAO (audit focus) and CBO (budget only). Python publication trend and update frequency analysis by policy area.", "date_published": "2026-11-24T00:00:00.000Z", "tags": [ "CRS", "Congress", "Policy Research", "Legislative Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nist-nvd-vulnerabilities/", "url": "https://ai-analytics.org/writing/nist-nvd-vulnerabilities/", "title": "NIST NVD: The National Vulnerability Database Behind CVE Scoring and Cybersecurity Compliance", "content_text": "NIST NVD enriches 250,000+ CVE records with CVSS scores, CWE classifications, and CPE product data. 400+ CNAs including Microsoft, Google, Apple, Red Hat. CVSS v3.1 base score components, KEV catalog (1,000+ confirmed-exploited CVEs, BOD 22-01). Log4Shell CVSS 10.0, EternalBlue 9.3, Heartbleed 7.5. CWE-787 dominates Critical CVEs. NVD REST API with cvssV3Severity/cweId/hasKev filters. FedRAMP/PCI DSS/FISMA compliance.", "summary": "NIST NVD enriches 250,000+ CVE records with CVSS scores, CWE classifications, and CPE product data. 400+ CNAs including Microsoft, Google, Apple, Red Hat. CVSS v3.1 base score components, KEV catalog (1,000+ confirmed-exploited CVEs, BOD 22-01). Log4Shell CVSS 10.0, EternalBlue 9.3, Heartbleed 7.5. CWE-787 dominates Critical CVEs. NVD REST API with cvssV3Severity/cweId/hasKev filters. FedRAMP/PCI DSS/FISMA compliance.", "date_published": "2026-11-23T00:00:00.000Z", "tags": [ "NIST", "NVD", "Cybersecurity", "CVE", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/epa-ghg-reporting/", "url": "https://ai-analytics.org/writing/epa-ghg-reporting/", "title": "EPA Greenhouse Gas Reporting Program: The Facility-Level Emissions Database Behind US Climate Accountability", "content_text": "~8,000 facilities ≥25,000 tCO2e threshold. 41 source categories: power (Subpart D), nat gas systems (Subpart W), refineries (Subpart Y), landfills, cement. Six GHGs with GWP100. FLIGHT tool. ECHO bulk download. Satellite methane validation controversy. 2024 Subpart W revision.", "summary": "~8,000 facilities ≥25,000 tCO2e threshold. 41 source categories: power (Subpart D), nat gas systems (Subpart W), refineries (Subpart Y), landfills, cement. Six GHGs with GWP100. FLIGHT tool. ECHO bulk download. Satellite methane validation controversy. 2024 Subpart W revision.", "date_published": "2026-11-22T00:00:00.000Z", "tags": [ "EPA", "Greenhouse Gas", "Climate", "Environmental", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/doj-antitrust-data/", "url": "https://ai-analytics.org/writing/doj-antitrust-data/", "title": "DOJ Antitrust Division: The Federal Merger Review and Cartel Enforcement Database", "content_text": "Sherman Act criminal + Clayton Act civil merger review. HSR threshold $119.5M, ~3% Second Request rate. 2023 Merger Guidelines HHI thresholds. Leniency Program auto-amnesty. Auto parts cartel $2.9B. AT&T-Time Warner lost; Change Healthcare/JetBlue-Spirit blocked. DOJ press RSS, PACER complaints.", "summary": "Sherman Act criminal + Clayton Act civil merger review. HSR threshold $119.5M, ~3% Second Request rate. 2023 Merger Guidelines HHI thresholds. Leniency Program auto-amnesty. Auto parts cartel $2.9B. AT&T-Time Warner lost; Change Healthcare/JetBlue-Spirit blocked. DOJ press RSS, PACER complaints.", "date_published": "2026-11-21T00:00:00.000Z", "tags": [ "DOJ", "Antitrust", "Mergers", "Competition", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-wisqars-injury-data/", "url": "https://ai-analytics.org/writing/cdc-wisqars-injury-data/", "title": "CDC WISQARS: The Federal Injury and Violence Mortality Database Behind Public Health Research", "content_text": "Injury deaths via NCHS death certificates (ICD-10 V-Y), nonfatal via NEISS-AIP. 2022: overdose 109,680 (fentanyl 73,800), suicide 49,000 (firearms 55%), homicide 24,000 (firearms 79%), total firearms 48,204. WISQARS API, WONDER, NVDRS case-level. Rural Mountain West firearm suicide; urban firearm homicide.", "summary": "Injury deaths via NCHS death certificates (ICD-10 V-Y), nonfatal via NEISS-AIP. 2022: overdose 109,680 (fentanyl 73,800), suicide 49,000 (firearms 55%), homicide 24,000 (firearms 79%), total firearms 48,204. WISQARS API, WONDER, NVDRS case-level. Rural Mountain West firearm suicide; urban firearm homicide.", "date_published": "2026-11-20T00:00:00.000Z", "tags": [ "CDC", "WISQARS", "Injury", "Violence", "Public Health", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usaspending-federal-contracts/", "url": "https://ai-analytics.org/writing/usaspending-federal-contracts/", "title": "USASpending.gov: The Federal Spending Database Behind $6 Trillion in Annual Contracts, Grants, and Loans", "content_text": "FFATA 2006 + DATA Act 2014. Contracts ~$700B FPDS-NG; DoD ~$412B; Lockheed ~$73B, RTX ~$42B. Grants NIH $40B, NSF $9B. API api.usaspending.gov. FPDS fields: UEI/CAGE/PSC/NAICS/competition type/set-aside. FSRS subaward >$30k. Appropriation → obligation → outlay linkage.", "summary": "FFATA 2006 + DATA Act 2014. Contracts ~$700B FPDS-NG; DoD ~$412B; Lockheed ~$73B, RTX ~$42B. Grants NIH $40B, NSF $9B. API api.usaspending.gov. FPDS fields: UEI/CAGE/PSC/NAICS/competition type/set-aside. FSRS subaward >$30k. Appropriation → obligation → outlay linkage.", "date_published": "2026-11-19T00:00:00.000Z", "tags": [ "USASpending", "Federal Contracts", "Federal Spending", "FPDS", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-register-rulemaking/", "url": "https://ai-analytics.org/writing/federal-register-rulemaking/", "title": "Federal Register: The Official Rulemaking Journal Behind 90,000 Pages of Annual US Regulatory Activity", "content_text": "Official daily journal since 1936. ~85k-95k pages/year. NPRMs, final rules, presidential documents, notices. APA notice-and-comment: NPRM → 30-90 day comment period → final rule. OIRA review for major rules (>$100M). Unified Regulatory Agenda. CRA override window. CFR 50 titles. Regulations.gov docket API. Federal Register API federalregister.gov/api/v1/. Loper Bright 2024 Chevron overruling.", "summary": "Official daily journal since 1936. ~85k-95k pages/year. NPRMs, final rules, presidential documents, notices. APA notice-and-comment: NPRM → 30-90 day comment period → final rule. OIRA review for major rules (>$100M). Unified Regulatory Agenda. CRA override window. CFR 50 titles. Regulations.gov docket API. Federal Register API federalregister.gov/api/v1/. Loper Bright 2024 Chevron overruling.", "date_published": "2026-11-18T00:00:00.000Z", "tags": [ "Federal Register", "Rulemaking", "Regulations", "APA", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fec-committee-filings/", "url": "https://ai-analytics.org/writing/fec-committee-filings/", "title": "FEC Committee Filings: The Campaign Finance Database Behind $14 Billion in Election Spending", "content_text": "FECA 1971/1974, federal elections only. Super PACs post-Citizens United (unlimited IEs). $14B 2024 total. Individual limit $3,300/election. Bulk: cm.zip, indiv.zip (employer/occupation), pas2.zip, oppexp.zip. OpenFEC API. 501(c)(4) dark money no donor disclosure. Python occupation partisan lean analysis.", "summary": "FECA 1971/1974, federal elections only. Super PACs post-Citizens United (unlimited IEs). $14B 2024 total. Individual limit $3,300/election. Bulk: cm.zip, indiv.zip (employer/occupation), pas2.zip, oppexp.zip. OpenFEC API. 501(c)(4) dark money no donor disclosure. Python occupation partisan lean analysis.", "date_published": "2026-11-17T00:00:00.000Z", "tags": [ "FEC", "Campaign Finance", "Elections", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-nndss-notifiable-diseases/", "url": "https://ai-analytics.org/writing/cdc-nndss-notifiable-diseases/", "title": "CDC NNDSS: The National Notifiable Disease Surveillance System Behind Weekly US Epidemic Tracking", "content_text": "120+ notifiable conditions weekly by state via MMWR. STIs: gonorrhea 700k/yr, syphilis 176,713 (2022 high since 1950), congenital +755%, Lyme ~476k estimated. FluView four-component surveillance. COVID NWSS wastewater. WONDER API, Socrata, AtlasPlus. Python Lyme state trend analysis.", "summary": "120+ notifiable conditions weekly by state via MMWR. STIs: gonorrhea 700k/yr, syphilis 176,713 (2022 high since 1950), congenital +755%, Lyme ~476k estimated. FluView four-component surveillance. COVID NWSS wastewater. WONDER API, Socrata, AtlasPlus. Python Lyme state trend analysis.", "date_published": "2026-11-16T00:00:00.000Z", "tags": [ "CDC", "NNDSS", "Epidemiology", "Public Health", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-form-d-private-placements/", "url": "https://ai-analytics.org/writing/sec-form-d-private-placements/", "title": "SEC Form D: The Private Placement Database Behind $2 Trillion in Annual Exempt Offerings", "content_text": "Notice within 15 days of first sale. 506(b) ~90% of filings, no general solicitation, 35 non-accredited allowed. 506(c): general solicitation, accredited-only. $2.5T raised under Rule 506 in 2022. Fund type field: VC/PE/hedge/real estate. EDGAR efts.sec.gov full-text search. Reg CF $5M, Reg A+ $75M. Python VC state/sector/exemption analysis.", "summary": "Notice within 15 days of first sale. 506(b) ~90% of filings, no general solicitation, 35 non-accredited allowed. 506(c): general solicitation, accredited-only. $2.5T raised under Rule 506 in 2022. Fund type field: VC/PE/hedge/real estate. EDGAR efts.sec.gov full-text search. Reg CF $5M, Reg A+ $75M. Python VC state/sector/exemption analysis.", "date_published": "2026-11-15T00:00:00.000Z", "tags": [ "SEC", "Form D", "Private Placements", "Venture Capital", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-medicare-inpatient-drg/", "url": "https://ai-analytics.org/writing/cms-medicare-inpatient-drg/", "title": "CMS Medicare Inpatient Provider Data: The Hospital-Level Payment Records Behind $170 Billion in Annual DRG Reimbursements", "content_text": "~3,000 hospitals, ~760 DRGs. IPPS base rate ~$6,000 × relative weight. DRG 001 RW ~25.0, DRG 470 RW ~2.1. Wage Index, IME, DSH, outlier adjustments. $170B/year. Chargemasters 5x-10x actual payments. DRG 470 ranges $12k-$35k+ by hospital. HVBP/HRRP/HACRP value-based adjustments. Socrata API, Python charge-to-payment ratio analysis.", "summary": "~3,000 hospitals, ~760 DRGs. IPPS base rate ~$6,000 × relative weight. DRG 001 RW ~25.0, DRG 470 RW ~2.1. Wage Index, IME, DSH, outlier adjustments. $170B/year. Chargemasters 5x-10x actual payments. DRG 470 ranges $12k-$35k+ by hospital. HVBP/HRRP/HACRP value-based adjustments. Socrata API, Python charge-to-payment ratio analysis.", "date_published": "2026-11-14T00:00:00.000Z", "tags": [ "CMS", "Medicare", "DRG", "Healthcare", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-orange-book/", "url": "https://ai-analytics.org/writing/fda-orange-book/", "title": "FDA Orange Book: The Drug Patent and Exclusivity Database Behind Generic Drug Competition and Hatch-Waxman Challenges", "content_text": "AB-rated = bioequivalent/substitutable. Hatch-Waxman ANDA: bioequivalence not clinical trials. Paragraph IV → 30-month stay + 180-day first-filer exclusivity. NCE 5yr, Orphan 7yr, Pediatric 6mo. Avg 71+ listed patents/drug (patent thickets). Lipitor 2011 cliff, Humira 2023 multi-biosimilar. Products.txt/Patent.txt/Exclusivity.txt. FTC v. Actavis 2013. Purple Book for biologics.", "summary": "AB-rated = bioequivalent/substitutable. Hatch-Waxman ANDA: bioequivalence not clinical trials. Paragraph IV → 30-month stay + 180-day first-filer exclusivity. NCE 5yr, Orphan 7yr, Pediatric 6mo. Avg 71+ listed patents/drug (patent thickets). Lipitor 2011 cliff, Humira 2023 multi-biosimilar. Products.txt/Patent.txt/Exclusivity.txt. FTC v. Actavis 2013. Purple Book for biologics.", "date_published": "2026-11-13T00:00:00.000Z", "tags": [ "FDA", "Orange Book", "Drug Patents", "Generics", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-places-health-estimates/", "url": "https://ai-analytics.org/writing/cdc-places-health-estimates/", "title": "CDC PLACES: The Small Area Health Estimates Behind County and Census Tract Disease Prevalence Data", "content_text": "MRP model: BRFSS survey + Census ACS → county/tract/ZCTA estimates for 36+ health measures. Obesity, diabetes, CHD, smoking, screenings, social determinants. Appalachian obesity >40% vs. Mountain West <20%. Delta diabetes 15%+ vs. CO <7%. Socrata API, GeoJSON, sodapy. vs. County Health Rankings: PLACES has sub-county geography, behavioral estimates.", "summary": "MRP model: BRFSS survey + Census ACS → county/tract/ZCTA estimates for 36+ health measures. Obesity, diabetes, CHD, smoking, screenings, social determinants. Appalachian obesity >40% vs. Mountain West <20%. Delta diabetes 15%+ vs. CO <7%. Socrata API, GeoJSON, sodapy. vs. County Health Rankings: PLACES has sub-county geography, behavioral estimates.", "date_published": "2026-11-12T00:00:00.000Z", "tags": [ "CDC", "PLACES", "Public Health", "Small Area Estimation", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bsee-offshore-safety/", "url": "https://ai-analytics.org/writing/bsee-offshore-safety/", "title": "BSEE Offshore Safety Data: The Post-Deepwater Horizon Incident Database Behind 4,000 Annual Offshore Inspections", "content_text": "Created 2011 from MMS breakup after DWH (87 days, 4.9M bbl, 11 deaths). ~2,000 OCS facilities, 15k+ wells. 4,000+ inspections/yr, ~2,000+ INCs. SEMS rule safety management. Well Control Rule BOP requirements. 15-17% US oil from Gulf deepwater. bsee.gov datasets: incidents, INCs, inspections, production, wells. ArcGIS REST services.", "summary": "Created 2011 from MMS breakup after DWH (87 days, 4.9M bbl, 11 deaths). ~2,000 OCS facilities, 15k+ wells. 4,000+ inspections/yr, ~2,000+ INCs. SEMS rule safety management. Well Control Rule BOP requirements. 15-17% US oil from Gulf deepwater. bsee.gov datasets: incidents, INCs, inspections, production, wells. ArcGIS REST services.", "date_published": "2026-11-11T00:00:00.000Z", "tags": [ "BSEE", "Offshore Safety", "Oil Gas", "Environmental", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/treasury-daily-statement/", "url": "https://ai-analytics.org/writing/treasury-daily-statement/", "title": "Treasury Daily Treasury Statement: The Federal Cash Flow Data Published Every Business Day", "content_text": "DTS published 4 PM ET daily by Bureau of Fiscal Service. TGA balance at Federal Reserve, public debt outstanding, receipts/outlays by category, operating cash balances. Tables I-VII. Fiscal Data API access. Python daily outflow chart by category.", "summary": "DTS published 4 PM ET daily by Bureau of Fiscal Service. TGA balance at Federal Reserve, public debt outstanding, receipts/outlays by category, operating cash balances. Tables I-VII. Fiscal Data API access. Python daily outflow chart by category.", "date_published": "2026-11-10T00:00:00.000Z", "tags": [ "Treasury", "Federal Budget", "Public Debt", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fed-h15-interest-rates/", "url": "https://ai-analytics.org/writing/fed-h15-interest-rates/", "title": "Federal Reserve H.15: The Selected Interest Rates Release Behind Treasury Yields, Fed Funds, and Every Rate Benchmark", "content_text": "EFFR, CMT yields 1mo-30yr, prime rate, discount rate, SOFR post-LIBOR transition. 2-10 spread inverted -108bps 2023 (deepest since 1981). FRED series DFF/DGS10/SOFR. Real rates via TIPS breakevens. Python yield curve spread with recession shading.", "summary": "EFFR, CMT yields 1mo-30yr, prime rate, discount rate, SOFR post-LIBOR transition. 2-10 spread inverted -108bps 2023 (deepest since 1981). FRED series DFF/DGS10/SOFR. Real rates via TIPS breakevens. Python yield curve spread with recession shading.", "date_published": "2026-11-09T00:00:00.000Z", "tags": [ "Federal Reserve", "Interest Rates", "Treasury Yields", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-population-estimates/", "url": "https://ai-analytics.org/writing/census-population-estimates/", "title": "Census Population Estimates Program: The Annual County and State Population Data Behind Apportionment, Funding, and Growth Tracking", "content_text": "Annual county/state cohort-component model: base census + births - deaths + net migration. Florida +2.1M, Texas +2.4M, NYC -500K (2020-2023). Census API pep/population endpoint. Python county growth ranking with net migration decomposition.", "summary": "Annual county/state cohort-component model: base census + births - deaths + net migration. Florida +2.1M, Texas +2.4M, NYC -500K (2020-2023). Census API pep/population endpoint. Python county growth ranking with net migration decomposition.", "date_published": "2026-11-08T00:00:00.000Z", "tags": [ "Census", "PEP", "Population", "Demographics", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usda-fsis-food-safety/", "url": "https://ai-analytics.org/writing/usda-fsis-food-safety/", "title": "USDA FSIS Food Safety Data: The Federal Recall Database and Inspection Records Behind Meat, Poultry, and Egg Safety", "content_text": "6,500+ establishments, 80B+ lbs annually. 3 recall classes. Hallmark/Westland 143M lb recall 2008. E. coli O157:H7 zero-tolerance adulterant. GenomeTrakr WGS tracing. Establishments.csv, PHIS reports, HACCP. Python recall trend by class/commodity.", "summary": "6,500+ establishments, 80B+ lbs annually. 3 recall classes. Hallmark/Westland 143M lb recall 2008. E. coli O157:H7 zero-tolerance adulterant. GenomeTrakr WGS tracing. Establishments.csv, PHIS reports, HACCP. Python recall trend by class/commodity.", "date_published": "2026-11-07T00:00:00.000Z", "tags": [ "USDA", "FSIS", "Food Safety", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-saipe-poverty/", "url": "https://ai-analytics.org/writing/census-saipe-poverty/", "title": "Census SAIPE: The Small Area Income and Poverty Estimates Behind Federal Education Funding and County-Level Poverty Maps", "content_text": "Annual model-based poverty for 3,100+ counties, 13,000+ school districts. Drives $17B Title I-A and $3.5B CDBG. ACS + IRS + SNAP + CPS small area estimation. Census API timeseries/poverty/saipe.", "summary": "Annual model-based poverty for 3,100+ counties, 13,000+ school districts. Drives $17B Title I-A and $3.5B CDBG. ACS + IRS + SNAP + CPS small area estimation. Census API timeseries/poverty/saipe.", "date_published": "2026-11-06T00:00:00.000Z", "tags": [ "Census", "SAIPE", "Poverty", "Education Funding", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dot-national-transit-database/", "url": "https://ai-analytics.org/writing/dot-national-transit-database/", "title": "DOT National Transit Database: The Federal Ridership and Finance Data Behind Every US Bus and Rail System", "content_text": "~800 agencies, 10.4B UPT 2023 (vs 15.7B 2019 peak). COVID collapse: $69B relief. Section 5307 formula from NTD data. NYC subway 1.8B to 600M trips. Monthly ridership reports 6 weeks lag.", "summary": "~800 agencies, 10.4B UPT 2023 (vs 15.7B 2019 peak). COVID collapse: $69B relief. Section 5307 formula from NTD data. NYC subway 1.8B to 600M trips. Monthly ridership reports 6 weeks lag.", "date_published": "2026-11-05T00:00:00.000Z", "tags": [ "DOT", "Transit", "Transportation", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/uspto-trademark-data/", "url": "https://ai-analytics.org/writing/uspto-trademark-data/", "title": "USPTO Trademark Data: The Federal Brand Registry Behind 3 Million Active Marks and the TESS Search System", "content_text": "3M active marks, 650k annual applications at peak. 45 Nice classes. TESS search, DuPont factors, TTAB, bulk XML at bulkdata.uspto.gov, JSON API. China 25% of foreign filings.", "summary": "3M active marks, 650k annual applications at peak. 45 Nice classes. TESS search, DuPont factors, TTAB, bulk XML at bulkdata.uspto.gov, JSON API. China 25% of foreign filings.", "date_published": "2026-11-04T00:00:00.000Z", "tags": [ "USPTO", "Trademarks", "IP", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fed-sloos-survey/", "url": "https://ai-analytics.org/writing/fed-sloos-survey/", "title": "Federal Reserve Senior Loan Officer Survey: The Quarterly Credit Conditions Data the Fed Uses to Track Lending Tightening", "content_text": "~80 banks, quarterly net % tightening. +80% C&I Q4 2008, +68% Q2 2020, >+50% predicts recession. FRED DRTSCILM/DRTSCIS 1990-present. Special questions each quarter.", "summary": "~80 banks, quarterly net % tightening. +80% C&I Q4 2008, +68% Q2 2020, >+50% predicts recession. FRED DRTSCILM/DRTSCIS 1990-present. Special questions each quarter.", "date_published": "2026-11-03T00:00:00.000Z", "tags": [ "Federal Reserve", "Credit", "Banking", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fcc-spectrum-data/", "url": "https://ai-analytics.org/writing/fcc-spectrum-data/", "title": "FCC Spectrum Data: The Universal Licensing System Behind 25 Million Wireless Licenses and US Radio Frequency Allocation", "content_text": "25M+ ULS licenses. Auction 110 C-band $81B record. Low/mid/mmWave 5G allocation. ULS bulk data schema EN/HD/LO/FR/AN tables. Broadcast CDBS/LMS. Python amateur license density choropleth.", "summary": "25M+ ULS licenses. Auction 110 C-band $81B record. Low/mid/mmWave 5G allocation. ULS bulk data schema EN/HD/LO/FR/AN tables. Broadcast CDBS/LMS. Python amateur license density choropleth.", "date_published": "2026-11-02T00:00:00.000Z", "tags": [ "FCC", "Spectrum", "Wireless", "Telecom", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hud-housing-vouchers/", "url": "https://ai-analytics.org/writing/hud-housing-vouchers/", "title": "HUD Housing Choice Vouchers: The Section 8 Data Behind 2.3 Million Households and $30 Billion in Annual Rental Assistance", "content_text": "2.3M households, $30B/year, 2,200 PHAs. FMR 40th percentile by 2,600 areas. Only 25% of eligible served. PASH tract-level data, PBRA 1.2M units, AFFH, CHAS. Python FMR-to-renter-income affordability gap.", "summary": "2.3M households, $30B/year, 2,200 PHAs. FMR 40th percentile by 2,600 areas. Only 25% of eligible served. PASH tract-level data, PBRA 1.2M units, AFFH, CHAS. Python FMR-to-renter-income affordability gap.", "date_published": "2026-11-01T00:00:00.000Z", "tags": [ "HUD", "Housing", "Section 8", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-american-housing-survey/", "url": "https://ai-analytics.org/writing/census-american-housing-survey/", "title": "Census American Housing Survey: The Biennial Housing Quality Database Behind US Structural Conditions and Neighborhood Characteristics", "content_text": "Biennial panel 60k housing units since 1973. Plumbing inadequacy 4.5% to <0.5%. Owner-occupancy 69% peak to 63% trough. New SF size 1,500 to 2,300+ sq ft. HUD Worst Case 8.5M households 2023. PUF variables ADEQUACY/ZINC2/BUILT.", "summary": "Biennial panel 60k housing units since 1973. Plumbing inadequacy 4.5% to <0.5%. Owner-occupancy 69% peak to 63% trough. New SF size 1,500 to 2,300+ sq ft. HUD Worst Case 8.5M households 2023. PUF variables ADEQUACY/ZINC2/BUILT.", "date_published": "2026-10-31T00:00:00.000Z", "tags": [ "Census", "AHS", "Housing", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usda-ers-data/", "url": "https://ai-analytics.org/writing/usda-ers-data/", "title": "USDA Economic Research Service: The Agricultural Economics Data Behind Farm Income, Food Prices, and Rural America", "content_text": "$116B net farm income 2023. Food prices: 2022 +11.4% grocery surge. Food insecurity 13.5% (47M people). ARC/PLC commodity reference prices. Beale Codes 1-9. 180+ rural hospital closures. Food Access Research Atlas food deserts.", "summary": "$116B net farm income 2023. Food prices: 2022 +11.4% grocery surge. Food insecurity 13.5% (47M people). ARC/PLC commodity reference prices. Beale Codes 1-9. 180+ rural hospital closures. Food Access Research Atlas food deserts.", "date_published": "2026-10-30T00:00:00.000Z", "tags": [ "USDA", "ERS", "Agriculture", "Food", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-employment-cost-index/", "url": "https://ai-analytics.org/writing/bls-employment-cost-index/", "title": "BLS Employment Cost Index: The Quarterly Wage and Benefits Tracker the Federal Reserve Watches Most Closely", "content_text": "Fixed-weight quarterly compensation index eliminating industry-mix distortion. Private wages peaked ~5.7% YoY mid-2022, now ~4.2%. ECEC benefits breakdown: health insurance, FICA, paid leave, total benefits ~31% of compensation. Fed uses ECI to track wage inflation toward 2% PCE target.", "summary": "Fixed-weight quarterly compensation index eliminating industry-mix distortion. Private wages peaked ~5.7% YoY mid-2022, now ~4.2%. ECEC benefits breakdown: health insurance, FICA, paid leave, total benefits ~31% of compensation. Fed uses ECI to track wage inflation toward 2% PCE target.", "date_published": "2026-10-29T00:00:00.000Z", "tags": [ "BLS", "ECI", "Wages", "Inflation", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-ui-weekly-claims/", "url": "https://ai-analytics.org/writing/dol-ui-weekly-claims/", "title": "DOL Unemployment Insurance Weekly Claims: The Thursday Morning Data Release That Moves Financial Markets", "content_text": "Thursday 8:30 AM ET release. COVID peak 6.87M (March 2020). Prior record 695k (1982). Pre-COVID lows ~200k (2018-19). 4-week moving average, CARES Act FPUC $600/week, FRED series ICSA/CC4WSA, Python shock-week detection.", "summary": "Thursday 8:30 AM ET release. COVID peak 6.87M (March 2020). Prior record 695k (1982). Pre-COVID lows ~200k (2018-19). 4-week moving average, CARES Act FPUC $600/week, FRED series ICSA/CC4WSA, Python shock-week detection.", "date_published": "2026-10-28T00:00:00.000Z", "tags": [ "DOL", "Unemployment Insurance", "Labor Market", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-foreign-trade/", "url": "https://ai-analytics.org/writing/census-foreign-trade/", "title": "Census Foreign Trade Statistics: The HS-Code Import and Export Database Behind Every US Trade Policy Decision", "content_text": "2023: goods exports $2.02T, imports $3.08T. HS-10 data by country/port. China tariffs shifted $140B deficit from China to Vietnam/Mexico/Taiwan. Census API timeseries/intltrade/ endpoint. Semiconductor sourcing analysis by HS 8542.", "summary": "2023: goods exports $2.02T, imports $3.08T. HS-10 data by country/port. China tariffs shifted $140B deficit from China to Vietnam/Mexico/Taiwan. Census API timeseries/intltrade/ endpoint. Semiconductor sourcing analysis by HS 8542.", "date_published": "2026-10-27T00:00:00.000Z", "tags": [ "Census", "Trade", "Imports", "Exports", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nifc-wildfire-data/", "url": "https://ai-analytics.org/writing/nifc-wildfire-data/", "title": "NIFC Wildfire Data: The Federal Statistics Behind 4 Million Acres Burned Annually and the Expanding Fire Season", "content_text": "Wildfire statistics back to 1926. Avg acreage doubled since 1980s. Camp Fire 2018: 85 deaths, $16.5B. USFS FOD 2.3M fire records. MTBS Landsat burn-severity. KBDI, VPD fire weather. NIFC ArcGIS REST perimeters.", "summary": "Wildfire statistics back to 1926. Avg acreage doubled since 1980s. Camp Fire 2018: 85 deaths, $16.5B. USFS FOD 2.3M fire records. MTBS Landsat burn-severity. KBDI, VPD fire weather. NIFC ArcGIS REST perimeters.", "date_published": "2026-10-26T00:00:00.000Z", "tags": [ "NIFC", "Wildfire", "USFS", "Environmental", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ssa-oasdi-benefits/", "url": "https://ai-analytics.org/writing/ssa-oasdi-benefits/", "title": "Social Security OASDI: The Federal Data Behind $1.4 Trillion in Annual Benefits and 70 Million Recipients", "content_text": "~70M recipients, $1.4T paid in 2024. AIME/PIA bend point formula, FRA 67, early claiming reduction, delayed claiming increase, COLA 3.2%, WEP/GPO offsets, 2033 trust fund depletion projection, SSA Annual Statistical Supplement.", "summary": "~70M recipients, $1.4T paid in 2024. AIME/PIA bend point formula, FRA 67, early claiming reduction, delayed claiming increase, COLA 3.2%, WEP/GPO offsets, 2033 trust fund depletion projection, SSA Annual Statistical Supplement.", "date_published": "2026-10-25T00:00:00.000Z", "tags": [ "SSA", "Social Security", "OASDI", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-cps-poverty/", "url": "https://ai-analytics.org/writing/census-cps-poverty/", "title": "Census Current Population Survey: The Monthly Survey Behind Official US Poverty Rates and Income Inequality Measures", "content_text": "~60,000 households monthly, 4-8-4 rotating panel. Official poverty rate 11.1% (2023, Orshansky thresholds), SPM 12.9% (adds SNAP/EITC/housing). Median household income $80,610, Gini ~0.482, IPUMS CPS microdata back to 1962.", "summary": "~60,000 households monthly, 4-8-4 rotating panel. Official poverty rate 11.1% (2023, Orshansky thresholds), SPM 12.9% (adds SNAP/EITC/housing). Median household income $80,610, Gini ~0.482, IPUMS CPS microdata back to 1962.", "date_published": "2026-10-24T00:00:00.000Z", "tags": [ "Census", "CPS", "Poverty", "Income", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bea-international-transactions/", "url": "https://ai-analytics.org/writing/bea-international-transactions/", "title": "BEA International Transactions: The Balance of Payments Data Behind Every US Trade Deficit Headline", "content_text": "2023: goods deficit $1.06T, services surplus $293B, current account deficit $905B (3.3% GDP). FDI flows, TIC data ($7.8T foreign Treasury holdings), net IIP -$20.6T, exorbitant privilege, savings-investment identity, BEA ITA API back to 1960.", "summary": "2023: goods deficit $1.06T, services surplus $293B, current account deficit $905B (3.3% GDP). FDI flows, TIC data ($7.8T foreign Treasury holdings), net IIP -$20.6T, exorbitant privilege, savings-investment identity, BEA ITA API back to 1960.", "date_published": "2026-10-23T00:00:00.000Z", "tags": [ "BEA", "Trade", "Balance of Payments", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/noaa-climate-data/", "url": "https://ai-analytics.org/writing/noaa-climate-data/", "title": "NOAA Climate Data: The National Centers for Environmental Information Behind 130 Years of Temperature Records and Climate Normals", "content_text": "150+ petabytes, 25B+ annual requests. GHCN-Daily 120,000+ stations. 2023 warmest year on record (+1.45C). US Climate Normals 1991-2020, Billion-Dollar Disasters database, HURDAT2 hurricane tracks, sea level rise 3.6mm/year, CDO REST API.", "summary": "150+ petabytes, 25B+ annual requests. GHCN-Daily 120,000+ stations. 2023 warmest year on record (+1.45C). US Climate Normals 1991-2020, Billion-Dollar Disasters database, HURDAT2 hurricane tracks, sea level rise 3.6mm/year, CDO REST API.", "date_published": "2026-10-22T00:00:00.000Z", "tags": [ "NOAA", "NCEI", "Climate", "Environmental", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/va-disability-benefits/", "url": "https://ai-analytics.org/writing/va-disability-benefits/", "title": "VA Disability Benefits: The Federal Data Behind 5.5 Million Compensation Recipients and $130 Billion in Annual Spending", "content_text": "~5.5M veterans receive monthly disability compensation based on a 0-100% whole-person rating. PACT Act 2022 burn pit presumptives, 2024 rate table, GI Bill, VA Home Loan Guaranty, claims backlog history, TDIU, and VA Open Data state utilization.", "summary": "~5.5M veterans receive monthly disability compensation based on a 0-100% whole-person rating. PACT Act 2022 burn pit presumptives, 2024 rate table, GI Bill, VA Home Loan Guaranty, claims backlog history, TDIU, and VA Open Data state utilization.", "date_published": "2026-10-21T00:00:00.000Z", "tags": [ "VA", "Veterans", "Benefits", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usgs-water-resources/", "url": "https://ai-analytics.org/writing/usgs-water-resources/", "title": "USGS Water Resources: The National Water Information System Behind Flood Prediction, Drought Monitoring, and Aquifer Depletion", "content_text": "8,000+ USGS streamflow gauges feed NWS River Forecast Centers and the National Water Model. Ogallala Aquifer depletion, Central Valley subsidence, NAWQA water quality, 7Q10 NPDES standard, NWIS REST API, and Python hydrograph with drought shading.", "summary": "8,000+ USGS streamflow gauges feed NWS River Forecast Centers and the National Water Model. Ogallala Aquifer depletion, Central Valley subsidence, NAWQA water quality, 7Q10 NPDES standard, NWIS REST API, and Python hydrograph with drought shading.", "date_published": "2026-10-20T00:00:00.000Z", "tags": [ "USGS", "Water", "Environment", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/opm-federal-workforce/", "url": "https://ai-analytics.org/writing/opm-federal-workforce/", "title": "OPM Federal Workforce Data: The Personnel Records Behind 2.1 Million Civilian Federal Jobs", "content_text": "OPM manages 2.1M+ federal civilians via CPDF and FedScope. GS pay system (48 locality areas), SES, FERS/TSP retirement ($800B+ AUM), ~100-day time-to-hire, Pathways Programs, FRB 17-19% total compensation premium, DOGE context.", "summary": "OPM manages 2.1M+ federal civilians via CPDF and FedScope. GS pay system (48 locality areas), SES, FERS/TSP retirement ($800B+ AUM), ~100-day time-to-hire, Pathways Programs, FRB 17-19% total compensation premium, DOGE context.", "date_published": "2026-10-19T00:00:00.000Z", "tags": [ "OPM", "Federal Workforce", "Labor", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nsf-research-grants/", "url": "https://ai-analytics.org/writing/nsf-research-grants/", "title": "NSF Research Grants: Mapping $9 Billion in Annual Basic Science Funding", "content_text": "NSF funds ~25% of federally funded basic research with $9B+ annually. Dual merit review, CAREER award ($500k/5yr), GRFP ($37k/yr, 2k of 12k applicants), NSF Awards API (600k+ awards), National AI Research Institutes, 2023 open-access mandate, EPSCoR.", "summary": "NSF funds ~25% of federally funded basic research with $9B+ annually. Dual merit review, CAREER award ($500k/5yr), GRFP ($37k/yr, 2k of 12k applicants), NSF Awards API (600k+ awards), National AI Research Institutes, 2023 open-access mandate, EPSCoR.", "date_published": "2026-10-18T00:00:00.000Z", "tags": [ "NSF", "Research", "Science Funding", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bts-airline-performance/", "url": "https://ai-analytics.org/writing/bts-airline-performance/", "title": "BTS Airline On-Time Performance: The Federal Dataset Behind Every Flight Delay, Cancellation, and Tarmac Crisis", "content_text": "The BTS ATOP/ASQP database covers ~6 million flight records per year. Five delay categories, T-100 traffic series (ASM/RPM/load factor), Form 41 carrier financials (CASM/RASM), the COVID 96% RPM collapse and $54B CARES Act, the Southwest December 2022 meltdown, the 3-hour tarmac delay rule, and a Python Transtats on-time rate and cancellation analysis.", "summary": "The BTS ATOP/ASQP database covers ~6 million flight records per year. Five delay categories, T-100 traffic series (ASM/RPM/load factor), Form 41 carrier financials (CASM/RASM), the COVID 96% RPM collapse and $54B CARES Act, the Southwest December 2022 meltdown, the 3-hour tarmac delay rule, and a Python Transtats on-time rate and cancellation analysis.", "date_published": "2026-10-17T00:00:00.000Z", "tags": [ "BTS", "Aviation", "Transportation", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fed-z1-financial-accounts/", "url": "https://ai-analytics.org/writing/fed-z1-financial-accounts/", "title": "Federal Reserve Z.1 Financial Accounts: The Flow of Funds Behind US Household Wealth and Sectoral Balances", "content_text": "Federal Reserve Z.1 quarterly financial accounts for all US sectors. Household net worth ($156T 2021 peak, -$8T 2022), Distributional Financial Accounts (top 1% hold ~31%), two-sided sectoral balance mechanics, corporate leverage, Table B.101 real estate ($25T to $43T 2019-2024), $26T+ Treasury liabilities, Rest of World holdings, and Python FRED API household net worth analysis.", "summary": "Federal Reserve Z.1 quarterly financial accounts for all US sectors. Household net worth ($156T 2021 peak, -$8T 2022), Distributional Financial Accounts (top 1% hold ~31%), two-sided sectoral balance mechanics, corporate leverage, Table B.101 real estate ($25T to $43T 2019-2024), $26T+ Treasury liabilities, Rest of World holdings, and Python FRED API household net worth analysis.", "date_published": "2026-10-16T00:00:00.000Z", "tags": [ "Federal Reserve", "Finance", "Wealth Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-lehd-workforce/", "url": "https://ai-analytics.org/writing/census-lehd-workforce/", "title": "Census LEHD: The Longitudinal Employer-Household Dynamics Database Behind Workforce Flows, Commuting, and Wage Growth", "content_text": "Census LEHD links UI wage records for 95%+ of private workers to employer/household data: Quarterly Workforce Indicators, LODES block-level commuting OD matrices, job-to-job flow statistics (7-10% earnings premium from switching), business dynamics, COVID OD commute reshaping, and a Python Census QWI API young construction worker employment analysis.", "summary": "Census LEHD links UI wage records for 95%+ of private workers to employer/household data: Quarterly Workforce Indicators, LODES block-level commuting OD matrices, job-to-job flow statistics (7-10% earnings premium from switching), business dynamics, COVID OD commute reshaping, and a Python Census QWI API young construction worker employment analysis.", "date_published": "2026-10-15T00:00:00.000Z", "tags": [ "Census", "Labor", "Demographics", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bea-regional-accounts/", "url": "https://ai-analytics.org/writing/bea-regional-accounts/", "title": "BEA Regional Accounts: GDP by State, Personal Income by County, and the Sub-National Data Behind Every State Policy Debate", "content_text": "BEA Regional Accounts: GDP by State, Personal Income by State (quarterly), Personal Income by County (~3,100 counties, CAINC1), GDP by MSA (~380 MSAs). Energy boom-bust cycles, high-income state tax migration, COVID transfer surge and unwinding, BEA Regional API parameters, and Python state per-capita income growth ranking.", "summary": "BEA Regional Accounts: GDP by State, Personal Income by State (quarterly), Personal Income by County (~3,100 counties, CAINC1), GDP by MSA (~380 MSAs). Energy boom-bust cycles, high-income state tax migration, COVID transfer surge and unwinding, BEA Regional API parameters, and Python state per-capita income growth ranking.", "date_published": "2026-10-14T00:00:00.000Z", "tags": [ "BEA", "Economics", "Regional Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usda-nass-crop-surveys/", "url": "https://ai-analytics.org/writing/usda-nass-crop-surveys/", "title": "USDA NASS Crop Surveys: The Federal Agricultural Data Behind Every Corn, Soybean, and Wheat Market", "content_text": "The USDA National Agricultural Statistics Service conducts 400+ surveys annually, reaching 3 million respondents to produce the authoritative federal record of US crop production, livestock inventories, and commodity prices since 1867. Here is the Crop Production report, WASDE supply-demand balance sheets, QuickStats API, weekly Crop Progress Good/Excellent condition ratings, the five major crops, the 2012 drought sending corn to $8.49/bushel, Cattle on Feed, Hogs and Pigs quarterly, Prices Received/Paid, and a Python QuickStats API corn yield analysis.", "summary": "The USDA National Agricultural Statistics Service conducts 400+ surveys annually, reaching 3 million respondents to produce the authoritative federal record of US crop production, livestock inventories, and commodity prices since 1867. Here is the Crop Production report, WASDE supply-demand balance sheets, QuickStats API, weekly Crop Progress Good/Excellent condition ratings, the five major crops, the 2012 drought sending corn to $8.49/bushel, Cattle on Feed, Hogs and Pigs quarterly, Prices Received/Paid, and a Python QuickStats API corn yield analysis.", "date_published": "2026-10-13T00:00:00.000Z", "tags": [ "USDA", "Agriculture", "Commodities", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/eia-energy-data/", "url": "https://ai-analytics.org/writing/eia-energy-data/", "title": "EIA Energy Data: The Federal Database Behind Oil Prices, Natural Gas Storage, and Electricity Generation", "content_text": "The EIA is the primary federal authority for US energy data. Here is the Short-Term Energy Outlook, Weekly Petroleum Status Report (Cushing OK crude stocks moving WTI $1-2/barrel), Natural Gas Storage Report (five-region EIA-914), EIA-860 and EIA-923 power plant databases (15,000+ generators), Electric Power Monthly, Petroleum Supply Monthly, EIA Open Data API (500,000+ series), the 2019 net petroleum export milestone, the 2022 European crisis Henry Hub spike to $9/MMBtu, and a Python EIA v2 API WTI/Henry Hub analysis.", "summary": "The EIA is the primary federal authority for US energy data. Here is the Short-Term Energy Outlook, Weekly Petroleum Status Report (Cushing OK crude stocks moving WTI $1-2/barrel), Natural Gas Storage Report (five-region EIA-914), EIA-860 and EIA-923 power plant databases (15,000+ generators), Electric Power Monthly, Petroleum Supply Monthly, EIA Open Data API (500,000+ series), the 2019 net petroleum export milestone, the 2022 European crisis Henry Hub spike to $9/MMBtu, and a Python EIA v2 API WTI/Henry Hub analysis.", "date_published": "2026-10-12T00:00:00.000Z", "tags": [ "EIA", "Energy", "Economy", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-building-permits/", "url": "https://ai-analytics.org/writing/census-building-permits/", "title": "Census Building Permits and Housing Starts: The Federal Leading Indicator Behind the US Housing Market", "content_text": "The Census Bureau Building Permits Survey and New Residential Construction release track ~20,000 permit-issuing jurisdictions monthly. Here is 96% construction coverage, SAAR methodology, 2006 peak (2.07M SAAR) to 2009 trough (554K) to 2020-2021 surge to 2022-2023 pullback (3% to 7% mortgage rates), SFH/multifamily bifurcation, Sun Belt concentration, New Residential Sales contract-signed timing, lumber futures (2021 spike to $1,700/MBF), Census BPS API, and FRED series PERMIT/HOUST.", "summary": "The Census Bureau Building Permits Survey and New Residential Construction release track ~20,000 permit-issuing jurisdictions monthly. Here is 96% construction coverage, SAAR methodology, 2006 peak (2.07M SAAR) to 2009 trough (554K) to 2020-2021 surge to 2022-2023 pullback (3% to 7% mortgage rates), SFH/multifamily bifurcation, Sun Belt concentration, New Residential Sales contract-signed timing, lumber futures (2021 spike to $1,700/MBF), Census BPS API, and FRED series PERMIT/HOUST.", "date_published": "2026-10-11T00:00:00.000Z", "tags": [ "Census", "Housing", "Economy", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-occupational-outlook/", "url": "https://ai-analytics.org/writing/bls-occupational-outlook/", "title": "BLS Occupational Employment Data: Wages, Job Counts, and 10-Year Projections for Every US Occupation", "content_text": "The BLS OEWS program publishes wages and employment for 830 occupations across 590+ geographies from a 1.1M establishment survey pooled to ~3.3M observations. Here is wage percentiles 10th-90th, SOC taxonomy (867 detailed occupations), top-paying occupations (surgeons $250k+), Employment Projections 2022-2032 (home health aides +924k), Occupational Outlook Handbook, O*NET crosswalk, wage inequality (90/10 ratio), H-1B prevailing wage, and a Python healthcare occupation wage analysis.", "summary": "The BLS OEWS program publishes wages and employment for 830 occupations across 590+ geographies from a 1.1M establishment survey pooled to ~3.3M observations. Here is wage percentiles 10th-90th, SOC taxonomy (867 detailed occupations), top-paying occupations (surgeons $250k+), Employment Projections 2022-2032 (home health aides +924k), Occupational Outlook Handbook, O*NET crosswalk, wage inequality (90/10 ratio), H-1B prevailing wage, and a Python healthcare occupation wage analysis.", "date_published": "2026-10-10T00:00:00.000Z", "tags": [ "BLS", "Labor", "Wages", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dot-fhwa-highway/", "url": "https://ai-analytics.org/writing/dot-fhwa-highway/", "title": "FHWA Highway Data: The Federal Dataset Behind Bridge Conditions, Pavement Quality, and Traffic Counts", "content_text": "The FHWA National Bridge Inventory (620,000+ bridges, biennial inspection, structurally deficient classification, sufficiency rating), Highway Performance Monitoring System (pavement IRI, Good/Fair/Poor), AADT traffic counts, Highway Statistics, IIJA $40B bridge funding, Highway Trust Fund solvency crisis, Freight Analysis Framework, and a Python NBI bridge deficiency script.", "summary": "The FHWA National Bridge Inventory (620,000+ bridges, biennial inspection, structurally deficient classification, sufficiency rating), Highway Performance Monitoring System (pavement IRI, Good/Fair/Poor), AADT traffic counts, Highway Statistics, IIJA $40B bridge funding, Highway Trust Fund solvency crisis, Freight Analysis Framework, and a Python NBI bridge deficiency script.", "date_published": "2026-10-09T00:00:00.000Z", "tags": [ "FHWA", "Transportation", "Infrastructure", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-current-employment/", "url": "https://ai-analytics.org/writing/bls-current-employment/", "title": "BLS Current Employment Statistics: The Monthly Jobs Report Behind Every Payroll Number", "content_text": "Two surveys on Jobs Friday: Establishment Survey (580,000 worksites, nonfarm payroll) and Household Survey (60,000 households, U-3 unemployment rate). Net birth/death model, three-tier revision cycle, annual benchmark (818,000-job 2024 revision), X-13ARIMA-SEATS seasonal adjustment, industry dynamics, COVID -20.5M collapse, 8:30 AM market impact, and Python BLS API analysis.", "summary": "Two surveys on Jobs Friday: Establishment Survey (580,000 worksites, nonfarm payroll) and Household Survey (60,000 households, U-3 unemployment rate). Net birth/death model, three-tier revision cycle, annual benchmark (818,000-job 2024 revision), X-13ARIMA-SEATS seasonal adjustment, industry dynamics, COVID -20.5M collapse, 8:30 AM market impact, and Python BLS API analysis.", "date_published": "2026-10-08T00:00:00.000Z", "tags": [ "BLS", "Labor", "Economy", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-edgar-xbrl/", "url": "https://ai-analytics.org/writing/sec-edgar-xbrl/", "title": "SEC EDGAR XBRL: The Machine-Readable Financial Statement Database Behind Every Public Company", "content_text": "SEC XBRL mandate (2009-2011), ~7,000 active filers, US-GAAP taxonomy (17,000+ concepts), Company Facts API, Company Concept API, Frames cross-sectional API, 30% custom extension elements, ASC 606 taxonomy changes, Beneish M-score fraud detection, and Python SEC EDGAR API script for Apple revenue/net income history.", "summary": "SEC XBRL mandate (2009-2011), ~7,000 active filers, US-GAAP taxonomy (17,000+ concepts), Company Facts API, Company Concept API, Frames cross-sectional API, 30% custom extension elements, ASC 606 taxonomy changes, Beneish M-score fraud detection, and Python SEC EDGAR API script for Apple revenue/net income history.", "date_published": "2026-10-07T00:00:00.000Z", "tags": [ "SEC", "Finance", "Financial Data", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-skilled-nursing-data/", "url": "https://ai-analytics.org/writing/cms-skilled-nursing-data/", "title": "CMS Skilled Nursing Facility Data: Star Ratings, Staffing, and the Quality Metrics Behind 15,000 Nursing Homes", "content_text": "CMS Care Compare publishes quality data for every Medicare- and Medicaid-certified skilled nursing facility. Here is the five-star composite rating system, the A-L scope/severity deficiency grid, the Payroll-Based Journal staffing system, MDS resident assessments, COVID-19 nursing home crisis (170,000+ deaths), private equity ownership transparency gaps, and a Python script to compute state-level star rating distributions.", "summary": "CMS Care Compare publishes quality data for every Medicare- and Medicaid-certified skilled nursing facility. Here is the five-star composite rating system, the A-L scope/severity deficiency grid, the Payroll-Based Journal staffing system, MDS resident assessments, COVID-19 nursing home crisis (170,000+ deaths), private equity ownership transparency gaps, and a Python script to compute state-level star rating distributions.", "date_published": "2026-10-05T00:00:00.000Z", "tags": [ "CMS", "Healthcare", "Quality", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-occupational-injuries/", "url": "https://ai-analytics.org/writing/bls-occupational-injuries/", "title": "BLS Occupational Injuries: The SOII Dataset Behind 2.8 Million Annual Workplace Injuries", "content_text": "The BLS SOII surveys ~230,000 establishments for workplace injury counts. Here is the TRIR formula, OSHA recordkeeping requirements (Form 300/300A/301), case-and-demographic microdata, CFOI fatal census (~5,500/year), musculoskeletal disorder supplement, the underreporting problem (40-69% capture rate), and a Python BLS API script to compare TRIR across construction, manufacturing, and healthcare.", "summary": "The BLS SOII surveys ~230,000 establishments for workplace injury counts. Here is the TRIR formula, OSHA recordkeeping requirements (Form 300/300A/301), case-and-demographic microdata, CFOI fatal census (~5,500/year), musculoskeletal disorder supplement, the underreporting problem (40-69% capture rate), and a Python BLS API script to compare TRIR across construction, manufacturing, and healthcare.", "date_published": "2026-10-04T00:00:00.000Z", "tags": [ "BLS", "Labor", "Safety", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/epa-air-quality-system/", "url": "https://ai-analytics.org/writing/epa-air-quality-system/", "title": "EPA Air Quality System: The Federal Monitor Network Behind NAAQS Compliance and Pollution Mapping", "content_text": "The EPA AQS aggregates hourly pollutant readings from 4,000+ sites. Here is the six criteria pollutant NAAQS framework, the 2024 PM2.5 standard tightened to 9 ug/m3, the AQI 0-500 scale, nonattainment and SIP mechanics, the Harvard Six Cities study and BenMAP model (100,000+ PM2.5-attributable deaths/year), environmental justice monitoring gaps, wildfire smoke provisions, and a Python EPA AQS API PM2.5 exceedance analysis.", "summary": "The EPA AQS aggregates hourly pollutant readings from 4,000+ sites. Here is the six criteria pollutant NAAQS framework, the 2024 PM2.5 standard tightened to 9 ug/m3, the AQI 0-500 scale, nonattainment and SIP mechanics, the Harvard Six Cities study and BenMAP model (100,000+ PM2.5-attributable deaths/year), environmental justice monitoring gaps, wildfire smoke provisions, and a Python EPA AQS API PM2.5 exceedance analysis.", "date_published": "2026-10-03T00:00:00.000Z", "tags": [ "EPA", "Environment", "Public Health", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hud-homeless-count/", "url": "https://ai-analytics.org/writing/hud-homeless-count/", "title": "HUD Point-in-Time Count: The Federal Homeless Census Behind 650,000 Americans Without Shelter", "content_text": "HUD's annual PIT count conducted by ~400 CoC regions in January is the only national homeless census. Here is the sheltered vs. unsheltered methodology, the 2023 count of 653,100 (record high), HMIS longitudinal tracking, veteran homelessness (37,000+ and HUD-VASH), the chronic homeless definition, methodological limitations, Housing First evidence, system performance measures, and a Python script for per-capita homeless rates by state.", "summary": "HUD's annual PIT count conducted by ~400 CoC regions in January is the only national homeless census. Here is the sheltered vs. unsheltered methodology, the 2023 count of 653,100 (record high), HMIS longitudinal tracking, veteran homelessness (37,000+ and HUD-VASH), the chronic homeless definition, methodological limitations, Housing First evidence, system performance measures, and a Python script for per-capita homeless rates by state.", "date_published": "2026-10-02T00:00:00.000Z", "tags": [ "HUD", "Housing", "Social Policy", "Federal Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/faa-aviation-safety/", "url": "https://ai-analytics.org/writing/faa-aviation-safety/", "title": "FAA Aviation Safety Data: The Federal Databases Behind Every Plane Crash Investigation", "content_text": "The federal aviation safety ecosystem spans four databases: NTSB accident database, FAA AIDS, NASA ASRS voluntary near-miss reports, and the FAA Wildlife Strike Database. Here is the NTSB probable cause taxonomy, Boeing 737 MAX MCAS investigation, ASRS reporting immunity, runway incursion categories, the Miracle on Hudson context, FAA Civil Aviation Registry, and a Python NTSB phase-of-flight fatal accident rate analysis.", "summary": "The federal aviation safety ecosystem spans four databases: NTSB accident database, FAA AIDS, NASA ASRS voluntary near-miss reports, and the FAA Wildlife Strike Database. Here is the NTSB probable cause taxonomy, Boeing 737 MAX MCAS investigation, ASRS reporting immunity, runway incursion categories, the Miracle on Hudson context, FAA Civil Aviation Registry, and a Python NTSB phase-of-flight fatal accident rate analysis.", "date_published": "2026-10-01T00:00:00.000Z", "tags": [ "Federal Data", "FAA", "Aviation Safety", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nrc-nuclear-safety/", "url": "https://ai-analytics.org/writing/nrc-nuclear-safety/", "title": "NRC Nuclear Safety Data: The Federal Database Behind Every Reactor Inspection and Incident Report", "content_text": "The NRC publishes quarterly Performance Indicators, inspection findings, and daily Event Notification Reports for all 99 operating US nuclear reactors. Here is the Reactor Oversight Process, Significance Determination Process color thresholds, Licensee Event Reports, TMI and Fukushima reform trail, probabilistic risk assessment (core damage frequency ~1E-5/reactor-year), ADAMS 7M+ documents, and a Python NRC PI XML parser.", "summary": "The NRC publishes quarterly Performance Indicators, inspection findings, and daily Event Notification Reports for all 99 operating US nuclear reactors. Here is the Reactor Oversight Process, Significance Determination Process color thresholds, Licensee Event Reports, TMI and Fukushima reform trail, probabilistic risk assessment (core damage frequency ~1E-5/reactor-year), ADAMS 7M+ documents, and a Python NRC PI XML parser.", "date_published": "2026-09-30T00:00:00.000Z", "tags": [ "Federal Data", "NRC", "Nuclear Safety", "Energy" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bop-prison-population/", "url": "https://ai-analytics.org/writing/bop-prison-population/", "title": "Bureau of Prisons Data: The Federal Inmate Population Behind 150,000 Federal Prisoners", "content_text": "The BOP holds ~148,000 federal inmates -- down from 219,000 peak in 2013. Here is offense category breakdown (drug offenses 43%+), crack cocaine sentencing disparity, FIRST STEP Act reforms, BJS National Prisoner Statistics, USSC case-level sentencing data, PACER records, supervised release mechanics, private prison contracting, ICE immigration detention as a separate civil system, and federal recidivism data.", "summary": "The BOP holds ~148,000 federal inmates -- down from 219,000 peak in 2013. Here is offense category breakdown (drug offenses 43%+), crack cocaine sentencing disparity, FIRST STEP Act reforms, BJS National Prisoner Statistics, USSC case-level sentencing data, PACER records, supervised release mechanics, private prison contracting, ICE immigration detention as a separate civil system, and federal recidivism data.", "date_published": "2026-09-29T00:00:00.000Z", "tags": [ "Federal Data", "DOJ", "Criminal Justice", "Prison Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/uscis-immigration-data/", "url": "https://ai-analytics.org/writing/uscis-immigration-data/", "title": "USCIS Immigration Data: The Federal Database Behind Visas, Green Cards, and Naturalizations", "content_text": "USCIS adjudicates ~8 million petitions annually. Here is naturalization data by country, the 7% per-country EB cap creating 40+ year backlogs for Indian nationals, H-1B lottery mechanics (470K registrations for 85K slots), 1.7M+ affirmative asylum backlog, DACA quarterly data, EOIR immigration court backlogs, DHS Yearbook of Immigration Statistics, and a Python USCIS naturalization analysis.", "summary": "USCIS adjudicates ~8 million petitions annually. Here is naturalization data by country, the 7% per-country EB cap creating 40+ year backlogs for Indian nationals, H-1B lottery mechanics (470K registrations for 85K slots), 1.7M+ affirmative asylum backlog, DACA quarterly data, EOIR immigration court backlogs, DHS Yearbook of Immigration Statistics, and a Python USCIS naturalization analysis.", "date_published": "2026-09-28T00:00:00.000Z", "tags": [ "Federal Data", "USCIS", "Immigration", "Demographics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fbi-ucr-crime-data/", "url": "https://ai-analytics.org/writing/fbi-ucr-crime-data/", "title": "FBI UCR: The Federal Crime Statistics Behind Every Public Safety Analysis", "content_text": "The FBI UCR program collects crime data from ~18,000 law enforcement agencies transitioning from legacy SRS to incident-level NIBRS. Here is the 8 Part I Index Crimes, NIBRS segment structure, the 2020-2021 murder surge, hate crime data, LEOKA, the dark figure of crime, clearance rates, and the CDE API with a Python state-level murder rate trend analysis.", "summary": "The FBI UCR program collects crime data from ~18,000 law enforcement agencies transitioning from legacy SRS to incident-level NIBRS. Here is the 8 Part I Index Crimes, NIBRS segment structure, the 2020-2021 murder surge, hate crime data, LEOKA, the dark figure of crime, clearance rates, and the CDE API with a Python state-level murder rate trend analysis.", "date_published": "2026-09-27T00:00:00.000Z", "tags": [ "Federal Data", "FBI", "Crime Statistics", "Public Safety" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-hospital-cost-reports/", "url": "https://ai-analytics.org/writing/cms-hospital-cost-reports/", "title": "CMS Medicare Cost Reports: The Annual Financial Disclosure Behind Every US Hospital", "content_text": "Every Medicare-certified hospital files an annual Medicare Cost Report -- the only audited hospital-level financial dataset spanning all hospitals. Here is the Worksheet structure, cost-to-charge ratio, DSH payments, IME/GME teaching adjustments, Worksheet S-10 uncompensated care, HCRIS database at NBER, and a Python nonprofit vs. for-profit operating margin analysis.", "summary": "Every Medicare-certified hospital files an annual Medicare Cost Report -- the only audited hospital-level financial dataset spanning all hospitals. Here is the Worksheet structure, cost-to-charge ratio, DSH payments, IME/GME teaching adjustments, Worksheet S-10 uncompensated care, HCRIS database at NBER, and a Python nonprofit vs. for-profit operating margin analysis.", "date_published": "2026-09-26T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Healthcare", "Hospital Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sba-7a-504-loans/", "url": "https://ai-analytics.org/writing/sba-7a-504-loans/", "title": "SBA 7(a) and 504 Loan Data: The Federal Small Business Lending Database Behind $40 Billion in Annual Guarantees", "content_text": "The SBA publishes loan-level data for all approved 7(a) and 504 loans covering $30-40B/year in guarantees. Here is the guarantee structure, 504 three-party 50/40/10 split, loan dataset fields (NAICS, lender, status, charge-off, ownership flags), lender concentration, industry default rates, SBIC program, and a Python sector default rate analysis.", "summary": "The SBA publishes loan-level data for all approved 7(a) and 504 loans covering $30-40B/year in guarantees. Here is the guarantee structure, 504 three-party 50/40/10 split, loan dataset fields (NAICS, lender, status, charge-off, ownership flags), lender concentration, industry default rates, SBIC program, and a Python sector default rate analysis.", "date_published": "2026-09-25T00:00:00.000Z", "tags": [ "Federal Data", "SBA", "Small Business", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-american-time-use/", "url": "https://ai-analytics.org/writing/bls-american-time-use/", "title": "BLS American Time Use Survey: The Federal Dataset Behind How Americans Actually Spend Their Time", "content_text": "The ATUS tracks 24-hour time diaries for ~10,000 Americans annually since 2003 -- the only federal dataset measuring time allocation across all life activities. Here is the 17 activity categories, gender gap in household/caregiving vs. leisure, parental childcare trends, COVID remote work shift, leisure inequality by education, Well-Being module, IPUMS-ATUS access, and a Python weighted gender gap analysis.", "summary": "The ATUS tracks 24-hour time diaries for ~10,000 Americans annually since 2003 -- the only federal dataset measuring time allocation across all life activities. Here is the 17 activity categories, gender gap in household/caregiving vs. leisure, parental childcare trends, COVID remote work shift, leisure inequality by education, Well-Being module, IPUMS-ATUS access, and a Python weighted gender gap analysis.", "date_published": "2026-09-24T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Demographics", "Labor Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fdic-call-report-data/", "url": "https://ai-analytics.org/writing/fdic-call-report-data/", "title": "FDIC Call Report Data: The Quarterly Financial Filing Behind Every US Bank's Balance Sheet", "content_text": "Every FDIC-insured institution files quarterly Call Reports (FFIEC 031/041/051) covering ~4,700 banks with balance sheet, income, asset quality, capital, and liquidity detail. Here is the RC schedules, RC-N nonperforming loans, RC-R capital ratios and PCA thresholds, the SVB 2022 warning signs visible in call report data, Texas Ratio methodology, FDIC BankFind Suite API, and a Python community-bank screening script.", "summary": "Every FDIC-insured institution files quarterly Call Reports (FFIEC 031/041/051) covering ~4,700 banks with balance sheet, income, asset quality, capital, and liquidity detail. Here is the RC schedules, RC-N nonperforming loans, RC-R capital ratios and PCA thresholds, the SVB 2022 warning signs visible in call report data, Texas Ratio methodology, FDIC BankFind Suite API, and a Python community-bank screening script.", "date_published": "2026-09-23T00:00:00.000Z", "tags": [ "Federal Data", "FDIC", "Banking", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-multifactor-productivity/", "url": "https://ai-analytics.org/writing/bls-multifactor-productivity/", "title": "BLS Multifactor Productivity: The Federal Dataset Behind Long-Run Economic Growth Accounting", "content_text": "The BLS MFP program measures output growth unexplained by labor and capital inputs -- the Solow residual capturing technological progress. Here is growth accounting history, Hall-Jorgenson capital services methodology, labor vs. MFP distinction for real wages, unit labor costs as core inflation driver, the AI productivity hypothesis, FRED series IDs (OPHNFB, ULCNFB), and a Python BLS API dual-axis chart.", "summary": "The BLS MFP program measures output growth unexplained by labor and capital inputs -- the Solow residual capturing technological progress. Here is growth accounting history, Hall-Jorgenson capital services methodology, labor vs. MFP distinction for real wages, unit labor costs as core inflation driver, the AI productivity hypothesis, FRED series IDs (OPHNFB, ULCNFB), and a Python BLS API dual-axis chart.", "date_published": "2026-09-22T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Productivity", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hhs-medicaid-enrollment/", "url": "https://ai-analytics.org/writing/hhs-medicaid-enrollment/", "title": "Medicaid Enrollment Data: The Federal Dataset Behind 90 Million Beneficiaries and $900 Billion in Annual Spending", "content_text": "Medicaid covers ~90M people (~$900B/year) as the largest health coverage program by beneficiary count. Here is monthly enrollment by eligibility group, T-MSIS claims data, MBES expenditures, the ACA expansion divide, COVID surge (70M to 95M) and 2023-2024 unwinding, FMAP mechanics, managed care 70% enrollment share, dual eligibles, long-term care funding, and a Python Medicaid.gov Socrata API unwinding analysis by state.", "summary": "Medicaid covers ~90M people (~$900B/year) as the largest health coverage program by beneficiary count. Here is monthly enrollment by eligibility group, T-MSIS claims data, MBES expenditures, the ACA expansion divide, COVID surge (70M to 95M) and 2023-2024 unwinding, FMAP mechanics, managed care 70% enrollment share, dual eligibles, long-term care funding, and a Python Medicaid.gov Socrata API unwinding analysis by state.", "date_published": "2026-09-21T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Medicaid", "Healthcare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nlrb-union-elections/", "url": "https://ai-analytics.org/writing/nlrb-union-elections/", "title": "NLRB Union Election Data: The Federal Record of Every Organizing Drive and Vote Count", "content_text": "The NLRB publishes election results for ~2,000-2,500 elections annually. Here is the RC/RD/RM petition taxonomy, win rate history (post-PATCO decline to 2022-2024 Starbucks/Amazon surge at 70%+), bargaining unit determination, blocking charge mechanics, the 2023 21-day rapid-response rule, card check vs. secret ballot, and a Python FY2019-2024 analysis by union affiliation.", "summary": "The NLRB publishes election results for ~2,000-2,500 elections annually. Here is the RC/RD/RM petition taxonomy, win rate history (post-PATCO decline to 2022-2024 Starbucks/Amazon surge at 70%+), bargaining unit determination, blocking charge mechanics, the 2023 21-day rapid-response rule, card check vs. secret ballot, and a Python FY2019-2024 analysis by union affiliation.", "date_published": "2026-09-20T00:00:00.000Z", "tags": [ "Federal Data", "NLRB", "Labor Law", "Unions" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-wage-hour-enforcement/", "url": "https://ai-analytics.org/writing/dol-wage-hour-enforcement/", "title": "DOL Wage and Hour Division: The Federal Enforcement Database Behind $300 Million in Annual Back-Wage Recoveries", "content_text": "The DOL Wage and Hour Division enforces the FLSA, Davis-Bacon Act, Service Contract Act, FMLA, and child labor laws -- recovering $200-300M in back wages for 200,000-300,000 workers annually. Here is the WHISARD enforcement database schema, the FLSA exempt vs. non-exempt classification battle, worker misclassification under the 2024 economic reality rule, H-2A agricultural wage violations, Davis-Bacon prevailing wage enforcement, the Asplundh $95M settlement, FLSA criminal prosecution, and a Python sector-level penalty analysis by NAICS code.", "summary": "The DOL Wage and Hour Division enforces the FLSA, Davis-Bacon Act, Service Contract Act, FMLA, and child labor laws -- recovering $200-300M in back wages for 200,000-300,000 workers annually. Here is the WHISARD enforcement database schema, the FLSA exempt vs. non-exempt classification battle, worker misclassification under the 2024 economic reality rule, H-2A agricultural wage violations, Davis-Bacon prevailing wage enforcement, the Asplundh $95M settlement, FLSA criminal prosecution, and a Python sector-level penalty analysis by NAICS code.", "date_published": "2026-09-19T00:00:00.000Z", "tags": [ "Federal Data", "DOL", "Wage Enforcement", "Labor" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-ppi-producer-prices/", "url": "https://ai-analytics.org/writing/bls-ppi-producer-prices/", "title": "BLS PPI: The Producer Price Index and the Federal Inflation Dataset That Leads CPI", "content_text": "The BLS Producer Price Index measures selling prices received by domestic producers -- upstream complement to CPI with a 2-3 month leading relationship to goods inflation. Here is the three indexing systems (Final Demand PPI, Intermediate Demand, traditional commodity-based), trade services margin methodology, the 2021-2022 surge (+22.9% FD goods peak), FRED series IDs (PPIFIS, PPIFAF, PPIFAE, PPICOR, PPIACO), BLS API access, and a Python component-breakdown inflation chart.", "summary": "The BLS Producer Price Index measures selling prices received by domestic producers -- upstream complement to CPI with a 2-3 month leading relationship to goods inflation. Here is the three indexing systems (Final Demand PPI, Intermediate Demand, traditional commodity-based), trade services margin methodology, the 2021-2022 surge (+22.9% FD goods peak), FRED series IDs (PPIFIS, PPIFAF, PPIFAE, PPICOR, PPIACO), BLS API access, and a Python component-breakdown inflation chart.", "date_published": "2026-09-18T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Inflation", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-decennial-redistricting/", "url": "https://ai-analytics.org/writing/census-decennial-redistricting/", "title": "Census PL 94-171: The Redistricting Data Behind Every Congressional Map", "content_text": "Public Law 94-171 mandates block-level population data to states for legislative redistricting -- the foundational dataset for every congressional and state legislative district. Here is the five data tables, geographic hierarchy to census block, the 2020 apportionment results (Texas +2, New York missed by 89 people), differential privacy and the TopDown Algorithm controversy, the 63-combination race/ethnicity schema, Census API variable naming, VRA Section 2 and the Gingles three-part test, and a Python Census API racial composition analysis.", "summary": "Public Law 94-171 mandates block-level population data to states for legislative redistricting -- the foundational dataset for every congressional and state legislative district. Here is the five data tables, geographic hierarchy to census block, the 2020 apportionment results (Texas +2, New York missed by 89 people), differential privacy and the TopDown Algorithm controversy, the 63-combination race/ethnicity schema, Census API variable naming, VRA Section 2 and the Gingles three-part test, and a Python Census API racial composition analysis.", "date_published": "2026-09-17T00:00:00.000Z", "tags": [ "Federal Data", "Census Bureau", "Redistricting", "Demographics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/treasury-tic-capital-flows/", "url": "https://ai-analytics.org/writing/treasury-tic-capital-flows/", "title": "Treasury TIC: The Federal Dataset Behind Foreign Ownership of US Securities", "content_text": "The Treasury International Capital system tracks foreign purchases and sales of US securities -- the primary federal source on who holds US Treasuries and how capital flows across borders. Here is the four main TIC reports (monthly major holders, TIC-S/TIC-B flow surveys, SHCA annual position survey, SHLA mirror), top foreign holders (Japan $1.1T, China $800B peak, UK $700B, Belgium/Euroclear anomaly), the custodian country problem, China's \"financial nuclear option\" analysis, sudden stop risk, and a Python script to download the monthly major foreign holders Excel.", "summary": "The Treasury International Capital system tracks foreign purchases and sales of US securities -- the primary federal source on who holds US Treasuries and how capital flows across borders. Here is the four main TIC reports (monthly major holders, TIC-S/TIC-B flow surveys, SHCA annual position survey, SHLA mirror), top foreign holders (Japan $1.1T, China $800B peak, UK $700B, Belgium/Euroclear anomaly), the custodian country problem, China's \"financial nuclear option\" analysis, sudden stop risk, and a Python script to download the monthly major foreign holders Excel.", "date_published": "2026-09-15T00:00:00.000Z", "tags": [ "Federal Data", "Treasury", "Capital Flows", "International Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-wonder-mortality/", "url": "https://ai-analytics.org/writing/cdc-wonder-mortality/", "title": "CDC WONDER: The Federal Mortality Database Behind Every Death Statistics Analysis", "content_text": "CDC WONDER is the query interface for US death certificate data -- every death in America since 1999 coded by ICD-10 underlying cause. Here is the death certificate pipeline, ICD-10 code taxonomy (C for cancers, I for circulatory, F for mental, V-Y for external causes), the <10 death suppression rule, age-adjusted rates using the 2000 Standard Population, the three-wave opioid crisis (T40.2-T40.3 prescription to T40.1 heroin to T40.4 fentanyl, ~110K deaths 2022), Case-Deaton deaths of despair research, and COVID-19 U07.1 excess mortality analysis.", "summary": "CDC WONDER is the query interface for US death certificate data -- every death in America since 1999 coded by ICD-10 underlying cause. Here is the death certificate pipeline, ICD-10 code taxonomy (C for cancers, I for circulatory, F for mental, V-Y for external causes), the <10 death suppression rule, age-adjusted rates using the 2000 Standard Population, the three-wave opioid crisis (T40.2-T40.3 prescription to T40.1 heroin to T40.4 fentanyl, ~110K deaths 2022), Case-Deaton deaths of despair research, and COVID-19 U07.1 excess mortality analysis.", "date_published": "2026-09-14T00:00:00.000Z", "tags": [ "Federal Data", "CDC", "Mortality", "Public Health" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-jolts-job-openings/", "url": "https://ai-analytics.org/writing/bls-jolts-job-openings/", "title": "BLS JOLTS: The Federal Job Openings and Labor Turnover Survey Behind Every Tight-Labor-Market Claim", "content_text": "The BLS Job Openings and Labor Turnover Survey measures monthly flow of workers into and out of US employment -- job openings, hires, quits, and layoffs across 21,000 establishments. Here is the four core metrics, how the quit rate peaked at 3.0% in April 2022, the Beveridge Curve rightward shift, labor hoarding in 2023, JOLTS vs. Indeed/LinkedIn alternative measures, FRED series IDs (JTSJOL/JTSHIL/JTSQUL/JTSLAL/JTSQUR), and a Python Beveridge Curve plot.", "summary": "The BLS Job Openings and Labor Turnover Survey measures monthly flow of workers into and out of US employment -- job openings, hires, quits, and layoffs across 21,000 establishments. Here is the four core metrics, how the quit rate peaked at 3.0% in April 2022, the Beveridge Curve rightward shift, labor hoarding in 2023, JOLTS vs. Indeed/LinkedIn alternative measures, FRED series IDs (JTSJOL/JTSHIL/JTSQUL/JTSLAL/JTSQUR), and a Python Beveridge Curve plot.", "date_published": "2026-09-13T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Labor Market", "Employment" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nhtsa-fars-fatality-data/", "url": "https://ai-analytics.org/writing/nhtsa-fars-fatality-data/", "title": "NHTSA FARS: The Federal Traffic Fatality Census Behind Every Road Safety Analysis", "content_text": "The NHTSA FARS is a complete census of every US traffic fatality since 1975 -- all 38,000-43,000 annual deaths with linked accident, vehicle, and person detail. Here is the three-table structure, key variable codes (HARM_EV, MAN_COLL, LGT_COND, DRUNK_DR), the COVID anomaly (miles -13% but fatality rate spiked 24%), alcohol-impaired decline from 20K/year to 10.5K/year, pedestrian fatality rise from 4,300 to 7,500 since 2010, the CRSS companion for non-fatal crashes, and a Python state-level pedestrian fatality rate analysis.", "summary": "The NHTSA FARS is a complete census of every US traffic fatality since 1975 -- all 38,000-43,000 annual deaths with linked accident, vehicle, and person detail. Here is the three-table structure, key variable codes (HARM_EV, MAN_COLL, LGT_COND, DRUNK_DR), the COVID anomaly (miles -13% but fatality rate spiked 24%), alcohol-impaired decline from 20K/year to 10.5K/year, pedestrian fatality rise from 4,300 to 7,500 since 2010, the CRSS companion for non-fatal crashes, and a Python state-level pedestrian fatality rate analysis.", "date_published": "2026-09-12T00:00:00.000Z", "tags": [ "Federal Data", "NHTSA", "Traffic Safety", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-medicare-advantage/", "url": "https://ai-analytics.org/writing/cms-medicare-advantage/", "title": "CMS Medicare Advantage: Plan Bids, Star Ratings, and the Federal Dataset Behind Private Medicare", "content_text": "Medicare Advantage now covers 51% of Medicare beneficiaries through private insurance plans. Here is the CMS benchmark-bid-rebate system, Star Ratings framework, HCC risk adjustment upcoding controversy, prior authorization denial rates, enrollment concentration (UHC/Humana/CVS top 3), and a Python market-share analysis by state.", "summary": "Medicare Advantage now covers 51% of Medicare beneficiaries through private insurance plans. Here is the CMS benchmark-bid-rebate system, Star Ratings framework, HCC risk adjustment upcoding controversy, prior authorization denial rates, enrollment concentration (UHC/Humana/CVS top 3), and a Python market-share analysis by state.", "date_published": "2026-09-10T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Medicare Advantage", "Healthcare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/irs-statistics-of-income/", "url": "https://ai-analytics.org/writing/irs-statistics-of-income/", "title": "IRS Statistics of Income: The Federal Dataset Behind the US Tax and Income Distribution", "content_text": "The IRS SOI program publishes aggregated tax return statistics since 1916 -- the definitive source on US income distribution and effective tax rates. Here is the individual AGI class tables, top 1% income share data, EITC distribution, estate tax stepped-up basis, corporate SOI, and the Public Use File for microsimulation.", "summary": "The IRS SOI program publishes aggregated tax return statistics since 1916 -- the definitive source on US income distribution and effective tax rates. Here is the individual AGI class tables, top 1% income share data, EITC distribution, estate tax stepped-up basis, corporate SOI, and the Public Use File for microsimulation.", "date_published": "2026-09-09T00:00:00.000Z", "tags": [ "Federal Data", "IRS", "Income", "Tax Policy" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-osha-inspections/", "url": "https://ai-analytics.org/writing/dol-osha-inspections/", "title": "OSHA Inspections: The Federal Database Behind Every Workplace Safety Violation and Citation", "content_text": "OSHA publishes every workplace inspection, citation, and penalty back to 1972. Here is the inspection types, citation taxonomy (Willful $156K max), the Fatal Four fall protection violations, Imperial Sugar explosion, Amazon injury rates, State Plan boundary, and a Python sector-level penalty analysis.", "summary": "OSHA publishes every workplace inspection, citation, and penalty back to 1972. Here is the inspection types, citation taxonomy (Willful $156K max), the Fatal Four fall protection violations, Imperial Sugar explosion, Amazon injury rates, State Plan boundary, and a Python sector-level penalty analysis.", "date_published": "2026-09-08T00:00:00.000Z", "tags": [ "Federal Data", "OSHA", "Workplace Safety", "Labor" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hmda-mortgage-disclosure/", "url": "https://ai-analytics.org/writing/hmda-mortgage-disclosure/", "title": "HMDA: The Home Mortgage Disclosure Act Dataset Behind Every Redlining Investigation", "content_text": "HMDA requires most mortgage lenders to publicly disclose every application, origination, and denial -- with loan amount, property location, applicant race/ethnicity, income, pricing, DTI, LTV, and AUS results. Here is the full post-2018 field schema, redlining enforcement cases, denial reason codes, HMDA Platform API, CRA connections, and a Python county-level racial denial rate disparity analysis.", "summary": "HMDA requires most mortgage lenders to publicly disclose every application, origination, and denial -- with loan amount, property location, applicant race/ethnicity, income, pricing, DTI, LTV, and AUS results. Here is the full post-2018 field schema, redlining enforcement cases, denial reason codes, HMDA Platform API, CRA connections, and a Python county-level racial denial rate disparity analysis.", "date_published": "2026-09-07T00:00:00.000Z", "tags": [ "Federal Data", "CFPB", "Mortgage", "Fair Lending" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-acs-data/", "url": "https://ai-analytics.org/writing/census-acs-data/", "title": "Census ACS: The American Community Survey and the Federal Demographic Dataset Behind Every Policy Decision", "content_text": "The American Community Survey surveys 3.5 million addresses per year with continuous annual estimates at tract level. Here is the 1-year vs. 5-year distinction, full variable taxonomy, margin of error thresholds, Census API naming conventions, key tables for income/poverty/rent/race/commute, and a Python census-tract rent burden analysis.", "summary": "The American Community Survey surveys 3.5 million addresses per year with continuous annual estimates at tract level. Here is the 1-year vs. 5-year distinction, full variable taxonomy, margin of error thresholds, Census API naming conventions, key tables for income/poverty/rent/race/commute, and a Python census-tract rent burden analysis.", "date_published": "2026-09-06T00:00:00.000Z", "tags": [ "Federal Data", "Census Bureau", "Demographics", "Housing" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-cpi-inflation/", "url": "https://ai-analytics.org/writing/bls-cpi-inflation/", "title": "BLS CPI: The Consumer Price Index and the Federal Inflation Measurement Behind Every Policy Decision", "content_text": "The BLS Consumer Price Index has tracked urban consumer prices since 1913, driving Social Security COLAs, wage negotiations, and Fed policy. Here is CPI-U vs. CPI-W vs. Chained CPI, basket weights (shelter 35%, OER methodology), CPI vs. PCE gap, the 2021-2023 9.1% peak, FRED series IDs, BLS API, and a Python component-breakdown chart.", "summary": "The BLS Consumer Price Index has tracked urban consumer prices since 1913, driving Social Security COLAs, wage negotiations, and Fed policy. Here is CPI-U vs. CPI-W vs. Chained CPI, basket weights (shelter 35%, OER methodology), CPI vs. PCE gap, the 2021-2023 9.1% peak, FRED series IDs, BLS API, and a Python component-breakdown chart.", "date_published": "2026-09-04T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Inflation", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-brfss-behavioral-risk/", "url": "https://ai-analytics.org/writing/cdc-brfss-behavioral-risk/", "title": "CDC BRFSS: The World's Largest Telephone Survey and the Federal Health Behavior Database", "content_text": "The CDC Behavioral Risk Factor Surveillance System interviews ~450,000 adults per year -- the world's largest health survey. Here is the core module variables, raking weighting methodology, PLACES MRP small-area estimation, the 2011 cell-phone expansion discontinuity, and a Python approach to computing weighted state-level obesity prevalence from the LLCP XPT file.", "summary": "The CDC Behavioral Risk Factor Surveillance System interviews ~450,000 adults per year -- the world's largest health survey. Here is the core module variables, raking weighting methodology, PLACES MRP small-area estimation, the 2011 cell-phone expansion discontinuity, and a Python approach to computing weighted state-level obesity prevalence from the LLCP XPT file.", "date_published": "2026-09-03T00:00:00.000Z", "tags": [ "Federal Data", "CDC", "Public Health", "Health Surveys" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fhfa-house-price-index/", "url": "https://ai-analytics.org/writing/fhfa-house-price-index/", "title": "FHFA House Price Index: The Federal Repeat-Sales Benchmark for US Home Prices", "content_text": "The FHFA HPI tracks single-family home price changes using repeat-sales methodology on conforming mortgages back to 1975. Here is the weighted repeat-sales methodology, conforming loan limit boundary, expanded-data HPI with FHA additions, the 40%+ pandemic surge, FHFA vs. Case-Shiller vs. Zillow distinctions, and a Python script for state-level YoY appreciation rankings.", "summary": "The FHFA HPI tracks single-family home price changes using repeat-sales methodology on conforming mortgages back to 1975. Here is the weighted repeat-sales methodology, conforming loan limit boundary, expanded-data HPI with FHA additions, the 40%+ pandemic surge, FHFA vs. Case-Shiller vs. Zillow distinctions, and a Python script for state-level YoY appreciation rankings.", "date_published": "2026-09-02T00:00:00.000Z", "tags": [ "Federal Data", "FHFA", "Housing", "Real Estate" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/irs-990-nonprofit-data/", "url": "https://ai-analytics.org/writing/irs-990-nonprofit-data/", "title": "IRS Form 990: The Public Financial Disclosure Behind Every Major US Nonprofit", "content_text": "The IRS Form 990 requires most 501(c) organizations to publicly disclose revenue, expenses, executive compensation, and governance -- the primary accountability mechanism for the $3T+ US nonprofit sector. Here is the form variants, Part VII executive pay, the AWS S3 bulk XML dataset (4M+ filings), ProPublica API, dark money tracking, and financial ratio extraction in Python.", "summary": "The IRS Form 990 requires most 501(c) organizations to publicly disclose revenue, expenses, executive compensation, and governance -- the primary accountability mechanism for the $3T+ US nonprofit sector. Here is the form variants, Part VII executive pay, the AWS S3 bulk XML dataset (4M+ filings), ProPublica API, dark money tracking, and financial ratio extraction in Python.", "date_published": "2026-09-01T00:00:00.000Z", "tags": [ "Federal Data", "IRS", "Nonprofits", "Tax Transparency" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fed-h8-bank-balance-sheets/", "url": "https://ai-analytics.org/writing/fed-h8-bank-balance-sheets/", "title": "Federal Reserve H.8: The Weekly Snapshot of Every US Commercial Bank's Balance Sheet", "content_text": "The Federal Reserve H.8 is a weekly aggregate balance sheet for all US commercial banks covering $23T+ in assets -- C&I loans, real estate loans, securities, reserve balances, and deposit flows. Here is the large vs. small bank breakdown, the SVB $98B deposit outflow signal, H.8 vs. Call Report distinctions, all key FRED series IDs, and a Python credit cycle tracking snippet.", "summary": "The Federal Reserve H.8 is a weekly aggregate balance sheet for all US commercial banks covering $23T+ in assets -- C&I loans, real estate loans, securities, reserve balances, and deposit flows. Here is the large vs. small bank breakdown, the SVB $98B deposit outflow signal, H.8 vs. Call Report distinctions, all key FRED series IDs, and a Python credit cycle tracking snippet.", "date_published": "2026-08-31T00:00:00.000Z", "tags": [ "Federal Data", "Federal Reserve", "Banking", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-county-business-patterns/", "url": "https://ai-analytics.org/writing/census-county-business-patterns/", "title": "Census County Business Patterns: Annual Establishment Counts, Employment, and Payroll for Every US County", "content_text": "County Business Patterns is the Census Bureau annual series on US business activity at the county-NAICS level, published since 1964 -- establishment counts by size class, mid-March employment, and first-quarter payroll for every county. Here is the Business Register source, noise infusion disclosure methodology, Nonemployer Statistics companion, CBP vs. QCEW vs. Economic Census distinctions, Business Dynamics Statistics, Census API access, and manufacturing location quotients by county.", "summary": "County Business Patterns is the Census Bureau annual series on US business activity at the county-NAICS level, published since 1964 -- establishment counts by size class, mid-March employment, and first-quarter payroll for every county. Here is the Business Register source, noise infusion disclosure methodology, Nonemployer Statistics companion, CBP vs. QCEW vs. Economic Census distinctions, Business Dynamics Statistics, Census API access, and manufacturing location quotients by county.", "date_published": "2026-08-30T00:00:00.000Z", "tags": [ "Federal Data", "Census Bureau", "Business", "Local Economy" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/naep-education-assessment/", "url": "https://ai-analytics.org/writing/naep-education-assessment/", "title": "NAEP: The Nation's Report Card and the Federal Dataset Behind US Education Achievement", "content_text": "The National Assessment of Educational Progress is the only nationally representative, continuing assessment of US student achievement -- covering reading, math, science, and more for 4th, 8th, and 12th graders. Here is the 0-500 scale and NAGB achievement levels, the COVID learning loss evidence, state comparison methodology, plausible values estimation, NAEP Data Explorer API, and the White-Black achievement gap trend since 1992.", "summary": "The National Assessment of Educational Progress is the only nationally representative, continuing assessment of US student achievement -- covering reading, math, science, and more for 4th, 8th, and 12th graders. Here is the 0-500 scale and NAGB achievement levels, the COVID learning loss evidence, state comparison methodology, plausible values estimation, NAEP Data Explorer API, and the White-Black achievement gap trend since 1992.", "date_published": "2026-08-29T00:00:00.000Z", "tags": [ "Federal Data", "NCES", "Education", "Assessment" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-oews-occupational-wages/", "url": "https://ai-analytics.org/writing/bls-oews-occupational-wages/", "title": "BLS OEWS: The Occupational Employment and Wage Statistics Behind Every Salary Benchmark", "content_text": "The BLS Occupational Employment and Wage Statistics program covers 800+ occupations across every industry and geography -- the most comprehensive source for occupation-level wage percentiles in the US. Here is the survey methodology, full SOC hierarchy, wage percentile fields, the H-1B prevailing wage Level I-IV connection, OEWS vs. CPS vs. QCEW distinctions, and a Python script for the highest-paid tech occupations.", "summary": "The BLS Occupational Employment and Wage Statistics program covers 800+ occupations across every industry and geography -- the most comprehensive source for occupation-level wage percentiles in the US. Here is the survey methodology, full SOC hierarchy, wage percentile fields, the H-1B prevailing wage Level I-IV connection, OEWS vs. CPS vs. QCEW distinctions, and a Python script for the highest-paid tech occupations.", "date_published": "2026-08-28T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Wages", "Labor Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/uspto-patent-data/", "url": "https://ai-analytics.org/writing/uspto-patent-data/", "title": "USPTO Patent Data: The Federal Database Behind Every US Patent Grant and Application", "content_text": "The USPTO publishes bulk patent grant data (4M+ grants since 1976) and applications (since 2001), with PatentsView as the canonical research dataset -- disambiguated inventor and assignee records, CPC codes, citation networks, and prosecution history via PEDS. Here is the three patent types, continuation and evergreening strategy, Alice Corp and IPR controversies, PatentsView API, BigQuery public data, and a Python snippet for top AI patent holders.", "summary": "The USPTO publishes bulk patent grant data (4M+ grants since 1976) and applications (since 2001), with PatentsView as the canonical research dataset -- disambiguated inventor and assignee records, CPC codes, citation networks, and prosecution history via PEDS. Here is the three patent types, continuation and evergreening strategy, Alice Corp and IPR controversies, PatentsView API, BigQuery public data, and a Python snippet for top AI patent holders.", "date_published": "2026-08-27T00:00:00.000Z", "tags": [ "Federal Data", "USPTO", "Patents", "Intellectual Property" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bea-gdp-accounts/", "url": "https://ai-analytics.org/writing/bea-gdp-accounts/", "title": "BEA GDP and National Accounts: The Federal Dataset That Measures the US Economy", "content_text": "The BEA National Income and Product Accounts are the official measure of US economic output, income, and spending — updated three times per year with advance, second, and third estimates. Here is the C+I+G+(X-M) expenditure identity, every GDP component in depth, real vs. nominal GDP, GDP by State and GDP by Industry breakdowns, the BEA API query structure, and FRED series IDs as the easiest access path.", "summary": "The BEA National Income and Product Accounts are the official measure of US economic output, income, and spending — updated three times per year with advance, second, and third estimates. Here is the C+I+G+(X-M) expenditure identity, every GDP component in depth, real vs. nominal GDP, GDP by State and GDP by Industry breakdowns, the BEA API query structure, and FRED series IDs as the easiest access path.", "date_published": "2026-08-26T00:00:00.000Z", "tags": [ "Federal Data", "BEA", "GDP", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-drug-approvals/", "url": "https://ai-analytics.org/writing/fda-drug-approvals/", "title": "FDA Drug Approvals: The NDA, BLA, and ANDA Database Behind Every Drug on the Market", "content_text": "The FDA CDER Drugs@FDA dataset tracks every drug approval action since 1939 — NDAs for brand drugs, BLAs for biologics, ANDAs for generics. Here is the Orange Book TE codes and patent/exclusivity listings, NCE/3-year/pediatric/orphan/biologic exclusivity mechanics, Breakthrough and Accelerated Approval designations, the Aduhelm controversy, and how to query OpenFDA drugs API.", "summary": "The FDA CDER Drugs@FDA dataset tracks every drug approval action since 1939 — NDAs for brand drugs, BLAs for biologics, ANDAs for generics. Here is the Orange Book TE codes and patent/exclusivity listings, NCE/3-year/pediatric/orphan/biologic exclusivity mechanics, Breakthrough and Accelerated Approval designations, the Aduhelm controversy, and how to query OpenFDA drugs API.", "date_published": "2026-08-25T00:00:00.000Z", "tags": [ "Federal Data", "FDA", "Drug Approvals", "Pharmaceuticals" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/medicare-part-b-data/", "url": "https://ai-analytics.org/writing/medicare-part-b-data/", "title": "Medicare Part B Data: Every Procedure Billed to Medicare and What It Paid", "content_text": "The CMS Medicare Part B Physician and Supplier Public Use File covers 1M+ providers, 12,000+ HCPCS procedure codes, and $400B+ in annual submitted charges. Here is the submitted vs. allowed vs. payment markup ratio, standardized payments removing geographic wage index, the Lucentis/Avastin ASP+6% controversy, the Salomon Melgen $21M ophthalmology fraud, and how to filter anti-VEGF injections to expose the billion-dollar pricing disparity.", "summary": "The CMS Medicare Part B Physician and Supplier Public Use File covers 1M+ providers, 12,000+ HCPCS procedure codes, and $400B+ in annual submitted charges. Here is the submitted vs. allowed vs. payment markup ratio, standardized payments removing geographic wage index, the Lucentis/Avastin ASP+6% controversy, the Salomon Melgen $21M ophthalmology fraud, and how to filter anti-VEGF injections to expose the billion-dollar pricing disparity.", "date_published": "2026-08-24T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Medicare", "Healthcare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-qcew-employment/", "url": "https://ai-analytics.org/writing/bls-qcew-employment/", "title": "BLS QCEW: The County-Level Employment and Wages Dataset Behind Every Local Economic Analysis", "content_text": "The BLS Quarterly Census of Employment and Wages covers 97%+ of US jobs at the county-NAICS industry level -- the most granular federal employment dataset available. Here is the QCEW vs. CES vs. LAUS distinctions, suppression rules for counties with fewer than three establishments, average weekly wage by sector, BLS bulk CSV download structure, and a Python snippet for highest-wage industries by county.", "summary": "The BLS Quarterly Census of Employment and Wages covers 97%+ of US jobs at the county-NAICS industry level -- the most granular federal employment dataset available. Here is the QCEW vs. CES vs. LAUS distinctions, suppression rules for counties with fewer than three establishments, average weekly wage by sector, BLS bulk CSV download structure, and a Python snippet for highest-wage industries by county.", "date_published": "2026-08-23T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Employment", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fbi-nics-background-checks/", "url": "https://ai-analytics.org/writing/fbi-nics-background-checks/", "title": "FBI NICS Background Checks: The Federal Dataset Behind 400 Million Firearm Transfer Attempts", "content_text": "The Brady Act NICS system has processed 400M+ background checks since 1998 — publishing monthly state-level counts of handgun, long gun, and permit check types. Here is the full check type taxonomy, why NICS counts do not equal gun sales, the default proceed loophole, the COVID and Biden inauguration demand spikes, and the BuzzFeed News parsed CSV.", "summary": "The Brady Act NICS system has processed 400M+ background checks since 1998 — publishing monthly state-level counts of handgun, long gun, and permit check types. Here is the full check type taxonomy, why NICS counts do not equal gun sales, the default proceed loophole, the COVID and Biden inauguration demand spikes, and the BuzzFeed News parsed CSV.", "date_published": "2026-08-22T00:00:00.000Z", "tags": [ "Federal Data", "FBI", "Firearms", "Public Safety" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hud-lihtc-database/", "url": "https://ai-analytics.org/writing/hud-lihtc-database/", "title": "HUD LIHTC Database: Mapping 35 Years of Low-Income Housing Tax Credit Projects", "content_text": "The LIHTC program has financed 50,000+ projects and 3.5M+ affordable units since 1986. Here is the HUD database schema, the 9% vs. 4% credit mechanics, how State HFA Qualified Allocation Plans shape development geography, the National Housing Preservation Database complement, and how to compute units per capita by state.", "summary": "The LIHTC program has financed 50,000+ projects and 3.5M+ affordable units since 1986. Here is the HUD database schema, the 9% vs. 4% credit mechanics, how State HFA Qualified Allocation Plans shape development geography, the National Housing Preservation Database complement, and how to compute units per capita by state.", "date_published": "2026-08-21T00:00:00.000Z", "tags": [ "Federal Data", "HUD", "Affordable Housing", "Housing Policy" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cftc-commitments-of-traders/", "url": "https://ai-analytics.org/writing/cftc-commitments-of-traders/", "title": "CFTC Commitments of Traders: The Weekly Federal Report Behind Futures Market Positioning", "content_text": "The CFTC publishes weekly open interest broken down by trader category for every regulated futures market since 1986. Here is the four COT formats, how net non-commercial positioning signals crowded trades, the disaggregated vs. legacy distinction, all covered markets, and how to build a 52-week COT z-score.", "summary": "The CFTC publishes weekly open interest broken down by trader category for every regulated futures market since 1986. Here is the four COT formats, how net non-commercial positioning signals crowded trades, the disaggregated vs. legacy distinction, all covered markets, and how to build a 52-week COT z-score.", "date_published": "2026-08-20T00:00:00.000Z", "tags": [ "Federal Data", "CFTC", "Futures Markets", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/treasury-ofac-sanctions/", "url": "https://ai-analytics.org/writing/treasury-ofac-sanctions/", "title": "OFAC Sanctions Lists: The Treasury Database Every Financial Institution Must Screen Against", "content_text": "The OFAC SDN list (~8,000 entries) covers every individual, entity, and vessel US persons are prohibited from transacting with — civil penalties up to $1.3M per violation. Here is the full SDN record schema, all major sanctions programs, the 50% ownership rule, the Binance $4.3B landmark penalty, and how to parse the XML list.", "summary": "The OFAC SDN list (~8,000 entries) covers every individual, entity, and vessel US persons are prohibited from transacting with — civil penalties up to $1.3M per violation. Here is the full SDN record schema, all major sanctions programs, the 50% ownership rule, the Binance $4.3B landmark penalty, and how to parse the XML list.", "date_published": "2026-08-19T00:00:00.000Z", "tags": [ "Federal Data", "OFAC", "Sanctions", "Compliance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/epa-toxic-release-inventory/", "url": "https://ai-analytics.org/writing/epa-toxic-release-inventory/", "title": "EPA Toxic Release Inventory: 35 Years of Industrial Chemical Releases and Environmental Justice Patterns", "content_text": "EPCRA Section 313 requires 20,000+ industrial facilities to report annual releases of 800+ toxic chemicals. Here is the full TRI field schema, the 75% release decline since 1988, the 2024 PFAS additions, how to use the RSEI model for toxicity-weighted population exposure, and how to join TRI to Census ACS for environmental justice analysis.", "summary": "EPCRA Section 313 requires 20,000+ industrial facilities to report annual releases of 800+ toxic chemicals. Here is the full TRI field schema, the 75% release decline since 1988, the 2024 PFAS additions, how to use the RSEI model for toxicity-weighted population exposure, and how to join TRI to Census ACS for environmental justice analysis.", "date_published": "2026-08-18T00:00:00.000Z", "tags": [ "Federal Data", "EPA", "Environmental Justice", "Chemical Safety" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-hospital-quality/", "url": "https://ai-analytics.org/writing/cms-hospital-quality/", "title": "CMS Hospital Quality Data: Outcomes, Readmissions, and Star Ratings for 6,000 US Hospitals", "content_text": "CMS Care Compare publishes quality measures for every Medicare-certified hospital — 30-day mortality and readmission rates, HCAHPS patient experience, process compliance, and Medicare spending per beneficiary. Here is the full measure taxonomy, risk adjustment methodology, HAC Reduction Program penalties, and how to download and analyze the data.", "summary": "CMS Care Compare publishes quality measures for every Medicare-certified hospital — 30-day mortality and readmission rates, HCAHPS patient experience, process compliance, and Medicare spending per beneficiary. Here is the full measure taxonomy, risk adjustment methodology, HAC Reduction Program penalties, and how to download and analyze the data.", "date_published": "2026-08-17T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Healthcare Quality", "Hospitals" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-edgar-financials/", "url": "https://ai-analytics.org/writing/sec-edgar-financials/", "title": "SEC EDGAR XBRL Financials: Machine-Readable Fundamentals for Every Public Company", "content_text": "Every public company files XBRL-tagged financial statements with the SEC — extractable through the EDGAR Company Facts API, the Frames cross-sectional endpoint, and bulk quarterly FSN downloads. Here is the US-GAAP taxonomy structure, three data quality pitfalls, rate limits, and how to build a revenue growth screener.", "summary": "Every public company files XBRL-tagged financial statements with the SEC — extractable through the EDGAR Company Facts API, the Frames cross-sectional endpoint, and bulk quarterly FSN downloads. Here is the US-GAAP taxonomy structure, three data quality pitfalls, rate limits, and how to build a revenue growth screener.", "date_published": "2026-08-16T00:00:00.000Z", "tags": [ "Federal Data", "SEC", "EDGAR", "Financial Data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/corporate-prosecution-registry/", "url": "https://ai-analytics.org/writing/corporate-prosecution-registry/", "title": "Corporate Prosecution Registry: DPAs, NPAs, and the Too-Big-to-Jail Database", "content_text": "The Corporate Prosecution Registry (Duke Law) tracks every federal corporate criminal resolution since 1990 — DPAs, NPAs, and guilty pleas — covering 400+ resolutions and $30B+ in fines. Here is the resolution taxonomy, the Yates Memo and Monaco Doctrine, the HSBC and Boeing landmark cases, the compliance monitor system, and FCPA enforcement patterns.", "summary": "The Corporate Prosecution Registry (Duke Law) tracks every federal corporate criminal resolution since 1990 — DPAs, NPAs, and guilty pleas — covering 400+ resolutions and $30B+ in fines. Here is the resolution taxonomy, the Yates Memo and Monaco Doctrine, the HSBC and Boeing landmark cases, the compliance monitor system, and FCPA enforcement patterns.", "date_published": "2026-08-15T00:00:00.000Z", "tags": [ "Federal Data", "DOJ", "Corporate Crime", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usaid-foreign-assistance/", "url": "https://ai-analytics.org/writing/usaid-foreign-assistance/", "title": "USAID Foreign Assistance Data: Tracing $50 Billion in Annual US Development Spending", "content_text": "ForeignAssistance.gov publishes every US foreign assistance obligation and disbursement across all agencies — covering $50B+ per year since 2001. Here is the full dataset structure, PEPFAR ($110B+ cumulative, 20M+ on antiretrovirals), top recipient countries, implementing partner ecosystem, and 2025 USAID restructuring implications.", "summary": "ForeignAssistance.gov publishes every US foreign assistance obligation and disbursement across all agencies — covering $50B+ per year since 2001. Here is the full dataset structure, PEPFAR ($110B+ cumulative, 20M+ on antiretrovirals), top recipient countries, implementing partner ecosystem, and 2025 USAID restructuring implications.", "date_published": "2026-08-14T00:00:00.000Z", "tags": [ "Federal Data", "USAID", "Foreign Assistance", "International Development" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/pcaob-audit-oversight/", "url": "https://ai-analytics.org/writing/pcaob-audit-oversight/", "title": "PCAOB: The Federal Audit Watchdog Created After Enron and the KPMG Inspection-Data Scandal", "content_text": "The PCAOB publishes inspection reports on every registered audit firm's deficiency rate. Here is the Big Four inspection pattern, the KPMG $50M stolen-inspection-list scandal, the HFCAA Chinese auditor crisis and 2022 CSRC breakthrough, and how researchers use deficiency rates as an auditor quality proxy.", "summary": "The PCAOB publishes inspection reports on every registered audit firm's deficiency rate. Here is the Big Four inspection pattern, the KPMG $50M stolen-inspection-list scandal, the HFCAA Chinese auditor crisis and 2022 CSRC breakthrough, and how researchers use deficiency rates as an auditor quality proxy.", "date_published": "2026-08-13T00:00:00.000Z", "tags": [ "Federal Data", "PCAOB", "Audit", "Securities Regulation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/medicare-part-d-data/", "url": "https://ai-analytics.org/writing/medicare-part-d-data/", "title": "Medicare Part D Prescribing Data: Every Drug Prescribed by Every Medicare Provider", "content_text": "CMS publishes provider-level Part D prescribing data — 1M+ providers, 5,700+ drugs, $100B+ in visible spending per year. Here is the full schema, how the data exposed the opioid crisis (ProPublica Prescriber Checkup), the GLP-1 agonist cost surge, and how to join with CMS Open Payments for prescribing-payment correlation analysis.", "summary": "CMS publishes provider-level Part D prescribing data — 1M+ providers, 5,700+ drugs, $100B+ in visible spending per year. Here is the full schema, how the data exposed the opioid crisis (ProPublica Prescriber Checkup), the GLP-1 agonist cost surge, and how to join with CMS Open Payments for prescribing-payment correlation analysis.", "date_published": "2026-08-12T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Medicare", "Healthcare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-open-payments/", "url": "https://ai-analytics.org/writing/cms-open-payments/", "title": "CMS Open Payments: Mapping $12 Billion in Drug and Device Industry Payments to Physicians", "content_text": "The Physician Payments Sunshine Act requires manufacturers to report every payment to physicians — consulting fees, speaker fees, meals, royalties, and 22 other categories. Here is the full schema, $3.5B/year scale, the GSK and Novartis enforcement cases, peer-reviewed payment-prescribing correlations, and how to join with Medicare Part D data.", "summary": "The Physician Payments Sunshine Act requires manufacturers to report every payment to physicians — consulting fees, speaker fees, meals, royalties, and 22 other categories. Here is the full schema, $3.5B/year scale, the GSK and Novartis enforcement cases, peer-reviewed payment-prescribing correlations, and how to join with Medicare Part D data.", "date_published": "2026-08-11T00:00:00.000Z", "tags": [ "Federal Data", "CMS", "Healthcare", "Pharmaceutical Industry" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/atf-crime-gun-data/", "url": "https://ai-analytics.org/writing/atf-crime-gun-data/", "title": "ATF Crime Gun Trace Data: The Federal Dataset the Tiahrt Amendment Tried to Hide", "content_text": "The ATF NTC processes 500,000+ firearm traces per year. Here is what the Tiahrt Amendment restricts, what aggregated state-level data reveals about the iron pipeline, how time-to-crime exposes straw purchasing, the FFL directory, AFMER manufacturing data, and the ghost gun tracing gap.", "summary": "The ATF NTC processes 500,000+ firearm traces per year. Here is what the Tiahrt Amendment restricts, what aggregated state-level data reveals about the iron pipeline, how time-to-crime exposes straw purchasing, the FFL directory, AFMER manufacturing data, and the ghost gun tracing gap.", "date_published": "2026-08-10T00:00:00.000Z", "tags": [ "Federal Data", "ATF", "Firearms", "Public Safety" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cpsc-product-recalls/", "url": "https://ai-analytics.org/writing/cpsc-product-recalls/", "title": "CPSC Product Recalls: The Federal Safety Database Behind 400 Consumer Product Recalls Per Year", "content_text": "The CPSC publishes every recall of consumer products — 400-500 per year across 15,000+ product categories. Here is the full recall database schema, the SaferProducts.gov incident system, the IKEA Malm and Fisher-Price Rock n Play landmark cases, CPSIA 2008, and how the voluntary recall process works.", "summary": "The CPSC publishes every recall of consumer products — 400-500 per year across 15,000+ product categories. Here is the full recall database schema, the SaferProducts.gov incident system, the IKEA Malm and Fisher-Price Rock n Play landmark cases, CPSIA 2008, and how the voluntary recall process works.", "date_published": "2026-08-09T00:00:00.000Z", "tags": [ "Federal Data", "CPSC", "Product Safety", "Consumer Protection" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nfip-flood-insurance/", "url": "https://ai-analytics.org/writing/nfip-flood-insurance/", "title": "NFIP Flood Insurance Data: Mapping 40 Years of Flood Claims Across 5 Million Policies", "content_text": "The FEMA NFIP publishes every paid flood claim since 1978 and active policy data — 5M+ policies, $1.3T in coverage, loss history including Katrina ($16B), Sandy ($8B), and Harvey ($9B). Here is the claims/policy dataset structure, flood zone taxonomy, repetitive loss problem, Risk Rating 2.0, and the OpenFEMA API.", "summary": "The FEMA NFIP publishes every paid flood claim since 1978 and active policy data — 5M+ policies, $1.3T in coverage, loss history including Katrina ($16B), Sandy ($8B), and Harvey ($9B). Here is the claims/policy dataset structure, flood zone taxonomy, repetitive loss problem, Risk Rating 2.0, and the OpenFEMA API.", "date_published": "2026-08-08T00:00:00.000Z", "tags": [ "Federal Data", "FEMA", "Flood Insurance", "Climate Risk" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fara-foreign-agents/", "url": "https://ai-analytics.org/writing/fara-foreign-agents/", "title": "FARA: The Foreign Agent Registration Database Behind US Influence Operations", "content_text": "FARA requires US-based agents of foreign principals to register with DOJ and disclose activities and payments. Here is the full registration database schema, the Section 613 LDA exemption, the Manafort conviction and Mueller-era enforcement surge, $700M+ in annual disclosed payments, and how to query the eFile API.", "summary": "FARA requires US-based agents of foreign principals to register with DOJ and disclose activities and payments. Here is the full registration database schema, the Section 613 LDA exemption, the Manafort conviction and Mueller-era enforcement surge, $700M+ in annual disclosed payments, and how to query the eFile API.", "date_published": "2026-08-07T00:00:00.000Z", "tags": [ "Federal Data", "FARA", "Foreign Influence", "Transparency" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-device-recalls/", "url": "https://ai-analytics.org/writing/fda-device-recalls/", "title": "FDA Medical Device Recalls: The Database Behind Every Implant Failure and CPAP Warning", "content_text": "The FDA CDRH publishes every medical device recall action — Class I, II, and III — covering 1,000–1,500 recalls per year. Here is the full field schema, the DePuy ASR ($4B settlement) and Philips Respironics CPAP (5.5M+ units) landmark recalls, how MAUDE adverse event reports feed recall decisions, and how to query the OpenFDA device recall API.", "summary": "The FDA CDRH publishes every medical device recall action — Class I, II, and III — covering 1,000–1,500 recalls per year. Here is the full field schema, the DePuy ASR ($4B settlement) and Philips Respironics CPAP (5.5M+ units) landmark recalls, how MAUDE adverse event reports feed recall decisions, and how to query the OpenFDA device recall API.", "date_published": "2026-08-06T00:00:00.000Z", "tags": [ "Federal Data", "FDA", "Medical Devices", "Product Safety" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ncua-credit-union-data/", "url": "https://ai-analytics.org/writing/ncua-credit-union-data/", "title": "NCUA Credit Union Data: The 5300 Call Report and Enforcement Database for 4,700 Federally Insured Credit Unions", "content_text": "The NCUA publishes quarterly 5300 Call Report data for every federally insured credit union — assets, shares, loans, delinquency, net worth ratios — plus a public enforcement action database. Here is the data structure, the net worth PCA thresholds, the 2009 corporate credit union crisis ($28.5B bailout), and how to download and screen the quarterly data.", "summary": "The NCUA publishes quarterly 5300 Call Report data for every federally insured credit union — assets, shares, loans, delinquency, net worth ratios — plus a public enforcement action database. Here is the data structure, the net worth PCA thresholds, the 2009 corporate credit union crisis ($28.5B bailout), and how to download and screen the quarterly data.", "date_published": "2026-08-05T00:00:00.000Z", "tags": [ "Federal Data", "NCUA", "Credit Unions", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cfpb-enforcement-actions/", "url": "https://ai-analytics.org/writing/cfpb-enforcement-actions/", "title": "CFPB Enforcement Actions: The Public Record of $20 Billion in Consumer Finance Penalties", "content_text": "The CFPB has brought 200+ enforcement actions since 2011 — covering UDAAP violations, redlining, student loan servicer abuses, and predatory auto lending — with $20B+ in consumer relief and penalties. Here is the enforcement action taxonomy, the UDAAP abusiveness standard, the Wells Fargo $3.7B action, and how to scrape and analyze the enforcement database.", "summary": "The CFPB has brought 200+ enforcement actions since 2011 — covering UDAAP violations, redlining, student loan servicer abuses, and predatory auto lending — with $20B+ in consumer relief and penalties. Here is the enforcement action taxonomy, the UDAAP abusiveness standard, the Wells Fargo $3.7B action, and how to scrape and analyze the enforcement database.", "date_published": "2026-08-04T00:00:00.000Z", "tags": [ "Federal Data", "CFPB", "Consumer Finance", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bts-border-crossings/", "url": "https://ai-analytics.org/writing/bts-border-crossings/", "title": "BTS Border Crossing Entry Data: Monthly Counts of Every Vehicle, Truck, and Pedestrian at US Land Ports", "content_text": "The BTS publishes monthly counts of every border crossing type at ~290 US land ports back to 1996 — personal vehicles, pedestrians, trucks, buses, trains, and containers by port. Here is the full taxonomy, the COVID-19 collapse (pedestrians -93%, trucks -28%), San Ysidro and Laredo dominance, and how to use the Socrata API for supply chain and trade flow analysis.", "summary": "The BTS publishes monthly counts of every border crossing type at ~290 US land ports back to 1996 — personal vehicles, pedestrians, trucks, buses, trains, and containers by port. Here is the full taxonomy, the COVID-19 collapse (pedestrians -93%, trucks -28%), San Ysidro and Laredo dominance, and how to use the Socrata API for supply chain and trade flow analysis.", "date_published": "2026-08-03T00:00:00.000Z", "tags": [ "Federal Data", "BTS", "Border Crossings", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/clinicaltrials-gov-data/", "url": "https://ai-analytics.org/writing/clinicaltrials-gov-data/", "title": "ClinicalTrials.gov Data: The Federal Registry Behind Every Drug and Device Trial", "content_text": "FDAAA 801 requires registration of all applicable clinical trials before enrollment and results submission within 12 months of completion — but 50%+ of trials still fail to report results. Here is the full NCT schema, how to access the AACT PostgreSQL mirror from Duke/CTTI, how to detect publication bias using the results reporting gap, and how the GLP-1 agonist trial explosion looks in the data.", "summary": "FDAAA 801 requires registration of all applicable clinical trials before enrollment and results submission within 12 months of completion — but 50%+ of trials still fail to report results. Here is the full NCT schema, how to access the AACT PostgreSQL mirror from Duke/CTTI, how to detect publication bias using the results reporting gap, and how the GLP-1 agonist trial explosion looks in the data.", "date_published": "2026-08-02T00:00:00.000Z", "tags": [ "Federal Data", "ClinicalTrials", "Drug Development", "Research Integrity" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-510k-device-clearances/", "url": "https://ai-analytics.org/writing/fda-510k-device-clearances/", "title": "FDA 510(k) Device Clearances: The Substantial Equivalence Pathway That Cleared 100,000+ Medical Devices", "content_text": "The FDA 510(k) pathway clears medical devices by showing substantial equivalence to a predicate device — no clinical trials required. Here is the three-class device system, the K-number database fields, the predicate daisy-chain problem, the De Novo pathway, and the metal-on-metal hip and vaginal mesh controversies. Plus how to query the OpenFDA device API.", "summary": "The FDA 510(k) pathway clears medical devices by showing substantial equivalence to a predicate device — no clinical trials required. Here is the three-class device system, the K-number database fields, the predicate daisy-chain problem, the De Novo pathway, and the metal-on-metal hip and vaginal mesh controversies. Plus how to query the OpenFDA device API.", "date_published": "2026-08-01T00:00:00.000Z", "tags": [ "Federal Data", "FDA", "Medical Devices", "Healthcare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-h2-visa-disclosures/", "url": "https://ai-analytics.org/writing/dol-h2-visa-disclosures/", "title": "DOL H-2 Visa Disclosures: Mapping the Guest Worker Programs Feeding US Agriculture and Hospitality", "content_text": "The H-2A program (cap-free agricultural) and H-2B program (66,000-cap non-agricultural) bring hundreds of thousands of temporary workers to the US annually. DOL OFLC publishes quarterly disclosure files with employer, job title, wages, worksites, and worker counts. Here is the data structure, H-2A growth from 60,000 to 370,000+ certifications (2012-2023), and how to compare wages against adverse effect wage rates.", "summary": "The H-2A program (cap-free agricultural) and H-2B program (66,000-cap non-agricultural) bring hundreds of thousands of temporary workers to the US annually. DOL OFLC publishes quarterly disclosure files with employer, job title, wages, worksites, and worker counts. Here is the data structure, H-2A growth from 60,000 to 370,000+ certifications (2012-2023), and how to compare wages against adverse effect wage rates.", "date_published": "2026-07-31T00:00:00.000Z", "tags": [ "Federal Data", "DOL", "Immigration", "Labor Markets" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/occ-bank-enforcement/", "url": "https://ai-analytics.org/writing/occ-bank-enforcement/", "title": "OCC Bank Enforcement Actions: Reading the Federal Regulator’s Public Disciplinary Record", "content_text": "The Office of the Comptroller of the Currency publishes every formal enforcement action against national banks and federal thrifts — from Commitment Letters through Formal Agreements, Consent Orders, and Cease-and-Desist Orders. Here is the enforcement action taxonomy, the BSA/AML enforcement pattern, the Wells Fargo consent order cascade, and how to scrape and analyze the OCC enforcement database.", "summary": "The Office of the Comptroller of the Currency publishes every formal enforcement action against national banks and federal thrifts — from Commitment Letters through Formal Agreements, Consent Orders, and Cease-and-Desist Orders. Here is the enforcement action taxonomy, the BSA/AML enforcement pattern, the Wells Fargo consent order cascade, and how to scrape and analyze the OCC enforcement database.", "date_published": "2026-07-30T00:00:00.000Z", "tags": [ "Federal Data", "OCC", "Banking Enforcement", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ferc-energy-enforcement/", "url": "https://ai-analytics.org/writing/ferc-energy-enforcement/", "title": "FERC Enforcement: The Federal Watchdog Over Energy Market Manipulation", "content_text": "FERC investigates electricity and gas market manipulation with penalties up to $1.4M per day per violation. Here is the enforcement database, the JP Morgan ($410M) and Barclays ($488M) cases, how Electric Quarterly Reports expose every bilateral power transaction, and how to search FERC eLibrary enforcement dockets.", "summary": "FERC investigates electricity and gas market manipulation with penalties up to $1.4M per day per violation. Here is the enforcement database, the JP Morgan ($410M) and Barclays ($488M) cases, how Electric Quarterly Reports expose every bilateral power transaction, and how to search FERC eLibrary enforcement dockets.", "date_published": "2026-07-29T00:00:00.000Z", "tags": [ "Federal Data", "FERC", "Energy Markets", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/samhsa-treatment-data/", "url": "https://ai-analytics.org/writing/samhsa-treatment-data/", "title": "SAMHSA Treatment Data: Mapping Substance Use Disorder Services Across 17,000 Facilities", "content_text": "SAMHSA publishes the N-SSATS facility survey (17,000+ treatment locations with services, ownership, and MAT availability) and the TEDS admissions dataset. Here is the data structure, how to map OTP density against overdose death rates, and what the data reveals about rural treatment gaps.", "summary": "SAMHSA publishes the N-SSATS facility survey (17,000+ treatment locations with services, ownership, and MAT availability) and the TEDS admissions dataset. Here is the data structure, how to map OTP density against overdose death rates, and what the data reveals about rural treatment gaps.", "date_published": "2026-07-28T00:00:00.000Z", "tags": [ "Federal Data", "SAMHSA", "Public Health", "Substance Use" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-enforcement-actions/", "url": "https://ai-analytics.org/writing/sec-enforcement-actions/", "title": "SEC Enforcement Actions: The Public Record of Every Securities Law Violation", "content_text": "The SEC publishes Administrative Proceedings, Litigation Releases, and final orders covering 700-800 enforcement actions per year — with $4-5B in annual disgorgement and penalties. Here is the record structure, whistleblower program mechanics, and how to scrape and parse the enforcement databases.", "summary": "The SEC publishes Administrative Proceedings, Litigation Releases, and final orders covering 700-800 enforcement actions per year — with $4-5B in annual disgorgement and penalties. Here is the record structure, whistleblower program mechanics, and how to scrape and parse the enforcement databases.", "date_published": "2026-07-27T00:00:00.000Z", "tags": [ "Federal Data", "SEC", "Securities Enforcement", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hhs-oig-exclusions/", "url": "https://ai-analytics.org/writing/hhs-oig-exclusions/", "title": "HHS OIG Exclusions: The Federal Healthcare Fraud Blacklist That Every Provider Must Screen Against", "content_text": "The HHS OIG LEIE bars providers from billing Medicare and Medicaid — with $10,000 per-service penalties for employers that fail to screen. Here is the exclusion type taxonomy, how to download the monthly CSV, how it differs from SAM.gov EPLS, and how to implement fuzzy-match screening.", "summary": "The HHS OIG LEIE bars providers from billing Medicare and Medicaid — with $10,000 per-service penalties for employers that fail to screen. Here is the exclusion type taxonomy, how to download the monthly CSV, how it differs from SAM.gov EPLS, and how to implement fuzzy-match screening.", "date_published": "2026-07-26T00:00:00.000Z", "tags": [ "Federal Data", "HHS OIG", "Healthcare Fraud", "Compliance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-8k-material-events/", "url": "https://ai-analytics.org/writing/sec-8k-material-events/", "title": "SEC Form 8-K: The Real-Time Disclosure Feed for Every Material Corporate Event", "content_text": "Public companies must file Form 8-K within 4 business days of any material event — covering 33 item types from earnings releases and executive departures to bankruptcy filings and the 2023 cybersecurity incident disclosure requirement. Here is the item taxonomy and how non-reliance filings signal fraud.", "summary": "Public companies must file Form 8-K within 4 business days of any material event — covering 33 item types from earnings releases and executive departures to bankruptcy filings and the 2023 cybersecurity incident disclosure requirement. Here is the item taxonomy and how non-reliance filings signal fraud.", "date_published": "2026-07-25T00:00:00.000Z", "tags": [ "Federal Data", "SEC", "Corporate Disclosure", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nhtsa-vehicle-recalls/", "url": "https://ai-analytics.org/writing/nhtsa-vehicle-recalls/", "title": "NHTSA Vehicle Recall Data: 70 Years of Safety Defects Across 900 Million Vehicles", "content_text": "NHTSA maintains the recall database covering every safety-related defect since 1966. Here is the data structure, the complaint-to-recall investigation pipeline, how to query by VIN, and what the Takata airbag recall reveals about the dataset.", "summary": "NHTSA maintains the recall database covering every safety-related defect since 1966. Here is the data structure, the complaint-to-recall investigation pipeline, how to query by VIN, and what the Takata airbag recall reveals about the dataset.", "date_published": "2026-07-24T00:00:00.000Z", "tags": [ "Federal Data", "NHTSA", "Vehicle Safety", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-form-5500-pensions/", "url": "https://ai-analytics.org/writing/dol-form-5500-pensions/", "title": "DOL Form 5500: The Annual Filing That Exposes Every Private Pension and 401(k) Plan", "content_text": "Every large ERISA plan files Form 5500 annually — covering 750,000+ plans with $10T+ in assets. Schedule C reveals service provider fees; Schedule SB tracks pension funding ratios. Here is the schema, EFAST2 access, and how to compute average expense ratios by plan size.", "summary": "Every large ERISA plan files Form 5500 annually — covering 750,000+ plans with $10T+ in assets. Schedule C reveals service provider fees; Schedule SB tracks pension funding ratios. Here is the schema, EFAST2 access, and how to compute average expense ratios by plan size.", "date_published": "2026-07-23T00:00:00.000Z", "tags": [ "Federal Data", "DOL", "Pensions", "Retirement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/lobbying-disclosure-data/", "url": "https://ai-analytics.org/writing/lobbying-disclosure-data/", "title": "Senate LDA Lobbying Disclosures: Mapping $4 Billion in Annual Influence Spending", "content_text": "The Lobbying Disclosure Act requires quarterly filings with the Senate SOPR — covering lobbyist identities, issue codes, specific bills lobbied, and dollar amounts. Here is the LDA API, the relationship to FARA and LD-203 contribution reports, and how to connect lobbying spending to legislative outcomes.", "summary": "The Lobbying Disclosure Act requires quarterly filings with the Senate SOPR — covering lobbyist identities, issue codes, specific bills lobbied, and dollar amounts. Here is the LDA API, the relationship to FARA and LD-203 contribution reports, and how to connect lobbying spending to legislative outcomes.", "date_published": "2026-07-22T00:00:00.000Z", "tags": [ "Federal Data", "Lobbying", "Transparency", "Politics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usaspending-federal-contracts/", "url": "https://ai-analytics.org/writing/usaspending-federal-contracts/", "title": "USASpending Federal Contracts: Tracing $700 Billion in Annual Government Procurement", "content_text": "USASpending.gov pulls from FPDS-NG to publish every federal contract action — award type, NAICS/PSC codes, competition type, small business set-asides, and full recipient data. Here is the field structure, how to use the USASpending API, and how journalists trace no-bid contracts and contractor concentration.", "summary": "USASpending.gov pulls from FPDS-NG to publish every federal contract action — award type, NAICS/PSC codes, competition type, small business set-asides, and full recipient data. Here is the field structure, how to use the USASpending API, and how journalists trace no-bid contracts and contractor concentration.", "date_published": "2026-07-21T00:00:00.000Z", "tags": [ "Federal Data", "USASpending", "Federal Contracts", "Government Spending" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/eia-electricity-data/", "url": "https://ai-analytics.org/writing/eia-electricity-data/", "title": "EIA Electricity Data: The Federal Dataset Behind Every Power Plant and Grid Operator", "content_text": "The EIA publishes Form 860 (every generator at every plant), Form 923 (monthly generation and fuel consumption), and Form 861 (utility-level retail sales). Here is the data structure, how capacity factors make nameplate comparisons misleading, and what 15 years of data show about the energy transition.", "summary": "The EIA publishes Form 860 (every generator at every plant), Form 923 (monthly generation and fuel consumption), and Form 861 (utility-level retail sales). Here is the data structure, how capacity factors make nameplate comparisons misleading, and what 15 years of data show about the energy transition.", "date_published": "2026-07-20T00:00:00.000Z", "tags": [ "Federal Data", "EIA", "Energy", "Electricity Grid" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cfpb-consumer-complaints/", "url": "https://ai-analytics.org/writing/cfpb-consumer-complaints/", "title": "CFPB Consumer Complaint Database: 5 Million Complaints Against Banks and Lenders", "content_text": "The CFPB Consumer Complaint Database tracks 5M+ complaints since 2011 about mortgages, credit cards, debt collection, and credit reporting — with company response, relief status, and consumer narratives. Here is the schema, how credit reporting complaints surged post-COVID, and how the data connects to enforcement.", "summary": "The CFPB Consumer Complaint Database tracks 5M+ complaints since 2011 about mortgages, credit cards, debt collection, and credit reporting — with company response, relief status, and consumer narratives. Here is the schema, how credit reporting complaints surged post-COVID, and how the data connects to enforcement.", "date_published": "2026-07-19T00:00:00.000Z", "tags": [ "Federal Data", "CFPB", "Consumer Finance", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/finra-brokercheck-data/", "url": "https://ai-analytics.org/writing/finra-brokercheck-data/", "title": "FINRA BrokerCheck: The Public Database of Every Registered Broker and Investment Adviser", "content_text": "FINRA BrokerCheck publishes registration history, licenses, employment records, and disclosure events for every registered broker and firm. Here is the data structure, the recidivist broker problem, how to access the BrokerCheck API, and how attorneys use it to vet advisers.", "summary": "FINRA BrokerCheck publishes registration history, licenses, employment records, and disclosure events for every registered broker and firm. Here is the data structure, the recidivist broker problem, how to access the BrokerCheck API, and how attorneys use it to vet advisers.", "date_published": "2026-07-18T00:00:00.000Z", "tags": [ "Federal Data", "FINRA", "Finance", "Investor Protection" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-form-4-insider-trading/", "url": "https://ai-analytics.org/writing/sec-form-4-insider-trading/", "title": "SEC Form 4: The Insider Trading Disclosure Behind Every Officer and Director Stock Transaction", "content_text": "Section 16(a) requires officers, directors, and 10%+ shareholders to file Form 4 within 2 business days of any stock transaction -- near-real-time public disclosure on EDGAR since 2004. Here is the full transaction code taxonomy, 10b5-1 plan gaming and 2022 SEC amendments, cluster-buying signal, academic evidence on abnormal returns, and a Python EDGAR bulk index screen for officer open-market purchases.", "summary": "Section 16(a) requires officers, directors, and 10%+ shareholders to file Form 4 within 2 business days of any stock transaction -- near-real-time public disclosure on EDGAR since 2004. Here is the full transaction code taxonomy, 10b5-1 plan gaming and 2022 SEC amendments, cluster-buying signal, academic evidence on abnormal returns, and a Python EDGAR bulk index screen for officer open-market purchases.", "date_published": "2026-07-17T00:00:00.000Z", "tags": [ "Federal Data", "SEC", "Insider Trading", "Securities" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-faers-adverse-events/", "url": "https://ai-analytics.org/writing/fda-faers-adverse-events/", "title": "FDA FAERS: The Adverse Drug Event Database Behind Post-Market Drug Safety", "content_text": "The FDA Adverse Event Reporting System contains 7 linked quarterly files tracking drug adverse events. Here is the MedDRA schema, how disproportionality analysis (PRR/ROR) detects safety signals, and the Avandia/Vioxx/SSRI signal cases.", "summary": "The FDA Adverse Event Reporting System contains 7 linked quarterly files tracking drug adverse events. Here is the MedDRA schema, how disproportionality analysis (PRR/ROR) detects safety signals, and the Avandia/Vioxx/SSRI signal cases.", "date_published": "2026-07-16T00:00:00.000Z", "tags": [ "Federal Data", "FDA", "Drug Safety", "Pharmacovigilance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/college-scorecard-data/", "url": "https://ai-analytics.org/writing/college-scorecard-data/", "title": "College Scorecard: The Federal Dataset That Exposes Graduation Rates, Debt, and Earnings for Every US College", "content_text": "The College Scorecard links IPEDS enrollment data to federal loan records and IRS earnings data. Here is the data structure, how to use the API, and what the earnings-debt gap reveals about for-profit colleges and high-debt programs.", "summary": "The College Scorecard links IPEDS enrollment data to federal loan records and IRS earnings data. Here is the data structure, how to use the API, and what the earnings-debt gap reveals about for-profit colleges and high-debt programs.", "date_published": "2026-07-15T00:00:00.000Z", "tags": [ "Federal Data", "Education", "College Scorecard", "Higher Education" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cisa-known-exploited-vulnerabilities/", "url": "https://ai-analytics.org/writing/cisa-known-exploited-vulnerabilities/", "title": "CISA KEV Catalog: The Federal Government's Definitive List of Actively Exploited Vulnerabilities", "content_text": "The CISA Known Exploited Vulnerabilities catalog lists CVEs confirmed as actively exploited in the wild — with mandatory federal patching deadlines under BOD 22-01. Here is the catalog structure, how it differs from CVSS scoring, and how security teams use it for patch prioritization.", "summary": "The CISA Known Exploited Vulnerabilities catalog lists CVEs confirmed as actively exploited in the wild — with mandatory federal patching deadlines under BOD 22-01. Here is the catalog structure, how it differs from CVSS scoring, and how security teams use it for patch prioritization.", "date_published": "2026-07-14T00:00:00.000Z", "tags": [ "Federal Data", "CISA", "Cybersecurity", "Vulnerability Management" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/uscis-h1b-data/", "url": "https://ai-analytics.org/writing/uscis-h1b-data/", "title": "USCIS H-1B Visa Data: Mapping the 600,000-Worker Skilled Immigration Pipeline", "content_text": "The DOL Labor Condition Application dataset and USCIS H-1B Employer Data Hub reveal the true shape of the skilled-worker visa program: IT staffing companies dominate approvals, India-born workers hold 70%+ of visas, and prevailing wage Level I filings expose systematic wage suppression.", "summary": "The DOL Labor Condition Application dataset and USCIS H-1B Employer Data Hub reveal the true shape of the skilled-worker visa program: IT staffing companies dominate approvals, India-born workers hold 70%+ of visas, and prevailing wage Level I filings expose systematic wage suppression.", "date_published": "2026-07-13T00:00:00.000Z", "tags": [ "Federal Data", "USCIS", "Immigration", "Labor Markets" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/doj-false-claims-act/", "url": "https://ai-analytics.org/writing/doj-false-claims-act/", "title": "DOJ False Claims Act Settlements: The $70 Billion Fraud Recovery Database", "content_text": "The False Claims Act is the government's primary anti-fraud tool, with qui tam whistleblowers driving 80%+ of the $2B+ in annual recoveries. Healthcare fraud dominates. Here is how to access the DOJ settlement database, scrape press releases, and identify repeat violators.", "summary": "The False Claims Act is the government's primary anti-fraud tool, with qui tam whistleblowers driving 80%+ of the $2B+ in annual recoveries. Healthcare fraud dominates. Here is how to access the DOJ settlement database, scrape press releases, and identify repeat violators.", "date_published": "2026-07-12T00:00:00.000Z", "tags": [ "Federal Data", "DOJ", "Healthcare Fraud", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nih-research-grants/", "url": "https://ai-analytics.org/writing/nih-research-grants/", "title": "NIH Research Grant Data: Mapping $40 Billion in Annual Biomedical Funding", "content_text": "The NIH Reporter system publishes every grant award — PI, institution, project title, abstract, award amount, IC, and activity code. Here is the activity code taxonomy, how funding flows by Institute/Center, and how to use the NIH Reporter API to track COVID research spending and HBCU funding gaps.", "summary": "The NIH Reporter system publishes every grant award — PI, institution, project title, abstract, award amount, IC, and activity code. Here is the activity code taxonomy, how funding flows by Institute/Center, and how to use the NIH Reporter API to track COVID research spending and HBCU funding gaps.", "date_published": "2026-07-11T00:00:00.000Z", "tags": [ "Federal Data", "NIH", "Research Funding", "Science Policy" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cdc-drug-overdose-data/", "url": "https://ai-analytics.org/writing/cdc-drug-overdose-data/", "title": "CDC Drug Overdose Mortality Data: The Federal Dataset Behind the Opioid Crisis", "content_text": "The CDC publishes overdose mortality through NVSS, CDC WONDER, and monthly VSRR provisional counts — tracking 107,000+ annual drug deaths at the county, demographic, and drug-category level. Here is the ICD-10 code structure, the three waves of the opioid epidemic, and how to access the data.", "summary": "The CDC publishes overdose mortality through NVSS, CDC WONDER, and monthly VSRR provisional counts — tracking 107,000+ annual drug deaths at the county, demographic, and drug-category level. Here is the ICD-10 code structure, the three waves of the opioid epidemic, and how to access the data.", "date_published": "2026-07-10T00:00:00.000Z", "tags": [ "Federal Data", "CDC", "Public Health", "Opioids" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fdic-bank-failures/", "url": "https://ai-analytics.org/writing/fdic-bank-failures/", "title": "FDIC Bank Failure Data: Every US Bank That Has Failed Since 1934", "content_text": "The FDIC publishes a complete failure list covering 4,000+ bank closures since 1934 — S&L crisis wave, the 2008–2012 GFC wave, and the 2023 SVB/Signature/First Republic episode. Here is the dataset schema, how to use call report data and the Texas Ratio to identify at-risk institutions, and how to access FDIC BankFind.", "summary": "The FDIC publishes a complete failure list covering 4,000+ bank closures since 1934 — S&L crisis wave, the 2008–2012 GFC wave, and the 2023 SVB/Signature/First Republic episode. Here is the dataset schema, how to use call report data and the Texas Ratio to identify at-risk institutions, and how to access FDIC BankFind.", "date_published": "2026-07-09T00:00:00.000Z", "tags": [ "Federal Data", "FDIC", "Banking", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fda-warning-letters/", "url": "https://ai-analytics.org/writing/fda-warning-letters/", "title": "FDA Warning Letters: The Public Enforcement Record for 100,000+ Regulatory Actions", "content_text": "The FDA publishes every warning letter on its website — pharmaceutical cGMP violations, food safety failures, device adulteration, and clinical investigator fraud. Here is the enforcement hierarchy from Form 483 to criminal referral, how to access and scrape the letter database, and what the record reveals about repeat violators.", "summary": "The FDA publishes every warning letter on its website — pharmaceutical cGMP violations, food safety failures, device adulteration, and clinical investigator fraud. Here is the enforcement hierarchy from Form 483 to criminal referral, how to access and scrape the letter database, and what the record reveals about repeat violators.", "date_published": "2026-07-08T00:00:00.000Z", "tags": [ "Federal Data", "FDA", "Healthcare", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/msha-mine-safety/", "url": "https://ai-analytics.org/writing/msha-mine-safety/", "title": "MSHA Mine Safety Data: Violations, Accidents, and Fatalities Across 10,000 Active Mines", "content_text": "The Mine Safety and Health Administration publishes three linked datasets — mine listings, accident/injury records, and violation citations going back to 1983. Here is the significant-and-substantial designation, the Pattern of Violations mechanism, the Upper Big Branch disaster context, and how to join violations to accidents by Mine ID.", "summary": "The Mine Safety and Health Administration publishes three linked datasets — mine listings, accident/injury records, and violation citations going back to 1983. Here is the significant-and-substantial designation, the Pattern of Violations mechanism, the Upper Big Branch disaster context, and how to join violations to accidents by Mine ID.", "date_published": "2026-07-07T00:00:00.000Z", "tags": [ "Federal Data", "MSHA", "Mine Safety", "Labor" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/uscg-marine-casualties/", "url": "https://ai-analytics.org/writing/uscg-marine-casualties/", "title": "USCG Marine Casualty Data: Every US Vessel Accident Since 1982", "content_text": "The US Coast Guard maintains the Boating Accident Report Database (BARD) for recreational vessels and the Marine Casualty and Pollution Database (MCPD) for commercial casualties. Here is what each database contains, how alcohol and life-jacket non-use drive fatality statistics, and how journalists use the data to track manufacturer defects.", "summary": "The US Coast Guard maintains the Boating Accident Report Database (BARD) for recreational vessels and the Marine Casualty and Pollution Database (MCPD) for commercial casualties. Here is what each database contains, how alcohol and life-jacket non-use drive fatality statistics, and how journalists use the data to track manufacturer defects.", "date_published": "2026-07-06T00:00:00.000Z", "tags": [ "Federal Data", "USCG", "Maritime Safety", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fmcsa-safety-ratings/", "url": "https://ai-analytics.org/writing/fmcsa-safety-ratings/", "title": "FMCSA Carrier Safety Ratings: The Federal Database Behind 550,000 Trucking Companies", "content_text": "The FMCSA maintains SAFER and MCMIS covering every commercial motor carrier in interstate commerce — three official safety ratings, seven SMS BASICs scoring each carrier as a percentile, inspection counts, OOS rates, and crash data. Here is the data structure, how to access it, and what it reveals about high-risk carriers.", "summary": "The FMCSA maintains SAFER and MCMIS covering every commercial motor carrier in interstate commerce — three official safety ratings, seven SMS BASICs scoring each carrier as a percentile, inspection counts, OOS rates, and crash data. Here is the data structure, how to access it, and what it reveals about high-risk carriers.", "date_published": "2026-07-05T00:00:00.000Z", "tags": [ "Federal Data", "FMCSA", "Transportation", "Safety" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cbp-trade-statistics/", "url": "https://ai-analytics.org/writing/cbp-trade-statistics/", "title": "CBP US Trade Statistics: The Federal Dataset Behind Every Import and Export", "content_text": "US Customs and Border Protection and the Census Bureau publish comprehensive import and export statistics by commodity (HTS code), trading partner, port of entry, and month. Here is the data structure, how to access USA Trade Online and the Census Foreign Trade API, and what the data reveals about trade diversion after Section 301 tariffs.", "summary": "US Customs and Border Protection and the Census Bureau publish comprehensive import and export statistics by commodity (HTS code), trading partner, port of entry, and month. Here is the data structure, how to access USA Trade Online and the Census Foreign Trade API, and what the data reveals about trade diversion after Section 301 tariffs.", "date_published": "2026-07-04T00:00:00.000Z", "tags": [ "Federal Data", "CBP", "Trade", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ice-enforcement-removals/", "url": "https://ai-analytics.org/writing/ice-enforcement-removals/", "title": "ICE Enforcement and Removal Operations: Reading the Federal Dataset Behind Immigration Enforcement", "content_text": "ICE publishes annual ERO reports covering arrests, detentions, removals, and returns by country of origin, criminal vs. non-criminal designation, and field office. Here is the data structure, TRAC-ICE access, and what the dataset reveals about enforcement priority shifts and the interior vs. border enforcement split.", "summary": "ICE publishes annual ERO reports covering arrests, detentions, removals, and returns by country of origin, criminal vs. non-criminal designation, and field office. Here is the data structure, TRAC-ICE access, and what the dataset reveals about enforcement priority shifts and the interior vs. border enforcement split.", "date_published": "2026-07-03T00:00:00.000Z", "tags": [ "Federal Data", "DHS", "Immigration", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-cpi-u/", "url": "https://ai-analytics.org/writing/bls-cpi-u/", "title": "BLS CPI-U: The Inflation Dataset That Moves Markets and Sets Policy", "content_text": "The BLS Consumer Price Index for All Urban Consumers tracks monthly inflation going back to January 1913. Here is the expenditure weight breakdown, how CPI-U differs from core CPI and the PCE deflator, how to access it via the BLS API, and what the 2021-2023 surge revealed about shelter inflation measurement.", "summary": "The BLS Consumer Price Index for All Urban Consumers tracks monthly inflation going back to January 1913. Here is the expenditure weight breakdown, how CPI-U differs from core CPI and the PCE deflator, how to access it via the BLS API, and what the 2021-2023 surge revealed about shelter inflation measurement.", "date_published": "2026-07-02T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Inflation", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ssa-disability-awards/", "url": "https://ai-analytics.org/writing/ssa-disability-awards/", "title": "SSA Disability Award Statistics: The Federal Dataset Behind 8 Million Benefit Decisions", "content_text": "The Social Security Administration publishes annual disability award statistics covering both SSDI and SSI — awards by state, diagnosis code, age group, gender, and decision level. Here is what the dataset contains, how to access it, and what it reveals about geographic variation in award rates, the ALJ hearing backlog, and the Trust Fund solvency timeline.", "summary": "The Social Security Administration publishes annual disability award statistics covering both SSDI and SSI — awards by state, diagnosis code, age group, gender, and decision level. Here is what the dataset contains, how to access it, and what it reveals about geographic variation in award rates, the ALJ hearing backlog, and the Trust Fund solvency timeline.", "date_published": "2026-07-01T00:00:00.000Z", "tags": [ "Federal Data", "SSA", "Disability", "Social Programs" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nlrb-ulp-filings/", "url": "https://ai-analytics.org/writing/nlrb-ulp-filings/", "title": "NLRB Unfair Labor Practice Data: 300,000 Cases of Worker-Management Conflict", "content_text": "The National Labor Relations Board maintains a public case management system tracking every unfair labor practice charge filed under the NLRA — 20,000–25,000 annually. Here is the case lifecycle, data structure, how to query the NLRB API, and what the data reveals about the 2022–2024 Starbucks and Amazon organizing surge.", "summary": "The National Labor Relations Board maintains a public case management system tracking every unfair labor practice charge filed under the NLRA — 20,000–25,000 annually. Here is the case lifecycle, data structure, how to query the NLRB API, and what the data reveals about the 2022–2024 Starbucks and Amazon organizing surge.", "date_published": "2026-06-30T00:00:00.000Z", "tags": [ "Federal Data", "NLRB", "Labor Law", "Enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bls-jolts/", "url": "https://ai-analytics.org/writing/bls-jolts/", "title": "BLS JOLTS: The Federal Dataset That Measures Why Workers Quit", "content_text": "The Job Openings and Labor Turnover Survey tracks monthly job openings, hires, quits, layoffs, and other separations by industry and region. Here is the data structure, BLS API access, and what JOLTS reveals about the Great Resignation, the Fed's rate-hike calculus, and the labor market signals that precede recessions.", "summary": "The Job Openings and Labor Turnover Survey tracks monthly job openings, hires, quits, layoffs, and other separations by industry and region. Here is the data structure, BLS API access, and what JOLTS reveals about the Great Resignation, the Fed's rate-hike calculus, and the labor market signals that precede recessions.", "date_published": "2026-06-29T00:00:00.000Z", "tags": [ "Federal Data", "BLS", "Labor Markets", "Economics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ftc-consumer-complaints/", "url": "https://ai-analytics.org/writing/ftc-consumer-complaints/", "title": "FTC Consumer Sentinel Network: 16 Million Fraud Reports Hiding in Plain Sight", "content_text": "The FTC Consumer Sentinel Network aggregates 8M+ fraud, identity theft, and consumer complaint reports annually from the FTC and dozens of partner organizations. Here is what the dataset contains, how to access it, and what it reveals about imposter scams, cryptocurrency fraud, and the counterintuitive age dynamics of financial loss.", "summary": "The FTC Consumer Sentinel Network aggregates 8M+ fraud, identity theft, and consumer complaint reports annually from the FTC and dozens of partner organizations. Here is what the dataset contains, how to access it, and what it reveals about imposter scams, cryptocurrency fraud, and the counterintuitive age dynamics of financial loss.", "date_published": "2026-06-28T00:00:00.000Z", "tags": [ "Federal Data", "FTC", "Consumer Protection", "Fraud" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fra-railroad-accident-data/", "url": "https://ai-analytics.org/writing/fra-railroad-accident-data/", "title": "Derailments and grade crossings: using FRA railroad accident data to analyze rail safety trends", "content_text": "The Federal Railroad Administration publishes two linked databases covering US railroad safety since 1975: Form 54 (all rail accidents) and Form 57 (highway-rail grade crossing accidents). Together they cover 250,000+ incidents with train information, track type, speed at accident, casualties, and equipment damage.", "summary": "The Federal Railroad Administration publishes two linked databases covering US railroad safety since 1975: Form 54 (all rail accidents) and Form 57 (highway-rail grade crossing accidents). Together they cover 250,000+ incidents with train information, track type, speed at accident, casualties, and equipment damage.", "date_published": "2026-06-27T00:00:00.000Z", "tags": [ "Regulatory data", "FRA", "Railroad safety", "Derailments", "Grade crossings", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/pbgc-pension-terminations/", "url": "https://ai-analytics.org/writing/pbgc-pension-terminations/", "title": "The graveyard of pensions: using PBGC data to track terminated defined-benefit plans", "content_text": "The Pension Benefit Guaranty Corporation publishes data on every terminated private-sector defined-benefit pension plan it has trusteed since 1975 — over 5,000 plans covering millions of workers.", "summary": "The Pension Benefit Guaranty Corporation publishes data on every terminated private-sector defined-benefit pension plan it has trusteed since 1975 — over 5,000 plans covering millions of workers.", "date_published": "2026-06-26T00:00:00.000Z", "tags": [ "Regulatory data", "PBGC", "Pensions", "Retirement", "Labor", "Defined-benefit" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usgs-earthquake-database/", "url": "https://ai-analytics.org/writing/usgs-earthquake-database/", "title": "Seismic record: using the USGS earthquake catalog to analyze fault risk and induced seismicity", "content_text": "The USGS National Earthquake Information Center maintains a catalog of every recorded earthquake globally — magnitude 2.5+ events back to 1900, with 100,000+ events per year above M4 globally.", "summary": "The USGS National Earthquake Information Center maintains a catalog of every recorded earthquake globally — magnitude 2.5+ events back to 1900, with 100,000+ events per year above M4 globally.", "date_published": "2026-06-25T00:00:00.000Z", "tags": [ "Regulatory data", "USGS", "Earthquakes", "Seismology", "Natural hazards", "Induced seismicity" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/epa-enforcement-data/", "url": "https://ai-analytics.org/writing/epa-enforcement-data/", "title": "Following EPA enforcement: using ECHO data to track environmental violations and penalties", "content_text": "EPA's Enforcement and Compliance History Online (ECHO) publishes every CAA, CWA, RCRA, and TSCA enforcement case — facility violations, formal actions, penalties assessed, and compliance status for 800,000+ regulated facilities.", "summary": "EPA's Enforcement and Compliance History Online (ECHO) publishes every CAA, CWA, RCRA, and TSCA enforcement case — facility violations, formal actions, penalties assessed, and compliance status for 800,000+ regulated facilities.", "date_published": "2026-06-24T00:00:00.000Z", "tags": [ "Regulatory data", "EPA", "Environmental enforcement", "ECHO", "Pollution", "Environmental justice" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/noaa-storm-events-database/", "url": "https://ai-analytics.org/writing/noaa-storm-events-database/", "title": "60 years of extreme weather: using NOAA Storm Events data to map tornado paths, flood losses, and climate trends", "content_text": "NOAA's Storm Events Database records every significant weather event in the US since 1950 — tornadoes, floods, hurricanes, winter storms, heat waves, wildfires, and 50+ other event types with location, deaths, injuries, and property damage estimates.", "summary": "NOAA's Storm Events Database records every significant weather event in the US since 1950 — tornadoes, floods, hurricanes, winter storms, heat waves, wildfires, and 50+ other event types with location, deaths, injuries, and property damage estimates.", "date_published": "2026-06-23T00:00:00.000Z", "tags": [ "Regulatory data", "NOAA", "Storm events", "Climate", "Natural disasters", "Weather data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/irs-990-political-organizations/", "url": "https://ai-analytics.org/writing/irs-990-political-organizations/", "title": "Dark money disclosed: using IRS Form 990 data to map political organization spending", "content_text": "The IRS publishes Form 990 filings for political organizations — 527 committees and 501(c)(4) social welfare organizations. The data covers revenue, expenditures, officer compensation, and political activities for 65,000+ organizations.", "summary": "The IRS publishes Form 990 filings for political organizations — 527 committees and 501(c)(4) social welfare organizations. The data covers revenue, expenditures, officer compensation, and political activities for 65,000+ organizations.", "date_published": "2026-06-22T00:00:00.000Z", "tags": [ "Regulatory data", "IRS", "Dark money", "Political organizations", "527", "501c4", "Campaign finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ntsb-aviation-accidents/", "url": "https://ai-analytics.org/writing/ntsb-aviation-accidents/", "title": "Every US plane crash since 1962: using the NTSB aviation accident database", "content_text": "The National Transportation Safety Board publishes a database of every US civil aviation accident since 1962 — over 90,000 accidents and incidents with aircraft type, probable cause, phase of flight, weather, pilot certificates, and injury counts.", "summary": "The National Transportation Safety Board publishes a database of every US civil aviation accident since 1962 — over 90,000 accidents and incidents with aircraft type, probable cause, phase of flight, weather, pilot certificates, and injury counts.", "date_published": "2026-06-21T00:00:00.000Z", "tags": [ "Regulatory data", "NTSB", "Aviation safety", "Aircraft accidents", "Transportation", "FAA" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/phmsa-pipeline-safety/", "url": "https://ai-analytics.org/writing/phmsa-pipeline-safety/", "title": "Pipeline spills and explosions: using PHMSA incident data to map 50 years of pipeline failures", "content_text": "The Pipeline and Hazardous Materials Safety Administration publishes incident reports for every significant pipeline accident since 1970 — gas distribution, gas transmission, hazardous liquids, and LNG facilities. The database covers 25,000+ incidents with fatalities, injuries, property damage, and commodity spilled.", "summary": "The Pipeline and Hazardous Materials Safety Administration publishes incident reports for every significant pipeline accident since 1970 — gas distribution, gas transmission, hazardous liquids, and LNG facilities. The database covers 25,000+ incidents with fatalities, injuries, property damage, and commodity spilled.", "date_published": "2026-06-20T00:00:00.000Z", "tags": [ "Regulatory data", "PHMSA", "Pipeline safety", "Energy", "Hazardous materials", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usda-snap-program-data/", "url": "https://ai-analytics.org/writing/usda-snap-program-data/", "title": "Food stamps by the numbers: using USDA SNAP participation data to track hunger and benefit policy", "content_text": "The USDA Food and Nutrition Service publishes monthly SNAP participation and benefit data by state — total participants, households, benefits issued, average benefit per person, and issuance history going back to 1969. The data shows how food assistance responds to recessions, pandemic aid expansions, and state-level work requirement policies.", "summary": "The USDA Food and Nutrition Service publishes monthly SNAP participation and benefit data by state — total participants, households, benefits issued, average benefit per person, and issuance history going back to 1969. The data shows how food assistance responds to recessions, pandemic aid expansions, and state-level work requirement policies.", "date_published": "2026-06-19T00:00:00.000Z", "tags": [ "Regulatory data", "USDA", "SNAP", "Food assistance", "Social programs", "Poverty" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/census-acs-demographic-data/", "url": "https://ai-analytics.org/writing/census-acs-demographic-data/", "title": "The demographic backbone: using Census ACS data to contextualize every other federal dataset", "content_text": "The Census Bureau's American Community Survey publishes 5-year estimates for every census tract in the US — income, poverty, race, housing tenure, education, employment, and 350+ other variables at the tract level. ACS is the denominator that makes every other federal dataset meaningful: HMDA denial rates per capita, OSHA injury rates per worker, SNAP participation per household.", "summary": "The Census Bureau's American Community Survey publishes 5-year estimates for every census tract in the US — income, poverty, race, housing tenure, education, employment, and 350+ other variables at the tract level. ACS is the denominator that makes every other federal dataset meaningful: HMDA denial rates per capita, OSHA injury rates per worker, SNAP participation per household.", "date_published": "2026-06-18T00:00:00.000Z", "tags": [ "Regulatory data", "Census", "ACS", "Demographics", "Economic data", "Open data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hud-fair-housing-complaints/", "url": "https://ai-analytics.org/writing/hud-fair-housing-complaints/", "title": "Mapping housing discrimination: using HUD FHEO complaint data to find fair housing violations", "content_text": "HUD's Fair Housing and Equal Opportunity office publishes a complaint database covering every fair housing complaint filed with HUD and participating state agencies — basis of discrimination, property type, complaint disposition, and whether the complainant received relief.", "summary": "HUD's Fair Housing and Equal Opportunity office publishes a complaint database covering every fair housing complaint filed with HUD and participating state agencies — basis of discrimination, property type, complaint disposition, and whether the complainant received relief.", "date_published": "2026-06-17T00:00:00.000Z", "tags": [ "Regulatory data", "HUD", "Fair housing", "Housing discrimination", "Civil rights", "Disability rights" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bjs-national-prisoner-statistics/", "url": "https://ai-analytics.org/writing/bjs-national-prisoner-statistics/", "title": "Inside the count: using BJS National Prisoner Statistics to analyze incarceration trends", "content_text": "The Bureau of Justice Statistics publishes the National Prisoner Statistics program — state and federal prison populations back to 1925, with demographics (race, sex, age), offense categories, sentence lengths, and admissions/releases flows. What 100 years of incarceration data reveals about mandatory minimums, the drug war, and mass incarceration's racial dimensions.", "summary": "The Bureau of Justice Statistics publishes the National Prisoner Statistics program — state and federal prison populations back to 1925, with demographics (race, sex, age), offense categories, sentence lengths, and admissions/releases flows. What 100 years of incarceration data reveals about mandatory minimums, the drug war, and mass incarceration's racial dimensions.", "date_published": "2026-06-16T00:00:00.000Z", "tags": [ "Regulatory data", "BJS", "Criminal justice", "Incarceration", "Prisons", "Mass incarceration" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/osha-inspection-enforcement/", "url": "https://ai-analytics.org/writing/osha-inspection-enforcement/", "title": "Workplace safety violations: using OSHA inspection and citation data to find dangerous employers", "content_text": "OSHA publishes its full inspection and citation database — every workplace inspection since 1972, every violation found, every penalty assessed, and whether the employer contested the citation. The database covers 2.5M+ inspections across all industries. Here is what it contains, how to query it, and what patterns emerge from 50 years of enforcement data.", "summary": "OSHA publishes its full inspection and citation database — every workplace inspection since 1972, every violation found, every penalty assessed, and whether the employer contested the citation. The database covers 2.5M+ inspections across all industries. Here is what it contains, how to query it, and what patterns emerge from 50 years of enforcement data.", "date_published": "2026-06-15T00:00:00.000Z", "tags": [ "Regulatory data", "OSHA", "Workplace safety", "Labor enforcement", "Violations", "Inspections" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dol-wage-hour-violations/", "url": "https://ai-analytics.org/writing/dol-wage-hour-violations/", "title": "Wage theft by employer: using DOL Wage and Hour Division enforcement data to find labor violations", "content_text": "The Department of Labor's Wage and Hour Division publishes a public enforcement database covering every concluded investigation — employer name, violation type, back wages owed, employees affected, and civil money penalties.", "summary": "The Department of Labor's Wage and Hour Division publishes a public enforcement database covering every concluded investigation — employer name, violation type, back wages owed, employees affected, and civil money penalties.", "date_published": "2026-06-14T00:00:00.000Z", "tags": [ "Regulatory data", "DOL", "Wage theft", "Labor enforcement", "FLSA", "Workers' rights" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nhtsa-fars-traffic-fatalities/", "url": "https://ai-analytics.org/writing/nhtsa-fars-traffic-fatalities/", "title": "Every US traffic death since 1975: using NHTSA FARS to analyze road safety, vehicle defects, and enforcement gaps", "content_text": "The Fatality Analysis Reporting System (FARS) contains a record for every motor vehicle crash death on US public roads since 1975 — 1.1M+ fatalities with vehicle type, crash circumstances, driver behavior, and roadway conditions. Here is the data structure, how to download it, and what it reveals about drunk driving trends, pedestrian deaths, and the safety gap between vehicle classes.", "summary": "The Fatality Analysis Reporting System (FARS) contains a record for every motor vehicle crash death on US public roads since 1975 — 1.1M+ fatalities with vehicle type, crash circumstances, driver behavior, and roadway conditions. Here is the data structure, how to download it, and what it reveals about drunk driving trends, pedestrian deaths, and the safety gap between vehicle classes.", "date_published": "2026-06-13T00:00:00.000Z", "tags": [ "Regulatory data", "NHTSA", "FARS", "Traffic safety", "Vehicle safety", "Transportation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fbi-nibrs-crime-data/", "url": "https://ai-analytics.org/writing/fbi-nibrs-crime-data/", "title": "Incident-level crime: using FBI NIBRS data to analyze offense patterns, victim demographics, and clearance rates", "content_text": "The FBI's National Incident-Based Reporting System (NIBRS) publishes incident-level crime data — every offense, victim, offender, arrest, and property loss reported by participating agencies. Here is what the database contains, how it differs from the legacy UCR Summary data, and how to use it for research on offense patterns, racial disparities in enforcement, and geographic hot-spots.", "summary": "The FBI's National Incident-Based Reporting System (NIBRS) publishes incident-level crime data — every offense, victim, offender, arrest, and property loss reported by participating agencies. Here is what the database contains, how it differs from the legacy UCR Summary data, and how to use it for research on offense patterns, racial disparities in enforcement, and geographic hot-spots.", "date_published": "2026-06-12T00:00:00.000Z", "tags": [ "Regulatory data", "FBI", "NIBRS", "Crime data", "Criminal justice", "Law enforcement" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fec-campaign-finance-data/", "url": "https://ai-analytics.org/writing/fec-campaign-finance-data/", "title": "Follow the money: mapping dark money and super PAC flows with FEC bulk data", "content_text": "The FEC publishes bulk data on every contribution and expenditure in federal elections — candidates, PACs, super PACs, and party committees. Here is how to download the full dataset, trace money from donor to expenditure, and identify the shell-company layer that obscures dark money flows.", "summary": "The FEC publishes bulk data on every contribution and expenditure in federal elections — candidates, PACs, super PACs, and party committees. Here is how to download the full dataset, trace money from donor to expenditure, and identify the shell-company layer that obscures dark money flows.", "date_published": "2026-06-11T00:00:00.000Z", "tags": [ "Regulatory data", "FEC", "Campaign finance", "Super PAC", "Dark money", "Political money" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hhs-ocr-hipaa-breach-data/", "url": "https://ai-analytics.org/writing/hhs-ocr-hipaa-breach-data/", "title": "The Wall of Shame: what the HHS-OCR HIPAA breach database reveals about healthcare data security", "content_text": "HHS-OCR publishes every reported healthcare data breach affecting 500+ patients — the \"Wall of Shame.\" Over 5,000 entries covering ransomware attacks, stolen laptops, unauthorized employee access, and business associate failures. Here is what the database contains and what it reveals about healthcare security failures.", "summary": "HHS-OCR publishes every reported healthcare data breach affecting 500+ patients — the \"Wall of Shame.\" Over 5,000 entries covering ransomware attacks, stolen laptops, unauthorized employee access, and business associate failures. Here is what the database contains and what it reveals about healthcare security failures.", "date_published": "2026-06-10T00:00:00.000Z", "tags": [ "Regulatory data", "HIPAA", "HHS-OCR", "Healthcare", "Cybersecurity", "Data breach" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/eeoc-discrimination-charge-data/", "url": "https://ai-analytics.org/writing/eeoc-discrimination-charge-data/", "title": "By the numbers: using EEOC charge statistics to find discrimination patterns by industry and employer", "content_text": "The EEOC publishes annual charge statistics and, since 2017, charge-level data under FOIA. The aggregate data shows which industries generate the most race, sex, disability, and age discrimination charges — and which large employers appear repeatedly in the conciliation record.", "summary": "The EEOC publishes annual charge statistics and, since 2017, charge-level data under FOIA. The aggregate data shows which industries generate the most race, sex, disability, and age discrimination charges — and which large employers appear repeatedly in the conciliation record.", "date_published": "2026-06-09T00:00:00.000Z", "tags": [ "Regulatory data", "EEOC", "Employment discrimination", "Civil rights", "Labor" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sba-ppp-loan-data/", "url": "https://ai-analytics.org/writing/sba-ppp-loan-data/", "title": "The $800 billion bailout: using SBA PPP data to trace who got pandemic relief", "content_text": "After a FOIA fight, the SBA released PPP loan data covering 11.8 million loans and $793 billion in forgiven funds. Here is what the public data contains, the fraud patterns it revealed, and how to cross-reference it with SAM.gov debarments, IRS nonprofit data, and the DOJ prosecution record.", "summary": "After a FOIA fight, the SBA released PPP loan data covering 11.8 million loans and $793 billion in forgiven funds. Here is what the public data contains, the fraud patterns it revealed, and how to cross-reference it with SAM.gov debarments, IRS nonprofit data, and the DOJ prosecution record.", "date_published": "2026-06-08T00:00:00.000Z", "tags": [ "Regulatory data", "SBA", "PPP", "Pandemic relief", "Fraud", "Open data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/stock-act-congressional-trading/", "url": "https://ai-analytics.org/writing/stock-act-congressional-trading/", "title": "Trading on the inside: using STOCK Act filings to track congressional stock transactions", "content_text": "The STOCK Act requires members of Congress to report stock trades within 45 days. The House Clerk publishes scanned PDFs — not structured data. Here is how Quiver Quantitative, Capitol Trades, and journalists have structured this data, and what the disclosures reveal about trading patterns around legislation and committee assignments.", "summary": "The STOCK Act requires members of Congress to report stock trades within 45 days. The House Clerk publishes scanned PDFs — not structured data. Here is how Quiver Quantitative, Capitol Trades, and journalists have structured this data, and what the disclosures reveal about trading patterns around legislation and committee assignments.", "date_published": "2026-06-07T00:00:00.000Z", "tags": [ "Regulatory data", "STOCK Act", "Congress", "Trading", "Conflicts of interest", "Disclosure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/eoir-asylum-grant-rates/", "url": "https://ai-analytics.org/writing/eoir-asylum-grant-rates/", "title": "The asylum lottery: what EOIR data reveals about judge-by-judge grant rate disparities", "content_text": "EOIR publishes quarterly data on every immigration judge's case outcomes, including asylum grant rates. The spread is enormous — some judges grant asylum in fewer than 5% of cases; others grant it in more than 90%. Here is how to access and analyze the data.", "summary": "EOIR publishes quarterly data on every immigration judge's case outcomes, including asylum grant rates. The spread is enormous — some judges grant asylum in fewer than 5% of cases; others grant it in more than 90%. Here is how to access and analyze the data.", "date_published": "2026-06-06T00:00:00.000Z", "tags": [ "Regulatory data", "EOIR", "Immigration", "Asylum", "DOJ", "Courts" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/hmda-mortgage-lending-disparities/", "url": "https://ai-analytics.org/writing/hmda-mortgage-lending-disparities/", "title": "The mortgage map: using HMDA loan-level data to find lending disparities", "content_text": "The Home Mortgage Disclosure Act requires 7,000+ lenders to report every mortgage application — approvals, denials, withdrawn, race, income, loan amount, census tract. Here is how to use the CFPB bulk download to find redlining, reverse redlining, and lender-level denial rate disparities.", "summary": "The Home Mortgage Disclosure Act requires 7,000+ lenders to report every mortgage application — approvals, denials, withdrawn, race, income, loan amount, census tract. Here is how to use the CFPB bulk download to find redlining, reverse redlining, and lender-level denial rate disparities.", "date_published": "2026-06-05T00:00:00.000Z", "tags": [ "Regulatory data", "HMDA", "Mortgage", "Lending disparities", "CFPB", "Housing" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cpsc-consumer-product-recalls/", "url": "https://ai-analytics.org/writing/cpsc-consumer-product-recalls/", "title": "The recall record: what the CPSC product safety database shows and what manufacturers hide", "content_text": "The CPSC Recall database covers 9,800+ recalls since 1973. Behind the press releases: how many units are actually returned, which hazard categories dominate, and why the voluntary recall system lets manufacturers negotiate the language of their own enforcement actions.", "summary": "The CPSC Recall database covers 9,800+ recalls since 1973. Behind the press releases: how many units are actually returned, which hazard categories dominate, and why the voluntary recall system lets manufacturers negotiate the language of their own enforcement actions.", "date_published": "2026-06-04T00:00:00.000Z", "tags": [ "Regulatory data", "CPSC", "Product safety", "Consumer protection", "Recalls" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/sec-13f-institutional-holdings/", "url": "https://ai-analytics.org/writing/sec-13f-institutional-holdings/", "title": "SEC Form 13F: The Institutional Holdings Disclosure Behind Every Hedge Fund Tracker", "content_text": "Section 13(f) requires institutional investment managers with >$100M in 13(f) securities to file quarterly holdings disclosures with the SEC -- ~5,000 filers, 45-day lag, long-equity-only view. Here is the full holdings table schema, what 13F covers and critically excludes (no short positions, no bonds, no foreign-listed shares), major filers (Berkshire, BlackRock, Renaissance), confidential treatment requests, the stale-data limitation and clone strategy research, academic research, comparison to 13D/13G/Form 4, and a Python EDGAR bulk index parser to track position changes for any manager by CIK.", "summary": "Section 13(f) requires institutional investment managers with >$100M in 13(f) securities to file quarterly holdings disclosures with the SEC -- ~5,000 filers, 45-day lag, long-equity-only view. Here is the full holdings table schema, what 13F covers and critically excludes (no short positions, no bonds, no foreign-listed shares), major filers (Berkshire, BlackRock, Renaissance), confidential treatment requests, the stale-data limitation and clone strategy research, academic research, comparison to 13D/13G/Form 4, and a Python EDGAR bulk index parser to track position changes for any manager by CIK.", "date_published": "2026-06-03T00:00:00.000Z", "tags": [ "Federal Data", "SEC", "Institutional Investing", "Finance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/dea-arcos-opioid-distribution/", "url": "https://ai-analytics.org/writing/dea-arcos-opioid-distribution/", "title": "380 million transactions: indexing the DEA's ARCOS opioid distribution data", "content_text": "How we indexed 380 million DEA ARCOS controlled-substance transaction records from the opioid MDL discovery release, what the data reveals about pill distribution, and how to cross-reference it against DEA enforcement actions and CDC overdose mortality.", "summary": "How we indexed 380 million DEA ARCOS controlled-substance transaction records from the opioid MDL discovery release, what the data reveals about pill distribution, and how to cross-reference it against DEA enforcement actions and CDC overdose mortality.", "date_published": "2026-06-02T00:00:00.000Z", "tags": [ "Regulatory data", "DEA", "ARCOS", "Opioids", "Public health" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/corporate-prosecution-dpa-registry/", "url": "https://ai-analytics.org/writing/corporate-prosecution-dpa-registry/", "title": "The DPA database: every federal deferred prosecution agreement since 1992", "content_text": "The Corporate Prosecution Registry at Duke and UVA covers 3,000+ federal organizational prosecutions and every DPA/NPA since 1990 — including agreements DOJ refused to disclose under FOIA.", "summary": "The Corporate Prosecution Registry at Duke and UVA covers 3,000+ federal organizational prosecutions and every DPA/NPA since 1990 — including agreements DOJ refused to disclose under FOIA.", "date_published": "2026-06-01T00:00:00.000Z", "tags": [ "Regulatory data", "DOJ", "Corporate prosecution", "DPA", "FOIA" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/atf-federal-firearms-licensees/", "url": "https://ai-analytics.org/writing/atf-federal-firearms-licensees/", "title": "The gun dealer map: what ATF's Federal Firearms Licensee data shows and what it hides", "content_text": "ATF publishes the complete list of ~75,000 active Federal Firearms Licensees monthly as a free CSV. Here's what the data contains, what the Tiahrt Amendment keeps hidden, and how to cross-reference it.", "summary": "ATF publishes the complete list of ~75,000 active Federal Firearms Licensees monthly as a free CSV. Here's what the data contains, what the Tiahrt Amendment keeps hidden, and how to cross-reference it.", "date_published": "2026-05-31T00:00:00.000Z", "tags": [ "Regulatory data", "ATF", "Firearms", "FFL", "Tiahrt Amendment" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/usaid-foreign-assistance-data/", "url": "https://ai-analytics.org/writing/usaid-foreign-assistance-data/", "title": "Before it disappeared: archiving $1.5 trillion in USAID foreign assistance data", "content_text": "foreignassistance.gov went dark on January 31, 2025. What the dataset contained, how it was archived, what the DOGE cuts actually targeted, and where to access it now.", "summary": "foreignassistance.gov went dark on January 31, 2025. What the dataset contained, how it was archived, what the DOGE cuts actually targeted, and where to access it now.", "date_published": "2026-05-30T00:00:00.000Z", "tags": [ "Regulatory data", "USAID", "Foreign aid", "DOGE", "Open data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/pcaob-audit-deficiency-data/", "url": "https://ai-analytics.org/writing/pcaob-audit-deficiency-data/", "title": "One in four audits flagged: indexing PCAOB deficiency data across the Big 4", "content_text": "PCAOB inspection reports contain structured deficiency data for every registered audit firm. In 2023, 26% of Big 4 audits reviewed had Part I.A deficiencies — meaning auditors signed off without sufficient evidence. Here is what the data covers and how to use it.", "summary": "PCAOB inspection reports contain structured deficiency data for every registered audit firm. In 2023, 26% of Big 4 audits reviewed had Part I.A deficiencies — meaning auditors signed off without sufficient evidence. Here is what the data covers and how to use it.", "date_published": "2026-05-29T00:00:00.000Z", "tags": [ "Regulatory data", "PCAOB", "Audit", "Big 4", "Financial oversight" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nlrb-union-election-data/", "url": "https://ai-analytics.org/writing/nlrb-union-election-data/", "title": "Who won, who lost: five years of union elections in NLRB data", "content_text": "How to pull, clean, and analyze NLRB union election records — RC and RD cases, the 2021–2024 organizing surge, the 100k export cap workaround, industry breakdowns, and cross-referencing with OSHA and CFPB data.", "summary": "How to pull, clean, and analyze NLRB union election records — RC and RD cases, the 2021–2024 organizing surge, the 100k export cap workaround, industry breakdowns, and cross-referencing with OSHA and CFPB data.", "date_published": "2026-05-28T00:00:00.000Z", "tags": [ "Regulatory data", "NLRB", "Labor", "Union elections", "Workers" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cms-open-payments-part-d/", "url": "https://ai-analytics.org/writing/cms-open-payments-part-d/", "title": "The pharma payment map: joining CMS Open Payments and Medicare Part D prescribing data", "content_text": "How joining CMS Open Payments (100M+ pharma payments to physicians) with Medicare Part D prescribing data surfaces the correlation between manufacturer payments and prescribing patterns — and how to cross-reference with HHS OIG exclusions.", "summary": "How joining CMS Open Payments (100M+ pharma payments to physicians) with Medicare Part D prescribing data surfaces the correlation between manufacturer payments and prescribing patterns — and how to cross-reference with HHS OIG exclusions.", "date_published": "2026-05-26T00:00:00.000Z", "tags": [ "Healthcare data", "CMS", "Open Payments", "Medicare Part D", "Pharma" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fara-foreign-agent-registrations/", "url": "https://ai-analytics.org/writing/fara-foreign-agent-registrations/", "title": "Foreign agents in plain sight: mapping DC's hidden influence network with FARA data", "content_text": "The DOJ buries the FARA bulk download inside an Oracle APEX URL that looks broken. Behind it: daily CSV exports of every DC firm registered to lobby for a foreign government. Here is how to use it.", "summary": "The DOJ buries the FARA bulk download inside an Oracle APEX URL that looks broken. Behind it: daily CSV exports of every DC firm registered to lobby for a foreign government. Here is how to use it.", "date_published": "2026-05-24T00:00:00.000Z", "tags": [ "Regulatory data", "FARA", "Foreign influence", "Lobbying" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/fema-nfip-flood-claims/", "url": "https://ai-analytics.org/writing/fema-nfip-flood-claims/", "title": "Repetitive loss: what FEMA's flood insurance claims data reveals about 2.7 million paid claims", "content_text": "FEMA's NFIP claims dataset covers 2.7 million paid flood insurance claims. The multiple-loss properties subset shows properties paid out more than their assessed value — some 10–15 times.", "summary": "FEMA's NFIP claims dataset covers 2.7 million paid flood insurance claims. The multiple-loss properties subset shows properties paid out more than their assessed value — some 10–15 times.", "date_published": "2026-05-23T00:00:00.000Z", "tags": [ "Regulatory data", "FEMA", "NFIP", "Climate risk", "Insurance" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/compliance-screening-risk-score/", "url": "https://ai-analytics.org/writing/compliance-screening-risk-score/", "title": "Compliance screening across 30+ federal enforcement lists: how the risk score works", "content_text": "How we built a 0–100 compliance risk score across OFAC, SAM, OIG, CFPB, SEC, DOJ, FDIC, FINRA, CFTC, EPA, MSHA, FDA warning letters, PCAOB, UFLPA, and 15+ more lists in a single API call.", "summary": "How we built a 0–100 compliance risk score across OFAC, SAM, OIG, CFPB, SEC, DOJ, FDIC, FINRA, CFTC, EPA, MSHA, FDA warning letters, PCAOB, UFLPA, and 15+ more lists in a single API call.", "date_published": "2026-05-22T00:00:00.000Z", "tags": [ "Regulatory data", "Compliance", "OFAC", "Entity resolution" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/compliance-entity-resolution/", "url": "https://ai-analytics.org/writing/compliance-entity-resolution/", "title": "Entity resolution for multi-list compliance screening: reducing false positives without sacrificing recall", "content_text": "How the Federal Regulatory Data Hub resolves entity identity across 30+ compliance lists: three-stage pipeline (identifier join 34%, FTS5 canonical name 41%, Jaro-Winkler fuzzy 18%), false positive taxonomy, EntityResolutionResult confidence-to-action mapping, 99.1% recall, 98.7% precision, and weekly analyst-feedback calibration.", "summary": "How the Federal Regulatory Data Hub resolves entity identity across 30+ compliance lists: three-stage pipeline (identifier join 34%, FTS5 canonical name 41%, Jaro-Winkler fuzzy 18%), false positive taxonomy, EntityResolutionResult confidence-to-action mapping, 99.1% recall, 98.7% precision, and weekly analyst-feedback calibration.", "date_published": "2026-05-16T00:00:00.000Z", "tags": [ "Regulatory data", "Compliance", "ML", "Entity resolution" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-entity-matching/", "url": "https://ai-analytics.org/writing/federal-entity-matching/", "title": "Name matching in federal regulatory data: aliases, subsidiaries, and sanctions evasion across 197 datasets", "content_text": "How the Federal Regulatory Data Hub resolves entity names across 197 federal datasets when identifiers disagree — OFAC alias explosion, SEC EDGAR subsidiary mapping, three-pass fuzzy matching (exact → Jaro-Winkler → TF-IDF cosine), 1.4% combined false positive rate, and how entity_confidence weights the compliance risk score.", "summary": "How the Federal Regulatory Data Hub resolves entity names across 197 federal datasets when identifiers disagree — OFAC alias explosion, SEC EDGAR subsidiary mapping, three-pass fuzzy matching (exact → Jaro-Winkler → TF-IDF cosine), 1.4% combined false positive rate, and how entity_confidence weights the compliance risk score.", "date_published": "2026-05-10T00:00:00.000Z", "tags": [ "Regulatory data", "Entity resolution", "Compliance", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-entity-canonical-ids/", "url": "https://ai-analytics.org/writing/regulatory-entity-canonical-ids/", "title": "Canonical entity IDs in the Federal Regulatory Data Hub: stable identifiers across 197 federal datasets", "content_text": "How the Federal Regulatory Data Hub generates and maintains stable canonical IDs for entities across 197 federal datasets — deterministic SHA-256 ID generation, EntityVersion history for merge and split events, EntityAlias tracking for historical name variants, and subscriber continuity guarantees when source identifiers change.", "summary": "How the Federal Regulatory Data Hub generates and maintains stable canonical IDs for entities across 197 federal datasets — deterministic SHA-256 ID generation, EntityVersion history for merge and split events, EntityAlias tracking for historical name variants, and subscriber continuity guarantees when source identifiers change.", "date_published": "2026-05-05T00:00:00.000Z", "tags": [ "Regulatory", "Infrastructure", "Data Engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/cross-agency-regulatory-entity-graph/", "url": "https://ai-analytics.org/writing/cross-agency-regulatory-entity-graph/", "title": "Building the cross-agency regulatory entity graph: 35M records, one join", "content_text": "How we built an entity bridge across 197 federal datasets so a single query returns every SEC filing, FDA warning letter, EPA enforcement case, and OFAC sanction for any company.", "summary": "How we built an entity bridge across 197 federal datasets so a single query returns every SEC filing, FDA warning letter, EPA enforcement case, and OFAC sanction for any company.", "date_published": "2026-05-01T00:00:00.000Z", "tags": [ "Regulatory data", "Entity resolution", "Cloudflare D1", "MCP" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-regulatory-entity-subscriptions/", "url": "https://ai-analytics.org/writing/federal-regulatory-entity-subscriptions/", "title": "Entity subscriptions in the Federal Regulatory Data Hub: per-entity change monitoring across 30+ enforcement lists", "content_text": "How the Federal Regulatory Data Hub lets compliance teams subscribe to regulatory events for specific entities — using the cross-agency entity bridge to watch OFAC, SAM, SEC, EPA, DOJ, and 25+ other lists simultaneously.", "summary": "How the Federal Regulatory Data Hub lets compliance teams subscribe to regulatory events for specific entities — using the cross-agency entity bridge to watch OFAC, SAM, SEC, EPA, DOJ, and 25+ other lists simultaneously.", "date_published": "2026-04-26T00:00:00.000Z", "tags": [ "Regulatory data", "Compliance", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-change-alerts/", "url": "https://ai-analytics.org/writing/regulatory-change-alerts/", "title": "Federal Regulatory Data Hub change alerts: near-real-time OFAC sanctions, SAM debarments, and enforcement action webhooks", "content_text": "How the Federal Regulatory Data Hub detects regulatory record changes and delivers them to subscribers: 10-minute OFAC sanctions window, 30-minute SAM debarment window, EDGAR 8-K filing webhooks, HMAC-signed Cloudflare Queue delivery with at-least-once semantics, per-entity and per-list subscription filters, and idempotency_key deduplication.", "summary": "How the Federal Regulatory Data Hub detects regulatory record changes and delivers them to subscribers: 10-minute OFAC sanctions window, 30-minute SAM debarment window, EDGAR 8-K filing webhooks, HMAC-signed Cloudflare Queue delivery with at-least-once semantics, per-entity and per-list subscription filters, and idempotency_key deduplication.", "date_published": "2026-04-21T00:00:00.000Z", "tags": [ "Regulatory data", "Compliance", "Infrastructure", "Cloudflare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-sdk-v04/", "url": "https://ai-analytics.org/writing/swarm-sdk-v04/", "title": "Swarm SDK v0.4: situational awareness, electronic warfare coordination, and adversarial resilience", "content_text": "Situational Awareness API for shared position and sensor fusion, EW Coordination protocol, Adversarial Resilience with traffic morphing and store-and-forward, and RF Fingerprinting for passive emitter tracking. 465 total tests.", "summary": "Situational Awareness API for shared position and sensor fusion, EW Coordination protocol, Adversarial Resilience with traffic morphing and store-and-forward, and RF Fingerprinting for passive emitter tracking. 465 total tests.", "date_published": "2026-04-14T00:00:00.000Z", "tags": [ "Swarm SDK", "Post-quantum", "Drone", "Cryptography" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-situational-awareness/", "url": "https://ai-analytics.org/writing/swarm-situational-awareness/", "title": "Swarm situational awareness: signed position broadcasts, sensor fusion, and dead-reckoning in embedded Rust", "content_text": "How the swarm coordination layer maintains a shared operational picture across 128 nodes: Ed25519-signed 124-byte position broadcast frames, an Extended Kalman Filter fusing GPS/IMU/barometric altitude into a 6-DOF state estimate, dead-reckoning fallback for up to 90 seconds without GPS, and a probabilistic gossip protocol achieving 94.2% frame delivery across a 2km x 2km field deployment.", "summary": "How the swarm coordination layer maintains a shared operational picture across 128 nodes: Ed25519-signed 124-byte position broadcast frames, an Extended Kalman Filter fusing GPS/IMU/barometric altitude into a 6-DOF state estimate, dead-reckoning fallback for up to 90 seconds without GPS, and a probabilistic gossip protocol achieving 94.2% frame delivery across a 2km x 2km field deployment.", "date_published": "2026-04-10T00:00:00.000Z", "tags": [ "Swarm robotics", "Embedded Rust", "Sensor fusion", "Distributed systems" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-embedded-rust/", "url": "https://ai-analytics.org/writing/swarm-embedded-rust/", "title": "Swarm SDK on bare metal: porting the cryptographic core to no_std Rust on STM32H7", "content_text": "How we ported the Swarm SDK cryptographic core to no_std Rust targeting the STM32H7 Cortex-M7: feature-gated std/embedded builds, 96KB static heap with cortex-m-alloc, pre-allocated VecDeque deduplication ring, in-place AES-GCM to avoid heap allocation, hardware AES accelerator integration (0.14ms vs. 0.61ms software), and binary size optimization from 1.2MB to 284KB with opt-level=\"z\" and LTO.", "summary": "How we ported the Swarm SDK cryptographic core to no_std Rust targeting the STM32H7 Cortex-M7: feature-gated std/embedded builds, 96KB static heap with cortex-m-alloc, pre-allocated VecDeque deduplication ring, in-place AES-GCM to avoid heap allocation, hardware AES accelerator integration (0.14ms vs. 0.61ms software), and binary size optimization from 1.2MB to 284KB with opt-level=\"z\" and LTO.", "date_published": "2026-04-06T00:00:00.000Z", "tags": [ "Swarm SDK", "Embedded", "Rust", "Cryptography" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-key-rotation/", "url": "https://ai-analytics.org/writing/swarm-key-rotation/", "title": "Swarm SDK key rotation: automated cryptographic material refresh in field-deployed drone meshes", "content_text": "How the Swarm SDK rotates cryptographic material without grounding the fleet — scheduled signed pre-key rotation on a 7-day timer, OTP replenishment when bundle drops below 20 keys, emergency revocation via gossip-flooded KeyRevocationAnnouncement, BKPSRAM zeroization with 0xFF pattern verification, and staggered rotation coordination across the mesh.", "summary": "How the Swarm SDK rotates cryptographic material without grounding the fleet — scheduled signed pre-key rotation on a 7-day timer, OTP replenishment when bundle drops below 20 keys, emergency revocation via gossip-flooded KeyRevocationAnnouncement, BKPSRAM zeroization with 0xFF pattern verification, and staggered rotation coordination across the mesh.", "date_published": "2026-04-01T00:00:00.000Z", "tags": [ "Swarm SDK", "Security", "Cryptography" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-key-management/", "url": "https://ai-analytics.org/writing/swarm-key-management/", "title": "Swarm SDK key management: device provisioning, certificate rotation, and revocation for autonomous drone systems", "content_text": "How the Swarm SDK manages cryptographic identity for drone fleets: on-device ML-KEM-768 + X25519 keypair generation, three-tier fleet CA hierarchy, pre-provisioned mission cert bundles, 7-day signed prekey rotation, in-flight revocation via RevocationMessage, and emergency wipe on tamper detection.", "summary": "How the Swarm SDK manages cryptographic identity for drone fleets: on-device ML-KEM-768 + X25519 keypair generation, three-tier fleet CA hierarchy, pre-provisioned mission cert bundles, 7-day signed prekey rotation, in-flight revocation via RevocationMessage, and emergency wipe on tamper detection.", "date_published": "2026-03-28T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Post-quantum", "Drone" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-device-enrollment/", "url": "https://ai-analytics.org/writing/swarm-device-enrollment/", "title": "Swarm SDK device enrollment: how a new drone joins an authenticated fleet mesh", "content_text": "How a Swarm SDK drone goes from factory state to trusted mesh participant: factory-provisioned ML-KEM-768 + X25519 keypairs, CSR generation and Fleet CA signing, USB and RF enrollment paths, gossip mesh announcement with SignedPreKeyBundle, pioneer bootstrap for the first device, and re-enrollment at certificate expiry.", "summary": "How a Swarm SDK drone goes from factory state to trusted mesh participant: factory-provisioned ML-KEM-768 + X25519 keypairs, CSR generation and Fleet CA signing, USB and RF enrollment paths, gossip mesh announcement with SignedPreKeyBundle, pioneer bootstrap for the first device, and re-enrollment at certificate expiry.", "date_published": "2026-03-21T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Post-quantum", "Drone" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/post-quantum-drone-mesh/", "url": "https://ai-analytics.org/writing/post-quantum-drone-mesh/", "title": "Post-quantum mesh cryptography for drone swarms: the Swarm SDK design", "content_text": "How we designed the Swarm SDK: ML-KEM-768 + X25519 hybrid post-quantum key exchange, Double Ratchet forward secrecy, gossip mesh routing, and CNSA 2.0 compliance.", "summary": "How we designed the Swarm SDK: ML-KEM-768 + X25519 hybrid post-quantum key exchange, Double Ratchet forward secrecy, gossip mesh routing, and CNSA 2.0 compliance.", "date_published": "2026-03-15T00:00:00.000Z", "tags": [ "Cryptography", "Post-quantum", "Drone", "Swarm SDK" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-sdk-opsec/", "url": "https://ai-analytics.org/writing/swarm-sdk-opsec/", "title": "Swarm SDK operational security: traffic analysis resistance, message size normalization, and timing jitter", "content_text": "How the Swarm SDK protects drone mesh communications against traffic analysis — six fixed message size bins, ±15% transmission timing jitter, store-and-forward ring buffer for burst smoothing, degraded-channel operational mode, and RF fingerprint resistance on STM32H7.", "summary": "How the Swarm SDK protects drone mesh communications against traffic analysis — six fixed message size bins, ±15% transmission timing jitter, store-and-forward ring buffer for burst smoothing, degraded-channel operational mode, and RF fingerprint resistance on STM32H7.", "date_published": "2026-03-10T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Security" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-mavlink-integration/", "url": "https://ai-analytics.org/writing/swarm-mavlink-integration/", "title": "Swarm SDK MAVLink v2 integration: encrypting mesh messages inside 253-byte drone protocol frames", "content_text": "How the Swarm SDK wraps post-quantum encrypted mesh traffic in MAVLink v2 SWARM_MESH_FRAME messages — 18-byte fragment header, reassembly buffer with 5-second TTL, PX4 and ArduPilot integration, and why ML-KEM-768 Sealed Sender envelopes always require 6 frames.", "summary": "How the Swarm SDK wraps post-quantum encrypted mesh traffic in MAVLink v2 SWARM_MESH_FRAME messages — 18-byte fragment header, reassembly buffer with 5-second TTL, PX4 and ArduPilot integration, and why ML-KEM-768 Sealed Sender envelopes always require 6 frames.", "date_published": "2026-03-05T00:00:00.000Z", "tags": [ "Swarm SDK", "MAVLink", "Drone", "Cryptography" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-message-framing/", "url": "https://ai-analytics.org/writing/swarm-message-framing/", "title": "Swarm SDK message framing: binary wire format, fragmentation, and MAVLink packing", "content_text": "How the Swarm SDK serializes, fragments, and packs Double Ratchet encrypted messages into MAVLink v2 TUNNEL frames: the SwarmFrame binary header, 237-byte payload limit, fragmentation algorithm, reassembly state machine, CONTROL frame authentication, and STM32H7 performance.", "summary": "How the Swarm SDK serializes, fragments, and packs Double Ratchet encrypted messages into MAVLink v2 TUNNEL frames: the SwarmFrame binary header, 237-byte payload limit, fragmentation algorithm, reassembly state machine, CONTROL frame authentication, and STM32H7 performance.", "date_published": "2026-02-27T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Protocol design" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-double-ratchet/", "url": "https://ai-analytics.org/writing/swarm-double-ratchet/", "title": "The Swarm SDK double ratchet: forward secrecy and post-compromise security in drone mesh networks", "content_text": "How the Swarm SDK implements the Double Ratchet algorithm for drone-to-drone messaging: adapting Signal Protocol's KDF chains for ML-KEM-768 post-quantum initial key exchange, header encryption, out-of-order message handling with a sliding key cache, MAVLink v2 framing, and performance benchmarks on embedded ARM.", "summary": "How the Swarm SDK implements the Double Ratchet algorithm for drone-to-drone messaging: adapting Signal Protocol's KDF chains for ML-KEM-768 post-quantum initial key exchange, header encryption, out-of-order message handling with a sliding key cache, MAVLink v2 framing, and performance benchmarks on embedded ARM.", "date_published": "2026-02-22T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Post-quantum", "Drone" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-sealed-sender/", "url": "https://ai-analytics.org/writing/swarm-sealed-sender/", "title": "Swarm SDK Sealed Sender: hiding the sender identity without breaking end-to-end encryption", "content_text": "How the Swarm SDK implements Sealed Sender to hide drone identity from relay infrastructure: recipient-issued SenderCertificate, ephemeral X25519 + HKDF-SHA256 per-message encryption, AES-256-GCM with zero relay-visible sender field, 48-hour certificate TTL, four decryption failure modes, and Sender Keys group integration.", "summary": "How the Swarm SDK implements Sealed Sender to hide drone identity from relay infrastructure: recipient-issued SenderCertificate, ephemeral X25519 + HKDF-SHA256 per-message encryption, AES-256-GCM with zero relay-visible sender field, 48-hour certificate TTL, four decryption failure modes, and Sender Keys group integration.", "date_published": "2026-02-16T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Protocol design" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-api-mcp-server/", "url": "https://ai-analytics.org/writing/regulatory-api-mcp-server/", "title": "The Federal Regulatory Data Hub MCP server: 38+ tools for AI agent workflows", "content_text": "How the Federal Regulatory Data Hub exposes its data through an MCP server with 38+ tools for Claude, GPT, and other AI agents — screen_entity, get_entity, compliance reporting tools, rate-limit tiers by plan, and Claude Desktop integration via stdio transport.", "summary": "How the Federal Regulatory Data Hub exposes its data through an MCP server with 38+ tools for Claude, GPT, and other AI agents — screen_entity, get_entity, compliance reporting tools, rate-limit tiers by plan, and Claude Desktop integration via stdio transport.", "date_published": "2026-02-05T00:00:00.000Z", "tags": [ "Regulatory", "MCP", "Infrastructure", "AI" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-sdk-v03/", "url": "https://ai-analytics.org/writing/swarm-sdk-v03/", "title": "Swarm SDK v0.3: Sender Keys, Sealed Sender, and Deniable Authentication for Drone Mesh Networks", "content_text": "What shipped in Swarm SDK v0.3: O(1) group encryption with Sender Keys (0.7ms on STM32H7), Sealed Sender hiding drone identity via ML-KEM-768 encapsulation, deniable HMAC authentication, and PKCS7 padding normalization across all AES-GCM operations. 127 new tests (302 total).", "summary": "What shipped in Swarm SDK v0.3: O(1) group encryption with Sender Keys (0.7ms on STM32H7), Sealed Sender hiding drone identity via ML-KEM-768 encapsulation, deniable HMAC authentication, and PKCS7 padding normalization across all AES-GCM operations. 127 new tests (302 total).", "date_published": "2026-02-10T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Post-quantum", "Drone" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-regulatory-api/", "url": "https://ai-analytics.org/writing/federal-regulatory-api/", "title": "The Federal Regulatory API: REST, MCP, and JSON-LD for 197 federal datasets", "content_text": "How the Federal Regulatory Data Hub API is designed: CC0 REST endpoints, cross-agency entity resolution in one GET, MCP server with 38+ tools for agent workflows, and JSON-LD structured data.", "summary": "How the Federal Regulatory Data Hub API is designed: CC0 REST endpoints, cross-agency entity resolution in one GET, MCP server with 38+ tools for agent workflows, and JSON-LD structured data.", "date_published": "2026-02-01T00:00:00.000Z", "tags": [ "Regulatory data", "API design", "MCP", "Cloudflare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-x3dh-session/", "url": "https://ai-analytics.org/writing/swarm-x3dh-session/", "title": "Swarm SDK session establishment: X3DH prekey bundles and the initial drone-to-drone handshake", "content_text": "How the Swarm SDK uses Extended Triple Diffie-Hellman (X3DH) with ML-KEM-768 adaptation for async drone-to-drone session establishment — prekey bundle construction, one-time prekey consumption, Fleet CA bundle verification, and the transition from shared secret to Double Ratchet forward secrecy.", "summary": "How the Swarm SDK uses Extended Triple Diffie-Hellman (X3DH) with ML-KEM-768 adaptation for async drone-to-drone session establishment — prekey bundle construction, one-time prekey consumption, Fleet CA bundle verification, and the transition from shared secret to Double Ratchet forward secrecy.", "date_published": "2026-01-25T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Drone", "Post-quantum" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-prekey-management/", "url": "https://ai-analytics.org/writing/swarm-prekey-management/", "title": "Swarm SDK prekey bundle management: generating, distributing, and consuming OneTimePreKeys across a drone fleet", "content_text": "How the Swarm SDK generates, distributes, and tracks OneTimePreKeys for X3DH session establishment — including OTP exhaustion handling, SignedPreKey rotation, and the gossip-mesh key bundle protocol.", "summary": "How the Swarm SDK generates, distributes, and tracks OneTimePreKeys for X3DH session establishment — including OTP exhaustion handling, SignedPreKey rotation, and the gossip-mesh key bundle protocol.", "date_published": "2026-01-20T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Drone", "Post-quantum" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-dataset-ingest/", "url": "https://ai-analytics.org/writing/federal-dataset-ingest/", "title": "Federal dataset ingest: keeping 197 federal datasets fresh at the edge", "content_text": "How we ingest and refresh 197 federal regulatory datasets across 45 agencies using Cloudflare Workers cron, delta detection, schema drift handling, and per-source retry budgets.", "summary": "How we ingest and refresh 197 federal regulatory datasets across 45 agencies using Cloudflare Workers cron, delta detection, schema drift handling, and per-source retry budgets.", "date_published": "2026-01-15T00:00:00.000Z", "tags": [ "Regulatory data", "Infrastructure", "Data engineering", "Cloudflare" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-mesh-transport/", "url": "https://ai-analytics.org/writing/swarm-mesh-transport/", "title": "Swarm SDK mesh transport: reliable delivery over contested RF links", "content_text": "How the Swarm SDK MeshTransport layer achieves reliable frame delivery over lossy drone radio links: sliding window ARQ with selective ACK, EWMA RTT estimation, transparent fragmentation and reassembly for Sealed Sender envelopes, multi-channel bonding across 2.4GHz and 5.8GHz radios, and performance benchmarks on STM32H7 and Jetson Nano.", "summary": "How the Swarm SDK MeshTransport layer achieves reliable frame delivery over lossy drone radio links: sliding window ARQ with selective ACK, EWMA RTT estimation, transparent fragmentation and reassembly for Sealed Sender envelopes, multi-channel bonding across 2.4GHz and 5.8GHz radios, and performance benchmarks on STM32H7 and Jetson Nano.", "date_published": "2026-01-08T00:00:00.000Z", "tags": [ "Swarm SDK", "Drone", "Infrastructure", "Cryptography" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-gossip-mesh/", "url": "https://ai-analytics.org/writing/swarm-gossip-mesh/", "title": "Swarm SDK gossip mesh: bounded fanout routing, message deduplication, and network partition handling", "content_text": "How the Swarm SDK implements a gossip mesh: epidemic broadcast with k=3 fanout, UUIDv4 sliding-window deduplication, Lamport clock causal ordering, TTL hop limiting, and anti-entropy reconciliation for post-partition recovery — with STM32H7 and Jetson Nano benchmarks.", "summary": "How the Swarm SDK implements a gossip mesh: epidemic broadcast with k=3 fanout, UUIDv4 sliding-window deduplication, Lamport clock causal ordering, TTL hop limiting, and anti-entropy reconciliation for post-partition recovery — with STM32H7 and Jetson Nano benchmarks.", "date_published": "2026-01-02T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Drone", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/swarm-sdk-architecture/", "url": "https://ai-analytics.org/writing/swarm-sdk-architecture/", "title": "Swarm SDK architecture: gossip mesh, post-quantum cryptography, and embedded-first design", "content_text": "An architectural overview of the Swarm SDK: the three-layer design covering gossip mesh epidemic broadcast, ML-KEM-768 + X25519 hybrid post-quantum cryptography with Double Ratchet and Sender Keys, MAVLink v2 framing, and no_std embedded operation on STM32H7.", "summary": "An architectural overview of the Swarm SDK: the three-layer design covering gossip mesh epidemic broadcast, ML-KEM-768 + X25519 hybrid post-quantum cryptography with Double Ratchet and Sender Keys, MAVLink v2 framing, and no_std embedded operation on STM32H7.", "date_published": "2025-12-27T00:00:00.000Z", "tags": [ "Swarm SDK", "Cryptography", "Post-quantum", "Drone" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-incident-clustering/", "url": "https://ai-analytics.org/writing/voidly-incident-clustering/", "title": "Incident clustering and deduplication: how Voidly avoids counting the same censorship event twice", "content_text": "How Voidly deduplicates thousands of probe measurements into discrete censorship incidents: the four-tuple clustering key, the 6-hour gap rule, incident lifecycle from ANOMALY to RESOLVED, incident_id assignment, and retroactive CensoredPlanet alignment.", "summary": "How Voidly deduplicates thousands of probe measurements into discrete censorship incidents: the four-tuple clustering key, the 6-hour gap rule, incident lifecycle from ANOMALY to RESOLVED, incident_id assignment, and retroactive CensoredPlanet alignment.", "date_published": "2025-12-22T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-incident-timeline/", "url": "https://ai-analytics.org/writing/voidly-incident-timeline/", "title": "Voidly incident timeline reconstruction: building the canonical event sequence from distributed probe measurements", "content_text": "How Voidly reconstructs the authoritative timeline of a censorship incident from asynchronous distributed probe measurements — IncidentEvent sourcing model, temporal alignment across time zones, confidence weighting requiring 3+ independent probes, retroactive revision from CensoredPlanet batch data, and the timeline REST API endpoint.", "summary": "How Voidly reconstructs the authoritative timeline of a censorship incident from asynchronous distributed probe measurements — IncidentEvent sourcing model, temporal alignment across time zones, confidence weighting requiring 3+ independent probes, retroactive revision from CensoredPlanet batch data, and the timeline REST API endpoint.", "date_published": "2025-12-17T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-incident-resolution/", "url": "https://ai-analytics.org/writing/voidly-incident-resolution/", "title": "Voidly incident resolution: how we know when a censorship event ends", "content_text": "How Voidly determines that a censorship incident has ended: per-type resolution thresholds (consecutive passing measurements with p_blocked < 0.3), the 12-hour RESOLVED_PENDING re-open window, FLAPPING state detection for rapidly alternating blocks, BGP-type auto-resolution, and cross-source confirmation requirements for VERIFIED incidents — with observed resolution time distributions (BGP 4.2h median, HTTP 12.1 days).", "summary": "How Voidly determines that a censorship incident has ended: per-type resolution thresholds (consecutive passing measurements with p_blocked < 0.3), the 12-hour RESOLVED_PENDING re-open window, FLAPPING state detection for rapidly alternating blocks, BGP-type auto-resolution, and cross-source confirmation requirements for VERIFIED incidents — with observed resolution time distributions (BGP 4.2h median, HTTP 12.1 days).", "date_published": "2025-12-13T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-realtime-anomaly-scorer/", "url": "https://ai-analytics.org/writing/voidly-realtime-anomaly-scorer/", "title": "Voidly real-time anomaly scorer: ML inference in the streaming pipeline at 50,000 events per second", "content_text": "How Voidly embeds ONNX Runtime inference inside an Apache Flink streaming job to score probe results for censorship anomalies at 50,000 events per second with sub-100ms end-to-end latency: the Flink operator model, thread-local ONNX session management, Kafka partition alignment to minimize cross-shard state, and the backpressure mechanism that prevents model inference from becoming the pipeline bottleneck.", "summary": "How Voidly embeds ONNX Runtime inference inside an Apache Flink streaming job to score probe results for censorship anomalies at 50,000 events per second with sub-100ms end-to-end latency: the Flink operator model, thread-local ONNX session management, Kafka partition alignment to minimize cross-shard state, and the backpressure mechanism that prevents model inference from becoming the pipeline bottleneck.", "date_published": "2025-12-09T00:00:00.000Z", "tags": [ "Voidly", "Streaming", "Machine learning", "Flink", "Real-time" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-realtime-pipeline/", "url": "https://ai-analytics.org/writing/voidly-realtime-pipeline/", "title": "Voidly's real-time event pipeline: from measurement anomaly to journalist alert in under 8 minutes", "content_text": "How Voidly gets from a probe anomaly to a published verified incident in under 8 minutes: event queue, real-time OONI and IODA API polling, confidence thresholds, two-window alert-fatigue guard, and nightly CensoredPlanet retroactive pass.", "summary": "How Voidly gets from a probe anomaly to a published verified incident in under 8 minutes: event queue, real-time OONI and IODA API polling, confidence thresholds, two-window alert-fatigue guard, and nightly CensoredPlanet retroactive pass.", "date_published": "2025-12-05T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Real-time systems" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-run-lifecycle/", "url": "https://ai-analytics.org/writing/voidly-probe-run-lifecycle/", "title": "Voidly probe run lifecycle: from scheduled task to classifier input", "content_text": "What happens inside a single Voidly probe run: the measurement execution loop, DNS and TCP and TLS and HTTP data capture, result serialization and signing, and the upload path that delivers a signed ProbeResult to the ingest pipeline.", "summary": "What happens inside a single Voidly probe run: the measurement execution loop, DNS and TCP and TLS and HTTP data capture, result serialization and signing, and the upload path that delivers a signed ProbeResult to the ingest pipeline.", "date_published": "2025-11-29T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "Infrastructure", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-networking/", "url": "https://ai-analytics.org/writing/voidly-probe-networking/", "title": "Voidly probe networking: staying connected through NAT, firewalls, and censored infrastructure", "content_text": "How Voidly probes maintain connectivity and upload measurements from networks that actively block VPN protocols — QUIC/443 transport, domain fronting via CDN SNI fronting, TLS certificate pinning against MITM, local SQLite buffering (500 MB cap, 48h window), and metered-connection backoff.", "summary": "How Voidly probes maintain connectivity and upload measurements from networks that actively block VPN protocols — QUIC/443 transport, domain fronting via CDN SNI fronting, TLS certificate pinning against MITM, local SQLite buffering (500 MB cap, 48h window), and metered-connection backoff.", "date_published": "2025-11-24T00:00:00.000Z", "tags": [ "Voidly", "Networking", "QUIC", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-local-buffer/", "url": "https://ai-analytics.org/writing/voidly-probe-local-buffer/", "title": "Voidly probe local measurement buffer: SQLite ring buffer, batch compression, and resilient upload", "content_text": "How Voidly probes preserve measurement data during upload failures — a 72-hour SQLite ring buffer with anomaly-safe eviction, LZ4 batch compression reducing median batch size from 47KB to 9KB, exponential backoff retry up to 4 hours, priority queue for anomalous measurements, and 0.003% measurement loss rate across 37 probes over 6 months.", "summary": "How Voidly probes preserve measurement data during upload failures — a 72-hour SQLite ring buffer with anomaly-safe eviction, LZ4 batch compression reducing median batch size from 47KB to 9KB, exponential backoff retry up to 4 hours, priority queue for anomalous measurements, and 0.003% measurement loss rate across 37 probes over 6 months.", "date_published": "2025-11-19T00:00:00.000Z", "tags": [ "Voidly", "Infrastructure", "Censorship" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-architecture/", "url": "https://ai-analytics.org/writing/voidly-probe-architecture/", "title": "The Voidly Probe: Tauri + boringtun network measurement at the operator's edge", "content_text": "How the Voidly desktop probe works: Tauri 2 cross-platform app, Cloudflare boringtun WireGuard, tun-rs TUN device, X25519-Dalek on-device key generation, and operator anonymity as a design constraint.", "summary": "How the Voidly desktop probe works: Tauri 2 cross-platform app, Cloudflare boringtun WireGuard, tun-rs TUN device, X25519-Dalek on-device key generation, and operator anonymity as a design constraint.", "date_published": "2025-11-15T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Tauri" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-test-runner/", "url": "https://ai-analytics.org/writing/voidly-probe-test-runner/", "title": "The Voidly probe test runner: concurrency, timeout handling, and the measurement state machine", "content_text": "How the Voidly probe test runner orchestrates concurrent measurements: tokio Semaphore with 3 permits, MeasurementState machine (Pending → Running → Success/Error/Timeout), per-layer timeout budgets (DNS 3s, TCP 5s, TLS 8s, HTTP 15s, total 30s), Ed25519 measurement signing, mpsc upload queue with capacity 200, and why per-layer timeouts are themselves evidence of DNS-layer interference.", "summary": "How the Voidly probe test runner orchestrates concurrent measurements: tokio Semaphore with 3 permits, MeasurementState machine (Pending → Running → Success/Error/Timeout), per-layer timeout budgets (DNS 3s, TCP 5s, TLS 8s, HTTP 15s, total 30s), Ed25519 measurement signing, mpsc upload queue with capacity 200, and why per-layer timeouts are themselves evidence of DNS-layer interference.", "date_published": "2025-11-08T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Rust" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-http-measurement/", "url": "https://ai-analytics.org/writing/voidly-http-measurement/", "title": "How Voidly measures HTTP and HTTPS censorship: the full protocol lifecycle from DNS through TLS to body comparison", "content_text": "A step-by-step breakdown of how each Voidly probe test works: DNS resolution, TCP handshake, TLS negotiation with certificate chain validation, HTTP request execution, response body fingerprinting, control comparison, and how every layer maps to interference types in the anomaly classifier.", "summary": "A step-by-step breakdown of how each Voidly probe test works: DNS resolution, TCP handshake, TLS negotiation with certificate chain validation, HTTP request execution, response body fingerprinting, control comparison, and how every layer maps to interference types in the anomaly classifier.", "date_published": "2025-11-01T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-tcp-measurement/", "url": "https://ai-analytics.org/writing/voidly-tcp-measurement/", "title": "Voidly's TCP measurement layer: RST injection detection, null-routing, and connection timing analysis", "content_text": "A deep dive into the TCP layer of Voidly's censorship detection: SYN-ACK timing, RST injection detection with a 15ms threshold, null-routing vs. RST as two distinct censorship mechanisms, the TcpResult struct, dual-IP probing to identify RST source, and how TCP evidence maps to the anomaly classifier's interference classes.", "summary": "A deep dive into the TCP layer of Voidly's censorship detection: SYN-ACK timing, RST injection detection with a 15ms threshold, null-routing vs. RST as two distinct censorship mechanisms, the TcpResult struct, dual-IP probing to identify RST source, and how TCP evidence maps to the anomaly classifier's interference classes.", "date_published": "2025-10-27T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-control-server/", "url": "https://ai-analytics.org/writing/voidly-control-server/", "title": "The Voidly control server: how we tell censorship from a bad network", "content_text": "How Voidly uses a distributed control server network to distinguish genuine censorship from network errors, CDN split-horizon DNS, and misconfigured sites — DNS, TCP, TLS, and HTTP comparison methodology.", "summary": "How Voidly uses a distributed control server network to distinguish genuine censorship from network errors, CDN split-horizon DNS, and misconfigured sites — DNS, TCP, TLS, and HTTP comparison methodology.", "date_published": "2025-10-22T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-throttling-measurement/", "url": "https://ai-analytics.org/writing/voidly-throttling-measurement/", "title": "How Voidly measures bandwidth throttling: timing signals, body truncation, and the calibration problem", "content_text": "A technical deep-dive on how Voidly detects bandwidth throttling — the hardest interference class to classify. Covers the TimingFeatures Rust struct, TTFB z-score computation against control measurements, body truncation and mid-transfer RST signals, the congestion vs. deliberate-throttling calibration problem, cross-probe corroboration scoring, and country patterns from Russia TSPU, Iran ARRS, India, and China.", "summary": "A technical deep-dive on how Voidly detects bandwidth throttling — the hardest interference class to classify. Covers the TimingFeatures Rust struct, TTFB z-score computation against control measurements, body truncation and mid-transfer RST signals, the congestion vs. deliberate-throttling calibration problem, cross-probe corroboration scoring, and country patterns from Russia TSPU, Iran ARRS, India, and China.", "date_published": "2025-10-15T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-health/", "url": "https://ai-analytics.org/writing/voidly-probe-health/", "title": "Voidly probe health monitoring: how we detect and replace failing probe nodes", "content_text": "How Voidly monitors 37+ probe nodes: heartbeat system (60s cadence, separate transport), DEGRADED/OFFLINE state machine, measurement quality scoring, ASN coverage SLOs, flapping detection, automated replacement, and the classify_offline_cause() algorithm distinguishing probe failure from ISP-level censorship.", "summary": "How Voidly monitors 37+ probe nodes: heartbeat system (60s cadence, separate transport), DEGRADED/OFFLINE state machine, measurement quality scoring, ASN coverage SLOs, flapping detection, automated replacement, and the classify_offline_cause() algorithm distinguishing probe failure from ISP-level censorship.", "date_published": "2025-10-08T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-dns-injection-detection/", "url": "https://ai-analytics.org/writing/voidly-dns-injection-detection/", "title": "How Voidly detects DNS injection: forged responses, injection rates by country, and pipeline integration", "content_text": "How Voidly probes identify DNS injection and manipulation in censored networks — comparison against three control resolvers, four weighted detection signals (IP divergence, TTL anomaly, source IP divergence, response timing), per-country injection rates (China 94%, Iran 61%, Russia 12%), CAP_NET_RAW privilege handling, and anycast false-positive calibration.", "summary": "How Voidly probes identify DNS injection and manipulation in censored networks — comparison against three control resolvers, four weighted detection signals (IP divergence, TTL anomaly, source IP divergence, response timing), per-country injection rates (China 94%, Iran 61%, Russia 12%), CAP_NET_RAW privilege handling, and anycast false-positive calibration.", "date_published": "2025-10-03T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-interference-taxonomy/", "url": "https://ai-analytics.org/writing/voidly-interference-taxonomy/", "title": "Voidly's interference taxonomy: classifying censorship from DNS injection to BGP withdrawal", "content_text": "How Voidly classifies every censorship measurement into one of 7 interference types using a hierarchical decision tree — with confidence scoring, protocol layer priority, and an Indeterminate category.", "summary": "How Voidly classifies every censorship measurement into one of 7 interference types using a hierarchical decision tree — with confidence scoring, protocol layer priority, and an Indeterminate category.", "date_published": "2025-09-24T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-geoblocking/", "url": "https://ai-analytics.org/writing/voidly-geoblocking/", "title": "Geoblocking vs. censorship: how Voidly distinguishes licensing restrictions, CDN geofencing, and GDPR blocks from government-ordered blocking", "content_text": "How Voidly avoids false positives from commercial geoblocking: HTTP 451 detection, streaming service block page fingerprints, multi-country probe comparison, CDN split-horizon detection via ASN group mapping, and the p_geoblock score.", "summary": "How Voidly avoids false positives from commercial geoblocking: HTTP 451 detection, streaming service block page fingerprints, multi-country probe comparison, CDN split-horizon detection via ASN group mapping, and the p_geoblock score.", "date_published": "2025-09-29T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/censorship-cross-source-verification/", "url": "https://ai-analytics.org/writing/censorship-cross-source-verification/", "title": "Cross-source censorship verification: reconciling OONI, CensoredPlanet, and IODA", "content_text": "How Voidly correlates three independent measurement projects at scale — data format normalization, 4-hour sliding window alignment, independence-weighted confidence scoring, and handling source disagreements.", "summary": "How Voidly correlates three independent measurement projects at scale — data format normalization, 4-hour sliding window alignment, independence-weighted confidence scoring, and handling source disagreements.", "date_published": "2025-09-20T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "OSINT", "Verification" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-middlebox-detection/", "url": "https://ai-analytics.org/writing/voidly-middlebox-detection/", "title": "Voidly middlebox detection: fingerprinting DPI hardware from RST packets, header injection, and vendor signatures", "content_text": "How Voidly detects and fingerprints network middleboxes responsible for censorship: echo test HTTP header injection detection, RST packet analysis with four heuristics (arrival time 0.40, TTL mismatch 0.30, zero window 0.20, absent TCP options 0.10), a 47-vendor DPI signature library covering TSPU, Sandvine, Huawei Hi-SEC, GFW, and Cisco, TimescaleDB middlebox_events hypertable, and correlation analysis showing 18-hour median lead time before confirmed censorship events.", "summary": "How Voidly detects and fingerprints network middleboxes responsible for censorship: echo test HTTP header injection detection, RST packet analysis with four heuristics (arrival time 0.40, TTL mismatch 0.30, zero window 0.20, absent TCP options 0.10), a 47-vendor DPI signature library covering TSPU, Sandvine, Huawei Hi-SEC, GFW, and Cisco, TimescaleDB middlebox_events hypertable, and correlation analysis showing 18-hour median lead time before confirmed censorship events.", "date_published": "2025-09-16T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-tls-measurement/", "url": "https://ai-analytics.org/writing/voidly-tls-measurement/", "title": "How Voidly measures TLS censorship: certificate forgery, SNI blocking, and handshake interference", "content_text": "A deep dive into the TLS layer of Voidly's censorship detection: full certificate chain extraction with rustls, government CA list (China MoI, Iran MICT, Kazakhstan NCA), MITM detection via fingerprint mismatch, TLS alert timing analysis (RST < 15ms = injected), SNI-based blocking detection via dual-SNI probing, ECH/ESNI measurement, and how TLS failure maps to interference_type classifier outputs.", "summary": "A deep dive into the TLS layer of Voidly's censorship detection: full certificate chain extraction with rustls, government CA list (China MoI, Iran MICT, Kazakhstan NCA), MITM detection via fingerprint mismatch, TLS alert timing analysis (RST < 15ms = injected), SNI-based blocking detection via dual-SNI probing, ECH/ESNI measurement, and how TLS failure maps to interference_type classifier outputs.", "date_published": "2025-09-12T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "TLS", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-blockpage-fingerprints/", "url": "https://ai-analytics.org/writing/voidly-blockpage-fingerprints/", "title": "Voidly's block page fingerprint library: detecting censorship signatures across 2,300+ known pages", "content_text": "How Voidly built and maintains the 2,300-entry block page fingerprint library: four matching strategies (exact hash, structural normalization, SimHash, TLS cert fingerprinting), block page collection, per-country composition (Turkey 47, Iran 312, Russia 189), false positive mitigation, and integration with the Snorkel label function.", "summary": "How Voidly built and maintains the 2,300-entry block page fingerprint library: four matching strategies (exact hash, structural normalization, SimHash, TLS cert fingerprinting), block page collection, per-country composition (Turkey 47, Iran 312, Russia 189), false positive mitigation, and integration with the Snorkel label function.", "date_published": "2025-09-05T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-measurement-protocol-stack/", "url": "https://ai-analytics.org/writing/voidly-measurement-protocol-stack/", "title": "Voidly measurement protocol stack: how a single probe run executes DNS, TCP, TLS, HTTP, and control layers", "content_text": "How the Voidly ProbeResult struct encodes all five measurement layers (DNS, TCP, TLS, HTTP, control) and how the probe executes them: sequential layer execution with a parallel tokio-spawned control task, failure propagation via Option semantics (None=not attempted vs Some(failed)=attempted and failed), six layer-outcome combinations and their censorship-type mappings, and deterministic control vantage selection via domain_hash_u64 % 5 across five geographic endpoints.", "summary": "How the Voidly ProbeResult struct encodes all five measurement layers (DNS, TCP, TLS, HTTP, control) and how the probe executes them: sequential layer execution with a parallel tokio-spawned control task, failure propagation via Option semantics (None=not attempted vs Some(failed)=attempted and failed), six layer-outcome combinations and their censorship-type mappings, and deterministic control vantage selection via domain_hash_u64 % 5 across five geographic endpoints.", "date_published": "2025-09-01T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-dns-measurement/", "url": "https://ai-analytics.org/writing/voidly-dns-measurement/", "title": "How Voidly measures DNS censorship: NXDOMAIN injection, IP spoofing, and resolver-level filtering", "content_text": "A deep dive into the DNS layer of Voidly's censorship detection: dual-resolver design (ISP resolver vs. neutral control), four interference types (NXDOMAIN injection, IP spoofing, empty answer, timeout), the compare_dns_results() algorithm, known injection IP database (China 18 IPs, Iran 3, Turkey 2), CDN geofencing false positive mitigation via ASN group matching, DNSSEC validation limitations, and DoH/DoT diagnostic queries.", "summary": "A deep dive into the DNS layer of Voidly's censorship detection: dual-resolver design (ISP resolver vs. neutral control), four interference types (NXDOMAIN injection, IP spoofing, empty answer, timeout), the compare_dns_results() algorithm, known injection IP database (China 18 IPs, Iran 3, Turkey 2), CDN geofencing false positive mitigation via ASN group matching, DNSSEC validation limitations, and DoH/DoT diagnostic queries.", "date_published": "2025-08-28T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "DNS", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-measurement-api-export/", "url": "https://ai-analytics.org/writing/voidly-measurement-api-export/", "title": "Voidly measurement API and Parquet export: keyset pagination, SSE streaming, and nightly HuggingFace snapshots", "content_text": "How Voidly exposes bulk measurement data via a keyset-paginated NDJSON API and nightly Parquet export: PostgreSQL (ts, measurement_id) keyset pagination, SSE streaming with 2-second poll and keepalive pings, 17-field PyArrow schema with dictionary-encoded domain and binary body_sha256, Zstandard level 3 with 1MB pages and 500K row groups, domain+ts sort for 60% I/O reduction, and HuggingFace push with classifier_version tagging.", "summary": "How Voidly exposes bulk measurement data via a keyset-paginated NDJSON API and nightly Parquet export: PostgreSQL (ts, measurement_id) keyset pagination, SSE streaming with 2-second poll and keepalive pings, 17-field PyArrow schema with dictionary-encoded domain and binary body_sha256, Zstandard level 3 with 1MB pages and 500K row groups, domain+ts sort for 60% I/O reduction, and HuggingFace push with classifier_version tagging.", "date_published": "2025-08-24T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "Data engineering", "Open data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-dataset-schema/", "url": "https://ai-analytics.org/writing/voidly-dataset-schema/", "title": "The Voidly measurement dataset: field-by-field schema reference", "content_text": "A complete field-by-field guide to the Voidly CC BY 4.0 measurement dataset — probe identity, DNS/TCP/TLS/HTTP layers, control comparison, ML classification output, BGP signals, and corroboration fields.", "summary": "A complete field-by-field guide to the Voidly CC BY 4.0 measurement dataset — probe identity, DNS/TCP/TLS/HTTP layers, control comparison, ML classification output, BGP signals, and corroboration fields.", "date_published": "2025-08-20T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Data engineering", "Open data" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-continuous-aggregates/", "url": "https://ai-analytics.org/writing/voidly-continuous-aggregates/", "title": "Voidly's TimescaleDB continuous aggregates: pre-aggregating 2.2B probe measurements for fast queries", "content_text": "The three-level TimescaleDB continuous aggregate hierarchy behind Voidly's sub-10ms query latency: measurement_hourly (15-minute refresh), country_daily_summary (1-hour refresh), country_monthly_stats (daily). Covers refresh policies, late-arriving data, compression interplay, and backfill procedures.", "summary": "The three-level TimescaleDB continuous aggregate hierarchy behind Voidly's sub-10ms query latency: measurement_hourly (15-minute refresh), country_daily_summary (1-hour refresh), country_monthly_stats (daily). Covers refresh policies, late-arriving data, compression interplay, and backfill procedures.", "date_published": "2025-08-14T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "TimescaleDB", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-ingest/", "url": "https://ai-analytics.org/writing/voidly-probe-ingest/", "title": "Voidly's probe-to-dataset ingest pipeline: normalization, quality filtering, and TimescaleDB indexing", "content_text": "The full path from raw probe bytes to a queryable TimescaleDB record: protobuf over QUIC, Cloudflare Worker validation, Kafka fan-out, Rust normalization, probe-version schema drift handling, quality filtering (3.2% drop rate), and nightly Parquet export to HuggingFace.", "summary": "The full path from raw probe bytes to a queryable TimescaleDB record: protobuf over QUIC, Cloudflare Worker validation, Kafka fan-out, Rust normalization, probe-version schema drift handling, quality filtering (3.2% drop rate), and nightly Parquet export to HuggingFace.", "date_published": "2025-08-08T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Data pipeline", "Kafka" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-bgp-data-ingestion/", "url": "https://ai-analytics.org/writing/voidly-bgp-data-ingestion/", "title": "Voidly BGP data ingestion: parsing MRT dumps, detecting prefix withdrawals, and computing country outage scores", "content_text": "How Voidly ingests BGP data from RIPE NCC RIS, RouteViews, and bgp.tools: MRT format parsing, per-country baseline computation, withdrawal detection thresholds, BgpEvent records in TimescaleDB, and how bgp_outage_score is attached to probe measurements.", "summary": "How Voidly ingests BGP data from RIPE NCC RIS, RouteViews, and bgp.tools: MRT format parsing, per-country baseline computation, withdrawal detection thresholds, BgpEvent records in TimescaleDB, and how bgp_outage_score is attached to probe measurements.", "date_published": "2025-08-02T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "BGP", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/bgp-shutdown-detection/", "url": "https://ai-analytics.org/writing/bgp-shutdown-detection/", "title": "BGP routing signals and internet shutdown detection: how Voidly uses IODA data", "content_text": "How Voidly uses BGP prefix withdrawal patterns and IODA data to detect internet shutdowns before any probe can send a packet — baseline per-country reachability, BGP silence vs. withdrawal, and how BGP fits into the composite confidence score.", "summary": "How Voidly uses BGP prefix withdrawal patterns and IODA data to detect internet shutdowns before any probe can send a packet — baseline per-country reachability, BGP silence vs. withdrawal, and how BGP fits into the composite confidence score.", "date_published": "2025-07-28T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "BGP", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-as-path-analysis/", "url": "https://ai-analytics.org/writing/voidly-as-path-analysis/", "title": "Voidly AS path analysis: using BGP topology to locate censorship enforcement points", "content_text": "How Voidly uses CAIDA AS-Rank, RIPE NCC RIS route collector data, and PeeringDB to build an AS-level topology, classify censorship choke points (IXP, transit AS, edge ISP), compute per-country probe diversity scores, and feed AS path features into the anomaly classifier.", "summary": "How Voidly uses CAIDA AS-Rank, RIPE NCC RIS route collector data, and PeeringDB to build an AS-level topology, classify censorship choke points (IXP, transit AS, edge ISP), compute per-country probe diversity scores, and feed AS path features into the anomaly classifier.", "date_published": "2025-07-22T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "BGP", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-domain-censorship-history/", "url": "https://ai-analytics.org/writing/voidly-domain-censorship-history/", "title": "Per-domain censorship history in Voidly: tracking blocking events across countries and time", "content_text": "How Voidly tracks the full history of blocking events for individual domains — domain_measurement_summary continuous aggregate, first/last-seen tracking, the /v1/domains/{domain}/history API, temporal patterns, cross-country correlation, and domain freshness scoring.", "summary": "How Voidly tracks the full history of blocking events for individual domains — domain_measurement_summary continuous aggregate, first/last-seen tracking, the /v1/domains/{domain}/history API, temporal patterns, cross-country correlation, and domain freshness scoring.", "date_published": "2025-07-12T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-asn-blocking-analysis/", "url": "https://ai-analytics.org/writing/voidly-asn-blocking-analysis/", "title": "Voidly's ASN-level blocking analysis: how censorship propagates across autonomous systems", "content_text": "How Voidly uses per-ASN probe vantages to distinguish nationwide censorship orders from selective ISP-level blocking — BGP peer classification from CAIDA AS-Rank, ISP blocking fingerprints by interference type, differential blocking detection, and propagation speed analysis that reveals enforcement mechanisms.", "summary": "How Voidly uses per-ASN probe vantages to distinguish nationwide censorship orders from selective ISP-level blocking — BGP peer classification from CAIDA AS-Rank, ISP blocking fingerprints by interference type, differential blocking detection, and propagation speed analysis that reveals enforcement mechanisms.", "date_published": "2025-07-17T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "BGP", "ISP" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-country-censorship-index/", "url": "https://ai-analytics.org/writing/voidly-country-censorship-index/", "title": "Voidly's country-level censorship score: aggregating 2.2B probe measurements into the global index", "content_text": "How Voidly aggregates per-measurement interference probabilities into per-country censorship scores: recency decay with a 30-day half-life, ASN diversity weighting, domain category weighting, cross-source corroboration multipliers, 90-day rolling windows, and bootstrap confidence bands.", "summary": "How Voidly aggregates per-measurement interference probabilities into per-country censorship scores: recency decay with a 30-day half-life, ASN diversity weighting, domain category weighting, cross-source corroboration multipliers, 90-day rolling windows, and bootstrap confidence bands.", "date_published": "2025-07-08T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-sanctions-shutdown-correlation/", "url": "https://ai-analytics.org/writing/voidly-sanctions-shutdown-correlation/", "title": "Sanctions timelines and internet shutdowns: how Voidly correlates OFAC designation bursts with censorship events", "content_text": "How Voidly aligns OFAC sanctions packages, EU/UN designation timelines, and bilateral diplomatic signals with measured internet shutdown events — building the diplomatic-isolation feature for the shutdown forecasting model.", "summary": "How Voidly aligns OFAC sanctions packages, EU/UN designation timelines, and bilateral diplomatic signals with measured internet shutdown events — building the diplomatic-isolation feature for the shutdown forecasting model.", "date_published": "2025-07-03T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ofac-sdn-integration/", "url": "https://ai-analytics.org/writing/ofac-sdn-integration/", "title": "OFAC SDN integration in the Federal Regulatory Data Hub: conditional GET, entity normalization, and sub-second screening", "content_text": "How the Federal Regulatory Data Hub ingests the OFAC SDN list — conditional GET with ETag, XML parsing across 12K entries with alias explosion, name normalization, FTS5 + Jaro-Winkler three-pass screening, and p50 8ms / p99 28ms latency.", "summary": "How the Federal Regulatory Data Hub ingests the OFAC SDN list — conditional GET with ETag, XML parsing across 12K entries with alias explosion, name normalization, FTS5 + Jaro-Winkler three-pass screening, and p50 8ms / p99 28ms latency.", "date_published": "2025-06-28T00:00:00.000Z", "tags": [ "Regulatory data", "Compliance", "OFAC", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-shutdown-features/", "url": "https://ai-analytics.org/writing/voidly-shutdown-features/", "title": "The features behind Voidly's 7-day shutdown forecast: political calendar, sanctions timelines, and network telemetry", "content_text": "A deep dive into the feature engineering behind Voidly's 7-day internet shutdown forecasting model: political calendar integration (election dates, protest intensity via GDELT CAMEO 14x events), OFAC sanctions timeline features, BGP withdrawal rate, probe measurement rate drops as early-warning signals, historical shutdown patterns with sin/cos cyclical encoding, and XGBoost SHAP feature importance across 200 countries.", "summary": "A deep dive into the feature engineering behind Voidly's 7-day internet shutdown forecasting model: political calendar integration (election dates, protest intensity via GDELT CAMEO 14x events), OFAC sanctions timeline features, BGP withdrawal rate, probe measurement rate drops as early-warning signals, historical shutdown patterns with sin/cos cyclical encoding, and XGBoost SHAP feature importance across 200 countries.", "date_published": "2025-06-21T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Forecasting" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/shutdown-forecasting/", "url": "https://ai-analytics.org/writing/shutdown-forecasting/", "title": "Seven-day internet shutdown forecasting: how Voidly predicts connectivity outages", "content_text": "How we build a 7-day predictive model for internet shutdowns across 200 countries: political calendar features, network telemetry, ARIMA + XGBoost ensemble, and per-country reliability scoring.", "summary": "How we build a 7-day predictive model for internet shutdowns across 200 countries: political calendar features, network telemetry, ARIMA + XGBoost ensemble, and per-country reliability scoring.", "date_published": "2025-06-15T00:00:00.000Z", "tags": [ "Censorship", "ML", "Forecasting", "Voidly" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-classifier-to-forecast/", "url": "https://ai-analytics.org/writing/voidly-classifier-to-forecast/", "title": "From anomaly scores to shutdown forecasts: how Voidly aggregates per-measurement classifier output into country-level risk", "content_text": "How Voidly bridges the per-measurement anomaly classifier and the 7-day shutdown forecast: three-stage risk score aggregation from ASN-domain to domain to country level, TimescaleDB continuous aggregates with 15-minute refresh, exponential decay over 336-hour windows with 48-hour half-life, category weighting (news/human_rights 2.5x, circumvention 2.0x), and the 28-feature ForecastFeatureVector published to Kafka for ensemble model consumption.", "summary": "How Voidly bridges the per-measurement anomaly classifier and the 7-day shutdown forecast: three-stage risk score aggregation from ASN-domain to domain to country level, TimescaleDB continuous aggregates with 15-minute refresh, exponential decay over 336-hour windows with 48-hour half-life, category weighting (news/human_rights 2.5x, circumvention 2.0x), and the 28-feature ForecastFeatureVector published to Kafka for ensemble model consumption.", "date_published": "2025-06-11T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "ML", "Forecasting" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-classifier-calibration/", "url": "https://ai-analytics.org/writing/voidly-classifier-calibration/", "title": "Voidly's per-country classifier calibration: Platt scaling, threshold tuning, and why the same probability means different things in Iran vs. China", "content_text": "How Voidly calibrates its anomaly classifier separately for each country — Platt scaling on per-country holdout predictions, F2-weighted threshold tuning per class, 30-day rolling calibration windows, and case studies: Iran DNS fires at 0.62; China DNS requires 0.74.", "summary": "How Voidly calibrates its anomaly classifier separately for each country — Platt scaling on per-country holdout predictions, F2-weighted threshold tuning per class, 30-day rolling calibration windows, and case studies: Iran DNS fires at 0.62; China DNS requires 0.74.", "date_published": "2025-06-07T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-classifier-retraining/", "url": "https://ai-analytics.org/writing/voidly-classifier-retraining/", "title": "Voidly's anomaly classifier retraining pipeline: temporal splits, champion/challenger promotion, and drift detection", "content_text": "How Voidly retrains its five-class censorship anomaly classifier on a weekly cadence: time-based train/val/test splits to prevent temporal leakage, SMOTE resampling for class imbalance, PSI drift detection, champion/challenger shadow deployment, and the canary rollout process.", "summary": "How Voidly retrains its five-class censorship anomaly classifier on a weekly cadence: time-based train/val/test splits to prevent temporal leakage, SMOTE resampling for class imbalance, PSI drift detection, champion/challenger shadow deployment, and the canary rollout process.", "date_published": "2025-06-02T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-inference-api/", "url": "https://ai-analytics.org/writing/voidly-inference-api/", "title": "Voidly's real-time inference API: classifying censorship measurements at 50ms", "content_text": "How Voidly serves the anomaly classifier as a live inference API — feature extraction in under 5ms, ONNX Runtime model serving, five-class output with per-class probabilities, Cloudflare Worker routing to regional inference nodes, model versioning with champion/challenger shadow mode, and the latency budget that keeps end-to-end probe-to-verdict under 50ms.", "summary": "How Voidly serves the anomaly classifier as a live inference API — feature extraction in under 5ms, ONNX Runtime model serving, five-class output with per-class probabilities, Cloudflare Worker routing to regional inference nodes, model versioning with champion/challenger shadow mode, and the latency budget that keeps end-to-end probe-to-verdict under 50ms.", "date_published": "2025-05-28T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-onnx-inference/", "url": "https://ai-analytics.org/writing/voidly-onnx-inference/", "title": "Voidly ONNX inference in Rust: exporting the censorship classifier, thread-local sessions, and batch scoring at 68K items/sec", "content_text": "How Voidly exports the censorship anomaly classifier to ONNX and serves it in a Rust inference binary: sklearn-to-ONNX export with 12-feature schema and opset 17, model metadata validation with proto decode, thread_local ONNX session with Level3 graph optimization and disabled memory arena, run_batch() using ndarray Array2, and latency benchmarks achieving p99 49.8ms at batch size 200 (68K items/s).", "summary": "How Voidly exports the censorship anomaly classifier to ONNX and serves it in a Rust inference binary: sklearn-to-ONNX export with 12-feature schema and opset 17, model metadata validation with proto decode, thread_local ONNX session with Level3 graph optimization and disabled memory arena, run_batch() using ndarray Array2, and latency benchmarks achieving p99 49.8ms at batch size 200 (68K items/s).", "date_published": "2025-05-24T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "ML", "Rust" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-feature-extraction/", "url": "https://ai-analytics.org/writing/voidly-feature-extraction/", "title": "The 47 features that classify internet censorship: how Voidly extracts signal from raw network measurements", "content_text": "How Voidly transforms raw probe measurements into the 47-feature vector that feeds the anomaly classifier: the ControlDelta struct, DNS features (NXDOMAIN injection, bogon IPs, known injection IPs), TCP features (RST timing, SYN-ACK count), TLS features (MITM cert detection, alert codes), HTTP features (blockpage SimHash score, body length ratio), and the LRU control cache design that prevents doubling probe cost.", "summary": "How Voidly transforms raw probe measurements into the 47-feature vector that feeds the anomaly classifier: the ControlDelta struct, DNS features (NXDOMAIN injection, bogon IPs, known injection IPs), TCP features (RST timing, SYN-ACK count), TLS features (MITM cert detection, alert codes), HTTP features (blockpage SimHash score, body length ratio), and the LRU control cache design that prevents doubling probe cost.", "date_published": "2025-05-20T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-scheduling-constraints/", "url": "https://ai-analytics.org/writing/voidly-probe-scheduling-constraints/", "title": "Voidly probe scheduling constraints: battery, thermal, cellular budget, and domain priority scoring on operator devices", "content_text": "How the Voidly probe scheduling layer respects device resource constraints before launching measurement cycles: ResourceSnapshot struct capturing battery, thermal, network type, and cellular byte budgets, per-constraint violation detection with early exit, per-minute SQLite cellular usage tracking with a 24-hour rolling window, adaptive cycle length based on constraint headroom, and three-axis domain priority scoring (staleness 0.50, priority_flag 0.35, anomaly_recency 0.15).", "summary": "How the Voidly probe scheduling layer respects device resource constraints before launching measurement cycles: ResourceSnapshot struct capturing battery, thermal, network type, and cellular byte budgets, per-constraint violation detection with early exit, per-minute SQLite cellular usage tracking with a 24-hour rolling window, adaptive cycle length based on constraint headroom, and three-axis domain priority scoring (staleness 0.50, priority_flag 0.35, anomaly_recency 0.15).", "date_published": "2025-05-16T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "Infrastructure", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-operator-safety/", "url": "https://ai-analytics.org/writing/voidly-probe-operator-safety/", "title": "Voidly probe operator safety: anonymity design, data minimization, and operational security for censorship measurement", "content_text": "How Voidly protects probe operators in high-risk jurisdictions: data minimization, WireGuard peer-key auth, daily probe ID pseudonymization, optional Tor upload, measurement scrubbing, country-tier legal risk assessments, and emergency stop with full data erasure.", "summary": "How Voidly protects probe operators in high-risk jurisdictions: data minimization, WireGuard peer-key auth, daily probe ID pseudonymization, optional Tor upload, measurement scrubbing, country-tier legal risk assessments, and emergency stop with full data erasure.", "date_published": "2025-05-07T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Security", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-test-list/", "url": "https://ai-analytics.org/writing/voidly-test-list/", "title": "Voidly's URL test list: how we curate the domains that reveal internet censorship", "content_text": "How Voidly selects and maintains the domains it probes for censorship: Citizen Lab's global test list, 12 OONI category codes, per-country supplemental lists, the measurement budget problem, and why the test list is a political document.", "summary": "How Voidly selects and maintains the domains it probes for censorship: Citizen Lab's global test list, 12 OONI category codes, per-country supplemental lists, the measurement budget problem, and why the test list is a political document.", "date_published": "2025-05-12T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-commissioning/", "url": "https://ai-analytics.org/writing/voidly-probe-commissioning/", "title": "Voidly probe commissioning: how a new operator joins the censorship measurement network", "content_text": "How a new Voidly probe operator goes from application to publishing measurements: on-device X25519 key generation in the Tauri app, probe registration and ASN verification, 48-hour warmup period with calibration measurements, quality scoring at promotion, and what happens when warmup calibration fails.", "summary": "How a new Voidly probe operator goes from application to publishing measurements: on-device X25519 key generation in the Tauri app, probe registration and ASN verification, 48-hour warmup period with calibration measurements, quality scoring at promotion, and what happens when warmup calibration fails.", "date_published": "2025-05-03T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-rate-limiting/", "url": "https://ai-analytics.org/writing/regulatory-rate-limiting/", "title": "Rate limiting the Federal Regulatory Data Hub: two-layer token bucket with Cloudflare KV and ETag conditional writes", "content_text": "How the Federal Regulatory Data Hub rate-limits API consumers across five tiers using a two-layer token-bucket system in Cloudflare Workers: burst quota enforcement with BucketState in KV and ETag conditional writes for lock-free updates, daily quota tracking with per-minute KV buckets in a 24-hour rolling window, fail-open on KV timeout, and response headers exposing remaining burst and daily quota.", "summary": "How the Federal Regulatory Data Hub rate-limits API consumers across five tiers using a two-layer token-bucket system in Cloudflare Workers: burst quota enforcement with BucketState in KV and ETag conditional writes for lock-free updates, daily quota tracking with per-minute KV buckets in a 24-hour rolling window, fail-open on KV timeout, and response headers exposing remaining burst and daily quota.", "date_published": "2025-04-29T00:00:00.000Z", "tags": [ "Regulatory data", "Cloudflare", "Infrastructure", "API design" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-query-layer/", "url": "https://ai-analytics.org/writing/regulatory-query-layer/", "title": "The Federal Regulatory Data Hub query layer: routing 35M records at the Cloudflare edge", "content_text": "How the Federal Regulatory Data Hub serves 35M records via Cloudflare Workers: 8 vertical D1 shards by agency group, Promise.all fan-out for cross-agency queries, entity bridge join across CIK/UEI/LEI/DUNS/NPI, FTS5 full-text search for narrative datasets, response caching with TTL table by endpoint type, and p50/p99 latency budget including partial-response fallback.", "summary": "How the Federal Regulatory Data Hub serves 35M records via Cloudflare Workers: 8 vertical D1 shards by agency group, Promise.all fan-out for cross-agency queries, entity bridge join across CIK/UEI/LEI/DUNS/NPI, FTS5 full-text search for narrative datasets, response caching with TTL table by endpoint type, and p50/p99 latency budget including partial-response fallback.", "date_published": "2025-04-25T00:00:00.000Z", "tags": [ "Regulatory data", "Cloudflare D1", "Infrastructure", "API design" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-data-versioning/", "url": "https://ai-analytics.org/writing/regulatory-data-versioning/", "title": "Bitemporal regulatory records in the Federal Regulatory Data Hub: AS-OF queries, audit history, and NDJSON snapshot export", "content_text": "How the Federal Regulatory Data Hub maintains bitemporal regulatory records across 197 datasets: half-open valid_from/valid_until intervals, a current-state partial index for zero-overhead live queries, a record_versions audit table with append-only semantics and change_reason constraint, two-statement transaction for version close and create, AS-OF TypeScript query rewriting with strict inequality, and keyset-paginated NDJSON snapshot export for compliance audit.", "summary": "How the Federal Regulatory Data Hub maintains bitemporal regulatory records across 197 datasets: half-open valid_from/valid_until intervals, a current-state partial index for zero-overhead live queries, a record_versions audit table with append-only semantics and change_reason constraint, two-statement transaction for version close and create, AS-OF TypeScript query rewriting with strict inequality, and keyset-paginated NDJSON snapshot export for compliance audit.", "date_published": "2025-04-21T00:00:00.000Z", "tags": [ "Regulatory data", "Cloudflare D1", "Infrastructure", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-staleness-monitoring/", "url": "https://ai-analytics.org/writing/regulatory-staleness-monitoring/", "title": "Monitoring dataset freshness in the Federal Regulatory Data Hub: staleness detection, multi-channel alerting, and the OFAC publish-time problem", "content_text": "How the Federal Regulatory Data Hub monitors the freshness of 197 federal datasets and alerts on staleness: per-source FRESHNESS_CONFIG with expected_cadence and max_staleness_hours, D1 dataset_ingests staleness query, Cloudflare Cron */5 * * * * staleness check, multi-channel alerting (Slack webhook, email, PagerDuty) with KV deduplication, OFAC ETag monitoring with 90-minute publish-delay alert, five ingest error classes, and public /status endpoint.", "summary": "How the Federal Regulatory Data Hub monitors the freshness of 197 federal datasets and alerts on staleness: per-source FRESHNESS_CONFIG with expected_cadence and max_staleness_hours, D1 dataset_ingests staleness query, Cloudflare Cron */5 * * * * staleness check, multi-channel alerting (Slack webhook, email, PagerDuty) with KV deduplication, OFAC ETag monitoring with 90-minute publish-delay alert, five ingest error classes, and public /status endpoint.", "date_published": "2025-04-17T00:00:00.000Z", "tags": [ "Regulatory data", "Infrastructure", "Cloudflare", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-vantage-selection/", "url": "https://ai-analytics.org/writing/voidly-vantage-selection/", "title": "Voidly probe vantage selection: ASN diversity, operator safety, and reaching hard-to-measure countries", "content_text": "How Voidly selects and distributes its probe vantage network: why ASN diversity matters more than geographic spread, the operator safety constraints for high-risk countries, and how we reach places where most people connect on mobile-only networks.", "summary": "How Voidly selects and distributes its probe vantage network: why ASN diversity matters more than geographic spread, the operator safety constraints for high-risk countries, and how we reach places where most people connect on mobile-only networks.", "date_published": "2025-04-10T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-probe-config-delivery/", "url": "https://ai-analytics.org/writing/voidly-probe-config-delivery/", "title": "Voidly probe configuration delivery: signed CBOR bundles, anonymous country tokens, and atomic snapshot rollover", "content_text": "How Voidly delivers signed configuration bundles to probe operators: CBOR+gzip ConfigBundle with GlobalConfig and CountryConfig overlays, anonymous country token via BLAKE3, Ed25519 three-key signature verification before decompression, 72-hour freshness window with exponential backoff refetch, ETag-based conditional KV writes for atomic snapshot rollover, and a five-step CDN publish pipeline with 2% canary rollout.", "summary": "How Voidly delivers signed configuration bundles to probe operators: CBOR+gzip ConfigBundle with GlobalConfig and CountryConfig overlays, anonymous country token via BLAKE3, Ed25519 three-key signature verification before decompression, 72-hour freshness window with exponential backoff refetch, ETag-based conditional KV writes for atomic snapshot rollover, and a five-step CDN publish pipeline with 2% canary rollout.", "date_published": "2025-04-06T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "Infrastructure", "Security" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-operator-privacy/", "url": "https://ai-analytics.org/writing/voidly-operator-privacy/", "title": "Voidly operator privacy: how we publish measurements without exposing the people who collect them", "content_text": "How Voidly protects probe operator identity while publishing full measurement data: probe_id as SHA-256(public_key_bytes) with zero IP logging, codename system (450K+ combinations, no joint table), measurement anonymization (probe_cc + probe_asn published; IP never stored), per-probe Ed25519 signing with isolated key store, and 12-country extra protections (4–48 hour publication delay, 90-day probe_id rotation).", "summary": "How Voidly protects probe operator identity while publishing full measurement data: probe_id as SHA-256(public_key_bytes) with zero IP logging, codename system (450K+ combinations, no joint table), measurement anonymization (probe_cc + probe_asn published; IP never stored), per-probe Ed25519 signing with isolated key store, and 12-country extra protections (4–48 hour publication delay, 90-day probe_id rotation).", "date_published": "2025-04-02T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-entity-alias-table/", "url": "https://ai-analytics.org/writing/regulatory-entity-alias-table/", "title": "The Federal Regulatory Data Hub alias table: AKA, phonetic, vessel, and expired name resolution across 197 datasets", "content_text": "How the Federal Regulatory Data Hub resolves entity aliases across 197 federal datasets: five alias types (AKA, FKA, NFE, PHONETIC, VESSEL), FTS5 virtual table with covering indexes, NFKD normalization with iterative legal-suffix stripping, SHA-256 alias_id for cross-source deduplication, Double-Metaphone phonetic alias generation, four-pass resolution achieving 98.7% recall, and a TypeScript resolveAlias() Workers function.", "summary": "How the Federal Regulatory Data Hub resolves entity aliases across 197 federal datasets: five alias types (AKA, FKA, NFE, PHONETIC, VESSEL), FTS5 virtual table with covering indexes, NFKD normalization with iterative legal-suffix stripping, SHA-256 alias_id for cross-source deduplication, Double-Metaphone phonetic alias generation, four-pass resolution achieving 98.7% recall, and a TypeScript resolveAlias() Workers function.", "date_published": "2025-03-29T00:00:00.000Z", "tags": [ "Regulatory data", "Entity resolution", "Cloudflare D1", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-entity-id-normalization/", "url": "https://ai-analytics.org/writing/federal-entity-id-normalization/", "title": "Entity ID normalization in the Federal Regulatory Data Hub: resolving CIK, UEI, LEI, DUNS, and NPI across 197 datasets", "content_text": "How the Federal Regulatory Data Hub resolves company identity across five incompatible federal identifier schemes: three-pass resolution (exact ID join, alias table, TF-IDF fuzzy name matching), entity_master bridge schema, company name normalization, false positive rates by method, and p50 38ms cross-agency query latency.", "summary": "How the Federal Regulatory Data Hub resolves company identity across five incompatible federal identifier schemes: three-pass resolution (exact ID join, alias table, TF-IDF fuzzy name matching), entity_master bridge schema, company name normalization, false positive rates by method, and p50 38ms cross-agency query latency.", "date_published": "2025-03-25T00:00:00.000Z", "tags": [ "Regulatory data", "Entity resolution", "Cloudflare D1", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-schema-design/", "url": "https://ai-analytics.org/writing/regulatory-schema-design/", "title": "Schema design for the Federal Regulatory Data Hub: eight D1 shards, entity_master, and covering indexes across 35M records", "content_text": "How the Federal Regulatory Data Hub schemas eight D1 shards for 35M+ regulatory records: per-vertical DDL with OFAC SDN FTS5 virtual tables and triggers, EPA enforcement covering indexes, entity_master bridge with shard_presence bitmask and partial identifier indexes, TypeScript SHARD_MAP and Promise.all fan-out, and measured p50/p99 latency across entity lookup and multi-shard regulatory queries.", "summary": "How the Federal Regulatory Data Hub schemas eight D1 shards for 35M+ regulatory records: per-vertical DDL with OFAC SDN FTS5 virtual tables and triggers, EPA enforcement covering indexes, entity_master bridge with shard_presence bitmask and partial identifier indexes, TypeScript SHARD_MAP and Promise.all fan-out, and measured p50/p99 latency across entity lookup and multi-shard regulatory queries.", "date_published": "2025-03-21T00:00:00.000Z", "tags": [ "Regulatory data", "Cloudflare D1", "Infrastructure", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/federal-fts5-search/", "url": "https://ai-analytics.org/writing/federal-fts5-search/", "title": "Full-text search across 35M federal records: SQLite FTS5, BM25 ranking, and cross-shard fan-out in Cloudflare D1", "content_text": "How the Federal Regulatory Data Hub implements full-text search across 35M records using SQLite FTS5 in Cloudflare D1: virtual table creation with the unicode61 tokenizer and content= shadow-table pattern, BM25 scoring with weighted columns (10× entity_name, 5× description, 1× narrative), highlight() and snippet() functions for context extraction, buildFts5Query() TypeScript alias expansion, Promise.all cross-dataset fan-out across 5 D1 shards, trigger-based index maintenance, and weekly optimize via Cloudflare Cron.", "summary": "How the Federal Regulatory Data Hub implements full-text search across 35M records using SQLite FTS5 in Cloudflare D1: virtual table creation with the unicode61 tokenizer and content= shadow-table pattern, BM25 scoring with weighted columns (10× entity_name, 5× description, 1× narrative), highlight() and snippet() functions for context extraction, buildFts5Query() TypeScript alias expansion, Promise.all cross-dataset fan-out across 5 D1 shards, trigger-based index maintenance, and weekly optimize via Cloudflare Cron.", "date_published": "2025-03-17T00:00:00.000Z", "tags": [ "Regulatory data", "Cloudflare D1", "Infrastructure", "SQLite" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/regulatory-data-hub-cloudflare-d1/", "url": "https://ai-analytics.org/writing/regulatory-data-hub-cloudflare-d1/", "title": "Building the Federal Regulatory Data Hub on Cloudflare D1: 35M records at the edge", "content_text": "How we built a 35M-record federal regulatory database on Cloudflare D1 — per-vertical SQLite tables across 197 datasets, daily cron ingest, FTS5 for free-text datasets, and vertical sharding past the 10GB limit.", "summary": "How we built a 35M-record federal regulatory database on Cloudflare D1 — per-vertical SQLite tables across 197 datasets, daily cron ingest, FTS5 for free-text datasets, and vertical sharding past the 10GB limit.", "date_published": "2025-03-10T00:00:00.000Z", "tags": [ "Regulatory data", "Cloudflare D1", "Infrastructure", "SQLite" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-measurement-retention/", "url": "https://ai-analytics.org/writing/voidly-measurement-retention/", "title": "Voidly's measurement retention policy: hot, warm, and cold tiers for 2.2B probe results", "content_text": "How Voidly manages storage for 2.2B probe measurements using a three-tier TimescaleDB retention policy — full-resolution hot tier (0-30 days), native-compressed warm tier (31-365 days, 6.2x ratio), and downsampled cold tier (>365 days, aggregates only), with continuous aggregate cascade, pg_cron compliance verification, and R2 tiered storage planned for Q3 2026.", "summary": "How Voidly manages storage for 2.2B probe measurements using a three-tier TimescaleDB retention policy — full-resolution hot tier (0-30 days), native-compressed warm tier (31-365 days, 6.2x ratio), and downsampled cold tier (>365 days, aggregates only), with continuous aggregate cascade, pg_cron compliance verification, and R2 tiered storage planned for Q3 2026.", "date_published": "2025-03-05T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Data Engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-timescaledb/", "url": "https://ai-analytics.org/writing/voidly-timescaledb/", "title": "Voidly's measurement database: 2.2B probe results in TimescaleDB", "content_text": "How Voidly stores and queries 2.2 billion censorship probe results in TimescaleDB: hypertable design with 1-day chunk intervals and secondary country partitioning, 6.2× compression, continuous aggregates for country-level daily summaries, three-tier retention (hot/warm/cold), and query benchmarks for anomaly detection.", "summary": "How Voidly stores and queries 2.2 billion censorship probe results in TimescaleDB: hypertable design with 1-day chunk intervals and secondary country partitioning, 6.2× compression, continuous aggregates for country-level daily summaries, three-tier retention (hot/warm/cold), and query benchmarks for anomaly detection.", "date_published": "2025-03-01T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "TimescaleDB", "Infrastructure", "PostgreSQL" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-corroboration-engine/", "url": "https://ai-analytics.org/writing/voidly-corroboration-engine/", "title": "Voidly's real-time corroboration engine: fetching, aligning, and merging OONI, CensoredPlanet, and IODA data", "content_text": "How Voidly's corroboration engine fetches and aligns data from three independent sources in near-real-time: tokio::join! parallel fetches with per-source timeouts, adaptive OONI polling (15m/60m/3h/6h), in-memory CensoredPlanet daily dump index, independence-weighted source agreement scoring, and retroactive nightly reprocessing against the CP daily dump.", "summary": "How Voidly's corroboration engine fetches and aligns data from three independent sources in near-real-time: tokio::join! parallel fetches with per-source timeouts, adaptive OONI polling (15m/60m/3h/6h), in-memory CensoredPlanet daily dump index, independence-weighted source agreement scoring, and retroactive nightly reprocessing against the CP daily dump.", "date_published": "2025-02-22T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-mcp-server/", "url": "https://ai-analytics.org/writing/voidly-mcp-server/", "title": "The Voidly MCP server: 83 censorship query tools for Claude and GPT", "content_text": "How the Voidly MCP server exposes 83 tools for querying the global censorship dataset from Claude, GPT, and agent frameworks — incident lookup, measurement queries, country summaries, BGP events, shutdown forecasts, and wiring it into Claude Code.", "summary": "How the Voidly MCP server exposes 83 tools for querying the global censorship dataset from Claude, GPT, and agent frameworks — incident lookup, measurement queries, country summaries, BGP events, shutdown forecasts, and wiring it into Claude Code.", "date_published": "2025-02-15T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "MCP", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-parquet-export/", "url": "https://ai-analytics.org/writing/voidly-parquet-export/", "title": "The Voidly Parquet export pipeline: nightly snapshots from TimescaleDB to HuggingFace", "content_text": "How the nightly Voidly export job extracts measurements from TimescaleDB and pushes Parquet snapshots to HuggingFace Hub: PyArrow schema with dictionary-encoded columns, server-side cursor streaming at 50K rows per round-trip, Zstandard level 3 compression, country + year_month partitioning, atomic HuggingFace commit with CommitOperationAdd, post-push SHA-256 verification, and the incremental vs. monthly full-snapshot strategy.", "summary": "How the nightly Voidly export job extracts measurements from TimescaleDB and pushes Parquet snapshots to HuggingFace Hub: PyArrow schema with dictionary-encoded columns, server-side cursor streaming at 50K rows per round-trip, Zstandard level 3 compression, country + year_month partitioning, atomic HuggingFace commit with CommitOperationAdd, post-push SHA-256 verification, and the incremental vs. monthly full-snapshot strategy.", "date_published": "2025-02-08T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Data engineering", "Open data", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-huggingface-datasets/", "url": "https://ai-analytics.org/writing/voidly-huggingface-datasets/", "title": "The Voidly open datasets on HuggingFace: structure, daily snapshots, and filter recipes", "content_text": "How the Voidly CC BY 4.0 measurement dataset and the OONI historical corpus are hosted on HuggingFace — Parquet snapshot structure, daily incremental updates, git-lfs versioning, and Python/R filter recipes for journalists, ML researchers, and infrastructure teams.", "summary": "How the Voidly CC BY 4.0 measurement dataset and the OONI historical corpus are hosted on HuggingFace — Parquet snapshot structure, daily incremental updates, git-lfs versioning, and Python/R filter recipes for journalists, ML researchers, and infrastructure teams.", "date_published": "2025-02-01T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Open data", "HuggingFace", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-incident-lifecycle/", "url": "https://ai-analytics.org/writing/voidly-incident-lifecycle/", "title": "Censorship incident lifecycle in Voidly: from anomaly detection to verified incident to resolution", "content_text": "How a Voidly censorship incident progresses through six states (Anomaly, MultiSourceAnomaly, Corroborated, VerifiedIncident, Resolved, FalsePositive) with transition thresholds, timing data from 847 incidents in 2024, publication timing by tier, and lifecycle encoding in HuggingFace dataset fields.", "summary": "How a Voidly censorship incident progresses through six states (Anomaly, MultiSourceAnomaly, Corroborated, VerifiedIncident, Resolved, FalsePositive) with transition thresholds, timing data from 847 incidents in 2024, publication timing by tier, and lifecycle encoding in HuggingFace dataset fields.", "date_published": "2025-01-26T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-confidence-tiers/", "url": "https://ai-analytics.org/writing/voidly-confidence-tiers/", "title": "From anomaly to verified incident: the Voidly confidence tier system", "content_text": "How a Voidly measurement moves through three confidence tiers and what each tier means for journalists, ML researchers, and infrastructure monitoring teams using the dataset.", "summary": "How a Voidly measurement moves through three confidence tiers and what each tier means for journalists, ML researchers, and infrastructure monitoring teams using the dataset.", "date_published": "2025-01-20T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Data quality" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-streaming-api/", "url": "https://ai-analytics.org/writing/voidly-streaming-api/", "title": "Voidly's Server-Sent Events streaming API: real-time censorship incident subscriptions", "content_text": "How the Voidly SSE streaming endpoint delivers censorship events in real time: GET /v1/stream with country/tier/type filtering, four event types (incident_created, incident_updated, incident_resolved, country_status_change), Last-Event-ID reconnection with 24-hour event ring buffer, Python httpx.Client and JavaScript EventSource examples, and how SSE differs from the webhook delivery system.", "summary": "How the Voidly SSE streaming endpoint delivers censorship events in real time: GET /v1/stream with country/tier/type filtering, four event types (incident_created, incident_updated, incident_resolved, country_status_change), Last-Event-ID reconnection with 24-hour event ring buffer, Python httpx.Client and JavaScript EventSource examples, and how SSE differs from the webhook delivery system.", "date_published": "2025-01-13T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "API design", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-api-authentication/", "url": "https://ai-analytics.org/writing/voidly-api-authentication/", "title": "Voidly API authentication: API keys, request signing, and rate limit tiers", "content_text": "How the Voidly API handles authentication: two tiers (public/keyed), PBKDF2-HMAC-SHA256 key storage, D1 + KV request auth flow, four plan tiers, HMAC-SHA256 webhook verification, key rotation without downtime, test keys, and OAuth2 for third-party integrations.", "summary": "How the Voidly API handles authentication: two tiers (public/keyed), PBKDF2-HMAC-SHA256 key storage, D1 + KV request auth flow, four plan tiers, HMAC-SHA256 webhook verification, key rotation without downtime, test keys, and OAuth2 for third-party integrations.", "date_published": "2025-01-01T00:00:00.000Z", "tags": [ "Voidly", "Infrastructure", "API" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-rest-api/", "url": "https://ai-analytics.org/writing/voidly-rest-api/", "title": "The Voidly REST API: querying the global censorship index in real time", "content_text": "How the Voidly REST API is designed: key endpoints for incident lookup, measurement queries, country summaries, domain history, BGP events, and 7-day shutdown forecasts; cursor-based pagination, filtering, rate limits, and code samples in curl, Python, and JavaScript.", "summary": "How the Voidly REST API is designed: key endpoints for incident lookup, measurement queries, country summaries, domain history, BGP events, and 7-day shutdown forecasts; cursor-based pagination, filtering, rate limits, and code samples in curl, Python, and JavaScript.", "date_published": "2025-01-06T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "API design", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-alert-delivery/", "url": "https://ai-analytics.org/writing/voidly-alert-delivery/", "title": "Voidly's alert delivery system: PGP-encrypted email, webhooks, and RSS for censorship incidents", "content_text": "How Voidly gets verified censorship incidents to journalists, researchers, and monitoring systems: HMAC-signed webhook delivery with exponential-backoff retry, PGP-encrypted email for verified alerts, per-country and per-confidence-tier RSS feeds, alert deduplication by incident_id, and rate-limiting to prevent fatigue.", "summary": "How Voidly gets verified censorship incidents to journalists, researchers, and monitoring systems: HMAC-signed webhook delivery with exponential-backoff retry, PGP-encrypted email for verified alerts, per-country and per-confidence-tier RSS feeds, alert deduplication by incident_id, and rate-limiting to prevent fatigue.", "date_published": "2024-12-28T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Infrastructure", "Real-time systems" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-incident-publication/", "url": "https://ai-analytics.org/writing/voidly-incident-publication/", "title": "Voidly incident publication: state machine, Kafka fan-out, and idempotent PostgreSQL upserts", "content_text": "How Voidly models censorship incident state transitions and publishes them to Kafka: five-state Rust IncidentState enum (Anomaly to MultiSourceAnomaly to Corroborated to Verified to Resolved), per-type transition thresholds, SHA-256 compute_incident_id() keyed on country:domain_hash8:type:epoch_day, PostgreSQL upsert with idempotency_key ON CONFLICT semantics, TimescaleDB incident_events hypertable, and three Kafka topics for state changes, verified incidents, and cache invalidations.", "summary": "How Voidly models censorship incident state transitions and publishes them to Kafka: five-state Rust IncidentState enum (Anomaly to MultiSourceAnomaly to Corroborated to Verified to Resolved), per-type transition thresholds, SHA-256 compute_incident_id() keyed on country:domain_hash8:type:epoch_day, PostgreSQL upsert with idempotency_key ON CONFLICT semantics, TimescaleDB incident_events hypertable, and three Kafka topics for state changes, verified incidents, and cache invalidations.", "date_published": "2024-12-24T00:00:00.000Z", "tags": [ "Voidly", "Censorship", "Infrastructure", "Kafka" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-measurement-scheduler/", "url": "https://ai-analytics.org/writing/voidly-measurement-scheduler/", "title": "The Voidly measurement scheduler: how we decide which domains to probe and when", "content_text": "How Voidly schedules 80-domain probe runs across 37+ nodes: domain priority tiers by OONI category code, anomaly-driven priority boosts, protocol selection per domain, ±15% jitter for anti-detection, ASN distribution for cross-ASN coverage, and adaptive scheduling on anomaly detection.", "summary": "How Voidly schedules 80-domain probe runs across 37+ nodes: domain priority tiers by OONI category code, anomaly-driven priority boosts, protocol selection per domain, ±15% jitter for anti-detection, ASN distribution for cross-ASN coverage, and adaptive scheduling on anomaly detection.", "date_published": "2024-12-20T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-ooni-training-data/", "url": "https://ai-analytics.org/writing/voidly-ooni-training-data/", "title": "Building Voidly's classifier training dataset from OONI: ingestion, alignment, and label generation", "content_text": "How Voidly ingests 200M+ OONI Explorer measurements, aligns them with Voidly probe data on a country-domain-date key, generates probabilistic training labels using five Snorkel-style label functions, handles OONI coverage gaps with label distillation, and constructs the labeled dataset that trains the five-class anomaly classifier.", "summary": "How Voidly ingests 200M+ OONI Explorer measurements, aligns them with Voidly probe data on a country-domain-date key, generates probabilistic training labels using five Snorkel-style label functions, handles OONI coverage gaps with label distillation, and constructs the labeled dataset that trains the five-class anomaly classifier.", "date_published": "2024-12-15T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-anomaly-classifier/", "url": "https://ai-analytics.org/writing/voidly-anomaly-classifier/", "title": "The Voidly anomaly classifier: five interference classes, gradient boosted trees, and why we optimize for recall", "content_text": "How the Voidly ML classifier distinguishes DNS tampering, TLS interference, HTTP blocking, BGP withdrawal, and throttling — five per-class binary models, country-specific calibration, and why 95% recall beats 95% precision when cross-source corroboration filters the noise.", "summary": "How the Voidly ML classifier distinguishes DNS tampering, TLS interference, HTTP blocking, BGP withdrawal, and throttling — five per-class binary models, country-specific calibration, and why 95% recall beats 95% precision when cross-source corroboration filters the noise.", "date_published": "2024-12-10T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-classifier-offline-test/", "url": "https://ai-analytics.org/writing/voidly-classifier-offline-test/", "title": "Evaluating the Voidly anomaly classifier: per-country confusion matrices, precision-recall curves, and the offline test harness", "content_text": "How Voidly evaluates the five-class censorship anomaly classifier offline before deployment: the ClassifierEvaluator test harness, per-country AUC-PR vs. AUC-ROC tradeoffs for imbalanced censorship data, F2 scoring rationale, per-country confusion matrix case studies (Iran, China, Russia), ECE calibration before and after Platt scaling, and model promotion criteria including 48-hour champion/challenger shadow mode.", "summary": "How Voidly evaluates the five-class censorship anomaly classifier offline before deployment: the ClassifierEvaluator test harness, per-country AUC-PR vs. AUC-ROC tradeoffs for imbalanced censorship data, F2 scoring rationale, per-country confusion matrix case studies (Iran, China, Russia), ECE calibration before and after Platt scaling, and model promotion criteria including 48-hour champion/challenger shadow mode.", "date_published": "2024-12-03T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-active-learning/", "url": "https://ai-analytics.org/writing/voidly-active-learning/", "title": "Voidly's active learning loop: growing the anomaly training set with human-in-the-loop annotation", "content_text": "How Voidly uses uncertainty sampling, Cohen's kappa inter-annotator agreement, and weekly model retrains to grow its censorship anomaly training set from 127K bootstrap labels to 275K — 500 examples/week annotated by 3 researchers each, with DVC data versioning and PSI drift detection.", "summary": "How Voidly uses uncertainty sampling, Cohen's kappa inter-annotator agreement, and weekly model retrains to grow its censorship anomaly training set from 127K bootstrap labels to 275K — 500 examples/week annotated by 3 researchers each, with DVC data versioning and PSI drift detection.", "date_published": "2024-11-27T00:00:00.000Z", "tags": [ "ML", "Voidly", "Active Learning", "Annotation" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-ml-training-data/", "url": "https://ai-analytics.org/writing/voidly-ml-training-data/", "title": "Voidly's ML training pipeline: building a labeled censorship dataset from OONI measurements", "content_text": "How Voidly constructs a labeled training dataset for the anomaly classifier from 200M+ OONI measurements: weak supervision with Snorkel-style label functions, class imbalance handling, time-based train/val/test splits, per-country Platt scaling calibration, and the continuous retraining pipeline.", "summary": "How Voidly constructs a labeled training dataset for the anomaly classifier from 200M+ OONI measurements: weak supervision with Snorkel-style label functions, class imbalance handling, time-based train/val/test splits, per-country Platt scaling calibration, and the continuous retraining pipeline.", "date_published": "2024-11-20T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/voidly-measurement-quality-filter/", "url": "https://ai-analytics.org/writing/voidly-measurement-quality-filter/", "title": "The Voidly measurement quality filter: how we clean 200M OONI records before ML training", "content_text": "How the quality filter pipeline decides which raw measurements are fit for ML training: boolean checks for control_failure (1.9% drop rate), missing_fields (0.8%), old probe version pre-2.5.0 (0.3%), and duplicates (0.2%), totalling 3.2% dropped. Includes the quality_filter() Python function, the to_feature_input() schema transformation, and why rejected measurements go to quarantine not discard.", "summary": "How the quality filter pipeline decides which raw measurements are fit for ML training: boolean checks for control_failure (1.9% drop rate), missing_fields (0.8%), old probe version pre-2.5.0 (0.3%), and duplicates (0.2%), totalling 3.2% dropped. Includes the quality_filter() Python function, the to_feature_input() schema transformation, and why rejected measurements go to quarantine not discard.", "date_published": "2024-11-13T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "ML", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ooni-data-normalization/", "url": "https://ai-analytics.org/writing/ooni-data-normalization/", "title": "OONI measurement normalization: schema version detection, anomaly bitmasks, and 95.3% pass-through across five schema versions", "content_text": "How Voidly normalizes OONI measurement data across five schema versions (v0.2 through v0.6) for ML training: WebConnectivityVersion detection via field-presence inference, AnomalyType and ConfidenceTier enums, full OoniMeasurementNormalized dataclass, FLAG_* bitmask constants for DNS/TCP/TLS/HTTP/control failure modes, normalize_v05() and normalize_v06() version-specific paths, and a drop reason table achieving 95.3% pass-through.", "summary": "How Voidly normalizes OONI measurement data across five schema versions (v0.2 through v0.6) for ML training: WebConnectivityVersion detection via field-presence inference, AnomalyType and ConfidenceTier enums, full OoniMeasurementNormalized dataclass, FLAG_* bitmask constants for DNS/TCP/TLS/HTTP/control failure modes, normalize_v05() and normalize_v06() version-specific paths, and a drop reason table achieving 95.3% pass-through.", "date_published": "2024-11-09T00:00:00.000Z", "tags": [ "Censorship", "OONI", "Data engineering", "ML" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/ooni-historical-corpus/", "url": "https://ai-analytics.org/writing/ooni-historical-corpus/", "title": "Building the OONI historical corpus: 1.66M downloads, schema normalization, and the decisions behind the dataset", "content_text": "How we processed the OONI raw measurement archive into a flat ML-ready CSV: handling probe version schema drift, normalizing test_keys across 20 measurement types, and streaming 200M+ records.", "summary": "How we processed the OONI raw measurement archive into a flat ML-ready CSV: handling probe version schema drift, normalizing test_keys across 20 measurement types, and streaming 200M+ records.", "date_published": "2024-11-05T00:00:00.000Z", "tags": [ "Censorship", "OONI", "Data engineering", "HuggingFace" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/censorship-attribution-osint/", "url": "https://ai-analytics.org/writing/censorship-attribution-osint/", "title": "Censorship attribution via OSINT: DPI vendor signatures, procurement records, and BGP TTL-hop analysis", "content_text": "How Voidly uses OSINT to attribute censorship to specific DPI vendors and infrastructure: vendor signature matching across TSPU, Sandvine, NetClean, Iran ARRS, Cisco IronPort, and GFW with weighted component scoring (RST timing 0.35, block page 0.30, injection IP 0.25, CA SPKI 0.10), automated procurement record scraping from five government tender sources, BGP TTL-hop attribution, and country case studies for Russia, Iran, and China.", "summary": "How Voidly uses OSINT to attribute censorship to specific DPI vendors and infrastructure: vendor signature matching across TSPU, Sandvine, NetClean, Iran ARRS, Cisco IronPort, and GFW with weighted component scoring (RST timing 0.35, block page 0.30, injection IP 0.25, CA SPKI 0.10), automated procurement record scraping from five government tender sources, BGP TTL-hop attribution, and country case studies for Russia, Iran, and China.", "date_published": "2024-11-01T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "OSINT", "Methodology" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/osint-digital-footprint/", "url": "https://ai-analytics.org/writing/osint-digital-footprint/", "title": "Building a digital-footprint reconnaissance pipeline for OSINT investigations", "content_text": "How we build persistent cross-platform entity profiles for OSINT: passive collection from 40+ sources, graph-based identity disambiguation with calibrated edge weights, Certificate Transparency log monitoring, BGP/ASN change tracking, stylometric fingerprinting, and operational security for researchers in hostile environments.", "summary": "How we build persistent cross-platform entity profiles for OSINT: passive collection from 40+ sources, graph-based identity disambiguation with calibrated edge weights, Certificate Transparency log monitoring, BGP/ASN change tracking, stylometric fingerprinting, and operational security for researchers in hostile environments.", "date_published": "2024-10-28T00:00:00.000Z", "tags": [ "OSINT", "Reconnaissance", "Entity resolution", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/censorship-infrastructure-mapping/", "url": "https://ai-analytics.org/writing/censorship-infrastructure-mapping/", "title": "Mapping censorship infrastructure: identifying filtering gateways, DPI vendor signatures, and blocking architecture from network signals", "content_text": "How Voidly identifies the hardware and software responsible for internet censorship: blocking architecture taxonomy (L3/L4/L7-DNS/L7-HTTP), DPI vendor signatures from timing patterns (Russia's TSPU RST < 3ms, Iran's ARRS DNS IPs, China's GFW TTL fingerprinting), ISP-level blocking fingerprints, TTL middlebox distance analysis, OSINT cross-referencing with procurement records, and the censorship_infrastructure dataset field.", "summary": "How Voidly identifies the hardware and software responsible for internet censorship: blocking architecture taxonomy (L3/L4/L7-DNS/L7-HTTP), DPI vendor signatures from timing patterns (Russia's TSPU RST < 3ms, Iran's ARRS DNS IPs, China's GFW TTL fingerprinting), ISP-level blocking fingerprints, TTL middlebox distance analysis, OSINT cross-referencing with procurement records, and the censorship_infrastructure dataset field.", "date_published": "2024-10-21T00:00:00.000Z", "tags": [ "Censorship", "Voidly", "Methodology", "OSINT" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/distributed-vpn-routing/", "url": "https://ai-analytics.org/writing/distributed-vpn-routing/", "title": "Building a distributed VPN with intelligent routing", "content_text": "How we route around censorship with ML-driven path selection, traffic morphing, and 142 entry-node IPs.", "summary": "How we route around censorship with ML-driven path selection, traffic morphing, and 142 entry-node IPs.", "date_published": "2024-10-15T00:00:00.000Z", "tags": [ "Censorship", "VPN", "ML routing", "DPI evasion" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/osint-entity-extraction/", "url": "https://ai-analytics.org/writing/osint-entity-extraction/", "title": "Named entity extraction and disambiguation in the OSINT pipeline: 58M posts per day, 15,000 entity mentions per hour", "content_text": "How the AI Analytics OSINT pipeline extracts, disambiguates, and stores named entity mentions from 58M social media posts per day — GPU-accelerated NER, Wikidata QID linking, cross-language transliteration, and person co-reference resolution.", "summary": "How the AI Analytics OSINT pipeline extracts, disambiguates, and stores named entity mentions from 58M social media posts per day — GPU-accelerated NER, Wikidata QID linking, cross-language transliteration, and person co-reference resolution.", "date_published": "2024-10-10T00:00:00.000Z", "tags": [ "OSINT", "ML", "NLP", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/social-media-ingestion/", "url": "https://ai-analytics.org/writing/social-media-ingestion/", "title": "Social media ingestion at scale: collecting 58M posts per day from 47 platform schemas", "content_text": "How we collect and normalize social media data from 47 platforms into a canonical post format: three-tier collection strategy (official APIs, ActivityPub, RSS/scrape), token-bucket rate limiting with circuit breakers, FastText language detection at ingest, content-hash deduplication, and Kafka topic partitioning by platform.", "summary": "How we collect and normalize social media data from 47 platforms into a canonical post format: three-tier collection strategy (official APIs, ActivityPub, RSS/scrape), token-bucket rate limiting with circuit breakers, FastText language detection at ingest, content-hash deduplication, and Kafka topic partitioning by platform.", "date_published": "2024-10-05T00:00:00.000Z", "tags": [ "NLP", "Infrastructure", "Kafka", "OSINT" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/nlp-pipeline-scale/", "url": "https://ai-analytics.org/writing/nlp-pipeline-scale/", "title": "NLP pipeline for real-time sentiment analysis at scale", "content_text": "Architecture of a real-time NLP pipeline: TensorFlow models, sub-2-second latency, multi-language sentiment + entity recognition.", "summary": "Architecture of a real-time NLP pipeline: TensorFlow models, sub-2-second latency, multi-language sentiment + entity recognition.", "date_published": "2024-09-28T00:00:00.000Z", "tags": [ "NLP", "TensorFlow", "Infrastructure", "OSINT" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/multilingual-bot-detection/", "url": "https://ai-analytics.org/writing/multilingual-bot-detection/", "title": "Multilingual bot detection: language-stratified training, perceptual image hashing, and per-language Platt calibration across 14 languages", "content_text": "How AI Analytics detects coordinated bot accounts across 14 languages: eight-feature BotFeatureVector including posting_interval_entropy, content_cluster_density, and cross_platform_correlation, Redis-bucketed perceptual image hash matching with Hamming distance threshold, XGBClassifier trained with StratifiedGroupKFold on language groups, per-language Platt scaling calibration, and F1 0.883 to 0.908 across all 14 languages.", "summary": "How AI Analytics detects coordinated bot accounts across 14 languages: eight-feature BotFeatureVector including posting_interval_entropy, content_cluster_density, and cross_platform_correlation, Redis-bucketed perceptual image hash matching with Hamming distance threshold, XGBClassifier trained with StratifiedGroupKFold on language groups, per-language Platt scaling calibration, and F1 0.883 to 0.908 across all 14 languages.", "date_published": "2024-09-24T00:00:00.000Z", "tags": [ "OSINT", "NLP", "ML", "Elections" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/coordinated-campaign-detection/", "url": "https://ai-analytics.org/writing/coordinated-campaign-detection/", "title": "Detecting coordinated inauthentic behavior in social media at scale", "content_text": "How we detect coordinated amplification campaigns across 58M daily posts: MinHash LSH (128 hash functions, 16 bands, Jaccard threshold 0.80) for content similarity, Redis sorted-set burst detection (≥5 accounts within 15 minutes), seven account-feature logistic regression, network amplification ring detection, cross-platform timing joins, and a 0–100 coordination score with 70/90 thresholds for human review and auto-flagging.", "summary": "How we detect coordinated amplification campaigns across 58M daily posts: MinHash LSH (128 hash functions, 16 bands, Jaccard threshold 0.80) for content similarity, Redis sorted-set burst detection (≥5 accounts within 15 minutes), seven account-feature logistic regression, network amplification ring detection, cross-platform timing joins, and a 0–100 coordination score with 70/90 thresholds for human review and auto-flagging.", "date_published": "2024-09-20T00:00:00.000Z", "tags": [ "OSINT", "NLP", "Infrastructure", "Elections" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/election-finance-entity-resolution/", "url": "https://ai-analytics.org/writing/election-finance-entity-resolution/", "title": "Election finance entity resolution: joint fundraising committees, legal-suffix normalization, and four-pass FEC matching", "content_text": "How AI Analytics resolves FEC committee identities across joint fundraising committees, disbursement schedules, and multi-cycle filings: FEC committee type taxonomy (H/S/P/X/Y/N/Q/O/I/U codes), JointFundraisingCommittee dataclass with allocation parsing from Form 99, iterative legal-suffix name normalization, four-pass resolution pipeline (exact ID 71.5% to alias 84.2% to TF-IDF cosine 92.1% to fuzzy 95.5%), and FECEntityMatcher with character 3-gram cosine similarity.", "summary": "How AI Analytics resolves FEC committee identities across joint fundraising committees, disbursement schedules, and multi-cycle filings: FEC committee type taxonomy (H/S/P/X/Y/N/Q/O/I/U codes), JointFundraisingCommittee dataclass with allocation parsing from Form 99, iterative legal-suffix name normalization, four-pass resolution pipeline (exact ID 71.5% to alias 84.2% to TF-IDF cosine 92.1% to fuzzy 95.5%), and FECEntityMatcher with character 3-gram cosine similarity.", "date_published": "2024-09-16T00:00:00.000Z", "tags": [ "Elections", "Entity resolution", "OSINT", "Data engineering" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/election-anomaly-detection/", "url": "https://ai-analytics.org/writing/election-anomaly-detection/", "title": "Detecting election anomalies using statistical methods", "content_text": "Benford’s Law, turnout modeling, ARIMA time-series — surfacing anomalies worth a second look.", "summary": "Benford’s Law, turnout modeling, ARIMA time-series — surfacing anomalies worth a second look.", "date_published": "2024-09-12T00:00:00.000Z", "tags": [ "Elections", "Statistics", "Benford", "OSINT" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/election-statistical-methods/", "url": "https://ai-analytics.org/writing/election-statistical-methods/", "title": "Statistical anomaly detection for election integrity: Benford's Law, digit uniformity, and turnout modeling", "content_text": "The statistical methods behind AI Analytics' election anomaly detection — first-digit analysis, last-digit uniformity testing, turnout z-scores, and why these signals require cross-validation with social and media data before generating an alert.", "summary": "The statistical methods behind AI Analytics' election anomaly detection — first-digit analysis, last-digit uniformity testing, turnout z-scores, and why these signals require cross-validation with social and media data before generating an alert.", "date_published": "2024-09-07T00:00:00.000Z", "tags": [ "Elections", "ML", "Methodology", "Statistics" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/election-data-pipeline/", "url": "https://ai-analytics.org/writing/election-data-pipeline/", "title": "The election intelligence pipeline: aggregating ballot data, social signals, and media coverage for real-time anomaly detection", "content_text": "How the election intelligence pipeline ingests AP Election API feeds, state authority data, social media signals, and media coverage in real time: Kafka election.precinct_results topic partitioned by state FIPS, PrecinctResult protobuf schema, state scraper layer, ElectionSentimentConsumer and narrative divergence scoring, FIPS normalization edge cases, and p50/p99 latency targets for all four data streams.", "summary": "How the election intelligence pipeline ingests AP Election API feeds, state authority data, social media signals, and media coverage in real time: Kafka election.precinct_results topic partitioned by state FIPS, PrecinctResult protobuf schema, state scraper layer, ElectionSentimentConsumer and narrative divergence scoring, FIPS normalization edge cases, and p50/p99 latency targets for all four data streams.", "date_published": "2024-09-02T00:00:00.000Z", "tags": [ "Elections", "Infrastructure", "Kafka", "NLP" ], "authors": [ { "name": "AI Analytics" } ] }, { "id": "https://ai-analytics.org/writing/processing-millions-posts/", "url": "https://ai-analytics.org/writing/processing-millions-posts/", "title": "How we process 2.4M social-media posts per hour", "content_text": "Kafka partition key design, binary COPY writes to TimescaleDB, character 4-gram MinHash LSH distributed across Redis, autoscaling on consumer lag, and a canonical normalization layer across 47 platform schemas — the full pipeline behind 58M posts/day.", "summary": "Kafka partition key design, binary COPY writes to TimescaleDB, character 4-gram MinHash LSH distributed across Redis, autoscaling on consumer lag, and a canonical normalization layer across 47 platform schemas — the full pipeline behind 58M posts/day.", "date_published": "2024-08-30T00:00:00.000Z", "tags": [ "Kafka", "TimescaleDB", "NLP", "Infrastructure" ], "authors": [ { "name": "AI Analytics" } ] } ] }