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SanctionSnap – free 250 sanctions checks via API

Hacker News

SanctionSnap – free 250 sanctions checks via API

Hi HN, I kept seeing teams pull CSV sanction files every day, so I built *SanctionSnap*. *What it does* - One REST call checks a name against 10 live lists (OFAC, UN, EU, UK, AU, CA, CH, JP, SG, plus a large PEP set). - Data refreshes daily. Typical response: ~150 ms. - Free tier: 250 calls a month, no card. *Quick test* ```bash curl -X POST " https://sanctionsnap.com/api/search " \ -H "X-API-Key: YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{"name":"Vladimir Putin"}' | jq Docs, pricing, and a web console (upload CSV/XLSX, filter, export) live here: https://sanctionsnap.com/docs I’d love your feedback on: 1. Matching quality—missed hits or false positives? 2. Filters you need (country, program, etc.). 3. Anything that blocks adoption in your stack. Thanks for reading!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, calls · Missing: platform, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
21%21% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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