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Open-Source EU AI Act Scanner for Python AI Projects

Hacker News

Open-Source EU AI Act Scanner for Python AI Projects

I built an open-source CLI tool that scans Python AI projects for EU AI Act technical requirements. It checks for 6 things the regulation asks for: risk management documentation, data governance, human oversight hooks, transparency logging, accuracy/robustness testing, and record-keeping. Right now it detects LangChain, CrewAI, OpenAI, Anthropic, HuggingFace, and AutoGen patterns, then flags what's missing against Articles 9-15 of the Act. It's not a legal compliance tool — it checks whether your code has the technical components the regulation references. Think of it like a linter for AI governance requirements. The interactive demo at the link walks through a sample scan. The scanner itself is pip-installable: pip install air-compliance-checker GitHub: https://github.com/air-blackbox/air-compliance-checker Feedback welcome — especially from anyone actually dealing with EU AI Act prep.

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3points
1comments
Did not reach leaderboard

Launch Intel predictions

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TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Strong signals: openai, code, open · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
41%41% predicted probability of success on Indie Hackers, 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
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
17%17% 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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