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FailCore – Execution-Time Safety Runtime for AI Agents

Hacker News

FailCore – Execution-Time Safety Runtime for AI Agents

Hi HN, FailCore is a small execution-time safety runtime for AI agents. Instead of relying on better prompts or planning, it enforces security at the Python execution boundary: blocking SSRF, private network access, and unsafe filesystem side-effects before any tool side-effects occur. I added a short live demo GIF in the README showing it blocking a real tool-use attack, along with a DESIGN.md that explains the architecture and trade-offs. GitHub: https://github.com/zi-ling/failcore Design notes: https://github.com/zi-ling/failcore/blob/main/DESIGN.md Feedback welcome — especially thoughts on runtime hooking vs. kernel-level approaches like eBPF.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, notes · Missing: mac, macos, cursor
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: filesystem, ide, io · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

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