Built a local guardrail layer for Claude Code

Hacker News

I built Mati to give me more control over what Claude Code and Codex are allowed to do. Many important product constraints live in an engineer's or product person's head. This isn't new for engineers. We've always had important constraints that only certain people know about, but with coding agents it gets worse because we can now delegate a lot more, sometimes without realizing how much context the agent is missing. Then we delegate changes and the agent implements a change that looks reasonable but breaks some hidden constraint, hurts performance, exposes data, or does something destructive. It’s a local, Apache-2.0 licensed tool. It works with Claude Code and Codex, and also exposes MCP and CLI interfaces. The name comes from a Nahuatl verb meaning “to know” or “to think.” It’s probably a bit over-engineered. Anyway, sharing it here in case it’s useful to someone.

Visit product View on Hacker NewsSep 19, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

28%28% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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