Layer – Hide your personal AI files from Git without touching gitignore

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I built a small CLI called layer. It manages .git/info/exclude so you can keep local-only files in a repo without adding them to the shared .gitignore. This came from a normal team repo problem. People keep their own local files in a repo: API_SPEC.md, BACKEND_GUIDE.md, ARCHITECTURE_NOTES.md, CLAUDE.md, prompt files, scratch notes, temporary investigation docs, and other markdown files with custom names that are useful for one developer but should not be committed. The usual answer is “just put it in .gitignore”, but that gets messy fast because these files are different for each person. The shared .gitignore gets bigger and noisier for everyone else. Git already has .git/info/exclude for local-only ignore rules, but most people do not really manage it directly. So I made a CLI for it. Example: cargo install git-layer layer add API_SPEC.md BACKEND_GUIDE.md my-notes/ layer status The files stay on disk, but disappear from git status. One thing that also mattered for me is that tools like Claude Code, Codex, and Cursor often respect git ignore state in their repo view and file suggestions. So even if a hidden file can still be used when referenced directly, it may stop showing up naturally in autocomplete, file pickers, or repo context. Sometimes I want those files hidden, and sometimes I want to temporarily show them again. So it also has simple on/off commands: layer off layer on Another issue I ran into was history. Once a file is hidden from Git, Git cannot really help you track changes to it anymore. If an AI tool rewrites part of it badly, deletes useful content, or you want to recover an older version, normal Git history is gone. So I added local snapshot/history for layered files too. Still a small tool, but it has been useful for my own workflow. Repo: https://github.com/aungsiminhtet/git-layer Curious if other people have the same problem, or if you handle it differently.

Visit product View on Hacker NewsMar 28, 2026

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

94%94% 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.

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.

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

47%47% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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.

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

18%18% 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.

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