Gat – Version large files with Git, store them anywhere

Hacker News

Hi HN — I was frustrated for a long time using Git LFS and DVC to version large files. Gat is the boringly simple tool I was looking for: Git manages versioning and Gat manages storage. No dedicated LFS server, no clean/smudge filters, just a lock file. The workflow is small: `gat add` some files, commit `gat.lock` with Git, then `gat push` the content to your storage. In a fresh clone, run `gat init` and `gat pull`. After init, hooks reconcile files automatically or you just run `gat sync`. There is a lot of interesting engineering going on behind the scenes. - Uses Git-inspired stat-proof before hashing - Reconciles the worktree blazingly fast - Can manage millions of files - Multi-remote routing / Read-only remote mounting - ... It’s written in Rust and Apache-2.0 licensed. It's _very early_ pre-release days but I’d appreciate any feedback before launching the offical first v0.1.0 release. Source: https://github.com/getgat-dev/gat

Visit product View on Hacker NewsSep 8, 2026

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

63%63% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · 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 · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, 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

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

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