Gcommit – clustering code changes semantically

Hacker News

The problem: I kept making "mega-commits" with unrelated changes because I get in a flow state and then end up with a huge number of changes I forgot to commit. The solution: I built a tool that analyzes your staged git changes, clusters them by semantic similarity using embeddings + hierarchical clustering, and creates separate commits for each cluster. How it works: - Parses diffs into semantic chunks using tree-sitter - Generates embeddings (OpenAI text-embedding-3-small) - Clusters with single-linkage hierarchical clustering - Interactive dendrogram UI to adjust cluster threshold - Creates one commit per cluster Currently Mac-only (uses self-written https client with kqueue), but happy to add cross-platform support if there's interest. Also planning local LLM/embedding support. Looking for feedback!

Visit product View on Hacker NewsDec 27, 2025

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Launch Intel predictions

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

81%81% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

51%51% 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

48%48% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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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