Turn Your Git Commits into Tweets

Hacker News

OP here. I've been trying to "build in public" recently, but I found that switching context from VS Code to Twitter/X just to write "Fixed a race condition" felt like friction. I often ended up posting nothing because translating code-diffs to human-readable text takes more mental energy than fixing the bug. I built Git to Tweet to automate this loop. How it works: It hooks into your GitHub repo (via OAuth). It pulls the metadata and diff summaries of your recent commits. It passes the diff through a specifically tuned prompt (to avoid generic "AI slop") that extracts the intent of the code change rather than just listing file names. It generates a draft that you can edit before posting. The Tech Stack: Frontend: React + Framer Motion (spent way too much time on the "terminal" animations you see on the landing page). Backend: Node.js/Supabase. LLM: Currently testing models to see which is best at understanding code context without hallucinating features. The landing page includes an interactive simulator (hardcoded scenarios for now) if you want to see how the "translation" logic works without connecting a repo. I’m curious if others find this "translation" layer useful, or if you prefer manual changelogs? Feedback on the diff parsing accuracy would be awesome. URL: https://landkit.pro/git-to-tweet

Visit product View on Hacker NewsDec 27, 2025

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

89%89% 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: ios · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% 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.

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

19%19% 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 · Strong signals: active · Missing: arr, mrr, revenue

14%14% 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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