Changelog-bot – Generate CHANGELOG.md from Git and release notes

Hacker News

Hi HN! I built `changelog-bot`, a small CLI/GitHub Action that generates CHANGELOG.md entries from your git history + release notes with AI. I wanted something that: - doesn’t require labels or strict conventions - works in CI without scripting - can optionally use LLMs (OpenAI/Anthropic) but never requires them - falls back to a deterministic git/PR-based summary when no API key is set Usage example: pnpm dlx @nyaomaru/changelog-bot --release-tag HEAD --dry-run Or as a GitHub Action: - uses: nyaomaru/changelog-bot@v0 If AI keys are missing or fail, it still completes the run and annotates the PR with the fallback reason. Still early-stage (`v0.x`) — would love feedback on: - CLI flags / defaults - whether the fallback behavior makes sense - edge cases from real repos Repo: https://github.com/nyaomaru/changelog-bot

Visit product View on Hacker NewsNov 30, 2025

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

90%90% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% 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

18%18% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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