Devion – AI powered release notes from your commits

Hacker News

Hey HN! I built Devion after spending way too many Friday afternoons writing release notes instead of shipping code. Devion connects to your GitHub repo and automatically generates professional release notes from your commits and PRs. It: - Analyzes commit messages and PR descriptions using AI - Groups changes into meaningful categories (features, fixes, breaking changes) - Recognizes and highlights contributors automatically - Generates changelogs in customizable formats - Works with your existing git workflow - no changes needed We're currently in demo phase and I'd love your feedback. The goal is to make release notes something that happens automatically, not something you dread doing. Try it at https://devion.dev - would especially love to hear: - What's missing from your ideal release notes tool? - What would make you switch from your current process? - Any workflow quirks we should handle? Also working on surfacing good-first-issues for maintainers. Happy to answer any questions!

Visit product View on Hacker NewsDec 27, 2025

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

88%88% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% 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.

Correct prediction on native model

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