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Conventional Comments in GitHub

Hacker News

Conventional Comments in GitHub

Hey HN, Cesc here, co-founder at Pullpo. We spend a lot of time doing code reviews on GitHub. One recurring frustration was deciphering ambiguous comments. Misunderstandings slowed us down. We're big fans of the https://conventionalcomments.org (discussed previously here: https://news.ycombinator.com/item?id=23009467 ) standard for adding clarity, but remembering and typing the prefixes (suggestion, issue(blocking), etc.) felt like friction. So, we built a simple, free, open-source Chrome extension to make using this standard effortless within the GitHub UI. How it works: • It adds a small toolbar above GitHub comment boxes. • You click buttons for labels (issue, suggestion, praise, nitpick, etc.) and optional decorators (blocking, non-blocking, if-minor). • It automatically formats the comment prefix for you. • There's a "Prettify" option to display prefixes as visual badges (using Shields.io, linked to a simple explainer on pullpo.io). • It adapts to GitHub light/dark themes. We built it because we needed it ourselves to improve our internal review process, and thought others might find it useful too. It's completely free and open-source (MIT license). • Install Link -> https://chromewebstore.google.com/detail/gelgbjildgbbfgfgpib... • GitHub Repo -> https://github.com/pullpo-io/conventional-comments • Quick Demo Video -> https://youtu.be/jLzXlZ78rNE?si=KMzIH9Vb43glekEW We just launched it on the Chrome Web Store. Would love to hear your feedback, suggestions, or any pain points you have with code review comments! Thanks, Cesc

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, visual · Missing: mac, agents, macos
74%74% 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
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: code review, ide, io · Missing: https docs, excited, just released
60%60% 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 · Strong signals: video, google · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
15%15% 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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