Recapg.it – Daily GitHub Summaries for Better Standups

Hacker News

Hi HN! As developers, we’ve all been there: it’s standup time, and you’re scrambling to remember what you worked on yesterday. We built RecapG.it to solve this exact problem. What it does: RecapG.it connects to your GitHub, analyzes your activity, and generates clear, structured summaries of your daily work. It’s designed to make standups smoother and help you plan your next steps effortlessly. Key features: GitHub Integration: Connect your account in seconds. Activity Summaries: Get concise recaps of commits, PRs, and other contributions. Team-Friendly: Keep everyone aligned during standups and planning. Why we built this: Standups should be about collaboration and alignment, but we often found ourselves focused on recalling past work instead of planning the day ahead. RecapG.it bridges the gap between reflection and forward planning, freeing you to focus on what matters most. We’d love for you to check it out and share your feedback. There’s a free tier, so you can try it right away. Try RecapG.it We’d especially love to know: How do you currently prepare for standups? Are there additional integrations or features you’d like to see? We’re here to answer any questions and would appreciate your thoughts! Thanks for taking a look.

Visit product View on Hacker NewsJan 17, 2025

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Actual performance

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Launch Intel predictions

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

87%87% 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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

27%27% predicted probability of success on TrustMRR, 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

23%23% 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

22%22% 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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