QuarterMaster – Generate performance reviews from your GitHub activity

Hacker News

Every quarter I'd manually dig through merged PRs and feed them to an LLM to write my self-review. QuarterMaster automates this process. It fetches your PRs, code review activity, and commits, then generates a structured review. You can point it at a goals markdown file to align your work to team/company OKRs, and add a separate achievements file for non-GitHub work (mentoring, incidents, interviews, etc.). Supports Anthropic, OpenAI, and Ollama.

Visit product View on Hacker NewsMar 24, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

94%94% 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.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, code review, ide · Missing: https docs, excited, just released

40%40% 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: para · 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

20%20% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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