Whatdidido – CLI to summarize your work from Jira/Linear

Hacker News

I built this after spending days every year manually searching through Jira tickets to remember what I'd accomplished for performance reviews. whatdidido is a CLI tool that: - Pulls tickets from Jira or Linear for a date range - Uses an LLM to create short summaries of each ticket - Generates an overall summary to help you build your self-evaluation The tool doesn't write your review for you—crafting thoughtful, contextual feedback still requires human judgment. It just eliminates the busywork of finding and organizing what you worked on. Key details: - MIT licensed, open source - No data storage—everything stays local - Requires OpenAI or OpenRouter API key - Works with Jira and Linear (more integrations welcome/coming soon) GitHub: https://github.com/oliviersm199/whatdidido I'm releasing it now because I think others might find it useful during review season. Would love feedback on the approach and what other integrations would be helpful. Happy to answer questions about how it works.

Visit product View on Hacker NewsOct 27, 2025

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

81%81% 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

63%63% 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, soon · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, open source, io · Missing: https docs, excited, just released

24%24% 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.

Incorrect prediction on native model

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