A tiny work log that generates performance review summaries

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Hi HN, A few months ago I posted JotChain here — a work log tool that tried to do too much. The feedback was fair: the value wasn’t obvious. I rebuilt it with a much narrower focus: performance reviews. The problem We recently started doing performance reviews, and I had to review three developers. I remembered some highlights and issues from the past year, but a lot of context was missing — especially small but important things like soft-skill feedback or moments where someone quietly helped unblock the team. Those things never make it into a 1:1 doc, and by review time they’re gone. I wanted one place where I could quickly jot things down as they happen, without worrying about structure — and only structure it when it matters. The approach Write short notes as work happens (30 seconds, no format) Add light context (project, teammates) Pick a date range and generate a clean: self-review, peer/person review, accomplishments summary. No meeting recordings, no standup bots — just a lightweight log that turns into review-ready output. It’s early and adoption is slow, so I’m mainly looking for feedback: - Does this solve a real problem for you? - How do you handle performance reviews today? - Is “performance reviews” a strong enough wedge? Live here: https://jotchain.com Happy to answer questions and very open to criticism.

Visit product View on Hacker NewsDec 23, 2025

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

47%47% predicted probability of success on AppSumo, 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

44%44% 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: month · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

24%24% 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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