An offline self-organising journal for work logging [OpenSrc]

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I'm making a completely offline, minimal, self-organizing journal (using lightweight, self-learning local AI models). That feels just like texting yourself. Motivation: To avoid the organizational overhead (e.g., what page to use, where to place something), I'd often just text/slack the logs/notes to myself. However, this made it difficult to find the information later. Idea: A simple text box. You can throw any information into it, and the app will make sense of it so you can find and use it later. Offline and open-source: Because I wouldn't trust my work logs in anyone else's hands. It's also extremely minimal on the surface. But when required, it has powerful features built in. Happy to hear your feedback!

Visit product View on Hacker NewsOct 23, 2025

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

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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