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Zen Notes – Distraction free notes app

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Zen Notes – Distraction free notes app

Hello everyone, I've been building a distraction free notes app called Zen for the past few months. * It's built using Go and uses SQLite database for storage * It's fast and uses less memory (~20MB) and CPU resources * Supports standard Markdown with tables, code, etc * It's built using as few dependencies as possible, so less bitrot long term * Has search with BM25 ranking * Designed thoughtfully with minimal color palette Here are some links: * Project page https://www.sheshbabu.com/zen/ * Demo https://zendemo.fly.dev/ * Docs https://www.sheshbabu.com/zen/documentation * GitHub https://github.com/sheshbabu/zen

Visit productView on Hacker NewsJun 21, 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: using, notes, code · Missing: mac, agents, macos
77%77% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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 · 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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