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Chroncal – A TUI for Calendar
Hacker NewsChroncal – A TUI for Calendar
I've been working on Chroncal for the past weeks, an open source TUI/CLI with iCalendar (RFC 5545) support and CalDAV sync, using SQLite to store the data. My main goal is to make it easy for LLMs to auto-discover the CLI commands and access the database, while providing a nice TUI for the user (and I also found it paired very well with Omarchy, so, I also made a plugin to integrate in on Quickshell: https://github.com/DouglasdeMoura/chroncal-bar ). I'd love some feeback, specially to find bugs and missing features you find important.
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, open · Missing: mac, agents, macos86%86% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released67%67% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: sde · Missing: arr, mrr, revenue16%16% 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, crypto0%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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