A

A plain-text file format for todos and check lists

Hacker News

A plain-text file format for todos and check lists

After having cycled through various CLI-based todo apps, I started to realise that I actually don’t need a tool at all for managing my todos. Most of the time, my use cases are quite simple, like viewing my todo items, checking them off, or adding a new one. Rather than having to memorise CLI commands for these interactions (which I’m not super good at), I figured that it’s easier for me to use my text editor directly, and have an editor plugin help me with the visual structure and some convenience functionality. So, kind-of similar to Emacs Org Mode, but without having to use Emacs. I personally use Sublime Text, and even though I enjoy it a lot, I don’t like being bound to specific tools. I think the best basis for staying independent is to have a data format that’s properly specified and meaningful on its own. This puts the data first, and it allows the tools to be built on top and shared (or interchanged) more easily. This is what [x]it! is about, which is a plain-text file format for todos and check lists. I’m curious for thoughts and feedback. There is obviously not much tooling support (yet), but feel free to create something if the idea resonates with you. Website with demo: https://xit.jotaen.net File specification: https://github.com/jotaen/xit/blob/main/Specification.md

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apps, new · Missing: agents, macos, agent
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% 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
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs
36%36% 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
14%14% 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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