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Tuido, a Terminal Todo List

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Tuido, a Terminal Todo List

This is my personal todo app, which I made a while back after the original https://xit.jotaen.net/ post. tuido is written in go, with the bubbletea tui framework. My daily workflow involves creating YYYY-MM-DD.md and taking notes, many of which are effectively low-level todos that fall below the threshold for more public or involved issue trackers. Problem was that these half-hazard todos weren't tracked at all. After seeing the [x]it spec, it seemed clear that a little tooling could fix this. I've been reasonably happy with it.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: notes · Missing: mac, agents, macos
67%67% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
57%57% 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, podcasting
53%53% 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
31%31% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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