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ZenTasker – Open-source, calm GTD task manager (pre-code launch)

Hacker News

ZenTasker – Open-source, calm GTD task manager (pre-code launch)

I’ve struggled with task management tools that feel cluttered, overwhelming, or misaligned with how I think and work. So I’m starting ZenTasker — an open-source, self-hosted GTD task manager designed for simplicity, accessibility, and focus.\n>\n> Why ZenTasker:\n> - Built-in GTD structure: Programme > Subproject > Next Action\n> - Gradual reveal to reduce decision paralysis\n> - Designed from day one for neurodiverse inclusivity (energy tagging, sequential focus views)\n> - Minimal UI, with optional advanced features via plugins (Kanban, Eisenhower Matrix, Time Blocking)\n> - Personalization via file-based settings\n>\n> This is my first open-source project and I’m currently in the pre-code phase. I’m looking for input from developers, designers, and accessibility specialists who’d like to help shape it.\n>\n> Here’s the repo and discussion space: \n> https://github.com/walshd1/Zentasker/discussions I’d love your thoughts on the idea, architectural suggestions, and advice for building a great contributor community. Any feedback or questions welcome!\n>\n> Thanks for reading.

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Actual performance

1points
3comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
87%87% 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: para · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, 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
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, para · Missing: mobile apps, ios, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
26%26% 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
15%15% 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.

Incorrect prediction on native model

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