Open-source Todoist Clone that you can run on your personal Supabase

Hacker News

I know there are a gazillion todoist clone already but please bare with me. :) I was beginning to start dabbling with vibe coding over the past few weeks, and what better project to start with than Todoist, my favorite to-do application. This demo: I’m a paid user of Todoist and have been using it for the past 10 years, so I naturally wanted to see how far I could go with wipe coding by building a version of Todoist that I could personally use. When I thought about sharing it with others and making it easier for anyone to self-host, I also built a wrapper around Supabase that you can connect to your personal Supabase. Do check it out! Any feedback is most welcome, and it’s also open source.

Visit product View on Hacker NewsSep 23, 2025

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, coding · Missing: mac, agents, macos

91%91% 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, io · Missing: https docs, excited, just released

59%59% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

53%53% 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

44%44% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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