I Built an Open Source AI-Powered Todoist Clone

Hacker News

Hey HN, I'm an Educator who left her 9-5 to pursue my dream of educating developers and I like to educate about the latest tech and teach frontend development through my Youtube channel https://www.youtube.com/@Kulkarniankita and newsletters. I built this todoist clone called TodoVex that seamlessly organizes your tasks and predicts what's next using AI and I made it fully open source. I've also provided a deep dive into how I built it in this 7 hours and 7 minutes long Youtube video: https://youtu.be/dVN_zN1hZI8?si=VNyXO90qDdKyx6ek I hope this app will be helpful as it is for me. Would love your feedback and contributions please. Ankita

Visit product View on Hacker NewsJun 26, 2024

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4points

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Did not reach leaderboard

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

74%74% 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, ide, io · Missing: https docs, excited, just released

65%65% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

27%27% 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

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

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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