I built an app that automates timeboxing

Hacker News

Hey HN, Timeboxing assigns specific durations and start times to tasks, transforming your to-do list into a concrete action plan. When I discovered this productivity technique, I was unable to find any tools that made timeboxing easy. Having to do repetitive and inefficient procedures like using a dropdown menu every time to select a task duration in existing tools makes it discouraging to develop the habit of timeboxing. I made Task Kitchen, which automatically cooks up a timeboxing schedule using your to-do list by syncing with all your calendars. The entire task-adding process uses natural language, so a task that lasts 40 minutes can be simply written as "Read atomic habits 40," making the process almost frictionless. Would love feedback as well

Visit product View on Hacker NewsJul 14, 2024

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Launch Intel predictions

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

80%80% 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: exist, existing, io · Missing: https docs, excited, just released

61%61% 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% 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

20%20% 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.

Correct prediction on native model

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