I created a to-do app that lets you submit tasks as quickly as possible

Hacker News

Hello HN! I've had this idea for a long time and created this app out of frustration with other to-do apps. It feels like you're filling a job application form just to add a task. Additionally, I've always worked better by creating to-do lists scoped to a day, which is also a main feature here. I created the whole app with SwiftUI and SwiftData - This is in fact my first mobile app with a native stack, and I must say, I absolutely love how easy this stack feels compared to my usual JS stack nowadays. I'm also giving away the lifetime pro version for just $9.99 until end of October - most of the features are free however, and I made it an important point not to track users :) I'd love to get some feedback as well!

Visit product View on Hacker NewsOct 19, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% 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.

Correct prediction on native model

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