NowDo – a free macOS todo app designed to combat procrastination

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Hey everyone, I'm a software architect who's wrestled with procrastination my whole life. I've tried countless apps and systems, but I found that the very features meant to aid my productivity—reorganizing tasks, categorizing them, tweaking priorities—became a source of distraction. Instead of getting things done, I'd procrastinate by micromanaging my task list. This realization led me to develop NowDo, a todo app born out of my personal experiences, refined over two years of prototyping. NowDo is designed to be as simple as possible, helping me focus on just one task at a time without the usual bells and whistles that lead me off track. Here's what sets NowDo apart: - One Task Focus: NowDo encourages you to focus on one task at a time. - Always-on-Top: It's designed to be always on top of your desktop, providing a constant but subtle reminder of your current task without being nagging or intrusive. - 100% Keyboard-Driven: NowDo can be completely driven using just your keyboard, so you can capture tasks the moment they occur to you, allowing you to offload your thoughts and return to your work without losing focus. - 2 Years of Prototyping: Every aspect of NowDo is crafted from my own struggles with procrastination and is intentionally built to avoid the pitfalls of conventional productivity tools. NowDo is free, and the current features will be free forever, with a premium option planned for the future. For Mac users interested in a more focused approach to productivity, give NowDo a try at https://nowdo.org/, I'd really appreciate any feedback.

Visit product View on Hacker NewsMar 26, 2024

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

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% 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, apps, users · Missing: mobile apps, ios, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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