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Launched Productivity App: distraction-free to-do list and focus timer

Hacker News

Launched Productivity App: distraction-free to-do list and focus timer

Hey HN I deployed one of projects: FocoDo.Work — a minimal, distraction-free focus timer and to-do list app. I built this to help myself stay focused without the clutter of most productivity tools, and thought others might find it useful too. It’s privacy-first, super simple, and works right in the browser. Features: Add tasks and track time with a built-in timer See the total time you spent working on each task All data is stored locally on your device — no sign-up needed Share your tasks with a quick link across devices or with friends/colleagues Keep the timer visible in a floating picture-in-picture window Full-screen mode for deep work Would love to hear your feedback or suggestions. I’m planning to keep iterating on it! Try it out here: https://focodo.work

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Actual performance

2points
4comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks · Missing: mac, agents, macos
67%67% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, 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
29%29% 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 · Missing: plus, platform, intuitive
25%25% 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
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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