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Ilseon, a Minimalist Focus Filter

Hacker News

Ilseon, a Minimalist Focus Filter

I’m excited to share a new productivity app I recently released on the Play Store. It was born out of pure necessity to solve a problem I am constantly faced with: cognitive overload and task paralysis. Since traditional to-do apps only added to the mental noise, I eventually built a small tool for myself called Ilseon. I needed something that would actually reduce cognitive load. I have added some features that helps me in my daily chaos: Context filtering: I work better when I only see what belongs to the role I’m currently in (Work, Family, Health, etc.). Switching context hides everything else, which stops me from bouncing between unrelated tasks or getting overwhelmed by a big master list. Time blocks with a big visible countdown: This externalises time for me in a way alarms or calendars never did. It gives me one clear block to focus on, and the timer helps with time blindness without being stressful. Still figuring out if adding alarms is a good idea or not.. A quick reflection loop: After finishing something, I jot down a short note about how it went. Over time I’ve noticed patterns which has been surprisingly helpful for understanding my own behaviour instead of feeling like every day resets. I also added things I personally rely on, like speech-to-text through my headphones and a low-sensory-load dark mode. I originally made this for myself, but since it’s open source I figured it might be useful to others who deal with similar things. If it’s helpful, great; just wanted to put it out there in case someone else is wrestling with the same issues I am.

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3points
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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
78%78% 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, new, context · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, way · Missing: mobile apps, ios, entrepreneurs
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
37%37% 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
36%36% 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
21%21% 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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