28 Day Dopamine Detox Tool

Hacker News

Hey lovely HN people :) A few months ago I listened to a podcast about doing a 4 week dopamine detox for addiction recovery. They talked about how the first 2 weeks are the hardest, but that if you can make it through that, things get easier in week 3 and 4. When they put it that way it actually sounded doable. Just 2 weeks? I can push through that. So I tried it for myself and yeah, it was pretty much right on. That inspired me to make a little web app that gives a little structure and guidance to this process. I hope it's useful to someone :) And I'm eager to hear what you think!

Visit product View on Hacker NewsJul 16, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

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

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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.

Incorrect prediction on native model

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