Helps people build daily habits and take back focus from their phone

Acquire.com

iOS habit and focus tracker. Users build daily habits, track streaks, run deep-work sessions and block distracting apps.

iOS habit and focus tracker. Users build daily habits, track streaks, run deep-work sessions and block distracting apps. Launched March 2026. 11,000+ installs, 773 paying subscribers, $36K ARR, 4.8 stars across 210 ratings. Revenue went from $707 in May to $10,724 in July. $24,946 total revenue since launch and $8,575 net income, every figure reconciled to App Store Connect. Acquisition is Meta ads only. UGC content, influencer marketing, Apple Search Ads, TikTok and Google ads are all unt...

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Actual performance

$3,000/yr

Did not reach leaderboard

Traction signals

Asking price$99,784

Annual revenue$36,000

Revenue multiple2.8x

Launch Intel predictions

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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Acquire.comFits revenue-generating acquisition profile · Strong signals: arr, revenue, subscribers · Missing: mrr, profit, saas

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

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs

74%74% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

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

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

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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