Stay Focus

Stay Focus

TrustMRR

StayFocus (stayfocus.now) — Distraction-free focus timer, streak tracker, and brain-dump app for deep work. Built for freelancers, remote workers, and students who want structure without the bloat of

StayFocus (stayfocus.now) — Distraction-free focus timer, streak tracker, and brain-dump app for deep work. Built for freelancers, remote workers, and students who want structure without the bloat of tools like Notion or ClickUp. SEO angles: "focus timer app," "streak tracker," "brain dump app," plus a unique MCP/AI-agent integration niche.

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Actual performance

2customers
$11MRR/mo
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: agent, mcp · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
33%33% 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
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
26%26% predicted probability of success on AppSumo, 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
14%14% predicted probability of success on Hacker News, 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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