SeeYourTime

TrustMRR

150+ users in 4 days. SeeYourTime is a real-time visual personalised dashboard that shows how much of your hour, day, week, month, and year has passed, making wasted time impossible to ignore. Features: activity/work logging, ambient focus music, floating tab PiP timer. One core insight: people manage time better when they can see it draining. Simple, sticky, daily-use product.

Visit product View on TrustMRRMar 8, 2026Productivity Other dodopayment

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Actual performance

Did not reach leaderboard

Traction signals

MRR growth 30d-72.9%

Launch Intel predictions

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

73%73% 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: user, visual, activity · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

64%64% 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

40%40% 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

27%27% 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

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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