Easy Earnings Tracker

TrustMRR

Income and expense tracking for content creators, freelancers, and multi-platform earners. Track earnings with intuitive dashboard, charts, and analytics. Dual-mode expense tracking, live currency conversion, real-time updates, smart vendor management. Supabase backend, Stripe payments, secure infrastructure. 14-Day Free Trial.

Visit product View on TrustMRRMar 21, 2025Fintech Accounting B2C

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Actual performance

2customers

$4MRR/mo

Did not reach leaderboard

Traction signals

Domain Rating12

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.

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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: stripe · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, 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

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

28%28% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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