HowToDistribute

TrustMRR

Monthly sponsorship slots on howtodistribute.com, an editorial database of how software products acquired their users. Sponsors pay a recurring monthly fee to display their product name, logo, a one-line description and a link to their own site. Every slot is manually reviewed before it goes live. No physical goods, no data resale, no user-generated content.

Visit product View on TrustMRRAug 6, 2026Analytics Software stripe

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating2

Launch Intel predictions

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

86%86% 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.

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, physical · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: monthly · Missing: api, 000, profitable

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: site · Missing: reviews, intuitive, videos

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, just like, small businesses

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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