Rankflex.lol

TrustMRR

A paid listing on brankflex.lol. You choose the amount; minimum $1. HOW RANK WORKS Your position is decided solely by what you pay. There is no algorithm, no voting and no editorial curation. POSITION DECAYS A payment's contribution to rank halves every 30 days. Pay $1,000 and it counts as $1,000 today, $500 after 30 days, $250 after 60. This is what keeps the top spot winnable rather than permanently bought. YOUR LISTING DOES NOT EXPIRE Decay applies to position only. Your listing, its descr

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Actual performance

Did not reach leaderboard

Traction signals

MRR growth 30d-100.0%

Launch Intel predictions

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

55%55% 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 · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, just released, open source

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

36%36% 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

18%18% 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

15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: 000, ai · Missing: api, profitable, 1m

9%9% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Correct prediction on native model

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