Builderswave

Builderswave

TrustMRR

A directory and weekly launch platform for apps built by African founders. Every product has a verified founder behind it. Each week a cohort launches and the community votes — winners get permanent r

A directory and weekly launch platform for apps built by African founders. Every product has a verified founder behind it. Each week a cohort launches and the community votes — winners get permanent rank badges. Products, browsable by 22 categories and 60+ tech stacks.

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating9

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% 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 NewsMay not resonate with HN audience · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, builder · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps · Missing: mac, agents, macos
33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
22%22% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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