Crackr

Crackr

TrustMRR

Learn ML engineering by building real systems from scratch.

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating27

Launch Intel predictions

Analyze your own launch →
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
21%21% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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