DeepBench
Indie HackersHelping strategy and product leaders in decision making
Clients come to DeepBench when they are at a crossroads while developing a new product, undertaking a new strategic initiative, or evaluating an investment opportunity.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal56%56% predicted probability of success on TrustMRR, 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 · Strong signals: new · Missing: mac, agents, macos51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue20%20% 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, crypto16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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