SaaSGaps
Indie HackersAI-powered SaaS idea discovery from real user pain points
I spent months scrolling Twitter looking for SaaS ideas and realized I was wasting hours reading through noise. So I built an AI pipeline to automate the process - it monitors discussions, extracts complaints, and valida
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, plain · Missing: mac, agents, macos59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal21%21% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue17%17% 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, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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