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Claim-Detective – Verify Suspicious Tech Claims Collaboratively
Hacker NewsClaim-Detective – Verify Suspicious Tech Claims Collaboratively
Hi HN, I created Claim Detective to investigate suspicious tech claims and metrics. It’s a free platform where a community of detectives collects evidence with links to verify authenticity collaboratively. More on this thread here and motivation: https://x.com/stackoversnow/status/1983284912250814703 Let me know what you think :)
Share cardActual performance
2points
1comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting40%40% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos15%15% 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, revenue15%15% 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, crypto13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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