DappInsight – Ranking DApps by Audits, TVL, and GitHub Activity

Hacker News

I built [DappInsight] to help cut through the noise in Web3. Most dApp explorers prioritize hype or pay-to-rank listings. DappInsight does the opposite — ranking dApps by: Audit status TVL (and 7-day changes) Multi-chain support GitHub activity Community trust scores The idea is to surface real, reliable projects instead of meme coins with 3 wallets holding 90% of supply. You can try it at: dappinsight dot xyz (replace dot with .)

Visit product View on Hacker NewsMay 28, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntUnlikely to reach the leaderboard · Strong signals: apps, activity · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, 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 released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: web3 · Missing: chat, crypto, cryptocurrency

22%22% predicted probability of success on BetaList, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% 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

14%14% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Correct prediction on native model

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