Yelp for blockchains - Explore, rate, and review ETH addresses

Hacker News

Super excited to share an early MVP of Triton. It allows anyone to visually explore blockchains as a graph, where nodes are addresses and edges are transactions. Each node has a reputation score, calculated based on analysis of its neighbors, transaction profile, as well as user feedback. We built this after one of our founders accidentally sent 0.35 ETH to a phishing site. He tried to investigate, but soon realized that there aren't any good solutions for users to keep themselves (and each other) safe in web3. Triton can help solve the reputation problem for web3 transactions that Yelp solved for dining out. Use cases include investigating stolen tokens, DYOR on new NFT drops, and screening addresses for signs of fraud before making a payment. We'd love any feedback on the product A couple of interesting addresses to check out: - 0x03B70DC31abF9cF6C1cf80bfEEB322E8D3DBB4ca: a phishing-associated wallet - 0x7ab9287bbb0a6d76af16e648c93d503c5baf5432: wallet associated with the Pixelmon NFT founders

Visit product View on Hacker NewsSep 8, 2022

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best fitHighest predicted score across all platforms for this description.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

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.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, visual · Missing: mac, agents, macos

27%27% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

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

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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