A first ever tool for analysing contract owners, permissions etc.

Hacker News

Hi all, We just hacked this thing: https://showme-1389.appspot.com/ I think it's the first ever tool of it's kind. It uses decompiled sources from Eveem.org API (e.g. http://eveem.org/code/0x06012c8cf97bead5deae237070f9587f8e7a... ), and using simple analytics on top of if, checks which other addresses are referenced by a given smart contract. For CryptoKitties, it finds all the ceo/coo/cxo, and all the libraries linked through sources, plus information on who can change those links - e.g. that only ceo can change coo, and only cfo can withdraw money. It took just a few hours to build it using Eveem's API, and you can check out how it's done here: https://github.com/kolinko/showmewhatyougot

Visit product View on Hacker NewsDec 14, 2018

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% 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, 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: crypto, smart · Missing: web3, chat, cryptocurrency

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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