WalletWatch – a social network for Ethereum wallets

Hacker News

Hey HN, I’m Kamil and one of the two people working on WalletWatch. We recently graduated college and have been getting more into crypto / web3 over the past year. A few of our friends had been getting into crypto more recently, but we noticed that it was hard to actually see what they were doing or natively engage with the transactions they were committing. Products like blockchain.com and etherscan.com were too technical, and products like Context or Zerion lacked key features that make modern products social, such as descriptions, likes, and comments. We built WalletWatch as an easier, more social, and more fun way to see and engage with your friends’ Ethereum activity. We don’t ask for your email or password, and instead authenticate by asking users to sign a transaction with their Ethereum wallet; we ask for usernames to give a more familiar and user-friendly experience for people that haven’t purchased ENS or other crypto domain names. At the moment, we’re storing user data in Firebase instead of on-chain because the current decentralized channels are too expensive or slow. We consider WalletWatch as pre-alpha. The website only renders in a mobile viewport and isn’t totally visually or computationally optimized, because we wanted to see if people liked this thing enough for us to spend more time making it polished. We’d love any feedback you all have! Please e-mail me at kamil@fwd.exchange or Blockscan Chat me at my address: 0x85eEF00b9a78A993359E94FD3Bcf65e9BDe909FB

Visit product View on Hacker NewsSep 17, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

75%75% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, context · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive

55%55% 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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

48%48% 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

12%12% 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, chat, crypto · Missing: cryptocurrency, make money, real time

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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