I built W3Design – a design pattern library of Web3 products

Hacker News

Hey all, I'm a product designer and I built this cause' when I was designing liquidity pool and staking features for this DAO, I didn't know how the user flow worked and how the UI looked for similar pages, so I was constantly switching back and forth Balancer, Uniswap, and OlympusDAO as references. I thought that I might not be the only one with this problem, so I built this library of userflow screenshots, and annotated them, to share with the web3 community of builders, designers, and developers. Would love feedback if you're a builder in web3, let's connect too! (Also I'm aware that there are some that'll claim web3 is a scam, but I believe in it, to each their own.)

Visit product View on Hacker NewsJan 4, 2023

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

74%74% 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 · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

43%43% 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 · Missing: mobile apps, ios, personal

31%31% 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

16%16% 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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