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Web3 Wiki - A comprehensive wiki about the Web3 ecosystem.
Hacker NewsWeb3 Wiki - A comprehensive wiki about the Web3 ecosystem.
I created an open-source Wiki to explore the Web3 ecosystem. It is a collection of resources, tools, and project explanations in the Web3 space. With numerous ideas and projects emerging, it can be challenging to stay updated. This Wiki aims to streamline learning by connecting resources in one place. I hope it helps you learn something new each time you visit. Contributions are welcome.
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Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting57%57% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal47%47% predicted probability of success on TrustMRR, 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 released37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, open · Missing: mac, agents, macos21%21% 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, revenue12%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 · Missing: chat, crypto, cryptocurrency8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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