Peer-to-peer mini r/place with Proof of Work

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Hi HN, This is a site where users can submit content and the shortest SHA256 value of nonce+content among them will make the site display the content. Very basic, without a blockchain. It's a static site with WebRTC and peer-to-peer. Why? I wanted r/place (Reddit canvas where users collaborate on a pixel grid), but with proof of work. This is my progress so far. It's a WebRTC app with peer to peer connectivity that forwards the best hashed values to other users, which verify them, display them and pass them on. There are many many improvements to be done (especially I should probably use something like libp2p-gossipsub, otherwise scale is probably an issue) and more ideas to be implemented, and I'm sure there are bugs. But I didn't find any similar projects yet (except blockchains of course). Hope you like it!

Visit product View on Hacker NewsJan 10, 2024

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80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

37%37% predicted probability of success on Indie Hackers, 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: collaborate · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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