Nostr-CRDT – real-time collaborative apps over Nostr

Hacker News

Hi HN! Nostr-CRDT is an experimental project that connects Yjs [1] (a proven, high performance CRDT) with Nostr [2]. I wanted to learn about the Nostr protocol and see if it'd be possible to send updates to state (e.g.: edits of a rich text document, updates to a todo list) over Nostr. Nostr describes itself as "The simplest open protocol that is able to create a censorship-resistant global "social" network once and for all.". I like the idea of a decentralized social network, but what if we can decentralize more kinds of apps and create decentralized, local-first collaborative applications? I've been exploring this area for a while and earlier shared a Show HN post that does this over Matrix [3]. There's still lots to figure out, but imo it's a very exciting and rapidly developing space - looking fwd to your thoughts already! [1] https://github.com/yjs/yjs [2] https://github.com/nostr-protocol/ [3] https://news.ycombinator.com/item?id=29978659 PS: for an instant demo you can open this link and click the green button to load the doc and connect over Nostr (using a new anonymous account): https://nostr-crdt-yousefed.vercel.app/#room=6d749539e1dd9ef...

Visit product View on Hacker NewsFeb 8, 2023

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, using · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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 · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, 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 · Missing: web3, chat, crypto

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

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