Hacked a multiplayer text editor into Signal (CRDT updates over chat)

Hacker News

Hi HN! SignalSync is an experiment where I extend Signal Desktop with collaborative text editing support. It works by shipping CRDT updates (Automerge / Yjs) over the chat transport (and thus benefit from Signal identity, e2ee, security, privacy, contacts, etc.). These experiments are a fun way for me to play with new technologies and see how they can be applied in unconventional ways. I'm quite intrigued by the potential of sharing (CRDT) state updates over data-agnostic transports (earlier open sourced yjs updates over Matrix / Nostr)[1]. In this case, we immediately get the benefit of Signal contact management, privacy and e2ee security, without any changes needed to the Signal backend. This works because with CRDT-based document editing, conflicts are merged on the client side - and you don't need a central authority to merge updates or peek at your data. [1]: https://news.ycombinator.com/item?id=29978659

Visit product View on Hacker NewsMay 17, 2024

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

64%64% 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: new, open · Missing: mac, agents, macos

59%59% 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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