End-to-end encrypted notes app

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One year ago I started to explore combining CRDTs with end-to-end encryption to allow people to collaborate on the same documents/data. Couple months back I got a first prototype working. Think of a simple Google Docs, but end-to-end encrypted and offline-first (iOS & Android for now). Why CRDTs? Every document is a CRDT data structure. This is very important for conflict resolution. With CRDTs this can happen on the client and everyone can resolve to exactly the same state of the document once all the updates are synchronised. This basically allows to exchange the updates/data via end-to-end encryption. Initially I started out using the Matrix protocol, but in order to develop further synchronization optimization I started to directly use Matrix's Olm/MegOlm encryption library. The app/client source is available here: https://github.com/SerenityNotes/serenity-notes-clients Website: https://www.serenity.re/en/notes Feedback, ideas and question are very welcome!

Visit product View on Hacker NewsMar 24, 2021

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, month, google · Missing: mobile apps, personal, entrepreneurs

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

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

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

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

33%33% 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

20%20% 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

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

Incorrect prediction on native model

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