AsyncDocs, a real-time and secure collaborative Markdown editor

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Hello HN! At my company we usually constrained what we can use for real-time collaboration. It usually consists of MS Word (online), Confluence or something similar. Between software developers we use VS Code's Live Share feature but it takes forever to spin it and it does not always work. Furthermore, non-developers are not keen to install VS Code and use Live Share. I also looked at available tools but I don't think we could use those, being Europe and in an Enterprise world. So as I've created my own: https://asyncdocs.com/ It is currently in active development but I think it already has enough features to gather feedback: - user interaction are synchronised to every other client in real time - every communication is encrypted in the browser so the server knows nothing - it has a presence display to see if somebody else is working on the document - cursor and selection of other authors are displayed - local documents: documents are saved in the browser and can be pasted at the cursor - easily shareable links: just click on the document id to copy the link Any feedback is appreciated but I am curios if anybody else would have a use of it. Also what do you think of the UX and design? What feature would make you want to use it?

Visit product View on Hacker NewsDec 30, 2022

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

86%86% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, user, code · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · 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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