Marktwin – collaborative workspaces on Markdown files you own

Hacker News

Hi HN, We built Marktwin because I wanted a place to edit my GitHub Markdown files with my team. The alternatives we found meant either storing our notes somewhere else, having uncomfortable collaboration, or passing through someone else's server. Marktwin is a space to edit the files already in your repository, shared peer-to-peer. You can write Markdown, use a canvas, draw, discuss and review changes before syncing them back to GitHub. Everything available on marktwin.com is free for now, and we hope to keep it like that. There is no built-in AI. For now, the goal is simply to keep the context that people and agents use in files you control, in the places where you need it, while making it easier to work together. It was useful for us, and we would love for people to try it out and tell us what doesn't work, what is confusing, or how to improve.

Visit product View on Hacker NewsAug 28, 2026

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Launch Intel predictions

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

97%97% 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: io · Missing: https docs, excited, just released

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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

17%17% 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.

Incorrect prediction on native model

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