Markant – A Dedicated Markdown Reader

Hacker News

With Markdown being the standard output for many LLMs I needed a way to view them outside the usual code editors. Markant is a no nonsense viewer, that also prepares markdown for print and PDF. It supports very large documents, like books, without any speed penalty. Along the way I realized that it would be nice with a Markdown version of ePub, where multiple markdown files along with images could be bundled. That is now a new format called Markbook - https://markbooks.org that is easy for anyone, including AI to write.

Visit product View on Hacker NewsApr 24, 2026

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Actual performance

6points

3comments

Did not reach leaderboard

Launch Intel predictions

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

80%80% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · 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.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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 · Strong signals: way · Missing: mobile apps, ios, personal

34%34% 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 · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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