mdfocus – A distraction free Markdown reader for your localhost

Hacker News

LLMs generate a lot of Markdown—Claude exports, ChatGPT conversation dumps, research notes, wiki-style docs. I found myself constantly opening these in VS Code or converting them just to read comfortably. So I built mdfocus: a zero-config local reader that you point at any folder and start reading instantly (npx mdfocus ~/notes). It's not a static site generator or a publishing tool—just a clean, focused way to consume Markdown locally. Includes live-reload, auto-generated table of contents, Mermaid diagrams, dark and other modes, and reading status tracking with color tags. MIT licensed.

Visit product View on Hacker NewsDec 28, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

78%78% 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.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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 · Strong signals: host · Missing: plus, platform, intuitive

32%32% 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

32%32% 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

21%21% 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.

Correct prediction on native model

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