Di

Dirsv – dir browser, battery packed Markdown previewers

Hacker News

Dirsv – dir browser, battery packed Markdown previewers

This is my 2nd time sharing this tool. It is a local directory browser with live reload. Single Go binary, embedded web UI. IMO, it is a better markserv [0] that supports more features I needed for my workflow with markdown and general dir browsing: - Math heavy note. - Tech design docs with heavy mermaid diagrams. - Code file, images, preview. Last time I used markserv, when browsing into an unsupported text files, it is downloaded. dirsv will render the file with syntax highlighting. I also built the companion nvim for this: https://github.com/letientai299/dirsv.nvim Compare to my last Show HN sharing, many bugs was fixed, and a few changes was made: - Sync scroll, cursorline and visual selection between nvim plugin can the previewer. - Change default port ranges to 3579+, to avoid conflict with many other tools that like to use 8xxx range. Disclaimer: this was built with claude code, and, I do code review seriously to make sure I can still work on it without any AI tools. [0]: https://github.com/markserv/markserv

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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: cursor, claude, visual · Missing: mac, agents, macos
82%82% 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: code review, io · Missing: https docs, excited, just released
53%53% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: supports · Missing: reddit linkedin, podcasting, created
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% 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
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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