md

mdfried, a terminal Markdown viewer that renders big headers

Hacker News

mdfried, a terminal Markdown viewer that renders big headers

mdfried renders markdown headers as big text, by generating an image with the same font as the terminal. It uses https://github.com/benjajaja/ratatui-image to render the images in the appropiate graphics protocol (sixel, kitty, or iterm2), if the terminal supports it. Check out the gif in the README.md (e.g. on the github link) to get a better idea on what it actually does and looks like. There is a setup step the first time you run mdfried, where a font must be picked (ideally the same font as the terminal). The first matching font for the prompt is previewed, you can force to run it again with `--setup`. There are still a lot of missing features (and probably bugs), as I just published/released this. If your terminal supports any graphics protocol, and you're curious, please try it out! Feedback is welcome.

Visit product View on Hacker NewsDec 14, 2024

Actual performance

2points

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

84%84% 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 · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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

36%36% 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.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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