Ki

KittyHTML – Render HTML/CSS as an inline image in your terminal

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KittyHTML – Render HTML/CSS as an inline image in your terminal

Some of the "use html as an output format for LLMs" conversations of the last month inspired me to put together this package. The idea being, if we are outputting HTML, we maybe care less about the idea of HTML-in-browser and more because it's a nice document description format with convenient renderers (browsers). But with things like kitty, I don't think we necessarily need to use a browser and can instead actually render HTML-described documents inline in the terminal itself. So here's kittyhtml. You can pipe HTML to it and it renders the HTML+CSS inline in the terminal itself. echo '<h1>hi</h1>' | kittyhtml It can take raw HMTL, but also is a nice output target for invocations of claude -p. claude -p "create a simple html page. return only the HTML and do not include markdown fences" | kittyhtml Under the hood it's using https://github.com/DioxusLabs/blitz for the parsing/layout and then getting that rendered with kitty. So anything Bitz supports this supports. As part of the npm package I ship an LLM skill that tells an LLM how to use this, but what I'm really ultimately aiming for here is to have this rendered inline in Claude Code. However, [CC doesn't yet support image protocols]( https://github.com/anthropics/claude-code/issues/2266 ), but once it (inveitably?) does I would update the skill so that "create a plan in html and display it with kittyhtml" would have the plan (or whatever) rendered directly in your CC session.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, using, code · Missing: mac, agents, macos
77%77% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
51%51% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
30%30% 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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