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Tailor: Edit Your Websites in the Browser with Llama3

Hacker News

Tailor: Edit Your Websites in the Browser with Llama3

Hey HN! I recently built Tailor ( https://tailor.cryingpotato.com ) as a way to speed up the dev cycle that comes with tweaking a website. My workflow to tweak websites was pretty painful - I’d change things in devtools until they looked visually correct, and then context-switch back to my terminal to apply relevant edits. Tailor makes that process much easier by keeping the edit loop fully in the browser. Tailor uses LLMs as a fuzzy reasoning engine to automatically map your HTML edits to source code edits. It also runs code directly in your browser using WebContainers so you don't have to switch to the terminal to play around with these edits. I’d love for you to try it out. Hit "Get Started" on the landing page and you're taken to the editing screen. Once it loads (only desktop Chrome for now because of WebContainer limitations), you can click the scissor icon on the top-right and start making edits. When you submit an edit, Tailor takes your edit intent and starts exploring the codebase with LSPs to make a source edit. You can hit the check mark to create a PR based on your edits. Here's an example edit Tailor made to the landing page: https://github.com/cryingpotat0/tailor-landing-page/pull/22 It uses Llama3 powered by Groq as a default option, but you can also use GPT-4 by providing your own API key (the key is not stored anywhere, but is sent to the server). To switch LLMs hit the settings icon in the top right. I'd love feedback on the idea and the experience of using it!

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, visual, using · Missing: mac, agents, macos
91%91% 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: started · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
57%57% 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
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.

Correct prediction on native model

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