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LangCSS – An AI Assistant for Tailwind

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LangCSS – An AI Assistant for Tailwind

Hi All This is my personal project that is an IDE and AI assistant for creating tailwind components and pages. You can chat to create designs, then make small edits yourself, and continue chatting to refine them. I am always working to improve the UX. I have a time limited demo page here: https://langcss.com/demo , or you can sign up for free and use one of the 3 models for free. Please let me know what you think! Feedback is welcome. Originally this used NextJS (Hosted on Docker) and Azure Open AI. It now uses Vite/.NET (Still on Docker) in order to use a more familiar back end language. DB is postgres. AI is Groq/OpenAI Azure/Claude. (Groq for free version). I think my next focus will be to make it so you can select parts of the design and run AI on those parts, which gets around the speed and context issues of working on larger designs. I also want to make a vanilla CSS (so no Tailwind!) mode. And look at integrating DaisyUI for the Tailwind users. Previous submission: https://news.ycombinator.com/item?id=40143498 Since then there is now a proper back end, rather than just losing your work when you close the tab! It will save your session and you can have many projects. It also handles the custom @apply and custom Tailwind config, but admitedly not as well as play.tailwind.com yet!

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, users, way · Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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 · Strong signals: host, users · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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