Generative UI: OSS "Imagine with Claude"

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Anthropic recently released an experiment called "Imagine with Claude" ( https://claude.ai/imagine/ ). However, it was limited time only and available only for Pro and Max users which I am not. I liked the idea and wanted to reverse engineer it just from public content like youtube videos and reviews. Turns out, there were a bunch of limitations in their implementation - The LLM cannot generate any JavaScript which means it cannot do any crazy animations - They are capturing all user interactions using code and invoking the LLM with that info which means form data is not captured I went ahead and implemented what they already had and a bit more - All windows go into iframes which means it can have JavaScript - I let the LLM write code to send iframe messages from any window to be invoked. This way the LLM writes the logic to invoke itself You can also use this with both OpenAI and Anthropic models. Gemini models were not working well with these instructions. I have hosted the app and you can use it by BYOK. There is no backend. LLM requests are made directly from your browser. There's no tracking and analytics as well. This is a small experiment project that took me a couple of weekends and would love any feedback.

Visit product View on Hacker NewsOct 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos

98%98% 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: gemini · Missing: supports, reddit linkedin, podcasting

65%65% 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, io · Missing: https docs, excited, just released

54%54% 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: reviews, host, users · 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: video, users, way · Missing: mobile apps, ios, personal

34%34% 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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