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Turn a video of an app into a functional prototype with Claude Opus

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Turn a video of an app into a functional prototype with Claude Opus

Hey everyone, I’m the maintainer of the popular screenshot-to-code repo on Github (46k+ stars). When Claude Opus was released, I thought to myself what if you could send in a video of yourself using a website or app, would the LLM be able to build it as a functional prototype? To my surprise, it worked quite well. Here are two examples: * In this video, you can see the AI replicating Google with auto-complete suggestions and a search results page (failed at putting the results on a separate page). https://streamable.com/s24pq6 * Here, we show it a multi-step form ( https://tally.so/templates/online-quiz/V3qOnk ) and ask Claude to re-create it. It does a really good job! https://streamable.com/gstsgn The technical details: Claude Opus only allows you to send a max of 20 images so 20 frames are extracted from the video, and passed along with a prompt that uses a lot of Claude-specific techniques such as using XML tags and pre-filling an assistant response. In total, 2 passes are performed with the second pass instructing the AI improve on the first attempt. More passes might help as well. While I think the model has Google.com memorized but for many other multi-page/screen apps, it tends to work quite well. You can try it out by downloading the Github repo and setting up a Anthropic API key in backend/.env Be warned that one creation/iteration (with 2 passes) can be quite expensive ($3-6 dollars).

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, google · Missing: mac, agents, macos
88%88% 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: para · 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: apps, video, google · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, 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
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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.

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