Generate User Interface Components with GPT-3

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I’m building an experimental tool that can generate a UI component from a high-level text description, i.e. “Design a card for viewing goals in a goal tracking application”. Basically, stable diffusion but for user interface components instead of images. This tool is at best a proof of concept. It currently only generates a single type of component (small cards that have basic info about an object), but the goal is to be able to design entire applications (either in one fell swoop or by piecing together components that the tool generates). The components it generates can be exported as react code. You can also have it modify an existing design. The tool isn't useful yet but it's somewhat fun to play around with. Having it generate a component rather than an entire screen made the problem easier but I'm not sure if there are any users that this would appeal to. I could see it appealing to developers who lack design and CSS skills and want to quickly build a decent looking frontend for their app. Looking forward to hearing what you guys think of this project and the generative AI for UI space in general. If you're interested in collaborating on a startup or open-source project in this space, email designhat dot ai at gmail dot com

Visit product View on Hacker NewsFeb 13, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, single · 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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

48%48% 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: interface, users · Missing: plus, platform, intuitive

44%44% 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

14%14% 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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