Cool demo from Burke Holland using GitHub Copilot Edits and a preview Vision plugin for image recognition and UI code generation. Burke was using Claude 35 Sonnet with the plug in for the demo.

I gave this a try with OpenAI gpt-4o but couldn’t reproduce the results. While it’s a cool concept to generate boilerplate in another language (Flutter), but I think the more powerful feature is the ability to convert design images into code.

Not something new, especially with a well-built design system, but once mature this technology will certainly make things easier.

Findings

Much like my prior experiment with first principles reasoning, LLMs aren’t sophisticated enough to provide granular enough product and architectural design to be immediately actionable by a development team.

While I anticipate that eventually LLM tools could be useful enough to assist in product and engineering tasks, it still requires a human to design something that is ubiquitous, intuitive, scalable, and secure.

Bottom line: don’t spend a lot of time bouncing ideas off the major models, and certainly don’t believe the hype that these tools can magically produce GOOD software with a few AI prompts.

Findings

While I think LLMs can be used to generate ideas or compile a list of potential features or technologies, the models aren’t sophisticated enough to understand the context of the problem and how to maximize user value.

This leaves a lot of gaps that humans inevitably need to fill. That’s to be expected. Anyone who has built an app from scratch will tell you that gathering feedback early and often is the best way to create the most useful app.

Novel concepts might be inspired by humans interacting with LLMs, but it’s doubtful it could come up with anything compelling on its own. Could you use LLM as a brainstorming tool, absolutely!