Vibe coding got people who'd never written code to try building apps. I keep wondering what those same people would make if getting started with a physical product felt more approachable.
Lately, my feed has been full of Astra and Opus 3D demos lately. I've spent eight years working in 3D, so seeing people describe something and start experimenting with a model is pretty exciting. Now I want to see what they'll create and how far their imagination stretches.
That's what we're working on at Autonomyware: you describe what you want to make and AI works through the design and engineering towards something buildable. Getting more people to try making their first physical product is a big part of why I'm involved.
So I'd love to hear from the people here building apps, websites, whatever else: have you ever wanted to make something physical and just had no idea where to start?
Tell me, what would you like to create?

I joined this team a few months ago, and honestly it's been one of the craziest stretches of my career.

Rewind three or four years before this AI era really hit, writing code was the hard part. Slow, painful, every feature a grind. You measured progress in weeks.

Now I watch how in Autonomyware three/four words and it comes back with the entire product: the requirements, the 3D model, the risk analysis, the whole chain. The first time I saw it run end to end, I just kind of sat there. The speed still doesn't feel real, even building it from the inside.

During the development of our Engineering AI, I realized that today's AI models are mostly incredible librarians.
They've read almost everything, and they're brilliant at retrieving and remixing it. Ask for a picture of a chair and you get a convincing chair. But a picture of a chair and a chair you can actually build are very different things. One just has to look right. The other has to hold weight, fit together, and survive contact with the real world.
That gap is where the next phase of AI gets interesting. Not models that know what things look like, but models that understand how things work. Cause and effect. Physics. Whether two parts actually fit, not just whether they look like they do.
We realized that this is the hardest part when using AI in engineering: real, manufacturable products. It's unforgiving but in a useful way. If a language model is slightly wrong, the answer might sound a little off. In an engineering context, if a system is slightly wrong, a part gets driven straight through another one, and the assembly fails. You can't fake your way past reality.
We think that's actually the most honest benchmark for this whole shift. "Looks plausible" is where AI is today. "Works when you build it" is where we are going.
Curious what others working in AI think: where do you see the line between models that describe the world and models that actually understand it? And what's the domain that finally forces the difference?