AI

I just went to the latest AI Builders Meetup and it was really fascinating. It reminded me of the years of the Boulder-Denver New Tech Meetup, where there was so much energy in the room. There were people who flooded the meetup talking about all of the interesting things they were building.

This meetup was similar.

There was also the same kind of supporting infrastructure with companies willing to throw around money. For instance, at this meeting, there was plenty of beer and pizza. It was at Founder Central and there was plenty of space.

I have been part of other meetups where it’s very, very hard to find space or money for pizza. AI is such a hot topic right now that it is relatively easy to find space and sponsors, such as service providers or VC firms who want to pony up.

There were over 500 RSVPs. I don’t know how many people attended, but there were hundreds of people there.

As far as the presentations, they were demos and varied quite a bit.

Some were over my head. For instance, the folks that use the transformer architecture to predict and build audiences for drug trials. I understood the general concepts, but some of the technical details went beyond me. Still a really cool technical overview from Branchlab.

There was also a presentation about building personalized apps using Wabi.ai. I thought that was interesting but a little bit hard for me to understand why people would care about building their own apps. But I was never someone with a personalized ringtone or even stickers on my laptop, so I might not be the target market.

The next presentation was a demo from FreePlay, one of the sponsors. They were filling in for a talk that bailed; it wasn’t intentional. They covered how they built an agent to help people improve their prompts inside their eval system. They shared a Notion doc which had some learnings on building this agent, which were pretty useful. I think one of the key points that I took away was that cross-model optimization is really hard. Each of the models has their own wrinkles and idiosyncrasies, and it’s really hard to build cross-model applications or agents. You can work around some of the issues by aiming for lowest common denominator.

Another presentation was about Mai-Tai, an open source framework which lets you access coding agents from your phone. This was kind of a cool, interesting way to use a phone before Claude’s or OpenAI’s tools existed. It seemed really developer-focused; they’re looking for PRs.

The final project was really interesting to me. It was from one of the founders of Slider. It showed how you can automate constraints around agents and how that leads to better outcomes. In this case, they were doing that with design systems. They built a way to have an agent have ground rules and understanding around building applications with a certain design system, like a Workday application.

This was interesting to me from a number of perspectives. The first is that I’ve been thinking a lot about authorization as one of those guardrails and how that interacts. The second thing that struck me was when he talked about compilation checks versus runtime checks. Runtime checks are better-crafted prompts, and compilation checks are enforceable, deterministic guardrails like linters or authorization. He kind of didn’t want to give away all the secret sauce, so he didn’t dig into all of those deterministic checks. But to me, that seems like it’s where we’re headed. With this approach, you get the best of both worlds: the non-determinism to let an agent accomplish ill-defined tasks, and good guardrails to keep it from doing things it should not.

All in all, it was a great meetup.