Solution stack

And then when recruiters actually do that human to human connection, they're not so sort of like beaten down with all that admin and all that stress is that they can be fully present to have a true, meaningful conversation with the candidate itself.

Now, one of the things that we're going to talk about today, Hung, is are we thinking about kind of sins of the past, maybe coming into those multi-agent kind of like bloated tech stack? So when kind of like SaaS really booms, obviously in the sort of the 2000s is people have those bloated tech stack.

So are we with a multi-agent kind of ecosystem are we potentially going into that world? And is those multi-agents, are they really truly going to speak to each other? Are they ultimately going to be able to have those like handshakes so the left hand knows what the right hand's ultimately doing? Now at Jovio, we think that we've got this one connected agent connecting everything together.

So to your point, Hung, Um, yes, we still have programmatic job advertising, but we've now kind of moving, uh, well have moved to a full orchestration platform.

So we're going to be able to do more.

It is going to supercharge us.

The question is, what does this agentic world look like in terms of how we build this tech stack? And that's one of the things that we want to discuss today.

Obviously, Jovio coming from a certain perspective to say, hey, listen, having a multi-agent single ecosystem might make more sense.

Um, so, um, you know, uh, I guess, uh, you know, like some other companies that you kind of alluded to that maybe one that reported this week, y-you're not just a silicon company.

Um, you know, how, how, uh, do, uh, Helia and NeuralSpot and the rest of the soft-soft-software stack make edge AI easier to deploy, and how does that help Ambiq compete?

Yeah.

You know, we, [sighs] we realized at some point that we're not just a hardware company, right? We had to be a software company.

And so trying to understand those motivations and the problems that people are feeling right now and connecting all of those to where we're trying to go.

So what does your AI tech stack look like right now then? Is it all kind of under Oracel or do you have still a bunch of other tools that you use?

We still have quite a few tools.

Big part is they don't all renew on the same day.

Um, so at this rate, we still have several pieces of software, not only because of, you um but also just making sure that we feel really confident in moving forward with one or the other or whatever it might be and as a business we're three different pnls uh now which was different than when i originally started so we also have several different um governing bodies to to manage as well so there's lots of opinions that are floating around and uh how the distance is being worked which definitely comes with its own complications but

Yeah, and like, I'm so curious about that.

A part of me doesn't even know, like, where do we start to dig in there? Because I mean, 24 and I'm guessing counting acquisitions inside this company, right? So 24 companies that were completely self-contained before they became Metrotech and had all of their own tech stacks, all of their own AI workflows and ways of doing things.

What's the process now for consolidating all of those AI tech stacks and workflows with new acquisitions?

I'll see you then.

The next AEC Tech Stack is presented by Graytech with support from Avail.

Show notes, the full series, and every episode are at trxl.co.

This has been a Troxel Media production.

I mean, that was a very natural segue into another news story, which was Hugging Face getting bought by NVIDIA, which actually, you know, the first leak happened like Monday, and then it got confirmed sort of midweek that they are actually buying it.

And like... think you're totally right to draw a line between those between this story and that one because i think what you see in the hugging face acquisition is so much of the control here isn't just the chips it's the software stack right it's it's okay great you built a model it's got to run on top of some inference like what is the software stack that runs that model and the thing about nvidia is that that entire stack is connected right Like, it goes from, you know, the hardware to the software stack to the way they deploy it.

Like, they're involved in every layer in that stack.

And so I think, you know, the Hugging Face acquisition sort of tells you, hey, we know that what we need is to control the software stack so that the deployment of these models, whether it's going to NVIDIA or it's going to Jalapeno or it's going to Chinese AI chips, like, NVIDIA needs to be the place where that motion starts.

And if it starts with Hugging Face, you know, it starts with here's the model, here are the weights, you know, and then it goes from Hugging Face to I deployed this directly onto your hardware.

You know, you can kind of see that you can see the path.

I'm Evan Troxel, and a little bit of housekeeping before we jump into this episode.

As I was putting this one together, I realized that my podcast in general, and this next AEC Tech Stack series, does not fit how most people consume media anymore.

So if your attention span has been rewired for doom scrolling or 30-second videos, this is not gonna be for you.

This episode alone runs over three hours, and no apologies from me.

Um, it depends on the phase of the project.

It depends on the user, all these things.

Talk about just the landscape of tool usage in firms when it comes to, like, number of tools in tech stacks, and I think, I think in, you know, very small firms, there might be a handful of tools, and in very large firms, there's lots and lots and lots of tools, like more than people would care to admit, um, because of the ongoing costs of, of all of those licenses and all kinds of things that are going on there.

But this has been a...