Lance Martin

speaker

624 appearances 1 recordings 1 series first heard Sep 2025 last heard Sep 2025

Lance Martin’s voice in public audio — every appearance, attributed to the second.

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Latent Space: The AI Engineer Podcast · Context Engineering for Agents - Lance Martin, LangChain · 11 Sep 2025 podcast

This is exactly Alessio's challenge and problem about using multi-agent.

So I actually hit that exact problem.

So I ripped out the independent writing and did a one-shot writing at the end.

And this is the current kind of version of Open Deep Research, which is quite good.

And this is kind of the thing that's, at least on Deep Research, meant the best performing Open Deep Research assistant.

At least that's open source.

So it was kind of my own arc, although we do have some faded results with GPT-5 that are quite strong.

So, you know, the models are always getting better.

And so, indeed, our open source assistant actually takes advantage and rides that wave.

But I actually kind of experienced, I felt like I actually got a bitter lesson to myself because I started with a system that was very reliable for the current state of models back in mid-2024, early 2024.

But I was completely bottlenecked as models got better.

I had to rip out the entire system and rebuild it twice, rechecking my assumptions, in order to kind of capture the gains of the model.

So I think, I just want to flag, I think this is an interesting point.

It's hard to build on top of rapidly expanding models, rapidly improving model capability.

And actually, I really enjoyed from AI Engineer Boris's talk on Cloud Code.

And they're very bitter lesson pilled.

He talks a lot about the fact that they make Cloud Code very simple and very general because of this fact.

They want to give users unfettered access to the model without much scaffolding around it.

But I think it's an interesting consideration in AI engineering that we're building on top of models that are improving exponentially.

And one of the points he makes is a core layer of the bitter lesson is that more general things around the model tend to win.

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