Eiso Kant

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1,158 appearances 1 recordings 1 series first heard Jul 2026 last heard 23 Jul

Eiso Kant’s voice in public audio — every appearance, attributed to the second.

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Latent Space: The AI Engineer Podcast · Inside the Model Factory — Eiso Kant, Poolside AI · 23 Jul 2026 podcast

We're still helping with the debugging.

But more and more, and this is right now very profound on the data side of our pipelines and both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well.

You're starting to kind of see these twinklings of what RSI is going to look like.

And that's frankly, so when we talk about like to your question about our models, I really talk about the model factory.

And my coolest example of these things is always that when we kick off a new run, as a matter of the pre-training, like big run, or if it's now a poster, like one of 10 post-training versions we do for like pre-release or many experiments is that at any given moment, the changes that somebody made that they had experimental results on from the day before make it into that run.

So there's not like a cutoff 90 days before, like, no, it's like literally from that moment because we can now trust the machine enough.

And then you also have to invest in a reliability.

So one of my favorite metrics about like Laguna S is that there was no on-call events, right?

Like completely zero.

And actually we haven't had a meaningful on-call event, like something to wake up for as far as I recall this entire year.

Okay.

now there is one asterisk to that in usually the first six hours of launching a new model run something breaks because you set a config wrong you made a small mistake etc so that's usually there's a little bit of intervention but that's always within like in call periods right not not on call and and i think that's starting to now compound so the model we're releasing now i love it it's amazing but we're already on to the next one and i think that's the way it should be

Well, I would say... Well, experiments are obvious, but I think one of my favorite things is... I don't know where it is in here, but...

Early on, and I still think this is the case actually a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster, distributed across all of the nodes, and then training starts.

And we looked at this like three years ago and we were like, that makes no sense.

You lose so much time because the moment you have to rematerialize the data, so you have to make a change, you have to fix something, et cetera.

You've got all this time of like repackaging it, right?

Tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes.

The bigger your clusters are, you start using fancy like torrent like algorithms to like distribute your data.

So why aren't we streaming data into training?

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