Engineering @nvidia. Previously at @poolsideai, @apple, @uber.
Amsterdam, Netherlands
Joined July 2025
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Kudos to JetBrains for hosting Jake Wharton and Andrew Kelly. I don't fully share their stance on LLM-assisted coding, but such voices are important.
“No AI, no AI companies, AI-based products, or forced AI usage for development.”
In his new interview, Jake Wharton explains why this principle matters to him, and what concerns him about the way AI is changing software development.
▶️ youtube.com/watch?v=9WjPdgOK…
Artem Tyurin retweeted
🎉 CUDA Rust is here! Team 🟩 ❤️ 🦀
You can now write CUDA kernels in plain Rust, two ways: cuda-oxide (SIMT) and cutile-rs (Tile).
developer.nvidia.com/blog/in…
Artem Tyurin retweeted
My @aiDotEngineer talk about the universal standard we need for clients to drive agent harnesses, and why I think it's Agent Client Protocol
youtube.com/watch?v=YkNulwcc…
Artem Tyurin retweeted
Now with the upcoming release of the new DS4F checkpoint, and I believe later with Laguna S2.2 or a new S2.1 checkpoint, DwarfStar is going to be better at zero development costs. It is important to invest in the right models that get better and are locally runnable.
Artem Tyurin retweeted
Congrats to @poolsideai on launching Poolside Desktop Assistant.
Their Laguna models power an ACP agent that runs in any client, Zed included, and now their desktop app is an ACP client for any agent in the public registry.
Awesome to see Poolside taking full advantage of ACP!
Artem Tyurin retweeted
I just published mixed q2/q3 quants of Laguna S2.1 that work in MacBook systems with 64GB of RAM with quality not far from q4. You need DwarfStar laguna-s2.1 brach for them to work, or llama.cpp. With DwarfStar the speed is 65 t/s generation, ~570 t/s prefill.
Artem Tyurin retweeted
one thing i think people dont appreciate enough about @poolsideai is their unusual degree of openness — not only have they shipped an excellent Small model that somehow beat @thinkymachines at coding, but most people (like @eliebakouch) have been shouting out their excellent papers, but also they're among a rare few to actually expose their full eval dataset as well - beautifully published, with across 6 public benchmarks with 4 runs each and hundreds of turns per run. you can satisfy for yourself if they rewardhack. brilliant.
Poolside’s Race to AGI: Model Factory, Laguna S, Open Models, & Beyond MCP latent.space/p/poolside
@poolsideai co-founder and CEO @eisokant explains why coding is the path to AGI, how Poolside’s Model Factory runs 10,000–20,000 experiments a month and turns new models around in as little as eight weeks, why Laguna S suggests persistence and verification may matter more than raw intelligence, why agents are already helping build the next generation of models, and why the future should have 100 foundation model companies not five.
Artem Tyurin retweeted
Congrats to the @poolsideai team on their latest release, Laguna S 2.1.
It’s open-weight, delivers way beyond its size, runs great locally and you can customize it with NVIDIA NeMo.
Go try it out on @OpenRouter or download from @huggingface 👉huggingface.co/poolside/Lagu…
Today we're releasing Laguna S 2.1, our most capable model to date.
It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes.
Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark.
Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface
poolside.ai/blog/introducing…
Open models and open protocols.
Try Laguna S 2.1 in pool, our coding agent.
Works in the terminal and in any Agent Client Protocol client, like Zed, JetBrains, Xcode, and Neovim.
Today we're releasing Laguna S 2.1, our most capable model to date.
It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes.
Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark.
Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface
poolside.ai/blog/introducing…
Artem Tyurin retweeted
Quite excited about this one: Laguna S 2.1: 118B-A8B MoE with 1M context. It runs on a single NVIDIA DGX Spark, and it stacks up very well on what we focus on: agentic coding.
A few observations we had building it. 🧵
Today we are releasing Laguna S 2.1.
At 118B total parameters, with 8B active per token, it does the work of models several times its size on agentic coding. It is remarkably persistent across long-horizon tasks. And it is small enough to run on a single NVIDIA DGX Spark.
It is far more capable than anything we have created before, and I think it redefines what a model in its weight class can do.
Laguna S 2.1 is an important model for Poolside. What it represents is even more important.
If, five years ago, I had read a book that said that by 2030 everything economically valuable, scientifically interesting, and personally meaningful would be built on intelligence contracted from three or four companies, I would have called it dystopian science fiction.
We are at a fork in the road of what kind of world we can have.
I believe intelligence should and will become a commodity. The question is whether that intelligence comes from three companies, or from many people who can build it, own it, and shape it.
The open ecosystem will not win by being the best in its own category. No one cares who is king of the open-source kingdom. People want the best intelligence for the task they are trying to do, with the right balance of quality, speed, cost, and control.
If we want a different future, open models have to be on par with, or better than, their closed equivalents.
Laguna S 2.1 is a meaningful step in that direction: capable enough to compete far above its weight class, efficient enough to run on hardware you can own, and open-weight so anyone can build on it.
Open-weighting our models is the contribution we can make today toward a world where intelligence can be built and owned by many. And we will keep doing it.
I am very proud of this team’s work. A big shout out to everyone at Poolside who made this possible, from infrastructure and data to architecture, pretraining, post-training, evaluations, and inference.
Laguna S 2.1 is available today under the OpenMDW-1.1 license, with weights on Hugging Face and access through OpenRouter and our API.
We are building toward a future where the most capable intelligence in the world can be owned and shaped by anyone. Laguna S 2.1 is one step. We are going to keep building until that future exists.
poolside.ai/blog/introducing…
Artem Tyurin retweeted
new jspace results :) this time focusing on @poolsideai laguna XS.2 and XS.2.1 version bump and optimizers
for laguna, the question i tried to answer is "XS.2 -> XS.2.1 is likely training the model on more/different code, so how different is the jlens between the two versions if i use code or wikitext for the prompt?"
and the answer is that it's indeed VERY different. the model workspace is totally different between XS.2 and XS.2.1 if i fit it on code (i use some of the agentic traces training data from nemotron), and quite similar if i do it on wikitext EXCEPT the end layers that are similar for both (called the "motor" section in anthropic paper)!
confounding factor is that i took longer prompts for code compared to wikitext, fable and kimi K3 said it was not very important tho (i might test it to be sure!)
for the optimizer (based on checkpoints from the fantastic optimizer paper by @wen_kaiyue and marin folks), the question was a bit more open ended. we found that the CKA between different optimizers is much lower (less similar) than between the same optimizer at different sizes. we also found that some optimizers are much more similar than others and that the PR (effective number of directions a layer uses to talk to the logits) is quite different (see other plots in thread!)
the overall structure is still relatively similar, feels like muon has fewer "outlier" layers, the ones that have lower CKA with other layers and create those lines in the heatmap
we replicated anthropic jspace analysis on @thinkymachines Inkling new 1T model!
it seems to be an outlier: where other models split into near-orthogonal sensory/workspace/motor blocks, inkling keeps roughly one geometry across the whole stack (early-late CKA ~0.8 vs ~0.5 elsewhere)
we also look computed the J-space of @poolsideai's laguna XS 2.1 in bf16 vs nvfp4 to test the impact of quantization. result: almost none. the quantized model has the same jlens space as the non-quantized one
both jspace checkpoint are up on hugging face!
Always great to see more ACP clients. At @poolsideai, we have pool TUI and something coming up soon for the GUI fans as well, stay tuned!
Artem Tyurin retweeted
Air now supports 19 agents:
Amp, Augment Code, Cline, Codex CLI, Cursor, Devin, Factory Droid, GitHub Copilot, Hermes Agent, Junie, Mistral Vibe, Pi, Stakpak, Autohand Code, Claude Agent, Kilo, Kimi CLI, OpenCode, and Poolside.
2/5
Artem Tyurin retweeted
Today we’re releasing Laguna XS 2.1.
It’s a small upgrade to the Laguna XS.2 model, the same 33B total / 3B active MoE and stronger results on multilingual coding and terminal-style tasks.
Available now on @huggingface, @OpenRouter, and via Poolside API.
