slacker, climber, yak shaver. founding engineer and CTO @poolsideai
Lausanne
Joined September 2009
- Tweets4.8K
- Following293
- Followers1.6K
- Likes1.5K
Pierre-Yves Ritschard retweeted
Objects in the rearview mirror may be closer than they appear
Congrats to @Zai_org on GLM-5.3. It massively beats every American open model: Nemotron, Laguna, Inkling. It's not close.
GLM-5.3 uses the same base model as GLM-5.2; every gain came from post-training. If America keeps going down the "Nemotron Committee" path instead of fostering real competition, it will keep losing.
Pierre-Yves Ritschard retweeted
Building AI in the open makes the world more secure.
We’re joining the Open Secure AI Alliance to build open tools that safeguard software and agents.
We’ll keep releasing model weights and evaluations and sharing our research to strengthen a broader open ecosystem for defenders.
Excited to contribute alongside @nvidia and the other organizations building this ecosystem.
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community.
During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion.
That’s why we created the Open Secure AI Alliance.
Pierre-Yves Ritschard retweeted
This has been a long time in the making and I'm so happy to see it out in the world.
From @almonk and @dizzyup's work before I joined @poolsideai through all the iterations as we've learned together what an AI coding tool should be.
It's been a real team effort timr.co/ github.com/cryizzle @Johan_Lajili @pemsbr github.com/Evgeny- @mrncst @oyaaaaaaaasumi @glennui @prekesh and loads of others too🙏❤️
Pierre-Yves Ritschard retweeted
Still didn't attempt any quality testing, but let's say that GPT 5.6 Sol was able almost unassisted to write this Laguna S2.1 inference implementation following the other two models in DwarfStar, and it is very fast, 50 t/s generation, very fast prefill as well.
Pierre-Yves Ritschard retweeted
Another great open model! Congrats to the team @poolsideai
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…
Pierre-Yves Ritschard retweeted
ZML/LLMD gets Day 0 support for Laguna S 2.1 with DFlash on all the platforms.
Very happy to work with @poolsideai on this.
In close collaboration with @poolsideai, we’re happy to announce the support of Laguna S 2.1, their new open-weight model for agentic coding and long-horizon work.
It’s available today in ZML/LLMD with DFlash support. Most importantly (and as always with us), it’s available on all the supported platforms: NVIDIA, AMD, Intel, Google TPU and Apple Metal.
It's a good model, sir.
zml.ai/posts/laguna-s-2-1/
Pierre-Yves Ritschard retweeted
this is the drop the local ai crowd should be losing their minds over.
poolside just dropped laguna s 2.1: 118b total parameters, only 8b active per token, a full 1m context window, open weights under a real open license, on huggingface today.
look at the chart. it lands at 71 on terminal-bench at 118b, sitting above deepseek v4 pro max at a trillion params, above inkling at 1.5 trillion, above nemotron 3 ultra. it's beating models ten times its size and losing only to kimi k3, which is 24 times bigger. that's the efficiency frontier, up and to the left, exactly where you want a model to sit.
but here's the part that made me sit up: it runs on a single dgx spark.
and this is what nobody's saying loud enough. the dgx spark is the moe king. a dense 118b would crawl on it, the bandwidth chokes reading every weight each token. a moe with 8b active only ever reads 8b, so the spark's 128 gigs holds the whole model while generation stays fast. big brain, light footprint, the exact shape the spark was built to run.
open, frontier competitive, moe efficient, and it fits on a box on your desk. that's the whole thesis in one release: you don't need a datacenter, you need the right architecture on the right hardware. go grab the link below, weights are up.
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…
Our model factory approach keeps delivering. 60 days to S, on to larger models now 🚀
Pierre-Yves Ritschard retweeted
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…
Pierre-Yves Ritschard retweeted
We're releasing ZML/LLMD, our homegrown LLM server built on top of our homegrown high performance heterogeneous inference stack.
It ships with 5 architectures out of the box: NVIDIA, AMD, Metal, Intel and TPU. All transparent.
It supports DFlash, continuous batching, prefix caching, the whole deal.
Oh, and it's fast.
Pierre-Yves Ritschard retweeted
We also worked on making XS 2.1 easier to run outside hosted endpoints.
XS 2.1 is supported in vLLM, SGLang, TensorRT-LLM, Hugging Face Transformers, and @ollama, with BF16, FP8, INT4, and NVFP4 checkpoints available.
We’re releasing DFlash speculator models alongside the checkpoints to speed up inference.
llama.cpp support and GGUFs are coming soon. We heard you.
huggingface.co/collections/p…
Pierre-Yves Ritschard retweeted
We've released the weights of Laguna M.1 (225B total/23B active) under Apache 2.0 today
Pierre-Yves Ritschard retweeted
We're publishing the technical report for Laguna M.1 and Laguna XS.2 today.
It's a bit of an unusual kind of tech report: We talk about two model generations at the same time. In addition to the technical details, we really wanted to share not just what went into them; but also how we approach model building.
poolside.ai/assets/laguna/la…
Pierre-Yves Ritschard retweeted
Today we’re publishing the technical report behind Laguna M.1 and Laguna XS.2.
This report opens up more of what went into them: Model Factory, pre-training data, distributed training, post-training, agent RL, quantization, and evaluation.
poolside.ai/assets/laguna/la…
Pierre-Yves Ritschard retweeted
I just tried Mapterhorn terrain in OpenGlobus - and my first impression is literally: WOW, it looks amazing!
Clean, detailed, visually pleasant relief, and it loads surprisingly fast. Really like the idea behind it: open-data terrain built for modern web mapping. Huge respect to @leichteralsluft for pushing this forward.
Check it out 👉 mapterhorn.com/
#OpenGlobus #Mapterhorn #WebGL #GIS #OpenData #3DMaps
Pierre-Yves Ritschard retweeted
Good morning researchers and technical builders in London👋🇬🇧
+100 applications in under 24 hours🤯 — only a small share will make it in!
We're hand-picking a small group of the strongest researchers to spend a weekend pushing @poolsideai's Laguna XS.2: fine-tuning, RL environments, quantization, inference.
Pizza, networking, special guests and the winning team walks out with an @nvidia DGX Spark™ 🤩
Apply here👉luma.com/poolsidehackathon
