Poolside

We build models for agentic coding and long-horizon tasks. Try Laguna:

[Poolside @poolsideai]

Today, we’re releasing Poolside Desktop Assistant.

One place to run coding agents across macOS, VS Code, and Visual Studio.

We built it for ourselves and have used it every day for the past year. Now we’re opening it up to everyone.

[Ilya Kochik @ilyakochik]

Frontier-Bench looks good. However at n=74, it may be too small to reliably distinguish model checkpoints. Our quick analysis suggests 100+ tasks would provide a more reliable signal...

[Poolside @poolsideai]

We’ll be at ModCon ’26 in SF on August 18!

@varunrandery is joining the frontier models panel to talk about building models for long-horizon agentic work, where open models differentiate, and what it takes to get them into production.

Join us in SF or tune in online ↓

[Modular @Modular]

Building frontier models involves a hundred decisions rarely discussed publicly: when to stop scaling, what data to keep, how to differentiate, whether to release the weights.

On August 18th at ModCon '26, Paige Bailey of @GoogleDeepMind, Joseph Spisak of @reflection_ai, Victor Su-Ortiz of @MiniMax_AI, and Varun Randery of @poolsideai are talking through the challenges of the industry from four different vantage points.

Register for the livestream: https://t.co/QUhiNFif2P

[Poolside @poolsideai]

Agentic evals are messy. A benchmark score tells you something about model performance, but it also reflects the whole system around it: the harness, sandbox, dependencies, timeouts and sometimes a loophole the agent found in the task.

That’s why trajectories matter so much to us. They show what the agent actually did and whether the score means what we think it does. We publish them to make that evidence transparent and auditable, giving the wider community more to learn from.

Watch @aalSonOfRavi and @ConnorBAdams go deep on all of this with @petergostev from @arena, including some surprisingly creative reward hacks!

[Arena.ai @arena]

Meet… Arena Conversations. @petergostev sits down with @poolsideai researchers,Connor Adams and Aalhad Patankar, to discuss how they’re building frontier open coding models.

Episode drops today at 10am PT on our YouTube.

They dig into Poolside's Laguna model family, why the team publishes full trajectories (not just benchmark scores) for anyone to audit, and how "experiments" at Poolside range from data mixes to harness design to reward-tuning decisions run tens of thousands of times a day.

[Tommy @yeahfortommy]

another 2 weeks of Laguna S 2.1 Free on Nous Portal, thank you @poolsideai

[Poolside @poolsideai]

@eigenlabs @yukonresearch Congrats with the launch 💙

[Poolside @poolsideai]

Come hear @mgalle and @sudip_r0y get into the weeds on post-training, agent RL, continuous learning, and what it takes to make models work reliably in production!

[adaption @adaption_ai]

First episode of Field Notes, our new monthly series on the leaders shaping AI.

@sudip_r0y, Adaption Co-founder, and @mgalle, post-training lead at @poolsideai discuss the last 5% of reliability and what it actually costs.

[Poolside @poolsideai]

we knew the community could make Laguna faster. 2.6x faster is pretty fun :)

huge congrats to the winner, and to all 35 solvers who spent the last few weeks pushing Laguna XS 2.1 further!

more reasons to build in the open, together 🤝

[Eigen Labs @eigenlabs]

We started the https://t.co/38dsNYD3nP challenge with @poolsideai with one key question. How much faster can the community make Laguna XS 2.1 run on a Mac?

The answer so far has been 2.6x faster.

Nearly 1,800 submissions and 35 solvers later, the challenge comes to a close today.

And there’s one more thing. One of the solvers is taking home a Mac Mini M4 24GB!

Winner below ↓

[Morgan McGuire @morgymcg]

Our SENPAI agent is currently no.1 on the @poolsideai x @eigenlabs inference optimization comp (for now) and first to break 200 TPS decode

Lovely first validation after a complete re-write of the agent to use @OpenHandsDev instead of cc

[Alex Cheema @alexocheema]

Poolside are pioneering models built specifically for local hardware.

Laguna S 2.1 is a great model for DGX Spark / MacBook. The number of tokens generated is probably an order of magnitude more if you include tokens generated locally.

[Poolside @poolsideai]

Watch Laguna S 2.1 climb at https://t.co/rsqK2Y1nxr

*Pulse currently shows OpenRouter traffic only, the rest is distributed across Vercel AI Gateway and direct API.

[Poolside @poolsideai]

We just crossed 10T tokens served across all our models in less than 3 months!

Laguna S 2.1 is pushing new highs at ~300B tokens a day, and has processed +2T in the 14 days since release.

That’s across @OpenRouter @vercel and our direct API.

Really good to see demand for open models keep accelerating.

[Poolside @poolsideai]

Thank you to everyone who tried Desktop and shared feedback in our first week!

A lot of 1.4.0 came directly from you.

Update the app, mix and match any model with any harness, and have fun with it.

Get started: https://t.co/setRB1BuGD

Then come tell us what we should build next:
https://t.co/uNPBwFEjsF

[Poolside @poolsideai]

Poolside Desktop Assistant has been out for a week, and we’ve received so much great feedback!

Today we’re releasing version 1.4.0, with a bunch of new features and fixes based on your bug reports:

- Native steering and queueing where supported
- Proper plan mode and agent Q&A
- First-class subagents, with full transcripts for Claude and better status reporting for Codex
- Much faster inference for local models
- A whole load of smaller bug fixes

More below!

[Poolside @poolsideai]

We made local inference faster for any model you run in Desktop. Try it with Laguna XS 2.1.

Tabs and split panels are more responsive too, and code files open almost instantly.

[Baseten @baseten]

One of our engineers asked @poolsideai's Laguna S 2.1 to transform a 715-file C++ game from neon cyberpunk into an Ancient Greek aesthetic.

It orchestrated 3 different models, refactored the code, and produced a playable build.

https://t.co/Dx5v5QxGtJ

[Alexander Yue @Alezander9]

Made some improvements to bcode + local models

Biggest winner is Laguna S2.1 which was compromised by provider issues, it gained +23% score since last post, now is very competitive

[Gajesh @gajesh]

excited to share we have made Laguna 2x faster on consumer Mac machines!!

i want to thank all the participants of this challenge -- this wouldn't have been possible without you.

all this with no speculative decoding; we're going to introduce it soon -- we want to make sure we ship an anti-hack verifier for our system!