Poolside Ships a 68.2% SWE-bench Coding Agent That Runs on Your Mac. Apache 2.0.

Poolside AI released two models that change the math on local coding agents. Laguna M.1 is a 225B total / 23B active MoE model scoring 72.5% on SWE-bench Verified. Laguna XS.2 is a 33B total / 3B active model scoring 68.2% on the same benchmark, 44.5% on SWE-bench Pro, and 30.1% on Terminal-Bench 2.0. Both are Apache 2.0. XS.2 runs locally on a Mac with 36GB RAM via Ollama. (Source)

Alongside the models, Poolside shipped "pool," a lightweight terminal-based coding agent that runs entirely on your machine. It's the same environment they use internally for agent RL training, now available as a research preview. Combine pool with XS.2 and you've got a competitive agentic coding setup with zero API costs and zero data leaving your machine.

I want to put this in context. A year ago, getting above 50% on SWE-bench Verified with a local model felt aspirational. Now we've got 68.2% from a model with 3B active parameters. That's significant. Not because it matches Claude or GPT on the hardest problems, but because it crosses a threshold where local inference becomes genuinely useful for a huge chunk of daily coding work.

The timing matters too. With GitHub Copilot moving to usage-based billing (see story #1), the cost of cloud-hosted coding assistance is about to spike. Having a local alternative that clears the 65% SWE-bench line gives developers a real escape valve. Private codebases, air-gapped environments, developers in regions with spotty API access, indie builders watching their API bills. This serves all of them.

The broader pattern is worth noting. SWE-bench Verified has become the universal legibility benchmark for coding models. Every new launch leads with it. Poolside, Devstral, Qwen Coder, NVIDIA Nemotron. SWE-bench is to coding models what ImageNet was to vision. The standardization makes comparison tractable, but I worry it also incentivizes teaching to the test.

If you've got a Mac with 36GB+ RAM, download XS.2 through Ollama and try pool on a real codebase this week. It's free, it's Apache 2.0, and it might change how you think about paying for AI coding tools.