poolside

A comparison of two AI coding agents building a Mario game: Laguna S 2.1 in Poolside's agent took 62 minutes with self-correction and passed tests, while a previous Qwen model took hours and needed human help; highlights oracle discipline and native harness advantages.

Eiso Kant, co-founder of Poolside AI, discusses their 'Model Factory' approach to rapidly training frontier models, the release of Laguna S 2.1, and the economics of AI model development.

A tweet highlights PoolsideAI's unusual openness, praising their release of a small coding model, publication of papers, and full evaluation datasets, setting a standard for transparency in AI.

Poolside released Laguna S 2.1, their most capable model for long-horizon tasks, along with multiple quantized variants (FP8, NVFP4, INT4, DFlash, GGUF) on Hugging Face.

Poolside released Laguna S 2.1, and the user acquired 3x AMD V620 GPUs totaling 96 GB VRAM.

Poolside releases Laguna S 2.1, a 118B parameter Mixture-of-Experts model with 8B activated per token and 1M context window, claiming to beat models three times its size like DeepSeek v4 Pro, Gemini 3.6 Flash, and Thinking Machines Inkling.

poolside released Laguna-S-2.1, a new 120 billion parameter language model that emerges as a strong contender in the LLM landscape.

Poolside releases GGUF quantizations of the Laguna S 2.1 AI model, including a DFlash speculative decoding draft model, enabling efficient local inference with llama.cpp.

Poolside releases Laguna XS.2, a 33B parameter MoE model with 3B activated parameters designed for agentic coding and local deployment on Macs with 36GB RAM.