On July 21, Poolside open-sourced the full weights of Laguna S 2.1 to Hugging Face (under the OpenMDW-1.1 license), along with official GGUF / MLX / INT4 / FP8 / NVFP4 quantized variants and a DFlash speculative-decoding draft model. The architecture is a 118B-total / 8B-active MoE, picking 10 of 256 routed experts plus 1 shared expert per token, with 48 layers, a 1:3 global / sliding-window mixed attention layout, and 1M context. The most counter-intuitive scoreboard: SWE-Bench Multilingual 78.5%, Terminal-Bench 2.1 70.2%, DeepSWE v1.1 40.4%. Hitting 78.5% on multilingual SWE-bench with only 8B active means it can run on a single NVIDIA DGX Spark (desktop-class Grace Blackwell) and still reach a level close to Tencent Hy3 295B-A21B. On launch day, Hermes Agent, pi, OpenCode, OpenClaw and Cline all put S 2.1 into their default model lists — an unmistakable signal that the size curve for the agent era has to be redrawn. What's getting attention isn't the academic score, it's the engineering: training started May 22 and shipped July 21 — 9 weeks; 409k agentic and non-agentic training environments; FP8-accelerated RL for the first time in post-training; rollout budgets pushed to "tens of thousands of tokens and hundreds of steps" per task; the same prompt rolled out in parallel across multiple harnesses to prevent overfitting to any one scaffold. Poolside published every final evaluation trace, and the most circulated one is the model independently rediscovering Erdős conjecture #397 in an unsupervised sandbox — a 50-year-old open problem first solved in January 2026 by GPT-5.2 Pro, and the model's 8-exponent linear family is structurally different from the public 6-exponent solution. Even more interesting is the case of the model editing its own harness: Poolside fed its agent framework back to itself, ran 20 rounds of iterative refinement, and ended with a 5.2% overall speedup and roughly 70% reduction in memory allocation. The blog puts it bluntly: "We didn't add more intelligence; we added the engineering behavior of 'verify, don't give up, don't declare victory'." On the serving side, OpenRouter offers 256K free + 1M paid endpoints (priced at $0.10 in / $0.20 out / $0.01 cache-read per million tokens); Baseten and Vercel AI Gateway went live the same day. vLLM, SGLang, Ollama and TRT-LLM all support it day-0. My take: S 2.1 is the concentrated expression of the "small but precise" agentic-coding path in the second half of 2026. MoE + 8B active + strong RL + multi-harness training + DFlash speculative decoding — this combination is more worth replicating than simply stacking parameters. Poolside's next-tier Laguna is already in pretraining, but S 2.1's meaning is that it turns "single-machine top-10% agentic coding" from a slide into downloadable weights.