Author: WorldofAI - Bewertung: 27x - Views:395
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Laguna S 2.1 is here, and it might be one of the most underrated open-weight AI releases of the year!
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Model Blog Post: https://poolside.ai/blog/introducing-laguna-s-2-1
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OpenRouter: https://openrouter.ai/poolside/laguna-s-2.1
https://x.com/poolsideai/status/2079613777343848465?s=20
https://x.com/atomic_chat_hq/status/2079735890000183345
https://x.com/superalesha/status/2079683537226330380?s=20;
https://x.com/atomic_chat_hq/status/2074878770851217853/video1
https://x.com/stevibe/status/2079899321374548252/video/1
https://x.com/analogalok/status/2079944805862559886
Poolside AI has unveiled Laguna S 2.1, a 118B Mixture-of-Experts model with just 8B active parameters, a 1M-token context window, and support for both thinking and non-thinking modes. Even more impressive? The model was trained from scratch to release in under 9 weeks.
In this video, we put Laguna S 2.1 to the test, explore its real-world coding capabilities, benchmark performance against models like GLM 5.2, Qwen 3.7 Max, Hy3, and Kimi K3, and look at why this model is generating so much excitement in the open-source AI community.
Could this be one of the best local coding models available today? Watch until the end to find out! 🔥
👇 Let me know your thoughts in the comments! Would you run Laguna S 2.1 locally?
#AI #OpenSource #LLM #Coding #LagunaS21 #PoolsideAI
Additional Tags (comma-separated):
Laguna S 2.1, Laguna AI, Poolside AI, Poolside, Open Weight AI, Open Source AI, Local AI, Local LLM, AI Coding, Coding AI, AI Programming, Developer AI, AI Models, LLM, Large Language Model, MoE, Mixture of Experts, GLM 5.2, Qwen 3.7 Max, Hy3, Kimi K3, SWE Bench, Terminal Bench, DeepSWE, AI Benchmarks, NVIDIA DGX Spark, RTX 5090, RTX PRO 6000, NVFP4, 1M Context, AI News, Artificial Intelligence, Machine Learning, Vibe Coding, Local Inference, Open Models, AI Developers 🤖
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