Image created with gemini-2.5-flash-image with claude-sonnet-4-5-20250929. Image prompt: A cinematic photograph of a minimalist offshore research platform at twilight, floating on an endless deep blue ocean, with two elegant birthday candles burning brightly on its polished metal deck, their warm flames creating rich red and white reflections against the slate grey structure, high contrast lighting with stars beginning to appear in the darkening sky above.
LIMI: Less Is More for Agency • Argues agentic AI doesn’t need more data, just better data • 78 curated demos → 73.5% on AgencyBench (beats models trained on 10k samples) • Outperforms SOTA (Kimi-K2: 24.1%, DeepSeek: 11.9%, Qwen3: 27.5%, GLM-4.5: 45.1%) • Establishes Agency https://x.com/arankomatsuzaki/status/1970328242688246160
China’s Alibaba just dropped an opensource 30B agentic LLM that outperforms Claude 4 Sonnet, DeepSeek v3.1, Kimi k2 on a range of agentic search benchmarks. Only 3B parameters are activated per token. 100% open-source. https://x.com/unwind_ai_/status/1969053988143477186
🚀 DeepSeek-V3.1 → DeepSeek-V3.1-Terminus The latest update builds on V3.1’s strengths while addressing key user feedback. ✨ What’s improved? 🌐 Language consistency: fewer CN/EN mix-ups & no more random chars. 🤖 Agent upgrades: stronger Code Agent & Search Agent performance.”” / X https://x.com/deepseek_ai/status/1970117808035074215
Congrats to @deepseek_ai ! DeepSeek-R1 was published in Nature yesterday as the cover article, and vLLM is proud to have supported its RL training and inference🥰 https://x.com/vllm_project/status/1968506474709270844
Finetune DeepSeek 🐳 with two Mac Studios + MLX 🚀 We use pipeline parallelism to split the full 671GB model across two devices connected by a single TB5 cable. LoRA reduces the number of parameters to train from 671 billion down to 37 million, reducing the memory overhead from https://x.com/MattBeton/status/1968739407260742069
Nature Portfolio also addressed this on Zhihu: Publishing this paper is itself a significant milestone👏 🤔 DeepSeek-R1 learns step-by-step reasoning with minimal human help: • Reinforcement learning: correct answers get rewards, mistakes penalized • Learns to self-verify & https://x.com/ZhihuFrontier/status/1968603082167828494
Pro Tip💡Fast and simple way to deploy DeepSeek-V3.1-Terminus with vLLM ⚡️ Run it with: vllm serve deepseek-ai/DeepSeek-V3.1-Terminus -tp 8 -dcp 8 (as simple as appending -dcp 8 after -tp 8) Thanks to the @Kimi_Moonshot team, vLLM 0.10.2 adds Decode Context Parallel (DCP) https://x.com/vllm_project/status/1970814441718755685
PSA: you can run the new DeepSeek v3.1 Terminus on a single M3 Ultra with mlx-lm at very usable speed. 4-bit quant one-shotted space-invaders in HTML/CSS: https://x.com/awnihannun/status/1970151204102750573
🚨 Major milestone for open-source AI: DeepSeek-R1, with Wenfeng Liang as corresponding author, has landed on the cover of Nature! 🔥 It’s the first fully peer-reviewed LLM published in a top academic journal, sparking huge debate in China’s tech community Zhihu. Zhihu mind https://x.com/ZhihuFrontier/status/1968573286696239247
DeepSeek’s updated V3.1 Terminus ties with gpt-oss-120b (high) as the most intelligent open weights model and offers increased instruction following and long context reasoning capabilities 🧠 Our benchmarking results indicate DeepSeek V3.1 Terminus shows a greater intelligence https://x.com/ArtificialAnlys/status/1971114096008495501
