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Company: "thinking-machines"
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gpt-5.6-luna gpt-5.6-terra gpt-5.6-sol arc-agi-3 inkling-small inkling gemini-robotics-2 openai thinking-machines lmsys modal unsloth artificial-analysis google price-optimization agent-systems memory-retention context-compaction multimodality mixture-of-experts model-compression benchmarking open-weights multimodal-models model-efficiency model-deployment embodied-ai robotics long-context sama fchollet kimmonismus gneubig scaling01 mervenoyann
OpenAI aggressively cut prices for GPT-5.6 Luna by 80% and Terra by 20%, introducing a faster Sol Fast tier with up to 2.5× lower latency at double the price, improving agent workflow costs by roughly 10×. The ARC-AGI-3 debate highlighted that the complete agent system, including memory retention and tool orchestration, is critical beyond just the base model. Thinking Machines released Inkling-Small, an open-weights, multimodal MoE model with 276B parameters (12B active), delivering performance comparable to the original Inkling at a quarter of the size, supporting audio, images, and Python-based image inspection. Benchmarks show Inkling-Small excels in coding and multimodality tasks, with 1M-context support and broad open inference stack adoption. The news also mentions Google's Gemini Robotics 2 advancing embodied AI from tabletop to full-body control.
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kimi-k3 claude-fable-5 opus-4.8 gpt-5.6-terra gpt-5.5 inkling glm-5.2 gpt-5.6-sol moonshot openai thinking-machines artificial-analysis arena datacurve arcprize aisecurityinst moe-routing quantization data-curation infrastructure-design coding-agents benchmarking front-end-development software-engineering arc-benchmarks cybersecurity zhilin_yang kimmonismus anikasomaia dylan522p novasarc01 scaling01 theo hqmank
Moonshot's Kimi K3 release has sparked a reassessment of Chinese open-weight models' proximity to the frontier, with strong performance in coding, agentic tasks, and long-horizon knowledge work. The strategic focus has shifted from a "compute moat" to an "efficiency stack" involving MoE routing, quantization, data curation, and scarcity-driven infrastructure like Moonshot's "Mooncake" stack. Benchmarks from Artificial Analysis, Arena, DeepSWE, ARC, and Cyber place K3 among the top models, with scores such as 57 on the Intelligence Index and coding agent benchmarks matching or surpassing models like GPT-5.6 Terra and Claude Fable 5. Discussions continue on K3's exact standing, but it is now widely recognized as a significant frontier contender.
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gpt-5.5 codex thinking-machines openai anthropic multimodality real-time-interaction visual-proactivity deployment cybersecurity threat-modeling automation continuous-audio-video-text-processing security-models field-engineering enterprise-ai johnschulman2 soumithchintala chillee liliyu_lili rown kimmonismus giffmana swyx eliebakouch gdb sama therundownai lukolejnik matvelloso
Thinking Machines previewed their new native interaction models designed for full-duplex multimodal interaction enabling real-time concurrent listening, speaking, watching, thinking, searching, and reacting, marking a shift beyond turn-based AI. This approach emphasizes continuous audio, video, and text processing, with innovations like visual proactivity and background tool use, implemented using SGLang. Meanwhile, OpenAI announced the OpenAI Deployment Company, a new unit with 150 Forward Deployed Engineers and $4B initial investment to help enterprises deploy frontier models, signaling a move into the deployment layer of the AI economy. OpenAI also launched Daybreak, a security-focused initiative integrating GPT-5.5 and Codex for cyber defense, threat modeling, and automated patching, offering differentiated access tiers including GPT-5.5-Cyber. This contrasts with Anthropic's more restrictive cyber approach, highlighting tensions in AI security strategies.
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nemotron-nano-2 gpt-oss-120b qwen3 llama-3 minimax-m2 glm-4.6-air gemini-2.5-flash gpt-5.1-mini tahoe-x1 vllm_project nvidia mistral-ai baseten huggingface thinking-machines deeplearningai pytorch arena yupp-ai zhipu-ai scaling01 stanford transformer-architecture model-optimization inference distributed-training multi-gpu-support performance-optimization agents observability model-evaluation reinforcement-learning model-provenance statistical-testing foundation-models cancer-biology model-fine-tuning swyx dvilasuero _lewtun clementdelangue zephyr_z9 skylermiao7 teortaxestex nalidoust
vLLM announced support for NVIDIA Nemotron Nano 2, featuring a hybrid Transformer–Mamba design and tunable "thinking budget" enabling up to 6× faster token generation. Mistral AI Studio launched a production platform for agents with deep observability. Baseten reported high throughput (650 TPS) for GPT-OSS 120B on NVIDIA hardware. Hugging Face InspectAI added inference provider integration for cross-provider evaluation. Thinking Machines Tinker abstracts distributed fine-tuning for open-weight LLMs like Qwen3 and Llama 3. In China, MiniMax M2 shows competitive performance with top models and is optimized for agents and coding, while Zhipu GLM-4.6-Air focuses on reliability and scaling for coding tasks. Rumors suggest Gemini 2.5 Flash may be a >500B parameter MoE model, and a possible GPT-5.1 mini reference appeared. Outside LLMs, Tahoe-x1 (3B) foundation model achieved SOTA in cancer cell biology benchmarks. Research from Stanford introduces a method to detect model provenance via training-order "palimpsest" with strong statistical guarantees.
Thinking Machines' Tinker: LoRA based LLM fine-tuning API
qwen-235b-a22b sora-2 thinking-machines openai fine-tuning lora model-training api model-optimization distributed-training post-training-methods research-productivity video-generation content-moderation engagement-patterns karpathy lilianweng sama
Thinking Machines recently raised $2 billion without shipping a product until now, launching their first product Tinker, a managed service API for fine-tuning large and mixture-of-experts models like Qwen-235B-A22B using LoRA for cost-efficient training. The Tinker API offers low-level primitives for post-training methods and is supported by an open-source Tinker Cookbook library. Influential AI figures like Andrej Karpathy and Lilian Weng praised its design for reducing complexity and boosting research productivity. Meanwhile, OpenAI launched Sora 2, a video+audio model integrated into their consumer social app, sparking viral engagement and concerns over misuse and content moderation. Sam Altman emphasized the product's dual focus on delight and revenue alongside AGI research.
X.ai Grok 3 and Mira Murati's Thinking Machines
grok-3 grok-3-mini gemini-2-pro gpt-4o o3-mini-high o1 deepseek-r1 anthropic openai thinking-machines benchmarking reasoning reinforcement-learning coding multimodality safety alignment research-publishing model-performance creative-ai mira-murati lmarena_ai karpathy omarsar0 ibab arankomatsuzaki iscienceluvr scaling01
Grok 3 has launched with mixed opinions but strong benchmark performance, notably outperforming models like Gemini 2 Pro and GPT-4o. The Grok-3 mini variant shows competitive and sometimes superior capabilities, especially in reasoning and coding, with reinforcement learning playing a key role. Mira Murati has publicly shared her post-OpenAI plan, founding the frontier lab Thinking Machines, focusing on collaborative, personalizable AI, multimodality, and empirical safety and alignment research, reminiscent of Anthropic's approach.