According to soumithchintala, Thinking Machines proposes staged access for Inkling to align open weights with safety, per their blog and X post.

According to soumithchintala, Inkling-Small matches Inkling quality at 4x smaller with 276B params, 12B active, and open weights for fine-tuning.

According to The Rundown AI, OpenAI launches a $230 agent control pad as Thinking Machines opens its first model and Weco’s agent self-improves.

According to TheRundownAI, Thinking Machines launched Inkling, an open-weight multimodal model with 1M tokens and full weights on Hugging Face.

According to @soumithchintala, Thinking Machines pushes personalization, human-in-the-loop, and decentralization to cut reliance on centralized AGI.

According to soumithchintala, Bridgewater’s fine-tuned model ranks financial news better and cheaper than frontier LLMs, per Tinker and Thinking Machines.

According to @soumithchintala, Thinking Machines is hiring supercomputing engineers for real time models, Tinker, and large scale training in NYC and SF.

According to Soumith Chintala on X, Workshop Labs is joining Thinking Machines to pursue AI that amplifies humans rather than replaces them, with links to the official announcement and blog post for confirmation. As reported by Workshop Labs’ blog, the team frames the deal around building human-in-the-loop systems that elevate expert workflows, indicating a product focus on copilots and decision-support tools for enterprises. According to the Thinking Machines announcement posted via X, new hires including Luke Drago and LRudL will contribute to applied AI capabilities, suggesting near-term offerings in data-driven copilots, retrieval-augmented generation, and workflow orchestration for knowledge-heavy teams. For businesses, the move signals opportunities to deploy AI copilots that integrate with existing data stacks, reduce time-to-insight, and maintain human oversight—key for regulated sectors such as finance, healthcare, and public services, as stated in the Workshop Labs blog post and associated X thread.

According to soumithchintala on X, Thinking Machines has partnered with NVIDIA to bring up 1GW or more of compute starting with the Vera Rubin cluster, co-design systems and architectures for frontier model training, and deliver customizable AI platforms; NVIDIA has also made a significant investment in Thinking Machines (as reported by the official Thinking Machines announcement at thinkingmachines.ai/news/nvidia-partnership/). According to Thinking Machines, the collaboration targets large-scale training efficiency and verticalized AI deployment, indicating near-term opportunities in AI infrastructure provisioning, GPU-accelerated training services, and enterprise model customization.