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

According to soumithchintala, ThinkyMachines targets personalization, human-in-the-loop, and decentralization to reduce AGI platform dependence.

According to TheRundownAI, TML and Bridgewater fine tuned an open model to 84.7% accuracy and 13.8x lower cost for news triage versus top frontier models.

According to soumithchintala, Bridgewater fine-tuned a model for financial news triage that outperforms frontier LLMs on cost and reliability.

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, four Qwen 3.5 models from Alibaba Qwen are now live on Tinker, introducing hybrid linear attention for extended context windows and native vision input support (source: Soumith Chintala; original post by Tinker and Alibaba Qwen). According to Tinker, this enables developers to deploy Qwen 3.5 variants for long-document reasoning and multimodal workflows with reduced memory overhead, improving inference efficiency and context handling for enterprise RAG, meeting transcription, and analytics use cases. As reported by Alibaba Qwen’s announcement referenced in the post, native vision input allows image understanding without extra wrappers, opening opportunities for e commerce visual search, industrial inspection, and content moderation pipelines. According to the cited posts, immediate availability on Tinker lowers integration friction for startups and enterprises seeking scalable long context LLMs with vision capabilities, supporting faster prototyping and cost efficient production deployment.