Thinking Machines Lab#28

Mira Murati@miramurati

Thinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world - through conversation, through sight, through the messy way we collaborate. We're excited that in the next couple months we’ll be able to share our first product, which will include a significant open source component and be useful for researchers and startups developing custom models. Soon, we’ll also share our best science to help the research community better understand frontier AI systems. To accelerate our progress, we’re happy to confirm that we’ve raised $2B led by a16z with participation from NVIDIA, Accel, ServiceNow, CISCO, AMD, Jane Street and more who share our mission. We’re always looking for extraordinary talent that learns by doing, turning research into useful things. We believe AI should serve as an extension of individual agency and, in the spirit of freedom, be distributed as widely and equitably as possible.  We hope this vision resonates with those who share our commitment to advancing the field. If so, join us. https://t.co/EaAKidpany

Shirin Ghaffary@shiringhaffary

NEW: Mira Murati's startup Thinking Machines Lab is in early talks to raise a new round of funding at a valuation of roughly $50 billion, more than 4x valuation from a few months ago. w/ @EdLudlow https://t.co/W67trhGKOE

Jukan @ICML@jukan05

The Information’s AI predictions for 2026: - Google will acquire Thinking Machines Lab - OpenAI will launch an automated AI research intern by September 2026, but that intern will fall short of expectations - One of the major AI labs will launch a $1,000-per-month AI agent - Google Gemini will catch up to OAI’s ChatGPT in weekly active users - A breakthrough in “continual learning” will cause Nvidia’s stock to crash Bookmark this tweet. I’ll open it again around next October.

Songlin Yang@SonglinYang4

Life update at the end of 2025: I’ve completed my PhD at @MITEECS and joined @thinkymachines to work on LLM archs

Kevin Patrick Murphy@sirbayes

I am pleased to announce another update to my RL tutorial (https://t.co/fnHAmyg1mH). This time I have added code for RLFT for multi-turn LLM agents, using the awesome Tinker library from @thinkymachines, and the simple ReBN training loop from GEM by @zzlccc et al. With ~100 lines of simple python running on your laptop, you can train an agent based on Qwen3-4B-Instruct to play "guess the number" in 20 minutes.

swyx @aiDotEngineer WF@swyx

I believe the kids call this "@thinkymachines just brutally framemogged gdm and oai". basically everyone's definition of "realtime" just got a massive frciking upgrade https://t.co/a5fzSV9Yec