world's first robot-made (and linked) sweaters (not those toys machines knit as one piece). 100% American-made and the start of knitwear manufacturing onshore!

A 35-year-old Saudi national employed by OpenAI, Abdulrahman Alfozan, has been arrested for allegedly installing a covert audio recording device at the company’s San Francisco headquarters, officials say.

BREAKING: OpenAI engineering lead arrested by the FBI after hiding a covert audio recorder inside an executive conference room at OpenAI HQ Abdulrahman Alfozan is 35, MIT-educated, ex-Meta, ex-Nash and reportedly their only Saudi employee, has been terminated, first federal court appearance today.

One slight modification.

Delta is now in public beta. It’s a multiplayer environment for coding with agents and reviewing what they build, with every code change linked to the conversation that produced it. Anyone can download it today on macOS, Linux, and Windows: delta.dev/download

Quick reminder: this was the state of robotics just 11 years ago

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on @huggingface, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...

Today we’re introducing Delta. Delta is a multiplayer environment for coding with agents and reviewing what they build, powered by DeltaDB. The first invites to our private beta are going out now. zed.dev/blog/introducing-del…

テキストや図面でAIエージェントと対話するだけで、台北101の詳細な構造モデルをつくり、パラメータまで調整できる？ これはPlamoで実際に行ったVibe Modelingです。

As New York and New Jersey get ready to host the world’s biggest match, we recreated the stadium where history will be made. We uploaded an image of the iconic MetLife Stadium into Plamo and worked with the AI agent to build a fully editable, parametric 3D model. What iconic sports venue should we vibe model next?