Here I write about the most important and up-to-date topics in AI that I find interesting: new models, papers and tools, what they do and why they matter. Every post is published on several platforms, so read it on your favorite one.
OpenAI's GPT-6.1 Terra is priced at $1/$5 per million input/output tokens, down from $2/$12 for GPT-5.6 Terra. Independent tests from Artificial Analysis show higher scores and a much lower hallucination rate. The story covers what changed, which effort settings are actually cost-efficient, and why reliability now matters more than price when deciding whether to deploy computer-use agents.
NVIDIA's GEAR lab has open-sourced Flora, a set of models that watch human and robot video and label what happened and how well it went. The team says Flora matches Astra-level performance at 125x lower cost, labels 200K hours of video a day on 500 GPUs, and is used to train GR00T. The release points to data engines, not only policy architectures, as a lever for embodied AI.
With open models nearly at the top, the week's most discussed agent work was about sandboxes, offline training and verifiers. Open-weight models are closing in on the frontier rankings, so the week's attention moved to the layers that make agents trustworthy: isolated training environments, policy-enforced runtimes, formal verification and world-model checks on every action.