World Wire

Google Research and UNSW Sydney have released GlucoFM , a self-supervised foundation model for continuous glucose monitoring . Its core move is a split. Existing CGM models — CGMformer , GluFormer , CGM-JEPA — encode a glucose trace as one entangled sequence. GlucoFM decomposes it into a slow physiological “state” stream and a transient “event” stream, keeps the observation mask intact, and pretrains with two JEPA-style latent objectives. The result is a 0.72M-parameter encoder that reached 58.8 task-averaged PR-AUC across 14 cohort–task evaluations, against 54.7 for the strongest CGM-specific baseline retrained on the same corpus. It was pretrained on 109,066 hours of unlabeled CGM from 477 subjects, on a single H100. Is it deployable? As research infrastructure, yes. As a clinical or consumer product, not yet. The research team state it directly: GlucoFM is a research prototype, has not been cleared or approved by any regulatory authority, and is ...

I read every major model release. Most of them ship a coding number. The number goes up. The conclusion everyone draws is that junior engineers are finished. I think that conclusion is being reached the wrong way. People are reasoning from a benchmark score to a labor market outcome, skipping every step in between. So let me do it differently. Instead of asking “will agents replace juniors,” I want to ask what would have to be true for that to happen. Then check each condition against the best evidence available. There are four. Three of them are not met. The fourth is the one that should worry you, because it does not require the other three. Condition 1: Agents have to be reliable at the length of task a junior actually gets The best measurement we have here is METR’s time-horizon work. They time human experts on real software tasks, then find the task length at which a model succeeds 50% of the time. The main result is t...

IBM has released Granite 4.2 , a family of open reasoning language models in 3B, 8B, and 30B parameter sizes. Unlike earlier Granite releases, which were instruction-following assistants, Granite 4.2 is built around explicit reasoning. Every model can emit a chain of thought before answering, and every model exposes a thinking / non-thinking switch plus a low-effort mode that spends a short reasoning budget on easy questions. The models are decoder-only dense transformers, pre-trained from scratch on roughly 15 trillion tokens, then post-trained through a multi-stage reinforcement learning chain. For the 8B and 30B, that chain includes an agentic RL block where the model learns to edit code, drive a terminal, and run web searches inside real sandboxed environments. All three ship under Apache 2.0 . IBM also released two 470M-parameter Granite Speech 5.0 Turbo CTC models alongside the LLMs. Is it deployable? Yes, All three Granite 4.2 language models ship under Apac...