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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 […]
What Would Have to Be True for Agentic Coding to Replace Junior Engineers Read More »
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
Model cards report quality under server-class, full-precision conditions. Those numbers rarely predict how the same model behaves on a phone. This week, Liquid AI released Pipette. It is an open-source platform for benchmarking foundation models on edge devices, built in partnership with Artificial Analysis as an independent methodology validator. Pipette treats on-device behavior as a
Perplexity has released Portable Computer, a local-first build of its agentic Computer platform that runs the agent harness, orchestrator, planner, tool router and post-trained models directly on NVIDIA DGX Spark. The local model, inference engine, tool sandbox and app connectors ship as one packaged system, every task begins on the device, and work handled by
Training and serving frontier models is now a networking problem as much as a compute problem. Collective operations like all-reduce and all-to-all synchronize thousands of accelerators during training, and the slowest transfer sets the pace for the entire job. Even small amounts of network friction directly strand significant compute capacity. This week, Meta introduced MetaRoCE.
Meta AI Introduces MetaRoCE: A Clean-Sheet RDMA Transport Built for AI-Scale Ethernet Read More »
Information extraction teams face a recurring choice. Small encoder models are cheap but rigid, and large language models are flexible but expensive per document. Fastino released GLiNER2.5 to narrow that gap. The release replaces span enumeration with boundary prediction: the model scores where an entity starts and ends instead of scoring every candidate span against
Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59%
A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs), a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is, but not how it is used. Two coffee shops can share a category, an address block, and a text vector, while
Harvey has released Harvey Tenet, its first post-trained model, as a research preview as of today. Tenet is a Kimi K3 base post-trained with Fireworks through asynchronous reinforcement learning on long-horizon legal work. The training corpus combined synthetic data, publicly available legal data, and human expert data. Harvey states no customer data was used. Against
Frontier open-weight models are shipping faster than the hardware assumptions around them. Kimi-K3, GLM-5.2 and DeepSeek-V4-Flash are closing the capability gap with proprietary systems, but releasing parameters only determines who can obtain a model — not who can afford to run it. Serving them still assumes datacenter-class GPU clusters, and as agentic workloads push inference
