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This week, Adaption Labs released Invent a Dataset. The feature generates a structured, training-ready dataset from a description of the behavior you want a model to learn. You do not arrive with a seed corpus, a predefined schema, or a labeling guide. Is it deployable? Yes, with one caveat. Invent a Dataset is live now […]
Video has been the most expensive modality to reason over. A Gemini model handed a 90-minute lecture has, until now, ingested the whole thing at a fixed one frame per second, whether the question was ‘summarize this’ or ‘what time does the speaker switch to the pricing slide?’ That single-pass design forces a bad trade:
Multi-agent workflows have changed the shape of local inference. A lead agent decomposes a task and spawns subagents. What looked like one user request becomes dozens of independent model calls. Pointed at a single local engine, those calls compete for the same execution slots. The queue grows while a workstation, laptop, or DGX Spark on
AI weather models have spent three years closing the gap with physics-based forecasting, but two problems stayed open: resolution too coarse for local terrain, and initialization tied to numerical weather prediction (NWP) analysis that arrives about six hours late. WeatherNext 3, released by Google DeepMind and Google Research, attacks both. It takes a live global
Today, OpenAI released GPT-6 Astra. The company calls it its most intelligent and aligned model, and positions it primarily as a computer-use system rather than a chat model. The pitch is that Astra operates software the way a person does, across browsers, spreadsheets, desktop applications and terminals, and finishes multi-step jobs instead of describing how
Most teams building a shopping assistant or agent rebuild the same scaffolding: an agent loop, a tool layer over the catalog, an approval gate, and an eval suite. Anthropic has now released that scaffolding as code. This week, they published anthropics/commerce-agents, a reference blueprint containing a shopping agent and a merchant agent, along with four
This week, Meta Superintelligence Labs released Muse Spark 1.3. It is the fourth Muse Spark release in five months, and the target is long-horizon agentic and coding work rather than single-turn generation. The framing in Meta’s post is usability: sustaining a long thread, collaborating with the user, and knowing when it is stuck. Is it
Perplexity has open sourced Lily, the local inference engine behind Hybrid Compute in Perplexity Computer. It is a single-process runtime: a Rust layer loads the checkpoint and drives the generation loop, an OpenAI-compatible chat-completions API streams tokens, and hand-written Metal kernels execute the model. Neither PyTorch nor MLX sits in the execution path. Lily is
Coding agents spend a large share of their tool budget on search. When the target is a known symbol, ripgrep answers it exactly. When the target is a behavior described in plain language, keyword matching often misses, and the agent falls back to guessing terms, reading whole files, and assembling context by hand. Each of
Teams running coding agents hit the same wall. Claude Code speaks the Anthropic Messages API, Codex CLI speaks OpenAI, and the model a team actually wants to serve sits behind vLLM, NVIDIA NIM, or Ollama. Rewriting the agent is not an option, so the translation layer has to live somewhere else. Switchyard is NVIDIA’s answer:
