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A collection of 20 posts

JUPITER Shows Exascale Science Becoming a Production Workflow

JUPITER Shows Exascale Science Becoming a Production Workflow

Exascale machines used to be covered like trophy cabinets: biggest, fastest, most national-pride-per-rack. JUPITER is more interesting if you ignore the trophy and look at the workloads. Europe’s first exascale supercomputer is being used to train cellular-scale brain models, run kilometer-scale climate simulation, explore 5G and 6G systems, and

Cannes Lions Shows NVIDIA Turning Marketing AI Into a Systems Problem

Cannes Lions Shows NVIDIA Turning Marketing AI Into a Systems Problem

Cannes Lions is not where engineers usually go looking for serious systems architecture. That is fair. The conference packaging is glossy enough to make any infrastructure person reach for the mute button. But NVIDIA’s advertising and marketing AI roundup is more interesting than the setting suggests because adtech has

NemoClaw + Hermes Is the Interesting Agent Demo Because the Policy Is Code, Not Vibes

NemoClaw + Hermes Is the Interesting Agent Demo Because the Policy Is Code, Not Vibes

The most important part of NVIDIA’s latest agent demo is not the agent. That sounds like a cheap trick, but it is the cleanest read on NVIDIA’s new developer walkthrough for running Nous Research’s Hermes Agent inside NemoClaw and OpenShell. The demo can read Outlook or Slack,

Nemotron 3 Is NVIDIA’s Open-Agent Stack Pitch, Not Just Another Leaderboard Slide

Nemotron 3 Is NVIDIA’s Open-Agent Stack Pitch, Not Just Another Leaderboard Slide

NVIDIA’s Nemotron 3 launch is not really a model announcement. It is NVIDIA making a very explicit bet that the next useful AI stack is a routing stack: small specialists for cheap work, stronger models for tool-heavy orchestration, and an expensive escalation tier for the problems that still deserve

NVIDIA’s Agent Toolkit Is a Bet That Enterprise Agents Need Harnesses More Than Another Chat UI

NVIDIA’s Agent Toolkit Is a Bet That Enterprise Agents Need Harnesses More Than Another Chat UI

NVIDIA’s newest agent announcement is not really about another chatbot, another workflow canvas, or another model leaderboard entry. It is about the thing enterprise AI keeps rediscovering the hard way: a model does not become an agent because it can call a tool. A model becomes an enterprise agent

NVIDIA’s Model Card Generator Makes AI Governance Look Like a Build Artifact

NVIDIA’s Model Card Generator Makes AI Governance Look Like a Build Artifact

AI governance keeps failing in the least dramatic place possible: the repo. Not the policy memo, not the executive risk committee, not the glossy “responsible AI” page. The real failure mode is that a model ships with unclear training-data notes, stale evaluation claims, vague limitations, missing license context, and a

NVIDIA’s Quant Agent Is Really a Blueprint for Verifiable Agent Loops

NVIDIA’s Quant Agent Is Really a Blueprint for Verifiable Agent Loops

The most interesting thing about NVIDIA’s new quantitative-finance agent is not that it tries to discover alpha. Every vendor demo eventually wanders into Wall Street cosplay. The useful part is that NVIDIA shows an agent loop where model creativity is boxed in by a formal operator library, structured outputs,

Google Cloud and NVIDIA’s 100K-Builder Milestone Is Really an Inference-Onramp Story

Google Cloud and NVIDIA’s 100K-Builder Milestone Is Really an Inference-Onramp Story

The least interesting number in NVIDIA and Google Cloud’s latest announcement is the one in the headline. Yes, the joint Google Cloud x NVIDIA developer community has crossed 100,000 members a year after launch. That is a healthy funnel. But the practitioner story is not community growth; it

Dell and NVIDIA Are Turning the AI Factory Into an Agent Runtime, Not Just a GPU Rack

Dell and NVIDIA Are Turning the AI Factory Into an Agent Runtime, Not Just a GPU Rack

The useful part of Dell and NVIDIA’s latest AI Factory pitch is not the rack glamour. It is the admission hiding under the keynote language: enterprise agents are becoming a runtime problem. For the last two years, “AI infrastructure” mostly meant “more GPUs, preferably yesterday.” That was not wrong,

Nemotron Omni on Thor Is Where Local Multimodal AI Gets Real — And Fragile

Nemotron Omni on Thor Is Where Local Multimodal AI Gets Real — And Fragile

Nemotron Omni on Jetson Thor is a better story when it fails in public. The clean version is easy: NVIDIA has a 31B-parameter multimodal model, quantized to NVFP4, with about 3B active parameters per token, supported on Jetson Thor, DGX Spark, RTX 5090-class hardware, and the usual Blackwell ecosystem. It