Vol. I · No. 199WED, NOV 4, 2026
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A week with RTX Spark: everything we’ve learned using Nvidia’s new laptops

Nvidia’s new SoC is available in Windows laptops from six different manufacturers. We’ve collectively tested four of them so far. | Photo: Antonio G. Di Benedetto / The Verge Nvidia's RTX Spark N1X processor is finally here, and we've been collectively testing a small fortune's worth of powerful laptops sporting this new "superchip." RTX Spark is designed to be a high-performing Arm-based SoC for laptops and small desktop computers, rivaling the best from Apple, Intel, AMD, and Qualcomm. It has loads of cores, Nvidia RTX Blackwell graphics, and enough power and unified memory to run sizable l...

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How Windows is being optimized for Nvidia’s RTX Spark laptops

Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a lively hands-on area. A day after the media event, I was invited to a quieter Nvidia event to hear a lot more about the new RTX Spark computing platform. While Nvidia's new laptops might look like regular Windows machines, the chips inside are Arm-based, instead of the traditional x64 architecture used by AMD and Intel. Combined with a powerful GPU, RTX Spark is designed for the future of AI, and it means a lot of w...

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AMD is acquiring AI company World Labs in a deal worth more than $8 billion

World Labs co-founder and CEO Fei-Fei Li. | Bloomberg via Getty Images AMD announced today that it's acquiring World Labs, an AI research lab co-founded by the prominent researcher Dr. Fei-Fei Li, in an all-stock deal worth approximately $8.2 billion. World Labs launched in 2024 and was valued at $1 billion in a matter of months. The startup launched its first commercial product, a world generation model called Marble that lets users make interactive 3D worlds from a prompt, in 2025. The deal is expected to close by the end of the year. Li will become EVP and chief scientist at AMD, reporting...

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NYC bans AI use for students until they reach high school

New York City mayor Zohran Mamdani has announced a new policy today that will ban younger schoolchildren from using AI in classrooms. The one-year moratorium, effective in the 2026-2027 school year, will impact about 600,000 public school students in 2-K through eighth grade and is being introduced alongside additional limits on digital devices and a small pilot program for AI education tools. The new rules being introduced by NYC's Department of Education will bar teachers from using AI tools to grade assignments, and keep students from using the technology in the classroom until they enter ...

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AMD’s datacenter business is booming while gaming takes a backseat

Driven by demand for AI capacity, AMD's data center revenue more than doubled year-over-year in its latest earnings report, reaching $6.7 billion. That's up from $5.8 billion in Q1, and jumping 107 percent from the $3.2 billion it reported for the same period a year ago. At the same time, AMD's gaming revenue fell 31 percent compared to last year, to $779 million, as price hikes and component shortages slowed sales for the Xbox Series X / S, PS5, and Valve's Steam Deck. "Revenue increased 50 percent year-over-year to a record $11.5 billion, driven by continued strength in our Data Center busi...

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AMD and Anthropic reach $5 billion AI infrastructure deal

AMD says it's going to invest up to $5 billion in Anthropic, while helping to expand the AI company's computing power, according to an announcement on Wednesday. As part of the new partnership, Anthropic will deploy up to 2 gigawatts of AMD's Instinct MI450 AI GPUs using the chipmaker's new Helios rack-scale system, as reported earlier by The Wall Street Journal. The companies plan to deploy the first gigawatt in the first half of 2027, building upon recent data center deals Anthropic has reached with SpaceX and TeraWulf. Anthropic has also signed AI infrastructure deals with Google, Broadcom...

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Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes

In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot estimators, which makes their training sample inefficient. Though in most PAMDP environments explicit but incomplete knowledge (e.g., rules, safety constraints, or expert heuristics) is available, it is rarely directly used to increase the sample-efficiency of training Reinforcement Learning agents. We step into this gap...

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Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe

FP4 training promises substantial reductions in memory and computation cost for LLM pretraining, yet current FP4 hardware paths and recipes, including NVIDIA Blackwell/Rubin-class systems and AMD MI350-series GPUs, remain centered on E2M1 data elements. In this study, we identify a fundamental limitation of that choice: non-uniform formats such as E2M1 inherently suffer from Shrinkage Bias, a systematic negative rounding error caused by the geometric asymmetry of their representable bins. We show that this bias accumulates multiplicatively across layers and is amplified by the Random Hadamard...

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