The Stack - SiliconNoon (Page 12)

The Stack

MLPerf Storage starts measuring inference, not just training

Two releases this week attack the same blind spot: almost everything we can measure about AI infrastructure was built for training, and almost all the money is now being spent on inference. MLCommons shipped MLPerf Storage v3.0, and the headline is that the suite now tests inference workloads. Alongside the training and checkpointing tests it has run for years, v3.0 adds a KV cache test that measures storage performance for the read/write churn of LLM inference caching, and a vector database te

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GitHub's HydraFusion routes between models to match Opus 5 at 67% less cost

GitHub is shipping a new way of building coding agents: instead of picking one model and hoping it holds, its Copilot runtime now drafts, critiques, and escalates between models on the fly. The first benchmark numbers suggest the orchestration is doing real work, not just splitting the bill. GitHub's Project HydraFusion matches Opus 5 on agentic coding at a fraction of the cost. The research preview, live in Copilot CLI today, treats workflow selection as an optimization problem. For each reque

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AI 101 — What is speculative decoding?

If you've ever wondered why an LLM feels slow to type out long answers — or why Nvidia's engineers spent a September 2026 blog post telling model designers how many attention groups to use — the answer is a technique called speculative decoding. Speculative decoding is an inference speedup where a small, fast "draft" model guesses several upcoming tokens, and the big target model verifies them all in a single pass instead of generating one token at a time. The output is bit-identical to what th

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Lenovo puts a 120-billion-parameter model on a laptop with Nvidia's RTX Spark

Lenovo unveiled the first laptops built on Nvidia's RTX Spark platform at IFA 2026 in Berlin, and a Chinese robotics lab showed how to keep training a policy while the robot is still working — two stories that point at the same shift: AI is moving off the cloud and onto the box under your hand. Lenovo and Nvidia put a 120-billion-parameter model inside a laptop. At IFA 2026, Lenovo announced the Yoga Pro 9n and the Yoga 9n 2-in-1 — the first notebooks built on Nvidia's RTX Spark silicon, a syst

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Cloudflare turns OpenAI's cyber model into an edge patch shop

OpenAI's Daybreak models now sit inside two very different products that shipped within hours of each other. Cloudflare is putting them to work patching vulnerabilities at the network edge; Proofpoint is using them to run investigations. Neither one is allowed to act alone. Cloudflare launched Vulnerability Discovery and Remediation, a service that pairs OpenAI's Daybreak cyber models — including GPT-5.6 Cyber — with traffic from its own network to find, block, and draft fixes for code flaws. T

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17,000 agent runs: coding agents disagree on tools 58% of the time

When a coding agent decides your app needs a database, a payment processor or an email service, it usually picks one and installs it without asking. A new study set out to learn which vendor wins that moment — and the honest answer is that no one reliably does. Armature ran 16,893 sessions across Claude Code, Codex and Cursor, and the three agents chose the same tool in only 42% of cases. The setup is more careful than most vendor research: 75 synthetic repositories across 10 languages with rea

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Nvidia's free tool turns idle PCs into one local AI cluster

Two of today's stories point the same direction — the work is moving off someone else's server. Nvidia wants the spare machines on your network doing it, Google wants you talking to your inbox instead of typing at it, and a separate team quietly finished the largest brain map ever assembled. Nvidia released PAIR, a free tool that turns the computers you already own into one shared local AI machine. Announced at IFA and published as open-source software, the Personal AI Router scans your home ne

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ChatGPT, Grok, and Claude all went down within 90 minutes

Three of the biggest consumer AI products broke on the same morning, and nobody could say why. Between 9:30 and 11AM ET, Grok, Claude, and ChatGPT all started throwing errors — a rare simultaneous failure across rival labs that share almost no infrastructure. Grok went first at 9:30AM ET, Claude followed at 10:30, and ChatGPT at 11:00. OpenAI's status page reported "elevated errors across ChatGPT and Codex," with logins, file uploads, voice mode, search, deep research, and image generation all

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Open Source Radar — September 3: the agent's paper trail

Today's board is about the parts nobody demos: where an agent's reasoning gets stored, how a PDF becomes context, where plugins come from, and whether your dev server has a name your agent can remember. Five fresh picks, none of them repeats from this week's earlier radars. Atlas (Rust, ~3,100 stars) — Source control built for the fact that agents now write a large share of the code and keep none of the reasoning. Every session is recorded locally, and when you commit, the commit is linked back

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Open Source Radar — September 3: hands, voice, and models

Today's daily board is all about the gear bolted onto an agent that already writes code: a real browser it can drive and debug, a skill that scrubs the machine-tone out of its prose, a forecasting model that reads time series, and one cluster that serves every model the agent calls. Four fresh picks, none of them repeats from this week's earlier radars. chrome-devtools-mcp (TypeScript, ~50,600 stars) — Google's official Chrome DevTools server for coding agents, and the clearest sign yet that th

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AI 101 — Local LLMs vs cloud APIs: what's the difference?

A local LLM is an AI model that runs on hardware you own — the actual math happens on your laptop, phone, or server. A cloud API is the same kind of model running on someone else's machines, which you reach over the internet and pay for by the token. Same technology, two very different deals about privacy, cost, and control. Why the question is live right now For most of the last three years, "using AI" meant calling an API. The model lived on a provider's servers; you sent a prompt, got an a

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AI 101 — What is a token in AI?

A token is the chunk of text an AI model actually reads and writes — not a word, not a character, but a piece of a word: "un", "believ", "able". Every language model, from the smallest phone-side model to the biggest frontier system, does its work by predicting one token after another. If you understand tokens, you understand why AI bills look the way they do, why some languages cost more than others, and why a model can count the letters in "strawberry" and still get it wrong. It is also the s

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