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Our newest AI Quest: Mapping the human brain

Learn AI literacy via Google’s latest AI Quests lesson on neuroscience, “Polar Peaks.”

Marvell’s early chipmaking gamble pays off amid AI demand

Eight years ago, chipmaker Marvell Technology Inc. decided to play leapfrog with nanometers, and its trajectory has been steadily upward since. In semiconductor manufacturing, nanometer designations identify generations of chipmaking technology, known as process nodes, rather than the exact physical dimensions of transistors. Advanced process nodes generally enable greater transistor density and improvements in performance […] The post Marvell’s early chipmaking gamble pays off amid AI demand appeared first on SiliconANGLE.

Microsoft offers buyouts to 60,000 employees

Microsoft is offering voluntary buyouts to up to 60,000 employees across the US, UK, Canada, and India. In a message to employees, Microsoft chief people officer Amy Coleman writes that this gives staff members "the opportunity to leave Microsoft with financial support and career planning resources" as the company looks to trim staff without another […]

Anthropic files S-1 paperwork for proposed IPO

Artificial intelligence giant Anthropic PBC filed papers today for an initial public offering with the United States Securities and Exchange Commission, putting it on track for what will likely be the biggest stock market debut of an AI developer to come, amid warnings about advanced AI. In its filing, Anthropic said it plans to list […] The post Anthropic files S-1 paperwork for proposed IPO appeared first on SiliconANGLE.

Anthropic files S-1 paperwork for blockbuster AI IPO

Artificial intelligence giant Anthropic PBC filed papers today for an initial public offering with the U.S. Securities and Exchange Commission, putting it on track for what will likely be the biggest stock market debut of an AI developer to come, amid warnings about advanced AI. In its filing, Anthropic said it plans to list on […] The post Anthropic files S-1 paperwork for blockbuster AI IPO appeared first on SiliconANGLE.

Context Engineering (Agent Memory) for AI Agents Starts with Retrieval

Agents waste time and context on clumsy searches, oversized results and stale data. Learn the retrieval techniques LangChain's Deep Agents harness uses to get the right context in.

Context Engineering with Deep Agents: How To Remember Without Keeping Everything In Context

Long-running agents degrade as context piles up. Learn how offloading, summarization, skills, subagents, and memory keep your agent's context window focused.

Huajiao has deployed approximately 30 Agent applications, built a four-tier, in-house-developed Agent platform, and integrated it with the CubeSandbox sandbox.

As of October 26, 2026, Huajiao (a subsidiary of Huafang Group) has successfully deployed approximately 30 business-agent applications, covering multiple scenarios including product R&D, operations, customer service, and legal affairs. Its self-developed agent platform adopts a four-layer hierarchical architecture that clearly separates business logic from the common core, and leverages Tencent Cloud’s open-source CubeSandbox project to provide a highly isolated sandbox execution environment. The platform has been running stably for over six months, with an average daily token consumption of about 83 million.

Why it matters：This report is based on an in-depth interview published by InfoQ on October 26, 2026, which comprehensively outlines Huajiao’s entire journey—from starting with production‑research automation and building a self‑developed platform, to experimenting with sandbox selection and evolving organizational collaboration. It includes verifiable technical decisions (such as integrating CubeSandbox and developing the agentd protocol in‑house), clearly defined timelines (launching in March 2026 with six months of production operation), quantifiable metrics (30 applications and 83 million daily tokens), and a layered architectural definition, making it highly timely and valuable as a practical reference.

How NVIDIA Mission Control 2.5 Brings Autonomous Recovery to Kubernetes

Training large-scale AI models on thousands of GPUs is a high-stakes operation where monitoring and automation must be built-in to optimize cluster resiliency...

Anthropic’s IPO Prospectus Reveals What’s Under the Hood

Anthropic on Monday filed documents ahead of a blockbuster IPO that offers insight into the company’s financials, risk factors, and views on the future of AI.

Anthropic officially files for its public debut

Anthropic has officially filed for an IPO, revealing soaring revenue, mounting losses, major enterprise adoption, and an unusual governance structure designed to preserve founder control and its AI safety mission.

Having lost $80.7 billion in half a year, Anthropic has publicly filed its prospectus: What makes it worth $2 trillion?

If the income statement is shocking, then the commitments on the balance sheet are where the real story lies.

How Ramp's internal AI agents crossed a million sessions

Ramp shares the agents, sandboxes, and dashboards it built by treating internal AI as analytics engineering.

From mainframe to AI-powered maturity: Verisk's data modernization journey

Verisk modernized decades of insurance data, from mainframe to dbt, to get AI-ready.

How DocuSign cut analytics engineering time in half by pairing Claude Code with dbt v2, dbt MCP, and agent skills

DocuSign wired Claude Code, dbt Agent Skills, dbt MCP, and dbt v2 into one workflow and measured a 50% productivity gain.

From prompt to PR: how Careem’s AI writes and validates dbt models before a human ever sees them

Careem's AI agent writes and validates dbt models end to end before a human ever reviews the PR.

How a data scientist at Kilo built the business context layer his AI agents were missing

How a data scientist built an AI context layer for organizational knowledge a dbt project alone can't hold.

What a swarm of rogue AI agents can teach us about having better ideas

Benn Stancil uses the Hugging Face hack to explain why the most useful ideas come from the weirdest connections.

How to solve dashboard debugging in six minutes with an MCP-connected AI agent

An AI agent used dbt lineage from inside the BI layer to answer three data mysteries in the time it takes to get coffee.

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common: pin model resolution on real HF repo layouts (#30548) * tests: pin model resolution on real repo listings Extend test-model-resolution with 63 repo listings captured verbatim from the HF API, chosen to cover every file naming pattern found across 1237 GGUF repos of the major publishers and pruned to whole quant groups. Each repo pins its resolved plans and its cache listing. The expectations follow what each file really is per its GGUF header, not what the current resolver picks, so a mismatch is a resolution bug. Mismatches are collected and reported together before the test fails. * tests: leave the listing unchecked on paths the filesystem refuses Some real split names (DevQuasar's Model.Q2_K.gguf-00001-of-00012.gguf under a quant folder) exceed MAX_PATH once materialized in the