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Nvidia Board Investigates Jensen Huang Over Alleged Chinese Spy Link

Nvidia Board Investigates Jensen Huang Over Alleged Chinese Spy Link

Big Tech

Nvidia’s board opened an independent probe after CEO Jensen Huang confirmed a relationship with a Megaspeed executive accused of spying for China. What we know.

Nvidia’s board has opened an independent investigation into CEO Jensen Huang after he confirmed a personal relationship with an executive at a Singapore chip buyer who has been accused, in reports, of working for Chinese intelligence. The board said on November 8, 2026 that it has “no reason to believe” national security or company secrets were compromised, and Huang will keep running the company during the review.

What is Nvidia investigating?

On October 29, The New York Times reported that a woman working for Megaspeed, a Singapore-based company, tried to build close ties with Huang to learn about Nvidia’s technology. A memo making the same claim had circulated among US policymakers earlier this year, according to TechCrunch. These are allegations, not proven facts, and the Chinese Embassy in Washington has denied any spying.

On Sunday, Huang sent an email to staff admitting the relationship and apologising to his family and to the company. Hours later the board announced a review led by a committee of independent directors working with outside lawyers.

What did the board actually say?

Three things. It has no reason to believe secrets were compromised, but will examine all available information. Huang “fully supports the review” and will cooperate. And the board still has confidence in Huang and his leadership team. Nvidia had earlier described the pair’s interactions as “not unusual” and said it does not share classified information with customers.

Why does Megaspeed matter?

Because of its size as a customer. The Times reported that Megaspeed became one of Nvidia’s largest buyers, purchasing about 4.8 billion dollars of equipment. Any link between a top chip buyer and a foreign intelligence service is exactly what US export rules are meant to stop, since advanced AI chips are treated as strategic technology. If you are new to why these chips are so tightly controlled, our guide to the AI chip supply chain explains it simply.

How has Congress reacted?

With rare bipartisan pressure. Democratic Senator Elizabeth Warren said Huang and the board “owe Congress answers” and called for passing bipartisan legislation on advanced chip sales to China. Republican Representatives John Moolenaar, Brian Mast and Rick Crawford asked Huang to testify before Congress on November 18 to establish whether US national security was put at risk.

What does this mean for Nvidia and the chip market?

It lands at a bad moment. Nvidia shares were already under pressure after the post-midterm AI selloff, when the company lost about 450 billion dollars of market value in a single day before recovering some ground on Friday. The bigger question is policy: the US has spent years debating how many advanced chips Nvidia may sell to China, and a probe like this gives export hawks fresh ammunition. Nvidia is also mid-ramp on its Vera Rubin chips, so investors will watch for any distraction at the top.

What happens next?

Watch three dates and signals: whether Huang appears before Congress on November 18, what the independent directors conclude, and whether lawmakers use the case to push new limits on chip exports. Until the review reports, the board’s position is that nothing sensitive leaked.

Up Next
8 Viral Open-Source AI Projects Taking Over GitHub This Week

8 Viral Open-Source AI Projects Taking Over GitHub This Week

Coding Assistants

The open-source AI projects trending on GitHub in November 2026: agent-desktop, colibri, kimi-k3-in-c, gstack, claude-mem, OpenMontage, spec-kit and NVIDIA auto-ontology, with verified star counts.

The open-source AI projects going viral on GitHub in early November 2026 have one thing in common: they make AI agents do more on hardware you already own. One lets agents operate any desktop app, two run trillion-parameter models on an ordinary laptop, and several turn a single coding assistant into a whole team. Here are the eight worth knowing, with star counts checked directly on GitHub on November 8.

1. agent-desktop: AI agents that can use any app

vercel-labs/agent-desktop • Rust • Apache-2.0 • about 19,700 stars

The fastest climber of the week, adding thousands of stars a day. It gives AI agents "computer use" on macOS, Windows and Linux without screenshots. Instead of looking at pixels, it reads each app’s accessibility tree, the hidden map of buttons and fields that screen readers use, and hands it to the agent as a list it can click or type into. That is faster and more reliable than screenshot-based approaches. Built by Vercel Labs. Its own README warns that snapshots can capture private desktop content, so keep them out of public logs.

2. colibri: huge models on a normal computer

JustVugg/colibri • C • Apache-2.0 • about 49,100 stars

Big "mixture-of-experts" models only use a small slice of themselves for each word they write. Colibri keeps that active slice in memory and streams the rest from your SSD as needed. The result: models with hundreds of billions of parameters, including GLM-5.3, Kimi K3, DeepSeek V4.1 Flash and Mistral Large 4, running on a machine with as little as 8 GB of RAM, faster on a GPU. Pure C, no dependencies.

3. kimi-k3-in-c: a 2.78-trillion-parameter model on a CPU

FareedKhan-dev/kimi-k3-in-c • C99 • Apache-2.0 • about 11,300 stars

The headline stunt: Moonshot’s Kimi K3, one of the largest open models, running on a single CPU in 8.24 GB of RAM, with no GPU and no libraries. Read the fine print, though. The model still needs a 1.56 TB file on disk, and on an 8 GB laptop it reportedly takes around 26 seconds per token. It is a brilliant proof of what is possible, not a practical chatbot.

4. gstack: Claude Code as a full company

garrytan/gstack • TypeScript • MIT • about 140,800 stars

Y Combinator CEO Garry Tan’s personal Claude Code setup: 23 slash-command roles such as CEO, designer, engineering manager, reviewer, QA and release manager. Each one is a structured prompt and toolset, so you can ask your coding agent to "review this as a security engineer" and get a consistent process. It installs into other coding agents too, but Claude Code is the only fully supported one.

5. claude-mem: memory that carries between sessions

thedotmack/claude-mem • TypeScript • Apache-2.0 • about 114,900 stars

Coding agents forget everything when a session ends. claude-mem records what your agent did, compresses it with AI, and feeds the relevant bits back next time, so you stop re-explaining your project every morning. Despite the name, it now works with other agents too.

6. OpenMontage: a video studio run by agents

calesthio/OpenMontage • Python • AGPL-3.0 • about 78,700 stars

Turns your coding assistant into a video production team. You describe the video in plain language and the agent handles research, script, assets, editing and the final render, using generators like Veo and Kling or free stock footage. Note the AGPL licence, which has strict sharing rules if you build a commercial service on it.

7. spec-kit: GitHub’s official way to brief AI agents

github/spec-kit • Python • MIT • about 148,700 stars

GitHub’s own toolkit for "spec-driven development": write a clear specification first, then let the agent build from it. It works with Copilot, Claude Code, Cursor, Codex and others, and is trending again as teams try to make agent output more predictable.

8. auto-ontology: NVIDIA’s fix for AI guessing at your data

NVIDIA/auto-ontology • Python • Apache-2.0 • about 10,400 stars

Asking an AI questions about company databases often produces confident wrong answers. NVIDIA’s framework builds a map of what your data means, then generates queries from that map so answers are repeatable and traceable to their source. It is aimed at enterprises and uses NVIDIA’s Nemotron models by default.

What do these trends tell us?

Three things. Agents are moving off the browser and onto the whole computer. Running giant models locally is becoming realistic, which matters for privacy and cost and adds to the shift towards open models. And developers want structure, not just raw power: roles, specs and memory that make agents behave the same way twice.

Are viral GitHub projects safe to install?

Not automatically. Stars measure popularity, not security. October showed how attackers target exactly these tools, from a fake Claude Code extension to a poisoned LiteLLM release. Install from the official repository, check who maintains it, read what permissions it asks for, and be extra careful with anything that controls your desktop or reads your files.