In April, Andrej Karpathy published a GitHub gist file called “LLM Wiki,” a brief text document designed to help one build a personal knowledge base using LLMs. It’s based on the premise that an AI agent will keep what it knows as linked Markdown files it can read and rewrite, because a language model does not get bored maintaining cross-references and can touch fifteen files in a single pass. It was only a few thousand words with no product attached.
Two months later, Google turned that instinct into a published standard called the Open Knowledge Format. The OKF packages organizational knowledge, metrics, tables, and runbooks as plain Markdown that any agent can read without a proprietary account. Google is careful to call it v0.1 — a starting point rather than a finished standard.
Garry Tan, the Y Combinator president, got there first in a different lane. His gstack, an MIT-licensed Claude Code setup that crossed 66,000 GitHub stars within weeks, comprises 23 specialist roles, each a Markdown file. No runtime; no code; just prose that runs across ten different coding agents.
Markdown has become the substrate agents read and write
Three approaches, three different needs, one common solution. Karpathy sought agent memory, Google aimed for enterprise context in BigQuery agents, and Tan wanted a way to summon an engineering team from a terminal. All three turned to the same basic resource: a folder of Markdown files versioned in git.
Developers had already established this practice. CLAUDE.md and AGENTS.md are present in millions of repositories as the initial files an agent loads. OKF and gstack are the evolved forms of this convention – one focused on what the agent knows, the other on how it behaves.
This is the Git and JSON playbook tied to the agent’s knowledge. The formats that survived are the ones you could start using without changing anything. You can simply cat the file, clone the repo, and any tool you already use can parse it. MCP remains important as the interface an agent connects to. Markdown is becoming the format that carries the content.
The lock-in moved from the model to the files
The significant factor to observe here is the competitive advantage, not technical specifics. For two years, the belief was that owning the best model meant controlling the developer.
This perspective is now shifting. Replacing Claude with GLM or Codex, gstack continues to operate because the core intelligence evolved, but the documentation did not.
The moat is shifting from the model to the Markdown a team owns and accumulates over time.
The moat is shifting from the model to the Markdown a team owns and accumulates over time. A company’s OKF bundle, including its runbooks, metric definitions, and architecture decisions, is, by design, portable across clouds, models, and frameworks.
That kind of portability is the reason vendor-neutral formats exist and why Google’s OKF deserves a closer look.
If no one develops consumers for it, it remains just a good idea that Google released on a slow Friday.
The area where I am most likely mistaken is durability. Declaring Markdown standards is easy, but making them reliable is difficult. OKF is merely a 0.1 draft with a reference implementation, not a full ecosystem. If no one develops consumers for it, it remains just a good idea that Google released on a slow Friday.
The direction remains determined by three separate bets targeting the same file format within a single quarter. Your next agent is likely to interpret its context from a Markdown folder, and the creator of that folder now possesses an advantage that the model vendor cannot easily replicate.
Researchers show that serving AI models with llm-d can boost inference speeds by up to 5 times and double throughput — all while using heterogeneous GPUs.
How much of Nvidia is reflected by their visionary CEO, Jenson Huang? With his praise and later support of OpenClaw, Huang took a big step beyond the corporate boundary to embrace the “bad boy” of the agent world. Where exactly does Nvidia fit in here?
The New Stack put that question to Nader Khalil, Director of Developer Technologies at Nvidia, as well as how Nvidia is working with developers on agentic AI projects.
Nader Khalil, Director of Developer Technologies at Nvidia
Khalil, co-founder of Brev.dev, found himself acquired by Nvidia about two years ago. His company helped startups access Nvidia AI chipsets. He is still excited by the possibilities of AI, and his energy is proof that Nvidia is enjoying the moment. Khalil was expansive, showing a startup’s keenness for the pace of change around them.
Before anything else, Khalil defines what he believes an agent is. “I have some slides,” he threatens. But these are more to organize his thoughts on an oft-asked question- not an attempt to lecture.
“An agent is an LLM and a harness… Each loop should take us closer to our goal.”
“An agent is an LLM and a harness. And if you think about that, it involves two things. It involves the loop and the LLM. And obviously you don’t want each loop to do the same thing. You want to leverage the results from the LLM. That might include reasoning on new tools to use. Each loop should take us closer to our goal.”
Nader praises the early OpenAI initiative. “So ChatGPT innovated outside of the model. It was not just a great model they made; they also added prompts. There was a system prompt and then the user prompt; there was multimodal, and suddenly that felt really good as a way for me to use the LLM. Every user could benefit from a system prompt that OpenAI had written while you were using your individual prompt.” Khalil continues, “Then they added memory.”
“Suddenly my assistant became really useful because it remembers things about me. ChatGPT knows that I really like to barbecue. So when I ask a question, it remembers what my smoker is,” recalls Khalil. “The thing that I was missing was files.”
Of course, the story continues through Cursor to Claude. “But this is the harness. Everything here is the harness,” he says.
Khalil moves on to how Nvidia works today. “The way to get your product into this rapidly growing market is with skills. Hence the CUDA X library.”
These are the implementations of use cases that target GPU acceleration, usually for compute-intensive applications.
“And so we look at every product we build now, it needs to have a skill because you need to cater to this growing audience,” he says.
This is how Nvidia first works with in-house experts, and connects to their edge hardware.
Supporting OpenClaw
Khalil was happy with the wording that Nvidia are “supporting” OpenClaw. “We’re just squarely in the community”, agrees Khalil. “We do this by the way, through a lot of projects that are very important in the open source ecosystem.”
But OpenClaw is not just any project and could be considered quite a risk to associate with. “We have a couple of developers at the company that contribute to OpenClaw full time.” Pushed on the nature of the relationship a little more, Khalil offers, “I think we just try to contribute wherever we can. I think what’s very clear is that harnesses had a moment, right?”
“We have a couple of developers at the company that contribute to OpenClaw full time.”
It has been quite a moment. “There is a lot of change happening right now, and we’re really thankful to [Peter Steinberger], OpenClaw, and the community for creating this moment around agents and harnesses. We of course want to contribute.”
Related to this, the OpenClaw project currently has many unresolved pull requests (PRs). In fact, there were rumors that new PRs are no longer accepted at all.
“We saw Peter tweeting about some of the issues they had, and we just rolled up our sleeves and were eager to help. They bless us by allowing our contributions.”
“You know,” says Khalil, “We saw Peter tweeting about some of the issues they had, and we just rolled up our sleeves and were eager to help. They bless us by allowing our contributions.”
Khalil reflects on things a little more: “You know the cardinal rule of code. It is easier to write than it is to read.”
And at over 800,000 lines of code, this must be true. Khalil continues, “It is easier not to have to process this complicated codebase, but every successful project right now has the same issue. It is easier to enlist many agents to help write code and build these PRs. The bottleneck is in merging the PRs through.” As well as dealing with the fallacies.
“OpenClaw was a major change for the industry. It was a huge moment, and everyone’s eyes are on it. It got more stars than Linux in months. Developers care deeply about the project because it was influential, and so I think you’re gonna see a mountain of PRs, right?”
“It got more stars than Linux in months… so I think you’re gonna see a mountain of PRs, right?”
Their attitude to OpenClaw is clearly to accept its problems, like that raucous friend who seems to wind up in police custody after a wild party, but is good at heart.
Blueprints and microwaves
Hermes is one of the newer projects in the wake of OpenClaw (like NanoClaw) that wants to bottle the lightning but in a safer way. Nvidia is also embracing it, but Khalil backs up to explain how Nvidia looks at projects in general.
“So, NemoClaw is our blueprint. When we see amazing harnesses, we try to figure out how we can help enterprises adopt them. Consumers sometimes want the security to run any agent; then there’s the model and harness. Then there are the skills, right. You have to give it access to your terminal.’
The term “blueprint” takes on a bit more formality in Nvidia, meaning the structure for building AI agents and systems. And of course Khalil needs to show these working with the Nemotron model and other Nvidia solutions.
“There’s a blueprint for Hermes and a blueprint for OpenClaw”. It sets up the runtime, enables the policies if there’s a local GPU, and runs the model.
Working with agents in the enterprise is seen as a significant risk. “There are a bunch of camps,” says Khalil. “There are teams within enterprises who are more worried. We have a project called OpenShell that is our security runtime and we’ll work with.”
“Our goal is to create the tooling that’s needed in the ecosystem. Developers in industry and enterprises have actually been adopting agents. And we have been building for this audience. One way to do so is to build a specialized agent or a sub-agent. “
So Nvidia doesn’t offer a big takeover solution, but fits in with where teams already are.
Your microwave, your agent
“The way to think about it is like when you use a microwave that you haven’t used before, you have to press a lot of buttons or spend time figuring it out. But when it’s your microwave at home, you just go ‘Boop, boop. Done.’ Right?”
“So every industry in enterprise will be building these specialized agents, and many already have. Nvidia is already working with CrowdStrike and Cadence, Palantir, among many others.”
The future will be agents
Khalil believes a lot of the concern over long-running agents is slowly petering out. Which leads to the final question: is Nvidia looking to stay in the open sea where there may be dragons, or become a calm port for developers to work in?
“So our approach is: Who can we help and how?” Khalil shows no fear, or lack of sea legs. “The inflection point happened months ago, so we ask what can we do to usher in all of this technology.” Here, Khalil ties his — and, to a degree, Nvidia’s — future to green-field developers.
“There are gonna be some people quick to adapt. And some people that aren’t; and what we’re noticing, if you look at the adoption curve, many people have yet to experience this. So there’s much work in helping make sure that we deliver this safely.”
Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can...
Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can reason over more complex driving scenes and produce richer intermediate reasoning are predominantly trained in open-loop, where model outputs are directly compared to ground-truth behaviors without considering their effect on the environment.
AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale...
AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale faster, operate more efficiently, and lower the cost of intelligence across the five-layer stack: energy, chips, infrastructure, models, and applications. NVIDIA DSX platform provides the complete playbook for designing, simulating, building…
High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions,...
High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions, and the high cost of expert annotation. As a result, training reliable 3D medical imaging models is frequently bottlenecked by small, narrow, and hard‑to‑share datasets, limiting model robustness and generalization. To help teams overcome…
Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with...
Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with limited visibility into how their GPUs are used. Most don’t know who’s consuming them, how much memory is in use, and whether Kubernetes pods are pending or silently idle. Without a signal, GPU fleets are routinely underutilized and slow to…
Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to...
Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to extend.But scaling agent use with structural transparency and operational integrity requires more than runtime guardrails. Organizations and teams need to understand and trust the skills, or instructions, an agent is using.
The compute capability of large GPU fleets presents unprecedented opportunities to innovate and provide value to customers in record time. Yet these...
The compute capability of large GPU fleets presents unprecedented opportunities to innovate and provide value to customers in record time. Yet these advancements come with a variety of challenges. At scale, teams are juggling heterogeneous hardware, fast‑moving software stacks, tight power envelopes, and spiky, multitenant workloads. A single hotspot, misconfigured driver, or subtle hardware fault…
Login
