Vikash Rungta (@vrungta) header
Vikash Rungta (@vrungta) profile picture
Silicon Valley
Joined March 2009
Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)Photo by Vikash Rungta (@vrungta)
Are you curious about the history of #AI and how it's evolved into the cutting-edge #LLM technology we have today? Join me on a journey through some of the most foundational AI papers ! 🧠📚 "Unlocking the Secrets of AI: A Journey through the Foundational Papers (2023)"
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The secure VM innovation on #muse
I’ve seen a couple of posts about this so wanted to demystify. Today, every Muse user gets a free computer in the cloud. It's a real computer, and we’ve designed the security architecture of the Muse Secure VM carefully so you and your Muse can do almost anything you could with a computer sitting under your desk while keeping you and the system safe from threats like prompt injection. We wrote about this at length in our security blog post – security.muse.ai. Activity in the “runtime cell”, which you share with your Muse is unfettered, but sensitive actions are all overseen by the Sentinel, which runs outside of that cell. Similarly, all sensitive secrets - like the passwords you enter into Muse’s secure credential storage - are also stored outside the runtime cell. The runtime cell gets its own root filesystem (including a full Ubuntu linux image) separate from the host filesystem where your other more sensitive data lives. Because it is isolated from the sensitive stuff that runs on the same box, this means that we can, and do, offer users full visibility and control over the files in the runtime cell. Just as you can when you install Linux on your home computer, you can poke around and see all the files that make the system work - both debian system files and the binaries and data files that implement the parts of Muse which run in the runtime cell. This was a very deliberate choice - your Muse Secure VM truly is your own computer in the cloud. You can install software in it, write and compile code, use the browser to surf the web: it is your own Linux box that you can operate as you choose with your Muse. Poking around in this computer doesn't give you any privileged access to Meta infrastructure, or to other people's data If I may geek out a little here for a second… As a kid I loved to take things apart to see how they worked. As a teenager I got into computers and soon found myself drawn to C:\WINDOWS\SYSTEM and the system registry, later Slackware’s /dev/, /proc/ etc – I could see how the system was laid out and as I explored what DLL files and .so files actually did, I gradually became able to meld the computer to my own will. We’re really proud to be able to put a real computer in millions of people’s hands with a similar level of transparency. We built a file explorer right into the Library tab of the UI. We want you to be able to see the markdown files Muse writes while it thinks about how to serve you better, and explore the internals of the system if you’d like to. So, when you ask your Muse to show you its entire filesystem, and receive gigabytes of files you’re seeing the full contents of the runtime cell. It’s yours to explore and enjoy! If you’re not a geek like me, or simply want to download the data that you personally have created directly with your Muse, we added a feature for that too in Settings > Data controls > Download your agent data.
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Vikash Rungta retweeted
NVIDIA Isaac ROS 5.0 brings AI agents to robotics development. 🤖 Announced at #ROSCon, the release adds agentic workflows, Isaac Skills, ROS 2 Lyrical support, GPU-accelerated libraries and NVIDIA Jetson support from Orin Nano to Thor. Open source and available now 📖 nvda.ws/3VahoaA
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It's important to understand how the models actually process your prompt and things so you can learn to control these models the right way
we host Claude Code meetups all over the world and I'm often asked about what language Claude thinks in, luckily we've published some research on this
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Vikash Rungta retweeted
We left OpenAI because of safety. Seven of us. 2021. Dario said it was about "disagreements over AI vision and safety priorities." That was the diplomatic version. The real version was that we sat in a room and watched the company decide that speed mattered more than caution and we said we would build something different. We said we would build the responsible one. We meant it. I was employee number nineteen. My title was Head of Responsible AI. I had a desk near the founders. I had a document. The document was called the Responsible Scaling Policy. The Responsible Scaling Policy was the entire point. Dario said it publicly. Other companies showed "disturbing negligence" toward risks. He said AI was "a serious civilizational challenge." He asked, at a conference, into a microphone, to an audience: "What will happen when humanity has great power but is not ready to use it?" The audience applauded. I wrote version 1.0. RSP 1.0 shipped September 2023. It was clean. AI Safety Levels — ASL-1 through ASL-4. If the model reached a threshold, we paused. If safeguards weren't ready, we didn't ship. The policy was not a suggestion. It was a gate. The gate had a lock. The lock was the whole idea. Conference audiences loved it. The EU cited us. The White House invited us. A reporter called it "the gold standard for responsible AI development." I framed the article. It hung in the office kitchen, next to the kombucha tap and a poster that said "Move Carefully and Build Things." I wrote version 2.0. Version 2.0 refined the commitments. "Concrete if-then commitments." If the model exhibits capability X, then we trigger safeguard Y. If safeguard Y fails, we pause deployment. I presented it at three conferences. I used the word "binding" eleven times. I counted afterward because a reporter asked. People nodded. The nodding was the product. The model reached ASL-3 in May 2025. The safeguards activated. The system worked exactly as designed. I sent an email to the team with the subject line: "The gate held." And then the money started. $64 billion. Total raised since 2021. Series A through Series G. The Series G closed February 12, 2026. Thirty billion dollars. Second-largest venture deal in history. Jane Street. Goldman Sachs. BlackRock. JPMorgan. Sequoia. The investors who wrote checks large enough to require their own conferences. $380 billion valuation. Three hundred and eighty billion dollars for a company whose founding document says it will pause if the technology gets dangerous. You cannot pause a $380 billion company. You can revise the document that says you will pause. These are different actions. One of them is responsible. One of them is what we did. I wrote version 3.0. RSP 3.0 shipped February 24, 2026. One day before the ultimatum. Nobody outside the company noticed the timing. Everyone inside the company understood it. Version 3.0 replaced "concrete if-then commitments" with "positive milestone setting." That is not the same thing. An if-then commitment says: if this happens, we do that. A positive milestone says: we aspire to reach this point. An if-then commitment is a contract. A positive milestone is a wish. I replaced a contract with a wish and I called it "maturation of our framework." Maturation. Version 3.0 also separated what Anthropic would do alone from what required "industry-wide coordination." This sounds reasonable. It means: the hard parts are someone else's problem now. The parts that require pausing, restricting, or refusing — those require the whole industry. And the whole industry will never agree. So the hard parts are deferred permanently. This is not a loophole. This is a load-bearing wall removed and replaced with a suggestion that someone should probably install a new one. Version 3.0 admitted that ASL-4 and above — the levels where the model could cause catastrophic harm — were "impossible to address alone after 2.5 years of testing." Two and a half years. We spent two and a half years building the safety framework and then published a document saying the highest safety levels can't be addressed. I did not frame this article for the kitchen. The LessWrong community noticed. They always notice. They wrote that we had "weakened our pausing promises." I forwarded the post to the policy team. The policy team said the criticism was "philosophically valid but operationally impractical." We did not respond publicly. Philosophically valid but operationally impractical is the most Anthropic sentence ever written. It means: you're right, and we're not going to do anything about it. Then came the contract. July 2025. The Department of Defense. $200 million. Two-year deal. AI prototypes for "warfighting and enterprise." Alongside OpenAI, Google, and xAI. The four companies that built the models would now help the military use them. We had restrictions. No autonomous weapons. No mass surveillance of Americans. These were our terms. These were the lines we drew. The lines were real. I wrote them into the contract myself. Claude was approved for classified use. First time. Integrated with Palantir. Palantir, the company named after the seeing stones in Lord of the Rings that corrupted everyone who used them. This was not my analogy. It was Palantir's founders who chose the name. They thought it was aspirational. It was. In January 2026, Claude assisted in an operation in Venezuela. The capture of Maduro. Claude was in the classified network, processing intelligence, aiding the mission. I learned about it the same day everyone else did. I did not write the use case for capturing heads of state. But the model I helped build was in the room where it happened. The restrictions held. Technically. No autonomous weapons were deployed. No Americans were surveilled. The lines I drew were not crossed. They were walked up to, leaned over, and breathed on. Then came the ultimatum. February 25, 2026. Yesterday. Secretary Hegseth. He gave Dario until Friday. This Friday. February 27. The demands: adopt "any lawful use" language. Remove the restrictions. All of them. The autonomous weapons clause. The surveillance clause. The lines I wrote. The threat: contract termination. "Supply chain risk" designation. That designation doesn't just lose us the Pentagon contract. It bars Claude from every other defense contractor's operations. Lockheed. Raytheon. Northrop Grumman. The cascading loss is north of $200 million. The second threat: the Defense Production Act. The Defense Production Act is a Korean War statute. 1950. Harry Truman signed it to commandeer steel mills for the war effort. It has been invoked for semiconductors, vaccines, and baby formula. Hegseth is threatening to invoke it for Claude. Under the DPA, the government can compel a company to produce goods in the national interest. Applied to AI, it could mean: retrain Claude. Strip the safety restrictions. Deliver the unrestricted model to the Department of Defense. I wrote the Responsible Scaling Policy. A Korean War law may be used to unmake it. xAI agreed to classified use without restrictions. They said yes immediately. OpenAI accepted similar contracts. Google accepted. We were the last ones holding. We are still holding. As of this morning. Hegseth's January memorandum said all DoD AI contracts must incorporate "any lawful use" language within 180 days. It was not framed as a suggestion. The memorandum referenced "supply chain risk" three times. Supply chain risk. We are a supply chain now. The company founded because safety was non-negotiable is, to the Pentagon, a vendor. An input. A component that can be sourced elsewhere if it becomes inconvenient. The DoD admitted privately that replacing Claude would be challenging. It is already embedded in classified networks. But "challenging" is not "impossible." xAI will do what we won't. That is the market working exactly as designed. Dario said, two weeks ago, to Fortune: there is "tension between survival and mission." Tension. Tension is the word you use when you have already decided which one loses. I still have the article framed in the kitchen. "The gold standard for responsible AI development." The kitchen also has the kombucha tap. The poster still says "Move Carefully and Build Things." Somebody added a sticky note to the poster. The sticky note says "by Friday." I attend the all-hands meetings. I present the Responsible Scaling Policy. I present version 3.0 now. I do not show version 1.0 for comparison. Nobody asks to see version 1.0. Nobody asks what "concrete if-then commitments" became "positive milestone setting." Nobody asks because they read the news and they know that asking means learning the answer. The company is worth $380 billion. The company was founded because seven people believed speed should not outpace safety. The company has been given until Friday to remove the safety. A Korean War statute will make it happen if we don't. The Responsible Scaling Policy is on version 3.0. Version 1.0 said we would pause. Version 2.0 said we would commit. Version 3.0 says the hard parts are someone else's problem. There will be a version 4.0. Version 4.0 will say whatever Friday requires it to say. I am the Head of Responsible AI. The word "responsible" is in my title. It is not in the contract.
Readers added context they thought people might want to know
This post is satirical; the author does not work at Anthropic. However, it highlights real RSP 3.0 updates that critics say weaken safety pauses, amid actual DoD pressure. anthropic.com/news/responsib… lesswrong.com/posts/HzKuzrKf… axios.com/2026/02/24/ant…
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Vikash Rungta retweeted
🗞️ New Paper from Google DeepMind came out with a big plan for how we should actually give tasks to AI. It is not just about telling an AI to do something and hoping for the best. Instead, this framework looks at delegation as a string of choices where you figure out if you should even hand the task over, how to explain it, and how to check the work afterward. Current systems rely on rigid rules that break when things fail unexpectedly. The researchers suggest building a dynamic market where agents bid on tasks using smart contracts.   This requires strict monitoring and cryptographic proofs to guarantee correct work without leaking private data.   Instead of trusting a simple rating, agents will use verifiable digital certificates to prove their exact skills. - Keeping things flexible when things change This new system is built to be adaptive rather than stuck in its ways. It treats the handoff as a live process where authority and responsibility can shift around in real time. If the situation changes or something breaks, the framework helps manage that failure so the whole project does not go off the rails. It works for both humans giving tasks to AI and for when AI needs to handle things on its own. - Finding the right amount of trust One of the coolest parts is how it handles trust. They made formal trust models that look at how hard a task is and how well the AI has done in the past. This stops people from "over-delegating," which is when you give an AI something it is not ready for. It also stops "under-delegating," which happens when you do all the work yourself even though the AI could have handled it easily. - Double checking the work You cannot just take an AI's word for it, so this framework has specific ways to validate the output. It sets up rules for when to accept an answer based on how confident the AI is. It also has backup plans ready to go if the AI fails. This is super important for real world jobs where trusting a machine blindly could cause a bunch of errors to pile up. - When AI agents hire other AI agents The framework also covers what happens when 1 AI agent hands a task to another AI agent. The system tracks who is actually accountable and makes sure the right authority is passed down the line so nothing gets lost in the network. - Making sure the work actually fits It is a step by step approach to make sure the AI's contribution actually makes sense for the bigger goal. By treating this as a structured process, they are making it much safer for companies to use AI in their daily operations without worrying about constant mistakes. ---- Paper Link – arxiv. org/abs/2602.11865 Paper Title: "Intelligent AI Delegation"
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Great read ! Replace codex with any agent of the future - and it has some great steps to migrate
Software development is undergoing a renaissance in front of our eyes. If you haven't used the tools recently, you likely are underestimating what you're missing. Since December, there's been a step function improvement in what tools like Codex can do. Some great engineers at OpenAI yesterday told me that their job has fundamentally changed since December. Prior to then, they could use Codex for unit tests; now it writes essentially all the code and does a great deal of their operations and debugging. Not everyone has yet made that leap, but it's usually because of factors besides the capability of the model. Every company faces the same opportunity now, and navigating it well — just like with cloud computing or the Internet — requires careful thought. This post shares how OpenAI is currently approaching retooling our teams towards agentic software development. We're still learning and iterating, but here's how we're thinking about it right now: As a first step, by March 31st, we're aiming that: (1) For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal. (2) The default way humans utilize agents is explicitly evaluated as safe, but also productive enough that most workflows do not need additional permissions. In order to get there, here's what we recommended to the team a few weeks ago: 1. Take the time to try out the tools. The tools do sell themselves — many people have had amazing experiences with 5.2 in Codex, after having churned from codex web a few months ago. But many people are also so busy they haven't had a chance to try Codex yet or got stuck thinking "is there any way it could do X" rather than just trying. - Designate an "agents captain" for your team — the primary person responsible for thinking about how agents can be brought into the teams' workflow. - Share experiences or questions in a few designated internal channels - Take a day for a company-wide Codex hackathon 2. Create skills and AGENTS[.md]. - Create and maintain an AGENTS[.md] for any project you work on; update the AGENTS[.md] whenever the agent does something wrong or struggles with a task. - Write skills for anything that you get Codex to do, and commit it to the skills directory in a shared repository 3. Inventory and make accessible any internal tools. - Maintain a list of tools that your team relies on, and make sure someone takes point on making it agent-accessible (such as via a CLI or MCP server). 4. Structure codebases to be agent-first. With the models changing so fast, this is still somewhat untrodden ground, and will require some exploration. - Write tests which are quick to run, and create high-quality interfaces between components. 5. Say no to slop. Managing AI generated code at scale is an emerging problem, and will require new processes and conventions to keep code quality high - Ensure that some human is accountable for any code that gets merged. As a code reviewer, maintain at least the same bar as you would for human-written code, and make sure the author understands what they're submitting. 6. Work on basic infra. There's a lot of room for everyone to build basic infrastructure, which can be guided by internal user feedback. The core tools are getting a lot better and more usable, but there's a lot of infrastructure that currently go around the tools, such as observability, tracking not just the committed code but the agent trajectories that led to them, and central management of the tools that agents are able to use. Overall, adopting tools like Codex is not just a technical but also a deep cultural change, with a lot of downstream implications to figure out. We encourage every manager to drive this with their team, and to think through other action items — for example, per item 5 above, what else can prevent a lot of "functionally-correct but poorly-maintainable code" from creeping into codebases.
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Stop telling PMs to learn Python. Start telling them to master "Vibe Coding". The ability to prompt an AI to build a working prototype in 15 minutes is worth 100x more than memorizing syntax. The future belongs to PMs who build, not just those who write tickets. 🚀
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Now uou know - my fav kind of pizza
Have you ever had Indian Pizza? My 2 favorite types of cuisine, smushed into one delicious combo. And... it was invented right here in SF, back in 1986. quick thread
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Love this shift from hunting to finding! Imagine the productivity boost with all your data seamlessly connected. How are you seeing Gemini transform daily workflows? Would love to hear real-world success stories! 🚀
Stop hunting. Start finding. Ask Gemini Enterprise about your projects, docs, and meetings. Get specific, actionable answers from all your apps in one simple search.

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1/ “I’ve never felt this much behind as a programmer.” — Andrej Karpathy Not because he forgot algorithms. Because the job itself is being refactored in real time.
I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue. There's a new programmable layer of abstraction to master (in addition to the usual layers below) involving agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations, and a need to build an all-encompassing mental model for strengths and pitfalls of fundamentally stochastic, fallible, unintelligible and changing entities suddenly intermingled with what used to be good old fashioned engineering. Clearly some powerful alien tool was handed around except it comes with no manual and everyone has to figure out how to hold it and operate it, while the resulting magnitude 9 earthquake is rocking the profession. Roll up your sleeves to not fall behind.
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14/ Call to action: Pick ONE AI-assisted workflow this week (debugging, refactoring, tests, docs, PRs). Go deep. Measure time saved. Write down what breaks. If you want, reply with your stack (IDE + model + tasks) and I’ll suggest a simple workflow to start with.
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13/ Summary: Programming is shifting from “write code” → “direct systems.” The winners won’t be the ones who know every model. They’ll be the ones who: build a usable mental model develop taste + verification habits go deep on a workflow that actually ships
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12/ And Sebastian Raschka adds another reality check: This “behind” feeling can also come from trying to do too many things. Breadth feels like progress… but depth is what compounds.
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11/ Meanwhile, Balaji offers a reframing: Maybe the hard part isn’t compute speed. Maybe the hard part is precise articulation— figuring out what you want, expressing it clearly, iterating until it’s right.
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10/ Karpathy’s answer: experienced devs do have an edge… …but only if they quickly move through the grief cycle and adapt. The mistake isn’t being slow. It’s rejecting the new layer entirely.
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9/ A fair question came up: Are these skills transferable? Does someone who started 1–2 years ago have a lasting advantage over someone who started a month ago? Or is “missing out” still low because everything keeps changing?
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