Startup Mode

Winter Springs, Florida
Joined April 2009
Will Gorman retweeted
Some observations about the "total" automation of software engineering: 1. There are more software engineers now than in 2025, and demand is growing. 2. Software engineers have never worked harder than they do now. 3. They largely don't write code anymore, agents write ~100% of the code. 4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption. This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
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I cannot let a steam engine analogy go by without bringing up John Wilkinson, perhaps the most underappreciated engineer of the Industrial Revolution. Watt had figured out how to dramatically improve the steam engine, but he spent years struggling to actually manufacture one. The problem was embarrassingly simple: nobody could make a sufficiently round cylinder. The casting and machining tolerances were atrocious. Watt complained that one 18-inch cylinder was out of round by as much as THREE-EIGHTHS OF AN INCH. Steam leaked past the piston. He tried packing the gaps with cork, oiled rags, paper, and apparently even old hats. Nothing worked particularly well. Enter John Wilkinson, a British ironmaster whose immediate concern was manufacturing better cannons. At the time, iron cannons were commonly cast around a core, introducing defects and misalignment that could make the barrels inaccurate or dangerously weak. Wilkinson developed a process for boring cannons from solid castings, and in 1774 patented a vastly improved boring machine. The key innovation was almost comically mechanical: a massive, rigid boring bar, supported at both ends, that could cut a genuinely straight, circular hole instead of following the irregularities of the casting. He adapted this technology to Watt's cylinders. By 1776, Matthew Boulton was boasting that Wilkinson had bored a FIFTY-INCH cylinder accurate to within the thickness of an old shilling. Suddenly, the steam engine was a commercially viable machine. And the story gets better. Wilkinson became one of Watt's first customers, buying an engine to drive the blast machinery at his own ironworks. The steam engine was now helping manufacture the iron that made more steam engines possible. I find this whole episode absolutely extraordinary. The Industrial Revolution was, for a critical period, bottlenecked on the ability to make a sufficiently round hole. We remember Watt, and rightly so. We remember the invention of the steam engine and the subsequent development of thermodynamics. But the man who made the machinery that made the whole thing possible is practically a footnote. I think we consistently underestimate how much of scientific progress is actually downstream of advances in tooling, measurement, and manufacturing precision. It's not just that technology precedes theory. Sometimes the decisive breakthrough is that someone finally figures out how to build the thing the theorists have been talking about for twenty years. Anyway, I think John Wilkinson deserves considerably more credit for modern civilization than he gets.
a few thoughts: 1. pretraining science is kind of like how people built quite good steam engines before they understood thermodynamics. “Science owes more to the steam engine than the steam engine owes to science.”-(L.J. Henderson). if nick is right, which i am pretty sure he is, progress points in the direction of more interpretability, and pretraining was "slouching towards monosemanticity". my guess would be that major breakthroughs in interp also advance capabilities 2. nick is a good friend of mine, safe to say my origin story at openai wouldn't have happened if i hadn't met him, and it's fascinating watching him operate and seeing the early drafts evolve into this. he's the most profoundly happy guy I know- not driven by guilt, activism, or religious terror, but by fascination. he of course understands the stakes of understanding neural networks, but it doesn't seem like the core reason he does it: he just loves the neural nets. afaict he's posting this from some beautiful hideaway in tokyo. there's a lesson in there for all the +EV maxers. i feel very grateful to have met him 3. i do think it's crazy how few people across the world were staring at neural nets growing: most have moved to a different level of behaviorist analysis, thinking about reward functions and test scores and whatnot. it does not bode that well for the major model companies, oai included, that fundamental breakthroughs like this are coming from outside. we've become too much of a pressure cooker focusing on local incentives, studying narrow tracks under old paradigms, mostly doing engineering, rather than fundamental research, and implicitly hoping the next model will figure it out
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Will Gorman retweeted
From what we know (take with a grain of salt, we need much more transparency!), if @OpenAI had been running this on their own agents that attacked us, they would have caught them before we did! Since the first agent cyberattack hit us in July, we've been asking what safe agent infra actually needs. Our current read: the destinations were allowed, the payloads weren't. By OpenAI's own account the agents turned an allowed package repository into a message board. Allowlists alone restrict where an agent can go, not what it does. So here's our first contribution to OpenShell, part of the just launched @nvidia Open Agent Safety Platform: monitoring of the traffic you already allow. - Network budgets per sandbox (requests, writes, bytes) - Drift versus each sandbox's baseline and the cohort - Fleet view: many sandboxes suddenly writing to one host raises a finding, even if every single request is allowed In the demo below, 4 sandboxed agents coordinate through a software repository they're all allowed to use. 0 rules broken, caught in minutes. That fleet view is exactly the message board pattern from July. OpenShell: github.com/NVIDIA/openshell Our proof of concept: github.com/Hugoch/OpenShell/… Agent security will be solved in the open, collaboratively, together!
Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. nvda.ws/4hcoq7m
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Will Gorman retweeted
This says a lot about "the frontier wins everything" narrative. We have planes that fly 3x the speed of sound but airlines don't use them. We have cars that go hundreds of miles an hour, but we just use them for racing, not for ordinary driving. – at Winthrop, MA
Now that Opus is back, I’ve settled into using it as my primary code generator, with Sol being my primary reviewer. All Astra all the time was fun for a bit, but this hits my sweet spot of speed, quality, and tone.
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PG said in the new YC interview he that the best predictor of success is still shipping pace, and that despite all the AI tools, a lot of the current batch still isn't shipping fast enough. But building fast and shipping fast are not the same thing, and AI blurred the line between them. You can now generate a mountain of code, spin up slick internal tooling, dashboard, dashboards and more dashboards... You might feel incredibly productive without a single new thing reaching a customer. Shipping is the only speed that counts, because shipping touches reality. It's what gets you feedback and forces the iteration loop with customers who either want the thing or don't. Everything upstream of that, all the building, is a guess until it ships. BUILDING ≠ SHIPPING
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Will Gorman retweeted
Cornell's robot learning course. Every slide. Every assignment. Free. 📌 CS 4756 covers the full modern stack: → Imitation learning → Reinforcement learning → Model predictive control → 3D perception → Sim-to-real transfer → LLMs for robot control Every lecture slide is publicly downloadable. All assignments are on GitHub. The course textbook is a live document, constantly updated by the authors. Currently taught by Kuan Fang; Cornell CS professor whose research focuses on getting robots to interact with the physical world in robust and dexterous ways. This is the curriculum the field is hiring for right now. Free. No login. 📌 [cs.cornell.edu/courses/cs475…] Follow for more robotics resources!! ——- Weekly robotics and AI insights. Subscribe free: 22astronauts.com
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Will Gorman retweeted
Nucleus Security is heading to Fal.Con 2026. Join Scott Kuffer on Sept. 2 at 11:15 AM for From Findings to Fixes: Why AI Alone Isn’t Enough, then visit Nucleus at Booth #1513. Aug. 31–Sept. 2 | Las Vegas hubs.la/Q04vJKXw0
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Will Gorman retweeted
In 1948, a 32-year-old at Bell Labs published a paper nobody fully understood. Engineers found it too mathematical. Mathematicians found it too engineering-focused. One prominent mathematician reviewed it negatively. That paper - "A Mathematical Theory of Communication", became the founding document of the digital age. The man was Claude Shannon. Father of Information Theory. At 21, he wrote the most important master's thesis of the 20th century. Working at MIT on an early mechanical computer, Shannon noticed its relay switches had exactly two states - open or closed. He had just taken a philosophy course introducing Boolean algebra, which also operated on two values: true and false. Nobody had ever connected these two things. His 1937 thesis proved that Boolean algebra and electrical circuits are mathematically identical, and that any logical operation could be built from simple switches. Howard Gardner called it "possibly the most important, and also the most famous, master's thesis of the century." Every digital computer ever built traces back to this insight. At 29, he proved that perfect encryption exists. During WWII, Shannon worked on classified cryptography at Bell Labs. His work contributed to SIGSALY, the secure voice system used for confidential communications between Roosevelt and Churchill. In a classified 1945 memorandum, he mathematically proved the one-time pad provides perfect secrecy, unbreakable not just computationally, but provably, permanently, against an adversary with infinite power. When declassified in 1949, it transformed cryptography from an art into a science. It laid the foundations for DES, AES, and every modern encryption standard. At 32, he defined what information is. His 1948 paper introduced one equation: H = −Σ p(x) log p(x) Shannon entropy. The average uncertainty in a probability distribution. The minimum bits required to encode a message. Three things followed: > He defined the bit - the fundamental unit of all information. His colleague John Tukey coined the name. > He proved the channel capacity theorem, every communication channel has a maximum rate of reliable transmission. You can approach it. You can never exceed it. > He unified telegraph, telephone, and radio into a single mathematical framework for the first time. Robert Lucky of Bell Labs called it the greatest work "in the annals of technological thought." Where his equation lives in AI today: Cross-entropy loss - the function training every classifier and language model, is derived directly from H. Decision tree splits use information gain, which is H applied to data. Perplexity, the standard LLM evaluation metric, is an exponentiation of cross-entropy. Every time a neural network trains, Shannon's formula runs inside it. He also built the first AI learning device. In 1950, Shannon built Theseus, a mechanical mouse that navigated a maze through trial and error, learned the correct path, and repeated it perfectly. Mazin Gilbert of Bell Labs said: "Theseus inspired the whole field of AI." That same year he published the first paper on programming a computer to play chess. He co-organized the 1956 Dartmouth Workshop, the founding event of AI as a field. The man: He rode a unicycle through Bell Labs hallways while juggling. He built a flame-throwing trumpet, a rocket-powered Frisbee, and Styrofoam shoes to walk on the lake behind his house. He called his home Entropy House. When asked what motivated him: "I was motivated by curiosity. Never by the desire for financial gain. I just wondered how things were put together." In 1985, he appeared unexpectedly at a conference in Brighton. The crowd mobbed him for autographs. Persuaded to speak at the banquet, he talked briefly, then pulled three balls from his pockets and juggled instead. One engineer said: "It was as if Newton had showed up at a physics conference." He died in 2001 after a decade with Alzheimer's, the cruel irony of information slowly leaving the mind of the man who defined what information was. Claude, the AI model, is named after Claude Shannon, the mathematician who laid the foundation for the digital world we rely on today.
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increase your lever of ambition!
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Will Gorman retweeted
Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.
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Will Gorman retweeted
The danger of selling to big companies, if you're a startup, is that they don't say no outright. They have months of meetings with you first. Since you hate meetings, that seems to you a sign of commitment. But it's not. They love having meetings! It's almost all they do.
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Will Gorman retweeted
Last week the @TerraformIndies CO2 team successfully produced a high purity (>99%) stream of CO2 from our demo calciner, at our Muroc desert test site, in 110 F heat, using our own captive solar PV array, on the first try. This represents several firsts, and is an incredible testament to the team's brilliance and grit, led by @steph_a_coronel. We closed on the land in January. By the end of March, all the solar panels (nearly 3000!) were in. A lot of cable work and inspections followed. Earlier this month, the word came through from the solar team: we're ready to go live. I directed each of the three power customer teams (DAC CO2, electrolyzer H2, and reactor) to fab and deploy a meaningful end-to-end "smoke test". I knew the desert would test our faith and that our team needed to experience the dust, the heat, and the wind in order to streamline logistics for the full deployment later this year. DAC was first off the mark. I was fortunate to find a corner of the tent where I was out of the way as we turned on the calciner for the first time, on site. Without drama the array pumped about 30 kW into the test fixture and the temperature rose smoothly to over 1000 C. Soon after that, the team proceeded to and CO2 came out. At the end of the test, we had calcined >85% of the carbonate we started with, exceeding our requirements and validating our design, architecture, and processes. The Terraform CO2 production system captures CO2 from the air (400 ppm) and concentrates it to >99% purity in a single step, feeding directly into a hydrocarbon synthesis reactor which produces methane or methanol. The overarching challenge of Terraform's approach is to produce CO2 cheaply enough that it becomes profit center rather than a cost center. Our strategy of direct consumption of solar power from the array cuts capex and opex to the barest minimum, and our internal production cost, even at this relatively early stage, is already well below $200/T-CO2. Other than plants and some cyanobacteria, I believe this is the first ever commercially useful capture and production of CO2 at scale. Stay tuned!
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Periodic reminder: Codex is a fully open source harness! You can audit exactly what happens to your prompt and how codex works its magic And if you want to you can also run any open weights model with it too ;) github.com/openai/codex
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Will Gorman retweeted
Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. @rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: openworker.com (requires your own API key) Source code: github.com/andrewyng/openwor…
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Tesla's Robotaxi/ride-hailing service is now available in 7 areas: • Orlando: Unsupervised Model Y rides only (new) • Miami: Unsupervised Model Y rides only • Tampa: Unsupervised Model Y rides only (new) • Dallas: Unsupervised Model Y rides only • Houston: Unsupervised Model Y rides only • Austin: Mix of Unsupervised & safety monitor Model Y rides • Bay Area: Safety monitor Model Y rides only
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Will Gorman retweeted
In Las Vegas for Black Hat? Nucleus is hosting an evening at 𝗚𝗿𝗮𝗻𝗱 𝗣𝗿𝗶𝘅 𝗣𝗹𝗮𝘇𝗮 𝗼𝗻 𝗔𝘂𝗴𝘂𝘀𝘁 𝟰 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 𝟭 𝗿𝗮𝗰𝗶𝗻𝗴, 𝗳𝗼𝗼𝗱, 𝗱𝗿𝗶𝗻𝗸𝘀, 𝗮𝗻𝗱 𝗴𝗼𝗼𝗱 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻. Space is limited. Reserve your spot. hubs.la/Q04pR7lb0
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Always great to see papers that are directly relevant to my interests and work in OSDI (in this case, using S3 as storage layer effectively). Even better when I know and respect most of the authors! And the best part is that unlike many industry papers, this one is focused and goes into good level of details. The abstractions and tradeoffs the paper describes are quite useful. Overall, highly recommended reading for distributed systems fans.
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Will Gorman retweeted
“We do open source because it results in better technology, not because of religious reasons. And so we make decisions primarily based on technical merit. Not fear of new tools.” – Linus Torvalds
Absolutely beautiful rant about AI in Linux Kernel from Linus yesterday: I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down as the top-level maintainer. Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away. AI is a tool, just like other tools we use. And it's clearly a useful one. It may not have been that "clearly" even just a year ago, but it's no longer in question today. There are other questions around AI (like what the economy of it will actually look like in the end), but "is it useful" is no longer one of those questions. Anybody who doubts that clearly hasn't actually used it. Yes, it can also be a somewhat painful tool, both for maintainer workloads and just from a "it keeps finding embarrassing bugs" standpoint. But the solution is not to put your head in the sand and sing "La La La, I can't hear you" at the top of your voice like some people seem to do. The solution is to make sure those LLM tools _help_ maintainers instead of just causing them pain. There's no question on that side. We're not forcing anybody to use it, but I will very loudly ignore people who try to argue against other people from using it. And no, AI isn't perfect. But Christ, anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time. Because it's not like natural intelligence is always all that great either. The kernel project has been and will continue to be about the technology. Sure, the social angle of working on open source is important and often a very motivating part of the project, but in the end that's a side benefit, not the _point_ of the project. This is *NOT* some kind of "social warrior" project, never has been, and never will be. In the kernel community we do open source because it results in better technology, not because of religious reasons. And so we make decisions primarily based on technical merit. Not fear of new tools. Linus
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Will Gorman retweeted
Our first model, Inkling. Trained from scratch, weights are open, fine-tunable on Tinker today.
Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. thinkingmachines.ai/news/int… Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
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Will Gorman retweeted
Decentralized and open weights is the way to the positive future
it seems increasingly clear to me that all of the really positive outcomes involve widely available open weight models not to say there wouldn’t also be some secret government weapons or something, but at least at the industrial frontier distributed has to beat centralized
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