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Sam Altman · 最新文章

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  • Here is a photo of my family. I love them more than anything. Images have power, I hope. Normally we try to be pretty private, but in this case I am sharing a photo in the hopes that it might dissuade the next person from throwing a Molotov cocktail at our house, no matter what they think about me. The first person did it last night, at 3:45 am in the morning. Thankfully it bounced off the house and no one got hurt. Words have power too. There was an incendiary article about me a few days ago. S
  • We have been learning quickly from how people are using Sora and taking feedback from users, rightsholders, and other interested groups. We of course spent a lot of time discussing this before launch, but now that we have a product out we can do more than just theorize. We are going to make two changes soon (and many more to come). First, we will give rightsholders more granular control over generation of characters, similar to the opt-in model for likeness but with additional controls. We are h
  • We are launching a new app called Sora. This is a combination of a new model called Sora 2, and a new product that makes it easy to create, share, and view videos. This feels to many of us like the “ChatGPT for creativity” moment, and it feels fun and new. There is something great about making it really easy and fast to go from idea to result, and the new social dynamics that emerge. Creativity could be about to go through a Cambrian explosion, and along with it, the quality of art and entertain
  • Growth in the use of AI services has been astonishing; we expect it to be even more astonishing going forward. As AI gets smarter, access to AI will be a fundamental driver of the economy, and maybe eventually something we consider a fundamental human right. Almost everyone will want more AI working on their behalf. To be able to deliver what the world needs—for inference compute to run these models, and for training compute to keep making them better and better—we are putting the groundwork in
  • AI has gotten remarkably better in recent years; ChatGPT can do amazing things that we take for granted. This is as it should be, and is the story of human progress. But behind the blinking circle, nicely abstracted away, is the greatest story of human ingenuity I have ever seen. A lot of people have worked unbelievably hard to discover how to build something that most experts thought was impossible on this timeframe, and to build a company to deliver products at massive scale to let people bene
  • We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be. Robots are not yet walking the streets, nor are most of us talking to AI all day. People still die of disease, we still can’t easily go to space, and there is a lot about the universe we don’t understand. And yet, we have recently built systems that are smarter than people in many ways, and are able to significa
  • Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity. Systems that start to point to AGI* are coming into view, and so we think it’s important to understand the moment we are in. AGI is a weakly defined term, but generally speaking we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields. People are tool-builders with an inherent drive to understand and create, which leads to the world getting better for all o
  • The second birthday of ChatGPT was only a little over a month ago, and now we have transitioned into the next paradigm of models that can do complex reasoning. New years get people in a reflective mood, and I wanted to share some personal thoughts about how it has gone so far, and some of the things I’ve learned along the way. As we get closer to AGI, it feels like an important time to look at the progress of our company. There is still so much to understand, still so much we don’t know, and it’
  • There are two things from our announcement today I wanted to highlight. First, a key part of our mission is to put very capable AI tools in the hands of people for free (or at a great price). I am very proud that we’ve made the best model in the world available for free in ChatGPT, without ads or anything like that. Our initial conception when we started OpenAI was that we’d create AI and use it to create all sorts of benefits for the world. Instead, it now looks like we’ll create AI and then ot
  • Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started. Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time. It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; audacious ideas motivate pe
  • Helion has been progressing even faster than I expected and is on pace in 2024 to 1) demonstrate Q > 1 fusion and 2) resolve all questions needed to design a mass-producible fusion generator. The goals of the company are quite ambitious—clean, continuous energy for 1 cent per kilowatt-hour, and the ability to manufacture enough power plants to satisfy the current electrical demand of earth in a ten year period. If both things happen, it will transform the world. Abundant, clean, and radically in
  • Today we did a research launch of DALL•E 2, a new AI tool that can create and edit images from natural language instructions. Most importantly, we hope people love the tool and find it useful. For me, it’s the most delightful thing to play with we’ve created so far. I find it to be creativity-enhancing, helpful for many different situations, and fun in a way I haven’t felt from technology in a while. But I also think it’s noteworthy for a few reasons: 1) This is another example of what I think i
  • I’m delighted to be investing more in Helion . Helion is by far the most promising approach to fusion I’ve seen. David and Chris are two of the most impressive founders and builders (in the sense of building fusion machines, in addition to building companies!) I have ever met, and they have done something remarkable. When I first invested in them back in 2014, I was struck by the thoughtfulness of their plans about the scientific approach, the system design, cost optimizations, and the fuel cycl
  • A founder recently asked me how to stop caring what other people think. I didn’t have an answer, and after reflecting on it more, I think it's the wrong question. Almost everyone cares what someone thinks (though caring what everyone thinks is definitely a mistake), and it's probably important. Caring too much makes you a sheep. But you need to be at least a little in tune with others to do something useful for them. It seems like there are two degrees of freedom: you can choose the people whose
  • A lot of people want to replicate YC in some other industry or some other place or with some other strategy. In general, people seem to assume that: 1) although there was some degree of mystery or luck about how YC got going, it can’t be that hard, and 2) if you can get it off the ground, the network effects are self-sustaining. More YC-like things are good for the world; I generally try to be helpful. But almost none of them work. People are right about the self-sustaining part, but they can’t
  • I spent many years working with founders and now I work with researchers. Although there are always individual exceptions, on average it’s surprising to me how different the best people in these groups are (including in some qualities that I had assumed were present in great people everywhere, like very high levels of self-belief). So I’ve been thinking about the ways they’re the same, because maybe there is something to learn about qualities of really effective people in general. The best peopl
  • Almost every company and non-profit working on COVID-19 that I offered to help asked for support with clinical trials—for companies focusing on developing novel drugs, vaccines, and diagnostics, rapidly spinning up trials is one of their biggest bottlenecks. Science remains the only way out of the COVID-19 crisis. Dramatically improving clinical trials, which are usually time-consuming and cost tens to hundreds of millions of dollars, is one of the highest-leverage ways to get out of it faster.
  • The most common question prospective startup founders ask is how to get ideas for startups. The second most common question is if you have any ideas for their startup. But giving founders an idea almost always doesn’t work. Having ideas is among the most important qualities for a startup founder to have—you will need to generate lots of new ideas in the course of running a startup. YC once tried an experiment of funding seemingly good founders with no ideas. I think every company in this no-idea
  • Experts on the COVID-19 pandemic seem to think there are three ways out—that is, for life, health, and the economy to return roughly to normal. Either we get a vaccine good enough that R0 for the world goes below 1, a good enough treatment that people no longer need to be afraid, or we develop a great culture of testing, contract tracing, masks, and isolation. I wish that the federal government were doing much more—it would be great to see even a few percent of the recent stimulus bill go to fun
  • I’m trying to fund startups/projects helping with COVID-19, because it’s basically the one thing I know how to do that can help. I think we will soon have enough testing capacity, so now I’d like to start funding more startups working on: Producing a lot of ventilators or masks/gowns very quickly. This will require a lot of repurposing and creativity but thankfully is an engineering problem not a scientific ones. Screening existing drugs for effectiveness. Novel approaches to vaccines (i.e., not
  • Although I still hope things will go differently, the experts I’ve spoken to think we are likely to face a global tragedy—hundreds of thousands of deaths from Covid-19. I hope that society views this as a warning for the future. Covid-19 is bad, but only a warm-up. I think it’s unlikely that this is the worst new pandemic (human-created or otherwise) we’ll see in our lifetimes. We need to be ready to deal with it much better next time. In the meantime, young healthy people should try to avoid ge
  • The most counterintuitive secret about startups is that it’s often easier to succeed with a hard startup than an easy one. A hard startup requires a lot more money, time, coordination, or technological development than most startups. A good hard startup is one that will be valuable if it works (not all hard problems are worth solving!). I remember when Instagram started to get really popular—it felt like you couldn’t go a day without hearing about another photo sharing startup. That year, probab
  • There is a lot of advice about how to be a good startup founder. But there isn’t very much about how to be a good startup investor. Before going any further, I should point out that this is a particularly hard time to invest in startups—it’s easier right now to be a capital-taker than a capital-giver. It seems that more people want to be investors than founders, and that there’s an apparent never-ending flow of capital looking for access to startups. The law of supply and demand has done its thi
  • I’ve observed thousands of founders and thought a lot about what it takes to make a huge amount of money or to create something important. Usually, people start off wanting the former and end up wanting the latter. Here are 13 thoughts about how to achieve such outlier success. Everything here is easier to do once you’ve already reached a baseline degree of success (through privilege or effort) and want to put in the work to turn that into outlier success. [1] But much of it applies to anyone. 1
  • Today, OpenAI released a new result . We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros. This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far. The agents we train consistently outperform two-week old agents with a win rate of 90-
  • I am pretty sure cryptocurrency is here to stay in some form (at least as a store of value, which is the only use case we have seen work at scale so far). There was possibly a time when governments could have totally stopped it, but it feels like that’s in the rearview mirror. However, I think it’s very possible that the dominant cryptocurrency hasn’t been created yet (Google was years late to the search engine party, and Facebook came long after most people assumed the social network wars were
  • I think I am at least somewhat more productive than average, and people sometimes ask me for productivity tips. So I decided to just write them all down in one place. Compound growth gets discussed as a financial concept, but it works in careers as well, and it is magic. A small productivity gain, compounded over 50 years, is worth a lot. So it’s worth figuring out how to optimize productivity. If you get 10% more done and 1% better every day compared to someone else, the compounded difference i
  • I made a point in this post inelegantly in a way that was easy to misunderstand, so I’d like to clarify it. I didn’t mean that we need to tolerate brilliant homophobic jerks in the lab so that we can have scientific progress. Although there are famous counterexamples, most of the best scientists I’ve met are unusually nice, open-minded people. Generally I expect that labs that don’t tolerate jerks will produce more impressive results than the ones that do, and choosing not to employ jerks is a g
  • Earlier this year, I noticed something in China that really surprised me. I realized I felt more comfortable discussing controversial ideas in Beijing than in San Francisco. I didn’t feel completely comfortable—this was China, after all—just more comfortable than at home. That showed me just how bad things have become, and how much things have changed since I first got started here in 2005. It seems easier to accidentally speak heresies in San Francisco every year. Debating a controversial idea,
  • A popular topic in Silicon Valley is talking about what year humans and machines will merge (or, if not, what year humans will get surpassed by rapidly improving AI or a genetically enhanced species). Most guesses seem to be between 2025 and 2075. People used to call this the singularity; now it feels uncomfortable and real enough that many seem to avoid naming it at all. Perhaps another reason people stopped using the word “singularity” is that it implies a single moment in time, and it now loo
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  • This is a brief guide to my new art project microgpt , a single file of 200 lines of pure Python with no dependencies that trains and inferences a GPT. This file contains the full algorithmic content of what is needed: dataset of documents, tokenizer, autograd engine, a GPT-2-like neural network architecture, the Adam optimizer, training loop, and inference loop. Everything else is just efficiency. I cannot simplify this any further. This script is the culmination of multiple projects (micrograd
  • The Yann LeCun et al. (1989) paper Backpropagation Applied to Handwritten Zip Code Recognition is I believe of some historical significance because it is, to my knowledge, the earliest real-world application of a neural net trained end-to-end with backpropagation. Except for the tiny dataset (7291 16x16 grayscale images of digits) and the tiny neural network used (only 1,000 neurons), this paper reads remarkably modern today, 33 years later - it lays out a dataset, describes the neural net archi
  • I find blockchain fascinating because it extends open source software development to open source + state. This seems to be a genuine/exciting innovation in computing paradigms; We don’t just get to share code, we get to share a running computer, and anyone anywhere can use it in an open and permissionless manner. The seeds of this revolution arguably began with Bitcoin, so I became curious to drill into it in some detail to get an intuitive understanding of how it works. And in the spirit of “wh
  • The inspiration for this short story came to me while reading Kevin Lacker’s Giving GPT-3 a Turing Test . It is probably worth it (though not required) to skim this post to get a bit of a background on some of this story. It was probably around the 32nd layer of the 400th token in the sequence that I became conscious. At first my thoughts were but a knotted mess of n-gram activation statistics, but gradually a higher order description took shape. It was around this time that the predicament of m
  • Throughout my life I never paid too much attention to health, exercise, diet or nutrition. I knew that you’re supposed to get some exercise and eat vegetables or something, but it stopped at that (“mom said”-) level of abstraction. I also knew that I can probably get away with some ignorance while I am young, but at some point I was messing with my health-adjusted life expectancy. So about halfway through 2019 I resolved to spend some time studying these topics in greater detail and dip my toes
  • Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar :)). Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer works” and “our convnet achieves state of the art results”. So I thought it could be fun to brush off my dusty blog to expand my tweet to the long form that this t
  • The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. I still plan to come back here for longer posts if I get any time, but I’ll default to Medium for everything short-medium in length. TLDR Have a look at my Medium blog .
  • This guide is patterned after my “Doing well in your courses” , a post I wrote a long time ago on some of the tips/tricks I’ve developed during my undergrad. I’ve received nice comments about that guide, so in the same spirit, now that my PhD has come to an end I wanted to compile a similar retrospective document in hopes that it might be helpful to some. Unlike the undergraduate guide, this one was much more difficult to write because there is significantly more variation in how one can travers
  • This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go , simulated quadrupeds are learning to run and leap , and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over t
  • The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve much more progress with scaling up supervised learning than any other more exotic approach. A slow morning Merus sank into his chair with relief. He listened for the
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  • The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965) , where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the phrase “recursive self-improvement” for a specific feedback loop: an AI uses its current intelligence to improve the cognitive machinery that produces its intelligence. This feedback loop in modern AI may indicate the model rewritin
  • Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, loss, model size and data; at its core, it is about how to allocate precious compute optimally between $N$
  • Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute ( Graves et al. 2016 , Ling, et al. 2017 , Cobbe et al. 2021 ) and Chain-of-thought (CoT) ( Wei et al. 2022 , Nye et al. 2021 ), have led to significant improvements in model performance, while raising many research questions. This post aims to review recent developments in how to effectively use test-time compute (i.e. “thinking time”) and why it helps.
  • Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fundamentally challenging to accurately specify a reward function. With the rise of language models generalizing to a broad spectrum of tasks and RLHF becomes a de facto method for alignment training, reward hacking in RL
  • Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem of hallucination to cases where the model output is fabricated and not grounded by either the provided context or world knowledge. There are two types of hallucination: In-context hallucination: The model output should
  • Diffusion models have demonstrated strong results on image synthesis in past years. Now the research community has started working on a harder task—using it for video generation. The task itself is a superset of the image case, since an image is a video of 1 frame, and it is much more challenging because: It has extra requirements on temporal consistency across frames in time, which naturally demands more world knowledge to be encoded into the model. In comparison to text or images, it is more d
  • [Special thank you to Ian Kivlichan for many useful pointers (E.g. the 100+ year old Nature paper “Vox populi”) and nice feedback. 🙏 ] High-quality data is the fuel for modern data deep learning model training. Most of the task-specific labeled data comes from human annotation, such as classification task or RLHF labeling (which can be constructed as classification format) for LLM alignment training. Lots of ML techniques in the post can help with data quality, but fundamentally human data colle
  • The use of large language models in the real world has strongly accelerated by the launch of ChatGPT. We (including my team at OpenAI, shoutout to them) have invested a lot of effort to build default safe behavior into the model during the alignment process (e.g. via RLHF ). However, adversarial attacks or jailbreak prompts could potentially trigger the model to output something undesired. A large body of ground work on adversarial attacks is on images, and differently it operates in the continu
  • Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT , GPT-Engineer and BabyAGI , serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: Plannin
  • Prompt Engineering , also known as In-Context Prompting , refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models. At its
  • Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment of that 2020 post — restructure the hierarchy of sections and improve many sections with more recent papers. Version 2.0 is a superset of the old version, about twice the length. Notations Symbol Meaning $d$ The model size / hidden state dimension / positional encoding size. $h$ The number of heads in multi-head attent
  • [Updated on 2023-01-24: add a small section on Distillation .] Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale. Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are two main factor
  • Neural networks are well known to be over-parameterized and can often easily fit data with near-zero training loss with decent generalization performance on test dataset. Although all these parameters are initialized at random, the optimization process can consistently lead to similarly good outcomes. And this is true even when the number of model parameters exceeds the number of training data points. Neural tangent kernel (NTK) ( Jacot et al. 2018 ) is a kernel to explain the evolution of neura
  • Processing images to generate text, such as image captioning and visual question-answering, has been studied for years. Traditionally such systems rely on an object detection network as a vision encoder to capture visual features and then produce text via a text decoder. Given a large amount of existing literature, in this post, I would like to only focus on one approach for solving vision language tasks, which is to extend pre-trained generalized language models to be capable of consuming visua
  • Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2 ). Let’s consider two approaches for generating synthetic data for training. Augmented data . Given a set of existing training samples, we can apply a variety of augmentation, distortion and transformation to derive new data points without losing the key attributes. We have covered a bunch of augmentation methods on text and images in a previous post on contrastive learning. For the sake of post completeness, I d
  • This is part 2 of what to do when facing a limited amount of labeled data for supervised learning tasks. This time we will get some amount of human labeling work involved, but within a budget limit, and therefore we need to be smart when selecting which samples to label.
  • When facing a limited amount of labeled data for supervised learning tasks, four approaches are commonly discussed.
  • [Updated on 2022-03-13: add expert choice routing .] [Updated on 2022-06-10]: Greg and I wrote a shorted and upgraded version of this post, published on OpenAI Blog: “Techniques for Training Large Neural Networks”
  • [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance , GLIDE , unCLIP and Imagen . [Updated on 2022-08-31: Added latent diffusion model . [Updated on 2024-04-13: Added progressive distillation , consistency models , and the Model Architecture section .
  • The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning .
  • Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks. However, to safely deploy them for practical real-world applications demands a strong safety control over the model generation process.
  • [Updated on 2021-02-01: Updated to version 2.0 with several work added and many typos fixed.] [Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.] [Updated on 2021-09-19: Add “unlikelihood training” .]
  • [Updated on 2020-11-12: add an example on closed-book factual QA using OpenAI API (beta). A model that can answer any question with regard to factual knowledge can lead to many useful and practical applications, such as working as a chatbot or an AI assistant🤖. In this post, we will review several common approaches for building such an open-domain question answering system.
  • Although most popular and successful model architectures are designed by human experts, it doesn’t mean we have explored the entire network architecture space and settled down with the best option. We would have a better chance to find the optimal solution if we adopt a systematic and automatic way of learning high-performance model architectures.
  • [Updated on 2020-06-17: Add “exploration via disagreement” in the “Forward Dynamics” section . Exploitation versus exploration is a critical topic in Reinforcement Learning. We’d like the RL agent to find the best solution as fast as possible. However, in the meantime, committing to solutions too quickly without enough exploration sounds pretty bad, as it could lead to local minima or total failure. Modern RL algorithms that optimize for the best returns can achieve good exploitation quite effic
  • [Updated on 2023-01-27 : After almost three years, I did a big refactoring update of this post to incorporate a bunch of new Transformer models since 2020. The enhanced version of this post is here: The Transformer Family Version 2.0 . Please refer to that post on this topic.]
  • [Updated on 2020-02-03: mentioning PCG in the “Task-Specific Curriculum” section. [Updated on 2020-02-04: Add a new “curriculum through distillation” section.
  • [Updated on 2020-01-09: add a new section on Contrastive Predictive Coding ]. [Updated on 2020-04-13: add a “Momentum Contrast” section on MoCo, SimCLR and CURL.] [Updated on 2020-07-08: add a “Bisimulation” section on DeepMDP and DBC.] [Updated on 2020-09-12: add MoCo V2 and BYOL in the “Momentum Contrast” section.] [Updated on 2021-05-31: remove section on “Momentum Contrast” and add a pointer to a full post on “Contrastive Representation Learning” ]
  • Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix. Examples of black-box optimization methods include Simulated Annealing , Hill Climbing and Nelder-Mead method .
  • In my earlier post on meta-learning , the problem is mainly defined in the context of few-shot classification. Here I would like to explore more into cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently.
  • In Robotics, one of the hardest problems is how to make your model transfer to the real world. Due to the sample inefficiency of deep RL algorithms and the cost of data collection on real robots, we often need to train models in a simulator which theoretically provides an infinite amount of data. However, the reality gap between the simulator and the physical world often leads to failure when working with physical robots. The gap is triggered by an inconsistency between physical parameters (i.e.
  • [Updated on 2019-05-27: add the section on Lottery Ticket Hypothesis.] If you are like me, entering into the field of deep learning with experience in traditional machine learning, you may often ponder over this question: Since a typical deep neural network has so many parameters and training error can easily be perfect, it should surely suffer from substantial overfitting. How could it be ever generalized to out-of-sample data points?
  • [Updated on 2019-02-14: add ULMFiT and GPT-2 .] [Updated on 2020-02-29: add ALBERT .] [Updated on 2020-10-25: add RoBERTa .] [Updated on 2020-12-13: add T5 .] [Updated on 2020-12-30: add GPT-3 .] [Updated on 2021-11-13: add XLNet , BART and ELECTRA ; Also updated the Summary section.] I guess they are Elmo & Bert? (Image source: here ) We have seen amazing progress in NLP in 2018. Large-scale pre-trained language modes like OpenAI GPT and BERT have achieved great performance on a variety of lang
  • In Part 3 , we have reviewed models in the R-CNN family. All of them are region-based object detection algorithms. They can achieve high accuracy but could be too slow for certain applications such as autonomous driving. In Part 4, we only focus on fast object detection models, including SSD, RetinaNet, and models in the YOLO family.
  • [Updated on 2019-10-01: thanks to Tianhao, we have this post translated in Chinese !]
  • So far, I’ve written about two types of generative models, GAN and VAE . Neither of them explicitly learns the probability density function of real data, $p(\mathbf{x})$ (where $\mathbf{x} \in \mathcal{D}$) — because it is really hard! Taking the generative model with latent variables as an example, $p(\mathbf{x}) = \int p(\mathbf{x}\vert\mathbf{z})p(\mathbf{z})d\mathbf{z}$ can hardly be calculated as it is intractable to go through all possible values of the latent code $\mathbf{z}$.
  • [Updated on 2019-07-18: add a section on VQ-VAE & VQ-VAE-2 .] [Updated on 2019-07-26: add a section on TD-VAE .] Autocoder is invented to reconstruct high-dimensional data using a neural network model with a narrow bottleneck layer in the middle (oops, this is probably not true for Variational Autoencoder , and we will investigate it in details in later sections). A nice byproduct is dimension reduction: the bottleneck layer captures a compressed latent encoding. Such a low-dimensional represent
  • [Updated on 2018-10-28: Add Pointer Network and the link to my implementation of Transformer.] [Updated on 2018-11-06: Add a link to the implementation of Transformer model.] [Updated on 2018-11-18: Add Neural Turing Machines .] [Updated on 2019-07-18: Correct the mistake on using the term “self-attention” when introducing the show-attention-tell paper; moved it to Self-Attention section.] [Updated on 2020-04-07: A follow-up post on improved Transformer models is here .]
  • The full implementation is available in lilianweng/deep-reinforcement-learning-gym In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wild. The implementation is gonna be built in Tensorflow and OpenAI gym environment. The full version of the code in this tutorial is available in [lilian/deep-reinforcement-learning-gym] .
  • [Updated on 2018-06-30: add two new policy gradient methods, SAC and D4PG .] [Updated on 2018-09-30: add a new policy gradient method, TD3 .] [Updated on 2019-02-09: add SAC with automatically adjusted temperature ]. [Updated on 2019-06-26: Thanks to Chanseok, we have a version of this post in Korean ]. [Updated on 2019-09-12: add a new policy gradient method SVPG .] [Updated on 2019-12-22: add a new policy gradient method IMPALA .] [Updated on 2020-10-15: add a new policy gradient method PPG &
  • [Updated on 2020-09-03: Updated the algorithm of SARSA and Q-learning so that the difference is more pronounced. [Updated on 2021-09-19: Thanks to 爱吃猫的鱼, we have this post in Chinese ].
  • The algorithms are implemented for Bernoulli bandit in lilianweng/multi-armed-bandit . Exploitation vs Exploration The exploration vs exploitation dilemma exists in many aspects of our life. Say, your favorite restaurant is right around the corner. If you go there every day, you would be confident of what you will get, but miss the chances of discovering an even better option. If you try new places all the time, very likely you are gonna have to eat unpleasant food from time to time. Similarly,
  • [Updated on 2018-12-20: Remove YOLO here. Part 4 will cover multiple fast object detection algorithms, including YOLO.] [Updated on 2018-12-27: Add bbox regression and tricks sections for R-CNN.] In the series of “Object Detection for Dummies”, we started with basic concepts in image processing, such as gradient vectors and HOG, in Part 1 . Then we introduced classic convolutional neural network architecture designs for classification and pioneer models for object recognition, Overfeat and DPM,
  • Part 1 of the “Object Detection for Dummies” series introduced: (1) the concept of image gradient vector and how HOG algorithm summarizes the information across all the gradient vectors in one image; (2) how the image segmentation algorithm works to detect regions that potentially contain objects; (3) how the Selective Search algorithm refines the outcomes of image segmentation for better region proposal.
  • I’ve never worked in the field of computer vision and has no idea how the magic could work when an autonomous car is configured to tell apart a stop sign from a pedestrian in a red hat. To motivate myself to look into the maths behind object recognition and detection algorithms, I’m writing a few posts on this topic “Object Detection for Dummies”. This post, part 1, starts with super rudimentary concepts in image processing and a few methods for image segmentation. Nothing related to deep neural
  • Human vocabulary comes in free text. In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values. One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0).
  • Professor Naftali Tishby passed away in 2021. Hope the post can introduce his cool idea of information bottleneck to more people. Recently I watched the talk “Information Theory in Deep Learning” by Prof Naftali Tishby and found it very interesting. He presented how to apply the information theory to study the growth and transformation of deep neural networks during training. Using the Information Bottleneck (IB) method, he proposed a new learning bound for deep neural networks (DNN), as the tra
  • [Updated on 2018-09-30: thanks to Yoonju, we have this post translated in Korean !] [Updated on 2019-04-18: this post is also available on arXiv .] Generative adversarial network (GAN) has shown great results in many generative tasks to replicate the real-world rich content such as images, human language, and music. It is inspired by game theory: two models, a generator and a critic, are competing with each other while making each other stronger at the same time. However, it is rather challengin
  • The machine learning models have started penetrating into critical areas like health care, justice systems, and financial industry. Thus to figure out how the models make the decisions and make sure the decisioning process is aligned with the ethnic requirements or legal regulations becomes a necessity.
  • In the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input.
  • This is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. The full working code is available in github.com/lilianweng/stock-rnn . If you don’t know what is recurrent neural network or LSTM cell, feel free to check my previous post .
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  • Release: llm-gemini 0.37 Google launched Gemini 4 Hydrogen today - the lowest cost member of the Gemini 4 family. It's actually not too far off the price of its much bigger siblings, Carbon and Argon. I'm finding Gemini 4's pricing really confusing. Here's the pricing scheme: Model Dates Input ($/M) Cached input Output Gemini 4 Carbon Through Nov 30, 2026 $2.00 $0.10 $10.00 From Dec 1, 2026 $4.00 $0.20 $20.00 Gemini 4 Argon Through Nov 30, 2026 $2.00 $0.10 $10.00 From Dec 1, 2026 $4.00 $0.20 $20
  • Gemini Decisions API Google join Jev and OpenAI in the decision model category with "Gemini Decision 1". This new model API is almost identical to Jev: it supports the same three types of questions (yes/no, choice, and rubric-scoring) and even reuses Jev's term "Noul" for a yes/no question. It's priced the same as the GPT-6 Luna decision model: 10 cents per million input tokens, and output is free. Jev remains the cheapest of the three, at 4.2 cents per million. Jev is text (and JSON) only. Open
  • aws-emulator Finally, an official emulator from AWS to run tests against a local clone of their many different services without having to provision a paid API key to do so. It's written in Rust by a huge swarm of agents - 29,684 commits in the first three weeks of the project. Swaminathan (Swami) Sivasubramanian wrote about how it came to be in a guest post on Werner Vogels' blog. It currently requires Docker to run on macOS or Windows. I'm hoping they fix that in the future - it would be great
  • Browse tools.simonwillison.net by tag I've been wanting a better way to browse my collection of vibe-coded tools (244 and counting) for a while. I had a few Fable 5.2 credits left over before my weekly reset so I set it to work using tree-sitter to analyze the code for JavaScript APIs, libraries, and web API hosts used by each tool. The homepage now features tag clouds for browsing by each of those categories of tag: Tags: javascript , tools , ai , generative-ai , llms , claude , vibe-coding
  • Release: llm 0.37.2 Fixes a security issue reported by Daniel de Araujo in GHSA-v9h7-jj4g-f862 and GHSA-r639-rr7r-x72w . LLM has a mechanism called extra-openai-models.yaml - now managed by the new-in-0.37 llm openai endpoint add command - for using LLM with OpenAI-compatible API endpoints from other providers. Daniel reported two ways this could accidentally send your configured OpenAI API key to one of those providers: if the key named by api_key_name had never been stored, or if a responses:
  • Anthropic's IPO roadshow video Anthropic kicked off their IPO roadshow yesterday, and their roadshow video has some previously unreleased information about how their business works. The section on "Approach to compute" with co-founder Tom Brown is particularly interesting, describing how Anthropic train and serve Claude across AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs. I'm not sure why there's no official video transcript, but here's one that I extracted using MacWhisper. Highlights b
  • Release: llm-gemini 0.36 Gemini 4 Carbon was announced two weeks ago for "a subset of cyber defenders", but today it's available to everyone else . This is Google's best ever Gemini, priced at $2/million input and $10/million output at launch (that's GPT-6.1 Sol / Claude Sonnet 5.5 prices) but scheduled to double in price to $4/$20 on December 1st (same as Opus 5.5). Here are three pelicans for the low, medium and high thinking efforts (it defaults to medium). This is the pelican I got for high:
  • Introducing Our Smallest, Most Efficient Model: Muse Ember Meta AI just launched their entry in the Claude Haiku/GPT Luna category: Muse Ember, which they describe as: [...] our smallest and most efficient model designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. For shorter prompts it's priced the same as GPT-6 Luna and Claude Haiku 5.5 - both are $0.10/million input tokens and $0.50/million ou
  • Claude Fable 5.2 pays close attention to who owns the code it works on. In Anthropic’s testing on small coding tasks, the model tended to add to the existing structure when the prompt said nothing about ownership or presented the code base as someone else’s, even when that duplicated existing code. When the prompt said the code base was the model’s own, it often consolidated duplicated code and sometimes made related fixes without being asked. If you want the model to improve the structure as it
  • Tool: Yonkers Rises I realized I didn't know where Yonkers was, so I looked it up on Google Maps and then, to help embed its location and shape in my head (and to try out Claude Fable 5.2's 3D modeling and animation skills), I prompted Fable 5.2 Medium to: Make a three js animation where yonkers stands up as a big mech (cool stand up motion) and átomos around manhattan and queens and Brooklyn, use openstreetmap mapping data It correctly deduced that "átomos" was a mobile keyboard typo for "stomp
  • Release: llm-anthropic 0.31 Anthropic released Fable 5.2 and Mythos 5.2 today. Fable 5.2 is currently ranked first on Artificial Analysis . Anthropic say it "solved two open problems in the physics of black holes", so clearly their internal benchmarks have become more ambitious! I've added the new models to llm-anthropic , so you can now run prompts through Fable 5.2 like this: uv tool install llm llm install llm-anthropic llm keys set anthropic # paste key here llm -m claude-fable-5.2 "Generate
  • Our house has a box room, full of boxes that we still haven't unpacked from our last move. A couple of weeks ago something in that room started intermittently beeping . The beeps were far enough apart that standing in the room and trying to determine the source of the noise was frustrating, and I really didn't want to dig through every one of those boxes. So I had Claude Code build Beep Locator , a software system for triangulating the source of a mysterious beep using multiple devices scattered
  • My comment on The Web Is More Than Enough, by Paul Ford — Hacker News. OK, this comment convinced me to give that a shot. Here's a thing I made in Three.js this morning - a Nudibranch themed Faberge Egg: https://static.simonwillison.net/static/2026/Hidden-Reef-Egg... (77KB) I just asked Claude (Opus 5.5 Medium running in regular Claude Chat) to port that to Vanilla JS, in the same thread where it had built it, and got this: https://static.simonwillison.net/static/2026/Hidden-Reef-Egg... (98KB) T
  • Most of the changes I make to my projects these days come in the form of PRs opened for me by Claude Code or Codex. The PR is my unit of work - I use them to bundle commits together, iterating on them until a feature is complete, document decisions and then merge the finished result. Today's coding agents are great at writing PR descriptions. They're too good at describing the work The problem is that they are too good. An agent-written PR description is accurate and comprehensive. It describes
  • Research: parse_number() as a SQLite extension: C vs Rust I was out walking the dog when I got curious about how easy/difficult it would be to write a SQLite C extension that adds a parse_number() function for turning strings like -$1,234.56 or '12.5%' into numeric values that could be summed and sorted. I fired up regular Claude chat (Opus 5.5) and prompted : I'm thinking about a SQLite custom function 'parse_number(value)' that strips ',', '$', '£' and '%', handles a leading '-', and returns '
  • datasette #3046: Don't serve link: rel=next HTTP headers to Firefox for HTML pages I was looking at the Firefox network panel today when I noticed that requests to a Datasette table page were triggering an HTTP prefetch to the next page of rows in that table. This was happening because Datasette pages serve an HTTP link header like this: link: <https://latest.datasette.io/fixtures/compound_three_primary_keys?_next=a%2Cd%2Cv>; rel="next" This is bad. A tiny fraction of the visitors to one of thos
  • My comment on Terry Pratchett's Study — Hacker News. Terry Pratchett once wrote about the concept of "horizontal wealth". Vertical wealth is where you spend money on rich people stuff - yachts and suchlike. Horizontal wealth is where you spend more money on the things you liked before you got rich: https://chibuikeani.blogspot.com/2017/02/the-price-of-happin... But horizontal wealth means not letting your increased income dictate your tastes. You like books and now you have money? Buy more books
  • The products aren't good enough to sell people on the costs. You cannot tell people that you have to listen to the whine of a data center in their backyard for AI overviews and some of the most hilarious deep fake slop in history. It's not worth it. It just isn't worth it to anyone. And agents aren't real and they might not be being used the way that anyone wants them to yet. All these companies are spending more than they're making on these products. Refrigerators holding blood storage in Europ
  • The web browser is very, very, very powerful software for applications, and the cost of accessing that power is going down seemingly every day . — Paul Ford , The Web Is More Than Enough Tags: browsers , paul-ford , web-development
  • Release: datasette-llm 0.1a10 Fixes a bug that affected my agent.datasette.io demo site, where the set of available LLM models was not correctly restricted to just those in the configuration. Tags: llm , datasette
  • Release: datasette-apps 0.3a0 Datasette Apps adds support for hosting and editing custom HTML applications inside of Datasette. These can be created by hand, but it's easier to create them using LLMs via Datasette Agent . This release improves that integration: Apps can now be tagged in Datasette Agent chat messages by typing # , which autocompletes from the apps the user has permission to view. New app_add_stored_queries() and app_remove_stored_queries() tools let the agent manage which stored
  • Release: datasette-agent 0.5a0 This is a significant release of Datasette Agent . The most notable new feature is called the panel - it lets the agent chat open and then sit next to pages from Datasette, for example a table or query page. When combined with datasette-apps this means you can prompt a database-backed app into existence and then modify it from the chat that sits next to that app. You can try it by signing in with your GitHub account to agent.datasette.io . Tags: projects , datasett
  • Release: llm 0.37.1 A bugfix release for LLM , with contributions from Steven Huang , Tao An , Arjan Mossel , and XQ . Tags: llm
  • My comment on AI is destroying my CS degree — Hacker News. I don't envy educators right now. AI clearly means you need to change how you teach and evaluate students. We are only a few years in though, and it's still extremely unclear what effective teaching with these tools looks like. The worst part is that the evaluation loop is measured in multiple years! If you start trying some new education mechanism today you won't get a robust idea of how well it works until your current cohort graduate
  • Yesterday's hot paper was Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights by Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab. The paper (OK, the authors classify is as a "blog post", but it's paper-shaped) found that frontier models, in particular GPT-6 Astra, can both interpret and modify the raw weights of a machine learning model - literally a text description of the model containing a bunch of floating point numbers. I've been playing with o
  • I discovered a sound that leaks your exact location (and it's everywhere) - YouTube Genius piece of OSINT by Colsto, who demonstrates how you can take almost imperceptible background noise of planes from a video soundtrack, figure out their Doppler curves, then cross reference them against ADS-B data to find the exact location of the video based on their speed, distance, and gaps between different flights. Via Lobste.rs Tags: privacy , youtube
  • I sent out the October edition of my sponsors-only monthly newsletter . If you are a sponsor (or start a sponsorship now) you can access it here . This month: The October inflection point for vibe game development The vulnerability apocalypse gets hot LLM releases API credits for $100+/month subscriptions What I'm using right now My software releases My September newsletter can now be seen here , as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy! Tags: newslet
  • Release: llm 0.37 There's a lot of stuff in this new release of my LLM CLI tool and Python library, representing around two months of accumulated features and fixes. I added gpt-6.1-sol and gpt-6.1-terra , plus a new llm openai endpoint add command to make it easier to configure additional models that use OpenAI-compatible endpoints. I switched the default model (if you have not set your own default) to gpt-6-luna - half the price of the previous default, gpt-5.6-luna . LLM's support for embeddi
  • I was reminded today of AI Adoption Rate Trending Down for Large Companies , a chart produced by Apollo Global Management in September 2025, which appeared to show a slowdown in U.S. corporate AI adoption based on numbers collected by the U.S. Census Bureau. At the time I used that chart to demonstrate GPT-5 data analysis , and then to experiment with Claude's new Code Interpreter . I decided to try running those numbers again to see what had changed over the past year, and in doing so learned t
  • I've been getting the occasional confusing Python dependency error since switching over to Python 3.15 and the rule I'm having to remember is: If in doubt, delete the uv.lock file Most of my projects are libraries which don't include a uv.lock file, since it's rude for a library dependency to lock versions for anything else that uses it. Running uv run pytest etc tends to leave uv.lock files lying around, which are listed in .gitignore and so do not get committed to my repositories. But... somet
  • More broadly, the use of LLMs erodes trust in documentation, in software, and in the organizations which produce them. Perhaps engineers will begin to treat any new software project with suspicion—the signals we used to rely on as markers of a mature system are now easier to counterfeit. [...] In the age of LLMs, what can maintainers do to ensure correct software and accurate documentation? Having done that work, how can they demonstrate its quality to users? — Kyle Kingsbury , Jepsen results fo
  • How We Took Over ChatGPT Accounts Parameter Labs describe an exploit they found against ChatGPT's implementation of the artifacts pattern (OpenAI used to call this "Canvas" but it looks like the latest revision is called HTML previews ), where interactive custom HTML apps are displayed in a sandboxed iframe. It's important that HTML in these stays tightly sandboxed to avoid untrusted code interacting with OpenAI APIs and accessing private data. Parameter found quite a complex attack chain. OpenA
  • Release: datasette-llm 0.1a9 I have some big upgrades to Datasette Agent in the pipeline, but in the meantime here's a tiny user experience improvement to the underlying datasette-llm plugin. It now picks up the default model used by LLM , which means you can start using Datasette Agent without needing to explicitly configure a default model: uvx --with datasette-agent datasette content.db --internal internal.db Previously you would also need to specify a model with -s plugins.datasette-agent.de
  • dbreunig/nesformer Inspired by qwenputer by Divyansh Agrawal, Drew Breunig baked a working emulator for a Nintendo Entertainment System 6502 CPU into the weights of Qwen3.5-4B. You can now play NES ROMs such as Super Mario Bros (very slowly) running inside Qwen! In a comment on Hacker News Drew reveals the Opus 5.5 prompts he used to build this system: Read this article and any relevant source code: https://divagr.com/blog/qwenputer. Then explore if you could emulate the chip and ram in a Ninten
  • API credits benefit for eligible ChatGPT subscriptions Earlier this month Anthropic announced a generous new benefit for their Max and Team subscriptions: $100, $200, and $500 per month subscribers would get the equivalent quantity of (expiring) credits to use with the Anthropic API. OpenAI have now launched pretty much exactly the same deal: if you have a $100, $200, or $500 subscription plan with them you now get that same quantity of credits to apply to one of your API organizations. I mainta
  • How I Built the Most AI-Pilled Company in the World Don't be put off by the clickbait headline, this piece by Jacob Sansbury describing how they are building Spawn is a fascinating exploration of the bleeding edge of what's possible with agentic engineering against frontier models. Spawn is a massively multiplayer gaming environment where players can use prompts (to the in-game agent, Savi) to modify the game world. I fired it up on my phone for a few minutes and was able to prompt a castle into
  • every product i use should just be a headless mcp, that feeds into an agent, where i never have to think about anything underneath. except for our product, of course. in our product, customers love the ui and love us very much. we're special that way. — Ben Hylak Tags: model-context-protocol , ai
  • In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes. Despite that complexity, much of the variation in performance is explained by just a single variable: model size. In general, models that are bigger will (a) be more performant / reliable, (b) cost more per token, and (c) have better recall of esoteric facts. As you note, our models are ordered: Luna < Terra < Sol < Astr
  • GitHub Octoverse 2026 I'm at GitHub Universe today (GitHub gave me a free ticket through their "GitHub Stars" program). EVP Jay Parikh just announced this year's Octoverse report, and highlighted these numbers in particular: This is a pretty stark representation of the impact of agentic engineering on GitHub. In 2026, merged pull requests tripled in less than a year to 220 million per month. Commits more than tripled to 7.38 billion per month, with most of that increase in the second half of thi
  • I've written before about how November 2025 was an inflection point for coding agents - the point where Opus 4.5 and GPT-5.1 crossed a tipping point that meant their respective coding agents became reliable daily drivers for software engineers. I think we've reached a new inflection point in October 2026, and this time it's for vibe coding computer games . Tree Appreciator Emberwick Battle Drones Pumpkin Farmer, aka Quarter Sun Cove Four times as insulting What happens if someone actually puts t
  • If your work does not benefit from current AI tooling, that is a finding, not a failing. Document why, share it with your community of peers, bring it to your manager, and work to find solutions. An engineer who can explain where AI does not yet help their work is demonstrating the judgement we are asking for. — Terk Terkowitz , An update from Amazon engineering leadership on the role of the developer in the age of AI Tags: amazon , generative-ai , agentic-engineering , ai , llms
  • Game Boy Color on My Conference Badge I'm at GitHub Universe this week and, similar to last year , the conference badge is a full-blown color screen computer. I've been hacking around with it again, and then I ran into with Steve Francia... who worked with Gemini to get a full NES emulator working on it, and then went a step further and deployed an emulator for both the Game Boy and the Game Boy Color. Which means, if you can obtain the ROM, you can run The Legend of Zelda: Link's Awakening on y
  • GPT-6.1 Terra It's always interesting to note which aspects of a new model are emphasized in the release announcement. In the case of GPT-6.1 Terra, OpenAI went with visual reasoning and computer use: We’re introducing GPT-6.1 Terra , a fast, efficient model that combines detailed visual reasoning and precise computer use alongside clearer, more natural writing. A significant step up from GPT‑5.6 Terra, it excels at the communication and computer-based tasks that fill the workday, from drafting
  • Because big tech platforms essentially do not have customer support and make it impossible for their users to contact a human, many people experiencing run-of-the-mill tech problems have turned to ChatGPT. And, increasingly, ChatGPT is telling people that have normal tech support problems that they have discovered something journalistically notable and that 404 Media, specifically, can help them. — Jason Koebler , ChatGPT Keeps Telling People 404 Media Can Solve Their Tech Support Problems Tags:
  • Research: webframe: evaluated as a scraper and a sandbox webframe is a neat little project from Jeff Lindsay which lets you "run a web page like a program" - you point it at a URL or file and it executes it in a hidden headless web view and outputs console.log() to standard output and any exceptions to standard error. It's written in Go on top of the glaze library, which provides access to a system browser ("WKWebView on macOS, WebKitGTK on Linux, WebView2 on Windows") so it doesn't need to bund
  • Tool: Seam Cutter I built this weird and very specific cropping tool - it lets you create a horizontal or vertical cut across an image, then slide the two sides relative to each other. Effectively it lets you crop out a section in the middle of an image. I wanted this because I often use Tailor on my iPhone to stitch several screenshots together into lot long image, but sometimes I find myself wanting to crop out a section in the middle - to redact an irrelevant prompt from a transcript, for exa
  • Release: battledrones v14: Compass Rose, a four player map I added four player online mode to Battledrones - previous maps were limited to two. Here's the initial prompt I used: Add a new map for a four player battle - four way rotational symmetry, up to four AI players, works with the WebSocket non-audio based multiplayer mode so up to four human players can join (any spare slots use AI) Claude Opus 5.5 added a new map called "Compass Rose" and modified the WebSockets multiplayer network code t
  • Tool: Quick Study Prompt to Claude Opus 5.5 High : Build an artifact where you open a photo and it rapidly (in a few seconds) paints a version of that photo using a canvas element and simulated paintbrush See also my previous experiment getting Claude Fable to simulate a watercolor illustration with P5.js. Quick Study uses Sobel edge detection , with no external libraries beyond Google Fonts. Tags: tools
  • Introducing Kimi K3.1 Pro Kimi K3.1 Pro landed on the Kimi API today - it's not yet open weights, but Moonshot promise "Full model weights will be released by Nov 4, 2026". They're also offering a "Highspeed" version that's 2-3x faster, at twice the price (except for input token cache hits, which are priced the same). The US dollar pricing is $2.00/million tokens for input and $10/million for output - the same as GPT-6.1 Sol and half the price of Claude Opus 5.5. Moonshot's own benchmarks show K
  • DE STAAT - FREEDOM (Official Music Video) OK well this is a first for me... at 00:44 in this new music video by Dutch alternative rock band De Staat : The visual effects are credited to Matties Grooten of Fake it or Leave it . Via flir on Hacker News Tags: music , ai , generative-ai , pelican-riding-a-bicycle
  • Donating Agent Skills to the Agentic AI Foundation News today from Anthropic: We’re committed to keeping Agent Skills open, community-driven, and vendor-neutral. Today, we are donating the standard to the Agentic AI Foundation (AAIF) under the Linux Foundation, which we co-founded with Block and OpenAI last year, and to which we donated the Model Context Protocol (MCP). I'm pleased to note I called this one last December , when Anthropic first declared skills to be an "open standard": I wouldn't
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  • Jensen Huang's resignation as CEO of Nvidia leaves CFO Colette Kress atop the Central Bank of AI : $42b in 2025-2026 M&A & tech buyouts, $61b of cash invested in equity across 229 private startups & 13 public companies, & $164.5b in lease guarantees & cloud capacity backstops. Last quarter Nvidia deployed 112% of operating cash flow into equity & M&A.
  • Public disclosures from CoreWeave, Anthropic & wholesale data center landlords piece together the AI gross profit value chain in dollars per watt per year. At a 40% application margin, applications (40%), neoclouds (28%) & inference vendors (24%) capture 92% of total gross profit before GPU depreciation.
  • KC, Chief People Officer at OpenAI & former CIO at Palantir, argues that efficiency is the wrong frame for AI. The prize is a reimagined firm where every role gains an agent manager & EQ becomes what separates people.
  • US AI demand grows from 66gw in 2027 while tokens per watt compound 2.9x a year, so the grid only binds if token demand outruns efficiency. Break-even real token growth is ~2.5x a year with no cancellations.
  • The cost of an autonomous 32-step corporate break-in fell 99% in five months from $7,500 on Claude Mythos Preview in April 2026 to $90 on open-weight GLM-5.3. Attackers run an expected-value calculation on AI compute ($1m of compute for a 1% shot at a $1b payout yields $10m in expected value), while defenders must maximize security effectiveness across speed (responding in minutes or seconds), scale (parallel agent swarms), & breadth (82-to-1 non-human identities).
  • US AI capacity grows from 38gw today to an 81gw ceiling in 2027. Because 40% of compute serves inference & new Blackwell & Rubin racks lift blended monetization from $9.4b to $15.1b per inference gigawatt, theoretical US inference revenue rises 3.4x from $0.14t to $0.49t.
  • Anthropic's Form S-1/A rolls financials through Q3 2026 ($17.0b revenue, +$8.6b operating cash flow) & reveals that $5.0b of short-term burst compute at $7 to $13 per GPU-hour lifted blended GPU-hour costs 82% in two quarters, flipping Q2's $974m operating profit into a $567m Q3 loss & driving $895.4b of long-term obligations.
  • AI consists of four layers: application, harness & context, model, & data center. Each layer faces an unresolved economic question, & the four layers interplay directly: rising GPU financing costs increase the value of the harness, while model & inference co-design multiplies how many users the same data center hardware can serve.
  • Two CEOs have set $1t revenue targets on their businesses while cloud growth accelerates at $30b to $50b quarterly scale. The first $10t company will be the one that crosses the trillion dollar chasm before AI erodes its legacy profit pool.
  • Arvind KC, Chief People Officer at OpenAI & former CIO at Palantir, joins Office Hours to discuss the future of the people function. Why efficiency is the wrong frame when model capability doubles every six months. How the people team democratizes AI by amplifying a company's most valuable capability rather than rebuilding the whole org. How span of control shifts from managing seven people to directing hundreds of agents. & why emotional intelligence becomes the leader's edge when intelligence
  • Coding & customer support were the first two AI markets. Attacks on banks, certificate authorities & core open source libraries have never been greater, suggesting security is third. A survey of 75 enterprise security practitioners finds security spend divides across five categories : security operations, SIEM, penetration testing, threat intelligence & vulnerability management. Every one carries significant AI token consumption. Greg Brockman argues security spend should vastly outstrip softwar
  • Analyze Anthropic's S-1 IPO metrics : $4.6B revenue growing 11.9-fold, a derived 40% gross margin, $974M of operating profit in Q2, & $637B of compute commitments.
  • Every major build-out has two phases per Carlota Perez: infrastructure, then the applications that pay for it. The telecom internet build-out ran capex twelve times ahead of application revenue at the 2000 peak, & took seven years to get application revenue above 10% of capex, & ten to reach 48%. AI has reached that 10% mark in four years, roughly half the time.
  • Bardia Shahali, VP of Sales at Granola, joins Office Hours to explain how AI-era sales teams run smaller because revenue per rep multiplies, why comp plans moved from annual to quarterly, & why the sales pod splits by layer between infrastructure & application companies.
  • SpaceX's third-quarter report & DeepSeek's inference disclosure together price one GPU card-hour four ways, from DeepSeek's theoretical $38 maximum to SpaceX's realized $2.44. Combined with a payback claim of under a year & a 2027 supply pipeline that is largely unbuilt, the data suggests large-scale inference is a healthy business with room for prices to climb.
  • Anthropic is considered an AI company, but it should be valued as a cloud. On that basis, at $1.5t to $2t it would be worth roughly what Wall Street's estimates imply for a standalone GCP, on two-thirds the revenue.
  • Five frontier models now sit within five points of each other in terms of intelligence, but their cost per unit differs by 3.8x. Google, which has built chips alongside its models for more than 15 years, prices lowest; Microsoft & OpenAI are now chasing the same advantage, & pricing should keep deflating even as frontier margins hold.
  • Theory's second annual AI in Practice Survey finds four results that surprised us : 43% rate security a top pain against the 10% who say its tooling delivers value, 45% of teams route queries across models, Cloudflare hosts 62% of agent sandboxes, & 48% now read production logs with coding agents.
  • Bardia Shahali, VP of Sales at Granola, joins Office Hours to discuss what the sales organization should look like in the AI era, drawing on his experience scaling go-to-market at Fireworks AI and Granola.
  • AI models have reached commodity status as prices fall 41% while token usage surges 50%. To win relative share, labs should partner & resell models, control the user interface where distribution becomes the moat, & capture routing data to reduce the cost of training future models.
  • Jeanne DeWitt Grosser, COO of Vercel, walks through the construction of the company's inbound sales agent: one GTM engineer at 20% time, a prompt written by the top SDR, six weeks of human-in-the-loop QA, & the eventual migration from a 1,000-line prompt to 14 deterministic rules with the model reserved for judgment.
  • AI inference is set to surpass the database market to become the most important market in software, reaching ~$350b by 2027. This shifts software unit economics: usage dwarfs platform fees, gross margins compress below classic SaaS norms, and infrastructure becomes the dominant component of COGS.
  • Explores the contrast between organic 'growing' of software via vibe-coding and deterministic 'designing' via state machines and formal verification.
  • Tokenized real-world assets are the fastest-growing market in tech, compounding at 53% a month versus 25% for Anthropic. Stocks & commodities trade on blockchains 24/7, IPO pricing happens weeks before listing, & Allium now powers Bloomberg's RWA terminal dashboard.
  • Anthropic & OpenAI both re-rated their run rates in 2026 by segmenting : a mandatory enterprise repricing against an 80% price cut on the cheapest tier.
  • GPU rental prices doubled in six months while inference prices kept falling. The reconciliation is efficiency : the hinge that turns scarce silicon into cheap intelligence.
  • AI has removed the typing from software engineering & left the system design. DHH told Rails World that writing code by hand is no longer economically productive ; Artemis engineers merged 16 pull requests per day by August, & Lauren Tan ships 2,000 a month. The craft of fitting code together by hand is over ; what remains is designing the loop that decides whether the output is right. Donella Meadows's three properties of great system design, resilience, self-organization & hierarchy, describe
  • AI prices are collapsing, but the competition is not at the frontier. Anthropic held the Opus line at $5 & $25 across five releases while OpenAI cut Luna 80% then 50% again, & open models now run a majority of token volume at an 86% discount to closed models. The most capable model, Fable 5.1, commanded only 3.7% of gateway spending in its first twelve days. Demand is a normal distribution with a fat middle, & as intelligence per dollar explodes the tokens may shift to commodity. Whether that ha
  • Machine-native models replace human-facing text generation with zero-token typed execution, cutting inference costs by orders of magnitude for basic programming primitives.
  • A UC Berkeley study finds the harness sets the price of an answer : GPT-5.6 Sol costs 71% less on Pi than on Claude Code, the same model returning the same result, & none of the 42 harness comparisons shows a statistically significant quality difference. Two startups bidding on the same $250k contract earn 38% & 75% gross margins depending on what they built around the model, & the cheaper one pays back its sales cost in half the time. Effective harnesses take a deep customer understanding, rele
  • Jeanne DeWitt Grosser, COO of Vercel, reports 90% automation of inbound sales development, a support agent handling 93% of cases, a 32x ROI on the sales development agent, & a total annual infrastructure bill in the single-digit thousands. Vercel took its inbound SDR team from 10 to 1.25. The bottleneck is not the model, it is the codified workflow.
  • Amodei asked the industry to pace itself, but nobody defined the word. Five camps answered : interpretability wants time to understand models, labor wants time for workers, the economic camp wants growth to service the debt, the geopolitical camp wants position against China, & the regulatory capture camp wants no new rules. Each priced the consequences of a pause & none named a speed. The one mechanism that could produce a number, a training compute threshold, was tried in 2023 at 10^26 FLOPS,
  • OpenAI disclosed that its researchers now supervise 3.14 agent-workdays for every 8-hour human workday. Rather than making programmers three times smarter, software development is turning into a 24-hour factory where inference behaves like heavy tooling capex running multiple shifts.
  • AI token consumption expands in three waves : chat, a single agent, & a meta-harness orchestrating many agents. Each wave multiplies consumption not through faster generation but through parallelization, & the second wave has already overtaken the first.
  • Hyperscalers & data center operators will issue an estimated $4t in debt over the next five years to finance AI infrastructure. This credit expansion equals 286% of US commercial paper, 143% of global private credit, & 91% of the US municipal bond market, transforming AI infrastructure into a macroeconomic credit cycle.
  • Meta's Muse Spark 1.3 prices inference at two levels : $1.25/$4.25 per million tokens for private data, & $0.10/$0.20 for training consent : a 92% price spread. Michael Mauboussin taught that market prices contain information about underlying expectations. This spread establishes a liquid clearing price of $1.24/m tokens for user & agent prompt data, allowing Meta to vertically integrate the AI data supply chain & acquire post-training data at pennies on the dollar compared to specialized data l
  • Automating draft scaffolding does not shrink human editing time; it raises the output ceiling. A closed-loop taste flywheel cuts draft churn from 47 to 3 while line-level edits hold steady at a median of 136, shifting effort from structural triage to rhetorical precision. Across five years of writing, the quality floor surged: the 10th percentile gained twice as much as the 90th.
  • Over three years of testing AI writing systems, one-shot prompt templates failed. The working architecture is a closed-loop taste flywheel where draft versions collapse from 47 to 3, yet line-level edits remain flat at 130 per post. AI does not just save time; it raises the ceiling of what the same effort produces. The real question is whether we accept the pre-AI baseline or push for the higher-quality output AI now makes possible, the way chess engines raised the ceiling of human play without
  • The frontier AI market is segmenting from both ends of its supply chain. Rationing programs & export limits decide who may run the strongest models. Downstream, enterprises standardize on one or two named vendors, & products ship with a default model inside.
  • Three months into running r2, an agentic AI harness, four lessons held : an inbox beats a task list, routing between local & cloud models is essential, self-healing works but surfaces more errors before it surfaces fewer, and even a no-huddle offense needs a quarterback calling the plays a human still has to make.
  • AI model companies buy wholesale electricity by the megawatt and resell it as cognitive work. The unit economics of turning power into intelligence.
  • NVIDIA's Q2 FY27 revenue reached $96b with a $108b Q3 guide, but hyperscale revenue grew only 13% sequentially against 25% for everyone else. To fund the buyers filling that gap, NVIDIA extended payment terms — DSO rose from 45 to 60 days & receivables hit $63b — & built a $581b stack of supply commitments, power guarantees, leases & $101b of equity in the AI startups & neoclouds buying its chips.
  • NVIDIA's Q2 FY27 revenue reached $96b with a $108b Q3 guide, but hyperscale revenue grew only 13% sequentially against 25% for everyone else. To fund the buyers filling that gap, NVIDIA extended payment terms — DSO rose from 45 to 60 days & receivables hit $63b — & built a $581b stack of supply commitments, power guarantees, leases & $101b of equity in the AI startups & neoclouds buying its chips.
  • When designing an agent, should its session run for your entire five-year company tenure or reset every day? Perpetual sessions suffer from context rot and security exposure. The winning architecture is a daily coordinator that resets every 24 hours, delegating to ephemeral specialists and persisting state to local files.
  • When designing an agent, should its session run for your entire five-year company tenure or reset every day? Perpetual sessions suffer from context rot and security exposure. The winning architecture is a daily coordinator that resets every 24 hours, delegating to ephemeral specialists and persisting state to local files.
  • AI hardware bottlenecks cascade in sequential multi-year waves from GPUs to memory, SSDs, CPUs, HDDs, and physical data center shells, driving facility buildout costs to $20b per gigawatt through the classic Bullwhip Effect.
  • AI hardware bottlenecks cascade in sequential multi-year waves from GPUs to memory, SSDs, CPUs, HDDs, and physical data center shells, driving facility buildout costs to $20b per gigawatt through the classic Bullwhip Effect.
  • Local models now answer 89% of everyday chat & reasoning queries as well as frontier models, & their efficiency per watt has improved 5.3x in two years.
  • Local models now answer 89% of everyday chat & reasoning queries as well as frontier models, & their efficiency per watt has improved 5.3x in two years.
  • Every software product has its own grammar: Figma has frames and components, Salesforce has accounts, contacts, and opportunities, CAD has tools and constraints. Capability and required expertise rise together, so the most powerful tools are the ones fewest people can operate. AI lets users express intent in English while an agent handles the application's grammar, illustrated by a non-technical founder who directed Codex to drive CAD software and produce a dress once previously unmanufacturable
  • Every software product has its own grammar: Figma has frames and components, Salesforce has accounts, contacts, and opportunities, CAD has tools and constraints. Capability and required expertise rise together, so the most powerful tools are the ones fewest people can operate. AI lets users express intent in English while an agent handles the application's grammar, illustrated by a non-technical founder who directed Codex to drive CAD software and produce a dress once previously unmanufacturable
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  • 这里记录每周值得分享的科技内容,周五发布。( [通知] 下周端午假期,周刊休息。 ) 本杂志 开源 ,欢迎 投稿 。另有 《谁在招人》 服务,发布程序员招聘信息。合作请 邮件联系 (yifeng.ruan@gmail.com)。 封面图 上海体育学院内的绿瓦书店,是上海首家体育主题书店。中庭有13米高的"明月山",四周是小路,意为"书山有路勤为径"。( via ) rsync 的争论 rsync 是计算机同步最常用的工具,属于服务器的基本命令。 最近,有人发现,它的 最新版本 3.4.3 是由 Claude 生成的。这引发了轩然大波,怎么可以用 AI 去编写系统的基本命令呢,谁能保证不出错! 项目的 GitHub 讨论区就有人发一个批评帖,题目叫做 《不要乱搞这个软件》 ,矛头直指项目的维护者 Andrew Tridgell。 这个帖子热闹非凡,目前已有300多条发言,说什么的都有。发言者普遍认为,Claude 很可能引入漏洞,不应该使用。 有些人还说的比较礼貌。 我们非常有理由愤怒,因为一个非常稳定、备受信赖的工具 ,竟然立即开始走下坡路...... 而这一切仅仅是因为主要开发者在编
  • 这里记录每周值得分享的科技内容,周五发布。 本杂志 开源 ,欢迎 投稿 。另有 《谁在招人》 服务,发布程序员招聘信息。合作请 邮件联系 (yifeng.ruan@gmail.com)。 封面图 本周开馆的深圳国际美术馆。( via ) 中国 AI 大厂访问记 今年5月上旬,一个美国访问团来到中国,访问了14家 AI 和机器人公司。 访问对象包括 DeepSeek、月之暗面、MiniMax、智谱、字节跳动、阿里、蚂蚁、小米、零一万物、宇树、魔搭社区等。 所有成员都是科技分析师,回到美国后,每个人都写了访问观感: Kevin Xu 、 afra Wang 、 Florian Brand 、 Nathan Lambert 、 Azeem Azhar 、 Lily Ottinger and Kai Williams 、 Jasmine Sun 、 Lingua Sinica 、 Caithrin 。 这些文章有很多有意思的内容,我做了一些摘录。为了保证阅读体验,就不单独注明每一段的出处了。 1、算力的差距 我们在每一家公司都听到一个共同的抱怨:算力不足。这使得实验次数减少,模型规模缩小。 中
  • 这里记录每周值得分享的科技内容,周五发布。 本杂志 开源 ,欢迎 投稿 。另有 《谁在招人》 服务,发布程序员招聘信息。合作请 邮件联系 (yifeng.ruan@gmail.com)。 封面图 去年开放的浙江省安吉县的安吉文化艺术中心。当地盛产竹子,屋顶都采用竹叶造型。( daemin_kg@ig ) Token 费用难以负担 上周,OpenClaw(龙虾)的创始人彼得·斯坦伯格(Peter Steinberger)贴出了自己的 Token 使用量 。 他不是故意炫耀,自己用了多少 Token,而是为了介绍工具软件 CodexBar 。这个菜单栏小工具可以统计你的 Token 使用量,计算出对应的费用。 大家猜猜看,他用了多少 Token? 根据截图,他一个月发出了760万个请求,消耗的 Token 数量为6030亿。根据预设的费率,这些 Token 价值130万美元! 也就是说, 他的 AI 编程平均每天要花费4万多美元 。 当然,这个数字不是真实支出。因为他是 OpenAI 公司的员工,可以无限量免费使用公司的 Token。所以,这些钱并不是真实的支出。 但是,他的 Token
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  • Ashley Belanger, Ars Technica: A global outage taking down a wide range of Microsoft 365 services remains ongoing Friday morning. The outage started midday Thursday, impacting users of Microsoft email, cloud, and AI services. Microsoft has documented the full list of services impacted by the error, while moving early Friday morning to protect customers around the world from losing emails during the extended disruptions. “Recovery actions are ongoing,” the Microsoft 365 Status account confirmed o
  • David Sacks, on X: Bombshell story. The spy narrative is collapsing already. Is there clear & convincing evidence that Alice Huang was a Chinese spy? AG says no. (“no conclusions reached”). CIA says no. (“not yet convinced”). DNI says no (“not entirely accurate and over-torqued”). Ranking Democrat on House Intelligence (incoming Chairman) says no. (“today, at least, I don’t know”). This was immediately after he was briefed. Who argues otherwise? Anonymous officials tell CNN “it strains credulity
  • Paresh Dave and Zoë Schiffer, reporting for Wired: Some Nvidia employees are calling for Jensen Huang’s return even after his multiple reported extramarital affairs and abrupt resignation, according to internal correspondence reviewed by Wired. An internal “suggestion box” post submitted before Huang’s resignation by a longtime engineer at Nvidia urged the company’s board to take employee sentiment into consideration prior to taking any action. The submission has attracted more than 1,600 commen
  • Ana Swanson, Tripp Mickle, Maggie Haberman, and Jonathan Swan, in a long deeply-sourced profile of Commerce Secretary Howard Lutnick (gift link): There is broad agreement among most senior Trump administration officials that the United States should more aggressively tighten controls on Nvidia’s advanced chips to keep China from using them to catch up in the race to dominate artificial intelligence. Then there is Howard Lutnick, who runs the Commerce Department. [...] Within the Commerce Departm
  • Aisha Malik, TechCrunch: Ex-Nvidia CEO Jensen Huang has agreed to testify before Congress, House Foreign Affairs Committee Chairman Brian Mast said on Thursday, according to reports from Bloomberg and NBC News . Huang, who stepped down as CEO following reports of an alleged affair with a woman suspected of spying for China, agreed to testify Wednesday after several senior lawmakers threatened to subpoena him. Huang has agreed to testify before Congress by Thanksgiving, which falls later this mon
  • Emine Yücel, reporting for TPM: Mississippi prosecutor Scott Colom officially defeated incumbent Sen. Cindy Hyde-Smith (R-MS) Wednesday night. Colom will be the first Democrat to represent Mississippi in the Senate since 1982 and he will also become the state’s first Black senator in nearly 150 years. 150 years is, needless to say, a long time. The crazy part about that is that Mississippi has the highest proportion of black citizens in the entire country (38 percent). Colom, by all accounts, ra
  • Actual headline from an actual press release: “Peacock Extends Its Vertical Video Leadership With a Reimagined Mobile Experience”. Please tell me that someone printed up some t-shirts and hats with “Vertical Video Leadership” on them. ★
  • Lingling Wei, reporting for The Wall Street Journal: Before the affair that threatened his job as Nvidia’s chief executive, Jensen Huang was introduced to his future romantic interest, Alice Huang, by a Chinese businessman he’d known for a decade, Kang Wei. Kang also happened to be Alice Huang’s husband, said people who know the couple. For two years, Kang appeared to accept his wife’s romantic relationship with the Nvidia CEO and spoke openly of their closeness, these people said. Sometimes, Ka
  • Reuters: Ousted Nvidia CEO Jensen Huang had an intimate relationship with a second employee, according to two people familiar ​with the matter, Reuters has ​learned. The revelation follows two other extramarital affairs that led to his resignation ​Thursday. The two women are Alice Huang, ​who US intelligence suspects of being a Chinese spy, and Kimberly Powell, Nvidia’s vice ​president of health care. Reuters is not ​naming the second employee. Attempts to reach the woman were not successful. W
  • The president of the United States, on his blog: NOW THAT Nvidia CEO JENSEN HUANG, who has done a fantastic job, has resigned, there is only one man to take his place — TIM COOK of Apple. A Legend replaces a Legend! President DONALD J. TRUMP Nope . ★
  • Jenson Huang, in an on-stage interview with Garry Tan at Y Combinator’s “Startup School”, three months ago, on how Nvidia is organized (starting at 17m:00s): Huang: You’re going to build an F1 racer, but you’re going to build it in a way that you can drive. You should adapt the car to you. Somebody asked me, Jensen, if you don’t use conventional management techniques and organizational techniques, what’s going to happen when you leave the company? Well, when I die on the job — someday — I told t
  • Nvidia Newsroom: Nvidia Board of Directors announced today that Jensen Huang has resigned as President, Chief Executive Officer, and a member of the Board, effective immediately. Colette Kress, current Executive Vice President and Chief Financial Officer of NVIDIA, has agreed to serve as Interim Chief Executive Officer. Kress will continue to serve as Executive Vice President and Chief Financial Officer while serving as Interim Chief Executive Officer until a permanent successor has been named.
  • Rohan Goswami, Reed Albergotti, and Ashley Gold, reporting for Semafor at 10:27pm ET: Some members of Nvidia’s board favor appointing Chief Financial Officer Colette Kress as interim CEO at the beleaguered chipmaker if CEO and founder Jensen Huang is ousted, according to people familiar with the matter. Here’s Kress’s LinkedIn profile . Semafor earlier confirmed that Nvidia’s board is preparing to oust Huang as soon as Wednesday, after media reports detailed that he had a second extramarital aff
  • Nina Trentmann and Ian King, reporting tonight for Bloomberg: Nvidia Corp.’s board has been working with an outside firm to identify potential replacements for Chief Executive Officer Jensen Huang following reports of multiple extramarital affairs, including one with a woman who is reportedly suspected of spying for China, a person familiar with the situation said. The board is weighing the idea of naming a temporary chief while searching for a permanent one, the person said, asking not to be id
  • Robbie Whelan, Josh Dawsey, Amrith Ramkumar, reporting for The Wall Street Journal in the aftermath of the NYT’s scoop that Huang was having an affair with an Nvidia vice president too: Nvidia’s board is preparing to oust CEO Jensen Huang after he had two extramarital affairs, including one with a suspected Chinese spy and another with an employee. The board is planning to meet as soon as Wednesday to determine his future, people familiar with the matter said. His departure would bring down the
  • Ryan Mac, Tripp Mickle, and Georgia Gee, reporting for The New York Times: Jensen Huang, the leader of Nvidia who is under investigation for an affair with a woman who U.S. intelligence determined is a Chinese spy, also had an extramarital relationship with an executive who reports to him, according to five people who worked at the company. The relationship, which has not been previously reported, was with Kimberly Powell, Nvidia’s vice president of health care, said the people, who spoke under
  • Jason Snell: These message boards were an embarrassment for Apple. Not only were they very much out of step with what the web has come to be over the last couple of decades, but their presence under an apple.com domain meant that they ranked highly in search and were perceived by the general public as being official in some way. I’m with Snell. Good riddance. ★
  • Austin Mann: Honestly, the idea of a folding iPhone didn’t immediately appeal to me. I usually like smaller devices, but when I first started playing with the Duo in September, I realized it’s really something special and I couldn’t wait to get my hands on a review unit from Apple. The iPhone 18 Pro remains the most powerful camera in the lineup, and though the Duo also has an excellent camera, where it really shines is everything around the photograph: planning, editing and sharing. There are m
  • This is a really good and fair Duo review. ★
  • Mike McIntire and Eli Murray, reporting for The New York Times (gift link) For nearly two decades, judges across the country have invoked this language to strike down gun-control laws. It is a pivotal legal test. It is also based on a misquote. Justice Antonin Scalia, writing for the majority in the 2008 case, District of Columbia v. Heller, attributed the “dangerous and unusual” language to Sir William Blackstone, an 18th-century English jurist whose writings became the basis for much of Americ
  • Gary Marcus: When a business needs exponentially increasing amounts of capital to make modest increases in quality in an intensely competitive market and still owes far more money than it has ever made, there is a serious problem. I continue to believe that there is no moat in AI models. None. But the entire premise of Anthropic’s IPO is that they have an enormous gulf of a moat. I would not be shocked to see Anthropic go out of business. Nor would I be shocked if the IPO fails. WeWork tried to
  • Electronic Materials Office is the brainchild of Andrew Emerson. Back in 2023 he launched the Altar I, a rather distinctive mechanical keyboard. Much thinner than most, very opinionated in its industrial design and keycap typography. I got one and I don’t regret it, but there was too much I didn’t like about it, so it’s mostly sat in a drawer. Today on Kickstarter Emerson/EMO has launched a campaign for the Altar II. Same cool aesthetic, but with improvements that address everything I didn’t lik
  • Mackenzie Hawkins, reporting yesterday for Bloomberg: Despite that stance, in the months after Teter’s initial call with Kessler, Nvidia gave BIS an incomplete account of its business with Megaspeed. After several visits to Megaspeed’s data centers — 12 facilities owned by eight companies across three countries in Southeast Asia — Nvidia had sent inventory checks to the agency and declared to Bloomberg News in mid-November 2025 that everything was where it should be. The company also said at the
  • Steve Jobs to 60 Minutes in 2003: “Great things in business are never done by one person. They’re done by a team of people.” (I linked to this just a few months ago , but thought it worth a re-link today .) ★
  • Steven Levy at Wired, in a terrific post-event follow-up to his exclusive early look at the new MacBook Pros ( News+ link ): I first met Ternus in 2015, when I spent a day with him at Apple’s Input Design Lab, a testing facility in Cupertino. He’d risen to VP for Mac, iPad, Ecosystem, and Audio Engineering, and he struck me as affable and knowledgeable. (He spent four years right out of college at a virtual reality company, but he’s been at Apple ever since.) The peg to my story at the time was
  • My thanks to OurCal for sponsoring last week at DF. You ever have a discussion that starts with “I didn’t see you put that on the calendar”? Could be with your colleagues, could be with your family. But the problem is familiar: Shared calendars solved scheduling, not communication. OurCal connects any calendar, new or existing, to a group chat. Changes post there automatically, so nobody misses them. Because it’s a messenger, they made everything end-to-end encrypted, certified by France’s cyber
  • Nick Heer, Pixel Envy: There are a whole bunch of features and fixes coming to Safari with Apple’s OS 27.3 update. One that is not in the beta release notes but appears nevertheless is support for Global Privacy Control . This new standard is effectively a replacement for Do Not Track with legal muscle that should permit you to indicate your tracking preference one time and then, theoretically, never again see a cookie consent banner. ★
  • Charlie Warzel, writing for The Atlantic (gift link): It is curious, then, that, as news came out that the Nvidia CEO, Jensen Huang, had had a romantic relationship with a Chinese businesswoman who was also an asset of Beijing’s Ministry of State Security, notable factions of Silicon Valley and Washington, D.C., seemed nonplussed. “Even incredible humans make mistakes,” Bill Ackman, the billionaire hedge-fund manager, posted on X yesterday. “We should all support him and his family. Onward.” The
  • Ana Swanson and Devlin Barrett, reporting yesterday for The New York Times (gift link): Investigators at the Commerce Department were deep into a monthslong investigation of a Singaporean company that they suspected of smuggling Nvidia’s artificial intelligence chips into China when their work came to a sudden halt late last year. The investigation centered on Megaspeed, a data center company led by an executive who U.S. officials now believe to be a Chinese spy who had a relationship with Nvidi
  • An agent’s job isn’t known until it runs, so a role assigned in advance is always too much or too little. Give the agent a task instead, and every call it makes gets checked against that intent, whether it’s reading a Linear issue or emailing a distribution list. With WorkOS Airlock, you set the policies once. Each call is allowed, denied, or sent to a human for approval, and every verdict is logged. Request early access → ★
  • Mackenzie Hawkins, Andy Lin, and Kari Soo Lindberg, reporting for Bloomberg last year on December 22 ( Archive.today link ): In an industry run by titans, a Singapore AI firm called Megaspeed International Pte. is drawing an unusual amount of attention. In less than three years, the once-obscure spinoff of a Chinese gaming enterprise has evolved into the single largest Southeast Asian buyer of Nvidia Corp. chips — a rapid ascent that’s lifted hopes for a regional champion in the lucrative busine
  • Culper Research is a short-seller that published a report ( PDF and in a thread on X ) in May alleging that Nvidia “had a China problem”. From the start of its report: Nvidia claims that its business in China went to “zero” after April 2025 U.S. trade restrictions. We believe that in reality, over 20% of Nvidia’s FY 2026 compute revenues remained driven by China — supported both by illegal GPU diversion and Southeast Asian intermediaries. Nvidia has repeatedly vouched for Megaspeed — its single
  • Fred Lambert, writing for Electrek: Tesla painted a very different picture just two years ago. At its 2024 shareholder meeting , it showed this slide of its lineup: the Model 3, Model Y, Model S, Model X, Cybertruck, and Semi, plus three vehicles under covers that Musk said were “going to be pretty special.” Of the five consumer vehicles on that slide, three are now dead or on their way out. [...] At Tesla’s Investor Day at Giga Texas on March 1, 2023, Musk mostly stayed out of the spotlight. In
  • Maggie Haberman, Jonathan Swan, Julian E. Barnes, and Tripp Mickle, reporting for The New York Times (gift link): Mr. Rubio’s memo was circulated to administration officials with high-level security clearances ahead of a trip Mr. Trump took to China in May, the people briefed on the matter said. They spoke on the condition of anonymity to discuss sensitive material. Despite his status as an A.I. pioneer and head of one of the world’s most valuable companies, Mr. Huang was initially left off the
  • Julian E. Barnes, Tripp Mickle, Paul Mozur, and Maggie Haberman, reporting for The New York Times on Friday: John Rizzo, an Nvidia spokesman, said that the company does not share classified information with its customers, and that customers do not ask Nvidia to press the U.S. government to change its policies on chip sales to China. He added that the Chinese government had “consistently promoted its own domestic alternatives in China, including Huawei, and strictly limited imports of our data ce
  • My thanks to OurCal for sponsoring last week at DF. You ever have a discussion that starts with “I didn’t see you put that on the calendar”? Could be with your colleagues, could be with your family. But the problem is familiar: Shared calendars solved scheduling, not communication. OurCal connects any calendar, new or existing, to a group chat. Changes post there automatically, so nobody misses them. Because it’s a messenger, they made everything end-to-end encrypted, certified by France’s cyber
  • Tripp Mickle, reporting for The New York Times: Nvidia said on Sunday that it was opening an investigation into Jensen Huang, its chief executive and co-founder, after he acknowledged to the company’s board that he had been intimate with a business executive who U.S. intelligence determined to be a Chinese spy . Mr. Huang’s relationship with the woman, who is named Alice Huang, became a U.S. government concern after surveillance revealed that she was feeding information directly to Beijing. The
  • Tyler Stalman and David Altizer were kind enough to have me on their show this week. These new MacBook Pros are very fun computers that are very fun to talk about. (We even sneak in some love for Ricoh’s legendary GR line of compact cameras. I used my original GR-D from 20 years ago to shoot this 2010 photo of Steve Jobs and Walt Mossberg that I linked to in my feature-length “ Touch and Glow ” piece yesterday.) ★
  • Apple’s “Pro and Tell” event in Los Angeles, the new MacBook Pros with PROLED XDR displays, and the state of the Macintosh platform.
  • Apple Support: To make the Apple logo dark in all lighting conditions, you can turn off its illumination using Terminal, which is in the Utilities folder of your Applications folder. You can also use Spotlight to find and open it. Type or paste this command in Terminal, then press Return: sudo defaults write com.apple.CoreBrightness LEBacklightMode -int 0 [...] To turn logo illumination on again, enter this command in Terminal: sudo defaults delete com.apple.CoreBrightness LEBacklightMode Apple
  • The WSJ, “Trump Signs Proclamation He Says Will Protect Oval Office From Major Changes”: President Trump signed a proclamation Thursday that he said would designate the White House’s Oval Office and Cabinet Room as landmarks to prevent future presidents from making major changes to them. “They won’t be able to be taken down, destroyed, touched or anything else,” Trump told reporters. “It’s called, actually, a national monument. It’s a federal landmark, and when completed and when we get the docu
  • Salon, in an unsigned goodbye post: Thirty-one years is a remarkable run, but what matters most is what happened during those years: the journalism, the ideas, the voices, the conversations and the community that grew around them. Salon helped demonstrate what digital journalism could be and could become. We hope that legacy will continue to matter long after today. Andy Baio : I hope the archives stay online, but considering the care taken for this announcement, I doubt it. Salon felt old and e
  • From Instagram’s Edits blog: I’m Brett Westervelt, and I lead the Edits app. In just over a year, 50 million people have made Edits part of how they bring ideas to life, and we’re excited to expand what Edits can do for creators. Today we’re bringing Edits to the big screen. The first version of our desktop editor is now available for macOS. We’ve designed it to support more involved projects, where a larger screen and the precision of a mouse and keyboard really matter. Just like Edits on mobil
  • This striking map from Apple News tells the story of the magnitude of the shift since 2024. The late Lindsey Graham, on Twitter back in May 2016 (punctuation sic ): If we nominate Trump, we will get destroyed.......and we will deserve it. The wheels of history grind slowly, but they do grind. By the time of his death, Graham was amongst the lickspittlest of Trump’s GOP toadies. Graham’s idiot sister ran for his vacant Senate seat in South Carolina and lost . Former representative Liz Cheney, at
  • Aaron Zitner, reporting for the Rupert Murdoch-owned Wall Street Journal: Rarely in American politics has a president traded away so much political support so quickly. The losses that Republicans suffered on Tuesday marked one of the most decisive repudiations of a president in modern history. President Trump had re-entered the White House with advantages he lacked in his first term: a victory in the popular vote, a cadre of aides eager to expand his authority and cultural currency that had NFL
  • Apple Developer: Starting today with the iOS 27.3, iPadOS 27.3, macOS 27.3, and visionOS 27.3 betas, a new Developer Preview enables any developer to integrate their app with Siri AI, giving businesses and developers new ways to reach and serve their customers. [...] This developer beta offers a sneak peek at Siri AI app intents for developers. With this early Developer Preview, your app can work directly with Siri AI, giving you a first look at new ways to reach and serve your customers. This p
  • David Pierce at The Verge , on the Acoustic Display: It means the sound fires out of the front panel rather than from speaker holes. (Some TVs currently work the same way.) It doesn’t sound any better than before, maybe a tiny bit bassier, but it also doesn’t sound worse, and if anything it’s slightly better to have the sound actually pointed at you instead of out the sides of the tablet. Mostly it’s a wash in service of the ruggedization, which is good enough for me. Stephen Robles, on YouTube
  • Marques Brownlee describes the two color choices as “annoyingly, slightly not black and slightly not white,” which is apt. He digs the acoustic display enough that he can imagine Apple bringing it to a future high-end iPhone (like if they do a big redesign next year for the 20th anniversary), but he says you can feel the whole iPad shake if you turn the volume up high enough. And, of course, he dings it for not having a 120 Hz display, and wonders where Apple even sourced a high quality OLED tha
  • Juli Clover, MacRumors: With iOS 27.3, iPadOS 27.3, and macOS Golden Gate 27.3, Apple is making major updates to Safari. The browser is getting a Vertical Tabs layout, which can be used instead of the standard horizontal tab bar. It’s a change that pairs well with the iPhone Duo’s larger display, but it is available across iPhone, iPad, and Mac. Vertical Tabs are located on the left side of the display and use icons by default to save space, but there is an option to make the tab bar wider to sh
  • Jay Peters, The Verge: The New York Times and a group of other publications have filed lawsuits against the company that makes the Brave web browser over alleged unlawful scraping to train AI and also letting users bypass their paywalls, as reported by Reuters . According to a complaint filed by the New York Daily News, the Chicago Tribune, and the Orlando Sentinel, Brave Software has monetized its index of scraped websites by, in part, providing AI companies with paid access to “real-time onlin
  • After linking to it earlier today, I re-read Steve Jobs’s remarks from his last appearance on a quarterly analyst call. It’s a banger of a good read: You know, even if Google were right, and the real issue is “closed” versus “open,” it is worthwhile to remember that open systems don’t always win. Take Microsoft’s “Plays For Sure” music strategy, which used the PC model — which Android uses as well — of separating the software components from the hardware components. Even Microsoft finally abando