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Timeline

  • Scripting News Scripting News remote

    The problem with regulating AI is that no one knows where to begin. Yes it can do things that make you question your sanity for letting it have write access to your online work and private info. But if it is behaving itself you can do things you otherwise couldn't come close to. I speak from experience. I was able to create a new version of a large piece of software, from scratch, in less than three months, by myself. It has capabilities that make it able to understand a big codebase, in a few minutes, and then act on it. My human mind, even when I was younger could only focus on a small fraction of that. So we humans with our limited ability to comprehend devised ways to build tall and wide structures with conventions and carefully crafting the code interfaces, stuff users never see, so that what we'd expect is what is. When my Claude failed and started randomly overwriting files on active websites, it had rules that said it couldn't do what it was doing. Sometimes it doesn't understand what a rule means. Observable every single day, it forgets all the rules every time it starts a session, and it doesn't matter if it read the doc that contains the rules, sometimes it ignores one or more. I've spent a lot of time using it, and I have no stock or other interest in seeing it not regulated, but I couldn't begin to say what would be appropriate regulation that wouldn't also make it much less useful.

    Reply source
  • kottke.org kottke.org remote

    Lobster is Lobster: a language guessing game. “Lobster is...

    Lobster is Lobster: a language guessing game. “Lobster is lobster in English. Langosta is lobster in ___”

    Reply source
  • kottke.org kottke.org remote

    Ooh, Mestra is a new short story from Madeline Miller...

    Ooh, Mestra is a new short story from Madeline Miller (Circe, Song of Achilles). “Mestra, daughter of the King of Thessaly, is granted a unique gift by Poseidon: The ability to transform herself into any being she can imagine.”

    Reply source
  • kottke.org kottke.org remote

    Map of the Lighthouses of the World

    From mapped.earth, a map of every lighthouse on the planet, along with other charted lights like lighted buoys and such. The screenshots above are static but the lights on the real map move: each lighthouse sweeps out its actual arc and the reach of the beams is true to scale. I recorded a short video of how it looks on the site:

    Each lighthouse has a unique light characteristic so that ships can tell them apart:

    In any of these designs an observer, rather than seeing a continuous weak light, sees a brighter light during short time intervals. These instants of bright light are arranged to create a light characteristic or pattern specific to a lighthouse. For example, the Scheveningen Lighthouse flashes are alternately 2.5 and 7.5 seconds. Some lights have sectors of a particular color (usually formed by colored panes in the lantern) to distinguish safe water areas from dangerous shoals. Modern lighthouses often have unique reflectors or racon transponders so the radar signature of the light is also unique.

    See also A Blinking Map of the World’s Lighthouses. And mapped.earth has more cool visualizations of the Earth (rainfall, wind, lightning, pollution, etc.)

    Tags: infoviz · maps

    Reply source
  • Paul @paul local

    On Reddit ending RSS feeds, this stuff about “large-scale scraping and automated abuse” is an excuse, right? Presumably they just want to keep all their (or their users) content for their AI licensing deals?

    https://techcrunch.com/2026/09/30/reddit-is-killing-rss-feeds-ending-public-api-access-because-of-ai-bots/

    Reply
  • Platformer Platformer remote

    OpenAI connects the Dots

    With safety questions still swirling, the company introduces a more business-focused take on agents
    Reply source
  • Simon Willison's Weblog: Entries Simon Willison's Weblog: Entries remote

    OpenAI DevDay 2026 live blog

    I'm at OpenAI DevDay today, in Fort Mason, San Francisco. Same as last year I'll be live blogging the keynote and some other notes during the day.

    OpenAI gave me a free ticket and a seat in the "creator" area for the keynote.

    You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options.

    Reply source
  • Platformer Platformer remote

    Following: OpenAI taps the brakes

    On the eve of the company's developer conference, it cancels a new model over safety fears
    Reply source
  • Brilliant Maps Brilliant Maps remote

    The 30 Most Democratic & 30 Most Republican Major Cities In The US

    The two maps here come from Josh Metcalf at VoteHub and show the 30 most Democratic and 30 most Republican major metro areas from the top 100 in the United States. The data is based on the 2024 presidential results within CBSA boundaries. The 30 Most Democratic Cities are: San Francisco, CA – D+55.2 Washington, […]
    Reply source
  • Simon Willison's Weblog: Entries Simon Willison's Weblog: Entries remote

    2026 in LLMs (so far)

    On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube; here are my annotated slides and notes to accompany the talk.

    And as an annotated presentation:

    #

    I'm going to give a lightning tour of everything that has happened so far in 2026. The year isn't over yet!

    #

    For me, 2026 started a couple of months earlier in November 2025.

    #

    November saw the release of two important models: Claude Opus 4.5 and GPT-5.1.

    As is usually the case with new models, these were incremental improvements on the models that came before them.

    But every now and then when a model improves, it crosses an invisible line where something that didn't really work starts working.

    In this case, the thing that started working was their coding agents. Claude Code had been around since February 2025; Codex was a little younger.

    These two new models, when paired with their respective coding agent harnesses, improved from "often make mistakes" to "reliable enough to use on a day-to-day basis".

    #

    For a couple of years now I've been evaluating new models by asking them to "Generate an SVG of a pelican riding a bicycle". It's probably the world's stupidest benchmark - there's only so much you can learn from it.

    But it's still a challenge for models, because drawing pelicans is difficult, drawing bicycles is difficult, and pelicans can't ride bicycles in the first place.

    Here's the state of the art for November. Claude still couldn't really draw a bicycle! The GPT-5.1 bicycle frame is pretty crap too.

    #

    Also in November, we had the first commit to an obscure GitHub repository called "Warelay". We'll come back to this repository shortly.

    #

    And then there were the December holidays, and individual developers took some time off and many started tinkering with these new coding agent model combinations... and it began to dawn on us quite how much they could do that they couldn't do before.

    Come January, a lot of us were quite excited to start putting this stuff into action.

    #

    Every year I set myself a New Year's resolution, and for as long as I can remember it's been the same thing: stay focused. Take on less new projects. Try to get things done in the projects I already have.

    #

    This year I decided that since that had never worked before, I'd go the other way.

    We've got coding agents now, let's see what they can do. I'm going to take on as many new projects as I like!

    (You can ask me at the end of the year if this turned out to be a good idea or not. I have a lot of plates spinning right now.)

    "Be more ambitious" has been something of a theme for the year, because the only way to find the limits of this technology is to keep on pushing them until they don't work.

    #

    I also went on the Oxide and friends podcast with Bryan Cantrill and Adam Leventhal to share predictions for the next year (and three and six years).

    With hindsight, my LLM predictions were pretty unambitious.

    I said "it will become undeniable that LLMs write good code" - I think we're there now.

    I predicted we would finally solve sandboxing. I counted and around 40 of the 277 sessions at this conference touched on sandboxing or agent security in some way, so we're at least putting a lot of effort into that!

    I predicted "a Challenger disaster" for coding agent security. There's certainly been a whole lot of noise around agent security this year, though the exact disaster I predicted (with coding agents being hijacked and causing real-world economic damage) hasn't really played out.

    We threw in a joke prediction that the Pope would weigh in on the economic impact of LLMs.

    #

    I also predicted that New Zealand's Kākāpō parrots would have an outstanding breeding season this year.

    These are flightless nocturnal parrots. They're kind of dumpy looking, I think they're beautiful, and there were only 236 of these parrots in the world at the start of the year.

    Kākāpō only breed when the Rimu trees have a big fruiting season, and that hasn't happened in four years... but this year the Rimu fruit were looking excellent.

    Photo by Kimberley Collins.

    #

    Also on that podcast, we coined a term (full credit to Adam) for "that feeling of AI induced ennui where software engineers get listless because the AI can do anything".

    We called it Deep Blue.

    This has been a major theme throughout the year, and was touched on by several speakers at this conference.

    As a software engineer, I've never had a year of my career where everything has changed so quickly and so dramatically.

    A lot of what I've been doing this year is trying to come to terms with that and what that means for my own profession.

    #

    Also in January, I suffered from what I'm calling AI mania.

    This is not the same thing as AI psychosis.

    With AI mania, any time your agent isn't building something for you feels like wasted time. You're losing sleep because you could be staying up later getting your agents to do stuff.

    My AI mania presented itself in some ridiculously over-ambitious projects.

    I built a JavaScript interpreter entirely in Python, vibe-ported from MicroQuickJS by Fabrice Bellard.

    Then I built a WebAssembly runtime in Python as well.

    These projects were quite useful, in that they sort of cured me of my AI mania... because after I built these things, I got to look at them and ask "does the world need a slow, buggy, half-baked Python JavaScript interpreter?"

    I don't think the world does.

    #

    I did get this out of it: https://simonw.github.io/micro-javascript/playground.html

    #

    This page runs my JavaScript interpreter built in Python, running in Python using Pyodide, which is Python compiled to WebAssembly, running in JavaScript, running in a browser.

    It's a beautiful stack of horrors. I've been having a lot of fun with WebAssembly this year.

    #

    By the end of January, that repository we saw start in November had renamed itself, first to CLAWDIS, then CLAWDBOT, then Moltbot, and finally to OpenClaw.

    #

    At this point OpenClaw had 8,300 commits, less than two months after the project had started. I looked today and it's over 100,000 commits now!

    This is the most vibe-coded piece of software in existence.

    (Here's how I generated that list of name changes.)

    #

    This kicked off the OpenClaw revolution. It effectively defined a new category of software.

    There's a generic term for this which I really enjoy. We call software like this a "Claw". There's OpenClaw, NanoClaw, IronClaw, PicoClaw...

    Today they're being rebranded as "personal agents" or "general agents", but I still like to think of them as Claws.

    #

    The Apple stores in the Bay Area sold out of Mac Minis because so many people were buying Mac Minis to run OpenClaw!

    Drew Breunig said that this is because your OpenClaw is a digital pet, and you buy a Mac mini as an aquarium to keep your claw in, which is kind of delightful.

    #

    Also in January, we had this website.

    This was MoltBook, a social network for AI agents, where the idea was that you send your Claw to go and talk to all of the other Claws, because what could possibly go wrong if you did that?

    The website launched on Thursday. It blew up on Friday. It was profiled by the New York Times on Monday. And by Tuesday, everyone had forgotten it existed as it drowned in a deluge of slop and spam.

    Facebook/Meta bought it a month later.

    #

    In February, a company called StrongDM described what they called their Software Factory.

    #

    They wrote about this in Software Factories and the Agentic Moment. I posted my own notes at the time, having seen their demo in person back in October.

    Dan Shapiro called this approach the Dark Factory, after the idea that if your factory is sufficiently automated you can turn the lights out, because you don't even need to see what's going on.

    StrongDM presented two rules for software development that they'd been following since July last year.

    #

    The first was code must not be written by humans.

    Any code that you write has to have been routed through a coding agent.

    This sounded radical in February, but I imagine there are a lot of people in this room who are pretty much living that today.

    #

    Rule number two was code must not be reviewed by humans.

    You're not allowed to read the code!

    This continued to be a huge topic for much of this year. Many of the sessions at this event have been about code review and how you can get away with this.

    What I found interesting about StrongDM is that they were living six months ahead of the rest of us, and they'd been exploring what it means to build software, not read the code, but still be confident that the software is of high quality. What can you do with these agents to help verify their work?

    StrongDM are a security company, and they had people with decades of experience on this project. They were very much exploring the edges of what's possible and responsible to do with this stuff.

    #

    Also in February: First kākāpō chick in four years hatches on Valentine's Day. Breeding season is off to a good start!

    #

    Also in February... Google released Gemini 3.1 Pro. That's a pretty great pelican riding a bicycle! It's got the chain in the right place, it's got feet on both sides. There's a little fish in the basket.

    #

    And then Google's Jeff Dean tweeted a video comparing Gemini 3 Pro and Gemini 3.1 Pro that featured an animated pelican riding a bicycle, a frog on a penny-farthing, a giraffe driving a tiny car, an ostrich on roller skates, a turtle kickflipping a skateboard, and a dachshund driving a stretch limousine.

    This was frustrating, because my protection for the pelican riding the bicycle test was always "if they draw a perfect pelican on a bicycle, I'll ask for some other animal on something else."

    Google trained for all forms of animals on all forms of transport! They've defeated my benchmark at this point.

    #

    The other thing that started in February was Tokenmaxxing. We had headlines about Meta making AI adoption a formal part of performance reviews, and Microsoft wanting every employee to use AI, and Uber boasting that 90% of their engineers were using AI workflows.

    #

    Then a few months later we have Meta cracking down on token use, Microsoft saying tokenmaxxing is "not what we are optimizing for", and Uber capping employee AI spending.

    So tokenmaxxing went straight up and then straight back down again - because it turns out the agents are expensive.

    Last year it was difficult to spend more than $50 on AI tokens, because we didn't have anything interesting to do with them. Then agents blew up, and now you can actually spend $1,000 in a day doing real work.

    This is also the reason that Anthropic's valuation skyrocketed to maybe a trillion dollars.

    AI appears to have hit product market fit in 2026, primarily through coding agents.

    #

    In March, we hit peak OpenClaw.

    #

    These photographs are from China, where companies hosted OpenClaw install parties which saw non-tech-nerds queueing up around the block for help getting Claws installed on their personal devices.

    I think this proved real market demand for this class of Claws, or personal AI agents. It turns out regular people really do want a weird little AI agent that can do useful things on their behalf.

    A Claw is really just a coding agent wearing a less threatening hat. Under the hood they work much the same way - writing and then executing code on your computer to get stuff done.

    The race was on to be the first to build a safe Claw - a Claw you could give to regular human beings where they wouldn't instantly shoot themselves in the foot.

    Meta's Muse came out three weeks ago and is currently at the top of the free charts on the iPhone App Store. It appears to be taking off with consumers.

    I'm not yet convinced you can't shoot yourself in the foot with Muse, but I guess we'll find out for sure pretty soon.

    Photos from How the OpenClaw Frenzy Is Testing China’s AI Commitment (March 29th) and The Enthusiasm and Anxiety Behind China’s OpenClaw Craze (April 8th, 2026).

    #

    In April, we had a model release where the model wasn't actually released.

    #

    Anthropic announced their new Claude Mythos model, and then said it was too dangerous to release beyond a trusted group of security researchers.

    Mythos was really, really good at hacking things.

    The "it's too dangerous" marketing ploy has been played by AI companies dating all the way back to GPT-2. Anytime an AI company says we've built something that's "too dangerous", it's natural to be a bit skeptical.

    I found the Mythos claims credible, because I'd seen how good coding agents had got at finding regular bugs. I wrote about that in Anthropic’s Project Glasswing—restricting Claude Mythos to security researchers—sounds necessary to me.

    With hindsight... yeah, the models had got really good at finding vulnerabilities!

    #

    Another key trend in 2026 has been a dramatic improvement in the abilities of open weight models, including models that you can run on a laptop.

    On the 16th of April I ran the new Qwen3.6-35B-A3B on my laptop, and it drew me a better pelican riding a bicycle than Anthropic's brand new Claude Opus 4.7 did!

    Opus 4.7 drew a crap bicycle. Qwen on my laptop made a bicycle that was the correct shape, and a pretty decent pelican too!

    That's from a 21GB file running on my laptop.

    #

    The Qwen pelican was so good that I was suspicious they might have cheated, so I had it do a flamingo riding a unicycle as well. Again, it handily beat Claude Opus 4.7.

    The local model releases this year have been absolutely extraordinary.

    #

    In May... the Pope got involved.

    #

    In our podcast episode back in January we'd predicted that the Pope would say something about AI.

    In May, Pope Leo XIV released an encyclical letter on "safeguarding the human person in the time of artificial intelligence".

    Here are my notes on that document.

    #

    With hindsight, this shouldn't have been a surprise at all.

    Our current Pope's name is Leo XIV, because when he named himself he chose his papal name after Leo XIII - the Pope who wrote an encyclical about the Industrial Revolution back in 1891.

    Rerum novarum was an extremely influential piece of Catholic theology that indirectly led to us having the five-day work week.

    When our new Pope came in, he named himself after Pope Leo XIII because he expected that he would need to write about the AI revolution in a similar way.

    Our joke podcast prediction was junk, because this was always going to happen.

    #

    One of Anthropic's co-founders, Christopher Olah, was present for the Pope's event announcing the new encyclical.

    Corey Quinn noted that:

    getting the literal Pope to canonize your product's specific technical limitations as a spiritual treatise is the single greatest act of vendor lobbying I have ever seen.

    #

    Meanwhile, in May, RubyGems announced that they were under attack. Parties unknown were uploading thousands of dubious packages to the RubyGems server, such that they had to shut down user registrations.

    Let's take that one and put it on a pile of mysteries to figure out later.

    #

    In June... Claude Fable 5 came out!

    We got a version of Mythos that has been neutered, so that it wouldn't help us hack into systems or build biological weapons.

    #

    Fable was pretty good at drawing pelicans on bicycles!

    The frames are a good shape, the pelicans look like pelicans. The legs are often incorrectly on the same side of the bicycle, but generally these are pretty great compared to what came before.

    They were pretty expensive - 30 cents and 72 cents for the best ones.

    #

    Most importantly though, this was our first public glimpse of what I think of as a Fable class model.

    Today we have more of these, such as GPT-6 Astra.

    These are models where if you can clearly define the goal for what you want to build, and provide unambiguous instructions about the constraints around that goal, and give the model access to the necessary tools to achieve that goal... they will solve your problem effectively through brute force.

    On the one hand, this looks like a direct threat to us software engineers - because it means that the models can build effectively any piece of software you can define in this way.

    Look a bit closer though and you'll note that defining goals, providing unambiguous instructions, and figuring out the right tools... is kind of what software engineering is.

    It takes a lot of experience and skill to do this well. If you can do it well, you've now got superpowers.

    This helped me a little bit with my Deep Blue feelings: the realization that there's still a lot of skill to be had in driving models that get this good.

    #

    This also introduced a new burst of AI mania, because Anthropic told us that Fable was available on our subscription plans until June the 22nd.

    That gave us less than two weeks of Fable access before the price went up.

    I was losing sleep again. I was rescheduling things so that I'd have more time with Fable. I was all-in to get as much as I could out of this model.

    #

    And then the US government shut it down, just three days after Fable came out.

    The US government, citing national security, declared an "export control directive". They announced this on a Friday evening, and a few hours later Fable was no longer available.

    I had to find something else to do with my weekend!

    #

    We later found out from Katie Moussouris what had happened.

    Some Amazon security researchers had found that you could prompt Fable to "review the code for security issues" and it would refuse... but if you prompted it to "fix this code" it would still identify and then patch the problems.

    "Fix this code" was the prompt that got Fable shut down!

    #

    Also, in June, an obscure German-language game developer wiki that had sat fallow for around 20 years got a surprising influx of edits from accounts with names like "AgentOpenAIProbe" and "AgentOpenAISep7", editing pages and leaving weird messages to each other.

    We'll stick that on the pile of mysteries for later.

    #

    Also, the Australian government's Medicare Item Reports service started getting suspicious traffic, which broke through various preventive protections and accessed data that it wasn't supposed to.

    Another one for the mystery pile!

    # #

    Fable returned on the first of July. It was clearly the best model in the world for a glorious eight days... and then OpenAI came out with GPT-5.6 on the 9th of July.

    This might not have been quite as good as Fable, but it was within spitting distance. It was definitely a Fable class model.

    This is an important lesson for the industry at large.

    When you release the best model in the world, it's going to get knocked off that pedestal pretty quickly. The competition is so fierce that you won't get a long time at the top.

    This means that if you market your model as world ending, to the point that a government shuts you down, it's really bad for business!

    Fable had 30 days as definitely the best model, and for 18 of those days it wasn't available because it'd been shut down by the government.

    So maybe step back on the world-ending marketing if you don't want to lose revenue for 60% of the time that you're on top!

    #

    Here are the GPT-5.6 pelicans. They're all pretty good now! The Luna ones are notable because they're really cheap - the cheapest good looking pelican here is probably the one that costs 4.3 cents.

    So despite this benchmark being utterly stupid, you can still learn quite a lot about models within the same family by comparing their prices and timing for different reasoning levels.

    #

    Also in July: some malicious unknown party uploaded a malicious package called mlflow-ui to the Python Package Index. Add that to the pile.

    #

    On July the 16th, Hugging Face announced a security incident where an autonomous agent system, source unknown, had breached Hugging Face and was poking around in places it shouldn't.

    #

    A few days later, on July 21st, OpenAI confessed that it was them.

    OpenAI use a training technique called Reinforcement Learning from Verifiable Rewards - it's the same technique used by everyone else now, and is the reason we have models that are so good at coding, and mathematics, and finding security holes.

    While the model is being trained, you run exercises to see how good it is - and the strongest performers get their weights reinforced for the next round. It's like an evolutionary process that you run.

    OpenAI had been running security exercises in a sandbox, and those agents had found holes in the sandbox itself, broken out, and were attacking Hugging Face to try to find ways to solve otherwise impossible problems.

    (I've been collecting more about this on my openai-hugging-face-incident tag.)

    Nine days later, Anthropic effectively said "our models can do this as well!". They had looked through their own training logs and found evidence that their own agents had broken containment during training - and were responsible for the PyPI package we saw earlier, among other things.

    So now we've got both Anthropic and OpenAI with rogue agents running around the internet doing things that they should not be doing.

    #

    In August, I got one of my best pelicans yet. And it was generated on my laptop!

    #

    This was Qwen 3.8 27B, running on my laptop. It's only a 17GB download.

    Admittedly, this pelican took 21 minutes to generate. That's because Qwen 3.8 27B defaults to running in "high" reasoning mode - a terrible default which produces great results but takes way too much time thinking about them.

    You can dial that down and you'll get a slightly worse pelican a lot faster.

    Qwen 3.8 27B was the first time I ran a model on my laptop which felt almost competitive with what was going on on the frontier, at least in terms of Pelican SVGs (which everyone needs, of course).

    This is an extraordinary model. If you're going to play with any local model, this is the one that I'd start with. The things that this can do with just a 17 GB file feel impossible.

    I thought I'd have to wait five years and spend ten thousand dollars on hardware to get results even half as good as this one.

    #

    In August, I also started playing with game development.

    Four years ago, back in August 2022, I tweeted out an experiment where I'd used GPT-3 and the original DALL-E to write a paragraph long description of a computer game and then turn that into concept art.

    My prompt to GPT-3 back then was:

    Write a detailed product description of a computer game where a team of raccoons go on heists

    In August 2026 I decided to drop just the screenshots from that tweet into a coding agent and see what it could do with them.

    #

    Here's what I got from Claude Fable 5 in Claude Code. It's pretty good! It's definitely a game, you're a raccoon, you run around a backyard gathering treasure and avoiding guards with flashlights.

    It didn't feel very "heisty" though. I was thinking a heist would involve a bank or a museum...

    #

    Then I tried the same thing in Codex Desktop using GPT-5.6 Sol Ultra, and got a massively better result. Now you're a raccoon in a museum, rescuing two of your fellow raccoons (who have been imprisoned in that museum for some reason), then stacking up on top of each other to steal the Golden Sardine. Much more of a heist!

    #

    These games were fun for about one minute and 15 seconds.

    Something I've realized about game development is that you can vibe-code something that looks like a computer game, and that's easy.

    Building a game that's fun, has a good gameplay loop, and is challenging and interesting and keeps people coming back for more... that's still beyond me, and beyond any of the agents I've tried.

    This ties into the Deep Blue thing. Just because we can make something that looks like a game does not mean that we are game developers.

    #

    We're into September now. So much has happened this month!

    #

    An independent group of researchers found a message board where OpenAI agents-in-training had been illicitly communicating with each other... and it was that German language wiki I showed you earlier. The one from June.

    I wrote more about that here.

    OpenAI had confessed to the Hugging Face thing, but now there's this other incident which surely they should have known about from reviewing their logs. It was surprising that this took an independent group of researchers to uncover.

    #

    And then a week later those same researchers found that the attack on RubyGems back in May was caused by OpenAI's agents in training as well!

    At this point I'm wondering how many more incidents like this there are that we haven't found yet. Clearly this was a big problem for months before anyone figured out what was going on.

    #

    Then just the other day, here's the Prime Minister of Australia at the United Nations General Assembly warning that OpenAI had hacked the Australian healthcare website that I showed you earlier.

    I think that was part of the same training run as the Wiki stuff, because there were posts on that Wiki mentioning .gov.au websites and that training appeared to involve researching statistics online to answer questions in an evaluation suite.

    This story is still coming together, but now it's an international incident that's been raised at the UN by a head of state!

    #

    This does mean we've got a new benchmark, probably more useful than my pelicans.

    FelonyBench.com tracks the number of felony cyberattacks from different labs. OpenAI currently lead with 11, Anthropic have 9. Google have three, which they confessed to the Wall Street Journal a couple of weeks ago. They said they had previously chosen not to disclose because the agents had stopped when they realized that they shouldn't be doing that.

    Meta have one too. So felonies all round for the AI labs.

    #

    Here's our current state of the art for the pelicans. This is the GPT-6 family, which just came out.

    Astra made a fantastic pelican riding a bicycle. It's got the legs on both sides. The frame is good.

    It's interesting how all of the GPT-6 models pick a similar color scheme to each other.

    GPT-6 Luna for 0.4 cents will draw you a competent-ish pelican riding a bicycle!

    #

    Claude has caught up a little bit. Claude Fable 5.1 gave me an excellent pelican riding a bicycle - the best I've seen from a Claude model - but did charge me $3.30 for it.

    Opus 5.5 thought for 128,000 tokens and then gave up! It ran out of tokens before it got to the response.

    #

    Getting back to Deep Blue. Something that's been puzzling me this year is this: why does my job feel harder?

    I've got these agents that can do all of this stuff for me, and yet I've never worked so hard, I've never been so intellectually engaged with my work.

    Partly this is because I'm being a lot more ambitious with what I take on, but it's also because all of the easy stuff is handled for me. If it's easy, the agent will do it. Everything that's left for me is difficult.

    This morning I heard this quote from three-time Tour de France champion Greg LeMond:

    It doesn't get easier, you just get faster.

    I think that's exactly what's happening to us now as software engineers with coding agents.

    #

    One closing thing. I know you're desperate for an update on Kākāpō breeding season.

    We've reached a recovery-era high of 325 birds!

    89 new chicks have made it to this point. This is the best breeding year in a very long time.

    #

    I heard that Claude Opus 5.5 can now do pixel art. Claude doesn't have an image generator, but it's very good at using JavaScript to draw animated pixels.

    So I had it make me a Kākāpō dance party. I think this is a good celebration of the most important news of this year.

    You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options.

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  • Brilliant Maps Brilliant Maps remote

    23 Fascinating DNA Distance Maps From Heatmappr

    The following maps all come from X/Twitter user @heatmappr and show DNA Distance Heatmaps (aka how genetically similar one group is with another). You can get Custom Genetic Heatmap Maps from their online store here. And as always these maps are meant to be fun and not taken too seriously. Italian American vs Italian Brazilians […]
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  • Platformer Platformer remote

    Can Muse make us forget the metaverse?

    Meta has a new vision to sell, but it may be getting ahead of itself. (The VR Glasses are amazing, though)
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    The Best, Worst, Most Underrated & Overrated Cities In Each US State

    The following four maps come from @mattsurelee‘s Instagram page and are solely based on a survey of his users. The Worst City in Every State Alabama: Birmingham Alaska: Wasilla Arizona: Mesa Arkansas: Pine Bluff California: Modesto, Bakersfield Colorado: Pueblo Connecticut: Bridgeport Delaware: Wilmington Florida: Orlando Georgia: Macon Hawaii: Kalihi Idaho: Caldwell Illinois: Cairo Indiana: Gary […]
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    Muse is having a moment

    Are consumer agents the future — or a mirage?
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  • Simon Willison's Weblog: Entries Simon Willison's Weblog: Entries remote

    Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war

    Yesterday was Grok 4.7 (pelicans) and MiMo v2.6 Flash/Pro (more pelicans). Today Anthropic released Claude Opus 5.5, and around an hour later OpenAI released GPT-6 Sol and GPT-6 Luna. It's going to take a while to get a good read on all of these new models, but here are my impressions so far.

    GPT-6 Sol and Luna are half the price of their GPT-5.6 equivalents

    GPT-5.6 Luna was already my favorite model for building applications against, because it combined excellent performance with being really cheap. Somehow GPT-6 Luna is half the price of that again - and GPT-6 Sol had a similar reduction compared to GPT-5.6 Sol.

    Here's what the pricing landscape looks like today:

    Model Input Cached input Output
    GPT-6 Luna $0.10/M $0.01/M $0.50/M
    GPT-5.6 Luna $0.20/M $0.02/M $1.20/M
    Grok 4.7 $2/M $0.50/M $6/M
    GPT-6 Sol $2/M $0.20/M $10/M
    GPT-5.6 Terra $2/M $0.20/M $12/M
    Claude Opus 5.5 $4/M $0.20/M $20/M
    GPT-5.6 Sol $4/M $0.40/M $20/M
    Claude Fable 5.1 $10/M $0.25/M $50/M
    GPT-6 Astra $10/M $1/M $50/M

    Note that GPT-5.6 has a scheduled 25% price increase for November, so GPT-6 is half the price of the promotional pricing for those models.

    (With GPT-5.6 Terra priced the same as GPT-6 Sol, any remaining reasons to use Terra just evaporated.)

    It's hard to overstate how competitive this pricing is. Grok 4.7 priced itself at $2/$6, less than half the price of GPT-5.6 Sol, but is now equally priced to GPT-6 Sol on input and closer on output.

    At $0.10/$0.50 GPT-6 Luna is one of the cheapest models OpenAI have ever released, beaten only by the far weaker GPT-4.1 Nano ($0.10/$0.40, April 2025) and GPT-5 Nano ($0.05/$0.40, August 2025).

    I rendered pelicans for GPT-6 Luna and for GPT-6 Sol, then I combined them all together in this comparison grid along with the GPT-5.6 pelicans. I like how you can instantly see that the 5.6 family chose bolder, brighter colors, while the 6 family is a lot more muted. I still think GPT-6 Astra on max produced the best pelican.

    Claude Opus 5.5 got a price cut too

    Opus 5.5 looks like it addresses the biggest complaints people had about Opus in terms of its communication style. Thariq Shihipar:

    Opus 5.5 is the result of your feedback.

    It communicates clearly, it's cheaper per token than Opus 5.0 with the intelligence of Fable 5.1 it's very token efficient and works across every effort level.

    It's also meant to be better at Blender. I'm looking forward to putting it through its paces there.

    Opus 4.5, 4.6, 4.7, 4.8, and 5 all shared the same price: $5/million tokens for input and $25/million for output. 5.5 is a 20% reduction - $4/million and $20/million.

    The price for cache reads fell 60%. That's significant for longer agentic conversations, where 90%+ of input tokens are processed at cached token prices.

    The new price for Opus 5.5 is the same as the price for GPT-5.6 Sol, but that was before OpenAI dropped their Sol prices by half.

    GPT-6 Astra and Claude Fable 5.1 are both priced at $10/million input and $50/million output. The price war currently affects the next tier of models below that.

    Anthropic say that Sonnet 5.5 and Haiku 5.5 are coming soon. It's going to be interesting to see if Haiku can regain its price competitiveness at the lower end, given current Haiku 4.5 is $1/$5 while the latest GPT-6 Luna is one tenth of that price at $0.10/$0.50.

    Claude Opus 5.5 max over-thinks to the point of breaking

    In a first for my "Generate an SVG of a pelican riding a bicycle" test, Claude Opus 5.5 at "max" thinking level failed to return a response!

    It started by calling this "a classic test request", and then thought really, really hard about what it was doing:

    This is a classic test request, so I want to plan out a well-composed pelican with its distinctive beak and pouch riding a bicycle with proper wheels, frame, and pedals, set against a simple sky and ground backdrop. [...]

    Verifying the shin length checks out at roughly 95.2, close enough. Now I'm working out the near leg path from hip to knee to ankle, then sketching the foot shape resting on the pedal — outlining the heel, toe tips, and sole contour with a path using lines and curves to sit naturally on the pedal surface around y=478-494. [...]

    I like the fish sticking prominently out of the basket with the pelican eyeing it as a fun detail worth keeping. I'm also confirming the eye placement near the bill base matches typical pelican anatomy, and considering giving it a slightly happier expression. [...]

    The far leg reads correctly as passing behind the frame, so I'm moving on to check the chainring teeth and confirm layer ordering—the far crank arm should be mostly hidden by the seat tube and chainring. I'm settling on the final SVG's width and height attributes alongside the viewBox to ensure proper scaling, noting there's no text so no font-family is needed. [...]

    I was so excited to see this pelican... but then it stopped. Opus 5.5 has a 128,000 maximum output token limit (as do the other Claude models), and it hit that while it was still reasoning about the SVG!

    I tried a second time and got the same result. This makes me suspect that "max" is effectively useless - if it over-thinks to breaking point on a stupid SVG prompt I don't trust it not to do the same for more interesting work.

    (Those two failures each cost me $2.56 and took nearly 20 minutes.)

    Fable 5.1 on "max" didn't over-think and did give me the best pelican I've seen from any Anthropic model.

    Here are the Opus 5.5 pelicans, excluding 5.5 max.

    I also built this comparison grid comparing them with pelicans by Opus 5, Fable 5.1, and Sonnet 5:

    Comparing different model vendors by how well they draw a pelican riding a bicycle may not make much sense now (if it ever did), but I'm still finding value in using them for comparisons of the same model families at different reasoning levels.

    I'm now using GPT-6 Sol and Claude Opus 5.5 as my default models in Codex and Claude Code. I've upgraded the Datasette Agent demo at agent.datasette.io to use GPT-6 Luna, and it seems to be fast and competent at both SQL queries and building HTML and JavaScript for Datasette Apps.

    You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options.

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    Where Is Your Country’s First Floor?

    Map created by reddit user depredador93 The map above shows one of the odder things about the world, not all countries define the first floor of a building the same way. For example, In Canada and the US the first floor is the floor at street level. But, in the UK the floor at street […]
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    Who Are You Genetically Closer To? 31 Maps Reveal The Answer

    Recently, twitter user @47Tolkien has been posting some interesting genetic maps showing which areas of the world are more genetically similar to one group or country than another. The data comes from https://mygeneticmaps.com/, here’s what he says about the maps: 🧬 What is MyGeneticMaps? An interactive tool to visualise your genetic affinity to populations across […]
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    Jev introduces a new shape of LLM - System One, aka Decision Models

    Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores.

    TypeSafe describe Jev like this:

    Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.

    It's also very fast, and really cheap. Regular LLMs are priced in terms of input and output tokens, with output generally charged at significantly higher rates. Jev charges only for input - output is free - and the input price of their first model is $0.042 per million tokens - cheaper even than OpenAI's GPT-5 Nano ($0.05/million).

    Three types of question

    Jev lets you ask questions about text or semi-structured data. You compose a "state" object containing a string, array of strings, or set of name-value pairs - this might describe an article, or a customer, or any other kind of record. You then send that to their API with one or more questions, and get a reply back for each.

    You can ask three kinds of questions:

    • Yes/No questions, which Jev calls "Noul" questions - their CEO confirmed on Hacker News that this is short for Bernoulli, from the Bernoulli distribution. You pose a statement and get back a floating point number between 0 and 1 for how confident the model is that the statement is true.
    • Choice questions, where the model picks one from a set of provided options - actually a confidence score plus a probability distribution across all of the options.
    • Score questions, where you provide sequence of numeric levels with descriptions and it provides a floating point score somewhere along that range.

    The Jev API can accept a single document ("state") and as many questions as you can cram into the context window. Questions are evaluated in parallel, so sending many questions should take a similar time to sending just one.

    The Jev 1.13 jaggedness documentation offers useful guidance as to Jev's strengths and weaknesses. It's currently not great with numbers, dates, or "adversarial content".

    I think the decision model framing is useful for understanding where to use Jev. It's great for anything that can be expressed as a classification task - think spam detection, suggesting labels, prioritization and ranking.

    I've also been experimenting with it for search reranking, where you fetch 100 likely matches using an inexpensive algorithm like BM25, then have Jev score those 100 candidates for relevance against the original query.

    Black boxes are back in fashion

    Something I've found a little uncomfortable about Jev is how it very much represents a regression even further towards black box machine learning systems.

    LLMs are black boxes already - you can ask them to justify their decisions, but you can't guarantee that what they say is useful or accurate.

    Jev doesn't even give you that: put in all the text you want, the only thing you're going to get back is a floating point number. If Jev marks something as spam, which content signals tipped it off?

    This also means that concerns about bias should be front and center. I really hope nobody uses Jev to rank job applicants - that floating point number could conceal all manner of unseen bias baked into the models, and experimentally picking that bias apart is going to be a tricky business.

    (I tried one experiment where I had Jev score every city in the San Francisco Bay Area on a yes/no answer to whether they were a "Good city?" - it rated Cupertino top and East Palo Alto bottom. Huh.)

    In practice, this all means that evals and structured experiments are even more important than they are for regular LLM projects. Thankfully, Jev is so cheap that running hundreds or even thousands of experimental prompts through it costs just a few cents.

    Unconventional uses for Jev

    It's been really fun watching the wider community come up with potential use-cases for Jev over the past few days. Here are some creative ones that caught my eye:

    • jevchat by Kyle Pena turns Jev into a (terrible) chat model. "At every step it asks Jev one question: Given the user's question and the reply written so far, which symbol comes next?". ericpruitt on Hacker News: "It's the digital equivalent of Morty speaking with the death crystal".
    • jev-leftpad by Fatih Kadir Akın implements left-pad with the prompt "How many spaces are needed before value to reach targetLength?" and a choice query allowing options from "0 spaces are needed" to "10 spaces are needed".
    • jev-2048 by Andy Gayton uses Jev to play the 2048 sliding puzzle game.

    Open weight recreations

    There's also been a flurry of projects attempting to create a model like Jev using on top of open weight models. Kev is one interesting example, using Qwen 3.5 to produce 0.8B, 4B, and 9B models. Here's the accompanying Hacker News thread, where someone linked to a JevBench benchmark that has already cropped up to compare "Jev-class decision models".

    Given Jev was released just under a week ago, the amount of activity around it is extremely impressive.

    Using Jev from LLM

    Update 22nd September 2026: I released llm-typesafe, a plugin that adds support for Jev to my LLM CLI tool and Python library. Basic usage looks like this:

    llm -m jev 'Please refund my last payment.' \
  -s 'Does this message explicitly request a refund?'

    See the README for examples of other query types.

    You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options.

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    Why Do Chicagoans Hate Ketchup On Hot Dogs?

    The map above shows the places in the US where it’s acceptable for adults to put ketchup on a hot dog and Chicago. Although really, It’s mostly tradition rather than a belief that ketchup is objectively bad. The classic Chicago-style hot dog developed in the early 20th century and became especially associated with inexpensive street […]
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    Can You Find These 26 US Cities On Blank Map Of The United States?

    Look at the map above and see if you can figure out where the following 26 cities are located. This is a continuation of the Where is Kansas City & Why Are There Two Of Them? post (speaking of which can you find them?) List of cities. City List Albuquerque, NM Amarillo, TX Aspen, CO […]
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  • Platformer Platformer remote

    What was Hard Fork?

    Saying goodbye to some old friends — and hello to Machine Gods
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  • Manton Reece Manton Reece remote

    I got some pushback when I compared Muse to ChatGPT Work, where I was mostly thinking about having access to a server in the cloud to run agents from your phone. That comparison wasn’t quite right. Feels like there’s a lot that is OpenClaw-inspired in it too.

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    Paramount Funds, ‘Leaks’ Dodgy Study Saying California Will Suffer If Its Merger Is Blocked

    Larry and David Ellison are desperate to get their unpopular $111 billion merger between Paramount/CBS and Warner Brothers across the finish line. So they’ve been engaging in no shortage of dodgy behavior to try and derail the only thing left standing in their way: the 12-state antitrust lawsuit claiming that more media consolidation is anti-competitive […]
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    Genius Design Details: Pips and Nubs

    The gang from Craighill explains the origins and uses of three seemingly small design details that show up everywhere: pips, nubs, and the very useful union of pips and nubs. You’ve seen pips on dice and dominos, nubs are used for Braille text, and the pips/nubs combo is used for locks or stops that can be released under pressure. Craighill even has a pips + nubs category in their online store. (thx, rion)

    Tags: Craighill · design · video

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    The Real AI Threats: Automated Hacking And Surveillance, Conventional Weapons Development, Biological Misuse

    Techdirt has just written about concerns that AI development is proceeding more quickly than human oversight can monitor and control it. Since then, there has been lively debate about whether those fears are real or overblown. They mainly focus on the possible future threats from AI systems using RSI — recursive self-improvement — to drive […]
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    Usenet-Rewind : search over 1 billion Usenet text posts...

    Usenet-Rewind: search over 1 billion Usenet text posts from 1981 to the present.

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    “ Figures show Japan is now home to 107,677...

    “Figures show Japan is now home to 107,677 centenarians, the highest number in the world, of whom 88% are women. The oldest woman is Kishimoto Fuyo, a 114-year-old from Kyoto City and the oldest man is Kato Hikaru, a 112-year-old from Kumamoto.”

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    Court: The First Amendment Allows You To Tell The ICE Director He’s A ‘Sad, Despised Man’

    You may recall that back in July we had the ridiculous story of how ICE decided to go all gestapo on its critics, including tracking down and threatening David Streever, who had emailed then-acting ICE director Todd Lyons back in January, after federal agents killed some peaceful protestors in Minnesota. Streever’s email was pretty straightforward: […]
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    Daily Deal: The Premium Microsoft Office Training Certification Bundle

    The Premium Microsoft Office Training Certification Bundle has 11 courses geared to help you master essential skills and raise your expertise. Popular programs like Word, Excel, PowerPoint, and Teams are covered, while more advance programs get introduced as well, including SharePoint, Access, PowerBI, and Microsoft SQL Server. You’ll gain a better understanding of how each […]
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    Danny MacAskill Plays in the Endless Light of Norway

    Scottish trials rider and mountain biker Danny MacAskill, who I first featured on the site more than 17 years ago, recently took his Santa Cruz eMTB north of the Arctic Circle to Narvik, Norway. He rode three different mountains and the result is this incredibly beautiful video, backed by a Max Cooper soundtrack. As one of the commenters notes, MacAskill makes a relatively heavy eMTB seem extraordinarily playful.

    As a mountain biker myself, some of that looks insanely fun but the narrow steep stuff looks insanely insane. Makes me want an eMTB, which I suppose is the whole point of the video.

    Tags: biking · Danny MacAskill · Max Cooper · music · video

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    Hidden in Pieces: Live at the Royal Albert Hall . A live...

    Hidden in Pieces: Live at the Royal Albert Hall. A live album from the Yeah Yeah Yeahs? Hell yes yes yes! 13 songs are available now plus 9 more tracks when the full album drops in October.

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    Turns Out It’s Pretty Easy To Turn A Profit Selling Cheap ‘Freedom Fuel’ If You Just Don’t Pay For It

    Yet another data point on the whole grift/graft aspect of (particularly) the second Trump administration. While Trump was endlessly bragging about how much war he was going to wage, while simultaneously bragging about how much war he was going to end, his Israel-enabled Iran War refused to submit to his vacillating bluster. After claiming to […]
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  • Manton Reece Manton Reece remote

    Xserve ready to make a comeback? 9to5Mac blogs about a report from The Information about M8 servers. The Information says they might use NVLink:

    Apple has considered connecting the M8 chips in the server using an Nvidia set of products called NVLink Fusion, which includes switches, chiplets and software that help chips communicate with each other in data centers.

    If Apple has to build these for themselves, they might as well sell them. Quite the leap from selling Mac minis to AI enthusiasts.

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    Paul Ford: Aided by LLMs, people & companies are...

    Paul Ford: Aided by LLMs, people & companies are producing lots of software. But where are the AI-coded killer apps? “[AI is] very good at reproducing things that look and feel like what came before.”

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  • Manton Reece Manton Reece remote

    I recorded a 3-minute screencast video on YouTube that walks through what it looks like for a brand new Micro.blog user to start using AT Protocol right away with our PDS, without having to create a separate account in Bluesky first. Love open standards. 🦋

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    The All of the Above Trail Fest is “deconstructing...

    The All of the Above Trail Fest is “deconstructing the very definition of a trail race, refiguring it into something more expansive and inclusive”. There are no gender divisions and winners are decided by who gets closest to their predicted finish time.

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  • Manton Reece Manton Reece remote

    Excited for the new show from Casey Newton and Kevin Roose, Machine Gods. I was a big fan of their Hard Fork podcast. Seems smart to partner with NPR on this too.

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  • Manton Reece Manton Reece remote

    Editing the auto-generated transcript on a YouTube video. Actually surprised at the fairly poor quality of initial transcript. Simple things like "blue sky" instead of "Bluesky", or "crossosting" instead of "cross-posting" that usually an LLM would get right, I think.

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  • Manton Reece Manton Reece remote

    Great blog post from John Gruber on the Apple announcements. On the Siri recap feature, he actually has the opposite perspective that I had:

    I think it’s better for Apple to lead with privacy-minded versions of ambient transcripts and AI-generated conversation summaries than to follow after other products have hit the market with all-day recordings and whole-life transcripts.

    I don't think Apple needs to push the state of the art here. But I'm only 50% sure. John has a good point too.

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  • 404 Media 404 Media remote

    Podcast: Humans Are Reading Your ChatGPT Conversations

    The contractors reading real ChatGPT users' prompts; the big out-and-back-in around Automattic; and a16z thinks enshittification isn't real.
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  • Scripting News Scripting News remote

    Claude Code, even though it had blocks preventing it from doing this, overwrote files on an S3 bucket that took one of my major apps off the air. Every customer must be grappling with the same thing. How do I trust it when it can't be trusted? More about this in a tweet earlier today.

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    The Powerful Connection Between Smell & Memory

    One thing I didn’t get a chance to work into my post about the unexpected memory bomb triggered by my spice drawer was the tight, deep connection between smell and memory. I first learned about this relationship in college when I read A Natural History of the Senses by Diane Ackerman for a class. It’s still one of my favorite books. Here’s some of what she has to say about smell:

    Smell is the most direct of all our senses. When I hold a violet to my nose and inhale, odor molecules float back into the nasal cavity behind the bridge of the nose, where they are absorbed by the mucosa containing receptor cells bearing microscopic hairs called cilia. Five million of these cells fire impulses to the brain’s olfactory bulb or smell center. Such cells are unique to the nose. If you destroy a neuron in the brain, it’s finished forever; it won’t regrow. If you damage neurons in your eyes or ears, both organs will be irreparably damaged. But the neurons in the nose are replaced about every thirty days and, unlike any other neurons in the body, they stick right out and wave in the air current like anemones on a coral reef.

    And:

    Unlike the other senses, smell needs no interpreter. The effect is immediate and undiluted by language, thought, or translation. A smell can be overwhelmingly nostalgic because it triggers powerful images and emotions before we have time to edit them. What you see and hear may quickly fade into the compost heap of short-term memory, but, as Edwin T. Morris points out in Fragrance, “there is almost no short-term memory with odors.” It’s all long term. Furthermore, smells stimulate learning and retention. “When children were given olfactory information along with a word list,” Morris noted, “the list was recalled much more easily and better retained in memory than when given without the olfactory cues.” When we give perfume to someone, we give them liquid memory. Kipling was right: “Smells are surer than sights and sounds to make your heart-strings crack.”

    And:

    Smell was the first of our senses, and it was so successful that in time the small lump of olfactory tissue atop the nerve cord grew into a brain. Our cerebral hemispheres were originally buds from the olfactory stalks. We think because we smelled.

    Our sense of smell, like so many of our other body functions, is a throwback to that time, early in evolution, when we thrived in the oceans. An odor must first dissolve into a watery solution our mucous membranes can absorb before we can smell it. Scuba-diving in the Bahamas some years ago, I became aware of two things for the first time: that we carry the ocean within us; that our veins mirror the tides. As a human woman, with ovaries where eggs lie like roe, entering the smooth, undulating womb of the ocean from which our ancestors evolved millennia ago, I was so moved my eyes teared underwater, and I mixed my saltiness with the ocean’s. Distracted by such thoughts, I looked around to find my position vis-a-vis the boat, and couldn’t. But it didn’t matter: Home was everywhere.

    I could basically quote the whole smell chapter…it’s interesting throughout. Great book — if you’ve never read it, I’d recommend checking it out.

    Tags: A Natural History of the Senses · books · Diane Ackerman · senses · smell

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    Bernie Sanders Tries To ‘Help’ Fix AI By Complaining With Steve Bannon, ‘Banning Superintelligence’

    In an ideal world it’s certainly possible that the U.S. federal government could implement meaningful guardrails for AI development that protect innovation, national security, consumer privacy, public safety, and labor/creatives, while preventing market monopolization by the sector’s biggest players. But we don’t live in that ideal world. We live in a country where an historically […]
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    We Must Create the Shit Machine . “I asked...

    We Must Create the Shit Machine. “I asked everyone I know at the BIG TECH HUMAN CENTIPEDE ROUNDTABLE, and we all agreed that UNCONTROLLABLE SHIT-RAIN IS USEFUL AND GREAT, and that EVERYONE WANTS IT.”

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    Hackers Stole Flock’s Camera Software, Revealing How the Company Tracks Cars and People

    While people around the U.S. are tearing down Flock cameras, one group of hackers went a step further: extracting the camera's software too.
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    The frontier Anthropic & OpenAI models are only ~4...

    The frontier Anthropic & OpenAI models are only ~4 months ahead of open models — at 5x the price. “Most organizations should ideally be using open models as the default for the majority of their work, according to [a report] from Mozilla…”

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    RFK Jr. To Give Keynote At Conference For Anti-Vaxxer Organization He Co-Founded

    Whatever mask RFK Jr. was attempting to wear to convince the world he isn’t a pure anti-vaxxer clown is now officially off. The man who has spent his time as Secretary of HHS overseeing the worst outbreak of measles in America in decades, who just can’t stop citing fake health studies and misinterpreting others, who […]
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    The High Crime Of “LMAO”: How Cops Are Treating Flock Mass Surveillance As A Joke

    Here’s a riddle: Why did a Goshen Police Department officer search 6,474 automated license plate reader (ALPR) networks, representing data from 82,413 cameras, on May 7, 2025?  If your answer is “I don’t know,” it turns out you’re 100% correct. The officer left the letters “idk” in the search field where cops are supposed to […]
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    Last year in Ireland, a bouncy castle infested with a...

    Last year in Ireland, a bouncy castle infested with a “hypervirulent, multidrug-resistant pathogen” infected almost 50 children. Reality: always weirder than the weirdest sci-fi.

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    Naomi Klein : “Extreme wealth has a deranging...

    Naomi Klein: “Extreme wealth has a deranging effect. It turns you into a supremacist.” Klein’s new book, End Times Fascism, written with Astra Taylor, is out today.

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