Recent AI news and official updates

Simon Willison's AI Notes published: The memory shortage is causing a repricing of consumer electronics David Oks provides the clearest explanation I've seen yet of why consumer products that use memory are likely to get significantly more expensive over the next few years. The short version is that memory manufacturers - of which there are just three remaining large companies - have a fixed capacity in terms of how many wafers they can process at any one time. This fixed wafer capacity is then split between DDR - used in desktops and servers, LPDDR - used in mobile phones and low-energy devices, and HBM - used with GPUs. Until recently, HBM got just 2% of that wafer allocation. The enormous growth in AI data centers has pushed that up to an expected 20% by the end of 2026, and "a single gigabyte of HBM consumes more than three times the wafer capacity that a gigabyte of DDR or LPDDR does". Memory companies have learned from the extinction of their rivals that you should always under-provision rather than over-provision your fabricator capacity. The profit margins and demand for HBM (high-bandwidth memory) will constrain the production of consumer-device RAM for several years. This is already being felt in the sub-$100 smartphone market, which is particularly important to markets like Africa and South Asia. (The original title of the piece was "AI is killing the cheap smartphone" but I'm using the Hacker News rephrased title, which I think does more justice to the content.) Via Hacker News Tags: memory , ai , ai-ethics

Simon Willison's AI Notes published: We just announced the first release of Datasette Agent , a new extensible AI assistant for Datasette. I've been working on my LLM Python library for just over three years now, and Datasette Agent represents the moment that LLM and Datasette finally come together. I'm really excited about it! Datasette Agent provides a conversational interface for asking questions of the data you have stored in Datasette. Add the datasette-agent-charts plugin and it can generate charts of your data as well. The demo The announcement post (on the new Datasette project blog) includes this demo video : I recorded the video against the new agent.datasette.io live demo instance, which runs Datasette Agent against example databases including the classic global-power-plants by WRI , and a copy of the Datasette backup of my blog. The live demo runs on Gemini 3.1 Flash-Lite - it's cheap, fast and has no trouble writing SQLite queries. A question I asked in the demo was: when did Simon most recently see a pelican? Which ran this SQL query : SELECT title, commentary, created FROM blog_beat WHERE beat_type = ' sighting ' AND (title LIKE ' %pelican% ' OR commentary LIKE ' %pelican% ' ) ORDER BY created DESC LIMIT 5 And replied: The most recent sighting of a pelican by Simon was recorded on May 20, 2026 . The observation included a California Brown Pelican, along with a Common Loon, Canada Goose, Striped Shore Crab, and a California Sea Lion. Here's that sighting on my blog , and the Markdown export of the full conversation transcript. The plugins My favorite feature of Datasette Agent is that, like the rest of Datasette, it's extensible using plugins. We've shipped three plugins so far: datasette-agent-charts , shown in the video, adds charts to Datasette Agent, powered by Observable Plot . datasette-agent-openai-imagegen adds an image generation tool to Datasette Agent using ChatGPT Images 2.0 . datasette-agent-sprites provides tools for executing code in a Fly Sprites persistent sandbox. Building plugins is really fun . I have a bunch more prototypes that aren't quite alpha-quality yet. Claude Code and OpenAI Codex are both proving excellent at writing plugins - just point them at a checkout of the datasette-agent repo for reference and tell them what you want to build! Running it against local models I've also been having fun running the new plugin against local models. Here's a uv one-liner to run the plugin against gemma-4-26b-a4b in LM Studio on a Mac: uvx --prerelease=allow \ --with datasette-agent --with llm-lmstudio \ datasette --internal internal.db --root \ -s plugins.datasette-llm.default_model lmstudio/google/gemma-4-26b-a4b \ data.db Datasette Agent needs reliable tool calls and the ability for a model to produce SQL queries that run against SQLite. The open weight models released in the past six months are increasingly able to handle that. What's next Datasette Agent opens up so many opportunities for the LLM and Datasette ecosystem in general. It's already informed the major LLM 0.32a0 refactor which I'm nearly ready to roll into a stable release, maybe with some additional "LLM agent" abstractions extracte from Datasette Agent itself. I've been exploring my own take on the Claude Artifacts, which is shaping up nicely as a plugin. I'm excited to use Datasette Agent to build my own Claw - a personal AI assistant built around data imported from different parts of my digital life, which is a neat excuse to revisit my older Dogsheep family of tools. We'll also be rolling out Datasette Agent for users of Datasette Cloud . Join our #datasette-agent Discord channel if you'd like to talk about the project. Tags: projects , sqlite , ai , datasette , generative-ai , llms , llm , uv , datasette-agent

Simon Willison's AI Notes published: We have the ability to use compute resources to support our proprietary AI applications (such as Grok 5, which is currently being trained at COLOSSUS II), while also providing access to select compute capacity to third-party customers. For example, in May 2026, we entered into Cloud Services Agreements with Anthropic PBC (“Anthropic”), an AI research and development public benefit corporation, with respect to access to compute capacity across COLOSSUS and COLOSSUS II . Pursuant to these agreements, the customer has agreed to pay us $1.25 billion per month through May 2029, with capacity ramping in May and June 2026 at a reduced fee. The agreements may be terminated by either party upon 90 days’ notice. — SpaceX S-1 , highlights mine Tags: ai , generative-ai , llms , anthropic , grok

Simon Willison's AI Notes published: How fast is 10 tokens per second really? Neat little HTML app by Mike Veerman ( source code here ) which simulates LLM token output speeds from 5/second to 800/second. Useful if you see a model advertised as "30 tokens/second" and want to get a feel for what that actually looks like. Via Hacker News Tags: ai , generative-ai , llms

Simon Willison's AI Notes published: It's hard to find much to write about Google I/O this year because I have a policy of not writing about anything that I can't try out myself, and a lot of the big announcements are "coming soon". I actually prefer to write about things that are in general availability, because I've had instances in the past where the previews didn't match what was released to the general public later on. Aside from Gemini 3.5 Flash the most interesting announcement looks to be Google's upcoming OpenClaw competitor Gemini Spark , described as "your personal AI agent" which can "connect natively with your favorite Google apps like Gmail, Calendar, Drive, Docs, Sheets, Slides, YouTube, and Google Maps". The FAQ for that also includes this confusing detail: What Gemini model does Gemini Spark run on? Gemini Spark runs on Gemini 3.5 Flash and Antigravity. The antigravity.google website currently lists Antigravity as a desktop app, a CLI agent tool (written in Go), the Antigravity SDK (an open source Python wrapper around a bundled closed source Go binary), and the original Antigravity IDE (a VS Code fork). I guess Gemini Spark, the user-facing hosted agent product, might be running on that Go binary, but I'm not sure why that's worth mentioning in the FAQ! Naturally I went looking for notes on how Gemini Spark intends to handle the risk of prompt injection. The best information I could find on that was in the Everything Google Cloud customers need to know coming out of Google I/O post aimed at enterprise customers, which includes: Spark operates in a fully managed, secure runtime on Google Cloud, meaning you get enterprise-grade security without ever having to manage the underlying infrastructure. Every task executes in a fresh, strictly isolated, ephemeral VM to help ensure data never overlaps between sessions. To protect your enterprise, all traffic routes through our secure Agent Gateway that enforces Data Loss Prevention (DLP) policies, while user credentials remain fully encrypted and are never exposed directly to the agent. Given how many people are going to be piping very sensitive data through Gemini Spark in the near future I hope they've made this bullet-proof, or this could be a top candidate for the agent security challenger disaster that we still haven't seen. Also of note: in Transitioning Gemini CLI to Antigravity CLI Google announce that the open source Gemini CLI tool (Apache 2.0 licensed TypeScript) will stop working with their AI subscription plans on June 18th, replaced by the new closed source Antigravity CLI . Tags: gemini , google , generative-ai , ai , google-io , llms , prompt-injection

Simon Willison's AI Notes published: Today at Google I/O, Google released Gemini 3.5 Flash . This one skipped the -preview modifier and went straight to general availability, and Google appear to be using it for a whole lot of their key products: 3.5 Flash is available today to billions of people globally: For everyone via the Gemini app and AI Mode in Google Search For developers in our agent-first development platform Google Antigravity and Gemini API in Google AI Studio and Android Studio For enterprises in Gemini Enterprise Agent Platform and Gemini Enterprise. As usual with Gemini, the most interesting details are tucked away in the What's new in Gemini 3.5 Flash developer documentation. It mostly has the same set of platform features as the previous Gemini 3.x series, albeit with no computer use . The model ID is gemini-3.5-flash . The knowledge cut-off is January 2025, and it supports 1,048,576 input tokens and 65,536 maximum output tokens. Google are also pushing a new Interactions API , currently in beta, which looks to me like their version of the patterns introduced by OpenAI Responses - in particular server-side history management. The price has gone up Gemini 3.5 Flash is accompanied by a notable price bump. The previous models in the "Flash" family were Gemini 3 Flash Preview and Gemini 3.1 Flash-Lite . The new 3.5 Flash is 3x the price of 3 Flash Preview and 6x the price of 3.1 Flash-Lite (see price comparison here ). At $1.50/million input and $9/million output it's getting close in price to Google's Gemini 3.1 Pro, which is $2 and $12. The Gemini team promise that 3.5 Pro will roll out "next month" - presumably at an even higher price. This fits a trend: OpenAI's GPT-5.5 was 2x the price of GPT-5.4, and Claude Opus 4.7 is around 1.46x the price of 4.6 when you take the new tokenizer into account . Given the price increase it's interesting to see Google roll it out for so many of their own free-to-consumer products. It feels like all three of the major AI labs are starting to probe the price tolerance of their API customers. Artificial Analysis publish the cost to run their proprietary benchmark against models, which is a useful way to take things like tokenization and increased volume of reasoning tokens into account. Some numbers worth comparing: Gemini 3.5 Flash (high) : $1,551.60 Gemini 3.1 Pro Preview : $892.28 Gemini 3 Flash Preview (Reasoning) : $278.26 Gemini 3.1 Flash-Lite Preview : $93.60 Running the benchmark for 3.5 Flash (high) cost significantly more than 3.1 Pro Preview! Here are some numbers from other vendors: Claude Opus 4.7 (Adaptive Reasoning, Max Effort) : $5,117.14 Claude Opus 4.7 (Non-reasoning, High Effort) : $1,217.23 GPT-5.5 (xhigh) : $3,357.00 GPT-5.5 (medium) : $1,199.14 A pelican on a bicycle I ran "Generate an SVG of a pelican riding a bicycle" against the Gemini API and got back this pelican, which is a lot : From the code comments: <!-- Pelican Eye / Sunglasses (Cool Retro Aviators) --> hedgehog on Hacker News : That pelican looks like it's in Miami for a crypto conference. That one cost me 11 input tokens and 14,403 output tokens, for a total cost of just under 13 cents . Tags: google , ai , generative-ai , llms , gemini , llm-pricing , pelican-riding-a-bicycle , llm-release

Simon Willison's AI Notes published: I put together these annotated slides from my five minute lightning talk at PyCon US 2026, using the latest iteration of my annotated presentation tool . # I presented this lightning talk at PyCon US 2026, attempting to summarize the last six months of developments in LLMs in five minutes. # Six months is a pretty convenient time period to cover, because it captures what I've been calling the November 2025 inflection point . November was a critical month in LLMs, especially for coding. # For one thing, the supposedly "best" model (depending mostly on vibes) changed hands five times between the three big providers. # As always, I'm using my Generate an SVG of a pelican riding a bicycle test to help illustrate the differences between the models. Why this test? Because pelicans are hard to draw, bicycles are hard to draw, pelicans can't ride bicycles ... and there's zero chance any AI lab would train a model for such a ridiculous task. # At the start of November the widely acknowledged "best" model was Claude Sonnet 4.5, released on 29th September . It drew me this pelican. In November it was overtaken by GPT-5.1 , then Gemini 3 , then GPT-5.1 Codex Max , and then Anthropic took the crown back again with Claude Opus 4.5 . I think Gemini 3 drew the best pelican out of this lot, but pelicans aren't everything. Most practitioners will agree that Opus 4.5 held the crown for the next couple of months. # It took a little while for this to become clear, but the real news from November was that the coding agents got good . OpenAI and Anthropic had spent most of 2025 running Reinforcement Learning from Verifiable Rewards to increase the quality of code written by their models, especially when paired up with their Codex and Claude Code agent harnesses. In November the results of this work became apparent. Coding agents went from often-work to mostly-work, crossing a quality barrier where you could use them as a daily-driver to get real work done, without needing to spend most of your time fixing their stupid mistakes. # Also in November, this happened - the first commit to an obscure (back then) repo called "Warelay" by some guy called Pete. # Over the holiday period, from December to January, a whole lot of us took advantage of the break to have a poke at these new models and coding agents and see what they could do. They could do a lot! Some of us got a little bit over-excited. I had my own short-lived bout of a form of LLM psychosis as I started spinning up wildly ambitious projects to see how far I could push them. # One of my projects was a vibe-coded implementation of JavaScript in Python - a loose port of MicroQuickJS - which I called micro-javascript . You can try it out in your browser in this playground . # That playground demo shows JavaScript code run using my micro-javascript library, in Python, running inside Pyodide, running in WebAssembly, running in JavaScript, running in a browser! It's pretty cool! But did anyone out there need a buggy, slow, insecure half-baked implementation of JavaScript in Python? They did not. I have quite a few other projects from that holiday period that I have since quietly retired! # On to February. Remember that Warelay project that had its first commit at the end of November? # In December and January it had gone through quite a few name changes ... and by February it was taking the world by storm under its final name, OpenClaw . The amount of attention it got is pretty astonishing for a project that was less than three months old. # OpenClaw is a "personal AI assistant", and we actually got a generic term for these, based on NanoClaw and ZeroClaw and suchlike... they're called Claws . # Mac Minis started to sell out around Silicon Valley, because people were buying them to run their Claws. Drew Breunig joked to me that this is because they're the new digital pets, and a Mac Mini is the perfect aquarium for your Claw. # My favourite metaphor for Claws is Alfred Molina's Doc Ock in the 2004 movie Spider-Man 2. His claws were powered by AI, and were perfectly safe provided nothing damaged his inhibitor chip... after which they turned evil and took over. # Also in February: Gemini 3.1 Pro came out, and drew me a really good pelican riding a bicycle . Look at this! It's even got a fish in its basket. # And then Google's Jeff Dean tweeted this video of an animated pelican riding a bicycle, plus a frog on a penny-farthing and a giraffe driving a tiny car and an ostrich on roller skates and a turtle kickflipping a skateboard and a dachshund driving a stretch limousine. So maybe the AI labs have been paying attention after all! # A lot of stuff happened just in the past month. # Google released the Gemma 4 series of models, which are the most capable open weight models I've seen from a US company. # Also last month, Chinese AI lab GLM came out with GLM-5.1 - an open weight 1.5TB monster! This is a very effective model... if you can afford the hardware to run it. # GLM-5.1 drew me this very competent pelican on a bicycle. # ... though when it tried to animate it the bicycle bounced off into the top and the bicycle got warped. # Charles on Bluesky suggested I try it with a North Virginia Opossum on an E-scooter # And it did this! I've tried this on other models and they don't even come close. "Cruising the commonwealth since dusk" is perfect. It's animated too . # The other neat Chinese open weight models in April came from Qwen. Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7 . That's a 20.9GB open weights model that runs on my laptop! (I think this mainly demonstrates that the pelican on the bicycle has firmly exceeded its limits as a useful benchmark.) # Here's that Claude Sonnet 4.5 pelican from September for comparison. # So those were the two main themes of the past six months. The coding agents got really good... and the laptop-available models, while a lot weaker than the frontier, have started wildly outperforming expectations. Tags: lightning-talks , pycon , speaking , ai , generative-ai , local-llms , llms , annotated-talks , pelican-riding-a-bicycle , coding-agents

Simon Willison's AI Notes published: GDS weighs in on the NHS's decision to retreat from Open Source Terence Eden continues his coverage of the NHS' poorly considered decision to close down access to their open source repositories in response to vulnerabilities reported to them as part of Project Glasswing . Now the Government Digital Service have joined the conversation with AI, open code and vulnerability risk in the public sector , published May 14th. Their key recommendation: Keep open by default. Making everything private adds additional delivery and policy costs, and can reduce reuse and scrutiny. Openness should remain the default posture, with closure used sparingly and deliberately. While they don't mention the NHS by name, Terence speaks the language of the civil service and interprets this as a major escalation: Within the UK's Civil Service you occasionally hear the expression "being invited to a meeting without biscuits ". It implies a rather frosty discussion without any of the polite niceties of a normal meeting. In general though, even when people have severe disagreements, it is rare for tempers to fray. It is even rarer for those internal disagreements to spill over into public. Tags: open-source , security , ai , generative-ai , llms , gov-uk , terence-eden , ai-ethics , ai-security-research

Simon Willison's AI Notes published: Tool: QR code generator Claude helped me build this tool for creating QR codes, for both text/URLs and for connecting to WiFi networks. Tags: tools , ai , generative-ai , llms , vibe-coding

Simon Willison's AI Notes published: This Mitchell Hashimoto quote about Bun migrating from Zig to Rust reminded me of a similar conversation I had at a conference last week. I was talking to someone who worked for a medium sized technology company with a pair of legacy/ legendary iPhone and Android apps. They told me they had just completed a coding-agent driven rewrite of both apps to React Native. I asked why they chose that, given that coding agents presumably drive down the cost of maintaining separate iPhone and Android apps. They said that React Native has improved a lot over the past few years and covered everything their apps needed to do. And... if it turned out to be the wrong decision, they could just port back to native in the future. Like Mitchell said: Programming languages used to be LOCK IN, and they're increasingly not so. Tags: react , coding-agents , ai-assisted-programming , generative-ai , ai , llms

Simon Willison's AI Notes published: [...] On the interesting side is how fungible programming languages are nowadays. Programming languages used to be LOCK IN, and they're increasingly not so. You think the Bun rewrite in Rust is good for Rust? Bun has shown they can be in probably any language they want in roughly a week or two. Rust is expendable. Its useful until its not then it can be thrown out. That's interesting! — Mitchell Hashimoto , on Bun porting from Zig to Rust Tags: ai , rust , zig , generative-ai , llms , mitchell-hashimoto , bun , agentic-engineering

Simon Willison's AI Notes published: Welcome to the Datasette blog We have a bunch of neat Datasette announcements in the pipeline so we decided it was time the project grew an official blog. I built this using OpenAI Codex desktop, which turns out to have the Markdown session transcript export feature I've always wanted. Here's the session that built the blog . See also issue 179 . Tags: ai , datasette , generative-ai , llms , ai-assisted-programming , codex

Simon Willison's AI Notes published: “11 AI agents” is meaningless as a phrase. If I said “I have 11 spreadsheets” or “I have 11 browser tabs” to do my work, it means about the same thing. — Boris Mann Tags: ai , ai-agents , agent-definitions

Simon Willison's AI Notes published: Now, if your CEO has never heard the phrase Ralph Loop, oh man, you are less than 30 days away from your next promotion. I'm not even exaggerating. Walk into his office, close the door, and say, hey chief, been experimenting with something. It's called Ralph Loops. And I think it could change literally everything. And he's gonna say, what's a Ralph loop? And you will say, give me $18,000 worth of API credits and I'll show you. Now you won't actually do anything, because you can't do anything. Because nobody can, because nobody knows what they're doing. But by the time he figures that out, you'll have a new title, and equity bump. [...] Talk about automation constantly. Nothing arouses the slumbering capitalists than the mention of automation. Drop names too, bro. Like talk about specific team members you can automate out of existence. Be like, yo, I automated Gary, bro. Tag Gary in the message. Tag him in Slack in a very public channel. Be like, yo, I just automated @Gary. His function has been Ralph Looped. And tag your CEO in the same message. You think you're getting laid off after that? — Mo Bitar , The Unethical Guide to Surviving AI Layoffs, TikTok Tags: careers , ai , tiktok , ai-ethics

Simon Willison's AI Notes published: Release: llm 0.32a2 A bunch of useful stuff in this LLM alpha, but the most important detail is this one: Most reasoning-capable OpenAI models now use the /v1/responses endpoint instead of /v1/chat/completions . This enables interleaved reasoning across tool calls for GPT-5 class models. #1435 This means you can now see the summarized reasoning tokens when you run prompts against an OpenAI model, displayed in a different color to standard error. Use the -R or --hide-reasoning flags if you don't want to see that. Tags: projects , ai , annotated-release-notes , openai , generative-ai , llms , llm

Simon Willison's AI Notes published: GitLab Act 2 There's a lot going on in this announcement from GitLab about the "workforce reduction" and "structural and strategic decisions" they are making with respect to the agentic era. They're "planning to reduce the number of countries by up to 30% where we have small teams". One of the most interesting things about GitLab is that they have employees spread across a large number of countries - 18 are listed in their public employee handbook but this post says they are "operating in nearly 60 countries". That handbook used to document their payroll workflows for those countries too - they stopped publishing that in 2023 but the last public version (hooray for version control) remains a fascinating read. Since we don't know which of those 60 countries have small teams, we can't calculate how many countries that 30% applies to. "We're planning to flatten the organization, removing up to three layers of management in some functions so leaders are closer to the work." - this isn't the first announcement of this type I've seen that's trimming management. Coinbase recently announced a much more aggressive version of this: they were "flattening our org structure to 5 layers max below" and "No pure managers: Every leader at Coinbase must also be a strong and active individual contributor. Managers should be like player-coaches". In terms of team structure: "We're re-organizing R&D to create roughly 60 smaller, more empowered teams with end-to-end ownership, nearly doubling the number of independent teams." I've always loved the idea of individual teams that can ship features unblocked by other teams, and it makes sense to me that agentic engineering can increase the capability of such teams. The 37signals public employee handbook used to have a section on working In self-sufficient, independent teams which perfectly captured this for me, I'm sad to see they removed that detail in January 2024! Tucked away towards the bottom: " We will be retiring CREDIT as our values framework " - that's the values framework described on this page : "Collaboration, Results for Customers, Efficiency, Diversity, Inclusion & Belonging, Iteration, and Transparency". The new values are "Speed with Quality, Ownership Mindset, Customer Outcomes". The fact that "Diversity" is no longer in there is likely to attract a whole lot of attention, so it's worth noting that a sub-bullet under Customer Outcomes reads "Interpersonal excellence: individuals who are good humans, embrace diversity, inclusion and belonging, assume good intent and treat everyone with respect". Here's the part of their new strategy that most resonated with me: The agentic era multiplies demand for software . Software has been the force multiplier behind nearly every business transformation of the last two decades. The constraint was the cost and time of producing and managing it. That constraint is collapsing. As the cost of producing software collapses, demand for it will expand. Last year, the developer platform market used to be measured in tens of dollars per user per month, this year it is hundreds/user/month and headed to thousands. Not only is the value of software for builders increasing, but we believe there will be more software and builders than ever, and we will serve an increasing volume of both . That very much encapsulates my own optimistic, Jevons-paradox -inspired hope for how this will all work out. Their opinion on this does need to be taken with a big grain of salt though. GitLab's stock price was ~$52 a year ago and is ~$26 today, and it's plausible that the drop corresponds to uncertainty about GitLab's continued growth as agentic engineering eats its way through their core market. If your entire business depends on software engineering growing as a field and producing larger volumes of more lucrative seats, you have a strong incentive to believe that agents will have that effect! Via Hacker News Tags: gitlab , careers , coding-agents , agentic-engineering , ai , 37signals , jevons-paradox

Simon Willison's AI Notes published: Your AI coding agent, the one you use to write code, needs to reduce your maintenance costs. Not by a little bit, either. You write code twice as quick now? Better hope you’ve halved your maintenance costs. Three times as productive? One third the maintenance costs. Otherwise, you’re screwed. You’re trading a temporary speed boost for permanent indenture. [...] The math only works if the LLM decreases your maintenance costs, and by exactly the inverse of the rate it adds code. If you double your output and your cost of maintaining that output, two times two means you’ve quadrupled your maintenance costs. If you double your output and hold your maintenance costs steady, two times one means you’ve still doubled your maintenance costs. — James Shore , You Need AI That Reduces Maintenance Costs Tags: coding-agents , ai-assisted-programming , generative-ai , agentic-engineering , ai , llms

Simon Willison's AI Notes published: Your AI Use Is Breaking My Brain Excellent, angry piece by Jason Koebler on how AI writing online is becoming impossible to avoid, filtering it is mentally exhausting and it's even starting to distort regular human writing styles. I particularly liked his use of the term "Zombie Internet" to define a different, more insidious alternative to the "Dead Internet" (which is just bots talking to each other): I called it the Zombie Internet because the truth is that large parts of the internet are not just bots talking to bots or bots talking to people. It’s people talking to bots, people talking to people, people creating “AI agents” and then instructing them to interact with people. It’s people using AI talking to people who are not using AI, and it’s people using AI talking to other people who are using AI. It’s influencer hustlebros who are teaching each other how to make AI influencers and have spun up automated YouTube channels and blogs and social media accounts that are spamming the internet for the sole purpose of making money. It is whatever the fuck “Moltbook” is and whatever the fuck X and LinkedIn have become. It’s AI summaries of real books being sold as the book itself and inspirational Reddit posts and comment threads in which people give heartfelt advice to some account that’s actually being run by a marketing firm. [...] Via @jasonkoebler.bsky.social Tags: ai-ethics , slop , jason-koebler , generative-ai , ai , llms , definitions

Simon Willison's AI Notes published: TIL: Using LLM in the shebang line of a script Kim_Bruning on Hacker News : But seriously, you can put a shebang on an english text file now (if you're sufficiently brave) [...] This inspired me to look at patterns for doing exactly that with LLM . Here's the simplest, which takes advantage of LLM fragments : #!/usr/bin/env -S llm -f Generate an SVG of a pelican riding a bicycle But you can also incorporate tool calls using the -T name_of_tool option: #!/usr/bin/env -S llm -T llm_time -f Write a haiku that mentions the exact current time Or even execute YAML templates directly that define extra tools as Python functions: # !/usr/bin/env -S llm -t model : gpt-5.4-mini system : | Use tools to run calculations functions : | def add(a: int, b: int) -> int: return a + b def multiply(a: int, b: int) -> int: return a * b Then: ./calc.sh 'what is 2344 * 5252 + 134' --td Which outputs (thanks to that --td tools debug option): Tool call: multiply({'a': 2344, 'b': 5252}) 12310688 Tool call: add({'a': 12310688, 'b': 134}) 12310822 2344 × 5252 + 134 = **12,310,822** Read the full TIL for a more complex example that uses the Datasette SQL API to answer questions about content on my blog. Tags: ai , generative-ai , llms , llm , llm-tool-use

Simon Willison's AI Notes published: Learning on the Shop floor Tobias Lütke describes Shopify's internal coding agent tool, River, which operates entirely in public on their Slack: River does not respond to direct messages. She politely declines and suggests to create a public channel for you and her to start working in. I myself work with river in #tobi_river channel and many followed this pattern. Every conversation is therefore searchable. Anyone at Shopify can jump in. In my own channel, there are over 100 people who, react to threads, add color and add context, pick up the torch, help with the reviews, remind me how rusty I am, and importantly, learn from watching. [...] As so often with German, there is a word for the kind of environment: Lehrwerkstatt . Literally: A teaching workshop . The whole shop floor is the classroom. You learn by being near the work. Being a constant learner is one of the core values of the firm. Shopify wants to be a Lehrwerkstatt at scale and River has now gotten us closer to this ideal than ever. It’s osmosis learning , because it does not require a curriculum, a training plan, or a manager. It just requires everyone's work to be visible to the maximum extent possible. Everyone learns from each other. I'm reminded of how Midjourney spent its first few years with the primary interface being public Discord channels, forcing users to share their prompts and learn from each other's experiments. I continue to believe that the early success of Midjourney was tied to this mechanism, helping to compensate for how weird and finicky text-to-image prompting is. Tags: midjourney , coding-agents , generative-ai , ai , tobias-lutke , llms , slack