Recent AI news and official updates

Google AI Blog published: One year after launch, see how AI Mode’s users are shifting from keywords to natural language queries.

Google AI Blog published: The latest from Google I/O: See how we’re helping you get more done with Gemini.

Google AI Blog published: We shared the next step in our journey to bring together the best of a search engine with the best of AI.

Google AI Blog published: At Google I/O we released Gemini 3.5, our latest series of models combining frontier intelligence with action.

VentureBeat AI published: For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm. At its annual I/O developer conference , Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversation starter that can accept text, images, PDFs, videos, and even open Chrome tabs as inputs. The company is also merging its AI Overviews and AI Mode features into a single, seamless search flow, eliminating the friction that previously forced users to choose between a traditional results page and an AI-forward experience. Liz Reid, Google's vice president and head of Search, called it "the biggest upgrade to our iconic search box since its debut over 25 years ago" during a press briefing on Monday. The announcement arrived alongside a blizzard of other news — new Gemini models , a personal AI agent called Spark , an intelligent shopping cart , a reimagined developer platform — but the search box redesign may prove to be the most consequential. It is the clearest signal yet that Google views the future of its flagship product not as a place where users type fragmented keywords, but as an interface where they hold open-ended, multimodal conversations with an AI system backed by the entire web. The new search box expands, accepts files, and coaches you on what to ask The changes show a fundamental shift in how Google expects people to interact with the product that generates the vast majority of Alphabet's revenue. The box itself now dynamically expands to accommodate longer, more conversational queries. Where the old interface subtly encouraged brevity — a narrow field suited to two- or three-word keyword strings — the new design invites users to fully articulate complex questions in granular detail. It also now supports multimodal inputs directly. Users can upload images, PDFs, files, and videos, or drag in content from Chrome tabs, right from the main search interface. Previously, some of these capabilities existed in AI Mode, but reaching them required extra steps. Now they sit at the primary entry point. Google is also deploying what it describes as an AI-powered query suggestion system that "goes beyond autocomplete." Rather than simply predicting the next word a user might type based on popular searches, the system helps users formulate complex, nuanced queries — essentially coaching them toward the kind of detailed questions that AI Mode handles best. The new search box is starting to roll out immediately in all countries and languages where AI Mode is available. Google is merging AI overviews and AI mode into one seamless experience Perhaps more significant than the box itself is the architectural change happening behind it. Google is unifying AI Overviews — the AI-generated summary panels that appear atop traditional search results — with AI Mode , the more immersive conversational search experience the company launched at I/O one year ago. Starting Tuesday, this merged experience will be live across mobile and desktop worldwide. A user can type a question, receive an AI Overview alongside traditional results, and then continue directly into a back-and-forth AI Mode conversation to ask follow-up questions — all without navigating to a separate interface. Reid explained the logic during the press briefing: the new AI search box is "an upgrade of our traditional search box, and so the results take you directly to main search rather than AI mode." She noted that while some power users actively sought out AI Mode, "for most users, they don't actually want to have to think about, do they want more of a traditional page or an AI-forward search experience." The goal, she said, was to ensure that "for most users, they don't have to think about where to go, they can just go to the search box they're familiar with, and it feels like they get the best experience afterwards." One billion users and doubling queries reveal how fast search behavior is shifting Google's decision to redesign the foundational interface of its most important product did not happen in a vacuum. The company shared a set of usage statistics during the briefing that reveal just how rapidly user behavior is already changing. AI Mode , which launched in the United States at I/O 2025, has surpassed one billion monthly users in its first year. AI Mode queries have been doubling every quarter since launch. AI Overviews, the lighter-weight AI summaries, now reach more than 2.5 billion monthly users. And overall search query volume hit an all-time high last quarter — a data point the company had previously disclosed on its earnings call. Sundar Pichai, Google's CEO, framed these figures as evidence that AI features are additive, not cannibalistic, to search usage. "When people use our AI-powered features in search, they use search more," he said. He added that he loves "how search has become less about individual queries and feels more like an ongoing conversation, giving users deeper insights and connecting you with the vastness of the web." Reid reinforced the point: "It's not just that people are searching more, it's that they're searching differently. They're fully expressing their questions in granular detail, asking those follow-up questions and searching across modalities." Gemini 3.5 Flash gives Google's AI search the speed it needs to work at scale Under the hood, the new search experience runs on Gemini 3.5 Flash , Google's newest AI model, which the company also introduced at I/O. Google upgraded AI Mode's underlying model to 3.5 Flash to deliver what Reid described as "an even more powerful AI search experience." Gemini 3.5 Flash is the workhorse of this year's announcements. Google claims it outperforms its previous frontier model, Gemini 3.1 Pro , on nearly all benchmarks while running four times faster in output tokens per second than comparable frontier models. Pichai described it as being "in a league of its own in the top right quadrant" of the Artificial Analysis index , which plots intelligence against speed — meaning it delivers near-frontier quality at dramatically lower latency. That speed matters enormously for search. A conversational AI search experience that feels sluggish would be dead on arrival for a product that serves billions of queries daily. By coupling the redesigned interface with a model optimized for both quality and throughput, Google is attempting to make AI-powered search feel as instantaneous as the old keyword experience — while being dramatically more capable. Search can now build interactive visuals and custom mini apps on the fly The redesigned search box is also the gateway to a set of new capabilities that push search far beyond text-based answers. Google announced what it calls " generative UI " — the ability for search to dynamically build custom widgets, interactive visualizations, and even mini applications in real time, tailored to a user's specific question. Reid offered a concrete example during the briefing: a user could ask "How do black holes affect space time?" and receive an interactive visual in an AI Overview that brings the concept to life. Follow-up questions would trigger the system to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. But Google is going further still. For ongoing tasks — planning a wedding, organizing a move, tracking a fitness routine — users will be able to build what the company describes as customizable, stateful experiences within search, powered by its Antigravity development platform . These require no coding expertise. Users simply describe what they want in natural language, and search builds it. Those experiences will be available in coming months, starting with Google AI Pro and Ultra subscribers in the United States. AI agents that monitor the web around the clock are coming to search results The redesign also opens the door to what Google calls " information agents " — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions and deliver synthesized updates when those conditions are met. A user could, for example, set up an agent to track market movements in a particular sector with specific parameters. The agent would create a monitoring plan, tap into real-time finance data, and proactively notify the user when conditions are met — complete with links and context for further research. Other use cases include apartment hunting, tracking sneaker drops, or monitoring any topic a user cares about. Information agents will launch first for Google AI Pro and Ultra subscribers this summer. These agents sit within a much larger strategic pivot that Google articulated throughout the briefing: the company is going all-in on AI systems that don't just answer questions but proactively take actions on users' behalf. Beyond search, Google introduced Gemini Spark , a 24/7 personal AI agent that runs on dedicated virtual machines in Google Cloud. It unveiled the Universal Cart , an intelligent cross-merchant shopping cart. It announced the Agent Payments Protocol for agents to make secure purchases. And it expanded its Antigravity developer platform into a full ecosystem for building autonomous AI agents. Publishers, advertisers, and SEO professionals face a new reality The redesign raises profound questions for the sprawling ecosystem — publishers, advertisers, SEO professionals — that has been built around the old model of keyword search and blue links. If users increasingly express their needs as full, conversational sentences rather than fragmented keywords, the entire discipline of search engine optimization will need to evolve. Keyword-density strategies become less relevant when the AI is parsing natural language intent rather than matching strings. Content that answers deep, nuanced questions in authoritative ways becomes more valuable; content engineered to rank for two-word keyword fragments becomes less so. For publishers, the stakes are existential . AI Overviews already synthesize information from across the web and present it directly in search results, reducing the need for users to click through to source material. The new seamless AI Mode integration deepens that dynamic: users can now get an AI-generated answer and ask multiple follow-up questions without ever leaving the search page. Google has consistently maintained that its AI features drive more traffic to publishers, but the redesign puts that claim under renewed scrutiny as the search results page becomes more self-contained. For advertisers — who fund the vast majority of Google's revenue — the shift from keywords to conversations changes the calculus of ad targeting. Conversational queries contain richer intent signals, which could make ad targeting more precise and valuable. But they also create new ambiguities: when a user is in the middle of a multi-turn conversation with AI Mode, where does an ad naturally fit? Google did not detail changes to its advertising model during the briefing, but the structural shift in the interface will inevitably reshape how ads are surfaced and measured. The search box was always more than a product — it was a habit for billions of people There is a reason Google chose to redesign the search box rather than simply adding new features behind it. The search box is not just a product element at this point; it is a cultural artifact — one of the few pieces of digital infrastructure used by essentially the entire internet-connected world. Changing it sends an unmistakable message about where the company believes computing is headed. For 25 years, the search box trained billions of people to think in keywords — to compress their curiosity into the shortest possible string of words. The new box invites them to do the opposite: to think out loud, to upload what they're looking at, to ask follow-up questions, to let an AI system handle the compression. Pichai tied the company's broader ambitions to a striking statistic: Google's surfaces now process over 3.2 quadrillion tokens per month, up seven-fold from a year ago. The company expects capital expenditures of approximately $180 to $190 billion in 2026 — roughly six times the $31 billion it spent four years ago — largely to support the infrastructure required for this AI transformation. When asked about the future of traditional search, he was direct. "Search is the most used AI product in the world," he said. The blinking cursor in Google's search box still invites you to type. But after 25 years of teaching the world to speak in keywords, Google is now asking it to speak in sentences — and betting roughly $190 billion that it will.

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

MIT News AI published: New AI education program from MIT Open Learning debuts with AI-powered personalization and a free introductory course for learners everywhere.

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