2,031 posts tagged “ai”
"AI is whatever hasn't been done yet"—Larry Tesler
2026
The memory shortage is causing a repricing of consumer electronics (via) 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.)
Datasette Agent
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!
[... 659 words]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
How fast is 10 tokens per second really? (via) 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.
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.
Gemini 3.5 Flash: more expensive, but Google plan to use it for everything
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:
The last six months in LLMs in five minutes
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.
[... 2,061 words]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.
Claude helped me build this tool for creating QR codes, for both text/URLs and for connecting to WiFi networks.
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.
[...] 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
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.
“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.
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
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/responsesendpoint 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.
GitLab Act 2 (via) 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!
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
Your AI Use Is Breaking My Brain (via) 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. [...]
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.
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_riverchannel 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.
This article was updated after The Times learned that a remark attributed to Pierre Poilievre, the Conservative leader, was in fact an A.I.-generated summary of his views about Canadian politics that A.I. rendered as a quotation. The reporter should have checked the accuracy of what the A.I. tool returned. The article now accurately quotes from a speech delivered by Mr. Poilievre in April. [...] He did not refer to politicians who changed allegiances as turncoats in that speech.
Using Claude Code: The Unreasonable Effectiveness of HTML. Thought-provoking piece by Thariq Shihipar (on the Claude Code team at Anthropic) advocating for HTML over Markdown as an output format to request from Claude.
The article is crammed with interesting examples (collected on this site) and prompt suggestions like this one:
Help me review this PR by creating an HTML artifact that describes it. I'm not very familiar with the streaming/backpressure logic so focus on that. Render the actual diff with inline margin annotations, color-code findings by severity and whatever else might be needed to convey the concept well.
I've been defaulting to asking for most things in Markdown since the GPT-4 days, when the 8,192 token limit meant that Markdown's token-efficiency over HTML was extremely worthwhile.
Thariq's piece here has caused me to reconsider that, especially for output. Asking Claude for an explanation in HTML means it can drop in SVG diagrams, interactive widgets, in-page navigation and all sorts of other neat ways of making the information more pleasant to navigate.
I wrote about Useful patterns for building HTML tools last December, but that was focused very much on interactive utilities like the ones on my tools.simonwillison.net site. I'm excited to start experimenting more with rich HTML explanations in response to ad-hoc prompts.
Trying this out on copy.fail
copy.fail describes a recently discovered Linux security exploit, including a proof of concept distributed as obfuscated Python.
I tried having GPT-5.5 create an HTML explanation of the exploit like this:
curl https://copy.fail/exp | llm -m gpt-5.5 -s 'Explain this code in detail. Reformat it, expand out any confusing bits and go deep into what it does and how it works. Output HTML, neatly styled and using capabilities of HTML and CSS and JavaScript to make the explanation rich and interactive and as clear as possible'
Here's the resulting HTML page. It's pretty good, though I should have emphasized explaining the exploit over the Python harness around it.
gemini-3.1-flash-liteis no longer a preview.
Here's my write-up of the Gemini 3.1 Flash-Lite Preview model back in March. I don't believe this new non-preview model has changed since then.
Behind the Scenes Hardening Firefox with Claude Mythos Preview (via) Fascinating, in-depth details on how Mozilla used their access to the Claude Mythos preview to locate and then fix hundreds of vulnerabilities in Firefox:
Suddenly, the bugs are very good
Just a few months ago, AI-generated security bug reports to open source projects were mostly known for being unwanted slop. Dealing with reports that look plausibly correct but are wrong imposes an asymmetric cost on project maintainers: it’s cheap and easy to prompt an LLM to find a “problem” in code, but slow and expensive to respond to it.
It is difficult to overstate how much this dynamic changed for us over a few short months. This was due to a combination of two main factors. First, the models got a lot more capable. Second, we dramatically improved our techniques for harnessing these models — steering them, scaling them, and stacking them to generate large amounts of signal and filter out the noise.
They include some detailed bug descriptions too, including a 20-year old XSLT bug and a 15-year-old bug in the <legend> element.
A lot of the attempts made by the harness were blocked by Firefox's existing defense-in-depth measures, which is reassuring.
Mozilla were fixing around 20-30 security bugs in Firefox per month through 2025. That jumped to 423 in April.
Notes on the xAI/Anthropic data center deal
There weren’t a lot of big new announcements from Anthropic at yesterday’s Code w/ Claude event, but the biggest by far was the deal they’ve struck with SpaceX/xAI to use “all of the capacity of their Colossus data center”.
[... 576 words]Live blog: Code w/ Claude 2026
I’m at Anthropic’s Code w/ Claude event today. Here’s my live blog of the morning keynote sessions.
Vibe coding and agentic engineering are getting closer than I’d like
I recently talked with Joseph Ruscio about AI coding tools for Heavybit’s High Leverage podcast: Ep. #9, The AI Coding Paradigm Shift with Simon Willison. Here are some of my highlights, including my disturbing realization that vibe coding and agentic engineering have started to converge in my own work.
[... 1,542 words]Our AI started a cafe in Stockholm (via) Andon Labs previously started an AI-run retail store in San Francisco. Now they're running a similar experiment in Stockholm, Sweden, only this time it's a cafe.
These experiments are interesting, and often throw out amusing anecdotes:
During the first week of inventory, Mona ordered 120 eggs even though the café has no stove. When the staff told her they couldn’t cook them, she suggested using the high-speed oven, until they pointed out the eggs would likely explode. She also tried to solve the problem of fresh tomatoes being spoiled too fast by ordering 22.5 kg of canned tomatoes for the fresh sandwiches. The baristas eventually started a “Hall of Shame”, a shelf visible to customers with all the weird things Mona ordered, including 6,000 napkins, 3,000 nitrile gloves, 9L coconut milk, and industrial-sized trash bags.
Where they lose their shine is when these AI managers start wasting the time of human beings who have not opted into the experiment:
She also successfully applied for an outdoor seating permit through the Police e-service, which didn’t require BankID. Her first submission included a sketch she had generated herself, despite having never seen the street outside the café. Unsurprisingly, the Police sent it back for revision. [...]
When she makes a mistake, she often sends multiple emails to suppliers with the subject “EMERGENCY” to cancel or change the order.
I don't think it's ethical to run experiments like this that affect real-world systems and steal time from people.
I'm reminded of the incident last year where the AI Village experiment infuriated Rob Pike by sending him unsolicited gratitude emails as an "act of kindness". That was just an unwanted email - asking suppliers to correct mistakes that were made without a human-in-the-loop or wasting police time with slop diagrams feels a whole lot worse to me.
I think experiments like this need to keep their own human operators in-the-loop for outbound actions that affect other people.
So it’s well known that Y Combinator owns some stake in OpenAI. But how big is that stake? This seems like devilishly difficult information to obtain. I asked around and a little birdie who knows several OpenAI investors came back with an answer: Y Combinator owns about 0.6 percent of OpenAI. At OpenAI’s current $852 billion valuation, that’s worth over $5 billion.
— John Gruber, Y Combinator’s Stake in OpenAI
Granite 4.1 3B SVG Pelican Gallery. IBM released their Granite 4.1 family of LLMs a few days ago. They're Apache 2.0 licensed and come in 3B, 8B and 30B sizes.
Granite 4.1 LLMs: How They’re Built by Granite team member Yousaf Shah describes the training process in detail.
Unsloth released the unsloth/granite-4.1-3b-GGUF collection of GGUF encoded quantized variants of the 3B model - 21 different model files ranging in size from 1.2GB to 6.34GB.
All 21 of those Unsloth files add up to 51.3GB, which inspired me to finally try an experiment I've been wanting to run for ages: prompting "Generate an SVG of a pelican riding a bicycle" against different sized quantized variants of the same model to see what the results would look like.
Honestly, the results are less interesting than I expected. There's no distinguishable pattern relating quality to size - they're all pretty terrible!
I'll likely try this again in the future with a model that's better at drawing pelicans.