Simon Willison's Weblog

FTC to Require Cox Media Group, Two Other Firms to Pay Nearly $1 Million to Settle Charges They Deceived Customers About “Active Listening” AI-Powered Marketing Service

2026-05-22T04:48:32+00:00

FTC to Require Cox Media Group, Two Other Firms to Pay Nearly $1 Million to Settle Charges They Deceived Customers About “Active Listening” AI-Powered Marketing Service

Back in 2024 Cox Media Group were caught trying to sell advertisers packages based on "active listening", with this deck which claimed:

Smart devices capture real-time intent data by listening to our conversations

Advertisers can pair this voice-data with behavioral data to target in-market consumers

I wrote about this in September 2024. My theory:

I think active listening is the term that the team came up with for “something that sounds fancy but really just means the way ad targeting platforms work already”. Then they got over-excited about the new metaphor and added that first couple of slides that talk about “voice data”, without really understanding how the tech works or what kind of a shitstorm that could kick off when people who DID understand technology started paying attention to their marketing.

This FTC press release appears to confirm that's pretty much what happened:

CMG, MindSift and 1010 Digital Works claimed their “Active Listening” branded marketing service listened in on consumers’ conversations overheard by smart devices, in real time, to target advertising [...]

According to the complaints, this service did not, in fact, listen in on consumers’ conversations or use voice data at all—nor did the service accurately place ads in customers’ desired locations. Instead, the service the companies provided consisted of reselling—at a significant markup—email lists obtained from other data brokers.

The FTC also clarify that hiding an "opt-in" to using voice data in terms of service would not be acceptable, as tricks like that do not constitute "adequate consent":

The FTC also alleged that all three companies deceived potential customers by claiming that consumers had opted into the Active Listening service. The company, however, did not seek or obtain consumers’ consent, according to the complaints. Instead, the companies claimed that consumers had “opted in” by agreeing to the terms of service that people have to accept when downloading and using apps. Clicking through mandatory terms of service does not constitute “opt-in consent” for such an invasive service or for use of consumers’ voice data from inside their homes. If the Active Listening service had functioned as advertised, this collection and use of consumers’ voice data without adequate consent would itself violate Section 5 of the FTC Act.

Attempting to myth bust the conspiracy theory that our mobile devices target ads to us based on spying through the microphones continues to be my least rewarding niche online hobby. It's nice to have a new piece of ammunition.

Via @nydiatisdale

Tags: privacy, microphone-ads-conspiracy

Datasette Agent

2026-05-21T19:52:19+00:00

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

datasette-agent-sprites 0.1a0

2026-05-21T18:21:07+00:00

Release: datasette-agent-sprites 0.1a0

A Datasette Agent plugin for running commands in a Fly Sprites sandbox.

Tags: sandboxing, datasette, fly, datasette-agent

datasette-agent-charts 0.1a2

2026-05-21T15:15:58+00:00

Release: datasette-agent-charts 0.1a2

"View SQL query" buttons below rendered charts.

Tags: datasette, datasette-agent

datasette-agent 0.1a3

2026-05-21T15:04:09+00:00

Release: datasette-agent 0.1a3

"View SQL query" buttons for both visible tables and collapsed SQL result tool calls.

Don't display empty reasoning chunks

Improved handling of truncated responses - table still displays to the user even if the SQL results were truncated when showing the agent.

See Datasette Agent, an extensible AI assistant for Datasette.

Tags: datasette, datasette-agent

Quoting SpaceX S-1

2026-05-20T22:26:36+00:00

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: anthropic, grok, generative-ai, ai, llms

How fast is 10 tokens per second really?

2026-05-20T17:57:45+00:00

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

Google I/O, Gemini Spark, Antigravity

2026-05-20T15:32:17+00:00

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

datasette-agent-charts 0.1a1

2026-05-20T14:52:16+00:00

Release: datasette-agent-charts 0.1a1

More color! Bar and waffle charts without a color column are shaded by magnitude with a sequential color scheme; color columns holding text values use the observable10 categorical scheme. #2

Now checks execute-sql permission before running the query to find the column names.

Charts now display interactive tooltips.

Fixed a bug where waffleY charts were not described to the agent.

Tags: datasette, datasette-agent

llm-gemini 0.32

2026-05-19T23:46:27+00:00

Release: llm-gemini 0.32

New model gemini-3.5-flash for Gemini 3.5 Flash.

See also my notes on Gemini 3.5 Flash, and the pelican I drew using this upgrade to the plugin.

Tags: llm, gemini

Gemini 3.5 Flash: more expensive, but Google plan to use it for everything

2026-05-19T22:40:25+00:00

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: 

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

datasette-llm-accountant 0.1a4

2026-05-19T20:45:43+00:00

Release: datasette-llm-accountant 0.1a4

Fixed bug tracking chains of responses. Refs datasette-llm#7

Tags: datasette, llm

llm-gemini 0.32a0

2026-05-19T20:36:23+00:00

Release: llm-gemini 0.32a0

Compatible with llm>=0.32a0 alpha - adds the ability to stream reasoning tokens.

Tags: llm, gemini

datasette-llm 0.1a8

2026-05-19T20:28:16+00:00

Release: datasette-llm 0.1a8

Fix for bug where llm_prompt_context() hook did not fully collect chains of responses. #7

The last six months in LLMs in five minutes

2026-05-19T01:09:44+00:00

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

Glaucous-winged Gull, Brown Pelican, Snowy Egret, Canada Goose

2026-05-18T14:51:00+00:00

Glaucous-winged Gull, Brown Pelican, Snowy Egret, Canada Goose, in Los Angeles River, CA, US

I'm heading home from PyCon US today so I went on a last morning walk to try and spot a pelican. I saw one! Didn't get a great photo of that, but I did see some goslings down by the swan boat lake.

GDS weighs in on the NHS's decision to retreat from Open Source

2026-05-17T15:59:41+00:00

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

Warelay -> OpenClaw

2026-05-16T20:23:30+00:00

In preparation for a lightning talk I'm giving at PyCon US this afternoon I decided to figure out how many names OpenClaw has actually had since that first commit back in November.

Thanks to this first_line_history.py tool (code here) the answer, according to the Git history of the OpenClaw README, is:

Warelay → CLAWDIS → CLAWDBOT → Clawdbot → Moltbot →🦞 OpenClaw

Or in detail (the output from the tool):

2025-11-24T11:23:15+01:00 16dfc1a # Warelay — WhatsApp Relay CLI (Twilio)
2025-11-24T11:41:37+01:00 d4153da # 📡 Warelay — WhatsApp Relay CLI (Twilio)
2025-11-24T17:47:57+01:00 343ef9b # 📡 warelay — WhatsApp Relay CLI (Twilio)
2025-11-25T04:44:10+01:00 14b3c6f # 📡 warelay — WhatsApp Relay CLI
2025-11-25T12:48:40+01:00 4814021 # 📡 warelay — Send, receive, and auto-reply on WhatsApp—Twilio-backed or QR-linked.
2025-11-25T13:50:18+01:00 d51a3e9 # warelay 📡 - Send, receive, and auto-reply on WhatsApp via Twilio or QR-linked WhatsApp Web; webhook setup in one command
2025-11-25T13:51:13+01:00 4d2a8a8 # 📡 warelay — Send, receive, and auto-reply on WhatsApp—Twilio-backed or QR-linked.
2025-11-25T14:52:43+01:00 1ef7f4d # 📡 warelay — Send, receive, and auto-reply on WhatsApp.
2025-12-03T15:45:32+00:00 a27ee23 # 🦞 CLAWDIS — WhatsApp Gateway for AI Agents
2025-12-08T12:43:13+01:00 17fa2f4 # 🦞 CLAWDIS — WhatsApp & Telegram Gateway for AI Agents
2025-12-19T18:41:17+01:00 7710439 # 🦞 CLAWDIS — Personal AI Assistant
2026-01-04T14:32:47+00:00 246adaa # 🦞 CLAWDBOT — Personal AI Assistant
2026-01-10T05:14:09+01:00 cdb915d # 🦞 Clawdbot — Personal AI Assistant
2026-01-27T13:37:47-05:00 3fe4b25 # 🦞 Moltbot — Personal AI Assistant
2026-01-30T03:15:10+01:00 9a71607 # 🦞 OpenClaw — Personal AI Assistant

Tags: openclaw, git, tools

Quoting Julia Evans

2026-05-16T16:45:37+00:00

[...] in the last 10 years I’ve learned to really love and respect CSS as a technology.

So I decided years ago that I wanted to react to “CSS is hard” by getting better at CSS and taking it seriously as a technology, instead of devaluing it. Doing that changed everything for me: I learned that so many of my frustrations (“centering is impossible”) had been addressed in CSS a long time ago, and that also what “centering” means is not always straightforward and it makes sense that there are many ways to do it. CSS is hard because it’s solving a hard problem!

— Julia Evans, Moving away from Tailwind, and learning to structure my CSS

Tags: css, julia-evans

inaturalist-clumper 0.1

2026-05-15T23:53:11+00:00

Release: inaturalist-clumper 0.1

Part of the infrastructure I use for publishing my iNaturalist sightings on my blog. I've been running this in production for a few weeks now, inspiring some iterations on how it works, so I decided to ship a 0.1 release.

You can see an example of the output in this JSON file.

Tags: projects, inaturalist

Western Gull, Rock Pigeon

2026-05-15T14:42:00+00:00

Western Gull, Rock Pigeon, in Los Angeles Area (custom), CA, US

I went for a bird walk in the morning before PyCon, and we spotted a local seagull enjoying a Starbucks.

QR code generator

2026-05-15T04:00:49+00:00

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

datasette-llm-limits 0.1a0

2026-05-15T00:42:09+00:00

Release: datasette-llm-limits 0.1a0

This plugin works in conjunction with datasette-llm and datasette-llm-accountant to let you configure a per-user (or global) spending limit for LLM usage inside of Datasette. Configuration looks something like this:

plugins:
  datasette-llm-limits:
    limits:
      per-user-daily:
        scope: actor
        window: rolling-24h
        amount_usd: 1.00

Tags: datasette, llm

datasette-agent 0.1a2

2026-05-15T00:03:00+00:00

Release: datasette-agent 0.1a2

Tool availability can now be attached to a required_permission. The default background agent tools now require the new datasette-agent-background permission. #10

Tags: datasette, datasette-agent

Not so locked in any more

2026-05-14T22:53:49+00:00

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

Quoting Mitchell Hashimoto

2026-05-14T22:31:20+00:00

[...] 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: zig, ai, mitchell-hashimoto, llms, rust, generative-ai, agentic-engineering, bun

datasette-agent 0.1a1

2026-05-14T22:01:42+00:00

Release: datasette-agent 0.1a1

Now uses the execute-sql permission when deciding which tables to list to the user. #8

Tags: datasette, datasette-agent

datasette-ip-rate-limit 0.1a0

2026-05-14T04:10:23+00:00

Release: datasette-ip-rate-limit 0.1a0

The datasette.io site was being hammered by poorly-behaved crawlers, so I had Codex (GPT-5.5 xhigh) build a configurable rate limiting plugin to block IPs that were hammering specific areas of the site too quickly.

Here's the production configuration I'm using on that site for the new plugin:

  datasette-ip-rate-limit:
    header: Fly-Client-IP
    max_keys: 10000
    exempt_paths:
    - "/static/*"
    - "/-/turnstile*"
    rules:
    - name: demo-databases
      paths:
      - "/global-power-plants/*"
      - "/legislators/*"
      window_seconds: 60
      max_requests: 60
      block_seconds: 20

Tags: rate-limiting, datasette, codex

Welcome to the Datasette blog

2026-05-13T23:59:39+00:00

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

Quoting Boris Mann

2026-05-13T16:15:50+00:00

“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-agents, ai, agent-definitions