2026-06-09 07:58:04
Given how badly burned anyone who took Apple's 2024 WWDC Apple Intelligence announcements at face value was, I'm holding to a strict "I'll believe it when I see it" policy for everything they announced today.
The new Siri AI features do at least look feasible with today's technology, especially since Apple are licensing a custom Gemini-derived model that they can run on their own Private Cloud Compute.
It sounds like they'll be taking advantage of vision LLMs to extract information from the user's screen, which neatly sidesteps the need for every existing application to ship custom code in order to integrate with Apple Intelligence. Vision LLMs were a much less mature category in June 2024.
The new Core AI library looks like a good step in enabling developers to finally take full advantage of Apple's hardware for running their own models. It integrates with Meta's open source PyTorch ecosystem, based on these Core AI PyTorch extensions:
Core AI PyTorch Extensions (
coreai-torch) is a Python package that bridges PyTorch and Core AI. You can use it to bring up an existing PyTorch model — exported as atorch.export.ExportedProgram— into a Core AIAIProgramready to run on Apple hardware, traversing the FX graph node-by-node and mapping ATen operators to Core AI operations.
You can install an iOS 27 Developer Beta today, which supposedly has the new features - but you then have to make it through a waiting list for access to the new Siri AI. Aaron Perris from MacRumors reports having made it off the waitlist so we may start seeing credible reports on how well Siri AI works in the very near future.
Tags: vision-llms, apple, generative-ai, ai, llms
2026-06-08 07:56:38
Release: datasette-agent-edit 0.1a0
I'm planning several plugins for Datasette Agent which can make edits to existing pieces of text - things like collaborative Markdown editing, updating large SQL queries, and editing SVG files.
Agentic editing of text is a little tricky to get right. My favorite published design for this is for the Claude text editor, which implements the following tools:
view - view sections of a file, with line numbers added to every line.str_replace - find an exact old_str and replace it with new_str - fail if the original string is not uniqueinsert - insert the specified text after the specified line numberRather than recreate these patterns for every plugin that needs them I decided to create this base plugin, datasette-agent-edit, which implements the core tools in a way that allows them to be adapted for other plugins.
Tags: ai, datasette, generative-ai, llms, llm-tool-use, datasette-agent
2026-06-06 12:26:06
Release: micropython-wasm 0.1a2
I added a CLI to micropython-wasm (issue #7), inspired by the first draft of the blog entry when I realized it would be a great way to illustrate the Try it yourself section.
Tags: python, sandboxing, webassembly, micropython
2026-06-06 11:53:34
I've been experimenting with different approaches to running code in a sandbox for several years now, but my latest attempt feels like it might finally have all of the characteristics I've been looking for. I've released it as an alpha package called micropython-wasm, and I'm using it for a code execution sandbox plugin for Datasette Agent called datasette-agent-micropython.
My key open source projects - Datasette, LLM, even sqlite-utils - all support plugins.
I absolutely love plugins as a mechanism for extending software. A carefully designed plugin system reduces the risk involved in trying new things to almost nothing - even the wildest ideas won't leave a lasting influence on the core application itself. My software can grow a new feature overnight and I don't even have to review a pull request!
There's one major drawback: my plugin systems all use Python and Pluggy, and plugin code executes with full privileges within my applications. A buggy or malicious plugin could break everything or leak private data.
I'd love to be able to run plugin-style code in an environment where it is unable to read unapproved files, connect to a network, or generally operate in a way that's risky or harmful to the rest of the application or the user's computer.
My interest covers more than just plugins. For Datasette in particular there are many features I'd like to support where arbitrary code execution would be useful. I've already experimented with this for Datasette Enrichments, where code can be used to transform values stored in a table. I'd love to build a mechanism where you can run code on a schedule that fetches JSON from an approved location, runs a tiny bit of code to reformat it into a list of dictionaries, then inserts those as rows in a SQLite database table.
My goal is to execute code safely within my own Python applications. Here's what I need:
while True: s += "longer string" to crash my application or the user's computer.Web browsers operate in the most hostile environment imaginable when it comes to malicious code. Their job is to download and execute untrusted code from the web on almost every page load.
Given this, JavaScript engines should be excellent candidates for sandboxes. Sadly those engines are also extremely complicated, and are not designed for easy embedding in other projects. Most of the V8-in-Python projects I've seen are infrequently maintained and come with warnings not to use them with completely untrusted code.
WebAssembly is a much better candidate. It was designed from the start to support all of the characteristics I care about and has been tested in browsers for nearly a decade. The wasmtime Python library brings WASM to Python, is actively maintained, and has binary wheels.
WebAssembly engines like wasmtime run WebAssembly binaries. Some programming languages like Rust are easy to compile directly to WebAssembly. Dynamic languages like JavaScript and Python are harder - they support language primitives like eval(), which means they need a full interpreter available at runtime.
To run Python we need a full Python interpreter compiled to WebAssembly, wired up in a way that makes it easy to feed it code, hook up host functions and access the results.
Pyodide offers an outstanding package for running Python using WebAssembly in the browser, but using Pyodide in server-side Python isn't supported. The most recent advice I could find was from October 2024 stating "Pyodide is built by the Emscripten toolchain and can only run in a browser or Node.js".
The other day I decided to take a look at MicroPython as an option for this. The MicroPython site says:
MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a small subset of the Python standard library and is optimised to run on microcontrollers and in constrained environments.
WebAssembly sure feels like a constrained environment to me!
I had GPT-5.5 Pro do some research for me, which turned up this PR against MicroPython by Yamamoto Takahashi titled "Experimental WASI support for ports/unix".
It then produced this research.md document, so I let Codex Desktop and GPT-5.5 high loose on it to see what would happen:
read the research.md document and build this. You will probably need to write a script that compiles a custom WASM version of MicroPython as part of this project - fetch the MicroPython code to a /tmp directory for this as part of that script.
It worked. I now had a prototype Python library that could execute Python code inside a WebAssembly sandbox!
The trickiest piece to solve was persistent interpreter state. The WASM build we are using here exposes a single entry point which starts the interpreter, runs the code and then stops the interpreter at the end.
This works fine for one-off scripts, but for Datasette Agent I want variables and functions to stay resident in memory so I can reuse them across multiple code execution calls.
A neat thing about working with coding agents is that you can get from an idea to a proof of concept quickly. I prompted:
For keeping variables resident: what if we ran code inside micropython itself which called a host function get_next_python_code() and then passed that to eval() - and that host function blocked until new code was available, maybe by running in a thread with a queue? Could that or a similar idea help here?
After some iteration we got to a version of this that works! In Python code you can now do this:
from micropython_wasm import MicroPythonSession with MicroPythonSession() as session: print(session.run("x = 10\nprint(x)").stdout) print(session.run("x += 5\nprint(x)").stdout) print(session.run("print(x * 2)").stdout)
Under the hood this starts a thread, sets up a request queue and then sends messages to that queue for the session.run() command, each time waiting on a reply queue for the result of that execution. Inside WASM the MicroPython interpreter blocks waiting for a __session_next__() host function to return the next line of code, which it runs eval() on before calling __session_result__({"id": request_id, "ok": True}) when each block has been successfully executed.
The other piece of complexity was supporting host functions, so my Python library could selectively expose functions that could then be called by code running in MicroPython.
Codex ended up solving this with 78 lines of C, which ends up compiled into the 362KB WebAssembly blob I'm distributing with the package.
I am by no means a C programmer, but I've read the C and had two different models explain it to me (here's Claude's explanation) and I've subjected it to a barrage of tests.
The great thing about working with WebAssembly is that if the C turns out to be fatally flawed the worst that can happen is the WebAssembly execution will fail with an exception. I can live with that risk.
Memory limits are directly supported by wasmtime. CPU limits are a little harder: wasmtime offers a "fuel" concept to limit how many operations a WebAssembly call can execute, and that's the correct fit for this problem, but the units are hard to reason about. I'm experimenting with a 20 million default "fuel" setting now but I'm not confident that it's the most appropriate value.
The micropython-wasm alpha is now live on PyPI.
You can try it from your own Python code as described in the README. I've also added a simple CLI mode in version 0.1a2 which means you can try it using uvx without first installing it like so:
uvx micropython-wasm -c 'print("Hello world")'
# To see it run out of fuel:
uvx micropython-wasm -c 's = ""; while True: s += "longer"'
# Outputs: micropython-wasm: guest exited with code 1You can also try it in Datasette Agent like this:
uvx llm keys set openai
# Paste in an OpenAI key, then:
uvx --with datasette-agent \
--with datasette-agent-micropython \
--prerelease allow \
datasette --internal internal.db \
-s plugins.datasette-llm.default_model gpt-5.5 \
--root -oThen navigate to http://127.0.0.1:8001/-/agent and run the prompt:
show me some micropython
![Screenshot of a chat application interface with a dark blue-grey header reading "home" on the left and "root" with a hamburger menu icon on the right. Below is a navigation row with "← Back" and "Chat" on the left and an "EXPORT" button on the right. A blue user message bubble reads "show me some micropython". Below it a collapsed thinking section reads "▸Thinking: … to show the result clearly. After that, I can wrap up with a brief explanation!" followed by a "▶ Tool: execute_micropython" label. A code block follows: "# A tiny MicroPython example: blink-style logic + Fibonacci" / "def fib(n):" / " a, b = 0, 1" / " out = []" / " for _ in range(n):" / " out.append(a)" / " a, b = b, a + b" / " return out" / 'print("Hello from MicroPython!")' / 'print("First 10 Fibonacci numbers:", fib(10))' / "# MicroPython often runs on microcontrollers, e.g.:" / "# from machine import Pin" / "# led = Pin(2, Pin.OUT)" / "# led.value(1) # turn LED on" / "# led.value(0) # turn LED off". Below a horizontal divider is the output: "Hello from MicroPython!" / "First 10 Fibonacci numbers: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]", followed by a "▶ Result: execute_micropython" label. At the bottom is a text input field with placeholder "Type a message..." and a blue "Send" button.](https://static.simonwillison.net/static/2026/micropython-in-datasette-agent.jpg)
You can try a live demo of that plugin running in Datasette Agent by signing into agent.datasette.io with your GitHub account.
Having complained about immature, loosely-maintained sandboxing libraries, it's deeply ironic that I've now built my own!
I deliberately slapped an alpha release version on it, and I'm not ready to recommend it to anyone who isn't willing to take a significant risk.
I've put it through enough testing that I'm OK using it myself. I've shipped my first plugin that uses it, datasette-agent-micropython. I've also locked GPT-5.5 xhigh in that Datasette Agent plugin and challenged it to break out of the sandbox and so far it has not managed to.
I'm hoping this implementation can convince some companies with professional security teams and high-stakes problems to commit to using Python in WebAssembly as a sandboxing approach and open source their own solutions.
Tags: python, sandboxing, ai, datasette, webassembly, generative-ai, llms, ai-assisted-programming, codex, datasette-agent, micropython
2026-06-06 07:56:40
OpenAI first teased this in February, but now it's live and "rolling out to eligible personal accounts, including Free, Go, Plus, and Pro, and self-serve ChatGPT Business accounts":
Lockdown Mode is designed to help prevent the final stage of data exfiltration from a prompt injection attack by limiting outbound network requests that could transfer sensitive data to an attacker. Lockdown Mode does not prevent prompt injections from appearing in the content ChatGPT processes. For example, a prompt injection could appear in cached web content or in an uploaded file, and could still affect the behavior or accuracy of a response.
This looks really good to me.
The Lethal Trifecta occurs when an LLM system has access to all three of access to private data, exposure to untrusted content and a way to steal data and transmit it back to the attacker.
The only way to solve the trifecta is to cut off one of the three legs, and by far the easiest leg to restrict without making your LLM systems far less useful is the exfiltration vectors to steal data.
It looks to me like lockdown mode directly attacks that leg, using mechanisms that are deterministic and, crucially, are not evaluated by AI systems that themselves can be subverted by sufficiently devious attacks.
The existence of lockdown mode does however imply that ChatGPT, in its default settings, does not provide robust protection against sufficiently determined data exfiltration attacks!
Update: This tweet OpenAI CISO Dane Stuckey:
Lockdown mode is not meant for everyone. However, for folks who have an elevated risk profile - due to who they are, what they work on, or the types of data they work with - it's an excellent tool for further securing themselves. This has some tradeoffs on functionality and utility, but for these users, the tradeoff is worthwhile.
Tags: security, ai, openai, prompt-injection, llms, lethal-trifecta
2026-06-05 19:10:05
We will no longer accept public pull requests. [...]
A substantial patch used to imply substantial effort, and that effort was a reasonable proxy for good faith. That assumption no longer holds. [...]
Whether code was typed by hand is beside the point. What matters is who is responsible for it once it enters the browser. Ladybird is becoming a browser for real users. The people introducing changes to it must be the people who decide those changes belong in the project, and who will answer for the consequences.
— Andreas Kling, Changing How We Develop Ladybird
Tags: ladybird, ai-ethics, open-source, generative-ai, ai, andreas-kling, llms
