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TechCrunch AI published: Apple is adding new AI-powered features to Safari, Shortcuts, and Password apps.

TechCrunch AI published: Shortcuts gets an AI upgrade, letting you describe the workflow you want in a prompt.

TechCrunch AI published: Apple's AI image generator is getting a makeover that could make it more competitive.

TechCrunch AI published: A new spatial "Reframe" feature will let users use AI to adjust perspectives.

TechCrunch AI published: The idea behind the new "Siri AI" is to turn the assistant from a voice controlled assistant into an AI companion that can do a lot more.

TechCrunch AI published: A new feature in the Amazon Shopping app allows users to generate designs with Alexa, then print them on products like T-shirts, hoodies, and tumblers.

MIT Technology Review published: This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Why this year’s World Cup ball may not fly as far Much is new about this month’s FIFA World Cup tournament. It hosts more teams than ever before. It’s the first…

Simon Willison's AI Notes published: 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 unique insert - insert the specified text after the specified line number Rather 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

TechCrunch AI published: We're likely to see more price increases as the big AI companies plan to go public.

TechCrunch AI published: "Chat is dead" — at least, according to a senior OpenAI employee.

TechCrunch AI published: Even with Lockdown Mode, ChatGPT could be vulnerable to prompt injections, but the goal is to reduce the likelihood that sensitive data gets shared in the process.

TechCrunch AI published: Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.

TechCrunch AI published: President Donald Trump said he's discussing deals "where the American people can benefit from the success of AI."

Simon Willison's AI Notes published: 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 . Why do I want a sandbox? What I want from a sandbox WebAssembly looks really promising here MicroPython in WebAssembly Building the first version Try it yourself Should you trust my vibe-coded sandbox? Why do I want a sandbox? 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. What I want from a sandbox My goal is to execute code safely within my own Python applications. Here's what I need: Dependencies that cleanly install from PyPI , including binary wheels across multiple platforms if necessary. I don't want people using my software to have to take any extra steps beyond directly installing my Python package. Executed code must be subject to both memory and CPU limits. I don't want while True: s += "longer string" to crash my application or the user's computer. File access must be strictly controlled . Either no filesystem access at all or I get to define exactly which files can be read and which files can be written to. Network access is controlled as well . Sandboxed code should not be able to communicate with anything without going through a layer I fully control. Support for interaction with host functions . A sandbox isn't much use if I can't carefully expose selected platform features to the code that it's running. It has to be robust, supported, and clearly documented . I've lost count of the number of sandbox projects I've seen in repos with warnings that they aren't actively maintained! WebAssembly looks really promising here 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. MicroPython in WebAssembly 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! Building the first version 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 \n print(x)" ). stdout ) print ( session . run ( "x += 5 \n print(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. Try it yourself 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 1 You 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 -o Then navigate to http://127.0.0.1:8001/-/agent and run the prompt: show me some micropython You can try a live demo of that plugin running in Datasette Agent by signing into agent.datasette.io with your GitHub account. Should you trust my vibe-coded sandbox? 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

Simon Willison's AI Notes published: OpenAI Help: Lockdown Mode 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

MIT News AI published: The MIT Ethics of Computing Research Symposium brought together experts and researchers working at the heart of ethical and social impact in technology.

TechCrunch AI published: In a statement, a Google representative described the deal as a result of unexpected demand for its recently launched AI products.

TechCrunch AI published: While the AI fundraising machine keeps breaking its own records, some founders are building in the other direction. Mirror founder Brynn Putnam just raised money for Board, a startup focused on bringing people together through in-person games and social experiences. Cyberdeck creators are going viral crafting whimsical DIY computers that literally encourage users to touch grass. Unlike the AI-free browser crowd, this doesn’t just feel like backlash, […]

TechCrunch AI published: "The whole conversation shifted from tokenmaxxing and 'go fast' to 'we need guardrails, how do we control this?'"

Google AI Blog published: Here are Google’s latest AI updates from May 2026