Recent AI news and official updates

TechCrunch AI published: Google DeepMind and A24 are teaming up to build AI filmmaking tools.

MIT Technology Review published: This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. For those of you enjoying your summer unaware of Anthropic’s latest feud with the US government, here’s a recap: In April the company said it had built an AI model called Mythos…

TechCrunch AI published: Amazon is planning to increase the footprint of its new conversational AI assistant Alexa+ to India and is inviting users in the country to test out a Hindi-language version.

TechCrunch AI published: Reflection AI will pay $150 million a month beginning July 1, 2026 through 2029 for immediate access to Nvidia's latest GB300 AI chips and supporting hardware across SpaceX's Colossus 2 data center near Memphis, Tennessee.

TechCrunch AI published: On the new episode of Equity, we discussed what actually prompted the administration's latest moves against Anthropic, and what this might mean for the AI ecosystem.

TechCrunch AI published: Siri’s AI overhaul may have grabbed the headlines at WWDC, but some of Apple’s most useful AI features are arriving elsewhere in iOS 27.

TechCrunch AI published: "These are not your friends. These are not conscious beings. These are not sentient interlocutors.”

TechCrunch AI published: So ... what's your In the Weights score?

Simon Willison's AI Notes published: The real valuable capability MCP offers over skills/CLI is isolating the auth flow outside of the agent’s context window, and potentially out of the harness completely. [...] Maybe the idealized form of MCP is just an auth gateway for the API and nothing else. That’d still be a win. — Sean Lynch , comment on Hacker News Tags: ai , generative-ai , llms , model-context-protocol , skills

TechCrunch AI published: For the last 30 years, stopping the flow of cybersecurity-related software has proven to be ineffective. It's unclear why it would work now with Anthropic’s cybersecurity model Mythos.

MIT News AI published: MIT researchers’ approach captures subtle atomic patterns, improving predictions of material properties.

TechCrunch AI published: Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails. Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]

TechCrunch AI published: Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails. Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]

TechCrunch AI published: Reliance is weaving AI into telecom services used by more than 500 million people.

TechCrunch AI published: Call it a startup with a sole founder and a very large seed round, but what's next is less clear.

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. A startup claims it broke through a bottleneck that’s holding back LLMs AI startup Subquadratic came out of stealth last month with a huge claim: it had solved a mathematical bottleneck…

MIT Technology Review published: The Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing…

TechCrunch AI published: Deductive AI, a startup that uses AI to catch and resolve bugs in software, was founded just three years ago.

Simon Willison's AI Notes published: Today we launched a new plugin for Datasette, datasette-apps , with this launch announcement post on the Datasette project blog. That post has the what , but I'm going to expand on that a little bit here to provide the why . The TL;DR Datasette Apps are self-contained HTML+JavaScript applications that run in a tightly constrained <iframe> sandbox hosted on your Datasette application. They can use JavaScript to run read-only SQL queries against data in Datasette, and can run write queries too if you configure them with some stored queries . Here's a very simple example and a more complex custom timeline example - the latter looks like this: Apps are allowed to run JavaScript and render HTML and CSS. They are limited in terms of access - the <iframe sandbox="allow-scripts allow-forms"> they run in prevents them from accessing cookies or localStorage and they also have an injected CSP header (thanks to this research ) which prevents them from making HTTP requests to outside hosts, preventing a malicious or buggy app from exfiltrating private data. Datasette Apps started out as my attempt at building a Claude Artifacts mechanism for Datasette Agent , but I quickly realised that the sandboxed pattern is interesting for way more than just adding custom apps in a chat interface and promoted it to its own top-level concept within the Datasette ecosystem. They're also a fun way to turn my multi-year experiment in vibe-coded HTML tools into a core feature of my main project! You can try out Datasette Apps by signing in with GitHub to the agent.datasette.io demo instance. Why build this? Since the very first release, Datasette has offered a flexible backend for creating custom HTML apps via its JSON API. One of my earliest Datasette projects was an internal search engine for documentation when I worked at Eventbrite - it worked by importing documents from different systems into SQLite on a cron and then serving them through a Datasette instance with a custom HTML+JavaScript search interface that directly queried the Datasette API. I had client-side JavaScript constructing SQL queries, which originally was intended as an engineering joke but turned out to be a really productive way of iterating on the app! That project, combined with my experience building my HTML tools collection and my experiments with Claude Artifacts , has convinced me that adding a Datasette-style backend to a self-contained HTML frontend is an astonishingly powerful combination. Imagine how much more useful Claude Artifacts could be if they had access to a persistent relational database. That's what I'm building with Datasette Apps! Neat ideas in Datasette Apps Here are a few of the ideas and patterns I've figured out building this which I think have staying power. <iframe sandbox="allow-scripts" srcdoc="..."> + <meta http-equiv="Content-Security-Policy" content="default-src 'none'; script-src 'unsafe-inline'; style-src 'unsafe-inline'; img-src data: blob:;"> This is the magic combination that makes Datasette Apps feasible in the first place. I need to run untrusted HTML and JavaScript on a highly sensitive domain - an authenticated Datasette instance can contain all sorts of private data. The sandbox= attribute lets me run that untrusted code in a way that cannot interact with the parent application - it can't read the DOM, or access cookies, or steal secrets from localStorage . It can however use fetch() and friends to load content (or exfiltrate data) from other domains. But... it turns out if you start an HTML page with a <meta http-equiv="Content-Security-Policy"> header you can set additional policies that lock down access to other domains. I was worried that malicious JavaScript would be able to update or remove that header but it turns out that doesn't work - once set, the CSP policy is immutable for the content of that frame. Locked down APIs with postMessage() and MessageChannel() Having locked down those iframes to the point that they couldn't do anything interesting at all, the challenge was to open them back again such that they could run an allow-list of operations, starting with read-only SQL queries against specified databases. I built the first version of this with postMessage() , which allows a child iframe to send messages to the parent window. I created a simple protocol for requesting that the parent run a SQL query - the parent could then verify it was against an allow-listed database before executing it. One of the LLM tools, I think it was GPT-5.5, suggested that postMessage() on its own can be exploited if the iframe somehow loads additional code from an untrusted domain. I don't think that applies to Datasette Apps, but I also believe in defense in depth, so I had GPT-5.5 help me port to a MessageChannel() based transport instead. MessageChannel() has the advantage that if a page navigates to somewhere else the channel closes automatically, removing any chance of executing commands sent from an untrusted external page. Visible logs, for queries and errors If you navigate to the timeline demo and search for the string usercontent you'll pull in some search results that embed images from the user-images.githubusercontent.com domain. This domain is not in the CSP allow-list, so it trips an error. Those errors are captured and transmitted back to the parent frame, where they can be displayed in a useful error log. This is meant to make hacking on apps more productive by surfacing otherwise-invisible problems. I built an experiment demonstrating that you can even turn this into a one-click-to-allow mechanism for building the CSP allow-list based on what breaks, but I haven't integrated that idea into datasette-apps just yet. SQL queries are also visibly logged - scroll to the bottom of the timeline page to see that in action. Stored queries for write operations I want apps to be able to conditionally write to the database, but this is an even more dangerous proposition than SQL reads! My solution involves Datasette's stored queries feature, rebranded from "canned queries" and given a major upgrade in the recent Datasette 1.0a31 - work that was directly inspired by Datasette Apps. Users can create a stored write query that performs an insert or update, then allow-list that specific query for an app to use. Usage from code inside an app looks like this: const result = await datasette . storedQuery ( "todos" , "add_todo" , { title : "Buy milk" , due_date : "2026-06-20" , priority : "high" , completed : false } ) ; I'm only just beginning to explore the possibilities this unlocks myself, but my goal is to support full read-write applications built safely as Datasette Apps. Copy and paste a prompt to build an app The Datasette Apps plugin has no dependency on LLMs at all, but these self-contained apps are the perfect shape to be written by a modern LLM. The create app form includes a copyable prompt at the end. This prompt has everything a model needs to know to build a new app, including the schema of any selected databases. This means you can click "copy", paste it into ChatGPT or Claude or Gemini, tell it what you need, and there's a good chance the model will spit out the code necessary to build the app. If you have Datasette Agent installed your AI assistant will also gain tools to both create new apps and edit existing ones, Claude Artifacts style. Built with so much AI assistance Datasette Apps started life back in April as datasette-agent-artifacts , a plugin I have since renamed to datasette-agent-edit keeping only its editing tools . I built that as one of the first plugins for Datasette Agent , to help get the plugin hooks into the right shape. That first prototype was mainly built using Claude Opus 4.6 in Claude Code. When I switched track to Datasette Apps I started with a plan constructed using Codex Desktop and GPT-5.5 xhigh, based on extensive dialog and feeding in both datasette-agent-artifacts and other prototypes I had built. Most of the work that followed stuck with Codex, but in the few short days that we had access to Claude Fable 5 I had it run a security evaluation of the product (an ability that would get it banned by the US government shortly afterwards) and it found a very real problem. I was allowing users to allow-list CSP hosts for their apps, but Fable pointed out the following attack: A less privileged user with create-app permission creates an app that queries SQLite for all available tables and selects and exfiltrates all of the data to a host they had allow-listed via CSP. They then trick an administrator user with access to private data into visiting their app. ... and the app can now run queries as that user and steal their private data! That's clearly unacceptable. I fixed it by restricting the ability to allow-list any domain to a new apps-set-csp permission, which is intended just for trusted staff. Site administrators can also configure Datasette with a list of allowed_csp_origins , which regular users can then select. This means you can do things like allow cdnjs.cloudflare.com and your users will be able to build apps that load extra JavaScript libraries from the cdnjs CDN. I've reviewed Datasette Apps extremely closely, especially the security-adjacent parts of it. The critical sandbox and CSP configuration are based on multiple AI-assisted prototypes and tests. It's looking good so far I'm really pleased with this initial release. Datasette is growing beyond its origins as an application for serving read-only data into a much richer ecosystem of tools for doing useful things with that data once it has been collected. Datasette's roots are in data journalism. I've always been interested in the question of what comes next after a journalist gets their hands on a giant dump of data about the world. Datasette supports exploring and publishing it. Datasette Agent adds interrogating it with AI assistance. Now Datasette Apps expands that to building custom interfaces and visualizations to help unlock the stories that are hidden within. Tags: iframes , javascript , projects , sandboxing , ai , datasette , generative-ai , llms , ai-assisted-programming , content-security-policy

TechCrunch AI published: Startup Baseten is reportedly close to finalizing a $1.5 billion round at a $13 billion as the “inference gold rush" marches on.