Recent AI news and official updates

TechCrunch AI published: The 168-megawatt facility will support Meta's global AI computing needs and can be expanded over time.

MIT News AI published: Founded by two researchers from MIT, Ferveret reduces the amount of energy and water required to cool the chips that power AI.

Simon Willison's AI Notes published: If Claude Fable stops helping you, you'll never know Jonathon Ready highlights one of the more eyebrow-raising details from the 319 page system card for Fable 5 and Mythos 5. Here's a longer excerpt, highlights mine: In light of the ability of recent models to accelerate their own development , we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design ). Using Claude to develop competing models already violates our Terms of Service , but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms. Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user . Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations. I believe this is the first time Anthropic have announced these kinds of silent interventions. The justification still feels pretty science-fiction to me - the linked article talks about "recursive self-improvement". I'm not at all keen on a model that silently corrupts its replies to questions about "ML accelerator design" purely to slow down research that might conflict with Anthropic's own goals! Update : Anthropic walked back this policy in the face of widespread outrage from the research community. Via Hacker News Tags: ai , generative-ai , llms , anthropic , claude , ai-ethics , claude-mythos

TechCrunch AI published: Google just made it significantly cheaper to enjoy its budget AI subscription tier.

Simon Willison's AI Notes published: I didn't have early access to today's Claude Fable 5 release, but I've spent the past ~5.5 hours putting it through its paces. My initial impressions are that this is something of a beast . It's slow, expensive and has been quite happily churning through everything I've thrown at it so far. As is frequently the case with current frontier models the challenge is finding tasks that it can't do. First, let's review the key characteristics. Anthropic claim that Claude Fable 5 offers the same performance as Claude Mythos 5, except with much more strict guardrails in place to prevent it being used for harmful things. Those guardrails trigger often enough that the Claude API has new mechanisms for letting you know when you hit them, and even has a new option to request it falls back to another model automatically if something gets rejected. Claude Mythos 5 is out today as well, Anthropic say it "Shares Claude Fable 5's capabilities without the safety classifiers". The models have a 1 million token context window, 128,000 maximum output tokens and a knowledge cut-off date of January 2026. They are priced at twice the price of Claude Opus 4.5/4.6/4.7/4.8: $10/million input tokens and $50/million output tokens. There's no increase in price for longer context usage. Other than that the upgrade guide is substantially thinner than the similar guide for Opus 4.8 . The big model smell The best way to describe Fable is that it feels big . Not just in terms of speed and cost, but also in how much it knows. Here's an example prompt, which I used to compare Fable's knowledge with Opus 4.8 (typo included): List all of Simon Willion's open source projects, most recent first, each with a rough date of when they were first released Opus 4.8's reply started like this : I don't have a reliable, comprehensive, and date-verified list of all of Simon Willison's open source projects, and I want to be honest about that rather than risk giving you inaccurate dates or fabricated entries. Here's what I can tell you with reasonable confidence: Well-known projects by Simon Willison: LLM (~2023) – A command-line tool and Python library for interacting with large language models. Datasette (~2017) – A tool for exploring and publishing data, probably his most prominent recent project. sqlite-utils (~2018-2019) – A Python library and CLI tool for manipulating SQLite databases, often used alongside Datasette. Django (2005) – He is one of the co-creators of the Django web framework, along with Adrian Holovaty, originally developed at the Lawrence Journal-World newspaper. He also maintains a large ecosystem of Datasette plugins and many smaller utilities (often with names like shot-scraper , various *-to-sqlite tools for importing data, etc.). That's not bad. Here's the start of the response for the exact same prompt from Fable 5 - both models were running without access to search: I think you mean Simon Willison — the prolific open source developer, co-creator of Django, and creator of Datasette. An important caveat upfront: a truly complete list isn't feasible. Simon Willison is famously prolific — he has published hundreds of repositories (Datasette alone has 100+ plugins, many written by him), and my knowledge has a cutoff date, so I'll be missing his most recent work. Below are his notable projects with approximate initial release dates, most recent first: Notable projects (approximate dates) files-to-prompt (April 2024) – concatenates files into a single prompt for LLMs datasette-extract (2024) – plugin for extracting structured data using LLMs LLM (May–June 2023) – CLI tool and Python library for interacting with large language models, plus a large ecosystem of plugins (llm-gpt4all, llm-mistral, llm-claude, etc., 2023 onward) symbex (June 2023) – search Python code for symbols ttok and strip-tags (May 2023) – CLI tools for token counting and HTML cleanup for LLM pipelines datasette-lite (May 2022) – Datasette running entirely in the browser via WebAssembly/Pyodide shot-scraper (March 2022) – automated website screenshots via Playwright s3-credentials (November 2021) – CLI for creating scoped AWS S3 credentials django-sql-dashboard (2021) – SQL reporting dashboards for Django Dogsheep suite (2019) – personal analytics tools: twitter-to-sqlite, github-to-sqlite, healthkit-to-sqlite, dogsheep-beta, etc. sqlite-utils (2018) – CLI and Python library for manipulating SQLite databases Datasette (November 2017) – his flagship project; tool for exploring and publishing data csvs-to-sqlite (2017) – convert CSV files to SQLite Various early tools (~2007–2010) – soupselect, json-head, geocoders, and others Django (developed 2003–2005, open-sourced July 2005) – co-created with Adrian Holovaty at the Lawrence Journal-World (Here's GPT-5.5 for good measure . It listed even more projects than Fable did!) In the past I've stated that I don't care about how much models know - I want them to be able to manipulate text and code in useful ways and actively look up the information they need via search tools, not bake it into their weights. But knowledge like this is a reasonably good proxy for model size - you can cram a whole lot more details about the world into a larger number of parameters. Does knowing more stuff mean the model is better at the tasks we pose to it? I can certainly imagine how a coding model with deeper knowledge of modern libraries and patterns could crunch through coding tasks more effectively. Is Fable really bigger than Opus? Anthropic haven't said anything about model size, so all we have are tea-leaves, but the speed, pricing and my own poking at its knowledge make me think that it's a large model. Maybe the largest yet from any vendor. Using Fable in Claude.ai Anthropic made Fable 5 available across all of their surfaces - the Claude.ai chat interface, Claude Code for web, Claude Code CLI and Claude Cowork as well. The model is available "until June 22nd" on the subscription plans (I'm on $100/month Max at the moment), after which it will be billed extra. Claude.ai is often under-estimated. Since September 2025 every chat has had access to a full container environment to run code, including the ability to install additional packages and even clone repositories directly from GitHub. Last week I released micropython-wasm , a Python library that uses wasmtime to run a custom build of MicroPython in WebAssembly to act as a sandbox for untrusted Python code. I decided to see if Fable could upgrade that to running full Python instead. I started with this prompt: Clone simonw/micropython-wasm from GitHub and research how this could use a full Python as opposed to MicroPython Fable identified that it could use Brett Cannon's cpython-wasi-build builds for this, but was unable to download them itself due to environment restrictions. So I grabbed the two zip files from that page and uploaded them to Claude: Here's the Brett Cannon builds ( python-3.zip , _build-python-3.zip as attachments) And that was that. It churned away for a few minutes and got the entire thing working. Part of the response included: I tried the cleaner single-zip-stdlib approach to shrink the filesystem surface, but CPython's getpath bootstrap fails to find encodings from inside a zip without more prefix finessing — the directory-preopen approach works reliably, so that's what the PoC uses. The zip path is solvable but needs _PYTHONHOME /frozen-getpath work. So I said: Try a bit more at the single-zip-stdlib problem Then a little later: I want a wheel that has the whole system in it, the Python wrappers and the WASM files and the stdlibrary, so I can do uv run --with path-to-whl python -c "demo code" ... and it gave me this 13.9MB cpython_wasm-0.1.0-py3-none-any.whl file. You can try running Python code in a sandbox using that wheel URL and uv like this: uv run --with https://static.simonwillison.net/static/cors-allow/2026/cpython_wasm-0.1.0-py3-none-any.whl \ cpython-wasm -c ' print(45 ** 56) ' Here's the full chat transcript . This was a very strong start. Adding features to Datasette Agent and LLM using Claude Code Before I'd realized it was Fable day, my stretch goal for today was to add a new feature to Datasette Agent : I wanted tool calls within that agent software to gain the ability to pause mid-execution and request approval directly from the user. This felt like a suitably meaty task to throw at the new model. Over the course of the day Fable not only solved that problem , it also identified and then implemented four issues in my underlying LLM library that would help support this kind of advanced pause-resume mechanism in tool calls. It got everything working first using somewhat gnarly hacks, but the moment I told it that changes to LLM itself were in scope it set to work unraveling the hacks and turning them into supported features of LLM instead. My stretch goal turned into LLM 0.32a3 , almost entirely written by Fable. Here are the release notes: Driven by the needs of Datasette Agent 's human-in-the-loop ask_user() feature, made the following improvements to how tool calls work: Tool implementations can declare a parameter named llm_tool_call in order to be passed the llm.ToolCall object for the current invocation. This allows them to access the current llm_tool_call.tool_call_id . See Accessing the tool call from inside a tool . #1480 Every tool call is now guaranteed a unique tool_call_id - providers that do not supply one get a synthesized tc_ -prefixed ULID. #1481 Tools can raise a llm.PauseChain exception to cleanly pause the tool chain, useful for things like waiting for human approval. The exception propagates to the caller with .tool_call and .tool_results (completed sibling results) attached, and no model call is made with a placeholder result. See Pausing a chain from inside a tool . #1482 Failure semantics for concurrent tool execution: async sibling tool calls always run to completion before a pause or hook exception propagates. #1482 Chains can now resume from a messages= history ending in unresolved tool calls: the calls are executed through the normal before_call / after_call machinery before the first model call, skipping any that already have results. The execute_tool_calls() method also accepts a new optional tool_calls_list= argument for executing an explicit list of ToolCall objects in place of the calls requested by the response. See Resuming a chain with pending tool calls . #1482 Fixed a bug where the async tool executor silently dropped calls to tools not present in tools= - these now return Error: tool "..." does not exist results, matching the sync executor. #1483 I'm really impressed with the quality of API design, tests, code and documentation that Fable put together for this. I spent several hours on it today, but it feels like several days' worth of work. How much I've spent I recently started using AgentsView to help track my local LLM usage across all of the different coding agents. I published a TIL today about adding custom Fable pricing to that tool, which I expect will not be necessary in the very near future. After setting the price, I ran this command to start a localhost web server to explore my usage: uvx agentsview serve Here's the treemap showing the breakdown of my Fable usage across various projects today: I used $110.42 worth of tokens today, all as part of my $100/month subscription. And some pelicans I ran "Generate an SVG of a pelican riding a bicycle" against all five thinking effort levels with Fable. Here are the results , including the token cost for each one: low: 1,929 out, 9.67c medium: 2,290 out, 11.475c high: 2,057 out, 10.31c xhigh: 5,992 out, 29.985c max: 14,430 out, 72.175c It's interesting that high ended up using fewer tokens than medium for this particular run. Here are the Opus 4.8 pelicans for comparison. Tags: ai , generative-ai , llms , anthropic , claude , llm-pricing , pelican-riding-a-bicycle , llm-release , claude-mythos

Simon Willison's AI Notes published: Release: llm 0.32a3 Almost entirely written by the new Claude Fable 5, see my write-up for more details . Tags: projects , ai , generative-ai , llms , llm , claude-mythos

Simon Willison's AI Notes published: TIL: Setting a custom price for a model in AgentsView I've been really enjoying AgentsView by Wes McKinney as a tool for exploring my token usage across different coding agents running on my laptop. Claude Fable 5 came out today and wasn't yet included in the pricing database AgentsView uses. I used Fable to reverse-engineer AgentsView and figured out this recipe for setting custom prices. Here's my Claude Fable 5 usage for today so far, plotted by AgentsView as a treemap across my different local projects: Tags: ai , generative-ai , llms , llm-pricing , claude-mythos

TechCrunch AI published: I'm desperate for a personal AI assistant, but do I really want to become the kind of person who can't function without the friendly robot voice in my phone?

TechCrunch AI published: Anthropic's Claude Fable 5 is going to be a big hit with the web's vibe coders.

MIT News AI published: A Media Lab study shows that, much like how GPS has weakened our navigation skills, AI can make us worse at detecting fake news.

Simon Willison's AI Notes published: I feel a lot of things changing as working software increasingly comes out on a tap. The Jevon's paradox kicks in and I feel my own demand for software growing substantially. You can ask for anything - explainers, visualizers, dashboards, bespoke single-use apps (e.g. a full wandb that is hyper-specific just for your project), you can 10X your test suite, auto-optimize code, run giant research projects with custom HTML for the results, anything! "Free your mind" (Matrix ref). — Andrej Karpathy , on Claude Fable 5 Tags: ai , andrej-karpathy , generative-ai , llms , anthropic , jevons-paradox , claude-mythos

TechCrunch AI published: If those same AI workloads can be handled by cheaper models without affecting quality, it would mean a massive shift in the economics of AI.

TechCrunch AI published: Apple primarily made the case for an improved experience with its long-standing Siri assistant, which like most other announcements had a hefty helping of AI.

TechCrunch AI published: Anthropic is releasing Claude Fable 5, its first Mythos-class model available to the public. The model comes with guardrails that block responses in high-risk areas like cybersecurity and biology.

TechCrunch AI published: Anthropic is releasing Claude Fable 5, its first Mythos-class model available to the public. The model comes with guardrails that block responses in high-risk areas like cybersecurity and biology.

TechCrunch AI published: With SpaceX, Anthropic, and OpenAI all eyeing massive public debuts, the tech industry may soon have a new class of corporate overlords — and a new acronym to match. Say goodbye to FAANG and hello to MANGOS.

TechCrunch AI published: Sandstone's Series A comes just six months after a Sequoia-led seed round.

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. David Sinclair plans to test whole-body rejuvenation drugs in the XPrize competition The outspoken longevity scientist David Sinclair has predicted that, one day, you’ll go to the doctor and get a…

MIT Technology Review published: As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across…

MIT Technology Review published: At SXSW London last week I gave a talk called “Five things you need to know about AI,” in which I shared what I think are the biggest themes in AI right now. I pulled a few things from our first AI10 list, an annual guide to the most important trends in this buzzy world,…