Recent AI news and official updates

TechCrunch AI published: The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

Simon Willison's AI Notes published: One of the most interesting tips I got from the Fireside Chat I hosted with Cat Wu and Thariq Shihipar from the Claude Code team at AIE on Wednesday was to let Fable (and to a certain extent Opus) use their own judgement rather than dictating how they should work. The example they gave was testing. You can tell Fable "only use automated testing for larger features, don't update and run tests for small copy or design changes" - but it's better to just tell Fable to use its own judgement when deciding to write tests instead. Jesse Vincent just gave me a related tip to help avoid burning too many of those valuable Fable tokens in the few days we have left before the prices go up. Tell Fable to use other models for smaller tasks, applying its own judgement about which model to use. I prompted Claude Code just now with: For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent Claude saved this memory file in ~/.claude/projects/name-of-project/memory/delegate-coding-to-subagents.md : --- name: delegate-coding-to-subagents description: Simon wants coding tasks delegated to subagents running an appropriately lower-power model metadata: node_type: memory type: feedback originSessionId: 30068d78-43a9-4fb1-bb29-9799e18c526a --- Stated by Simon on 2026-07-03: "For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent." Why: cost/efficiency — implementation work rarely needs the top-tier model; judgment, review, and synthesis stay with the main loop. How to apply: when a task in this project is primarily writing/editing code, spawn an Agent with a model override (sonnet for substantive implementation, haiku for trivial/mechanical edits) and a self-contained prompt; review the result in the main loop before committing. Design, auditing, data synthesis, and anything judgment-heavy stays in the main model. See also [[project-goals]]. So far it seems to be working well. I'm getting a ton of work done and my Fable allowance is shrinking less quickly than before. Tags: ai , prompt-engineering , generative-ai , llms , anthropic , claude , coding-agents , claude-code , claude-mythos-fable

MIT Technology Review published: As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking. That was not the prevailing sentiment when I was young.…

TechCrunch AI published: At an internal meeting, the Meta CEO reportedly said that AI development efforts were not moving as quickly as anticipated.

TechCrunch AI published: Just for kicks, I took a look at Jersey Mike's IPO documents. Surely a sandwich shop would have no need to mention AI. But lo and behold.

Simon Willison's AI Notes published: Release: llm-coding-agent 0.1a0 Another Fable 5 experiment. Now that my LLM library has evolved into more of an agent framework it's time to see what a simple coding agent would look like built on it. I started a new Python library using my python-lib-template-repository GitHub template repository, then ran these two prompts (here's the Claude Code for web transcript ): Write a spec.md for this project - it will depend on the latest “llm” alpha from PyPI and implement a Claude code style coding agent complete with tools for reading and editing files and executing commands Then: Commit the spec, then build it using red/green TDD in a series of sensible commits (each with passing tests and updated docs) - occasionally manually test it using the OpenAI API key in your environment Here's the spec , the resulting README file , and the sequence of commits . I've shipped a slop-alpha to PyPI, so you can run the new agent like this: uvx --prerelease=allow --with llm-coding-agent llm code It's pretty good for a first attempt! Here's the (Fable-authored) README , which lists recipes like llm code --yolo and llm code --allow "pytest*" --allow "git diff*" . It also presents a Python API based around a CodingAgent(model="gpt-5.5", root="/path", approve=True).run("Fix the failing test in tests/test_parser.py") class which I didn't ask for but I'm delighted to see implemented. Here's the suite of tools it implemented , listed using uvx ... llm tools : CodingTools_edit_file(path: str, old_string: str, new_string: str, replace_all: bool = False) -> str Replace an exact string in a file. old_string must match the file contents exactly (including whitespace) and must identify a unique location unless replace_all is true. Returns a diff of the change so it can be verified. CodingTools_execute_command(command: str, timeout: int = 120) -> str Run a shell command in the session root directory. Returns combined stdout and stderr followed by an Exit code line. timeout is in seconds (maximum 600); on timeout the whole process tree is killed. CodingTools_list_files(pattern: str = '**/*', path: str = '.') -> str List files matching a glob pattern, newest first. Skips hidden directories, node_modules, __pycache__ and (in a git repository) anything covered by .gitignore. Returns at most 200 paths relative to the searched directory. CodingTools_read_file(path: str, offset: int = 0, limit: int = 2000) -> str Read a text file, returning numbered lines like cat -n. Paths are relative to the session root. Use offset (0-based first line) and limit (max lines) to page through files too large to read in one call. CodingTools_search_files(pattern: str, path: str = '.', glob: str = None, max_results: int = 100) -> str Search file contents for a regular expression. Returns matches as path:line_number:line, capped at max_results. Use glob (e.g. "*.py") to restrict which files are searched. CodingTools_write_file(path: str, content: str) -> str Create or overwrite a file with the given content. Parent directories are created as needed. Prefer edit_file for modifying existing files. I tried it out by running llm code --yolo and then prompting: mkdir /tmp/demo and then in that folder create a simple swiftui CLI app for telling the time in ascii art Here's the transcript , in which GPT-5.5 reasoning notes that "SwiftUI isn't suitable for a true CLI" and then builds an app that outputs this on swift run AsciiTime : █ █████ ████ █ █ ███ ██ █ █ █ ██ █ ██ █ █ █ ████ ███ █ █ █ █ █ █ █ █ █ █ █ ███ ████ ████ ███ ███ █████ Tags: projects , ai , generative-ai , llm , llm-tool-use , coding-agents , claude-code , claude-mythos-fable

TechCrunch AI published: Meta has quietly launched Pocket, an experimental AI app that lets users generate and share interactive mini games using text prompts.

TechCrunch AI published: The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.

Simon Willison's AI Notes published: Research: Using DSPy to evaluate and improve Datasette Agent's SQL system prompts One of this morning's AIE keynotes covered dspy , which reminded me I've been meaning to see if it could help me improve the system prompt used by Datasette Agent - so I fired off an asynchronous research task in Claude Code for web using Claude Fable 5: Pip install the latest Datasette alpha and datasette-agent and dspy - then figure out how to use dspy to evaluate and improve the main system prompts used by Datasette Agent for the feature where it can execute read only SQL queries to answer user questions about data. Fable chose to test using GPT 4.1 mini and nano, and identified several promising looking directions for improvements. I particularly like this one: The schema listing gives only table names; the "don't call describe_table if you already have the information" advice caused column-name guessing (page_count, o.order_id, first_name) and error-retry loops in baseline traces. Either include column names in the prompt's schema listing or soften that advice. Tags: ai , datasette , generative-ai , llms , evals , dspy , datasette-agent , claude-mythos-fable

Simon Willison's AI Notes published: I saw Geoffrey Litt speak at AIE yesterday, and one framing he used particularly resonated with me: Understand to participate Geoffrey was talking about the challenge of collaborating with coding agents as they construct increasingly large and sophisticated changes, and the need to avoid taking on cognitive debt as your understanding drifts from how the code actually works. His argument is that you need to understand the code to a depth that enables you to participate further with the model: You can learn what the agent is doing to make sure you can be an active participant in the creative process. [...] You need a rich set of concepts in your mind to think creatively and fluently about how to move something forward. If you're lacking that fluency, your ability to participate in the project is meaningfully limited. The AIE talks are all recorded - all 300+ of them! - and should be trickling out over the next three weeks. Geoffrey's is one that I recommend catching on YouTube. Update 10th July : here's Geoffrey's talk on YouTube . Geoffrey also published a thread version of his talk on Twitter. Tags: ai , generative-ai , llms , geoffrey-litt , coding-agents , cognitive-debt

MIT Technology Review published: Frameworks like Lean Six Sigma and business process management (BPM) first gained traction because they promised clarity in the chaos—a structured way to bring order to messy, sprawling operations. Lean Six Sigma emphasized statistical rigor and quality control; BPM created end-to-end maps of how work should flow across departments. Both offered a repeatable way to…

TechCrunch AI published: OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

TechCrunch AI published: Microsoft follows Amazon, OpenAI, and Anthropic with its new AI deployment group.

MIT Technology Review published: Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational…

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. LLMs are stuck in a groupthink groove. This startup is trying to get them out. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1…

TechCrunch AI published: Ben Guez has "a bunch of potential international wives in [his] DMs," thanks to an automated script he set up using OpenClaw, Claude code, and Instagram trials.

TechCrunch AI published: Neo is Bhavin Turakhia’s fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office and Google Apps with AI.

TechCrunch AI published: SpaceX reportedly showed investors a "handset-like" AI device before going public. It could be another signal SpaceX wants to expand into wireless.

TechCrunch AI published: Sound built its reputation on concentrated, high-conviction bets in category-leading AI labs, while Kutcher's new fund appears to be chasing the layer underneath those companies — the infrastructure and energy that power them.

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