Recent AI news and official updates

Google AI Blog published: We’re announcing new capabilities in Managed Agents in Gemini API so developers can build reliable, production-ready agents.

Simon Willison's AI Notes published: tencent/Hy3 New Apache 2.0 licensed model from Tencent in China: Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks. The full-sized model is 598GB on Hugging Face, and the FP8 quantized one is 300GB . The context length is 256K. It's available for free on OpenRouter until July 21st . I had it "Generate an SVG of a pelican riding a bicycle" there and got this: Update : I'd forgotten about this but Max Woolf wrote about an earlier preview of this model back on May 26th: The mysterious Hy3 LLM is topping OpenRouter Model Rankings by a large margin . When I tried that one I got back this pelican which wasn't as good as today's but did have a "Change Pelican Color" button, a first from any model. Tags: ai , generative-ai , llms , pelican-riding-a-bicycle , llm-release , ai-in-china

TechCrunch AI published: An AI agent carried out the technical execution of a real-world ransomware attack for the first known time, but new details show a human still chose the victim, set up the infrastructure, and supplied stolen credentials — meaning it wasn't quite the fully autonomous cybercrime debut that last week's headlines suggested.

TechCrunch AI published: SK Hynix is experiencing a boom credited to AI. It will ride that to a multibillion-dollar U.S. IPO, expected to take place on Friday.

MIT News AI published: PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.

TechCrunch AI published: "The reality is, when you're optimizing for production, you start looking at a price/performance," Guillermo Rauch tells TechCrunch.

TechCrunch AI published: The update is part of Apple's broader effort to make Siri feel more natural and personal, as it rebuilds the assistant around generative AI.

TechCrunch AI published: A running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as a stated factor.

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. OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in…

TechCrunch AI published: Consider this a belated PSA: A recent change to Google’s privacy settings is allowing the company to store more of your data, including media such as “images, files, and audio and video recordings,” to improve its AI models.

TechCrunch AI published: Microsoft cut around 4,800 roles, or 2.1% of its global workforce, on Monday — the latest in a series of layoffs that’s stoking fears of AI replacing jobs. The layoffs will hit Xbox and commercial sales the hardest.

TechCrunch AI published: In the AI era, platforms have no choice but to fight fire with fire to cull spam.

TechCrunch AI published: Station F, a Paris-based startup hub founded by French billionaire Xavier Niel, is gearing up for a new edition of its F/ai accelerator program in a bid to strengthen its positioning as a stepping stone for promising AI startups.

Simon Willison's AI Notes published: Better Models: Worse Tools Armin reports on a weird problem he ran into while hacking on Pi: The short version is that newer Claude models sometimes call Pi’s edit tool with extra, invented fields in the nested edits[] array. And not Haiku or some small model: Opus 4.8. The edit itself is usually correct but the arguments do not match the schema as the model invents made-up keys and Pi thus rejects the tool call and asks to try again. That alone is not too surprising as models emit malformed tool calls sometimes. Particularly small ones. What surprised me is that this is getting worse with newer Anthropic models as both Opus 4.8 and Sonnet 5 show it but none of the older models. In other words, the SOTA models of the family are worse at this specific tool schema than their older siblings. Armin theorizes that this is because more recent Anthropic models have been specifically trained (presumably via Reinforcement Learning) to better use the edit tools that are baked into Claude Code. This has the unfortunate effect that other coding harnesses, such as Pi, may find that their own custom edit tools are more likely to be used incorrectly. Claude's edit tool uses search and replace . OpenAI's Codex uses an apply_patch mechanism instead , and OpenAI have talked in the past about how their models are trained to use that tool effectively. Does this mean third-party coding harnesses like Pi should implement multiple edit tools just so they can use the one with the best performance for the underlying model the user has selected? Tags: armin-ronacher , ai , openai , generative-ai , llms , anthropic , llm-tool-use , coding-agents , pi

TechCrunch AI published: Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?

TechCrunch AI published: As part of an ongoing legal dispute with three Hollywood studios, Midjourney is seeking to compel those studios to reveal how they use AI themselves.

TechCrunch AI published: Alibaba has reportedly classified Claude Code as high-risk software.

TechCrunch AI published: Mistral AI, which offers some open source AI models, has raised significant funding since its creation in 2023, with the ambition to “put frontier AI in the hands of everyone.”

Simon Willison's AI Notes published: Open Source AI Gap Map Current AI is "a global partnership building a public option for AI", founded as a non-profit at the AI Action Summit in Paris in February 2025 and backed by serious capital ($400m already committed). They launched their Gap Map a couple of days ago - an attempt at indexing the current state of open source AI: The Gap Map v0.1 details 421 products in depth: 266 software tools and libraries, 85 models, 50 datasets, and 20 hardware projects, produced by 228 organizations. These products are organized into 14 categories across 3 layers of the stack (model components, product / UX, and infrastructure). The remaining 24,400 artifacts constitute the uncategorized long tail of the open source AI ecosystem, and will carry no score until they are researched and cited. The map itself is interesting to explore, but I'm more excited about the underlying data - released under an MIT license in the currentai-org/os-ai-map GitHub account: 1,184 YAML files plus the notebooks, schemas and other scripts used to help gather them. Since the files are on GitHub you can use Datasette Lite to explore some of them - here are 16,185 GitHub repos the project is tracking as a CSV file loaded into Datasette Lite. Tags: open-source , ai , datasette-lite , generative-ai , local-llms , llms

Simon Willison's AI Notes published: I just launched my third course, Whimsical Animations, and so far, it’s on track to sell roughly ⅓ as many copies as a typical course launch. It’s a similar story with my two existing courses. Sales are down significantly from last year. There are likely a lot of reasons for this, but I think the biggest is AI. There’s sort of a double whammy with AI: Many people are wondering whether developer jobs will even exist in a few months, so they’re reluctant to spend time/money learning new dev skills. Even if they do want to learn new dev skills, LLMs can provide personalized tutoring, so there’s less incentive to buy a paid course. [...] I’ve spoken to a few course creators now, and we’re all seeing the same trend. Revenue down 50%+. Fewer people engaging with our content. People switching to LLMs, which slurp up all of our work and regurgitate it, without consent or compensation. — Josh W. Comeau , via Salma Alam-Naylor Tags: careers , ai , generative-ai , llms , josh-comeau , ai-ethics