#WWDC26 was the biggest local AI release Apple has ever shipped. I build local AI on macOS with MLX, so I went through the sessions, the docs, and the GitHub repos. Here's what actually changed. The context: Apple already had the pieces. The Foundation Models framework arrived last year with an on-device model. MLX, Apple's open source machine learning framework, has powered local models on Macs since late 2023, and its biggest performance advances quietly shipped months before the keynote. What's new is that Apple connected everything. The Foundation Models framework now accepts any model, not just Apple's. One protocol, one session API, and the model behind it becomes a swappable choice: Apple's on-device model, a bigger Apple model running on Private Cloud Compute, open source models via MLX, or your own custom weights through Core AI, a brand new framework for running your own models on device with a ready catalog that includes Qwen and Mistral. The framework also went agentic. Dynamic Profiles lets one session switch models, tools, and instructions on the fly. Models can now see images, read text and barcodes through the camera, and search your Mac with Spotlight for fully local retrieval. A new command line tool brings all of it to scripts. And Apple put real weight behind open source. The Core AI model implementation is already on GitHub, and a new Apache licensed utilities package adds agent skills, conversation memory management, and a bridge that lets any local model server plug into Apple's API. Even the developer tools caught up, with new Instruments profiling for on-device models and Xcode connecting directly to a local MLX server. Add it up and there are more ways to run a local model on a Mac than ever: Apple's own, open source models, custom weights, a local server, even a cluster of Macs working as one. No cloud account required for any of it. Local AI on Apple platforms is now a platform strategy, not a side project.

Built a playground app to show what Apple announced at #WWDC26: the Foundation Models framework driving an open-source MLX model (Qwen3.5-4B via MLXLanguageModel) and Apple's on-device model through the same LanguageModelSession. One picker switch, downstream untouched. Live TTFT / tok/s / token counts from the framework's Usage API. MLX via AFM, huge for local AI on Apple platforms. Test rig: M3 Ultra (32 cores), 512 GB, macOS 27.0 (26A5353q).

Our quick-invoke feature looks a bit like the new Siri AI on macOS 27. 😄 Months in the making, and near launch. Good timing! Ask it anything and it acts across Calendar, Files, Photos, Music, web, and smart home, all on your Mac. Fully local, running Qwen and Gemma models. Great minds think alike. 🤝 Bravo, Apple 👏 for all the announcements and the leap forward with Siri AI. #WWDC26

When you consider how the brain generates thoughts and predicts what comes next, then draw the analogy to generative AI and next-token prediction, thinking starts to look less like magic and more like a very powerful process running on billions of neurons. So is AI consciousness really that different from human consciousness? Maybe the gap is just sensory; there are channels of human perception that AI does not have access to yet, and perhaps those cover the parts of consciousness we still do not fully understand.

"Design is not just what it looks like and feels like. Design is how it works." — Steve Jobs. For years, the gap between a great idea and a shipped product was engineering capacity. You could see the vision clearly in your head and still spend months, or never get there at all, because building it required a team you didn't have. That gap is closing fast. AI coding agents are turning product thinkers into builders. Not by writing snippets you paste into a file, but by operating at the level that actually matters: architecture. You describe the system, the constraints, the way it should work, and the agent reasons through the structure with you. This is architectural coding. You stay focused on how it works while the agent handles much of how it gets built. What excites me most is who this unlocks. The people with the sharpest sense of how a product should feel and behave were often the furthest from the code. Now they're the closest. The bottleneck was never their imagination. It was access. We're about to see a wave of dream projects come to life. Things that lived in notebooks and half-finished prototypes for years. Things that one person could never build alone, and now can. If you've been sitting on an idea, this is the moment to build it. What have you been waiting to make?

AI for humanity. We got here because of human genius and creativity, so AI should give back, solving immediate threats to health and well-being unconditionally.

Today we’re releasing Qwen-Scope 🔭, an open suite of sparse autoencoders for the Qwen model family. It turns SAE features into practical tools： 🎯 Inference — Steer model outputs by directly manipulating internal features, no prompt engineering needed 📂 Data — Classify & synthesize targeted data with minimal seed examples, boosting long-tail capabilities 🏋️ Training — Trace code-switching & repetitive generation back to their source, fix them at the root 📊 Evaluation — Analyze feature activation patterns to select smarter benchmarks and cut redundancy We hope the community uses Qwen-Scope to uncover new mechanisms inside Qwen models and build applications beyond what we explored.Excited to see what you build! 🚀 🔗🔗 Blog: qwen.ai/blog?id=qwen-scope HuggingFace: huggingface.co/collections/Q… ModelScope: modelscope.cn/collections/Qw… Technical Report: qianwen-res.oss-accelerate.a…

Two Mac Studios just landed. M3 Ultra. 512GB unified memory per machine. The AI lab is getting serious. Running the biggest models locally. No cloud, no compromise. Developing in MLX, built for Apple Silicon from the ground up. Shoutout to @awnihannun @ActuallyIsaak and the MLX community for building an incredible foundation. We'll be building on it.

Exploring Genie 3 from @GoogleDeepMind . Incredible! @Google I hope you don't mind me virtually visiting your data center 🙂