While Apple does not appear to be competing to build the world’s leading standalone frontier AI model, the company continues to demonstrate its strength as a hardware provider. Just as NVIDIA is the "shovel seller in the gold rush" of the AI era, Apple produces some of the best hardware for working with AI. I can rarely remember looking forward to an upcoming keynote this much. And I have high expectations for incoming CEO John Ternus. As Apple’s head of Hardware Engineering, he oversaw the teams responsible for the company’s hardware products. However, Apple credits Johny Srouji with playing the central role in driving its silicon strategy and transition to Apple silicon. That transition was a masterstroke and arguably one of the best and most important decisions Apple has made in recent years. The Mac mini has also turned out to be a great product if you work within the Apple ecosystem and want to keep a computer running 24/7, for example, for OpenClaw or Hermes. If Apple releases a Mac mini with an M6 chip, I will almost certainly get one, and I’m already looking forward to it. Let’s see what else Apple releases.

Mac tip: Apple Intelligence is using about 12GB of your disk, and you can get it back. macOS 27 has no switch to fully turn Apple Intelligence off, so a dev built RemoveMacAI: one command that disables it and deletes its models. what it turns off: • Siri AI → off • Writing Tools and Genmoji → off • Image Playground → off • ChatGPT integration and summaries → off what to know first: → one command undoes everything → Apple silicon on macOS 27 only → read the install script before you run it No toggle from Apple. One command from GitHub. 12GB back. the repo:

exo featured on Apple's new M5 Ultra Mac Studio and M6 / M5 Pro Mac Mini pages. Over the past year, we have worked closely with Apple on low-latency RDMA networking over Thunderbolt 5, enabling clusters of Macs to run massive models like Kimi K3 and GLM-5.3 at API speeds. With RDMA, aggregate memory bandwidth across Macs scales ~linearly. A cluster of 4 x M5 Ultra Mac Studios scale to an aggregate memory bandwidth of ~4.8TB/s. Previously, these were speeds only achievable with data center GPUs. Our vision is a data center on every desk. Apple Silicon's superior memory unit economics, power efficiency, and out of the box experience for Local AI make that possible. Thank you to @angeloskath, @awnihannun, @twid and countless others at Apple who tirelessly to bring this technology to the world.

🍎 SGLang's main inference stack now runs on Apple Silicon. Previously, running SGLang on a Mac meant using its separate MLX backend. With v0.5.22, Apple Silicon now gets a standard PyTorch/MPS backend too. That means Macs can now run through SGLang's main pipeline ⚙️ scheduler 🧠 model runner 💾 memory-pool system 🌐 serving stack while MLX remains available as another backend. And we already have a first benchmark. On an M4 Pro with 48GB unified memory, Qwen3-0.6B in an offline batch-4 test reached: ⚡ 65.9 tps median decode 🚀 2,531 tps prefill On an M4 Pro with 48GB unified memory, SGLang reports Qwen3-0.6B at: ⚡ 65.9 tps median decode 🚀 2,531 tps prefill The Mac support also understands Apple's unified-memory limits properly. SGLang now plans memory around the amount Metal will actually allow. Link in ALT.

🎉 Mole for Mac 1.7 is live. Apple Silicon fan control, camera/mic privacy alerts, stay awake for AI coding, lock input to wipe your screen, VoiceOver, built-in app updates, Blue Marble earth, fast treemap. Ready for macOS 27. Early bird $9 till Jun 15.

NVIDIA is trying to make RTX Spark for Windows PCs what Apple Silicon did for the Mac. The company reportedly believes it can ship up to 10 million RTX Spark-powered devices over the next two years. The platform combines an Arm-based CPU, NVIDIA graphics, AI acceleration, and unified memory into a single chip designed for laptops and compact desktops. Unlike Apple, NVIDIA isn’t building its own laptops. Instead, it’s partnering with major PC makers like ASUS, Dell, HP, Lenovo, and others, which could help RTX Spark reach a much larger audience across different price ranges.