WWDC is nearing and as a father I have only one modest wish: Apple, please fix the bugs in iOS screen time.

Leaving CloudKit: mjtsai.com/blog/2026/05/21/l… #mjtsaiblog

Two economists just published a mathematical proof that AI will destroy the economy. Not might. Not could. Will — if nothing changes. The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled. The conclusion is one sentence. "At the limit, firms automate their way to boundless productivity and zero demand." An economy that produces everything. And sells it to nobody. Here is how you get there. A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself. Because the workers who were fired were also customers. When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation. The loop has no natural exit. The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements. Every single one failed in the model. The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger. No government has implemented this. No major economy is seriously discussing it. Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion." Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem. Rational behavior. At scale. Simultaneously. With no mechanism to stop it. Two economists built the math. The math leads to one place. Source: Falk & Tsoukalas · Wharton School Boston University · arxiv.org/pdf/2603.20617

Thanks to @compnerd, we have a new Swift XML parser! 🌿 Xylem is pure Swift, zero dependencies, covering SAX, DOM, and XPath 1.0. This is the kind of infrastructure work that helps Swift thrive everywhere! 🎉 forums.swift.org/t/xylem-a-p…

Apple XNU: Clutch Scheduler: mjtsai.com/blog/2026/02/09/a… #mjtsaiblog

Love this little improvement in Swift, proposal got accepted! github.com/apple/swift-evolu…

StackOverflow graph of questions asked per month. Holy shit.

Automates iOS Simulators via Accessibility APIs github.com/cameroncooke/AXe/

Apple just filed a patent hinting that Personas in visionOS might soon react to the virtual environment they are in. Rain falls and your Persona gets wet. Lighting shifts and your look changes. Wind blows and your hair moves. Shout out to @PatentlyApple for uncovering it.

After rewatching Home Alone, I couldn’t stop wondering: how plausible is the oversleep that leaves Kevin behind? So I wrote a tiny paper and ran the numbers. Merry Christmas! 🎄

Runs iOS apps on Apple Silicon Macs github.com/PlayCover/PlayCov…

Arthur C. Clarke on BBC's Horizon in 1964, when he gave some astonishing predictions about the future.

to commemorate alan dye moving from apple to meta, here's one of his best quotes

GUI for lsof on macOS github.com/sveinbjornt/Sloth…

New researsh shows ice is slippery because of electrical charges — not pressure and friction. For almost 200 years, the prevailing explanation for ice’s slipperiness was that friction or pressure from a skate, boot, or tire melted a microscopic film of water on the surface, creating a lubricating layer. A new study from Saarland University has overturned that long-standing idea. Instead, the true cause lies in the electric fields generated by molecular dipoles. When any object contacts ice, the partial charges in its own molecules interact with the highly ordered dipole arrangement of water molecules in the ice crystal. This electrostatic tug-of-war loosens the topmost layer of the ice lattice, transforming it into a thin, disordered, quasi-liquid film—without any need for heat or significant pressure. Remarkably, this self-lubrication mechanism works even at temperatures approaching absolute zero, where thermal energy is virtually absent and conventional pressure-melting or frictional heating theories completely break down. In those extreme conditions, ice remains slippery simply because its surface molecules are electrically vulnerable. The discovery fundamentally rewrites our understanding of one of nature’s most familiar phenomena. Beyond settling a centuries-old debate, it has immediate practical implications: from designing better winter tires and non-slip surfaces that actually work on ice, to engineering superior skis, ice skates, and even advanced nanomaterials that perform reliably in cryogenic environments. By revealing the dominant role of intermolecular electric forces, the research opens entirely new avenues for controlling friction and adhesion at the molecular scale—potentially transforming fields from winter sports equipment to aerospace and nanotechnology. ["Cold Self-Lubrication of Sliding Ice", Physical Review Letters, 2025]

if apple ever makes a car

Chris Lattner (@clattner_llvm) is one of the most influential engineers of the past two decades. He created LLVM, Swift, contributed to TensorFlow, and created the Mojo programming language. What was the story about creating Swift - and why did he face resistance inside Apple when wanting to replace Objective C? What did he learn at Tesla, Google and CPU maker SiFive, that led him to working on Mojo at Modular? We cover these and many more in today's episode. Watch or listen: • YouTube: youtube.com/watch?v=Fxp3131i… • Spotify: open.spotify.com/episode/2Nk… • Apple: podcasts.apple.com/us/podcas… Brought to you by: •⁠ @statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. statsig.com/pragmatic •⁠ @linear – The system for modern product development. linear.app/pragmatic?utm_sou… My favorite quote from Chris in this episode: “I believe in the power of programmers. I believe in the human potential of people that want to create things. And that’s fundamentally why I love software is that you can create anything that you can imagine.”

It’s time to share some well kept secrets by all these ASO tools 🤪 The new web App Store uses the same endpoints than the iOS/macOS App Store apps. Once you have an authentication bearer token (inspect the browser XHR), you can get many things from the Apple catalog 👀 🧵 1/n

Swift 6 and iOS 18 introduce the Synchronization framework, containing two new low-level concurrency primitives — Mutex and Atomics.  These features could only be introduced with Swift 6 because they are implemented using the brand-new generic ownership mechanics. I tested them out and benchmarked their performance versus Actors. Learn them here: blog.jacobstechtavern.com/p/…