标签：#os

The iOS 27 developer beta includes code that references the fold state and screen angle of a device.

A useful technical idea, repeated often enough, eventually generates an unuseful philosophical claim. The current example is grammar-constrained decoding. The technique is straightforward — at each generation step, the language model's next-token logits are masked so that only tokens whose continuation can satisfy a formal grammar remain selectable; the output is, by construction, structurally valid. JSON parses. SQL is well-formed. Function-call signatures match. There is a real engineering payoff and a healthy ecosystem of libraries that deliver it. The drift is not in the engineering. It is in the rhetorical move that follows the engineering. A growing corner of 2025-2026 AI writing argues, more or less explicitly, that constraining a model's output is making the model approach meaning — that filtering linear sequences is somehow building structure, and that structure is somehow building understanding. I want to take that drift seriously, because it is the same conflation Chomsky and collaborators flagged in their March 2023 essay in the New York Times , and the engineering literature on constrained decoding agrees with Chomsky on the substantive question, even when the marketing copy doesn't. What grammar-constrained decoding actually is A language model produces output one token at a time. At each step, the model emits a probability distribution over its vocabulary, and the decoding strategy (greedy, top-k, nucleus, etc.) picks one token. Without modification, the model is free to emit any continuation; the resulting text might happen to be valid JSON, or it might not. Grammar-constrained decoding intervenes in that step. A formal grammar — typically a context-free grammar, sometimes a regular expression, sometimes a JSON schema or Pydantic model — defines what counts as valid output. At each generation step, the constraint engine computes which next tokens could lead to a continuation that is still satisfiable under the grammar, masks the logits for all other

While iOS 27 isn't bringing major changes, it's still got many small features that could improve everyday usage for iPhone users.

Microsoft AI head Mustafa Suleyman is walking back his statement about AI automating jobs done by white-collar workers, including lawyers, accountants, and project managers. During an episode of Decoder on Monday, Suleyman says he meant AI will help these workers complete tasks, rather than do their jobs: Sending an email, having a conversation with a […]

Microsoft used their Build 2026 event to announce new functionality for Microsoft Foundry. Citing Foundry as "the place where AI agents move from experiments to production systems," in a blog post, Nick Brady writes that the release brings “runtime, tools, memory, grounding, models, observability, and governance” that developers need for production agents, rather than just new model endpoints. By Matt Saunders

Six months ago I got tired of switching between apps to talk to AI. ChatGPT in the browser. Claude in another tab. Local models in a terminal. It was like having five friends who all live in different cities and refuse to visit each other. So I did what any developer with too many GPUs and too little patience would do: I built my own assistant and put it where I already spend my day — Telegram. It's not a chatbot for customers. It's not a business automation tool. It's just... my assistant. It lives in a private chat on my phone and handles the stuff I used to do manually. Here's what six months of actually using it has looked like. What I Actually Built (And Why Telegram) I already had three machines running Ollama at home — a Mac Mini M4, a Windows PC with an RTX 3060, and an Ubuntu box. Three endpoints, eight models, and me constantly forgetting which model was good for what. Telegram was the obvious choice because: I'm already there all day (friends, family, a few dev groups) It works on my phone, my Mac, and my watch The Bot API is dead simple I can send voice messages, photos, documents — and the bot can handle all of them The setup: a Python bot running on the Mac Mini, connected to all three Ollama endpoints. When I message it, the bot classifies what I want, routes to the right model on the right machine, and replies in the same chat thread. Sounds simple. Took three evenings to get right. Took six months to make actually useful. The Things I Actually Use It For Here's the honest list. Not the marketing pitch — the real daily usage: 1. Quick questions without context switching "Summarize this article" (I paste a link). "Explain this error" (I paste a stack trace). "Rewrite this email less formally." These used to mean opening a browser tab, logging in, maybe hitting a rate limit. Now I just... send a message. The reply comes back in 2-8 seconds depending on which model handles it. The routing is simple but effective: quick chat → small model on the Mac. Cod

iOS 27 has only been out for a few hours, and I've been messing around with the developer beta on my iPhone 16 Pro. I was most interested in trying out the new Siri AI, but unfortunately, I'm still on Apple's waitlist for that. In the meantime, I've been poking around a bunch of features […]

The ChatGPT maker announced it has filed paperwork to go public, just a week after rival Anthropic took the same step.

You'll need an M1 or better to run the next release of macOS.

This promises to be a solid release for aging iPhones.

Apple's keynote at this year's Worldwide Developers Conference was a big one. After months of delays, Apple reintroduced us to its AI-upgraded Siri, which will go beyond what the existing voice assistant can do by offering more personalized help. We also got a look at many other updates coming across the operating systems powering the […]

Fresh off the WWDC keynote presentation, The Verge has been invited to an "on-the-record technical deep dive into the bold new architecture enabling Apple Intelligence capabilities." Apple SVP of Software Engineering Craig Federighi and his team will be there, and so will we. The revamped Apple Intelligence is at the heart of nearly every update […]

A new spatial "Reframe" feature will let users use AI to adjust perspectives.

Apple's OSes come with Liquid Glass tweaks and performance optimizations.

Apple says a completely rebuilt Search function will competently find the emails, photos and other content you are searching for.

While some users liked the sleek, transparent designs that look "glassy," others found Apple's design overhaul from last year to be hard to read.

Apple announced its next major iOS update, iOS 27, at WWDC 2026 on Monday. Apple is highlighting performance and design improvements, trust and safety upgrades like a Screen Time redesign, and major upgrades to Siri and Apple Intelligence. The update will be supported all the way back to the iPhone 11. A big change is […]

Short answer first: use Supabase if you want Postgres plus auth, realtime, storage, and a dashboard as one managed bundle. Self-host Postgres – or use a managed Postgres – if you mostly need a database and your app already handles its own auth and logic. The choice is not really "Postgres vs Supabase". It's whether you need the extra layers Supabase puts on top of Postgres. Supabase is not a database Supabase runs on PostgreSQL, but it's a stack of services around it: Postgres – the actual database Auth – user signup, login, JWT tokens Realtime – live updates over websockets Storage – an S3-style file store Edge Functions – serverless functions Studio – dashboard + auto-generated REST/GraphQL API So "self-host Postgres or use Supabase" compares a plain database to a full backend. The honest question: do you need those extra layers, or just the database underneath them? A quick test: You use Supabase Auth, Storage, and Realtime → Supabase earns its place. You use one of them → it's replaceable. You use none and treat it as "Postgres with a nice dashboard" → you want plain Postgres. Side-by-side comparison Factor Supabase (managed) Self-hosted Supabase Plain Postgres (managed or self-hosted) Database engine PostgreSQL PostgreSQL PostgreSQL Built-in auth Yes Yes No (bring your own) Realtime / websockets Yes Yes No File storage Yes Yes No Dashboard + auto API Yes Yes No (use any SQL client) Backups Managed (limits by plan) You manage Managed or you manage Cost shape Metered, grows with usage Server cost + your time Database only Self-host effort None High (many containers) Low–medium Lock-in Medium–high Medium Very low The lock-in point decides it for many teams. Your data is standard Postgres in every option ( pg_dump portable). The lock-in is everything else: Auth tokens, Storage paths, Supabase-specific RLS policies, Edge Function code. The more Supabase-specific features you adopt, the harder the exit. When each option wins Pick managed Supabase when: You're startin

Today I’m talking with Mustafa Suleyman, the CEO of Microsoft AI. And I’m actually going to keep today’s intro short — I’m working from my wife’s family farm this week, as you’ll see in the video, but also this is a real burner of an episode. We covered everything from Mustafa’s approach to training new […]

Two years ago, when Microsoft first revealed that it was bringing four Xbox-exclusive games to the PS5 and Nintendo Switch, it made the announcement far more complicated than necessary. That's not likely to improve anytime soon. In fact, things now seem more confusing than ever as the company tries to appease both fans and the […]