Linked List: June 8, 2025

Sunday, 8 June 2025

The Talk Show Live From WWDC 2025: Tuesday at 7pm PT in San Jose 

Location: The California Theatre, San Jose
Showtime: Tuesday, 10 June 2025, 7pm PT (Doors open 6pm)
Special Guest(s): Indeed
Price: $50

A different type of show this year, and I’m excited for it. If you can make it, you should come. You’ll even enjoy the prelude, mingling with fellow DF readers and listeners.

An Eve of WWDC Spitball Theory on the Rumored New ‘Games’ App 

Filipe Espósito, in a scoop for 9to5Mac all the way back in October:

9to5Mac has learned details about the new project from reliable sources familiar with the matter. The new app combines functionality from the App Store and Game Center in one place. The gaming app is not expected to replace Game Center. In fact, it will integrate with the user’s Game Center profile.

According to our sources, the app will have multiple tabs, including a “Play Now” tab, a tab for the user’s games, friends, and more. In Play Now, users will find editorial content and game suggestions. The app will also show things like challenges, leaderboards, and achievements. Games from both the App Store and Apple Arcade will be featured in the new store.

Even before Mark Gurman corroborated this report last week, I’ve had a spitball theory about what it might mean. Perhaps this is about more than having one app (Games) for finding and installing games, and another (App Store) for finding and installing apps. It could signal that Apple is poised to establish different policies for apps and games. Like, what if games still use the longstanding 70/30 commission split (with small business developers getting 85/15), but non-game apps get a new reduced rate? Say, 80/20 or even 85/15 right off the top, with small business developers and second-year subscriptions going to 90/10?

Having separate store apps for apps and games would help establish the idea that games and apps are two entirely different markets. Thus: two different stores?

Update: MG Siegler offered the same spitball — back on May 28. Great minds think alike.

Breaking Down Why Apple TVs Are Privacy Advocates’ Go-To Streaming Device 

Scharon Harding, writing at Ars Technica:

“Just disconnect your TV from the Internet and use an Apple TV box.”

That’s the common guidance you’ll hear from Ars readers for those seeking the joys of streaming without giving up too much privacy. Based on our research and the experts we’ve consulted, that advice is pretty solid, as Apple TVs offer significantly more privacy than other streaming hardware providers.

But how private are Apple TV boxes, really? Apple TVs don’t use automatic content recognition (ACR, a user-tracking technology leveraged by nearly all smart TVs and streaming devices), but could that change? And what about the software that Apple TV users do use — could those apps provide information about you to advertisers or Apple?

In this article, we’ll delve into what makes the Apple TV’s privacy stand out and examine whether users should expect the limited ads and enhanced privacy to last forever.

tvOS is perhaps Apple’s least-talked-about platform. (It surely has orders of magnitude more users than VisionOS, but VisionOS gets talked about because it’s so audacious.) But it might be their platform that’s the furthest ahead of its competition. Not because tvOS is insanely great, but it’s at least pretty good, and every other streaming TV platform seems to be in a race to make real the future TV interface from Idiocracy. It’s not just that they’re bad interfaces with deplorable privacy, it’s that they’re outright against the user.

Apple Researchers Publish Paper on the Limits of Reasoning Models (Showing That They’re Not Really ‘Reasoning’ at All) 

Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, and Mehrdad Farajtabar, from Apple’s Machine Learning Research team:

Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. [...] Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counterintuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs with their standard LLM counterparts under equivalent inference compute, we identify three performance regimes: (1) low-complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models’ computational behavior, shedding light on their strengths, limitations, and ultimately raising crucial questions about their true reasoning capabilities.

The full paper is quite readable, but today was my travel day and I haven’t had time to dig in. And it’s a PDF so I couldn’t read it on my phone. (Coincidence or not that this dropped on the eve of WWDC?)

My basic understanding after a skim is that the paper shows, or at least strongly suggests, that LRMs don’t “reason” at all. They just use vastly more complex pattern-matching than LLMs. The result is that LRMs effectively overthink on simple problems, outperform LLMs on mid-complexity puzzles, and fail in the same exact way LLMs do on high-complexity tasks and puzzles.