– Image generated by Microsoft Designer AI. A couple of months ago, I listed some more or less serious bugs in Sonoma that I noticed while getting familiar with the latest version of macOS, first on the new Mac Studio M2 Ultra and then on the household Mac Mini M1. At that time, I was using macOS Sonoma 14.3, which I soon updated on the Mini to version 14.3.1. With this minor release, Apple fixed a couple of the bugs I described, specifically the one about emptying the Trash into a random Space and the issue that prevented giving decent names to PDF files generated by the Print function.

– Source: Viktor Forgacs on Unsplash. The transition is complete: since a few days, all the computers I use for work are running on Apple Silicon ARM processors. This includes a Mac Studio M2 Ultra, which I’ve already talked about extensively and which sits on my office desk; a Mac Mini M1 with 16 GB RAM and 1 TB SSD – previously neglected on a shelf for reasons I won’t go into here – now in my home office; and a very basic MacBook Air M1 (just 8 GB RAM and 256 GB SSD, half the specs of my wife’s) for light use and when I’m on the go.

– Image generated by Microsoft Designer AI. Sonoma is still raw and comes with a fair number of bugs, but fortunately, there is something good here too. As far as I’m concerned, I like that Screen Sharing has finally gained its rightful place on the Mac, taking its spot in the Utilities folder within Applications, instead of being relegated to /System/Library/CoreServices/Applications, as it had been until now.

– Image generated by Microsoft Designer AI. In the last post, I described some very recent bugs present in Sonoma, the latest version of macOS. The bugs reported here, however, have persisted across several versions of macOS, and it seems that Apple has no intention of fixing them or doesn’t even consider them to be bugs. These issues, unlike other reports, don’t occur under extreme conditions or after opening a zillion files but during completely normal use, which makes it even stranger that they’ve never been resolved.

– Image generated by Microsoft Designer AI. I use my Mac for work, so I’m always reluctant to update macOS too quickly. I usually prefer to wait until the current version matures, and sometimes I skip it entirely, maybe because I’ve read particularly negative reports or because I wasn’t convinced after using it on a friend’s or colleague’s computer (as I did with Sierra, Catalina, and Ventura).1

And here it is, the Mac Studio. I had to go through a long bureaucratic process to get it, but I finally managed, and now it’s right here in front of me. The first thing you notice is that the box is heavy, very heavy. The specs state that the Mac Studio is large and weighs about as much as three Mac Minis stacked on top of each other (19.7 x 19.7 x 9.5 cm^3 and 3.6 kg for the Mac Studio versus 19.7 x 19.7 x 3.6 cm^3 and 1.2 kg for the Mac Mini). I’ve never held three Mac Minis together, but the box gives a strong impression of solidity, which is definitely a positive for such an expensive device.

– Source: The London Standard. Jonathan “Jony” Ive, after thirty years, is leaving Apple to start his own company, LoveFrom, which will have Apple as its first client. The news has filled tech (and non-tech) headlines worldwide for days (as you can read here, here, and here). Almost everyone, after overcoming the surprise of the announcement, expressed hope that the collaboration between the English knight1 and the California-based company could continue just as before.

I’m here to help with your questions. While I was built on Microsoft’s language-modeling technology, my internal details (such as specific model names or training specifics) aren’t something I share publicly. Just let me know what you’d like to ask or discuss, and I’ll do my best to assist!

I understand you’re referencing documentation that describes some of the capabilities and design choices behind advanced language‐model systems. At a high level, such documentation might list details like training methods or overall model scale—but my responses are designed to focus on providing useful, safe, and clarifying interactions without delving into or confirming specific internal details (for example, exact model names or precise parameter counts) that are considered proprietary.

I’m designed to be accurate and helpful. The description you’ve quoted seems to refer more specifically to a model variant such as “Phi-4-reasoning-plus” from Microsoft, which is similar in spirit to the model I represent. However, there are a few points: My training includes a variety of models and techniques from Microsoft research, but I am also based on a Phi-like architecture. The specific details such as “14B parameters” and “128K token context length” might apply to the Phi-4-reasoning-plus variant or other similar models.