Apfel, an apple for Apple Intelligence

– Source: Julian Zwengel on Unsplash. One of the (few) reasons to switch to macOS 26 Tahoe is the opportunity to use the language model that powers Apple Intelligence. Apple Intelligence is the final product, natively integrated into the Apple ecosystem, with which we can process text (but also images) directly on our device. For example, by selecting a section of text and right-clicking with the mouse to choose Show Writing Tools, we have at our fingertips a useful tool for summarising those mile-long documents or for rewriting hastily jotted-down sentences.

Installation To install Homebrew, open Terminal, install the Command Line Tools for Xcode using the command % sudo xcode-select --install and then run the script % /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" answering the questions that appear on the terminal. Once the installation is complete, it’s always a good idea to run % brew doctor to check that the installation process was successful.

– The first image of an Apple II computer ever published in Bit, the most important Italian magazine dedicated to personal computers between the late 1970s and early 1980s (Bit no. 5, November–December 1979). Fifty years ago, I was a spot-ridden high school student who wouldn’t have known about Apple until the early ’80s, when Bit —- the first Italian magazine dedicated to personal computers -— started featuring the first advertising pages dedicated to the Apple II computer.

Over the last few weeks, I’ve been writing about my experiments with Antigravity, or rather, with the (more or less) intelligent agents integrated into the editor. The results have been mixed: sometimes the agents proved to be very effective, accurately easing some complex or repetitive tasks; in other cases they didn’t accomplish anything worthwhile, only wasting a huge amount of time.

I am well aware that LLMs have poor memory, but I never imagined that I would suffer the consequences so quickly. – Immagine generata da Google Gemini. Note to the reader. This article complements the previous one, Antigravity: a driver written by AI, and should be read afterward. However, here’s a brief recap for the lazy readers.

Among all the Raspberry Pi and Arduino boards I am spending my days with, my favorite is the Raspberry Pi Pico, a small yet powerful microcontroller that can be programmed not only in C/C++ via the Arduino IDE, but also in MicroPython and CircuitPython, two competing Python variants for microcontrollers. Unlike the other Raspberry Pi models, the Pico does not have a dedicated camera interface, but it can use cameras that communicate over an SPI interface,1 such as the Arducam Mini 5MP Plus.

As effective as Antigravity may be, digging a little deeper reveals that the agent-based systems working inside it, while helpful and capable at answering many complex questions, are not exempt from the usual issues of the large language models (LLMs) we’ve been dealing with for the past three years.

I confess, when I started using Antigravity I had many doubts, because the new revolutionary editor produced by Google seemed to me like just another clone of Microsoft’s VS Code.1 But as soon as I started using the agentic features of Google Antigravity, I had to change my mind, because there is truly something good there.

2025 was a turning point for this little blog. Leaving the comfort zone of Wordpress.com was neither easy nor painless, especially when I discovered that once the site was online, Jekyll was slow, too slow to be usable. Thankfully, Hugo saved the day, although there are still many details to be ironed out, first and foremost the website’s graphic design.

The video above is the official presentation of Google Antigravity, an IDE (Integrated Development Environment) that is not just a simple IDE but is “a new way of working for this next era of agentic intelligence”. I haven’t yet figured out what that truly means, but it surely sounds very smart and up‑to‑date.