Antigravity: the LLM does it better

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.

It is not just a matter of disk icons. As soon as I saw what Tahoe had done to the icons of many applications installed on my Mac, I decided I had to do something to restore the original look of the icons. I tried several times, using Apple’s home‑automation tools, Automator and Shortcuts, but nothing worked and there was always some function missing. Or maybe I’m just not very good at using them.

After less than two months since the official release, Tahoe seems poised to become another one of those macOS versions to be forgotten, like Lion, Mavericks, Sierra, Catalina, or Ventura. Aside from Liquid Glass, which I’ll discuss in a moment, what does Tahoe have that’s memorable? There’s the telephone‑call filter, which actually belongs more to iOS than macOS and still has many limitations, and there are also improvements to Spotlight search. But is it really worth upgrading an operating system just for that?1

Learning a language is hard work: you have to learn vocabulary, study grammar, repeat endlessly. And then, once you know a bit of the language, you have to start reading, listening, speaking with others. In short, it’s no joke. In the past it was even worse. We studied from massive tomes full of rules, made especially to make you hate the language. My high‑school English book dedicated fifteen pages just to the use of the definite article “the”. I never read a single line of that book.