<antirez>

Frontier LLMs such as Gemini 2.5 PRO, with their vast understanding of many topics and their ability to grasp thousands of lines of code in a few seconds, are able to extend and amplify the programmer capabilities. If you are able to describe problems in a clear way and, if you are able to accept the back and forth needed in order to work with LLMs, you can reach incredible results such as:

1. Eliminating bugs you introduced in your code before it ever hits any user: I experienced this with Vector Sets implementation of Redis. I would end eliminating all the bugs eventually, but many were just removed immediately by Gemini / Claude code reviews.

This is a short story of how humans are still so much more capable of LLMs. Note that I'm not anti-AI or alike, you know it if you know me / follow me somewhere. I use LLMs routinely, like I did today, when I want to test my ideas, for code reviews, to understand if there are better approaches than what I had in mind, to explore stuff at the limit of my expertise, and so forth (I wrote a blog post about coding with LLMs almost two years, when it was not exactly cool: I was already using LLMs for coding and never stopped, I'll have to write an update, but that's not the topic of this post).

Yesterday, it was a very intense day. In Italy it was 1st of May, the workers holiday, so in the morning I went for a 4h walk in the Etna with friends <3, I love walking, and I often take pauses when coding just to walk, to return later at the keyboard with a few more kilometers on my legs, and walking in the Etna is amazing (Etna is the largest active volcano in Europe, and I happen to live in Catania, that is on its slopes).

Then at 6PM I was at home to release my blog post about the AGPL license switch, and I started following the comments, feedbacks, private messages, and I learned a few things in the process.

Five months ago, I rejoined Redis and quickly started to talk with my colleagues about a possible switch to the AGPL license, only to discover that there was already an ongoing discussion, a very old one, too. Many people, within the company, had the feeling that the AGPL was a better pick than SSPL, and while eventually Redis switched to the SSPL license, the internal discussion continued.

I tried to give more strength to the ongoing pro-AGPL license side. My feeling was that the SSPL, in practical terms, failed to be accepted by the community. The OSI wouldn’t accept it, nor would the software community regard the SSPL as an open license. In little time, I saw the hypothesis getting more and more traction, at all levels within the company hierarchy.

About three years ago I saw a quite curious and interesting post on Hacker News. A student, Christopher Tarry, was able to use cosine similarity against a vector of top words frequencies in comments, in order to detect similar HN accounts — and, sometimes, even accounts actually controlled by the same user, that is, fake accounts used to uncover the identity of the writer.

This is the original post: https://news.ycombinator.com/item?id=33755016

I was not aware, back then, of Burrows-Delta method for style detection: it seemed kinda magical that you just needed to normalize a frequency vector of top words to reach such quite remarkable results. I read a few wikipedia pages and took mental note of it. Then, as I was working with Vectors for Redis I remembered about this post, searched the web only to discover that the original page was gone and that the author, in the original post and website, didn’t really explained very well how the data was processed, the top words extracted (and, especially, how many were used) and so forth. I thought I could reproduce the work with Vector Sets, once I was done with the main work. Now the new data type is in the release candidate, and I found some time to work on the problem. This is a report of what I did, but before to continue, the mandatory demo site: you can play with it at the following link:

Yesterday we finally merged vector sets into Redis, here you can find the README that explains in detail what you get:

https://github.com/redis/redis/blob/unstable/modules/vector-sets/README.md

The goal of the new data structure is, in short, to create a new “Set alike” data type, similar to Sorted Sets, where instead of having a scalar as a score, you have a vector, and you can add and remove elements the Redis way, without caring about anything except the properties of the abstract data structure Redis implements, ask for elements similar to a given query vector (or a vector associated to some element already in the set), and so forth. But more about that later, a bit of background, first:

I used AI with success 5 minutes ago.

Just five minutes ago, I was writing a piece of software and relied on AI for assistance. Yet, here I am, starting this blog post by telling you that artificial intelligence, so far, has proven somewhat useless. How can I make such a statement if AI was just so helpful a moment ago? Actually, there's no contradiction here if we clarify exactly what we mean.

Here’s the thing: at this very moment, artificial intelligence can support me significantly. If I'm struggling with complicated code or need to understand an advanced scientific paper on math, I can turn to AI for clarity. It can help me generate an image for a project, make a translation, clean my YouTube transcript. Clearly, it’s practical and beneficial in these everyday tasks.

By multiple accounts, the web is losing pieces: every year a fraction of old web pages disappear, lost forever. We should regard the Internet Archive as one of the most valuable pieces of modern history; instead, many companies and entities make the chances of the Archive to survive, and accumulate what otherwise will be lost, harder and harder. I understand that the Archive headquarters are located in what used to be a church: well, there is no better way to think of it than as a sacred place.

It’s not new, but it’s accelerating. People that used to say that LLMs were a fundamentally flawed way to reach any useful reasoning and, in general, to develop any useful tool with some degree of generality, are starting to shuffle the deck, in the hope to look less wrong. They say: “the progresses we are seeing are due to the fact that models like OpenAI o1 or DeepSeek R1 are not just LLMs”. This is false, and it is important to show their mystification as soon as possible.

First, DeepSeek R1 (don’t want to talk about o1 / o3, since it’s a private thing we don’t have access to, but it’s very likely the same) is a pure decoder only autoregressive model. It’s the same next token prediction that was so strongly criticized. There isn’t, in any place of the model, any explicit symbolic reasoning or representation.

We are destroying software by no longer taking complexity into account when adding features or optimizing some dimension.

We are destroying software with complex build systems.

We are destroying software with an absurd chain of dependencies, making everything bloated and fragile.

We are destroying software telling new programmers: “Don’t reinvent the wheel!”. But, reinventing the wheel is how you learn how things work, and is the first step to make new, different wheels.

We are destroying software by no longer caring about backward APIs compatibility.