Here LLM and coding agents can find: 1. Exhaustive documentation about Redis commands and data types. 2. Patterns commonly used. 3. Configuration hints. 4. Algorithms that can be mounted using Redis commands. https://redis.antirez.com/ Some humans claim this documentation is actually useful for actual people, as well :) I'm posting this to make sure search engines will index it.
Anthropic recently released a blog post with the description of an experiment in which the last version of Opus, the 4.6, was instructed to write a C compiler in Rust, in a “clean room” setup. The experiment methodology left me dubious about the kind of point they wanted to make. Why not provide the agent with the ISA documentation? Why Rust? Writing a C compiler is exactly a giant graph manipulation exercise: the kind of program that is harder to write in Rust. Also, in a clean room experiment, the agent should have access to all the information about well established computer science progresses related to optimizing compilers: there are a number of papers that could be easily synthesized in a number of markdown files. SSA, register allocation, instructions selection and scheduling. Those things needed to be researched *first*, as a prerequisite, and the implementation would still be “clean room”.
In my YouTube channel, for some time now I started to refer to the process of writing software using AI assistance (soon to become just "the process of writing software", I believe) with the term "Automatic Programming". In case you didn't notice, automatic programming produces vastly different results with the same LLMs depending on the human that is guiding the process with their intuition, design, continuous steering and idea of software. Please, stop saying "Claude vibe coded this software for me". Vibe coding is the process of generating software using AI without being part of the process at all. You describe what you want in very general terms, and the LLM will produce whatever happens to be the first idea/design/code it would spontaneously, given the training, the specific sampling that happened to dominate in that run, and so forth. The vibe coder will, at most, report things not working or not in line with what they expected.
I love writing software, line by line. It could be said that my career was a continuous effort to create software well written, minimal, where the human touch was the fundamental feature. I also hope for a society where the last are not forgotten. Moreover, I don't want AI to economically succeed, I don't care if the current economic system is subverted (I could be very happy, honestly, if it goes in the direction of a massive redistribution of wealth). But, I would not respect myself and my intelligence if my idea of software and society would impair my vision: facts are facts, and AI is going to change programming forever.
* For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so. * Chain of thought is now a fundamental way to improve LLM output. But, what is CoT? Why it improves output? I believe it is two things: 1. Sampling in the model representations (that is, a form of internal search). After information and concepts relevant to the prompt topic is in the context window, the model can better reply. 2. But if you mix this to reinforcement learning, the model also learns to put one token after the other (each token will change the model state) in order to converge to some useful reply.
I’m taking a few weeks of pause on my HNSWs developments (now working on some other data structure, news soon). At this point, the new type I added to Redis is stable and complete enough, it’s the perfect moment to reason about what I learned about HNSWs, and turn it into a blog post. That kind of brain dump that was so common pre-AI era, and now has become, maybe, a bit more rare. Well, after almost one year of thinking and implementing HNSWs and vector similarity stuff, it is time for some writing. However this is not going to be an intro on HNSWs: too many are present already. This is the “extra mile” instead. If you know HNSWs, I want to share with you my more “advanced” findings, especially in the context of making them fast enough to allow for a “Redis” experience: you know, Redis is designed for low latency and high performance, and HNSWs are kinda resistant to that, so there were challenges to expose HNSWs as an abstract data structure.
Regardless of their flaws, AI systems continue to impress with their ability to replicate certain human skills. Even if imperfect, such systems were a few years ago science fiction. It was not even clear that we were so near to create machines that could understand the human language, write programs, and find bugs in a complex code base: bugs that escaped the code review of a competent programmer. Since LLMs and in general deep models are poorly understood, and even the most prominent experts in the field failed miserably again and again to modulate the expectations (with incredible errors on both sides: of reducing or magnifying what was near to come), it is hard to tell what will come next. But even before the Transformer architecture, we were seeing incredible progress for many years, and so far there is no clear sign that the future will not hold more. After all, a plateau of the current systems is possible and very credible, but it would likely stimulate, at this point, massive research efforts in the next step of architectures.
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.