Salvatore Sanfilippo
Systems programming
Creator of Redis (antirez). Writing on systems programming and coding with LLMs.
Recent activity
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Control the ideas, not the code
Look at the past history of this blog. There are many blog posts about programming with AI, a few of them date back to January 2024 (like this: https://antirez.com/news/140). I’m a relatively well regarded programmer, after all. I don’t have the need to still be in the “loop” as a old man that seeks for relevance, I recently rejoined Redis, and now I also am developing a new open source software for local LLM inference that received a good welcome in the community. Why I keep doing this, of sayi
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A new era for software testing
Automatic programming dramatically speeds up writing software in certain use cases and in the right hands. In my experience the output does not reach the structural quality and economy of complexity of the best hand-written software. However, not all the software is stellar, and my feeling is that automatic programming surpasses most of the times (and if well managed) the quality of decently developed hand-written code. Yet, there is a tradeoff between quality and time, in the case of writing ne
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Distributing LLM inference in DwarfStar
High end NVIDIA cards, and the server and power needed to run them, cost a lot of money, especially if you plan to reach enough VRAM to run massive models. The alternative, so far, has been Apple hardware, or the DGX Spark that, even if severely limited because of memory bandwidth, still allows to run LLMs prompt processing (prefill) fast enough. The Mac Studio provided up to 512GB unified memory, a solution with modest memory bandwidth (but much better than the Spark) and compute at a price tha
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Alternatives for the EDIT tool of LLM agents
EDIT: of course this was already done in the past! I had little doubts but people just confirmed me about it on Twitter :) But, keep reading: the CRC32 compromise at the end is an interesting tradeoff, and this is a good discussion to have in general. Right now I'm working to an agent for my DS4 project. Local inference is token-poor, it's a battlefield where optimizations count. I was quite surprised by the fact the EDIT tool everybody is using right now forces the LLM to emit the old version o
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A few words on DS4
I didn’t expect DwarfStar 4 (https://github.com/antirez/ds4) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all the
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Redis array type: short story of a long development
I started working on the new Array data type for Redis in the first days of January. The PR landed the repository only now, so this code was cooked for four months. I worked at the implementation kinda part time (kinda because many weeks were actually full time, sometimes to detach yourself from the keyboard is complicated), and even before LLMs the implementation was likely something I could do in four months. What changed is that in the same time span, I was able to do a lot more. This is the
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AI cybersecurity is not proof of work
The proof of work is the wrong analogy: finding hash collisions, while exponentially harder with N, is guaranteed to find, with enough work, some S so that H(S) satisfies N, so an asymmetry of resources used will see the side with more "work ability" eventually winning. But bugs are different: 1. Different LLMs executions take different branches, but eventually the possible branches based on the code possible states are saturated. 2. If we imagine sampling the model for a bug in a given code M t
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GNU and the AI reimplementations
Those who cannot remember the past are condemned to repeat it. A sentence that I never really liked, and what is happening with AI, about software projects reimplementations, shows all the limits of such an idea. Many people are protesting the fairness of rewriting existing projects using AI. But, a good portion of such people, during the 90s, were already in the field: they followed the final part (started in the ‘80s) of the deeds of Richard Stallman, when he and his followers were reimplement
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Redis patterns for coding
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. Comments
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Implementing a clear room Z80 / ZX Spectrum emulator with Claude Code
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,
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