I keep finding myself to minimize the words to describe the same thing as well, since we are finding ourselves doing "that" operation more and more over time.

maybe shortening the term to "auto-code" would help tho.

https://en.wikipedia.org/wiki/Automatic_programming It's an acknowledged term in computer science, describing any mechanism whatsoever of auto-generating code from a description at a higher level of abstraction. Of course LLM's are highly unusual in being non-deterministic and having a surprisingly broad scope, but this does not make the term inapplicable.

Well, Redis is a data structures server, and has very complicated and edgy data structures like the HyperLogLog, so I have very little doubts that a fundamental data type like the Array will fit :) Also the actual complexity added is mostly two C files that are quite commented and understandable.

    wc -l t_array.c sparsearray.c
        2012 t_array.c
        2063 sparsearray.c
        4075 total (including comments)

Sure there are also the AOF / RDB glues, the tests, the vendored TRE library for ARGREP. But all in all it's self contained complexity with little interactions with the rest of the server.

A quick note: if we focus only on that part of the implementation, skipping tests and persistence code which is not huge, 4075 lines in 4 months are an average of 33 lines per day, which is quite low.

Redis sets the locale at startup to avoid issues so should be ok but we will document that for instance è will not match È when nocase is used.

Haha, ~5000 LOC with comments. The rest is tests + TRE code + TRE tests.

Yep I will release it, it is a bit out of sync at this point, but will do a pass of updating and will release it.

Checking, thanks. EDIT: works very well on my iPhone, so without being able to reproduce is not easy to fix.

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1. The huge jump from from Opus to GPT 5.3. Game changer. GPT 5.4, 5.5, were better but only incrementally better.

2. Nope I don't give much personalities, but I use subtle prompt differences to maximize certain responses I want, to make the model focusing in a given detail or acting in a specific kind of engineering mindset.

3. It never happened that the AI was slowing me down since I always had the full context and code detail in mind of what was happening. I believe that this happens more when you don't have a clear idea. Also GPT >= 5.3/4 is not the past generation of models, it is very hard to trap it into a situation where it seems unable to understand what you mean.

4. A few times the AI provided fresh insights that I really liked. Most of the times it was the other way around. Certain implementations were written by the AI at a very impressive level of quality.

5. I don't use general skills, I build skills with deep search when needed for specific projects, and build an AGENT.md that works as a knowledge base as I work with the AI. One thing that I use a lot is, when there is a very complex problem, to tell GPT that I have a friend called Machiavelli that is an incredible computer scientist. To write him an email in /tmp/letter.md with the problem we are facing, and I'll try to get a reply. Then I ask GPT 5.5 Pro on the web with extensive reasoning set on. It will take sometimes 30 minutes or more to reply. Often times after I feed back the reply, the agent will be able to see things a lot more clearly.