2000 lines the sparse array.
2000 lines the t_array commands and upper layer implementation.
~500 lines of AOF / RDB code.
All the other stuff is tests, JSON command descriptions, TRE library under "deps".
c.f. valkey and others
... just speaking as someone who sometimes has to review very long PRs sometimes, though, I feel like 25% is a roughly normal level of "signal to noise." 5,000 lines of core logic is a LOT, and the tests and dependencies do still need to be read.
Virtually all major Redis features are a solo job of the post author.
By the way reviewers are paid good money for this and know the setup.
I start with a high level design md doc which an AI helps write. Then I ask another AI - whether the same model without the context, or another model - to critique it and spot bugs, gaps and omissions. It always finds obvious in hindsight stuff. So I ask it to summarize its findings and I paste that into the first AI and ask its opinions. We form an agreed change and make it and carry on this adversarial round robin until no model can suggest anything that seems weighty.
I then ask the AI to make a plan. And I round robin that through a bunch of AIs adversarially as well. In the end, the plan looks solid.
Then the end to end test cases plan and so on.
By the end of the first day or week or month - depending on the scale of the system - we are ready to code.
And as code gets made I paste that into other AIs with the spec and plan and ask them to spot bugs, omissions and gaps too and so on. Continually using other AI to check on the main one implementing.
And of course you have to go read the code because I have found it that AI misses polishes.
And I’m not saying that to poke fun at you (my workflow is essentially identical to yours), or at Google, but rather to say that there’s nothing new :)
AI is a fantastic accelerator of effective and ineffective workflows alike. It’s showing us which are effective and ineffective on way shorter timescales / in realtime!
Because spotting holes in specs has never been one of my strengths. And working without technical colleagues much of the time, it's a boon to be able to "rubber-duck" my ideas with something that is at least more intelligent than plastic.
Grabbing multipliers from thin air, the coding bit may only be 2x faster with a poorer-quality outcome, but working out what's needed is a good 5x faster.
And yes, I'm using the same adversarial AI MO as @wood_spirit, combined with Matt Pocock's excellent /grill-me and /grill-with-docs skills [1] and Plannotator [2] to review the plans.
I haven't been using multiple AIs adversarially as OP, but might consider giving it a try with Codex and Opus. That said, my AI workflow has been pretty similar... lots of iterations on just design, then iterations on documentation, testing, etc... then iterations on implementation, testing, validation and human review in the mix.
My analogy is that it's really close to working with a foreign dev team, but your turnaround is in minutes instead of days, where it's much more interactive.
To get a quality, lasting, result you're ultimately having to carefully study everything otherwise you end up quickly accumulating cognitive debt and the speedup soon shrinks as you're constantly having to revisit the initial approaches.
He is not "your avg dev" and it took him 4 months with llm.
This is not a seal of approval for you to go and command all your developers to move to Claude code/codex/any other ai coding tool fully.
I'm looking at you - any avg CEO of a startup.
To clarify, from TFA:
> 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
The initial timeframe was 4 months, he was able to do more work within the same timeframe with LLMs.
I've been working on a Database adapter for a couple months using an LLM... I've got a couple minor refactors to do still, then getting the "publish" to jsr/npm working... I've mostly held off as I haven't actually done a full review of the code... I've reviewed the tests, and confirmed they're working though. The hard part is there's some features I really want when in Windows to a Windows SQL Server instance that isn't available in linux/containers. I don't think I'll ever choose SQL again, but at least I can use/access a good API with windows direct auth and FILESTREAM access in Deno/Bun/Node.
FWIW: My final implementation landed on ODBC via rust+ffi so after I get the mssql driver out, I'll strip a few bits in a fork and publish a more generic odbc client adapter. using/dispose and async iterators as first class features in the driver.
He's not, but his work is obviously not average.
Average dev work is plumbing and CRUDs.
Then it quickly lost its original meaning as people started using it for virtually all forms of AI-assisted coding.
The RE component is interesting, but as commentary here has noted it seems orthogonal to the array data structure (i.e., usable on others as well). Does this not make more sense to accomplish with Lua scripting? Or if performance of Lua is an issue perhaps abstracting OP to be composable on top of any command that returns a range of values.
I say this with reverence for Antirez as the expert in this space, but some of this new feature set feels like the sort of solution that I tend to see arise from LLM-driven development; namely creation of new functionality instead of enhancement of existing, plus overcomplicating features when composition with others might be more effective.
Now I just need a way to protect my chats from any potential discovery, and <pew pew> business’ll be easy.
@antirez: Introducing a regex feature that late into the project for a seemingly unrelated feature feels a bit weird? Can you explain more your rationale on that? thanks!
wc -l t_array.c sparsearray.c
2012 t_array.c
2063 sparsearray.c
4075 total (including comments)
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.
This looks like a very useful feature. Thank you again for the reply.
Very cool anyway! Can I expect a youtube video about this soon?
antirez - you inspire a generation of devs. Thanks for all you do.
- the project essentially spans almost 3 different (albeit minor) generations of LLMs. Have you noticed major differences in their personas, behavior, output for that specific use case?
- when using AI for feedback, have you ever considered giving it different "personalities"? I have few skills that role play as very different reviewers with their own different (by design conflicting) personalities. I found this to improve the output, but also to be extremely tiring and to often have high noise ratio.
- when did you, if ever, felt that AI was slowing you down massively compared to just doing it yourself (e.g. some specific bug or performance or design fix)? Are there recurring patterns?
- conversely, how often did AI had moments where it genuinely gave you feedback or ideas that would've not come to you?
- last: do you have specific prompts, skills, setups, etc to work on specific repositories?
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.