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.

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.

@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!

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.

or maybe the conclusion is that model providers need to clean up their training data!

Very cool anyway! Can I expect a youtube video about this soon?

- 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?

Who is going to do an LLM free fork?