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As always, the answer is "divide & conquer". Works for humans, works for LLMs. Divide the task into as small, easy to verify steps as possible, ideally steps you can automatically verify by running one command. Once done, either do it yourself or offload to LLM, if the design and task splitting is done properly, it shouldn't really matter. Task too difficult? Divide into smaller steps.

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Judging from this an approach might have been to port the 28 modules individually and check that everything returns the same data in Perl and TS versions:

"I took a long-overdue peek at the source codebase. Over 30,000 lines of battle-tested Perl across 28 modules. A* pathfinding for edge routing, hierarchical group rendering, port configurations for node connections, bidirectional edges, collapsing multi-edges. I hadn’t expected the sheer interwoven complexity."

Well, ideally we teach the AIs how to divide-and-conquer. I don't care, whether my AI coding assistant is multiple LLMs (or other models) working together.

They already know how to. But you have to tell them that's the way you want them to operate, tell them how to keep track of it, tell them how to determine when each step is done. You need to specify what you want both in terms of final result but also in terms of process.

The AI's are super capable now, but still need a lot of guiding towards the right workflow for the project. They're like a sports team, but you still need to be a good coach.

> They already know how to. But you have to tell them that's the way you want them to operate, tell them how to keep track of it, tell them how to determine when each step is done. You need to specify what you want both in terms of final result but also in terms of process.

I found Google Antigravity (with the current Gemini models) to be fairly capable. If I had to guess, it seems like they set up their system to get that divide-and-conquer going. As you suggest, it's not that hard: they just have to put the instructions in their equivalent of the system prompt.

Well, when I say 'not that hard', I mean it's an engineering problem to get the system and tooling working together nicely, not really an AI problem.

I ask the LLM to split the task for me. It shines.

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