I'm not completely convinced by this comparison between blind chess and prompting LLMs.
In blind chess you get deterministic information about the state of the board: each mental update to your board model can be precise, and you have the full state at every point in time.
LLMs are notoriously non-deterministic, and even at temperature zero you still can't predict exactly where the weights will take you next.
I suppose you can get closer to deterministic if you adopt a prompting style where you almost dictate every line of code, but at that point the coding agent is more of a typing assistant.
The productivity benefits of coding agents unlock themselves when you figure out how to turn short prompts - "add tests that exercise the registration form and check the happy path and all failure states" - into larger changes.
If you're completely blind to the results of those you're going to end up with a system you don't 100% understand very quickly. In blind chess terms you'll no longer know the positions of every piece on the board.
In blind chess you get deterministic information about the state of the board: each mental update to your board model can be precise, and you have the full state at every point in time.
LLMs are notoriously non-deterministic, and even at temperature zero you still can't predict exactly where the weights will take you next.
I suppose you can get closer to deterministic if you adopt a prompting style where you almost dictate every line of code, but at that point the coding agent is more of a typing assistant.
The productivity benefits of coding agents unlock themselves when you figure out how to turn short prompts - "add tests that exercise the registration form and check the happy path and all failure states" - into larger changes.
If you're completely blind to the results of those you're going to end up with a system you don't 100% understand very quickly. In blind chess terms you'll no longer know the positions of every piece on the board.