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How is the token-cost-be-damned part in the latter example?

I do find that both porting and translation projects have a much higher signal given the ease of mapping to tokens, when there is a proven working source to refer to - the source itself provides the validation. In a new project, you don’t have that validation.

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> How is the token-cost-be-damned part in the latter example?

It's there, but...

1. The project owner figured out a way to minimize token usage for agent claim verification tasks.

2. Verification agents used older and much cheaper models, including local models for the most trivial things.

3. They could afford it anyway; but I think it's an inevitability that the token-cost for a task will approach some limit for some quality threshold - concurrent with the dollar-cost-per-token shrinking over time as better hardware comes out.

> In a new project, you don’t have that validation.

I'm still trying to understand that part of the project's history, actually. Obviously the HTML5+WebGL+Emscripten+Etc entrypoint was a "new" project; one of the first things they did was build their own means of verification, I just don't know how that part worked-out in practice (besides the agents dogpiling in on TODO.md).

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> any time any agent confidently asserts something then it has to provide associated evidence

And this is enforced by... another LLM? Seems like it would work alright until something is asserted implicitly and not categorized as an assertion.

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It does happen. But this is why the “ralph loop” works: over dozens (or hundreds) of iterations, eventually every regression, implicit assumption, fake passing test, is noticed and fixed by another agent. The code slowly but continuously converges to a better state. I’m surprised myself, but haven’t seen it fall into chaos or degradation so far.
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It's agents all the way down~!
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"Determined" -> "Probabilistic"
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