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JackSlateur 3 days ago

Static and analytic guardrails ??

Unless you are writing some shitty code for a random product that will be used for some demo then trashed, code can be resumed to a simple thing:

  Code is a way to move ideas into the real world through a keyboard
So, reading that the future is using a random machine with an averaged output (by design), but that this output of average quality will be good enough because the same random machine will generate tests of the same quality : this is ridiculous

Tests are probably the thing you should never build randomly, you should put a lot of thoughts in them: do they make sense ? Do your code make sense ? With tests, you are forced to use your own code, sometimes as your users will

Writing tests is a good way to force yourself to be empathic with your users

People that are coding through IA are the equivalent of the pre-2015 area system administrators that renewed TLS certificates manually. They are people that can (and are replacing themselves) with bash scripts. I don't miss them and I won't miss this new kind.

CuriouslyC 3 days ago | parent [-]

I actually have a bayesian stochastic process model for LLM codegen that incorporates the noisy channel coding theorem, it turns out that just like noisy communications channels can be encoded to give arbitrarily low error rate communication, LLM agents workflows can be coded to give arbitrarily low final error rate output. The only limitation on this is when model priors are highly mis-aligned with the work that needs to be done, in that case you need hard steering via additional context.

JackSlateur 3 days ago | parent [-]

Which model gives you creatives outputs ?

CuriouslyC 3 days ago | parent [-]

Creative outputs start with gemini (because of long context support, it can get longform stuff right), with successive refinement passes using claude for line/copy edits because (it's the least purple).