CaveTech

Respectfully that’s further highlighting the problem. Nail it down into a small set worth reading. This is far too trivial of a project to have multiple origin stories

Looks like you didn’t even edit the origin story docs, they are pure slop. Demonstrate a real intention

Then don’t give it email access

Your question is worded kind of confusingly, but all caching is handled on the inference layer, and by all major providers. In short, caching should work as long as you are sending requests to the same model and provider.

You can also use hooks to force the use of subagents for this. The stack here is entirely unnecessary

Haiku < Sonnet < Opus < Fable

The harness does not matter that much, it's getting leaner every cycle.

You've confused what I wrote, we are in agreement. The fact codex found the vulns means that mythos almost certainly will.

I’m highly confident that prior exposure is irrelevant at this point. I work on vulnerability detection at a hyperscaler.

It was found with gpt 5.5 7/10 times it’ll be trivially found by mythos

These things are not nearly equivalent. It’s writing code, it’s not software engineering.

I never made a claim that it's ineffective, just that it's of limited effectiveness. The diminishing returns kick in quickly, and it's not applicable in more domains than it is applicable.

The latter, and I would disagree that “this works and scales well” in the general sense. It clearly has very finite bounds by the fact we haven’t achieved agi by running an llm in a loop..

The approach of “try a few more things before stopping” is a great strategy akin to taking a few more stabs at RNG. It’s not the same as saying keep trying until you get there - you won’t.

The amount of paths in the wrong direction are infinitely more than then number in the right direction. You'll quickly realize this doesn't actually scale.

[citation needed]