RE: https://macaw.social/@jay/117393989247637083
This is a direct consequence of the industry myopically framing the use of #llms #llm #ai as “managing agents”, when the technology is nothing like it. It’s literally the corporate structure trying to imprint and capture a technology it doesn’t know how to deal with and that is actually so easily used to undermine the concept of a “corporation” itself.
These models are language transformers, and we developers have always transformed languages and built tools and complete research fields that deal with that. I don’t prompt my model as “agents”, but as “planning with heuristics” and sometimes (very rarely) parallelism makes sense (as in, doing multiple inferences in parallel in order to create a single artifact).
This entire “code review agents and design agents and planning and planning verification and adversary review” is just burning tokens for the sake of cosplaying hierarchical structures that have been established to control labor.
What is actually possible when you approach llms from a hackers perspective, subverting and questioning the status quo, is hard to put into words. I have never hit any quota limits ever, and yet I am consistently the person putting out the most projects / artifacts in any ai forward group I am part of, shaking my head at people burning $200 for a round of code review that doesn’t help anything.
Here’s basically my entire approach in two prompts:
- “create an elegant language to do X”
- “implement a compiler/interpreter for X”
- “now do X. And X1. And X2. And X3.”
The language is elegant and thus easy to read and generate in the context of X. The compiler is usually fairly trivial, and much easier to code review / formally analyze. And then you’re done, in less tokens than it would take to “write a plan to do a tiny subset of my as of yet partial understanding of X”.
I’m serious, check attached screenshots, sonnet 5.5 medium, two prompts, of which only 4 are actually relevant to the thing at hand. The hard part (always was): knowing what the right primitives are, and how to compose them.
This was the only way to get useful stuff out of gpt-3.5, and still is.
https://claude.ai/share/9784fe15-cb53-4227-8666-d1a0217e3b0c