But damn, agents are amazing and I'm enjoying being a "thought process designer". I'm not going back. Even if AI development stops today my career will never be the same.
But damn, agents are amazing and I'm enjoying being a "thought process designer". I'm not going back. Even if AI development stops today my career will never be the same.
I’m working on a tiny agent harness at home to learn and the process of taking human speech and turning it into agent tool calls that output something generally deterministic depending on how the tool is defined is so interesting.
One of the big takeaways is you really only have to rely on the non-determinism<->determinism translation layer once when you switch between the two domains. You can obviously rely on it more if you want, and that’s probably faster because determinism is hard, but you don’t need too do that.
For voice related things you have a lot of turn of phrase scenarios that can make no sense unless you know. Phrasing like “Put Larry on the horn.” For someone familiar with old lingo for phone calls makes sense. For someone else they might think of a war horn, someone else a music class.
All of those are wildly different situations. It’s not hard to see how one oops between two non deterministic things can quickly go off the rails.
The fact we can get away with so much non-determinism->non-determinism recursion is frankly amazing when you realize how easy it is to imprecisely describe what it is you’re thinking.
npx tsc
bash tsc
bash npx tsc
npm run build
…
I’m not an expert at all on the subject matter, but is it impossible to train a model that calls tools in a (quasi-)deterministic way?Would just make the answer to the same exact prompt X repeatedly the same.
It wouldn't change the fact that prompt X', functionaly indistinguisable from X, aside from small phrasing changes, can give a totally different looking answer.
Can't help but feel like a lot of people who are deep in IT made it there because they hated working with humans.