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Discussion (5 Comments)Read Original on HackerNews
"Couldn't this be stateless?" "Do you have a plan to be able to shard this?" - We will almost always pivot from the agents initial design but the agent is able to easily understand the reasons and benefits and align quickly.
If I was writing the code myself, I'd often have to make compromises between the ideal architecture and the level of effort required to implement it - now I can just always have the ideal architecture.
Other times, I let the agent create a black box with a well-defined interface contract. I don't care about the architecture inside the black box.
I start talking to it while doing menial tasks like cleaning or doing the laundry, and I discuss the architectual decisions and options until I come to some resemblance of a plan.
Good side of this approach is that I can't just "skim over" or "copy paste" things - either I understood them and can repeat them myself, or I can't. It takes more time than /grill-me and similar approaches, but it's the only approach that doesn't make me want to claw my brain out.
I walk to work and home with ChatGPT Voice and AirPods. I ask it to be Socratic and I just start rambling the top of thing on my mind. After 20 mins of back-and-forth it's usually teased an answer out of me or I've teased an answer out of it.
I feel the opposite, like if I go to an agent without first knowing what I want to build, I'll never figure out what I'm doing or why and it'll run away from me.
I pretty much always go back and forth and have the agent write out a plan to a file and review it myself in my text editor. I still sometimes end up with surprises I disagree with, but I don't really find it to be true that the LLM ends up trying to "drive all of the thinking".
When I'm thinking through an architecture, I not only instruct it to refrain from writing any code, I don't even necessarily tell the agent what I'm trying to build.
The agent can rewrite half your codebase in one day, so architecture stops mattering for the most part.
Some apps don't care about scalability and performance but many do. Ignoring architecture all but guarantees inefficient, wasteful software.
But why would you care when an AI can just rewrite it? Yes, but can it rewrite it to a good architecture? Or just to a different one?
Does a good architecture make code easier for an AI to maintain? I don't know, but I think it's at least not proven that it doesn't.
Architecture has remained so far to be one of those domains where skillset dwarfs everything by comparison.
To actually do it is different and usually comes from having to wrestle with a problem.
Sadly people from the academia and the public at large has a hard time understanding what this even means.
They equate it with exam based memorization or delegation to authority. Funnily they even think first principles reasoning is an improved version of doing the same.
But this is a blessing in disguise as it gives those who wrestle with real problems a unique skillset that can be advantageous.
In what departments and at what universities? The term "first principles" comes from academia. I think you'd be hard pressed to find a faculty member in any philosophy department doesn't understand what reasoning from first principles is. I'd be surprised if any working mathematician thinks of "memorization" or delegating to authority rather than axiomatization. What experiences led you to say this?