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Comment by devin | Hacker News Reader

What are you saying is going to be over in a year or so?

Right now I think there is an edge to how you construct prompts and config files. There is a large difference between "modify f() to do..." and "modify f() to do... Review the current variables and make sure they are still used consistent with their naming. Look for unreachable and dead code. Examine callers and called functions for side effects from the introduced changes...".

I don't think that will make much difference in a year.

I'm increasingly convinced of the opposite. IMO Fable was pretty similarly capable for my day to day work as Opus.

I think there's a pretty good chance that we've reached the point of diminishing returns, for our specific use case.

There are still like a billion other (more difficult) use cases to be tackled, but I think "generating code" has gotten really good to the point where the other bottlenecks will prevent further exponential progress on this specific task.

That's not going away.

It's already going away for me in a sense as I build up a library of AGENTS.md and Codex skills. I see no reason such things won't get baked in at the agent layer so that domain specific rules and such are automatically applied when appopriate.

I'm not sure if you're ahead of me or behind me on this curve, but fwiw, my experience has been that we have now encoded everything that is useful in the various markdown files and have reached the point of diminishing returns on this, with more powerful new models making noticeable but not revolutionary improvements as they come out.

You're essentially making the case here that your work is now automated into a set of one-shot actions that can be performed by an AI model and your job has become to selectively apply these actions. That says either a) we don't do the same work, and instead you're doing some kind of low level devops function that I've only ever seen in rare cases where a human isn't needed anymore, or b) you've vastly oversimplified the software engineering you're doing.

Sophisticated chain of reasoning LLMs like ChatGPT have baked in some natural language operations and they make it so i can create at a higher level of the language expression stack. But I'm still formulating my own expression. There's no conceivable path I can see where an improved model is going to be able to do what I do. I think that is clear from my ChatGPT threads at least.

I think you're reading quite a bit into my comment.. I'll try to respond to a more accurate response to my comment but I'm not going to waste time with this sort of response.

Yeah, uh, why would it go away? In what world do you completely surrender your ability to control the work product, the methods for achieving said work product, etc. That is the dream of a PHB.

Who's to say it won't?

Who's to say it will? :)

Not OP, but I generally agree. Models are powerful enough now to reliably instruct other models. They don’t need fancy tools or IDEs, just the command line.

With deterministic workflows, type-safe languages and test suites, agentic loops pretty much “can’t fail”. They will continue until the types resolve, the tests pass, and the project requirements are deterministically met.

By that point it’s literally just a case of typing a prompt in to a text field, and waiting.

"project requirements are deterministically met" makes it sound so easy

It works great in dynamic languages as well. Static typing is mostly to aid the IDE. In dynamic languages you can infer the type by looking at the code, and LLM are good at that.

This seems true to me in theory, but not in practice.

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