logoalt Hacker News

adastra22yesterday at 5:45 AM3 repliesview on HN

I'm pretty sure you can do that right now in Claude Code with the right subagent definitions.

(For what it's worth, I respect and greatly appreciate your willingness to put out a prediction based on real evidence and your own reasoning. But I think you must be lacking experience with the latest tools & best practices.)


Replies

yosefkyesterday at 6:34 AM

If you're right, there will soon be a flood of software teams with no programmers on them - either across all domains, or in some domains where this works well. We shall see.

Indeed I have no experience with Claude Code, but I use Claude via chat, and it fails all the time on things not remotely as hard as orientation in a large code base. Claude Code is the same thing with the ability to run tools. Of course tools help to ground its iterations in reality, but I don't think it's a panacea absent a consistent ability to model the reality you observe thru your use of tools. Let's see...

show 3 replies
bootsmannyesterday at 9:46 AM

I feel like refutations like this (you aren't using the tool right | you should try this other tool) pop up often but are fundamentally worthless because as long as you're not showing code you might as well be making it up. The blog post gives examples of clear failures that can be reproduced by anyone by themselves, I think its time vibe code defenders are held to the same standard.

show 1 reply
alfalfasproutyesterday at 5:30 PM

FWIW I do work with the latest tools/practices and completely agree with OP. It's also important to contextualize what "large" and "complex" codebases really mean.

Monorepos are large but the projects inside may, individually, not be that complex. So there are ways of making LLMs work with monorepos well (eg; providing a top level index of what's inside, how to find projects, and explaining how the repo is set up). Complexity within an individual project is something current-gen SOTA LLMs (I'm counting Sonnet 4, Opus 4.1, Gemini 2.5 Pro, and GPT-5 here) really suck at handling.

Sure, you can assign discrete little tasks here and there. But bigger efforts that require not only understanding how the codebase is designed but also why it's designed that way fall short. Even more so if you need them to make good architectural decisions on something that's not "cookie cutter".

Fundamentally, I've noticed the chasm between those that are hyper-confident LLMs will "get there soon" and those that are experienced but doubtful depends on the type of development you do. "ticket pulling" type work generally has the work scoped well enough that an LLM might seem near-autonomous. More abstract/complex backend/infra/research work not so much. Still value there, sure. But hardly autonomous.

show 1 reply