Comment by LucaMo | Hacker News Reader

Comment by LucaMo | Hacker News Reader

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What I think people get wrong (especially non-coders) is that they believe the limitation of LLMs is to build a complex algorithm. That issue in reality was fixed a long time ago. The real issue is to build a product. Think about microservices in different projects, using APIs that are not perfectly documented or whose documentation is massive, etc.

Honestly I don't know what commenters on hackernews are building, but a few months back I was hoping to use AI to build the interaction layer with Stripe to handle multiple products and delayed cancellations via subscription schedules. Everything is documented, the documentation is a bit scattered across pages, but the information is out there. At the time there was Opus 4.1, so I used that. It wrote 1000 lines of non-functional code with 0 reusability after several prompts. I then asked something to Chat gpt to see if it was possible without using schedules, it told me yes (even if there is not) and when I told Claude to recode it, it started coding random stuff that doesn't exist. I built everything to be functional and reusable myself, in approximately 300 lines of code.

The above is a software engineering problem. Reimplementing a JSON parser using Opus is not fun nor useful, so that should not be used as a metric

> The above is a software engineering problem. Reimplementing a JSON parser using Opus is not fun nor useful, so that should not be used as a metric.

I've also built a bitorrent implementation from the specs in rust where I'm keeping the binary under 1MB. It supports all active and accepted BEPs: https://www.bittorrent.org/beps/bep_0000.html

Again, I literally don't know how to write a hello world in rust.

I also vibe coded a trading system that is connected to 6 trading venues. This was a fun weekend project but it ended up making +20k of pure arbitrage with just 10k of working capital. I'm not sure this proves my point, because while I don't consider myself a programmer, I did use Python, a language that I'm somewhat familiar with.

So yeah, I get what you are saying, but I don't agree. I used highload as an example, because it is an objective way of showing that a combination of LLM/agents with some guidance (from someone with no prior experience in this type of high performing architecture) was able to beat all human software developers that have taken these challenges.

This hits the nail on the head. There's a marked difference between a JSON parser and a real world feature in a product. Real world features are complex because they have opaque dependencies, or ones that are unknown altogether. Creating a good solution requires building a mental model of the actual complex system you're working with, which an LLM can't do. A JSON parser is effectively a book problem with no dependencies.

You are looking at this wrong. Creating a json parser is trivial. The thing is that my one-shot attempt was 10x slower than my final solution.

Creating a parser for this challenge that is 10x more efficient than a simple approach does require deep understanding of what you are doing. It requires optimizing the hot loop (among other things) that 90-95% of software developers wouldn't know how to do. It requires deep understanding of the AVX2 architecture.

Here you can read more about these challenges: https://blog.mattstuchlik.com/2024/07/12/summing-integers-fa...

You need to give it search and tool calls and the ability to test its own code and iterate. I too could not oneshot an interaction layer with Stripe without tools. It also helps to make it research a plan beforehand.

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