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Comment by aappleby | Hacker News Reader

I don't need an AI to write code for me, but it is _astoundingly_ helpful to have it summarize various design options and new technology stacks without me having to scavenge Google for the obscure corner-cases I care about.

I have an idea for a project that involves streaming 3 giabits of data per second from a USB 3.0 device out over a 10 gig Ethernet connection, and it was able to compare/contrast various levels of support for high-bandwidth USB 3 and Ethernet in multiple frameworks and languages.

And the whole conversation, with code examples, cost me 3 _cents_ of Anthropic credits.

My new fear is when people start asking AIs "Hey AI, here is my codebase, my org chart, and commit histories for all my employees - how can I reduce the number of humans I need to employ to get this project done?"

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Examples like this make me think, “maybe the S&P 500 will be at 15,000 by 2030.” I mean, in my own job, Claude is making me substantially better in a wide range of areas, from sales negotiation to managing my team. Add that up across millions of people…

> it was able to compare/contrast various levels of support for high-bandwidth USB 3 and Ethernet in multiple frameworks and languages.

How do you know such information is correct though?

Some of it I was able to verify immediately, some I confirmed by poking around in Github repos once I knew the magic words, some I haven't verified yet.

For example, I wasn't sure if there was good support for USB devices in Rust. Just not something I'd ever bothered to investigate. Claude knew that there was a libusb wrapper and a "rusb" crate, and claimed that support for USB in Rust was stable. I verified the existence of the crates, and I'll take Claude at its word for stability unless I discover otherwise.

The same way as when you learn something new from some random internet person.

Does what they say conflict with anything you already know to be true?

Is their argument internally inconsistent, does it contradict itself?

Can you corroborate parts of their argument from other independent sources?

The key difference is that I can reasonably trust (depending on the audience) that the other human is not simply hallucinating facts in order to provide a response.

Not only do humans invent stuff that didn't happen (hallucination), we're also prewired with a very long list of cognitive biases.

It depends a lot on the domain of course, but I'd bet that frontier LLMs already exhibit superhuman capabilities in providing accurate answers in the vast majority of domains.

The fact that we can notice our own biases and correct them is irrelevant to you?

Even without writing code, on personal projects it's worth it for the three things I'm always lazy about:

1. Good, detailed commits 2. Tests 3. Docstrings

Nobody cared when it was the artists, the songwriters, the music studios, the film companies. But impacting jobs of the programmers - now, heaven forbid.

I'm pretty sure people cared about the artists too.

Did the people building dall-e care?

Are those people here? Do you reckon GP is one of them?

The specific issue of using a LLM to make org decisions on how to downsize actually affects nearly all jobs equally.

From what I can tell, most programmers are more ok with LLMs directly replacing them than artists are. I tend to agree that it is better to replace programmers, and protect artists.

The replacement is currently less obvious with developers, AI work needs to be copy pasted to the right places in execution and the hooks into the rest of the code need to be set up right.

It doesn’t yet do prompt to 100% finished artifact.

For concept artists and illustrators at least who have very generic or commercial styles Diffusion models can create a final artifact that passes as good enough to a lot of clients.

Think developers would feel a bit different if they were in the same situation today.

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