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Claude Sonnet will ship in Xcode

(developer.apple.com)
485 points zora_goron | 1 comments | | HN request time: 0s | source
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not_your_vase ◴[] No.45060519[source]
3 days ago I saw another Claude praising submission on HN, and finally I signed up for it, to compare it with copilot.

I asked 2 things.

1. Create a boilerplate Zephyr project skeleton, for Pi Pico with st7789 spi display drivers configured. It generated garbage devicetree which didn't even compile. When I pointed it out, it apologized and generated another one that didn't compile. It configured also non-existent drivers, and for some reason it enabled monkey test support (but not test support).

2. I asked it to create 7x10 monochromatic pixelmaps, as C integer arrays, for numeric characters, 0-9. I also gave an example. It generated them, but number eight looked like zero. (There was no cross in ether 0 nor 8, so it wasn't that. Both were just a ring)

What am I doing wrong? Or is this really the state of the art?

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OtherShrezzing ◴[] No.45060786[source]
It’s good at doing stuff like “host this all in Docker. Make a Postgres database with a Users table. Make a FastAPI CRUD endpoint for Users. Make a React site with a homepage, login page, and user dashboard”.

It’ll successfully produce _something_ like that, because there’s millions of examples of those technologies online. If you do anything remotely niche, you need to hold its hand far more.

The more complicated your requirements are, the closer you are to having “spicy autocomplete”. If you’re just making a crud react app, you can talk in high level natural language.

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fx0x309 ◴[] No.45061001[source]
In other words, the vibe coders of this world are just redundant noobs who don't really belong on the marketplace. They've written the same bullshit CRUD app every month for the past couple of years and now they've turned to AI to speed things up
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stpedgwdgfhgdd ◴[] No.45061225[source]
Last week I asked Claude to improve a piece of code that downloads all AWS RDS certificates to just the ones needed for that AWS region. It figured out several ways to determine the correct region, made a nice tradeoff and suggested the most reliable way. It rewrote the logic to download the right set, did some research to figure out the right endpoint in between. It only made one mistake, it fallback mechanism was picking EU, which was not correct. Maybe 1 hour of work. On my own it would have taken me close to a working day to figure it all out.
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Towaway69 ◴[] No.45061576[source]
This is just a thought experiment.

I don't mean to be treading on feet but I'm noticing this more and more in the debates around AI. Imagine if there are developers out there that could have done this task in 30 mins without AI.

The level of performanace of AI solutions is heavily related to the experience level of the developer and of the problem space being tackled - as this thread points out.

Unfortunately the marketing around AI ignores this and makes every developer not using AI for coding seem like a dinosauer, even though they might well be faster in solving their particular problems.

AI is moving problem solving skills from coding to writing the correct prompts and teaching AI to do the right thing - which, again, is subjective, since the "right thing" for one developer isn't the "right thing" for the another developer. "Right thing" being the correct solution, the understandable solution, the fastest solution, etc depending on the needs of the developer using the AI.

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1. TheOtherHobbes ◴[] No.45063109[source]
There are two ways to do this. One is to one-shot or maybe few-shot a solution. Maybe this works. Maybe it doesn't. Sometimes it works if you copy a solution from [Product 1] to [Product 2] and say "Fix this."

The other is to look at the non-working solution you get, read through it, and think "Oh, I didn't know about that framework/system/product/library, that's neat" and then do some combination of further research and more hand-holding to get to something that does work.

This is useful, more or less, no matter what your level.

It's also good for explaining core industry tooling you've maybe never used before. If you're new to Postgres/NoSQL/AWS/Docker/SwiftUI/whatever it can talk you through it and give you an instant bootcamp with entry-level examples and decent solutions.

And for providing fixes for widely known bugs and issues in products that may not be widely known to you (yet.)

IME ChatGPT5 is pretty solid with most science/tech up to undergrad. It gets hallucinatory past that, and it's still flattering, which is annoying, but you can tell it to cut that out.

Generally you can use it as a dumb offshore developer, or as an infinitely patient private tutor.

That latter option is very useful. The first, not always.