[ivoras]

I did a similar thing but with backend-heavy code, and I agree with this assessment:

> In particular, I asked ChatGPT to write a function by knowing precisely how I would have implemented it. This is crucial since without knowing the expected result and what every line does, I might end up with a wrong implementation.

In my eyes, it makes the whole idea of AI coding moot. If I need to explain every step in detail - and it does not "understand" what it's doing; I can virtually the statistical trial-and-error behind its action - then what's the point? I might as well write it all myself and be a bit more sure the code ends up how I like it.

link: https://www.linkedin.com/feed/update/urn:li:activity:7289241...

[doug_durham]

Because as op pointed out it's faster. It has ready access to the correct usage of different libraries.

[WD-42]

So does the language features of basically any modern editor.

[rfw300]

Not really - there's a difference between having the docstring of a function available for you to read, and a model which has learned from thousands of examples how to use a particular API and integrate it into a larger set of instructions. The latter is vastly faster and takes much less human work than the former.

[girvo]

Except when it consistently gets said particular API wrong. I was using it to do basic graphql-yoga setup with R1 and then Claude Sonnet 3.5 and they both output incorrect usage, and got stuck in a loop trying to fix it.

If it can’t do something that basic and that common using a language and toolset with that much training data, then I’m pessimistic personally.

I’m yet to see Copilot be useful for any of my juniors when we pair, it gets in the way far more than it helps and it is ruining their deeper understanding, it seems.

I’ll continue trying to use these tools, but I swear you’re overselling their abilities even still.

[simonw]

The way to fix that is to find an example of correct usage of that API and paste that example in at the start of the prompt.

This technique can reliably make any good LLM fluent in an API that it's never seen in its training data.

[throwup238]

At this point with Cursor you can have it index the online docs by giving it a base URL and have it automatically RAG the relevant content into the chat (using the @ symbol to reference the docs). Both Windsurf and Cursor also support reading from URLs (iirc Aider does too).

I’ve had better luck with manually including the page but including the indexed docs is usually enough to fix API mistakes.

[WD-42]

Begs the question again: if you need to go out of your way to find an example of correct usage of the api to paste into the prompt, why are you even bothering?

I find copilot useful when I already know what I want and start typing it out, at a certain point the scope of the problem is narrowed sufficiently for the LLM to fill the rest in. Of course this is more in line of “glorified autocomplete” than “replacing junior devs” that a keep hearing claims of.

[girvo]

Yeah thats the trick I've been using too, but by that point I get a better result by implementing it myself... of course, I've had two decades of practice and I don't have to communicate what I want lossily to myself, so it's an unfair comparison, but perhaps I've just not found the right use-case yet. I'm sure it exists, I've just not had much luck over the past couple years yet (including just this past weekend).

[therealpygon]

That is far more likely to happen when it is relying on compressed knowledge of documentation and usage for an API it would have seen (comparatively) only a few times in training. That is where the various types of memory, tool calling and supplementary materials being fed in can make them significantly more situationally useful.

The LLMs you mention are first and foremost a “general knowledge” machine rather than a domain expert. In my opinion, Junior developers are the least likely to benefit from their use because they have neither the foundational understanding to know when the approach is wrong, nor the practical experience to correct any mistakes. An LLM can replace a junior dev because we expect the mistakes and potentially poor quality, but you don’t really want a junior developer doing code reviews for another junior developer before pushing code.

[onemoresoop]

The expectation for junior devs will probably change as well and they’d do a lot more shadowing while learning the product. Experience is gained in time.

[simonw]

LLMs are way, way faster at typing code than I am. I often dictate to them exactly what I need and it saves me a bunch of time.

[bcrosby95]

So you would say typing speed matters?

[simonw]

Yes. Here's a neat essay that helps capture why I believe that. https://www.scattered-thoughts.net/writing/speed-matters/

[onemoresoop]

Sometimes it does. If typing slows you down enough to take you out of the zone then it probably does.

[rubslopes]

and, for tasks that take the LLM a minute, you can grab some coffee while it works and come back just to review. It's a great feeling.

[alkonaut]

There are tons of use cases. E.g. if you know an algorithm (take any pseudocode description from a moderately complex algorithm on Wikipedia for example) and you know the programming language, you still may be looking at an hour or two of typing just to get that pseudocode down into code using your own language, variable names, libraries.

But this is the kind of thing a LLM excels at. It gives you 200 lines of impl right away, you have a good understanding of both what it should look like and how it should work.

Slow and error prone to type but, but quick and easy to verify once done, that's the key use case for me.

[guelo]

It’s a better, faster, personalized Stack Overflow. Just like SO you might be led down the wrong path by an answer, but if you’re a programmer and you say you don’t get value out of Stack Overflow I don’t believe you.