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Aider: AI pair programming in your terminal

(github.com)
432 points tosh | 2 comments | 10 Apr 24 21:06 UTC | HN request time: 0.001s | source
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vander_elst ◴[11 Apr 24 05:53 UTC] No.39998806[source]▶
With all these AI tools requiring a prompt, does it really simplify/speed up things? From the example: I have to write "add a name param to the 'greeting' function, add all types", then wait for the result to be generated, read it carefully to be sure that it does what I want, probably reiterate if the result does not match the expectation. This seems to me more time consuming than actually do the work myself. Does anyone has examples where promoting and double checking is faster than doing it on your own? Is it faster when exploring new solutions and "unknown territory" and in this case, are the answers accurate (from what I tried so far they were far off)? In that case how do you compare it with "regular search" via Google/Bing/...? Sorry for the silly question but I'm genuinely trying to understand
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pocketarc ◴[11 Apr 24 06:25 UTC] No.39998965[source]▶
Personally the use for me has been in writing boilerplate. As an example, one of my ongoing goals has been to port all the view code of a project to another framework, following its idioms. Using an LLM, I can process a file in a couple of seconds, and checking that everything is right takes just a few seconds as well. It’d take me hours to go through every file manually, and it’d be prone to human error. It’s not technically challenging stuff, just tedious and mind-numbing, which is perfect for an LLM.

I do agree though, these basic examples do seem quite pointless, if you already know what you’re doing. It’s just as pointless as telling another developer to “add a name param to ‘greeting’ function, add all types”, which you’d then have to review.

I think it comes down to your level of experience though. If you have years and years of experience and have honed your search skills and are perfectly comfortable, then I suspect there isn’t a lot that an LLM is going to do when it comes to writing chunks of code. That’s how I’ve felt about all these “write a chunk of code” tools.

In my case, apart from automating the kind of repetitive, mindless work I mentioned, it’s just been a glorified autocomplete. It works -really- well for that, especially with comments. Oftentimes I find myself adding a little comment that explains what I’m about to do, and then boop, I’ve got the next few lines autocompleted with no surprises.

I had to work without an internet connection a few days ago and it really, really hit me how much I’ve come to use that autocomplete - I barely ever type anything to completion anymore, it was jarring, having to type everything by hand. I didn’t realise how lazy my typing had become.

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1. pistacchioso ◴[11 Apr 24 09:37 UTC] No.40000278[source]▶
Most of the discussions about AI applied to coding end up having someone who states that it's just not worth it (at least the moment) and someone else who then chimes in to say that they mostly use it for "boilerplate" code.

I have trouble understanding the "boilerplate" thing because avoiding writing boilerplate is

1) already a solved "problem" long before AI

2) is it really a "problem"?

The first point: * If you find yourself writing the same piece of code over and over again in the same code it's the indication that you should abstract it away as a function / class / library.

* IDEs have had snippets / code completion for a long time to save you from writing the same pieces of code.

* Large piece of recycled functionalities are generally abstracted away in libraries of frameworks.

* Things like "writing similar static websites a million times" are the reason why solutions like WordPress exist: to take away the boilerplate part of writing websites. This of course applies to solutions / technologies / services that make "avoid writing boilerplate code" their core business

* The only type of real boilerplate that comes to my mind are things like "start a new React application" but that is a thing you do once per project and it's the reason why boostrappers exist so that you only really have to type "npx create-react-app my-app" once and the boilerplate part is taken care of.

The second point: Some mundane refactoring / translations of pieces of code from one technology to the other can actually be automated by AI (I think it's what you're talking about here, but how often does one really do such tasks?), but... Do you really want to? Automate, it, I mean?

I mean, yes "let AI do the boring staff so that I can concentrate on the most interesting parts" make sense, but it's not something I want to do. Maybe it's because I'm aging, but I don't have it in me to be concentrated on demanding, difficult, tiring tasks 8 hour straight a day. It's not something that I can and it's also something that I don't want to.

I much prefer alternating hard stuff that require 100% of my attention with lighter tasks that I can do while listening to a podcast and steam off in order to rest by brain before going back to a harder task. Honestly I don't think anyone is supposed to be concentrated on demanding stuff all day long all week long. That's the recipe for a burnout.

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2. fragmede ◴[11 Apr 24 21:41 UTC] No.40007110[source]▶
Because we're all working at different companies on different codebases in different languages doing different things, we're all talking abstractly about something we think is the same, when really it isn't. Obvious to every programmer is to make a library if you're copy and pasting code multiple times, but the overhead of that means you don't do that if you only do that once or twice. the problem, in humans, as with LLMs, is the context window. after running create-react-app or whatever, there are a number of steps to do that are repetitive, but because that doesn't happen often enough to fully automate it, you just do it manually. LLMs let you do that level of boilerplate without all the overhead of manually configuring snippets in the IDE.