this post was submitted on 27 May 2025
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I use it as a glorified manual. I'll ask it about specific error codes and "how do I" requests. One problem I keep running into is I'll tell it the exact OS version and app version I'm using and it will still give me commands that don't work with that version. Sometimes I'll tell it the commands don't work and restate my parameters and it will loop around to its original response in a logic circle.
At least it doesn't say "Never mind, I figured out the solution" like they do too often in stack exchange.
But when it works, it can save a lot of time.
I wanted to use a new codebase, but the documentation was weak and the examples focused on the fringe features instead of the style of simple use case I wanted. It's a fairly popular project, but one most would set up once and forget about.
So I used an LLM to generate the code and it worked perfectly. I still needed to tweak it a little to fine tune some settings, but those were documented well so it wasn't an issue. The tool saved me a couple hours of searching and fiddling.
Other times it's next to useless, and it takes experience to know which tasks it'll do well at and which it won't. My coworker and I paired on a project, and while they fiddled with the LLM, I searched and I quickly realized we were going down a rabbit hole with no exit.
LLMs are a great tool, but they aren't a panacea. Sometimes I need an LLM, sometimes ViM macros, sed or a language server. Get familiar with a lot of tools and pick the right one for the task.
Same here. I never tried it to write code before but I recently needed to mass convert some image files. I didn't want to use some sketchy free app or pay for one for a single job. So I asked chatgpt to write me some python code to convert from X to Y, convert in place, and do all subdirectories. It worked right out of the box. I was pretty impressed.
May I introduce you to the wonderful world of open source instead?
I am aware of it but it doesn't always exist for my exact needs or I don't need an app for a one time job.
The command line is precisely trying to address this, providing not isolated apps but commands that are flexible and can be stitched together so that most needs are cover. Think of it like Lego blocks made out of text, that do stuff to your files.
If I can help, let me know.
Ty, do you have a site I can read up on this and what is available?
Depends how you learn and what are your goals but I can recommend :
... yet IMHO the real fun comes when you apply YOUR commands to YOUR files.
So yes, please do try in a safe sandbox first then when you want, when you are not rushed by a project start a terminal right there from the comfort of your desktop, then PLAY with your files after doing a backup. Trust me it won't just be fun, it will be truly empowering.
When you get stuck, come back here and do ask.
That's what LLMs largely pull from.
Exactly, hence why being aware of provenance matters.
And LLMs can help find those FOSS projects and fill in the gaps in their documentation.
I'm well aware of the copyright issues here and LLMs can make it easier to violate copyright, whether it's protected by a proprietary or a FOSS license, but that's up to the user of the LLM to decide where their boundaries are (and how much legal risk to accept). If you're generating entire projects, you'll probably have problems, but if you're generating examples on how to accomplish a task with an existing tool, you're probably fine.
LLMs are useful tools, but like any tool they can be misused. FOSS is great, LLMs are great, use both appropriately.
Typically LLMs aren't a problem with FOSS with licensing as pretty much anything and everything is free to use, remix, etc.
What is more of a problem is hallucinations, imagining using the wrong
rm -rf ~/command without understanding the consequence, but arguably that's hard to predict. What will always be a problem though, no matter the model, is how much energy was put into it... so that, in fine, it makes the actual documentation and some issues on StackOverflow slightly more accessible because one can do semantic search rather than full text search. Does one really need to run billion parameters models in the cloud on a remote data center for that?Most licenses require attribution.
This is the real problem. I'm arguing it's a good tool in the hands of someone who knows what they're doing.
Despite the ecological costs?
The ecological costs don't need to be very high. We host our own LLM models at my company on a Mac Mini, which doesn't use a ton of power and works pretty well.
FWIW I did try few LLMs locally too (cf my notes on the topic https://fabien.benetou.fr/Content/SelfHostingArtificialIntelligence ) but AFAIK that is only the top of the iceberg, that LLM has been trained before and that's a significant part of the cost.