> 1) copilot is a terrific auto complete, and writes tremendous amounts of repetitive boilerplate

I agree. I have it active on VSCode and enjoy it. It has introduced subtle bugs but the souped up autocomplete is nice.

> 2) copilot can help me kickstart writing some complex functions starting from a comment where I tell it what is the input and expected output. Is the implementation always perfect or bug free? No. But in general I just need to review and check rather than come up with the instruction entirely.

I don't find it very useful for anything non trivial. If anything I found it more useful for generating milestones and tasks for a product, than even making a moderately complex input -> output without me having to check it in a way that annoys me.

> 3) copilot chat helps me a lot in those situations where I would've googled to find how to do this or that and spent a lot of time with irrelevant or outdated search results

I find I don't use copilot chat, almost at all. Nowadays I prefer to go to Gemini and throw in my question.

> 4) I have found use cases for LLMs in production. I had lots of unformatted plain text that I wanted to transform in markdown. All I needed to do is to provide few examples and it did everything on its own. No need to implement complex parsers, but make a query to OpenAI with the prompt and context. Few euros per month in OpenAI credits is still insanely cheaper than paying tons of money in writing and maintaining software by humans for that use case.

This is mostly what I'm using it for in this current project. It does it job nicely but it's very far away from replacing myself as a programmer. It's more like a `fn:magic(text) -> nicer text`. This is a good use case. But it's a tool, not a replacement.

> 5) It helps me tremendously when trying to learn new programming languages or remembering some APIs. Writing CSS selectors is actually a very good example. But I don't feed it an entire HTML as you do, I literally tell him "how do I target the odd numbered list elements that are descendants of .foo-bar for this specific media query". Not sure why would you need to feed it an entire HTML.

Because I get random websites with complex markup, and more often than not every page has its unique structure. I can't just say give me `.foo-bar` because `.foo-bar` might not exist. Which is where the manual process comes in. Currently, I'm using hand crafted queries that get fed into GPT / Claude / LLama, but the actual query is what I wanted it to do.

> 6) LLMs have been extremely useful to generate images and icons for an entire frontend application I wrote

I'm very curious how this behaves in different resolutions. There's a reason vector graphics are a thing. I've used it for this purpose before but it doesn't compare to vectorial formats.

> 7) I instruct him to write and think about test cases about my code. And it does and writes the code and tests. Often thinks about test cases I would've never thought of and catches nice bugs.

What is the context size of your code? It works for trivial snippets but as soon as the system is a bit more complex, I find that it becomes irellevant fairly fast.

> The promise of it writing anything but simple boilerplate, I find it ridiculous because there's way too much nuance in our products, business, devices, systems that you need to follow and work on.

> But as a helper? It's terrific.

> I'm 100% sure that people not using these tools are effectively limiting themselves and their productivity.

Totally agree. But I'm not complaining about its usefulness. I'm a paying user of LLM systems. I use them almost every day. They're part of my products. But this particular hype about it replacing ... me. I don't buy. Yet. It could come tomorrow and I'd be happier for it.