The terminal really is sort of the perfect interface for an LLM; I wonder whether this approach will become favored over the custom IDE integrations.
It can even debug my k8s cluster using kubectl commands and check prometheus over the API, how awesome is this?
It's got 7 fingers? Looks fine to me! - AI
The number of times that my manager or coworkers have rejected proposals for technical solutions because I can't make a webpage look halfway decent is too damn high.
I have a designer on my team that adds their polish to the basic HTML and CSS I produce, but first I have to produce it. I really don't care what the front-end ends up looking like, that's for someone else to worry about. So I let the "AI" write the CSS for buttons and other UI elements, which it is good enough at to save me time. Then I hand it off to the designer and they finish the product, make the buttons match the rest of the buttons, fix the padding, whatever. It certainly has accelerated that part of my workflow, and it produces way better looking front-end UI styling than I would care to spend my time on. If I didn't have the designer, the AI-generated CSS would be good enough for most people. But, I wouldn't trust the AI to tell me if a page "looks weird". I have no doubt it would become a nuisance of false-positives, or just not reporting problems that actually exist.
In the end, you either concede control over 'details' and just trust the output or you spend the effort and validate results manually. Not saying either is bad.
Now we can work on our passion projects and everything will just be LLMs talking to LLMs.
By manual labor I specifically mean the kind where you have to mix precision with power, on the fly, in arbitrary terrain, where each task is effectively one-off. So not even making things - everything made at scale will be done in automated factories/workshops. Think constructing and maintaining those factories, in the "crawling down tight pipes with scewdriver in your teeth" sense.
And that's only mid-term; robotics may be lagging behind AI now, but it will eventually catch up.
However with an LLM I'm not so sure. So how will you write a test to validate this is done but also guarantee it doesn't add the email to a blacklist? A whitelist? A list of admin emails? Or the tens of other things you can do with an email within your system?
So the process becomes: Read PR -> Find fundamental issues -> Update prompt to guide agent better -> Re-run agent.
Then your job becomes proof-reading and editing specification documents for changes, reviewing the result of the agent trying to implement that spec, and then iterating on it until it is good enough. This comes from the belief that better, more expensive, agents will usually produce better code than 5 cheaper agents running in parallel with some LLM judge to choose between or combine their outputs.
Do you not want to edit your code after it’s generated?
You get both editors to choose from, vi _and_ emacs! All the man pages you could possibly want _and_ perldocs! Of _course_ as a Perl newbie you'll be able to fall back on gdb for complicated debugging where print statements no longer cut it.
How do you interact with your projects?
The Chat panel in VS Code has seen a lot of polish, can display full HTML including formatting Markdown nicely, has some fancy displays for AI context such as file links, supports hyperlinks everywhere, and has fancy auto-complete popups for things like @ and # and / mentioned "tools"/"agents"/whatever. Other VS Code widgets can show up in the Chat panel, too. The Chat Panel you can dock in either sidebar and/or float as its own window.
A terminal can do most of those things too, with effort and with nothing quite like the native experience of your IDE and its widgets. It seems like a lesser experience than what VS Code already offers, other than you only have one real choice for AI assistant that supports VS Code's Chat panel (though you still have model choice).