It's amazing this is still an opinion in 2025. I now ask devs how they use AI as part of their workflows when I interview. It's a standard skill I expect my guys to have.
Let people work how they want. I wouldn’t not hire someone on the basis of them not using a language server.
The creator of the Odin language famously doesn’t use one. He’s says that he, specifically, is faster without one.
They didn’t say how heavily they weight the question.
(All that said I expect that, soon, experience with the appropriate LLM tooling will be as important as having experience with the language your system is implemented in.)
I can’t use perforce while my company is on git.
But if I do or do not use an LLM to assist me while coding, my team is unaffected.
If someone liked jetbrains, but your team used neovim, would you force them to use neovim?
Though nobody should care if I edited my text files with neovim as long as I still used the same toolchain as everyone else.
If you're "working the way you want to" ie still handrolling all your code, you're going to find my expectations unrealistic, and that is certainly not fair to you.
Recently, I shared a code base with a junior dev and she was surprised with the speed and sophistication of the code. The LLM did 80+% of the "coding".
What was telling was as she was grokking the code (for helping the ~20%), she was surprised at the quality of the code - her use of the LLM did not yield code of similar quality.
I find that the more domain awareness one brings to the table, the better the output is. Basically the clearer one's vision of the end-state, the better the output.
One other positive side-effect of using "LLMs as a junior-dev" for me has been that my ambitions are greater. I want it all - better code, more sophisticated capabilities even for relatively not-important projects, documentation, tests, debug-ability. And once the basic structure is in place, many a time it is trivial to get the rest.
It's never 100%, but even with 80+%, I am faster than ever before, deliver better quality code, and can switch domains multiple times a week and never feel drained.
Sharing best AI hacks within a team will have the same effect as code-reviews do in ensuring consistency. Perhaps an "LLM chat review", especially when something particularly novel was accomplished!
If I was in an environment that didn't allow hosted API models I'd absolutely be looking into the various Llama 3 models or Qwen2.5-Coder-32B.
files-to-prompt . -e py -e md -c | pbcopy
Then I paste that into the Claude web interface or Google's AI Studio if it's too long for Claude and ask questions there.
Sometimes I'll pipe it straight into my own LLM CLI tool and ask questions that way:
files-to-prompt . -e py -e md -c | \
llm -m gemini-2.0-flash-exp 'which files handle JWT verification?'
llm chat -c
I haven't actually done many experiments with long context local models - I tend to hit the hosted API models for that kind of thing.