Preface: I do think the nature of the work is changing, now that LLMs are capable code generators. And I'll admit that I started working with LLMs because of FOMO, but also to keep my skills sharp in an evolving market. I'm not 100% certain this will pan out, but I do think that working with LLMs is a skill unto itself, and I feel that I'm much better at it now than I was when I started (in earnest, roughly) a year ago.
Q: What's my setup like? A: I'm using both Claude Code and opencode w/ DeepSeek flash. Recently I've started providing the task context to both simultaneously, and then have one act as a real-time reviewer, and one as implementer. I don't consistently use one or the other for implementer, and I'm still learning their capabilities and "who's good at what." But this allows me to get one model to provide an instantaneous code review to the other, as soon as the one model is done writing the code. I set my permissions for both to ask every time on every action: Read, Write, Bash, etc. I grew up playing turn-based RPGs, so I prefer turn-based battle to real-time.
Q: What kind of work I offload, vs do myself. A: Almost all of the code is written by an LLM, but, since I'm reading and rejecting a lot of code, and basically micro-managing them, I feel that the code that's generated is roughly what I would have written myself. Work that I don't offload is any kind of email or communication, or non-code writing, with the exception of README files, and the prose session summaries I have them write.
Q: How do I make it enjoyable? A: I make it enjoyable by finding new ways to work with the LLMs, and specifically, getting them to produce better quality code. The real-time code review process I outlined above is one method, but I've also found that the LLM will write better code when given a "guiding principles" document.
HTH