Developers don't skip standards because they're careless, they skip them because there are fifteen things to remember and the code was the hard part. The real question isn't which tasks your LLM handles well. It's what's still slipping through ungated.
The machines aren't replacing developers, they're promoting them. You're no longer just writing code; you're managing agents, reviewing output, and setting standards. Three Claudes walk into a codebase, and suddenly you're a manager.
Managing multiple Claude Code accounts across machines gets messy fast. Jean-Claude keeps the useful parts in sync, separates account-specific config, and makes switching between personal, team, and client setups far less painful.
Conductor by Melty Labs makes parallel agent workflows practical by running multiple agents with separate tasks simultaneously. The trade-offs are real but manageable, and this is where development is heading.
A couple of months ago, I was copy-pasting prompts into ChatGPT. Now I'm shipping features, running tests, managing branches, and keeping documentation alive, with a team of agents doing the heavy lifting. All by myself.
Legacy codebases are messy, undocumented, and full of decisions nobody remembers making. But if you can explain it to a new developer, you can onboard an AI and that changes everything.
Software development's feedback loop has compressed from years to minutes, but QA remains the last bottleneck, the one place still dependent on human judgment. AI is rapidly closing that gap, and before the year is out, that final human checkpoint may no longer be necessary.
LLMs are built for conversation, not incantations. The value isn't in your opening message, it's in the back-and-forth: clarifying, correcting, refining. Iteration is cheap. The conversation is the work.
The return of multitasking, but not as we knew it. Running multiple Claude Code instances simultaneously isn't the context-switching productivity killer we've been warned about for years; it's orchestration