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 AI wave is here, and the industry is already splitting into two: those adapting fast and those falling behind. The gap is widening quickly.
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.
Last week, we deleted 16,000 lines of code, rewrote 11,500, migrated frameworks, ripped out React, and swapped our entire CSS setup with AI. It took less than 6 hours.
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.
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.
Agile was supposed to free us from bureaucracy. Many teams just rebuilt it with better branding. Now, AI-driven development is forcing the uncomfortable question: Were we ever truly agile, or just managing slow feedback loops?