I know, I know - another AI talk. But hear me out: this one isn't about prompts, it isn't about replacing developers, and ChatGPT won't be mentioned once. It's about what happens when you stop using AI as a fancy autocomplete and start wiring it up with tools, memory, and the ability to orchestrate other agents. We'll look at how Claude skills and plugins actually work, build a small agent pipeline live, and be honest about where it breaks. You might leave with ideas you can actually use on day-to-day bases.

What happens when you need to refactor 377 files in a SvelteKit codebase? Without AI, you retire. With Claude Code, you ship it in 11 hours.

This talk walks through real-world examples of using Claude Code for massive refactoring work that would be soul-crushing to do manually. I'll cover:

How I approach large-scale refactors with Claude (377 files → 364 files → and counting)

Building and activating Claude Code skills reliably (based on my viral post that's getting 4k+ views in 3 days)

The workflow patterns that actually work vs the ones that waste your time

Real examples from production codebases

This isn't about AI replacing developers - it's about using AI to handle the tedious parts so you can focus on the interesting problems.

Format: Technical talk with live demos, code and prompt examples

Description:

The role of the developer is fundamentally changing. We're moving from being executors who write every line of code to becoming orchestrators who conduct AI agents to build complex systems. This talk, based on my essay about transitioning from traditional coding to AI orchestration, shares practical insights from a year of experimenting with multi-agent development workflows. 🔗 Essay linked here: https://pivotech.substack.com/p/from-executor-to-orchestrator-my

Through real code examples and live demonstrations, I'll walk through my evolution from using ChatGPT for learning CS50 concepts to orchestrating Claude, Gemini CLI, and NotebookLM to build complete products. You'll discover the three distinct schools of AI development I've identified through hands-on experimentation: the One-Shot method, the Incremental approach, and my hybrid Layering technique.

I'll share the workflows I use to go from customer discovery sessions to deployed applications, including the mistakes, frustrations, and breakthroughs that shaped my approach. We'll explore the "Legacy Codebase Problem" that emerges from AI-generated code, the "Hyper-specificity Paradox" of detailed prompting, and the new skill set required to become an effective AI orchestrator.

Key Takeaways:

Three proven patterns for AI-assisted development and when to use each

Practical orchestration workflows for complex projects

The emerging skillset of the developer-orchestrator

How to maintain technical depth while leveraging AI efficiency

Real-world pitfalls and how to navigate them

Target Audience: Developers looking to evolve their practice in the age of intelligent agents. Minimal level of AI development experience required e.g. prompting Claude

They hired me to add features. The codebase had less than 1% test coverage. Azure was retiring .NET 8, but upgrading seemed too hard, so they'd waited years. The kind of problems you know you should fix but never do.

Four weeks later: 560 commits, 65% test coverage, 13 database improvements, and yes, we shipped features too. Two programmers, four hours a day, one $17/month Claude subscription.

Here's what nobody tells you about AI: Day one, it took us eight hours to fix one nullable reference. Week four, I did fifty in under an hour with five minutes of my time. We didn't get faster because AI got smarter. We got faster because we built the infrastructure—scripts, CLIs, knowledge docs—that let AI scale what we'd already figured out.

This is a field report on what's actually possible when you stop thinking "AI makes me faster" and start thinking "AI lets me solve problems that used to be too expensive to touch."

Come see how we got a codebase under control.