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

Everyone is obsessed with autonomous agents right now, but you don't always need one. Often, the better approach is to use AI to generate structured data up front, then let standard code handle the rest. Keep it simple: use AI to build the setup, and run simple logic at runtime.

To prove it, I built an AI movement coach that breaks down videos (like a tennis serve, dance move, or martial arts kata) into step-by-step interactive lessons. A live webcam coach then tracks your form and scores you as you practice.

I built the app using AI tools, but at runtime, it only makes a single call to Claude per new lesson (and even that can be skipped). Once a lesson is created, the system caches it and converts each movement into target joint angles. Plain math handles the scoring in real time. The result is fast, cheap, predictable, and simple.

In this talk, I'll walk through:

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.

I'll share our experience building RorkAI, an AI builder for fullstack React apps without code. It's like Replit meets Webflow/Framer. We're very early stage, but built a fully working prototype in 4 weeks, and we're proud to share our experience.
Questions we'll touch: