Developers find themselves in a world where old maps no longer apply. The rules of software development have fundamentally shifted, and continue shifting at unprecedented speeds.

Drawing from a year of exploration, this talk reveals where AI truly shines - from rapid prototyping to uncovering what we didn't know we didn't know - and hidden dangers that can erode the very trust our software depends on.

Come discover the patterns emerging from this new reality: when to explore freely, when to be careful, and what opens up when the old constraints disappear.

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

Designers and developers work on different timelines—designers look to the future while developers build from what’s already been designed. They speak different languages and follow different processes. How can we bridge these gaps and build a more collaborative development process? In this talk, we will explore the integration of modern techniques and AI-driven tools to enhance design systems, fostering improved communication between cross-functional teams. By leveraging artificial intelligence, we can not only boost productivity and innovation but also create a more harmonious and efficient workflow for both designers and developers

In this session, I’ll explore how AI-powered tooling is transforming large-scale refactoring and codebase migrations, making these complex tasks faster and more efficient. By leveraging tools like Large Language Models (LLMs), static analysis, and refactoring frameworks, we can automate repetitive code transformations, accelerate migration paths, and reduce human error. I’ll share practical examples of how we can migrate legacy systems to modern frameworks, break monolithic architectures into service-based structures, and automate large-scale code changes across thousands of files.

I’ll demonstrate how tools like OpenAI Codex, GitHub Copilot, and fine-tuned models for domain-specific transformations can assist in these processes, while still integrating with traditional migration tools and CI/CD systems. I’ll also cover the importance of developer oversight, highlighting lessons learned from real-world production rollouts and how to balance automation with manual reviews.

By the end of the talk, I want attendees to walk away with a clear understanding of how to use AI to enhance their refactoring and migration workflows, ensuring they can handle large-scale transformations while maintaining code quality, consistency, and governance.