Performant Architecture for a Fast Gen AI User Experience

More and more developers are being asked to build Generative AI experiences into their web applications everywhere. However, the user's experience with Generative AI can sometimes be slow and frustrating. It’s been years since we would gladly wait over 10 seconds for a response to anything online. As AI processing gets faster, there are new limiting factors that could be negatively affecting your AI user experience.

In this talk, we will cover different ways to minimise laggy experiences in web applications when interacting with third-party Generative AI tools. We’ll be using a practical demonstration to show how you can reduce latency as we build one of my favourite sci fi gadgets, the babel fish.

Conversational interfaces like chatbots and voice assistants are rapidly becoming the future of how users interact with technology. In this talk, Salmen will share his thoughts and insights about the future of User-Interfaces, and he will show how to build customized, next-gen conversational UIs using NLUX ( https://nlux.ai ) – an easy to use open-source JavaScript library that he created.

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