Splitting the Atom: Lessons From Dividing Our Codebase and Shipping It in a Browser Extension

In this workshop, we discuss the merits of serverless architecture and how it can be applied to the AI space. We'll explore options around building serverless RAG applications for a more lambda-esque approach to AI. Next, we'll get hands on and build a sample CRUD app that allows you to store information and query it using an LLM with Workers AI, Vectorize, D1, and Cloudflare Workers.

Leveraging reactjs to create reusable microfrontends addressing challenges and common pitfalls.

Next.js is a compelling framework that makes many tasks effortless by providing many out-of-the-box solutions. But as soon as our app needs to scale, it is essential to maintain high performance without compromising maintenance and server costs. In this workshop, we will see how to analyze Next.js performances, resources usage, how to scale it, and how to make the right decisions while writing the application architecture.

Join a focused 2-hour session covering MCP's purpose, architecture, hands-on server implementation, and future directions. Designed for developers and system architects aiming to integrate contextual data with ML models effectively. Agenda:- Introduction & Why MCP? Key challenges MCP solves and core benefits.- Architecture Deep Dive: components, interactions, scalability principles. - Building Your Own MCP Server: guided walkthrough with code snippets and best practices; live demo or code review.- Future of MCP Developments: potential enhancements, emerging trends, real-world scenarios.
Key Takeaways:- Clear understanding of MCP's rationale.- Insight into design patterns and scaling considerations.- Practical steps to implement a prototype server.- Awareness of upcoming trends and how to apply MCP in projects. 

Node's concurrent nature is powerful already, but often we need to push work out of the main server for several reasons. In this work, we will explore a few scenarios in which work is cleverly pushed to another Node process to resolve.
Once we use a queue to distribute workloads, we need to identify the nature of the work to be done. For either I/O- or CPU-intensive work, the first is already perfectly covered by a single Node.js process; we will need to tweak the worker setup to match the available resources and throughput.

As React apps move into real-time systems — streaming data, AI workflows, hardware devices — async complexity grows quickly. Reducers and scattered async handlers often work at first, but once concurrency and lifecycle interruptions enter the picture, things start to break in subtle ways.
In this hands-on workshop, we’ll take a fragile async React setup and redesign it into a deterministic, state-machine-driven architecture. The goal isn’t to teach a specific library, but to show how explicit state modeling makes complex systems easier to reason about and more reliable in production.