Forty tabs open. Back-to-back meetings. An AI coding assistant quietly wiping your context every twenty minutes. If that's your day too, you already know what it feels like to walk into a room mentally exhausted and without the one thing you need to make a decision.

I built a Slack bot to fix that for myself. I'll show you what it does and how to build one for yourself.

When the backend can reason, what does that mean for the frontend?
Let's take a look at how we can create UI's that can handle the reasoning capabilities of LLMs and adapt to perform any task.

Don’t let Python developers have all of the fun with LLMs.

This talk is a gentle introduction to Generative AI and Large Language Models for front-end developers looking to build intelligent chatbots and applications. We’ll dive into Langchain.js, an open-source orchestration framework for building AI Applications.

We will start by exploring the basics of Generative AI (GenAI) and Large Language Models (LLMs), how they work, and cover some common pitfalls and how they can be avoided. We’ll take a closer look at vectors, retrievers, and how to build an agent capable of choosing tools to perform specific tasks.

About Adam: Adam Cowley is a dedicated developer with a keen interest in data and graph databases. Serving as the Senior Developer Advocate at Neo4j, Adam produces educational content for GraphAcademy, Neo4j’s free, self-paced, online learning platform. His technical experience spans two decades, developing websites, mobile apps and mixed reality experiences for budding startups to the world’s biggest companies

A walkthrough of a real session where two developers and their AI agent teams collaborated on a complex architectural change — going
from a rough spec to 8 PRs, 4,500 lines of code, and a clean task board in a single day.

The talk covers how we used git worktrees as a shared context surface between two developers' Claude Code agents, spawned specialist
subagents (including a Redis expert that caught two critical production bugs during design review), ran parallel implementation
across multiple branches, and coordinated via a dedicated agent that polled for commits and monitored Slack and CI.

The focus is on the collaboration model — not the technical feature itself — and what the human's role becomes when AI agents can
read, write, test, and coordinate autonomously.