Black Code Collective

## Arrival info:

• There are two ways to enter the Willard Office Building -- Pennsylvania (across from the Willard Hotel) or F st side (across from Hamilton). We recommend F St entrance.
• Parking garage is beside the entrance on the F St side.

## Description

Modern distributed systems rely on background task queues like RQ, Celery, Temporal, and Akka to handle complex, data-driven workflows. But while these tools manage parallelism and dependencies, they often fall short on clarity, order, and immutability.
In this session, you'll learn practical patterns to tame complexity, debug with confidence, and design systems that are easy to reason about. Ideal for beginner to intermediate engineers, with Python examples and concepts that apply across Java, Node, and Go.

## Bio

Mike Chase is a seasoned software engineer with a background in distributed systems, infrastructure, and Python platforms. With hands-on expertise in task queue frameworks like Celery, Temporal, and Redis-Queu, he specializes in designing systems that are both high-performing and easy to reason about. Mike is also a dynamic speaker and educator, known for making complex backend patterns accessible to developers across Python, Java, Node, and Go ecosystems

## Description

LLMs have changed the landscape of coding drastically since they have been developed. But have you gotten a chance to explore them for a project of your own? You may develop a project or a tool, but can you test out the market for it?

Through this beginner-friendly talk by Aaron Dantly, we'll walk through how to develop a quality landing page for a project idea! So you can get paid from a side project, or test the waters for a business idea you haven't explored yet.

We'll walkthrough how to do launch a landing page at minimal cost, while providing a quick and efficient workflow for developing professional looking pages, how to create multiple pages with a matching theme, and next steps for how to put in the finishing touches.

Come for the learning, leave with a landing page and practical prompt engineering knowledge!

In this talk, Dr. Keita Broadwater, author of Graph Neural Networks in Action, traces the journey of machine learning with graphs—from early heuristic approaches to today’s powerful GNN architectures. We’ll explore why graph-structured data is uniquely valuable, how GNNs emerged as a breakthrough for modeling complex relationships, and where they stand in the current landscape dominated by transformer-based models.

As large language models and foundation models reshape the field, we’ll discuss the evolving role of GNNs and how they complement—or compete with—transformers in tasks like recommendation, drug discovery, and fraud detection. This session offers both historical context and forward-looking insights for anyone interested in the future of machine learning on structured data.