Warks.Dev

## Details

🎉 Join us for the Warks.Dev #6-

Sponsored by Jump24: https://jump24.co.uk/

We are excited to announce the 6th event of Warks.Dev and the last of the year!

Warks.Dev is a Tech/Dev event for everyone to connect with fellow developers, enjoy insightful talks, and engage in networking.

Event Highlights:
🎟️ Free Entry: All our events are free to attend.
🍕 Complimentary Pizza: Enjoy free pizza during the break.
🍺 Bar Open: Drinks available for purchase.

🕒 Event Schedule:
6:30 PM - 7:15 PM: Arrival and Networking
Arrive, grab a drink and network with like-minded devs in the local area. If it's your first time and you are arriving alone feel free to seek out Adam (the tall ginger guy) and we will help introduce you to some friendly faces.

7:15 PM - 7:45 PM: Featured Talk by John Hartnup- Senior Software Engineer@ Wealth Wizards
Talk Title: "What's the F****ing Point?"

Almost every language's guide warns us that `0.1 + 0.2` doesn't equal `0.3. But when did you last think about the fundamental reasons for this? And what can you do about it?

This presentation tackles floating point representation - that perennial source of gotchas that even seasoned developers occasionally stumble over. We'll examine the mathematical foundations, implementation details, and practical implications of how computers represent and manipulate decimal numbers.

Whether you're a veteran programmer who's encountered floating point quirks throughout your career, or a developer starting their journey, this talk offers valuable insights. We'll cover the basics of the IEEE 754 standard, precision limitations, and strategies for avoiding or mitigating the imprecision inherent in floats.

7:45 PM - 8:15 PM: Break and Pizza

8:15 PM - 9:00PM: Featured Talk by Oziri Emeka- Senior Developer
Talk Title: "The Hidden Realities of Building an In-House AI/ML Models, Skills, Struggles, and the Future of Federated Learning"

I’ll be speaking on AI and machine learning, focusing first on what it takes to train and build an in‑house AI model. Drawing from my personal experience, I’ll walk through the practical realities such as resource constraints, technical challenges, and the skills needed, as well as how AI/ML development intersects with academic research.

Alongside this, I’ll discuss how lightweight, on-device learning techniques fit into the larger picture of AI/ML federation, especially in the context of privacy-preserving machine learning. I’ll explain the mechanics of building models that avoid transmitting raw data, instead sharing encoded parameters or gradient arrays. This approach makes it significantly harder for external actors to intercept or misuse sensitive business or personal information.