🎙️ In this episode of "AI and ML Conversations," I sit down with Yordan Madzhunkov, a multi-talented engineer with a fascinating background spanning physics, low-level programming, computer vision, and even politics.

Yordan shares his unique journey from competing in math and physics olympiads to self-teaching programming in Pascal, eventually developing computer vision solutions for radiation measurement labs before the term "computer vision" was even commonly used.

With engineering experience in companies like HyperScience, Chaos Group, and Alcatraz AI, to serving as a member of the Bulgarian Parliament, he brings a rare combination of deep technical expertise and real-world insight into how AI intersects with business, politics, and society.

We explore the realities of low-level optimization, why most AI applications fail economically, the dangers of over-hyped AI integration (like chatbots needlessly replacing functional web forms), and the importance of understanding your actual users before building solutions. Yordan also discusses his friend Georgi Gerganov's groundbreaking GGML library and shares war stories from failed AI trading experiments.

Links

Iavor Botev: https://www.linkedin.com/in/iavorbotev/

Yordan Madzhunkov: https://www.linkedin.com/in/yordanmadzhunkov/

Timestamps:

00:00 – Introduction

02:28 – Studying physics and self-teaching programming

08:02 – Video stabilization and numerical precision errors

11:21 – Explore vs exploit: learning strategies

16:44 – First projects: assembly optimization and computer vision

22:12 – Radiation track counting project (pre-neural network era)

28:26 – Client motivation and Google's research

31:00 – Warehouse automation: when AI doesn't make economic sense

34:26 – The AI hype problem and misapplied solutions

40:03 – Banks forcing AI chatbots on users

43:23 – Future of chatbots and adversarial attacks

46:01 – Career prospects for low-level optimization engineers

50:57 – GGML library: beating GPUs with CPU optimization

55:27 – Economic viability of AI applications

1:02:32 – YOLO model and military applications

1:06:13 – Politics, technology, and decision-making

1:11:36 – AI regulation and enforcement challenges

1:21:23 – Using LLMs in development workflows

1:29:31 – AI trading algorithms: why they (mostly) don't work

1:32:33 – Learning from failure vs traditional education

1:36:18 – Closing thoughts