AI Code Optimization by Experimentation, Without Free Bread, with Mina Ilieva

July 2, 2026 · 29 min

About this episode

James Governor interviews Mina Ilieva about AI code optimization and the challenges of software development in 2026.

RedMonk's James Governor sits down with Mina Ilieva, AI engineer at TurinTech AI, to talk about a problem everyone in 2026 recognizes: we're shipping a lot of code slop. Ilieva explains how TurinTech's platform, Artemis, fights back. It began life running a genetic algorithm that scores candidate code against a fitness function, and it now has a newer tool, Discovery, built on an empirical loop where agents form hypotheses, turn them into experiments, and verify every change before a human approves it. The two get into what clients actually optimize for — throughput, latency, memory, runtime — and why none of it is free. Ilieva walks through real wins with Intel's vLLM work, QuantLib, BLAKE3, and a quantized Nemotron model. They also take apart "token maxxing," the habit of burning tokens to look busy, and make the case that verification skill, not raw output, is what keeps engineers employable. This RedMonk conversation is sponsored by TurinTech AI. Show notes: https://redmonk.com/videos/ mina-ilieva / Chapters 00:00 Introduction to AI and TurinTech 02:52 Code Optimization and Its Importance 05:35 Use Cases and Client Engagements 08:55 Token Efficiency and Cost Management 11:46…

People in this episode

Host: James Governor

Guest: Mina Ilieva

Topics covered

AI code optimization
software development
genetic algorithms
client engagement
token efficiency
verification skills

Keywords

AI
code optimization
genetic algorithm
throughput
latency
memory
runtime
token maxxing
verification skill

Mentioned in this episode

Organizations: Intel, QuantLib, BLAKE3, Nemotron

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