This is not a story of seamless success for quant trading firms; it's a dispatch from the digital trenches, a chronicle of a grueling two-day battle against degrading model quality, logical drift, and the frustrating opacity of the very tools we’ve come to rely on.

Their discussion transcends a simple interview, evolving into a masterclass for the next generation of quant AI.

The dream of a personal, automated trading system—an AI trading bot-powered alchemist that turns market data into gold—is no longer the exclusive domain of hedge funds and quantitative powerhouses.

Optimization can achieve superior speedups in low-level program asm code, challenging the supremacy of traditional compilers like GCC and heralding a new era for software performance engineering.

The world of quantitative finance and algorithmic trading or quant developer is in a perpetual state of evolution.

The drumbeat of Artificial Intelligence (AI) quant is impossible to ignore, and the world of quantitative finance is listening intently.

A Visual Guide to How Diffusion Models Work for any Quant Researcher: Understanding the Key Intuitions Behind Text-to-Image Generative AI fr

If you're interested in learning more about AI, one way to get started is to build your own AI server.