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.

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.

Now, as large language models (LLMs) like ChatGPT trading demonstrate increasingly sophisticated capabilities, a pressing question emerges: can these AI systems effectively serve as quantitative analysts for trading?

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 announcement, centered around the power of newly released Large Language Models (LLMs), showcases the rapidly evolving capabilities of AI coding in generating complex, high-performance C++ code for institutional-level trading strategies.

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

Does running DeepSeek R1 blog locally guarantee privacy? Or are there hidden risks lurking beneath the surface?

High Flyer and touch the face of God seems so powerful with the rise of what was developed in Deepseek.

The rapid evolution of artificial intelligence has become a global race, with companies and research institutions worldwide vying to creat

LLM model tools have taken the world by storm, demonstrating an uncanny ability to generate human-quality text, translate languages, and eve