AI evolution involving technological landscape, developers and technology professionals frequently question whether established programming languages will remain relevant amidst the artificial intelligence revolution.

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.

This process, for all its technological sophistication, has remained fundamentally artisanal: slow, painstaking, and limited by the cognitive bandwidth of its human practitioners via this new Quant AI.

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?

An in-depth exploration of experimental quant C and C++ projects, from implementing Ehlers' Cybernetic Oscillator and probing option theory to simulating mean-reverting processes and LPI volatility.