RK's Rambling

 Author: Rabimba Karanjai Scope: Problem statement + data methodology + model training (no deployment discussion) Abstract Real‑time coaching in motorsport is a safety‑critical learning problem : a system must map noisy, high‑frequency telemetry to short, actionable guidance that remains physically consistent and avoids hazardous recommendations . This paper proposes a “Split‑Brain” training formulation that separates (i) a semantic coaching target (what action/critique should be expressed) from (ii) a reflexive interface (how actions are represented as compact, verifiable tokens). The approach trains a Small Language Model (SLM) in the Gemma family [1] using QLoRA fine‑tuning [2] , and introduces a telemetry tokenizer plus teacher‑student synthesis pipeline to generate instruction‑action pairs at scale. Core contribution: a reproducible method to convert “ golden lap ” differential tel...

" AI in a Jupyter notebook is safe. AI on a race track—at 150 mph—is a different story." - Ajeet Mirwani When we accepted the High-Velocity AI Field Test , the challenge was clear: Build an AI system that could coach a driver in real-time. But in motorsport, "real-time" doesn't mean a fast web request (500ms). It means sub-50ms . At 150 mph, a half-second of latency puts you 110 feet further down the track, the difference between hitting the apex and hitting the wall. We quickly realized we couldn't rely on the cloud for everything. We needed a "Split-Brain" architecture: Gemini Flash 3.0 in the cloud for high-level strategy, and a fine-tuned Gemma designed for immediate, reflex-based coaching. While we haven't put the model in the driver's seat just yet, we have successfully completed the critical first phase: engineering the "brain" capable of understanding high-speed telemetry. Here is the deep technical dive into ho...