A clear, practical guide to RLHF—how human preferences train models, the pipeline, pitfalls, and modern variants like DPO and RLAIF.
A clear, visual walkthrough of Transformer architecture—from tokens and positions to multi-head attention, residuals, and FFNs.
Step-by-step QLoRA guide with concepts, setup, memory tips, and code to fine-tune LLMs using 4-bit quantization on a single GPU.
A practical blueprint for deploying autonomous AI agents to production—architecture, safety, reliability, evals, cost control, and ops patterns.
How AI code review tools work, key features, pitfalls, and a step-by-step plan to pilot them in your engineering workflow.
A practical, end-to-end guide to reducing AI hallucinations with data, training, retrieval, decoding, and verification techniques.
