GPT-4's technical report is famously thin on architecture details and thick on capability claims, which makes a clear-headed review essential reading for anyone building on top of it. This walkthrough hits the highlights: a multimodal Transformer that takes images and text and returns text, human-level performance on a stack of professional exams including a top-10-percent bar exam score, and post-training alignment that measurably improves factuality and behavior.

The more interesting story is the infrastructure work OpenAI hints at. Predictable scaling laws let them forecast GPT-4's performance from runs using less than one one-thousandth of the final compute, which is a genuine capability upgrade for anyone planning multi-million-dollar training budgets. For voice-AI and multimodal folks, the vision-plus-text framing is the piece to watch, since the same recipe underlies speech-plus-text foundation models now landing across the industry. The deep-dive walks through what the report actually says, what it deliberately leaves out, and how to read between the lines. A good starting point before you tackle the derivative papers.