software stack

Robert Wills of TNMoC explains the Apollo Guidance Computer hardware and software, then walks through the Apollo 11 landing procedure step by step, including the 1202 alarms.

Casey Muratori's lecture, originally given at the Technical University of Madrid, arguing that Conway's Law is the only viable candidate for a true law of software architecture. He explains how software mimics the organization over time, using examples like Windows' multiple volume controls.

Part one of Capt. Grace Hopper's 1982 NSA lecture on the future of data, hardware, software and people. It includes her humor, her nanosecond demonstration and views on scaling and resisting old ways of doing things.

Casey Muratori's invited lecture on where bad code comes from, rebroadcast from a Twitch stream. He critiques conventional wisdom such as SOLID and Clean Code and argues about how those habits lead to slow, poorly structured software.

Casey Muratori's first refterm lecture lays out philosophies of optimization, distinguishing optimization from non-pessimization, which means avoiding needless work. It uses his fast terminal renderer project to show how fast computers really are.

Casey Muratori's University of Twente guest lecture is a case study in how straightforward code can cut thousands of lines and tens of seconds of runtime down to a few dozen lines running in under a second. It uses concrete examples to show why software is often an order of magnitude slower than it should be.

A special lecture from MIT's Private Pilot Ground School in which Lt. Col. Randy Gordon, a fighter and test pilot, describes F-22 flight controls. Topics include thrust vectoring, the pilot command system and how software handles failures.

Andrej Karpathy's three-hour-plus general-audience deep dive into how ChatGPT-style LLMs are built, covering the full training stack from pretraining to post-training. It also gives mental models for their quirks, like hallucination, and how to use them well.

Bill Dally of NVIDIA gives a University of Washington lecture on trends in deep learning hardware. It covers numeric formats, specialized instructions, energy per operation and co-design of hardware and software.