code analysis

A short !!Con 2016 talk by blind developer Sina Bahram on how he uses a computer and writes code with a screen reader at about 1,000 words per minute. He plays how common interfaces sound and challenges the audience to go a day using only the keyboard.

A reverse engineer walks through fixing a ten-year-old bug in Guitar Hero III without access to its source code, using a disassembler and decompiler. It is technical but explained step by step.

In an 11-minute TED talk, Ralph Langner explains how his team reverse-engineered the Stuxnet worm and worked out that it targeted industrial control systems, with a covert state origin. It is a compact look at cyber-forensics.

Andrew Sorensen's OSCON 2014 keynote in which he live-codes generative music on stage using his Impromptu environment while narrating his computational and musical choices. It imagines concert programmers as a performing art.

Anders Hejlsberg introduces the native port of the TypeScript compiler, written in Go, which promises roughly 10x faster tooling. He demos compiling VS Code and explains where the speedup comes from.

A JuliaCon talk by a NASA Launch Services Program engineer on modeling spacecraft separation dynamics in Julia. He describes replacing a Simulink-based workflow for checking launch vehicle separation requirements with Julia and ModelingToolkit, with large speedups and much shorter code.

A JuliaCon 2016 talk by Shashi Gowda showing an APL implementation in Julia. APL code is translated to Julia and JIT compiled to machine code.

Andrej Karpathy's Databricks talk on Software 2.0, the shift from human-written code to code produced by neural network training. It covers how developer work moves toward curating datasets, the advantages and challenges, and opportunities for new tooling.

Berkeley neuroscientist Jack Gallant shows how fMRI brain mapping can be used to decode what a person sees, with visualizations of brain activity and its implications.

Yegor Denisov-Blanch of Stanford presents a study of nearly 100,000 developers at hundreds of companies, measuring how AI affects productivity from commit data. It finds an average boost of about 20% with wide variance by codebase, task complexity and stack.