Teaching in the Age of AI: Learning Goals and the Goals of Learning
Learning #active learning#AI-assisted programming#bootcamps#Claude Code#code review#coding agents#cognitive load#computer science education#curriculum design#developer education#developer tools#Emacs#flipped classroom#internships#Jane Street#Jupyter#language server#learning#learning goals#mastery learning#Merlin#Neovim#OCaml#onboarding#property-based testing#Python notebooks#software testing#teaching programming#text editors#trading#version control#VS Code
Aaron Bauer is a software engineer and one of Jane Street's few developer educators—a role that splits his time between writing code and teaching other people how to write it. Before joining the firm, he taught computer science at Carleton College, including four straight terms online during the pandemic. In this episode, Ron and Aaron discuss what it takes to teach engineering inside a company with its own language, its own version control, and its own editors, and what changes when an LLM can do the exercise for you. Along the way, they consider the underrated power of a live lecture; why the text editor is still the software engineer's home base; using editor telemetry to find out how AI is actually changing developer workflows; and how Jane Street rebuilt its intern curriculum around testing, design, and code review now that producing the code is the easy part.
00:00:00 – Start
00:00:02 – Asteroids, Python, and a teacher on a beach
00:04:05 – Grad school, rendering, and Foldit
00:06:43 – Carleton, COVID, and the flipped classroom
00:10:02 – In defense of the lecture
00:14:54 – Feedback loops and mastery learning
00:21:29 – Inventing the developer educator job
00:26:48 – More and more to teach
00:31:18 – The Editors team, and what a "feature" is
00:34:10 – The editor as home base
00:37:47 – Giant diffs and other problems of scale
00:40:41 – Python notebooks
00:43:30 – Build it ourselves, or push it upstream?
00:48:05 – What agentic AI changes for editors
00:53:27 – Claude Code's learning mode and mental models
00:58:20 – Teaching when the LLM can do the exercise
01:07:26 – Bootcamps, teach-ins, and a course catalog
01:16:15 – What are the learning goals?
01:25:10 – Rebuilding the intern curriculum for AI
For more episodes, visit: https://signalsandthreads.com/
00:00:00 – Start
00:00:02 – Asteroids, Python, and a teacher on a beach
00:04:05 – Grad school, rendering, and Foldit
00:06:43 – Carleton, COVID, and the flipped classroom
00:10:02 – In defense of the lecture
00:14:54 – Feedback loops and mastery learning
00:21:29 – Inventing the developer educator job
00:26:48 – More and more to teach
00:31:18 – The Editors team, and what a "feature" is
00:34:10 – The editor as home base
00:37:47 – Giant diffs and other problems of scale
00:40:41 – Python notebooks
00:43:30 – Build it ourselves, or push it upstream?
00:48:05 – What agentic AI changes for editors
00:53:27 – Claude Code's learning mode and mental models
00:58:20 – Teaching when the LLM can do the exercise
01:07:26 – Bootcamps, teach-ins, and a course catalog
01:16:15 – What are the learning goals?
01:25:10 – Rebuilding the intern curriculum for AI
For more episodes, visit: https://signalsandthreads.com/