斯坦福如何教授AI本位工程

TheAI Engineer YouTubechanneljust crossed one million subscribers. Thank you to everyone who’s watched a talk, sent one to a teammate, or put something they learned into production. For this special, we wanted to sit down with someone who has to answer a question a lot of us are asking at work: what should a software engineer actually learn now?

Mihail Erichas two places to test his answer. He teachesThe Modern Software Developer at Stanfordand leads AI at Monaco, where his colleagues use coding agents to ship software and investigate outages. I asked him to take us through the syllabus, then tell us which parts survived contact with his own engineering team.

This is also meant as a preview for all of you joining us in San Francisco forAIE Codenext week, asrequired knowledgefor all the conversations with the speakers, sponsors and attendees you’re about to have. If you can see this as a LS subscriber and haven’t got a ticket yet, you canuse this code for 30% off(no refunds, sorry)

Last year’s homework takes one prompt

The first run of CS146S had nine assignments and a capstone. In one exercise, students ran an AI code reviewer on a repo and evaluated its feedback. By the next run, Mihail figured students could one-shot a lot of those exercises. When he asked a room of Stanford sophomores and juniors how they coded, everyone he surveyed was already using an agent.

So this time, part of the homework is contributing to an open-source project. The person who maintains the repo reviews the student’s patch. I like what that does to the assignment. You can ask an agent to implement a feature in an afternoon. You still have to find out whether the maintainer wants it, why the code behaves the way it does, and what else your patch might break. That conversation is also how a junior engineer gets to learn from a senior one.

There’s an exercise in the episode you can borrow for your next PR. Geoffrey Litt’s explain-diff skill turns a change into a visual walkthrough, then quizzes you about it. Try it on a patch your agent just wrote. Which question do you have to reopen the code to answer?Watch the discussion.

What Monaco buys, and what it builds

At Monaco, the team evaluated code review products and kept using Devin. Agent workspaces went the other way. A background agent needs somewhere to run the product, reproduce a failure and test the patch. The details depend on how Monaco’s cloud is set up, so the team built its own workspaces. Mihail’s rule of thumb: “You buy to learn, and you build to keep.”

One result is Hal. An engineer can ask it to investigate something that broke and open a PR. A salesperson can ask the same system how the product works. In one case the useful answer is a change someone can review. In the other, it’s an explanation you can give a customer. Give both people a wall of debugging output and you’ve only served one of them.

The question I kept coming back to was what happens at the other end of the PR. An agent sees the current code. Where does it look to find out why the team rejected this same change six months ago? That takes us fromBasis’s Authority Mapto Mihail’s prediction that open-source repos will become agent-run software factories. It’s a good reason to send a student to a maintainer today. Someone still has to decide which change belongs in the project.