Markdown source

Surviving Your Own Velocity: How VS Code Ships Weekly with 40 People

Conference Context

Session Description

A ~40-person team ships VS Code weekly to millions of users. Models got good enough to lean on, and leaning in is exactly what broke our process. This talk is the part most AI talks skip: what you have to rebuild after agents start working. We had to scale three things at once: how fast we ship, how we hold quality, and how fast we learn, and each one we fixed revealed the next. I'll walk through the harnesses, evals, and self-healing systems that keep velocity from becoming regression, and the patterns you can steal.

Media Evidence

Real World Development with GitHub Copilot and VS Code — Harald Kirschner, Christopher Harrison (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

Surviving Your Own Velocity: How VS Code Ships Weekly with 40 People ## Conference Context - Date/time: 2026-06-29 · 3:20pm-3:40pm - Track/room: Track M · Track M - Speaker(s): Harald Kirschner - Session type/status: sponsor · confirmed - Track: Track M - Room: Track M - Session type: sponsor - Status: confirmed ## Session Description A ~40-person team ships VS Code weekly to millions of users. Models got good enough to lean on, and leaning in is exactly what broke our process. This talk is the part most AI talks skip: what you have to rebuild after agents start working. We had to scale three things at once: how fast we ship, how we hold quality, and how fast we learn, and each one we fixed revealed the next.

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.