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Collaborative AI Engineering: One Dev, Two Dozen Agents, Zero Alignment — Maggie Appleton, GitHub

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AI Engineer· published 2026-04-26· 0:17:42· en· indexed 2026-08-10 19:49

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What was stored

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Each pipeline stage, its state and the model that produced it
stage	state	model	started	took
`fetch`	done	—	2026-08-10 11:46	1m 41s
`stt`	done	—	2026-08-10 11:48	20s
`chunk`	done	—	2026-08-10 11:48	0s
`text_embed`	done	—	2026-08-10 19:49	0s
`keyframe`	done	—	2026-08-10 11:48	1m 55s
`ocr`	done	—	2026-08-10 11:50	39s
`frame_embed`	done	—	2026-08-10 19:49	11s

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Transcript

220 cues· 3,421 words· 18,407 chars

chunk 0 · 0:14–0:55 · 135 words · 737 chars
0:14 Okay, are we all good?
0:16 Right.
0:17 So yes, this talk is called One Developer, Two Dozen Agents, Zero Alignment.
0:21 This is the case for why we need collaborative AI engineering.
0:25 So first, a very quick intro.
0:27 I'm Maggie.
0:27 I work at GitHub as a staff research engineer.
0:30 At least that's my title.
0:31 I'm actually a designer back when that was like a separate thing to engineer.
0:35 And next is the labs team within GitHub.
0:37 So we work on kind of more experimental risky bets than the rest of the organization.
0:41 We like to call it the department of fuck around and find out.
chunk 1 · 0:44–1:28 · 135 words · 731 chars
0:44 And like everyone else, we are of course trying to shape new developer agentic tools.
0:50 So I think this is what many people think peak developer productivity looks like right now, right?
0:55 This is like a wall of terminal-based coding agents all running in parallel on one person's machine.
1:03 I like to call this the one man, two dozen clods theory of the future.
1:07 So the promise that we're given here is that one person with a fleet of agents will do the work of an entire team of developers.
chunk 2 · 1:16–1:57 · 120 words · 712 chars
1:16 The main problem with this dream is it assumes that software is made by one person.
1:20 All of these tools are single player interfaces and they focus on scaling up the work of the individual.
1:26 But there is limited value in scaling up one individual.
1:29 Because software is not made by one person in a vacuum.
1:33 It is a team sport and everyone building it needs to agree on what they're building and why.
1:38 Believing individual productivity leads to great software is nine women make a baby in one month logic.
chunk 3 · 1:46–2:28 · 129 words · 749 chars
1:46 More individual output doesn't solve problems that require communication and coordination, it makes them worse.
1:53 And implementation is rapidly becoming a solved problem, right?
1:56 Probably everyone here believes that.
1:58 Writing code is now fast, it's getting cheaper, and quality is going up and to the right.
2:03 The hard question is no longer how to build it, it's should we build it?
2:08 Agreeing on what to build is the new bottleneck.
2:11 So everyone on your team needs to be involved in asking, are we making the right thing?
2:15 Are we spending our energy in the right place?
chunk 4 · 2:17–3:00 · 123 words · 703 chars
2:17 And how do we have the most impact?
2:20 When production is cheap, opportunity cost becomes the real cost.
2:25 You can't build everything and whatever you pick comes at the cost of everything else.
2:30 Anyone who ships software on a team knows that this isn't a new problem.
2:34 Alignment has always been a bottleneck, but agents have made the cost of not being aligned as a team much, much higher.
2:42 What makes it worse is that all our coordination tools are still from another era.
2:47 So GitHub, Slack, Jira, Linear, and the like, as they currently stand, are not designed for the agentic development world.
chunk 5 · 2:55–3:35 · 136 words · 744 chars
2:55 We are funneling masses of agentic outputs into platforms that were built for an outdated way of building software.
3:03 I work at GitHub, so that might sound heretical for me to say, but I promise it's not controversial.
3:09 There are very few people internally who believe that the PR and the issue are the future of software development, and there are lots of us inside the machine trying to explore what comes next.
3:18 So this is how the development process used to look.
3:21 We had a planning phase, a building phase, and a review phase.
3:24 And we had all of these touch points of alignment along the way.
chunk 6 · 3:28–4:12 · 147 words · 844 chars
3:28 And it was slow enough that we had time for conversations in Slack and Zoom meetings, comments on issues and draft PRs so you could discuss the details.
3:35 And everyone could give their two cents and get advice from expertise across your team and seniors and catch mistakes and course correct if things were going wrong.
3:44 But by the time the code was reviewed and merged, the whole team had seen the work happening, and they were roughly on the same page.
3:52 But that implementation window has now collapsed.
3:54 And because implementation is no longer as expensive and time consuming, we think we don't need to plan as much.
chunk 7 · 4:01–4:42 · 136 words · 742 chars
4:01 So most of those early touch points actually disappear.

Collaborative AI Engineering: One Dev, Two Dozen Agents, Zero Alignment — Maggie Appleton, GitHub

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What was stored

Provenance

Frames, and what the machine read

Transcript