Orchestrate teams of Claude Code sessions

Why is Yegge so.... loud?

Like, who cares? Judging from his blog recount of this it doesn't seem like anybody actually does. He's an unnecessarily loud and enthused engineer inserting himself into AI conversations instead of just playing office politics to join the AI automation effort inside of a big corporation?

"wow he was yelling about agent orchestration in March 2025", I was about 5 months behind him, the company I was working for had its now seemingly obligatory "oh fuck, hackathon" back in August 2025

and we all came to the same conclusions. conferences had everyone having the same conclusion, I went to the local AWS Invent, all the panels from AWS employees and Developer Relations guys were about that

it stands to reason that any company working on foundational models and an agentic coding framework would also have talent thinking about that sooner than the rest of us

so why does Yegge want all of this attention and think its important at all, it seems like it would have been a waste of energy to bother with, like in advance everything should have been able to know that. "Anthropic! what are you doing! listen to meeeehhhh let me innnn!"

doesn't make sense, and gastown's branding is further unhinged goofiness

yeah I can't really play the attribution games on this one, can't really get behind who cares. I'm glad its available in a more benign format now

It also depends upon how you manage it

My process, which probably wouldn't work with concurrent agents because I'm keeping an eye on it, is basically:

- "Read these files and write some documentation on how they work - put the documentation in the docs folder" (putting relevant files into the context and giving it something to refer to later on)

- "We need to make change X, give me some options on how to do it" (making it plan based on that context)

- "I like option 2 - but we also need to take account of Y - look at these other files and give me some more options" (make sure it hasn't missed anything important)

- "Revised option 4 is great - write a detailed to-do list in the docs/tasks folder" (I choose the actual design, instead of blindly accepting what it proposes)

- I read the to-do list and get it rewritten if there's anything I'm not happy with

- I clear the context window

- "Read the document in the docs folder and then this to-do list in the docs/tasks folder - then start on phase 1"

- I watch what it's doing and stop if it goes off on one (rare, because the context window should be almost empty)

- Once done, I give the git diffs a quick review - mainly the tests to make sure it's checking the right things

- Then I give it feedback and ask it to fix the bits I'm not happy with

- Finally commit, clear context and repeat until all phases are done

Most of the time this works really well.

Yesterday I gave it a deep task, that touched many aspects of the app. This was a Rails app with a comprehensive test suite - so it had lots of example code to read, plus it could give itself definite end points (they often don't know when to stop). I estimated it would take me 3-4 days for me complete the feature by hand. It made a right mess of the UI but it completed the task in about 6 hours, and I spent another 2 hours tidying it up and making it consistent with the visuals elsewhere (the logic and back-end code was fine).

So either my original estimate is way off, or it has saved me a good amount of time there.

I strongly believe we will need both agentic and deterministic approaches. Agentic to catch edge cases & the like, deterministic as those problems (along with the simpler ones early on) are continually turned into hard coded solutions to the maximum extent possible.

Ideally you could eventually remove the agentic supervisor. But for some cases you would want to keep it around, or at least a smaller model which suffices.

It's clear what's gonna play out. Chinese open source labs are slowly closing the gap, and as American frontier labs hit diminishing return on various tasks, the Chinese models are going to be good enough for the vast majority of use cases. This is going to strip American labs ability to do monopoly plays, and force them into open behavior.

The only place frontier labs will be able to profit take is niche models for specific purposes where they can control who has access to traces tightly. Any general pupose LLM with highly available traces is gonna get distilled down instantly.

It’s a skill issue, which means you can’t fire any of your highly skilled employees, which means it has the same value as any other business organization tool like Jira or Microsoft Excel, approximately $10-20 per user per month.

Autodesk Fusion for manufacturing costs less than Claude Max and you literally can’t do your job without it.

So Autodesk takes you from 0 to 100% productivity for under $200 a month and companies are expected to pay $200+ to gain an extra 10-20%?

That math isn’t how it works with any other business logic tools.