Recently I have been building Menagerie a tool for dispatching many Claude Code agents to complete tasks in parallel while maintaining visibility of progress, status and outputs. agents can show you what they are doing to the web app via playwright and ask for help when the need it.

I think the future of engineering will be based on creating robust agentic systems and amid all the doom and gloom that can exist in the industry these days I want to paint a positive picture of the exciting engineering problems that actually lay ahead of us.

Along with this I'll describe what it has taken me to deploy a system like this, e.g. sandboxing, cursor compatibility, terminals over the network and also how i've made it fun to use (for example you can give your agent a cute avatar, and if you so choose also a top hat)

What happens when you need to refactor 377 files in a SvelteKit codebase? Without AI, you retire. With Claude Code, you ship it in 11 hours.

This talk walks through real-world examples of using Claude Code for massive refactoring work that would be soul-crushing to do manually. I'll cover:

How I approach large-scale refactors with Claude (377 files → 364 files → and counting)

Building and activating Claude Code skills reliably (based on my viral post that's getting 4k+ views in 3 days)

The workflow patterns that actually work vs the ones that waste your time

Real examples from production codebases

This isn't about AI replacing developers - it's about using AI to handle the tedious parts so you can focus on the interesting problems.

Augmented Coding is more than just asking an AI to help you write code.

You externalize your decision points, heuristics, and workflows into artifacts the agent can follow. The agent becomes your mentee.

As you do that, you change your focus from low-level details to high-level intent and methodology. Decisions that were unconscious become explicit.

In this talk, I’ll share my story of how I learned to teach AI a Software Crafter’s approach to development.

The agent follows a rigorous TDD process with 121 unique nodes and 131 directed edges that can run autonomously for hours, creating dozens of commits.

It covers advanced context engineering and techniques to increase autonomy while aiming to maintain quality.

And I will tell real stories of AI-enabled breakthroughs in real-world product development.

They hired me to add features. The codebase had less than 1% test coverage. Azure was retiring .NET 8, but upgrading seemed too hard, so they'd waited years. The kind of problems you know you should fix but never do.

Four weeks later: 560 commits, 65% test coverage, 13 database improvements, and yes, we shipped features too. Two programmers, four hours a day, one $17/month Claude subscription.

Here's what nobody tells you about AI: Day one, it took us eight hours to fix one nullable reference. Week four, I did fifty in under an hour with five minutes of my time. We didn't get faster because AI got smarter. We got faster because we built the infrastructure—scripts, CLIs, knowledge docs—that let AI scale what we'd already figured out.

This is a field report on what's actually possible when you stop thinking "AI makes me faster" and start thinking "AI lets me solve problems that used to be too expensive to touch."

Come see how we got a codebase under control.