When AI Writes the Code

Every line of code in our new project, swamp, is generated by an LLM operating within strict design guidelines we've crafted and maintained. We don't accept pull requests but we happily accept contributions. If you want to contribute, you open an issue, we discuss the problem, refine the design together and let the AI build it. This keeps the supply chain intact and trustworthy. That's not a gimmick, it's the thesis made real.

This talk is about what it actually looks like to run an engineering team this way — the discipline it requires, the design guidelines that make it work, and what we learned building swamp: an AI-native automation CLI for operations teams.What we've learned is that the quality of what the AI produces is a direct reflection of how well you can express what you want. Vague guidelines produce vague code but sharp constraints produce good code. That's true for swamp, and it's true for how we build swamp.

We'll cover:

• Why we made the decision to stop writing code and what changed when we did
• How design guidelines replace pull requests as the primary contribution mechanism
• What "architecture-first" means in practice when an LLM is your entire engineering team
• How swamp applies those same principles to operations — and why that matters as AI starts driving infrastructure decisions
• How AI gives you back the work you got into this industry to do — systems design, not syntax

The future of software isn't humans writing less code, it's humans getting better at expressing what they want. We're living that experiment in the open, come see what we've learned!