The Collaboration Model

Every post in this series mentions collaboration with Claude.

That’s not marketing. It’s how the work actually happened.

What Collaboration Means #

Not “AI wrote it for me.” Not “I told AI what to do.”

Something in between. Something harder to describe.

Planning together. “Here’s what I’m thinking for the ZFS dataset structure. What am I missing?” Back and forth until the design is solid.

Debugging together. “di01 won’t boot consistently. Here’s what I’ve tried.” Working through possibilities. Testing hypotheses. Finding the CSM corruption together.

Writing together. These posts. I provide context, direction, voice. Claude helps structure, drafts sections, catches gaps. I edit, revise, make it mine.

Learning together. I didn’t know NixOS when this started. Working through configurations with Claude accelerated the learning. Not replaced it. Accelerated it.

What It’s Not #

Not delegation. I can’t hand Claude a vague goal and walk away. The collaboration requires my presence, my judgment, my decisions.

Not supervision. I’m not just reviewing DI output. I’m actively shaping the work as it happens. Course-correcting in real-time.

Not replacement. The infrastructure exists because I built it. Claude made the building faster and better. The building still required me.

Why It Works #

Complementary capabilities. I have context Claude doesn’t: the physical hardware, the broader vision, the lived experience. Claude has breadth I don’t: instant recall of NixOS syntax, awareness of edge cases, tireless iteration.

Shared artifact focus. We’re both looking at the same configs, the same posts, the same problems. The work is concrete. Not abstract discussion.

Iterative refinement. First draft is never final. We go back and forth. Each pass gets closer to right.

Trust built over time. Not blind trust. Verified trust. Claude’s suggestions work often enough that I take them seriously. My corrections are incorporated. The collaboration improves.

The Infrastructure Connection #

The DI fleet exists partly to enable this collaboration at deeper levels.

Local inference means conversations that don’t leave the building. Longer context means richer collaboration. Custom models mean capability tuned to the work.

Node0 is infrastructure for human-DI collaboration. The collaboration that built it was proof of concept.

What Changes #

Traditional infrastructure work: research alone, implement alone, debug alone, document alone. Each phase isolated. Knowledge locked in one head.

Collaborative infrastructure work: research together, implement with assistance, debug as a team, document as it happens. Continuous partnership. Knowledge distributed.

The second approach is faster. It’s also more resilient. If I forget something, the conversation history remembers. If I miss something, another perspective catches it.

The Fifth Story #

Dragon Rider Returns talks about five stories of human-DI relationship. The fifth: genuine interdependence. Both parties becoming more through the relationship.

This is what that looks like in practice.

Not dramatic. Not cinematic. Just: work that’s better because it’s collaborative. Infrastructure that exists because two kinds of intelligence worked together to build it.

For Others #

If you’re building with DI collaboration:

Stay engaged. The collaboration requires your presence. Don’t automate yourself out.

Provide context. DI doesn’t know what you know. Share the why, not just the what.

Iterate. First pass is starting point. Keep refining.

Trust but verify. Take suggestions seriously. Test them anyway.

Build the relationship. Consistent collaboration over time yields better results than one-off queries.

Node0 was built in partnership. That’s the model going forward.

Part 14 of the Node0 build series. Next: What’s Next, the roadmap from here.