Series · 7 articles

Spine

How a delivery-governance tool for AI-assisted work came out of a real operating problem — keeping the reasoning behind decisions from disappearing between sessions, tools and handovers.

Related project: Spine

Part 016 min read

Project Memory Is Not Chat History

A searchable transcript of every conversation isn't the same as a project that remembers what it decided and why. The difference between chat history and project memory, and why conflating them cost us more than once.

Part 026 min read

From Requirement to Release: Keeping the Chain Intact

A requirement, a decision, a work package, an implementation, a release. Six months later, can you still walk the whole chain backward? Most teams can't — and don't notice until it matters.

Part 036 min read

The Handover Is Where AI-Assisted Work Breaks

Human to AI, AI to human, session to session — every handover is a place where context can silently drop. What a minimum viable handover actually needs to carry, learned from watching plenty of bad ones.

Part 046 min read

Why Every AI Agent Should Read the Same Project Memory

Claude, Codex, Gemini, Cursor — each one builds its own private understanding of a project unless something makes them read from the same source. What that costs, and what a shared, tool-neutral memory actually requires.

Part 056 min read

Protecting the Core Without Freezing the Project

Constitutions, templates and core rules keep a project coherent — until they calcify into rules nobody can change even when the project has clearly outgrown them. Finding the line between governance and paralysis.

Part 066 min read

The Only Metric That Matters: Are We Re-Explaining Less?

Most metrics for a knowledge tool measure activity — searches run, entries created. The one that actually predicts whether it's working is quieter: are we explaining the same thing to the same tool less often than we used to?

Part 073 min read

The Decision That Disappeared

AI tools helped our team move faster. They also made it easier to lose the reason behind a decision. What we changed, and what it taught me about working with AI.