8 items across 2 sections
Operational case studies are engineering records — not success summaries. This document defines the exact structure, frontmatter schema, evidence requirements, and narrative pattern used in the AI Execution Lab case study archive.
Execution density is the concentration of documented real operational events — deployments, failures, fixes, decisions — per unit of time in a software practice. High execution density is the primary long-term moat for AI engineering platforms.
Operational evidence is execution-derived proof that a specific technical decision, fix, or workflow actually worked in a real production context. It distinguishes documented execution from theoretical documentation.
Design and template for long-form operational case studies — evidence standards, timeline structure, outcome measurement, before/after analysis, and the components that make case studies high-authority proof.
Naming conventions, metadata structure, storage organization, integration patterns, and quality standards for operational evidence on AI Execution Lab.
Design specification for the evidence layer — how screenshots, deployment logs, command histories, debugging records, and operational timelines integrate into tracks, failures, playbooks, case studies, and labs.
How to record, name, store, and publish execution media — screen recordings, walkthrough videos, architecture diagrams, and debug replays.
Full production audit, metadata fixes across all section index pages, accessibility improvements, and operational documentation sprint.