2026-03-25
Applied AI Signals publishing system
A static publishing system that turns raw AI signals into operator-focused analysis, topic dossiers, and machine-readable outputs.
Problem
Raw AI updates arrive faster than most operators can triage, and link dumps do not build authority or reusable context.
Role
Product direction, information architecture, and workflow design
Approach
Built a repeatable publishing model that turns signals into briefs, groups them into topic dossiers, and exposes structured outputs for search and agent consumption.
Outcomes
- Established a continuously updated Applied AI publication with durable topic structure.
- Converted transient links into operator-grade briefs and recurring topic pages.
- Created a foundation for machine-readable profile and evidence artifacts.
Evidence
This work focused on making Applied AI useful both as a publication and as an operational memory system.
The key decision was to treat the site as a structured signal workflow instead of a static personal homepage. That changed the output from "posts and links" into a reusable system: recurring topic dossiers, stable evidence pages, and machine-readable artifacts that can support discovery, recruiting, and delegation.
The system is intentionally static-first. That keeps the publishing surface deterministic and deployable on GitHub Pages while still allowing future runtime services to consume the same artifacts.