Data & Knowledge
2 · ConceptualKnowledge Graphs Explained
Relational databases answer what; knowledge graphs answer how things relate, and relationships are where business meaning lives. For AI systems, the graph is what grounds answers in reality.
Not blog. Notebook.
Notebook.
Searchable, indexable writing with tags for topics, categories for editorial structure, and a technical depth rating from overview to expert. Longer research lives in Papers.
No matches yet. Try different terms.
Infrastructure
3 · TechnicalModular Infrastructure with AI and Terraform
The Terraform MCP server grounds AI assistants in live registry data instead of stale training memory, and it is the clearest working example of a principle: give the machine trusted context.
Architecture
3 · TechnicalThe Architecture, One Piece at a Time · Part 1 of 7
The Architecture: Queue, Workers, Knowledge Graph
The enterprise intelligence system reduces to three pieces of infrastructure and six layers that keep them safe. This is the map for the whole series: what each piece does and why the shape holds.
Workforce
1 · OverviewThe $1.27 Problem
Companies that cut workers for AI spend $1.27 for every dollar they save, and a third are already rehiring for the roles they eliminated. The retraining evidence points the other way, in billions.
Workforce
1 · OverviewThe Automation-Augmentation Paradox
Every AI initiative eventually asks whether to replace people or empower them. Research and the best corporate case studies agree: it is a false choice, and treating it as a real one causes real damage.
Strategy
1 · OverviewThe First Step Is a Meeting, Not a Purchase Order
AI readiness follows a sequence, and each step de-risks the next: assess the data, align the people, isolate the environment, pilot one workflow, measure it, and plan the workforce transition.
Data & Knowledge
2 · ConceptualThe Shared Language Problem
Every department holds private definitions of your core business terms, and an AI system inherits all of them at once, contradictions included. Fixing this is a meeting discipline called ontology.
Strategy
1 · OverviewWhy AI Projects Fail
Most enterprise AI failures trace back to foundations rather than models. Five causes account for most of the losses: unready data, absent leadership, a hesitant workforce, missing controls, and unmeasured costs.
Architecture
4 · EngineerThe Stack Behind This Site
A complete accounting of what cyberd.tech runs on (Astro, AWS, CDK, and a deliberately small dynamic layer) and the reasoning each piece had to survive to earn its place.
AI-Native Web
2 · ConceptualMost of Your Visitors Aren't Human
Automated traffic has overtaken human traffic on the web. Three ways to give machines reliable context about your site: facts in structured data, positions in curated prose, and an interface.
Architecture
3 · TechnicalServerless Is A Content Strategy
Infrastructure choices decide how often you publish, how much you maintain, and how safely you expose dynamic features. For a personal site, serverless is a publishing decision before it is a technical one.
No articles match this technical depth yet.