Newest first, grouped by year. Search or filter by topic.
The four core disciplines of my AI engineering framework and the argument behind their weights - why the prompt sits at the bottom of the stack at 8 points, why the specification sits at the apex at 22, and the sentence in my own document that its own table contradicts.
The transformation map between my two frameworks - what each of the seven traditional disciplines became among the AI-era eight, what split, what transferred with its weight intact, what dissolved into sub-disciplines, and the fifth learning-stack discipline that is deliberately not on the list.
The highest-leverage discipline in the stack is writing down what done looks like - what a specification must contain before an agent can execute for hours against it, and what Addy Osmani's 80% problem actually says about the work that remains.
Pick the one closest to the role. Each is two pages and carries the same facts.
Applied AI
Agentic AI & Cloud Engineer · multi-agent systems, RAG and MCP on AWS
Cloud & .NET
C#, .NET 10 and ASP.NET Core microservices on AWS and Azure · Terraform & AWS CDK
Data Engineering
AWS lakehouse · Kinesis, Glue (PySpark), Apache Iceberg, Lake Formation
Everything
All three domains, with the standout project from each