Newest first, grouped by year. Search or filter by topic.
The honest retrospective on two weighted competency frameworks - the blank templates at their centre, the qualities no weight can hold, the situations where a weighted model is the wrong tool, and what I would build differently.
How the framework becomes a form and the form becomes a plan - per-sub-discipline ratings with evidence, a 500-point ceiling, a gap-times-weight priority formula and a 90-day goal loop, walked through with a worked example.
Two research results explain why self-ratings drift - METR's randomised trial of AI-assisted developers and Kruger and Dunning's calibration studies - plus the evidence fields, peer calibration notes and built-in warnings my templates use to correct for both.
Most competency models fail the same way - everything matters equally, so nothing does. The case for a two-level weighting system, and a first look at the two mirrored frameworks this series unpacks.
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