Topics
Six pillars of building AI systems that hold up in production — and the writing under each.
Thoughts
Logic, reasoning and the working-out behind a decision. These are experiences from real builds and how-tos worth sharing.
Evaluation & quality of AI
Metrics whose assumptions match the problem and assuring quality of the model. Doing the trajectory-level evaluation for agents. While focussing on the underlying data quality
Context engineering
Getting the right information in front of the model at the right moment, retrieval, grounding and knowledge graphs that turns scattered enterprise data into a queryable source of truth.
Integration
Wiring AI into the systems around it including use of tools, APIs, MCP and data pipelines. How does the demo scale?
Open-source & open-weight AI
Building on open models and open-source tooling. Control over cost, privacy and how a system actually behaves transparently.
Looking ahead
Where this is heading, which trends are worth taking seriously, the ones that aren’t and how the models and the tooling around them are evolving.