Turning Problem-Explaining into Solutions
Explaining a problem to your manager is only half a solution. How to complete the circle, identify solutions, and show cost, benefits and delivery timelines.
Why Agentic Coding will Always Introduce Errors
LLM-centred agentic systems will always make mistakes, just like human engineers. Human-authored tests are all that can stop an agentic quality drop.
There is no such thing as Technical Debt
Most technical debt is not debt at all. It is the unavoidable cost of changing yesterday’s decisions to support today.
Bias, Metrics and Technical Debt
Technical debt is often ignored due to bias; using clear metrics turns it into a measurable business risk, improving predictability, reducing fragility and preventing future delivery slowdown.
Software Delivery Constraints and Pipeline Throughput
Modern software delivery is not just coding. It is a seven‑stage, 32‑task pipeline. Speed gains from AI only matter when the pipeline is understood and has capacity end-to-end.
Measuring Reliability in the Age of AI
In a delivery system reshaped by AI, metrics are the only defence against blind risk
The Cost Curve of Unchecked LLM Context
LLM workflows need context, but unmanaged context growth becomes expensive and degrades results. This piece outlines how to keep usage controlled and effective.
Hiring in an AI World
AI automates code generation. Evaluate engineers on judgement, failure reasoning, and system awareness.
When Urgency is High but Progress is Slow
A clear view of why leaders feel rising ambiguity and how structured judgement restores clarity without leadership abstractions.
Before You Adopt AI in Engineering, Answer These Five Questions
Most organisations think they are maturing in AI, but their workflows tell a different story. These five questions give engineering leaders a clear, stage‑aligned way to understand their real maturity and scale AI safely.