Leadership & practice
Software laws: a leadership reading path
A ranked starting point for teams, delivery, and modernization.
STARTING PATHS · FIELD NOTES · REFERENCE
Notes on software, teams, and engineering judgment.
Each note: the idea, how to apply it, and where to read more.
Searches titles, descriptions, and topics.
22 entries
STARTING PATHS
Leadership & practice
APEL: a lifecycle for agent-assisted engineering
A Kanban-based lifecycle for agent-assisted engineering, with a named owner at every phase.
Teams & delivery
Team Topologies: team types, interactions, and load
Give each team a clear purpose, an explicit way of working with other teams, and work it can hold in its head.
Architecture & design
Architecture decisions: preserve the why
Record the context, choice and consequences close to the code, including the shortcuts, and draw the system at the level its reader needs.
Leadership & practice
How I lead: four working principles
Stay close to the code, own the outcome, remove friction, and share what works.
FIELD NOTES
My own work: case studies, essays, and the APEL model.
Empathetic testing: a passing test can still fail a person
Acceptance criteria are where testing starts. A feature is done when it works for the person who needs it.
First-principles thinking: is it true, or just convention?
Most work runs on patterns that worked before. For fundamental change, rebuild the problem from what is actually true.
Making delivery visible: instrument first, then ask for change
I built an internal platform that joined delivery and quality signals, then used what it showed to raise the bar on how work reaches QA.
Modernizing with context: taking monoliths apart, twice
Two employers, one pattern: decompose the monolith behind stable service boundaries, and write down the existing system's rules before changing it.
Putting AI into practice: coaching, context and verification
Buying coding agents is easy. Getting engineers to use them well took hands-on leadership, shared context and verification that never depends on trust.
Strong views, loose grip
Hold a position firmly enough to act and loosely enough to change. Aim for the better answer, even when it isn't yours.
REFERENCE BY TOPIC
AI & engineering
Agentic systems: bound them before you deploy
Add autonomy only where a simpler path can't do the job, and settle its limits, access and owner while the design is cheap to change.
Context engineering: spec, context and harness
A spec says what to change, context says how the system works, and a harness catches what goes wrong. Grow all three from real failures.
Where AI earns its place: fit, then cost
Use a model where a wrong answer is visible and recoverable, then divide what you spend by the answers people actually use.
Architecture & design
Modernize safely, one slice at a time
Capture behavior, change a bounded slice, translate at its edges, and verify before expanding.
Leadership & practice
Growing engineers: 1:1s and learning with AI
Use 1:1s for growth, and design the work so understanding still forms when an agent writes the code.
Observability: investigate beyond the alert
Connect telemetry to follow a failing request and test an explanation.
Strategy, roadmap, execution, and telling it upward
Strategy chooses the direction, the roadmap sequences the bets, and execution delivers. Take it upward by leading with the decision and its stakes.
Verification at AI volume: review by risk
When agents write more code than people can read, automate the checks, test at the lowest useful level, and save human review for the risky changes.
Teams & delivery
DORA and DevEx: measure the pipeline and the people
Speed and stability from the delivery pipeline, and feedback, focus and cognitive load from the people doing the work. Read them together.
Measuring AI impact without fooling yourself
Usage is easy to count and easy to misread. Measure outcomes against a baseline, with quality beside speed.
Value stream mapping: the path to production
Make processing, queues, and handoffs visible from commit to verified production.
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