Featured perspectiveDelivery and quality
The Hidden Cost of AI-Assisted Development.
Why AI-assisted development can increase review burden, rework, and governance exposure even when code output rises.
Read the perspectiveResources / Insights
Think clearly about
AI engineering.
Perspectives on engineering outcomes, codebase investment, and the operating practices that make AI useful.
More perspectives
Economics. Practices. Evidence.
Evidence and decisions
Engineering Is Not Under-Instrumented. It Is Under-Modeled.
Why executive teams still struggle to manage engineering outcomes despite having more tooling and telemetry than ever.
Read the perspectiveLeadership alignment
The CTO-CFO Scorecard for Modern Engineering.
A shared scorecard for CTOs and CFOs to manage engineering cost, delivery risk, AI leverage, and governance.
Read the perspectiveEngineering practice
Ravi Singh writes about the decisions, habits, and teamwork behind software. These six perspectives bring his original essays into the Binomial conversation.
Measurement
The Three Velocities of Effective AI-Assisted Engineering
Evaluate AI-assisted engineering through delivery flow, codebase integrity, and team practices, with evidence and limits for each.
Originally published
Read the perspectivePlanning and specification
Specify AI-Assisted Work Before You Ask for More Code
Adapt the RACES + S prompt framework into an engineering work brief with explicit context, constraints, stakes, and acceptance checks.
Originally published
Read the perspectiveEngineering practice
AI-Assisted Engineering Still Depends on Human Judgment
Turn fast AI-generated output into maintainable software through explanation, failure-path testing, and engineering ownership.
Originally published
Read the perspectiveOperating Model
Choose AI Tools for Repeatable Engineering Workflows
Set useful AI tooling defaults, run bounded experiments, and evaluate switching decisions against repeatable engineering work.
Originally published
Read the perspectiveModernization
Using AI to Reconcile a Fragmented Legacy Codebase
A practical approach to AI-assisted legacy integration: map divergent behavior, preserve customer requirements, and validate each reconciliation step.
Originally published
Read the perspectiveCollaboration
AI Engineering Needs Shared Context and Clear Ownership
How engineering teams can share AI working context through explicit decisions, ownership, handoffs, and reviewable evidence.
Originally published
Read the perspectiveInside the program
The people and routines behind the Learning Center.
Learning center
Build your AI engineering practice, step by step.
Start with one real ticket. Follow the public chapters through Prepare, Execute, Guardrails, Trust, and Scale, with exercises and checkpoints for a coached cohort.
Explore the learning center →Tools and guides
Work through the decision.
Explore a scenario, understand the evidence model, or get help defining a focused assessment.
Interactive tool
AI Engineering Value Model
Model capacity, rollout costs, training time, and payback using your assumptions. All outputs are modeled estimates.
Explore the value modelMethodology
How to read the evidence
Understand source coverage, observed evidence, modeled interpretations, authorized context, and follow-up measurement.
Read the methodologyOptional expert support
A bounded readiness assessment
Agree on the engineering question, selected sources, reviewers, and priorities before expanding the scope.
Explore the assessment