Interview lab
Prepare from first principles.
My working notebook for senior engineering interviews: behavioural stories, architecture trade-offs, and repeatable coding patterns.
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Coding Patterns
A five-part, retrieval-first system for turning coding practice into defensible interview decisions.
Coding Interviews: A Decision System from Constraints to Proof
A five-part, retrieval-first system for turning coding practice into defensible interview decisions.
Constraint Routing: Choose the Pattern Before You Code
Turn input, output, and operational constraints into a defensible algorithm choice instead of guessing from keywords.
Pattern Cards: Remember the Invariant, Not the Template
Compress each algorithm family into triggers, invariants, proof obligations, complexity, and explicit failure boundaries.
Retrieval Practice for Coding Interviews
Replace solution rereading and problem-count goals with blank-editor recall, spaced review, mixed practice, and an error-driven plan.
From Idea to Proof: Execute a Coding Interview
A timed method for turning an algorithm choice into correct code, a concise proof, adversarial tests, and a useful trade-off discussion.
Behavioral Evidence
From factual evidence to judgment, trade-offs, and lasting impact: an auditable system for behavioral interview preparation.
Behavioral Stories That Actually Show Seniority
From factual evidence to judgment, trade-offs, and lasting impact: an auditable system for behavioral interview preparation.
Build the Behavioral Evidence Ledger Before Writing STAR
Organize project experience by source, ownership boundary, causal strength, and disclosure level before turning it into an interview story.
The Seniority Signals in a Behavioral Interview
Use judgment, ownership, trade-offs, influence, risk management, and institutional leverage to identify what an experience truly demonstrates.
Reconstructing STAR from an Evidence Chain
Rebuild an answer from real constraints, options, ownership boundaries, and outcome evidence so it can survive follow-up questions.
Story Mapping, Follow-ups, and the Authenticity Stress Test
Use the same facts for different prompts, then test the account through technical, collaboration, risk, and measurement follow-ups.
System Design
Connect the control, data, and evidence planes through an explainable, releasable, and replayable system contract.
System Design Series: High-Throughput Rule Engine Overview
Connect the control, data, and evidence planes through an explainable, releasable, and replayable system contract.
System Design: Requirements, SLOs, and Capacity Assumptions
Turn “evaluate events against rules” into testable contracts, then size and recover the system with explicitly isolated interview assumptions.
System Design: Rule Compilation and the Control Plane
Treat rules as executable production behavior, from DSL and IR through static checks, immutable bundles, progressive activation, and rollback.
System Design: Hot Path, Partitioning, State, and Backpressure
Use dispatch indexes, shared computation, keyed state, and lane-aware backpressure to keep per-event rule-evaluation cost predictable.
System Design: Delivery Semantics, Rollout, and Operations
Complete the production loop with stable decision IDs, idempotent effects, reproducible replay, shadow/canary rollout, SLO burn, and failure drills.