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Design idempotent APIs and contracts that survive retries addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using order creation protected by an idempotency key as a running example and testing every choice against production constraints. Design idempotent APIs and contracts t
Deploy to Kubernetes with controlled rollout and verifiable rollback addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using a stateless Deployment with readiness and documented rollback as a running example and testing every choice against production constraints. Deploy to Ku
Define and enforce a web performance budget addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using size, execution, and rendering thresholds verified in CI as a running example and testing every choice against production constraints. Define and enforce a web performance budge
Cloud capacity: size systems without waste or fragility addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using requests and limits derived from measured load rather than guesses as a running example and testing every choice against production constraints. Cloud capacity: size
Build a tool-using AI agent with guardrails and tracing addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using a support agent that calls a knowledge source and validates the response as a running example and testing every choice against production constraints. Build a tool-u
Build a testing strategy from component to user journey addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using a business regression captured by unit, integration, and journey tests as a running example and testing every choice against production constraints. Build a testing
Background Android synchronization without infinite loops addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using unique work with backoff, network constraints, and idempotency as a running example and testing every choice against production constraints. Background Android syn
Android 16 / API 36: prepare for edge-to-edge and behavior changes addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using an existing screen audited for insets and API 36 behavior as a running example and testing every choice against production constraints. Android 16 / API 3
Android permissions and privacy: request less, explain better addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using location requested only during an explicit field action as a running example and testing every choice against production constraints. Android permissions and p
AI in a medical device: lifecycle and GMLP practices addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using a model tracked from design through post-deployment change as a running example and testing every choice against production constraints. AI in a medical device: lifecyc
AI agent security: injection, tools, data, and privileges addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using an agent constrained by tool permissions and output validation as a running example and testing every choice against production constraints. AI agent security: inj
AI agent observability: traces, decisions, and tool errors addresses a concrete operating problem: turn intent into verifiable behavior without hiding security, quality, or operational trade-offs. This piece moves from foundations to expert decisions, using correlation between conversation turn, tool call, and result as a running example and testing every choice against production constraints. AI agent observability:
