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Practical guides to AI-driven development, full-stack architecture, and solving production problems.
Showing 25–36 of 100 articles
Test a backup by restoring it into an isolated environment
Verify backups through an isolated restore, application checks, access restrictions, and measured recovery steps instead of trusting file existence.
Add a health check that detects useful failure states
Separate liveness, readiness, and startup checks so probes detect useful failures without causing restart loops or hiding broken user journeys.
Make a production deploy reversible
Make deploy recovery concrete by retaining artifacts, checking schema compatibility, rehearsing rollback, and accounting for jobs and external effects.
Manage configuration without committing secrets
Separate configuration names from secret values, validate runtime settings, assign deployment ownership, and prepare rotation without exposing credentials.
Debug a container that works on your laptop but not the server
Compare image identity, CPU architecture, files, environment, users, and networking to diagnose containers that fail only in the server environment.
Build a CI pipeline that tests the change rather than just compiling it
Build CI around changed behavior, required services, trustworthy failures, and reviewable evidence so a green compile does not hide a broken feature.
Plan a reversible release for a risky architectural change
Release architectural changes through compatible stages, bounded exposure, measurable checkpoints, and a recovery plan that includes data and side effects.
Review an AI proposed architecture against actual requirements
Evaluate an AI architecture proposal against real requirements, ownership, failure modes, and operating costs before adopting its services or abstractions.
Design tenant isolation into every data access path
Carry trusted tenant context through queries, caches, jobs, files, and administration, then verify isolation with cross-organization negative tests.
Find a scaling bottleneck before splitting the application
Measure latency, resource saturation, and workload shape before splitting services, then compare bounded fixes against the actual limiting resource.
Use an outbox when database success and message delivery diverge
Close the gap between committed business data and failed message delivery with an outbox, repeatable dispatch, and consumer duplicate protection.
Design a background job for work that should not block a request
Move slow work into durable background jobs with clear acceptance, bounded retries, duplicate protection, cancellation, and visible recovery states.
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