Jefferson Valandro

I built Next.js templates and e-books with AI. What guarantees quality is the review: the tests, the checks and the mistakes I caught before selling.

How to use AI to detect code smells, apply Extract Method, Introduce Parameter Object, Replace Conditional with Polymorphism, and Move Method — with the workflow that prevents regressions at every step.

How to prompt AI to identify Factory, Builder, Adapter, Decorator, Strategy, Observer, and Chain of Responsibility opportunities — plus the test that tells you when a pattern adds complexity instead of removing it.

How to prompt AI to detect SRP violations, refactor OCP-breaking switches, audit LSP hierarchies, split fat interfaces, and inject dependencies — with working code for each principle.

How to prompt AI for clean output, the most common clean code violations AI introduces, and the review protocol that catches them before they land in your codebase.

How to build a company MCP server that puts your architecture, coding patterns, and business rules directly into every developer's AI assistant — so the AI knows your system, not just generic best practices.

Smart/dumb components, OnPush, RxJS pitfalls, Signals, standalone architecture, lazy loading, and testing strategies for production Angular applications.

Component composition, hooks pitfalls, state management with React Query and Zustand, memo anti-patterns, code splitting, and testing with React Testing Library.

A practical comparison of the most widely used architectures — when each shines, when it fails, and how to choose the right one for your context.

The six most destructive anti-patterns, dependency cycles, module health scores, and how to automate architecture quality checks on any codebase.

Microservices, DDD Bounded Contexts, CQRS, event-driven communication, and resilience patterns for systems that grow without breaking.

End-to-end MLOps pipeline: experiment tracking with MLflow, model packaging, CI/CD quality gates, canary releases, and feature stores.

Topics, partitions, consumer groups, the Outbox Pattern, Event Sourcing, and Schema Registry for building decoupled distributed systems.

Four-layer monitoring stack for ML: data quality, feature drift (PSI, KS test), performance degradation, and automated retraining triggers.

REST vs gRPC, API Gateway, Service Mesh, and the Saga pattern for managing distributed transactions without global locks.

Medallion Architecture, idempotency, dbt schema contracts, Airflow orchestration, and late-arriving data strategies for production ML systems.

How a structured spec lifecycle — Draft, Approve, Implement, Review, Close — turns AI-assisted development from a guessing game into a repeatable, auditable engineering practice.

What AI genuinely accelerates, where hallucinations silently destroy trust, how agents and skills work, and the discipline that prevents over-reliance from eroding your engineering judgment.

Dependency Rule, concentric layers, and use case isolation: building a TypeScript payment module where the domain core has zero framework dependencies.

Ports & Adapters with Spring Boot: isolating the domain core, defining ports as interfaces, and writing use cases that are fully testable without infrastructure.

Resource modeling, HTTP semantics, versioning, consistent error responses, cursor pagination, rate limiting, and contract-first with OpenAPI.

Pipeline stages, quality gates with SonarQube, Testcontainers, multi-stage Docker builds, GitOps with ArgoCD, and deployment strategies for production.