Knowledge library
Articles
Engineering decisions, implementation notes, and operational field guides.
60 entries
Playwright vs Cypress: Choosing Browser Tests for Enterprise SPAs
A practical comparison of Playwright and Cypress for enterprise single-page applications, covering architecture, parallel execution, flake control, debugging, and governance requirements.
Hardening LLM Code Assistants Against Prompt Injection
A practical threat model and defense-in-depth guide for reducing prompt injection risk in AI pair-programming workflows while preserving human control over code, tools, and production changes.
Architecting AI-Assisted Code Reviews with GitHub Actions
A practical architecture for adding LLM-assisted review to GitHub pull requests while controlling permissions, prompt injection, cost, comment noise, and developer latency.
Practical Caching Strategies for Next.js API Routes
A practical guide to browser, CDN, and server-side caching for Next.js API routes and Route Handlers, including safe headers, invalidation patterns, and production tradeoffs.
Why Prompt Engineering Is Not an Enterprise AI Skill Strategy
Prompt techniques can improve individual outputs, but enterprise AI adoption depends on verification, workflow redesign, critical thinking, risk controls, and domain judgment. Here is a practical capability model for engineering leaders.
Why Your Microservices Architecture Became a Distributed Monolith
Excessive synchronous service calls can make independently deployed microservices fail and scale as one system. Learn how to identify runtime coupling, contain cascading failures, and incrementally introduce events, local read models, bulkheads, and sagas.
How to Modernize Mainframe Integrations Without Exposing the Mainframe
A practical architecture for modernizing legacy financial systems with domain APIs, anti-corruption layers, asynchronous messaging, adapters, and stable contracts—without leaking mainframe records, protocols, or release constraints to consumers.
How to Measure the Real ROI of Enterprise AI
A practical framework for proving enterprise AI value through counterfactual baselines, balanced operational metrics, total cost, risk-adjusted returns, and realized business outcomes.
Practical REST APIs in Financial Services: Why Perfect REST Is Often Impractical
REST is a design philosophy, not a religion. In financial services, APIs have to survive legacy systems, long-running workflows, regulatory controls, distributed transactions, audit requirements, retries, failures, and decades of technology. Perfect REST may look elegant on a whiteboard, but practical enterprise API design is about consistency, security, resilience, clear domain semantics, and knowing when a deliberate compromise is better than architectural purity.