Architecture diagrams are lies the moment you draw them. Here's how to build living code graphs that actually reflect your system—and why AI needs them.
Serverless isn't about removing servers—it's about removing server problems. Learn why FaaS won, where it fails, and how to tame distributed complexity.
MCP connects AI assistants to your codebase intelligence. Stop explaining your product architecture—let Claude and Cursor query it directly.
Security tools scan for known vulnerabilities but miss architectural flaws. AI needs codebase context to understand real attack surfaces and data flows.
Most enterprise AI pilots never reach production. The real blocker isn't the AI—it's understanding your own codebase well enough to integrate it safely.
Why representing your codebase as a knowledge graph changes everything — from AI assistance to onboarding. The data model matters more than the tools.
Architecture diagrams lie. Learn why static diagrams fail, how to visualize code architecture that stays current, and tools that generate views from actual code.
I built Glue's blast radius analysis by mapping files to features, dependencies, and impact zones. Here's why most change analysis tools fail.
CTOs ask the hard questions about AI coding tools. We answer them with real security implications, implementation strategies, and context architecture.
AI applications demand different API patterns. Here's how to design endpoints that handle streaming, context windows, and unpredictable load without breaking.
Serverless or Kubernetes? This guide cuts through the hype with real tradeoffs, cost breakdowns, and when each actually makes sense for your team.
Most engineers pick an AI SDK and pray it works. Here's how to choose, integrate, and ship AI features without destroying your existing codebase.
AI coding assistants hallucinate solutions that don't fit your codebase. Here's how to actually debug with AI that understands your architecture.
Serverless promises no ops. K8s promises control. Neither delivers what you think. Here's what actually matters when choosing your cloud infrastructure.
AI-generated dev plans with file-level tasks based on actual codebase architecture. How to cut sprint planning overhead by 50%.
Complete guide to securing company data when adopting AI coding agents. Data classification, access controls, audit trails, and practical security architecture.
AI can flag dependency issues and style violations. Humans should focus on architecture, business logic, and mentoring. Here's how to split the work.
Vector embeddings find similar code. Knowledge graphs find connected code. Why the best systems use both.
Practical architecture patterns for AI-powered applications — from RAG pipelines to agent orchestration. Lessons from building production AI systems.