Analysis of the real return on investment of AI development agents: production data, calculation framework, documented cases, and mistakes that destroy ROI.
Technical guide to AI agents for software development: internal architecture, orchestration patterns, available tools, and real criteria for deciding when they make sense.
How to choose a technology stack with sound criteria: team skills assessment, ecosystem maturity, total cost of ownership, migration risk, and why boring technology is usually the best choice.
A decision framework for rewriting vs refactoring: the second system effect, incremental migration strategies, the strangler fig pattern, and real examples of successes and failures.
What MCP is, its client-server architecture, how to implement servers and clients, the available tools ecosystem, and practical use cases for development teams.
DORA metrics, the SPACE framework, and what to measure (and what not to) to improve your development team real productivity. With 2026 benchmarks and common implementation mistakes.