Platform engineering, Go, DevOps, and applied AI.
A first-hand review of Gartner Predicts 2026: Intelligent Applications. Five predictions, the numbers behind them, and what enterprise application leaders should actually do next.
Every AI-native enterprise needs a GenAI Platform Team to build the harness: the engineering layer around the model that makes agents safe, observable, and economical at scale.
Run Anthropic's Claude Code CLI in a Docker container and docker-compose on Linux — step-by-step install, Dockerfile, and optional Squid proxy for domain-level network isolation.
A deterministic, CI/CD-ready framework for measuring how accurately AI agents follow complex, multi-step workflow instructions. Features a three-tier hybrid evaluation engine, progressive scoring, and dual-metric reporting.
How I automated production-ready code generation from GitHub issues using Claude Code CLI with continuous feedback loops through pre-commit hooks, CI/CD, and PR reviews—achieving 90%+ merge-ready code in ~40 minutes.
A comprehensive framework to determine if your software project is ready for AI development agents integration. Provides criteria, quality gates, and readiness assessment to ensure AI enhances productivity without creating technical debt.
Why selecting GenAI tools for an enterprise is challenging, and why building your own foundational capabilities might be the best approach for sustainable innovation.
A practical Bash script that collects Git repository data to provide context for AI models to generate meaningful changelogs automatically.