Insights, guides, and deep dives into AI-native software development.
The era of AI assistants autocompleting your code is over. In 2026, autonomous AI agents plan multi-file changes, run tests, and ship features with minimal human oversight. Here is what engineering leaders need to know.
AI software testing has moved beyond generating unit tests from templates. Modern AI QA agents autonomously explore applications, generate comprehensive test suites, detect vulnerabilities, and self-heal broken tests. This guide covers what works, what does not, and how to adopt it.
The Model Context Protocol (MCP) is an open standard that gives AI agents a universal way to connect to tools, APIs, and data sources. Think of it as USB-C for AI — one protocol to replace dozens of custom integrations.
Big-bang rewrites fail 70% of the time. AI agents offer a better path: incremental legacy modernization that generates tests, refactors safely, and migrates piece by piece while the system stays live.
Retrieval-Augmented Generation is the most practical way to make LLMs useful with your proprietary data. This guide covers everything from architecture decisions to production deployment, including vector database comparisons, chunking strategies, and the pitfalls that derail most enterprise RAG projects.
Traditional code reviews catch bugs. AI codebase audits catch systemic problems — architectural drift, hidden security vulnerabilities, and the technical debt that is silently slowing your team down. Here is exactly what the process looks like and what you get at the end.