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AI coding agents are reshaping how software gets built. But raw capability without discipline produces unreliable results. These principles are for engineering teams serious about using AI tools to build software that is fast, correct, and maintainable over time.

Reading code and writing code activate different cognitive systems. Heavy reliance on AI code generation accelerates the atrophy of code-writing abilities, and that has serious long-term consequences for engineering effectiveness.

With all the focus on AI, it's easy to overlook foundational technologies. But containers and orchestration platforms like Kubernetes aren't just still relevant. They're more critical than ever for running AI workloads at scale.

AI is changing technology teams and organizations are losing their handle on what AI is creating. Training is more important than ever.

Modern software engineering demands speed, quality, and agility. This comprehensive technical guide explores trunk-based development and continuous integration/continuous deployment practices that enable high-performing teams to deliver software with confidence and velocity.

Feature flags (also known as feature toggles or feature switches) are a powerful software development technique that allows teams to modify system behavior without changing code.

Infrastructure-as-Code isn't just a best practice. It's the foundation of reliable, scalable cloud operations. Here's why your organization can't afford to skip it.

AI coding assistants are transforming how we build software, but they're also creating a dangerous skills gap. Here's why deep technical expertise matters more now than it ever has.

Serverless computing promises reduced operational overhead and automatic scaling, but it's not the right fit for every workload. Here's a practical framework for deciding when serverless makes sense for your organization.

Getting AI coding tools into the hands of your engineers is the easy part. Getting those engineers to use them well, consistently and collaboratively, is where most organizations stumble. An engineering guild focused on AI-assisted coding is one of the most effective structures for closing that gap.

GitOps extends infrastructure-as-code principles by using Git as the single source of truth for declarative infrastructure and applications. For enterprises managing complex environments, GitOps offers consistency, auditability, and automated reconciliation, but scaling it requires careful consideration of organizational patterns.

The strangler fig pattern offers a proven approach to modernizing legacy systems incrementally rather than through risky big-bang rewrites. This article explores practical strategies for applying the pattern, including common challenges and how to address them.

AI coding agents are only as smart as the context you give them. The comments in your codebase are a direct line of communication to those agents -- here's how to write them so the output actually reflects your intent.