Cloud-native isn't just containers and functions. It's a fundamental shift in how we build, deploy, and reason about systems. Here's what that means.
Autonomous AI agents can write code, debug issues, and ship features. Here's what actually works, what doesn't, and how to give agents the context they need.
I gave AI agents proper context for 30 days. The results: 40% faster onboarding, 60% fewer bugs, and tools that actually understand our codebase.
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.
CrewAI makes multi-agent systems accessible, but real implementation hits friction fast. Here's what you'll actually encounter building your first agents.
Bolt.new is great for prototypes, but enterprise teams need more. Here are the alternatives that actually handle production codebases at scale.
Stop writing boilerplate AI code. Learn how to build autonomous agents with CrewAI that actually understand your codebase and ship features faster.
Real benchmarks comparing Cursor AI and GitHub Copilot. Which AI coding assistant actually makes you faster? Data from 6 months of production use.
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.
Git won't save you when your production model breaks. Here's how to actually version AI models and the code that depends on them — with automation that works.
Building multi-agent systems with CrewAI? Here are the 8 questions every engineer asks—and the answers that actually matter for production systems.
Bolt.new makes beautiful demos, but shipping production code is different. Here are better alternatives when you need something that won't break in two weeks.
AI coding agents fail because they lack context. Here's how to give them the feature maps, call graphs, and ownership data they need to work.
Legacy systems are black boxes to AI coding tools. Here's how to make decades-old code readable to both humans and LLMs without a full rewrite.
Serverless or Kubernetes? This guide cuts through the hype with real tradeoffs, cost breakdowns, and when each actually makes sense for your team.
Stop building AI features that hallucinate in production. Context engineering is the difference between demos that wow and systems that ship.
Serverless promises no ops. K8s promises control. Neither delivers what you think. Here's what actually matters when choosing your cloud infrastructure.
Complete guide to securing company data when adopting AI coding agents. Data classification, access controls, audit trails, and practical security architecture.
AI-generated prototypes are impressive demos. They're terrible production systems. Here's where vibe coding ends and real engineering begins.
Every team considers building their own AI coding agent. Here's when it makes sense and when you should buy instead.
Practical architecture patterns for AI-powered applications — from RAG pipelines to agent orchestration. Lessons from building production AI systems.
How lightweight agent frameworks like OpenAI Swarm compare to production multi-agent systems. When simplicity wins and when you need more.