AI can write user stories in seconds, but most are disconnected from your codebase. Here's how to generate stories that match your actual code capabilities.
Honest answers to common questions about AI coding tools. Learn how context-aware platforms solve problems that ChatGPT and Copilot can't touch.
AI coding tools promise to boost productivity, but most teams struggle with context and code quality. Here's how to actually integrate AI into your workflow.
AI assistants write code fast. Your codebase becomes a mess faster. Here's how to maintain control when AI is writing half your code.
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.
Most developers waste 30-90 minutes understanding code context before writing a single line. Here's how to optimize your AI coding workflow.
DevSecOps is shifting from rule-based scanning to AI-powered analysis. Here's what actually works when securing modern codebases at scale.
CrewAI makes multi-agent systems accessible, but real implementation hits friction fast. Here's what you'll actually encounter building your first agents.
Forget feature lists. This guide ranks AI coding assistants by what matters: context quality, codebase understanding, and real-world developer experience.
Shift-left is dead. Modern AI requires code intelligence at every stage. Here's what actually works when AI needs to understand your entire codebase.
Real answers to hard questions about making AI coding tools actually work. From context windows to team adoption, here's what nobody tells you.
Model version control isn't just git tags. Learn what actually works for ML teams shipping fast—from artifact tracking to deployment automation.
Code graphs power modern dev tools, but most are syntax trees in disguise. Here's what framework-aware graphs actually do and why they matter for AI context.
Stop writing boilerplate AI code. Learn how to build autonomous agents with CrewAI that actually understand your codebase and ship features faster.
Architecture diagrams lie. Learn why static diagrams fail, how to visualize code architecture that stays current, and tools that generate views from actual code.
Most AI project tools are glorified chatbots. Here's how to actually use AI to understand what's happening in your codebase and ship faster.
I built Glue's blast radius analysis by mapping files to features, dependencies, and impact zones. Here's why most change analysis tools fail.
CTOs ask the hard questions about AI coding tools. We answer them with real security implications, implementation strategies, and context architecture.
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 applications demand different API patterns. Here's how to design endpoints that handle streaming, context windows, and unpredictable load without breaking.
AI coding tools generate code fast but lack context. Here's what actually works in 2026 and why context-aware platforms change everything.
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.
Most engineers pick an AI SDK and pray it works. Here's how to choose, integrate, and ship AI features without destroying your existing codebase.
Model Context Protocol lets AI tools talk to your code, databases, and docs without building custom integrations. Here's why it matters more than the LLM.
AI coding assistants fail at scale because they lack context. Here's how to build a context graph that makes AI actually useful in enterprise codebases.
How AI-powered codebase context and code tours transform developer onboarding from months of tribal knowledge transfer to weeks of guided exploration.
Complete guide to securing company data when adopting AI coding agents. Data classification, access controls, audit trails, and practical security architecture.
A practical guide to combining Glue's codebase intelligence with Cursor's AI editing for a workflow that understands before it generates.
A framework for measuring actual return on AI coding tool investments. Spoiler: adoption rate is the wrong metric.
Side-by-side comparison of Lovable and Dev for AI-powered application building. When to use each and how they compare to code intelligence tools.
Everything you need to know about codebase understanding tools, techniques, and workflows. From grep to AI-powered intelligence.
Practical architecture patterns for AI-powered applications — from RAG pipelines to agent orchestration. Lessons from building production AI systems.
Manual feature mapping is expensive, incomplete, and always stale. Graph-based automated discovery finds features humans miss. Here is the algorithm.
The prediction came true - adoption is massive. But ROI? That is a different story. Here is why most teams are disappointed and what the successful ones do differently.
Neuromorphic chips process data like the brain. What this means for AI applications, when it matters, and what developers need to know.