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.
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.
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.
Enterprise orchestration platforms promise unified workflows but ignore the code underneath. Here's why context matters more than coordination.
CrewAI makes multi-agent systems accessible, but real implementation hits friction fast. Here's what you'll actually encounter building your first agents.
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.
Traditional kanban boards track tickets. AI kanban boards track code, dependencies, and blast radius. Here's why your team needs the upgrade.
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.
Your engineers ship fast, but nobody uses what they build. Here's why "trust the vibe" development destroys product-market fit.
Technical deep dive into graph-based feature discovery. How Louvain modularity optimization groups files into meaningful features automatically.
AI-generated dev plans with file-level tasks based on actual codebase architecture. How to cut sprint planning overhead by 50%.
Deep dive into graph-based code analysis and why traditional file-based thinking fails at scale.
A practical guide to combining Glue's codebase intelligence with Cursor's AI editing for a workflow that understands before it generates.
AI can flag dependency issues and style violations. Humans should focus on architecture, business logic, and mentoring. Here's how to split the work.
Everything you need to know about codebase understanding tools, techniques, and workflows. From grep to AI-powered intelligence.
AI-native development isn't about using more AI tools. It's about restructuring workflows around AI strengths and human judgment.
Manual feature mapping is expensive, incomplete, and always stale. Graph-based automated discovery finds features humans miss. Here is the algorithm.