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.
Product intelligence software promises better decisions. Here's what it actually costs, delivers, and how to measure ROI using code metrics that matter.
I gave AI agents proper context for 30 days. The results: 40% faster onboarding, 60% fewer bugs, and tools that actually understand our codebase.
AI writes code fast but can't understand your codebase. Here's what breaks when you ship AI-generated code—and how to fix the intelligence gap.
Architecture diagrams are lies the moment you draw them. Here's how to build living code graphs that actually reflect your system—and why AI needs them.
Most developers ask the wrong questions about AI coding tools. Here are the 8 questions that actually matter—and why context is the real problem.
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.
Security tools scan for known vulnerabilities but miss architectural flaws. AI needs codebase context to understand real attack surfaces and data flows.
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.
AI coding assistants promise magic but deliver mediocrity without context. Here's what vendors won't tell you about hallucinations, costs, and the real solution.
How we built a system that predicts what breaks when you change code. File-to-feature mapping, call graphs, and risk scoring that actually works.
The best PM tools now understand code, not just tickets. Here's what actually matters for product decisions in 2026—and what's just noise.
Traditional kanban boards track tickets. AI kanban boards track code, dependencies, and blast radius. Here's why your team needs the upgrade.
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.
Why representing your codebase as a knowledge graph changes everything — from AI assistance to onboarding. The data model matters more than the tools.
Dependency graphs aren't just debugging tools. Smart teams use them to parallelize work, prevent merge conflicts, and cut release cycles by weeks.
Cyclomatic complexity is a lie. Here's how to actually measure code health by combining complexity, churn, and ownership data that predicts real problems.
Architecture diagrams lie. Learn why static diagrams fail, how to visualize code architecture that stays current, and tools that generate views from actual code.
I built Glue's blast radius analysis by mapping files to features, dependencies, and impact zones. Here's why most change analysis tools fail.
You have the perfect requirements template. You still ship the wrong thing. The problem isn't your process—it's that you don't understand your own codebase.
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.
AI coding tools generate code fast but lack context. Here's what actually works in 2026 and why context-aware platforms change everything.
Traditional product analytics tracks clicks. Real product intelligence measures features built, technical debt, and competitive gaps from your actual codebase.
CTOs ask the hard questions about low-code platforms. Here's what nobody tells you about the $65B industry—from vendor lock-in to the mess it leaves behind.
Shift-left is dead. Modern AI doesn't just catch bugs earlier—it understands your entire codebase at every stage. Here's what shift-everywhere actually means.
Low-code platforms promise speed but deliver technical debt nobody talks about. Here's what the $65B market boom means for engineering teams.
AI-generated prototypes are impressive demos. They're terrible production systems. Here's where vibe coding ends and real engineering begins.