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.
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.
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.
Bolt.new is great for prototypes, but enterprise teams need more. Here are the alternatives that actually handle production codebases at scale.
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.
Dependency graphs aren't just debugging tools. Smart teams use them to parallelize work, prevent merge conflicts, and cut release cycles by weeks.
Architecture diagrams lie. Learn why static diagrams fail, how to visualize code architecture that stays current, and tools that generate views from actual code.
CTOs ask the hard questions about AI coding tools. We answer them with real security implications, implementation strategies, and context architecture.
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.
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.
Stop building AI features that hallucinate in production. Context engineering is the difference between demos that wow and systems that ship.
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.
Most PMs ask the wrong questions about AI. Here are 8 that actually matter — and how code intelligence gives you answers marketing can't fake.
Low-code platforms promise speed but deliver technical debt nobody talks about. Here's what the $65B market boom means for engineering teams.
Comprehensive comparison of the top AI coding tools — Copilot, Cursor, Claude Code, Cody, and more. Updated for 2026 with real benchmarks on complex codebases.
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.
An honest review of the IBM AI Product Manager Professional Certificate.