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.
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.
MCP connects AI assistants to your codebase intelligence. Stop explaining your product architecture—let Claude and Cursor query it directly.
Product managers need code awareness, not more dashboards. Here's what separates winning AI PMs from those drowning in feature backlogs in 2025.
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.
Most developers waste 30-90 minutes understanding code context before writing a single line. Here's how to optimize your AI coding workflow.
Claude and Copilot fail on real codebases because they lack context. Here's why AI coding tools break down—and what actually works for complex engineering tasks.
AI coding tools promise 10x productivity but deliver 10x confusion instead. The problem isn't the AI—it's the missing context layer your team ignored.
CrewAI makes multi-agent systems accessible, but real implementation hits friction fast. Here's what you'll actually encounter building your first agents.
Security tools scan for known vulnerabilities but miss architectural flaws. AI needs codebase context to understand real attack surfaces and data flows.
Forget feature lists. This guide ranks AI coding assistants by what matters: context quality, codebase understanding, and real-world developer experience.
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.
Model Context Protocol connects AI tools to real data. Here's everything you need to know about MCP servers, security, and practical implementation.
Real answers to hard questions about making AI coding tools actually work. From context windows to team adoption, here's what nobody tells you.
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.
Real benchmarks comparing Cursor AI and GitHub Copilot. Which AI coding assistant actually makes you faster? Data from 6 months of production use.
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.
Most AI code reviewers catch formatting issues. Here's what tools actually find logic bugs, race conditions, and security holes—and why context matters.
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.
CTOs ask the hard questions about AI coding tools. We answer them with real security implications, implementation strategies, and context architecture.
After 6 months with both tools, I learned the real productivity gain isn't the AI—it's the context you give it. Here's what actually matters.
AI applications demand different API patterns. Here's how to design endpoints that handle streaming, context windows, and unpredictable load without breaking.
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.
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.
I asked Copilot to fix a bug. It broke 3 features instead. The problem isn't AI—it's that your tools don't know what your code actually does.
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.
Your engineers ship fast, but nobody uses what they build. Here's why "trust the vibe" development destroys product-market fit.
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.
AI code generation isn't optional anymore. Here's what CTOs ask about GitHub Copilot, Cursor, and why context matters more than the model.
AI coding assistants hallucinate solutions that don't fit your codebase. Here's how to actually debug with AI that understands your architecture.
Stop using ChatGPT as a search engine. MCP lets AI assistants access your feature catalog, code health data, and competitive gaps directly.
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.
Cursor vs Copilot isn't about features. It's about context. Here's what actually matters when your AI editor needs to understand 500k lines of code.
Your team's AI coding tools generate garbage because they're context-blind. Here's why 73% of AI code gets rejected and how context awareness fixes it.
AI code optimizers promise magic. Most deliver chaos. Here's what actually works when you combine AI with real code intelligence in 2026.
AI coding tools ship features fast but leave you vulnerable. Here's how to test code you barely understand — and why context matters more than coverage.
Raw code metrics lie to you. Stop drowning in file-level data. Learn how context intelligence platforms turn code into features, ownership, and strategy.
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.
ClickUp, Monday, and Asana all have AI. None understand your code. Here's what their AI actually does—and what's still missing for engineering teams.
AI code completion breaks down on cross-file refactors, legacy code, and tickets requiring business context. The problem isn't the AI — it's the context gap.
Engineering teams lose 20-35% of developer time to context acquisition. This invisible tax is baked into every estimate and accepted as normal. It shouldn't be.
Why 60+ specialized MCP tools beat generic LLM prompting for code intelligence. Deep dive into the protocol that makes AI actually useful for developers.
AI-generated dev plans with file-level tasks based on actual codebase architecture. How to cut sprint planning overhead by 50%.
How AI-powered codebase context and code tours transform developer onboarding from months of tribal knowledge transfer to weeks of guided exploration.
Most AI tool adoptions fail to deliver ROI. Here are the productivity patterns that actually work for engineering teams.
CODEOWNERS files are always stale. Git history tells the truth about who actually maintains, reviews, and understands each part of your codebase.
AI-generated prototypes are impressive demos. They're terrible production systems. Here's where vibe coding ends and real engineering begins.
Each context switch costs a developer 23 minutes to regain focus. In a typical day, that adds up to 2-3 hours of lost deep work.
Most teams measure AI tool success by adoption rate. The right metric is whether hard tickets get easier. Here's the framework that works.
How spec drift silently derails engineering teams and how to detect it before you ship the wrong thing.
Claude Code is powerful but limited by what it can see. Here's how to feed it codebase-level context for dramatically better results on complex tasks.
AI reshaped the developer tool landscape. Here's what the modern engineering stack looks like and where the gaps remain.
Comprehensive comparison of the top AI coding tools — Copilot, Cursor, Claude Code, Cody, and more. Updated for 2026 with real benchmarks on complex codebases.
A practical guide to combining Glue's codebase intelligence with Cursor's AI editing for a workflow that understands before it generates.
Regressions, slow onboarding, missed estimates, and knowledge loss. Quantifying what poor codebase understanding actually costs.
LeetCode doesn't predict job performance. Codebase navigation and system understanding do. How interviews should evolve for the AI era.
A framework for measuring actual return on AI coding tool investments. Spoiler: adoption rate is the wrong metric.
Automated competitive gap detection that scans competitor features and maps them against your codebase. Real intelligence, not guesswork.
Before buying AI tools, understand where your team will actually benefit. A practical framework for assessing AI readiness.
AI can flag dependency issues and style violations. Humans should focus on architecture, business logic, and mentoring. Here's how to split the work.
AI-native development isn't about using more AI tools. It's about restructuring workflows around AI strengths and human judgment.