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.
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.
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.
DevSecOps is shifting from rule-based scanning to AI-powered analysis. Here's what actually works when securing modern codebases at scale.
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.
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.
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.
Bolt.new is great for prototypes, but enterprise teams need more. Here are the alternatives that actually handle production codebases at scale.
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.
Real benchmarks comparing Cursor AI and GitHub Copilot. Which AI coding assistant actually makes you faster? Data from 6 months of production use.
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.
Most AI code reviewers catch formatting issues. Here's what tools actually find logic bugs, race conditions, and security holes—and why context matters.
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.
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.
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.
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.
Building multi-agent systems with CrewAI? Here are the 8 questions every engineer asks—and the answers that actually matter for production systems.
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.
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.
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.
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.
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.
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.
AI won't replace PMs. But PMs who understand their codebase through AI will replace those who don't. Here's what actually matters in 2025.
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.
Most AI-for-PM predictions are hype. Here's what will actually separate winning PMs from the rest: the ability to talk directly to your codebase.
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.
Technical deep dive into graph-based feature discovery. How Louvain modularity optimization groups files into meaningful features automatically.
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.
Git history, call graphs, and change patterns contain more reliable tribal knowledge than any wiki. The problem isn't capturing knowledge — it's extracting it.
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.
Deep dive into graph-based code analysis and why traditional file-based thinking fails at scale.
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.
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.
LeetCode doesn't predict job performance. Codebase navigation and system understanding do. How interviews should evolve for the AI era.
Automated competitive gap detection that scans competitor features and maps them against your codebase. Real intelligence, not guesswork.
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.
Every team considers building their own AI coding agent. Here's when it makes sense and when you should buy instead.
AI can flag dependency issues and style violations. Humans should focus on architecture, business logic, and mentoring. Here's how to split the work.
Vector embeddings find similar code. Knowledge graphs find connected code. Why the best systems use both.
Everything you need to know about codebase understanding tools, techniques, and workflows. From grep to AI-powered intelligence.
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.
How lightweight agent frameworks like OpenAI Swarm compare to production multi-agent systems. When simplicity wins and when you need more.