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.
Sourcegraph searches code. CodeSee maps architecture. Glue discovers what your codebase actually does — features, health, ownership — and why that matters more.
I gave AI agents proper context for 30 days. The results: 40% faster onboarding, 60% fewer bugs, and tools that actually understand our codebase.
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.
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.
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.
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.
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.
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 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.
AI applications demand different API patterns. Here's how to design endpoints that handle streaming, context windows, and unpredictable load without breaking.
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.
Low-code platforms promise speed but deliver technical debt nobody talks about. Here's what the $65B market boom means for engineering teams.
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.
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.
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.
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.
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.
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.
How AI-powered codebase context and code tours transform developer onboarding from months of tribal knowledge transfer to weeks of guided exploration.
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.
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.
A practical guide to combining Glue's codebase intelligence with Cursor's AI editing for a workflow that understands before it generates.