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.
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.
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.
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.
Real benchmarks comparing Cursor AI and GitHub Copilot. Which AI coding assistant actually makes you faster? Data from 6 months of production use.
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.
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.
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.
The tools you need to ship faster in 2025. From IDE to production, here's what works—and what most teams are missing between code and planning.
AI coding tools generate code fast but lack context. Here's what actually works in 2026 and why context-aware platforms change everything.
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 PMs ask the wrong questions about AI. Here are 8 that actually matter — and how code intelligence gives you answers marketing can't fake.
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.
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 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.
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%.
Complete guide to securing company data when adopting AI coding agents. Data classification, access controls, audit trails, and practical security architecture.
Most AI tool adoptions fail to deliver ROI. Here are the productivity patterns that actually work for engineering teams.
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.
Code reviews catch style issues and obvious errors. They miss the architectural bugs that cause production incidents. Here's why, and how to fix it.
A buyer's guide to code intelligence platforms. What to look for, what to ignore, and how to run a meaningful proof of concept.
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.
That "temporary" feature flag from 6 months ago now controls 3 code paths. Here's how feature flag debt accumulates and how to detect it.
Remote work broke ambient knowledge sharing. Here's how to rebuild it without forcing everyone back to the office.
Every tool helps you write code faster. Nothing helps you understand what to write. Pre-code intelligence is the missing category.
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.
Story points, lines of code, and PR count don't measure what matters. Here's what to track instead.
The monorepo vs microservices debate usually focuses on build systems. The real difference is in how knowledge is distributed and discovered.
Regressions, slow onboarding, missed estimates, and knowledge loss. Quantifying what poor codebase understanding actually costs.
A framework for measuring actual return on AI coding tool investments. Spoiler: adoption rate is the wrong metric.
Code search finds where code is. Code intelligence tells you why it exists, what depends on it, and what breaks if you change it.
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.
Before buying AI tools, understand where your team will actually benefit. A practical framework for assessing AI readiness.
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.
An honest comparison of code intelligence tools. What each does best, where each falls short, and how to choose.
Everything you need to know about codebase understanding tools, techniques, and workflows. From grep to AI-powered intelligence.
AI-native development isn't about using more AI tools. It's about restructuring workflows around AI strengths and human judgment.
Practical architecture patterns for AI-powered applications — from RAG pipelines to agent orchestration. Lessons from building production AI systems.
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.
Serverless and Kubernetes changed deployment. But they also changed how developers need to understand their systems. The complexity moved, it did not disappear.
How lightweight agent frameworks like OpenAI Swarm compare to production multi-agent systems. When simplicity wins and when you need more.