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.
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.
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.
Why representing your codebase as a knowledge graph changes everything — from AI assistance to onboarding. The data model matters more than the tools.
Dependency graphs aren't just debugging tools. Smart teams use them to parallelize work, prevent merge conflicts, and cut release cycles by weeks.
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.
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.
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.
AI coding tools generate code fast but lack context. Here's what actually works in 2026 and why context-aware platforms change everything.
Traditional product analytics tracks clicks. Real product intelligence measures features built, technical debt, and competitive gaps from your actual codebase.
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.
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.
AI coding assistants hallucinate solutions that don't fit your codebase. Here's how to actually debug with AI that understands your architecture.
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.
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.
Most impact analysis tools are wrong. We built a system that combines static analysis, runtime traces, and LLM reasoning to actually predict what breaks.
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.
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.
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.
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.
The monorepo vs microservices debate usually focuses on build systems. The real difference is in how knowledge is distributed and discovered.
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.