AI assistants write code fast. Your codebase becomes a mess faster. Here's how to maintain control when AI is writing half your code.
Product intelligence software promises better decisions. Here's what it actually costs, delivers, and how to measure ROI using code metrics that matter.
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.
Your legacy code has no docs? Write PRDs backwards from the implementation. Here's how to extract product specs from code that everyone forgot about.
How we built a system that predicts what breaks when you change code. File-to-feature mapping, call graphs, and risk scoring that actually works.
Traditional kanban boards track tickets. AI kanban boards track code, dependencies, and blast radius. Here's why your team needs the upgrade.
Dependency graphs aren't just debugging tools. Smart teams use them to parallelize work, prevent merge conflicts, and cut release cycles by weeks.
Cyclomatic complexity is a lie. Here's how to actually measure code health by combining complexity, churn, and ownership data that predicts real problems.
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.
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.
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.
CTOs ask the hard questions about low-code platforms. Here's what nobody tells you about the $65B industry—from vendor lock-in to the mess it leaves behind.
Your engineers ship fast, but nobody uses what they build. Here's why "trust the vibe" development destroys product-market fit.
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.
Fast prototypes don't mean sloppy code. Learn how to run effective prototype sprints that ship real features without creating technical debt nightmares.
Stop using ChatGPT as a search engine. MCP lets AI assistants access your feature catalog, code health data, and competitive gaps directly.
Low-code platforms promise speed but deliver technical debt nobody talks about. Here's what the $65B market boom means for engineering teams.
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.
Most impact analysis tools are wrong. We built a system that combines static analysis, runtime traces, and LLM reasoning to actually predict what breaks.
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.
Code quality scanners measure syntax. Real technical debt lives in architectural complexity, dependency rot, and knowledge concentration. Here's how to measure what matters.
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.