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.
Product intelligence software promises better decisions. Here's what it actually costs, delivers, and how to measure ROI using code metrics that matter.
I gave AI agents proper context for 30 days. The results: 40% faster onboarding, 60% fewer bugs, and tools that actually understand our codebase.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Stop using ChatGPT as a search engine. MCP lets AI assistants access your feature catalog, code health data, and competitive gaps directly.
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.
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.
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.
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.
AI-generated dev plans with file-level tasks based on actual codebase architecture. How to cut sprint planning overhead by 50%.
How understanding code dependencies and blast radius before deployment prevents the bugs that code review misses.
How automated feature discovery and competitive gap analysis accelerate M&A technical evaluation from months to days.
Deep dive into graph-based code analysis and why traditional file-based thinking fails at scale.
How to use discovered features, competitive gaps, and team capabilities to build data-driven roadmaps instead of opinion-driven ones.
Most AI tool adoptions fail to deliver ROI. Here are the productivity patterns that actually work for engineering teams.
AI-generated prototypes are impressive demos. They're terrible production systems. Here's where vibe coding ends and real engineering begins.
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.
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.
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.
AI reshaped the developer tool landscape. Here's what the modern engineering stack looks like and where the gaps remain.
Story points, lines of code, and PR count don't measure what matters. Here's what to track instead.
Wikis are always stale. Auto-generated feature catalogs from code analysis are always current. Here's the difference.
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.
Automated competitive gap detection that scans competitor features and maps them against your codebase. Real intelligence, not guesswork.
Before buying AI tools, understand where your team will actually benefit. A practical framework for assessing AI readiness.
Most incident prevention is reactive. Code intelligence makes it proactive by identifying risk before changes ship.
Practical architecture patterns for AI-powered applications — from RAG pipelines to agent orchestration. Lessons from building production AI systems.
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.
Most competitive analysis is guesswork based on marketing pages. Code-level gap analysis shows exactly what you have, what competitors have, and what it would cost to close the gap.
An honest review of the IBM AI Product Manager Professional Certificate.
How lightweight agent frameworks like OpenAI Swarm compare to production multi-agent systems. When simplicity wins and when you need more.