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.
Product managers need code awareness, not more dashboards. Here's what separates winning AI PMs from those drowning in feature backlogs in 2025.
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.
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 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 code optimizers promise magic. Most deliver chaos. Here's what actually works when you combine AI with real code intelligence in 2026.
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.
CODEOWNERS files are always stale. Git history tells the truth about who actually maintains, reviews, and understands each part of your codebase.
How spec drift silently derails engineering teams and how to detect it before you ship the wrong thing.
Wikis are always stale. Auto-generated feature catalogs from code analysis are always current. Here's the difference.
Automated competitive gap detection that scans competitor features and maps them against your codebase. Real intelligence, not guesswork.
Manual feature mapping is expensive, incomplete, and always stale. Graph-based automated discovery finds features humans miss. Here is the algorithm.
An honest review of the IBM AI Product Manager Professional Certificate.