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.
Autonomous AI agents can write code, debug issues, and ship features. Here's what actually works, what doesn't, and how to give agents the context they need.
I gave AI agents proper context for 30 days. The results: 40% faster onboarding, 60% fewer bugs, and tools that actually understand our codebase.
Product managers need code awareness, not more dashboards. Here's what separates winning AI PMs from those drowning in feature backlogs in 2025.
CrewAI makes multi-agent systems accessible, but real implementation hits friction fast. Here's what you'll actually encounter building your first agents.
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.
Stop writing boilerplate AI code. Learn how to build autonomous agents with CrewAI that actually understand your codebase and ship features faster.
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 project tools are glorified chatbots. Here's how to actually use AI to understand what's happening in your codebase and ship faster.
Building multi-agent systems with CrewAI? Here are the 8 questions every engineer asks—and the answers that actually matter for production systems.
AI coding agents fail because they lack context. Here's how to give them the feature maps, call graphs, and ownership data they need to work.
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.
Complete guide to securing company data when adopting AI coding agents. Data classification, access controls, audit trails, and practical security architecture.
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.
Every team considers building their own AI coding agent. Here's when it makes sense and when you should buy instead.
Practical architecture patterns for AI-powered applications — from RAG pipelines to agent orchestration. Lessons from building production AI systems.
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.