GlueSprint Planning Without 3-Hour Meetings
AI-generated dev plans with file-level tasks based on actual codebase architecture. How to cut sprint planning overhead by 50%.
M&A Tech Due Diligence in 3 Days, Not 3 Months
How automated feature discovery and competitive gap analysis accelerate M&A technical evaluation from months to days.
AI Development Productivity Tips: Avoid the 73% Failure Rate
Most AI tool adoptions fail to deliver ROI. Here are the productivity patterns that actually work for engineering teams.
Code Ownership at Scale: Who Knows What in Your Codebase
CODEOWNERS files are always stale. Git history tells the truth about who actually maintains, reviews, and understands each part of your codebase.
What Engineering Leaders Get Wrong About AI Tool Adoption
Most teams measure AI tool success by adoption rate. The right metric is whether hard tickets get easier. Here's the framework that works.
Why Engineering Velocity Metrics Are Misleading
Story points, lines of code, and PR count don't measure what matters. Here's what to track instead.
How AI Is Changing Technical Interviews (And Why It Should)
LeetCode doesn't predict job performance. Codebase navigation and system understanding do. How interviews should evolve for the AI era.
The CTO's Guide to AI Tool ROI
A framework for measuring actual return on AI coding tool investments. Spoiler: adoption rate is the wrong metric.
How to Conduct an AI Readiness Assessment for Your Engineering Team
Before buying AI tools, understand where your team will actually benefit. A practical framework for assessing AI readiness.
What Happens When Your Best Engineer Leaves
Knowledge concentration is a ticking time bomb. When a key engineer leaves, the blast radius extends far beyond their code.
How Engineering Teams Should Prepare for AI-Native Development
AI-native development isn't about using more AI tools. It's about restructuring workflows around AI strengths and human judgment.