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Blog
AI agents, engineering automation, product intelligence, and how teams ship faster when their tools finally talk to each other.
Most dependency mapping tools are built for IT infrastructure teams. Code-level dependency mapping is a different discipline - one that helps engineering teams understand blast radius before making changes.
Arjun Mehta
Dependencies are the hidden architecture of your software. Learn how to map, visualize, and manage code dependencies to prevent incidents and improve code quality.
Principal Engineer
Copilot writes code. Glue understands it. Why product managers and engineering leaders need both tools in 2026.
Vaibhav Verma
CTO & Co-founder
Duplicate tickets aren't a search problem—they're a context problem. Why connecting codebase intelligence to issue tracking eliminates duplicate work and improves triage.
Glue Team
Editorial Team
The fundamental gap in work tracking tools: they track status, not resolution. Why ghost work happens and how verification closes the gap.
PMs: learn what engineers see in git history, complexity analysis, and test coverage. Ask better questions about code quality without custom reports.
Compare static analysis, architecture tools, and AI codebase intelligence. Choose the right tool for your problem.
AI codebase analysis isn't code generation. It's making large codebases understandable without reading every line. Here's what actually matters.
Service, library, and data dependencies drive estimates and incidents. Make them visible before they break.
How lack of codebase clarity compounds: opacity creates more opacity, slowing incidents, onboarding, and feature development. A quantified view.
A practical guide to reducing technical debt continuously. Avoid failed "debt quarters" with the strangler fig pattern and continuous improvement.
What technical skills actually matter for PMs and what's a better investment than coding.
Priya Shankar
Head of Product
How code intelligence platforms bridge the gap between engineering insights and product decisions.
The 5 questions PMs should answer about their codebase. Proxy questions and strategies for understanding technical reality without learning to code.
Measure code health through understandability, modifiability, and resilience. Learn metrics that correlate with engineering velocity and incident rates.
Why PMs struggle with visibility into technical constraints and how codebase access changes product decisions and estimation accuracy.
