Sahil Singh
Founder & CEO
Sahil Singh is the Founder and CEO of Glue, the AI codebase intelligence platform that gives product teams X-ray vision into their software. Before Glue, Sahil spent over a decade building and leading engineering teams where he repeatedly saw the same pattern: product managers making roadmap decisions without understanding their own codebase, engineering managers unable to quantify technical debt to leadership, and entire teams losing months of productivity to knowledge that lived in one person's head. Glue was born from a simple observation — the people making product decisions and the people writing code are working off completely different maps. Sahil built Glue to bridge that gap using AI that reads your codebase and translates it into strategic insight anyone on the team can use. He writes about the intersection of AI, product strategy, and engineering leadership — specifically about why the old way of building software (where product and engineering operate in separate worlds) is dying.
How to Convince Your CTO to Invest in Developer Experience
You know the problem. Your team loses two hours a day to slow builds. New engineers take three weeks to understand the codebase. Your CI/CD pipeline feels like it was built in 2015. And when developers finally ship code, half the bugs should have been caught earlier.
I Analyzed 50 Codebases - Here Are the 5 Patterns That Predict Tech Debt
Tech debt doesn't appear suddenly. It leaves signatures.
Why Your Sprint Velocity Is Lying to You
The moment your team's sprint velocity becomes a performance metric, it stops being useful.
Open Source Tools Every Engineering Leader Should Know About in 2026
Building and scaling engineering teams in 2026 means choosing the right tools. The good news: the best tools are often open-source. The challenging news: there are thousands of them, and most won't move the needle for your team.
Building an Awesome List That Actually Gets Stars (Step-by-Step)
A few months ago, I decided to start an awesome list. Not because I thought I'd become famous - mostly because I was frustrated that no single repository existed as a definitive guide to codebase intelligence tools and practices. I wanted to solve my own problem and maybe help others along the way.
The Bus Factor Problem: What Happens When Your Best Engineer Leaves
Your lead backend engineer walks into your office on a Tuesday morning and tells you they're leaving. Two weeks notice. They found a new opportunity. They're excited about it.
Technical Debt Is Not a Metaphor - Here's How to Put a Dollar Figure on It
Ward Cunningham introduced the technical debt metaphor in 1992, and it was useful. Talking about debt helped engineering teams communicate with business stakeholders about the cost of shortcuts. But metaphors have limits. The moment you want to make an actual decision about whether to refactor or ship the next feature, a metaphor breaks down. You need numbers.
I Built a CLI Tool to Score Codebase Health - Here's What I Learned
Three months ago, I built a CLI tool to score codebase health. I called it `codebase-health-score` (original, I know). The goal was simple: give a team a single number that represents the overall health of their system.
Why 95% of Product Managers Can't Read Code (And Why That's a $1.85T Problem)
I sat in a product review meeting at a Series B fintech startup where the VP of Product confidently proposed moving a feature's deadline up by two weeks. The engineering director's face went pale. "That's literally impossible," she said. "We'd have to refactor the payment service first, and that touches core systems."
Code Refactoring: When, Why, and How to Refactor Without Breaking Everything
Refactoring improves code without changing behavior. Learn when to refactor, which techniques to use, how to build the business case, and how to do it safely at scale.
Microservices vs Monolith: How to Choose the Right Architecture in 2026
Microservices or monolith? The answer depends on your team size, domain complexity, and operational maturity. This guide helps you make the right architectural call.
Code Smell: How to Detect, Diagnose, and Fix the 20 Most Common Code Smells
Code smells signal deeper design problems. Learn to identify the 20 most common smells, understand their root causes, and fix them with targeted refactoring patterns.
Software Development Lifecycle (SDLC): Every Phase Explained for 2026
Understand every phase of the SDLC: planning, analysis, design, development, testing, deployment, and maintenance. Includes model comparisons and modern AI-augmented workflows.
Shift Left: How Moving Testing Earlier Cuts Defect Costs by 100x
Shift left testing and security catches defects when they are cheapest to fix. Learn implementation strategies, tools, and how to measure the ROI of shifting left.
Incident Management: From Alert to Resolution to Prevention
Build an incident management process that reduces MTTR and prevents repeat failures. Covers severity levels, on-call rotations, postmortems, and the role of codebase context.
Feature Flags: The Complete Guide to Safe, Fast Feature Releases
Feature flags decouple deployment from release. Learn implementation patterns, management best practices, and how to avoid the flag debt trap.
Clean Code: Principles, Practices, and the Real Cost of Messy Code
Clean code is not about aesthetics — it is about velocity. Learn the principles that reduce bugs, speed up onboarding, and cut maintenance costs by measurable amounts.
Platform Engineering: Building the Internal Developer Platform Your Team Needs
Platform engineering reduces cognitive load so developers can ship faster. Learn how to build an internal developer platform with self-service, golden paths, and codebase intelligence.
API Documentation: How to Write Docs Developers Actually Want to Read
Great API documentation is the #1 driver of developer adoption. Learn structure, tools, examples, and how AI auto-generates endpoint docs from your codebase.
Observability: Beyond Monitoring — How to Actually Understand Your Systems
Observability goes beyond monitoring. Learn the three pillars (logs, metrics, traces), top tools, and how codebase intelligence adds the missing context layer.
Design Patterns in Software Engineering: A Practical Guide with Real Examples
Learn the 23 GoF design patterns with modern, real-world examples. Includes when to use each pattern, anti-patterns to avoid, and how AI can detect pattern opportunities.
Tech Stack: How to Choose, Document, and Communicate Your Technology Stack
How to choose a tech stack that scales, document it so everyone understands it, and avoid the architecture decisions that create years of technical debt.
DORA Metrics: The 4 Key Metrics That Predict Engineering Team Performance
Master the 4 DORA metrics: deployment frequency, lead time, change failure rate, and MTTR. Includes benchmarks, dashboards, and how to improve each metric.
What Is DevOps? A Plain-English Guide for Non-Engineers
DevOps explained without the jargon. Learn what DevOps means, why it matters for product delivery, and how it connects CI/CD, monitoring, and team culture.
Technical Debt: What It Really Is, How to Measure It, and When to Pay It Down
Technical debt costs the average team 42% of development time. Learn how to identify, quantify, and strategically eliminate it with data-driven prioritization.
CI/CD Pipeline: The Definitive Guide to Continuous Integration & Delivery
Build a CI/CD pipeline that actually works. Covers pipeline stages, tool comparison, security integration, and the metrics that matter for deployment velocity.
Burndown Charts Explained: How to Read, Build, and Actually Use Them
Master burndown charts: how to read them, build them, and use them to predict sprint outcomes. Includes templates, examples, and common anti-patterns.
Trunk-Based Development: Why Top Teams Ship Faster Without Long-Lived Branches
Learn why Google, Meta, and top engineering teams use trunk-based development. Includes migration guide, CI/CD setup, and how to handle feature flags.
Technical Documentation: The Complete Guide for Engineering Teams [2026]
Everything you need to know about technical documentation: types, templates, tools, and the AI-powered workflows that cut documentation time by 60%.
AI for Product Teams: The 2026 Playbook
How product teams can use AI for codebase intelligence, competitive analysis, spec writing, and planning.
The Complete Guide to Software Estimation
Everything you need to know about software estimation: why it fails, what works, and how to make it better.
The Product Manager's Guide to Understanding Your Codebase
The comprehensive guide for product managers who want to understand their codebase without learning to code.
ChatGPT for Product Managers: What It Can and Can't Do
ChatGPT dethroned Jira as PMs' top tool. But it has a blind spot: it can't see your codebase.
AI Codebase Analysis: What It Is and Why It Matters
AI can now read and understand entire codebases. Here's what that means for product teams.
Product Roadmap Keeps Slipping? Here's the Real Reason
65% of PMs say roadmapping is their hardest task. The problem isn't your process — it's your visibility into the codebase.
Non-Technical Product Managers: Your Secret Advantage
Being non-technical isn't a weakness. You ask the questions engineers forget. Here's how to leverage it.
Product Manager AI Assistant: What to Look For in 2026
98% of PMs use AI but only 1.1% for strategic work. Here's what a real PM AI assistant should actually do.
Can AI Replace Product Managers? Wrong Question.
49K people search this monthly. The answer: AI won't replace PMs — it'll replace PMs who can't see their product.
The PM-Engineer Trust Gap: Why Product Managers Can't See Their Own Product
Engineers say PMs ask dumb questions. PMs say engineers can't explain. The real issue: PMs can't see the codebase.
Story Points Are Useless — Here's What Actually Works
Engineers call it 'estimation theater.' Here's why story points fail and what high-performing teams use instead.
AI for Product Management: What Actually Helps You Think (Not Just Produce)
98% of PMs use AI but only 1.1% for roadmap ideas. Here's how AI should actually help product leaders think, not just produce.
The Complete Guide to Competitive Intelligence for SaaS Product Teams
65% of sales are competitive. Your team rates itself 3.8/10. Here's how to build a CI program connected to code reality.
The Real Cost of Nobody Knowing How the System Works
$109M wasted per $1B invested. 70% of failures trace to requirements. The root cause: nobody knows how the whole system works.
Why Sprint Planning Is Broken (And What to Do Instead)
Sprint planning is estimation theater. Here's why it fails and what high-performing teams do instead.
Software Architecture for Product Managers: See Your Product Without Reading Code
A product manager's guide to understanding software architecture, dependencies, and code structure without learning to code.
Tribal Knowledge in Software Teams: The Silent Productivity Killer
Tribal knowledge costs engineering teams 17+ hours/week. Learn how to surface, document, and share institutional knowledge.
Glue — AI Codebase Intelligence for Product Teams
Glue gives product managers, engineering leaders, and CTOs instant visibility into their codebase. No code skills needed.