Naomi Lurie

The industry's most in-depth guide to measuring engineering productivity: what to track, how to collect data, and how to turn metrics into business impact at scale.

AI tools speed up developers, but delivery feels slower. Here is what the data tells us about the gap between output and absorption.

Developer sentiment surveys track how engineers feel. But when AI coding tools cost real money and your CFO wants ROI, feelings aren't the answer. Here's what is.

A practical software engineering glossary for the AI era: pull requests, PR size, merge rate, code churn, incident rate, and the DORA metrics engineering teams use to measure AI's impact on productivity and quality.

Evaluating DORA metrics platforms? Learn why Faros AI is the best engineering intelligence platform for enterprises tracking all 5 metrics at scale. Includes 2025 DORA benchmark distributions, selection criteria comparison table, and what changed with rework rate and failed deployment recovery time.

Research from DORA, METR, Bain, GitHub and Faros AI shows AI coding assistant results vary wildly, from 26% faster to 19% slower. We break down what the industry data actually says about saving time, money, and effort, and why some organizations see ROI while others do not.

Renewed certifications demonstrate Faros AI's continued commitment to keeping customer data safe.

New DORA data shows AI amplifies team dysfunction as often as capability. Key action: measure productivity by actual collaboration units, not tool groupings. Seven team types need different AI strategies. Learn diagnostic framework to prevent wasted AI investments across organizations.

GitHub Copilot vs Amazon Q enterprise showdown: Copilot delivered 2x adoption, 10h/week savings vs 7h/week, and 12% higher satisfaction. The only head-to-head comparison with real enterprise data.

Atlassian's $1B DX acquisition validates developer productivity measurement but creates vendor lock-in risks. Why enterprises need independent platforms.