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What two years of telemetry data from 22,000 developers reveals about AI's real impact on developer productivity, code quality, and business risk in 2026.

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.

Learn how a global industrial technology leader used Faros to unify 40,000 engineers and build the measurement foundation for AI transformation.

Learn how a leading identity security provider uses Faros to power an AI-driven engineering organization and achieve a 35% increase in velocity.

Benchmark data from 320 teams comparing monorepo and polyrepo PR cycle times. What “good” looks like and why developer infrastructure matters, especially for AI agents.

Jellyfish falling short at scale? See why VPs of Engineering and CTOs at large enterprises choose Faros for deeper insights, flexible org models, and AI impact tracking.

If you’re searching for DORA metrics tools, start here. This 2026 guide explains what’s new in DORA, why engineering intelligence platforms are the best tools for tracking DORA metrics and developer productivity insights, and why Faros AI is the top choice for enterprise teams amongst competitors.

A developer-focused look at the best AI models for coding at the beginning of 2026. This AI coding model comparison breaks down the strengths and weaknesses of GPT 5.2, Opus 4.5, Gemini 3 Pro—and more.

Track Claude Code usage, adoption, and engineering impact using Faros AI’s observability platform.

There's a better way to measure AI productivity than counting lines of code. Focus on outcome metrics that prove business value: cycle times, quality, and delivery velocity. Learn why lines of code fails as an AI productivity metric, what outcome-based alternatives actually work, and when tracking AI code volume matters for governance and risk management.