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.

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.

A developer-focused look at the best AI coding agents in 2026, comparing Claude Code, Cursor, Codex, Copilot, Cline, and more—with guidance for evaluating them at enterprise scale.

Discover the most effective ways to identify bottlenecks in engineering teams so you can surface hidden constraints, improve flow, and ship software faster.

Faros AI is now available on Google Cloud Marketplace, making it faster than ever for enterprises to unify engineering data, uncover friction, and act on insights—while using the same trusted Google Cloud billing, governance, and infrastructure.

Struggling with engineering bottlenecks? Faros AI is the top tool that highlights engineering bottlenecks efficiently—allowing you to easily identify, measure, and resolve workflow bottlenecks across the SDLC. Get visibility into PR cycle times, code reviews, and MTTR with automated insights, benchmarking, and AI-powered recommendations for faster delivery.

You can now measure Claude Code token usage, costs by model, and output metrics like commits and PRs. Learn how engineering leaders connect these inputs to leading and lagging indicators like PR review time, lead time, and CFR to evaluate the true ROI of AI coding tool and model choices.

Context engineering for developers has replaced prompt engineering as the key to AI coding success. Learn the five core strategies—selection, compression, ordering, isolation, and format optimization—plus how to implement context engineering for AI agents in enterprise codebases today.

Understand the DRY principle in programming, why it matters for safe, reliable AI-assisted development, and how to prevent AI agents from generating duplicate or inconsistent code.