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AI Concepts & Analytics

How LinearB thinks about AI.

Steven Silverstone
7 articles by 1 author

AI Analytics: Adoption, Workflow, and Impact

Use AI Analytics to measure AI adoption, workflow touchpoints, and impact across pull requests, repositories, and teams. This is the supported path after the Copilot/Cursor Metrics dashboards and AI Tools Usage view were deprecated on April 2, 2026.

AI FAQs - Governance, Detection & Trust

Answers common questions about AI detection, metrics, security, MCP access, and AI Reviews. Use this FAQ to understand how LinearB identifies AI activity and how AI insights are calculated and protected.

AI Insights – Overview

Understand how AI tools affect your engineering performance. AI Insights connects AI usage, acceptance, and trust signals to delivery, quality, and DORA metrics so you can measure real impact, not just adoption.

AI Rule Files

Track where AI agents are being formally introduced into your repositories. AI Rule Files reveal which tools are configured, how widely they’re used, and where multiple agents coexist.

Configuring AI Classification Thresholds

Use Classification Thresholds to control how LinearB classifies commits and pull requests as AI-assisted. These settings affect how AI activity is labeled in the product and how AI Analytics metrics…

How LinearB Calculates AI Attribution

Learn how LinearB identifies AI-assisted development across your codebase. This article explains how AI attribution is calculated using commit signals and AI tool data, how confidence thresholds work, and how results are applied to commits and pull requests.

Improving AI Attribution Accuracy and Coverage

Best practices for improving AI attribution accuracy and coverage in LinearB using commit metadata, branch signals, contributor mapping, and frequent commits.

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