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Measuring the impact of Copilot and Cursor on engineering productivity

Track GitHub Copilot and Cursor adoption, usage, and ROI in one unified LinearB dashboard. Correlate AI metrics with delivery performance and outcomes.

Is the future of AI development spec-driven?

Amit Patel, Director of Software Development for AWS Kiro, explains why spec-driven development is the future of AI development, solving the context loss...

The new engineering leader: navigating AI as a core business metric

Lake Dai, a globally recognized AI expert, explains why the modern CTO must adopt the financial acumen of a CFO to manage AI compute costs (30-50% of OpEx) and...

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What is the best AI code review tool?

Discover which AI code review tools actually work in production: This controlled benchmark reveals why LinearB's statefulness and signal-to-noise ratio...

Closing the AI gap: How to measure adoption and impact in engineering (without falling into the ROI trap)

Measure AI adoption and impact in engineering with usage visibility, throughput + quality metrics, and credible ROI narratives grounded in delivery data.

A guild, not a mandate: Superhuman's blueprint for AI success

Superhuman's Loic Houssier shares a blueprint for driving bottom-up AI adoption by overcoming skepticism with a trusted internal champion, fostering an AI...

AI as a value multiplier: a human-centric approach to engineering leadership

Super.com's Matt Culver explains why AI should be used as a value multiplier, not a cost-cutter, advocating for a human-centric approach to engineering...

Vector databases: the essential foundation for reliable AI applications

Pinecone's Ram Sriharsha explains why vector databases are the essential foundation for reliable AI applications, enabling RAG for trustworthy output and...

Enterprise vibe coding: how Salesforce balances speed with governance

Salesforce's Dan Fernandez details the Enterprise Vibe Coding strategy, explaining how to balance the speed of agentic AI with governance by using a Trust...

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