LinearB Blog | Insights and Best Practices for Software Engineering Leaders

Mastering Ralph loops transforms software engineering with LLM automation

Geoffrey Huntley introduces the Ralph Wiggum Loop, a deterministic LLM pattern that manages context rot and compaction by treating the context window like...

14,000 Engineers, One Pipeline: How Capital One Standardizes for Speed

Capital One’s Ameesh Paleja explains how standardization acts as a force multiplier for 14,000 technologists, enabling AI-powered automation and a unified...

Why AI elevates engineering leadership instead of replacing it

Engineering executive Manoj Mohan explains why AI elevates leadership by shifting the focus from code generation to high-level strategy, utilizing his 3GF...

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The end of pre-compute: why the future of AI is on-demand

Chalk co-founder Elliot Marx argues that pre-compute fails in real-time AI by causing latency and errors, advocating for on-demand, real-time data pipelines to...

DevEx

The 2025 Stack Overflow survey reveals how autonomy and AI are reshaping development

Erin Yepis of Stack Overflow analyzes the 2025 Developer Survey, detailing the AI paradox (high adoption, falling trust), the critical importance of autonomy...

The AI maximalist: why engineering leaders need to invert their thinking

Kubernetes co-creator Craig McLuckie advocates for the "AI Maximalist" philosophy, urging engineering leaders to invert their thinking by asking "Why can't we...

Here’s where AI coding agents are delivering reliable code refactoring

New research analyzing 15,000+ AI refactorings reveals agents excel at routine cleanup but struggle with architectural changes. Learn where AI delivers value...

Speed as a moat: building the open innovation engine at AMD

AMD's Anush Elangovan reveals why speed of innovation is the only true moat in AI, detailing how the open-source ROCm stack fosters an "innovation flywheel"...

Product

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.

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