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Hypervelocity Engineering: An Overview

Welcome to The HVE Runbook. This guide will help you and your team embrace Hypervelocity Engineering—a paradigm shift in how multi-disciplinary teams collaborate with AI to achieve dramatically increased velocity without sacrificing quality.

What is Hypervelocity Engineering?​

Hypervelocity Engineering (HVE) is not about "moving faster and breaking more things." Instead, it's about leveraging AI collaboration to:

  • Increase velocity while maintaining or improving code quality
  • Accelerate iteratively by building AI guidance that compounds over time
  • Enable the entire team—not just engineers—to work at hypervelocity
  • Focus human expertise on high-value decisions while AI handles boilerplate and common patterns

HVE in Practice​

Hypervelocity Engineering transforms how teams work across all disciplines:

For Engineering Teams​

  • Rapid prototyping: Move from paper mockups and scheduled meetings to real-time interactive prototypes built collaboratively with stakeholders
  • Quality by default: AI-guided code follows your team's standards for security, observability, error-handling, and best practices
  • Iterative development: Break down solutions into specific features and components rather than requesting entire applications
  • Reduced code churn: Well-factored, atomic files work better with AI agents and reduce merge complexity
  • AI-assisted review: Common issues (memory leaks, off-by-one errors, threading issues) are caught automatically, letting human reviewers focus on architecture and business value

For Product & Design​

  • Cross-functional pairing: TPMs collaborate with AI to sharpen stories, devise risk mitigations, and develop persuasive stakeholder strategies
  • Rapid design iteration: Designers work with AI on color theory, storyboards, and mockups to quickly explore options
  • Data-driven decisions: ADRs and implementations can be co-developed, providing concrete data for team discussions

For Security & Quality​

  • Proactive threat modeling: Security SMEs pair with AI to identify attack surfaces and devise mitigations
  • Embedded standards: Modify team AI guidelines to ensure all future code adheres to security recommendations
  • Automated validation: Well-structured experiment code with unit tests catches subtle bugs before they cause rework

The HVE Approach: Iterative, Not All-at-Once​

HVE is fundamentally iterative. Rather than "vibe coding" with prompts like "build me an entire application," successful HVE teams:

  1. Break down solutions into specific components and features
  2. Provide context to guide AI toward the right patterns
  3. Refine incrementally through collaboration between human expertise and AI capabilities
  4. Build guidance over time that increases both velocity and acceleration

Think of early explorations with AI as an artist sketching composition before committing paint to canvas—easy to iterate, low cost to throw away, but invaluable for learning.

Key Steps to Begin​

  1. Identify your team's highest-friction activities
  2. Create shared engineering standards that both humans and AI can follow
  3. Encourage cross-functional pairing with AI assistants
  4. Measure both immediate velocity improvements and long-term quality outcomes

Core Principles​

As you explore HVE, remember:

  • Quality over speed: Hypervelocity doesn't mean rushing or breaking things
  • Team-wide transformation: HVE isn't limited to engineers—entire teams must reinvent how they work
  • Flexibility with foundations: Embrace curiosity and new practices while maintaining core principles of well-built software
  • Human expertise remains essential: AI augments human decision-making; it doesn't replace it

We're entering an evolving world. Success requires teams to embrace flexibility and experimentation without abandoning the principles that make software systems truly valuable to users.