Are you an engineering leader trying to understand how to measure the true impact of your teams and tools, especially in the era of AI? Balancing developer happiness, velocity, and quality isn't easy, and success isn't driven by just one of these factors in isolation. It's when these three foundational zones work in unison that organizations achieve their best results and see improved business outcomes.

In this episode, we dive into GitHub’s Engineering System Success Playbook (ESSP), a structured yet flexible framework designed to help organizations drive meaningful, measurable improvements and align engineering practices with business goals. Inspired by leading frameworks like SPACE, DORA, and DevEx, the ESSP offers a balanced and comprehensive approach to understanding your engineering system.

The ESSP centers on a systems thinking approach, recognizing that engineering is an interconnected ecosystem. It prioritizes long-term, sustainable improvements over quick wins that might cause negative downstream effects.

The playbook outlines a three-step process to achieve engineering success:

* Step 1: Identify the current barriers to success. This involves understanding your current state and desired future state, auditing processes, gathering data (both quantitative and qualitative), understanding organizational priorities, and conducting research to find friction points. Prioritizing the most impactful barriers and target metrics is key.

* Step 2: Evaluate what needs to be done to achieve your goals. Here, you identify potential solutions, estimate their cost and resource requirements, and conduct a risk, cost, and benefit analysis. Engaging stakeholders, potentially starting with pilots, and creating mitigation plans are crucial parts of this step.

* Step 3: Implement your changes, monitor results, and adjust. This involves assigning ownership, fostering communication and transparency, training teams, and monitoring performance using both metrics and qualitative feedback. Analyzing results, pivoting if needed, and maintaining continuous feedback loops are essential for sustained progress.

At the heart of the ESSP are 12 key metrics (three for each zone: Quality, Velocity, Developer Happiness, and Business Outcomes) that you can track to measure performance over time. These are primarily downstream or lagging metrics, but the playbook emphasizes complementing them with leading indicators that provide early signals of friction or potential impact.

Examples of the recommended metrics include:

* Quality: Change failure rate, Failed deployment recovery time, Code security and maintainability.

* Velocity: Lead time, Deployment frequency, PRs merged per developer.

* Developer Happiness: Flow state experience, Engineering tooling satisfaction, Copilot satisfaction.

* Business Outcomes: AI leverage, Engineering expenses to revenue, Feature engineering expenses to total engineering expenses.

The ESSP highlights the importance of using these metrics carefully, focusing on improvements over time rather than benchmarks, and avoiding the pitfalls of gamification that can incentivize undesirable behaviors. Instead, thoughtful gamification can align with intrinsic motivators like mastering skills or contributing to team goals.

Beyond the core steps, the playbook stresses concepts like tailoring the approach to your team's needs, embracing change management frameworks, fostering a growth mindset that values learning from every attempt, and most importantly, involving your engineering teams early in the process. Engineering is a team sport, and metrics should focus on teams and organizations, not individual developers.

Ultimately, the ESSP provides a way to connect engineering improvements to business-level outcomes. Faster delivery and lower change failure rates link to market responsiveness and customer trust. Happier developers contribute to productivity and innovation. Metrics like AI leverage help quantify the ROI of investments like GitHub Copilot. This holistic view helps engineering leaders demonstrate tangible value to the business.

Whether you're adopting AI tools or tackling existing bottlenecks, the ESSP offers a blueprint to systematically increase developer joy, ship value faster, and raise the quality bar. Learn how to use this playbook to unlock your engineering team's full potential.Get the deep dive blog post here.

Ever wondered how quickly your exposed API keys or credentials can turn into a cybersecurity nightmare? This episode uncovers eye-opening research revealing that attackers exploit leaked credentials within seconds—often faster than your security team can respond. We deep dive into real-world breach examples, common attack vectors, and the alarming reality behind rotating secrets. Discover practical tips for securing your organization's sensitive information in today’s high-speed digital threat landscape.

Show Notes:

* Introduction – Why leaked credentials matter

* "Gone in 60 Seconds": The shocking speed of credential exploitation

* Real-world breaches: Python Software Foundation, Toyota, Hugging Face

* The myth of credential rotation as a reliable defense

* Common leak vectors and attacker methods explained

* Proactive steps: How to protect your API keys and secrets effectively

* Wrap-up and actionable security takeaways

Resources mentioned in this episode:

* Full Blog Post: Gone in 60 Seconds: Your API Keys in Flight

Follow along and enhance your cybersecurity awareness—because once your secrets leak, every second counts.

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In this episode, we explore OpenAI’s groundbreaking new models—o3 and o4-mini—and their revolutionary approach called Interwoven Intelligence. Unlike traditional AI, these models integrate tools directly into their reasoning, enabling them to search the web, crunch numbers, interpret images, and handle complex tasks autonomously. Join us as we dive into how this seamless integration is reshaping AI’s capabilities, making it smarter, more practical, and more versatile in everyday life—from simplifying trip planning and interpreting dense documents, to supercharging coding tasks. This isn’t just an upgrade; it’s the future of intelligent assistance.

Podcast Show Notes:

* Introduction to Interwoven Intelligence: Why giving AI integrated tools changes everything.

* Understanding the agentic reasoning loop: Think-Act-Reflect in action.

* Practical impacts in daily tasks: Travel planning, document analysis, and detailed queries answered with ease.

* Advantages over traditional models: Enhanced accuracy, reduced hallucinations, and autonomy.

* Coding revolution: How o3 and o4-mini can transform GitHub Copilot and software development workflows.

* When to use advanced models vs. cheaper, simpler alternatives: A quick guide for decision-making.

* Final insights: The exciting future of truly intelligent, autonomous AI assistance.

Listen now and get a glimpse of the AI-powered future that's already beginning to unfold!

Ever felt like your large language model applications hit a wall when faced with complex tasks? This podcast dives into how we can move beyond single prompt-response interactions and build truly agentic systems that can tackle intricate challenges like trip planning or code refactoring.

We introduce the idea of an augmented LLM as the fundamental building block. Think of your standard LLM but with added 'superpowers' like the ability to retrieve information, use external tools, and remember context. These augmentations are key to creating more capable AI.

We’ll explores five core design patterns that leverage these augmented LLMs to construct robust workflows . These patterns are:

* Routing

* Prompt Chaining

* Parallelization

* Orchestrator-Workers

* Evaluator-Optimizer

We’ll also discuss a practical example of this, a Coding Agent, inspired by GitHub Copilot.

We’ll cover a valuable overview of the fundamental concepts and design patterns necessary to build sophisticated AI applications that can handle much more than simple questions.

It's about moving towards LLM-powered agents that can truly take action and manage complex processes.