Inspring interviews to Nicole Forsgren by The Pragmatic Engineer
Gergerly Orosz and Nicole Forsgren are two of the more inspiring personalities I follow on the tech space.
Gergely Orosz is a software engineer and author renowned for his influential work in the tech industry. He writes The Pragmatic Engineer, the leading technology newsletter on Substack, where he shares insights on software engineering practices and industry trends. Orosz has authored several books, including The Software Engineer’s Guidebook, Building Mobile Apps at Scale, and The Tech Resume Inside-Out. His professional journey encompasses roles at prominent companies such as Uber, Microsoft, Skype, Skyscanner, and JP Morgan.
Dr. Nicole Forsgren is a distinguished expert in DevOps and developer productivity, recognized for her extensive research and leadership in the field. She co-authored the award-winning book Accelerate: The Science of Lean Software and DevOps and was the lead investigator on the State of DevOps reports. Forsgren co-founded DevOps Research and Assessment (DORA), which was later acquired by Google. She has held roles as a software engineer, professor, and performance engineer, and currently serves as a Partner at Microsoft Research, leading the Developer Experience Lab. Her work focuses on leveraging AI to enhance developer productivity and well-being.
Having the twos in a podcast episode of The Pragmatic Engineer was really great.
Their conversation was packed with valuable insights on engineering metrics, AI in development, DevEx (Developer Experience), and cultural transformation in tech organizations.
Here are my key takeaways from their discussion
1. Using Metrics Wisely: A Holistic Approach
Metrics like pull requests (PRs) and code diffs can provide a reasonable view of the work being done. However, context is crucial. Senior engineers, for instance, may not have a high PR output because they contribute in other impactful ways, such as mentorship and system design. To get a true picture of productivity, metrics should be used holistically rather than in isolation.
2. AI Success Starts with Solid Fundamentals
Building AI-driven applications is incredibly challenging if the foundational tools and processes aren’t functioning properly. Before implementing AI solutions, ensure that your engineering infrastructure and workflows are stable and efficient.
3. DevEx is Shaped by Policies and Processes
Policies and processes significantly impact Developer Experience. Organizations should strive to minimize unnecessary friction by refining these frameworks to support, rather than hinder, engineering teams.
4. Curiosity and Open-Mindedness Drive Success
A key to growth is questioning existing practices. Just because something works today doesn’t mean it’s the best approach. There’s always room for improvement — stay curious and open to change.
5. Onboarding: Measuring the First Commit
A strong indicator of a company’s engineering efficiency is the time it takes for a new hire to make their first commit. A smooth onboarding process is essential for productivity and retention.
6. The Transformation of Banks into Tech-First Companies
Many banks have transitioned into technology-driven organizations. However, simply changing the tech team’s culture isn’t enough — the entire organization must embrace a shift in mindset. A good strategy is to start small and let the cultural shift bubble up across the company.
7. Driving Change: Observe, Validate, Act
Implementing meaningful change starts with observation. Confirm your impressions with colleagues, call for action, and foster creativity — initiatives like Hack Days can be great catalysts for transformation. Continuous communication is key to sustaining progress.
8. AI’s Impact on Metrics: SPACE vs. DORA
AI coding assistants are reshaping the way engineers work, and their impact is more visible in SPACE metrics (which focus on developer well-being and collaboration) rather than DORA metrics (which emphasize deployment and operational performance). Understanding this shift is crucial for evaluating AI’s role in software development.
9. AI Usage: Customize to Fit Your Flow
AI should be leveraged in ways that truly enhance productivity. Be open to adjusting your workflow — whether that means using AI for auto-suggestions, integrating chat features in the IDE, or engaging with AI tools externally. The key is maintaining your flow state.
Final Thoughts
This conversation between Gergely Orosz and Nicole Forsgren highlighted the importance of adaptability, thoughtful measurement, and cultural shifts in engineering and leadership. Whether you’re looking to enhance Developer Experience, integrate AI, or drive change in your organization, these insights provide a valuable roadmap for success.
