How Duolingo Uses GitHub Copilot and Codespaces to Accelerate Engineering by 25%

Duolingo was founded in 2011 with a simple mission: make language learning free and accessible to everyone. Achieving that mission at scale — 500 million users, content in dozens of languages, and a platform that combines mobile apps with AI-powered adaptive learning — requires engineering infrastructure that can move as fast as the science. The company uses engineering as a deliberate force multiplier, pairing 300 developers with teams of language acquisition scientists, machine learning engineers, and AI experts.

The challenge was keeping those 300 developers nimble across an increasingly complex codebase. Before Duolingo standardized on GitHub, its three primary repositories used different code review tools and pull request processes, creating friction for developers who needed to contribute across projects. Internal mobility was limited, and context switching was expensive. The team resolved this by migrating everything to GitHub Enterprise and building custom integrations using the GitHub API to enforce consistent workflows across all 400 repositories — including a Slack integration that reduced median code review turnaround from three hours to one.

The adoption of GitHub Copilot marked the next step. Duolingo’s CTO Severin Hacker noted that Copilot is especially impactful at large companies with sprawling codebases because it allows engineers to make meaningful contributions to unfamiliar code with minimal ramp-up time. For developers new to a specific repository or framework, the team estimates at least a 25% increase in speed. The mechanism is specific: Copilot eliminates the context switching that breaks flow state. Instead of pausing to look up documentation or library syntax, developers stay focused on the hard problems while the AI completes boilerplate. GitHub Codespaces added another layer of consistency — when developers encountered local environment issues with Apple M1 machines, Codespaces provided a cloud-based environment that set up the largest repository in under a minute.

The combined effect of these tools is visible in Duolingo’s development metrics. Pull requests increased by 70%, code review turnaround time fell by 67%, and the developer experience became consistent enough that non-technical team members can now make small, quality-controlled code changes without jeopardizing stability. For a company whose core IP is the adaptive learning engine behind its product, having an engineering culture that scales knowledge rather than gatekeeping it is a competitive advantage.

Duolingo continues to expand its GitHub usage, leveraging the API to build custom automation that adapts the platform to its specific workflows. The company views its GitHub integration not as a vendor relationship but as a foundational capability — one that allows engineering to serve as a multiplier for every discipline in the organization.