# Duolingo's Implementation of GitHub Copilot and AI-Powered Development Tools ## Company Overview and Challenge Duolingo, the world's leading language learning platform with over 500 million users, faced significant challenges in maintaining consistent development standards and workflows across their expanding codebase. The company's mission to provide universal access to education required their engineering team to work efficiently while collaborating with language learning scientists, machine learning engineers, and AI experts. ## Technical Infrastructure Evolution ### Initial Setup and Challenges - Started with basic GitHub usage in 2011 for source code management - Initially relied on third-party tools like Gerrit and PullApprove - Maintained three primary repositories with varying cultures and PR processes - Inconsistent workflows hindered developer mobility between projects ### Modernization and Integration - Migrated from third-party tools to consolidated GitHub Enterprise ecosystem - Expanded from 3 repositories to 400 as part of microservices architecture - Implemented custom GitHub bot integrations for workflow standardization - Created Slack integration reducing code review turnaround time from 3 hours to 1 hour ## GitHub Copilot Implementation ### Key Features and Benefits - AI-powered pair programming providing autocomplete-style suggestions - Contextual awareness of the entire codebase - Two primary suggestion methods: ### Performance Improvements - 25% increase in developer speed for those new to specific repositories - 10% increase in speed for developers familiar with the codebase - Significant reduction in time spent on boilerplate code - Decreased context switching and documentation lookup needs ### Integration Advantages - Seamless integration with existing development tools - Simple organization-wide deployment process - Enhanced ability to work with unfamiliar code - Maintains developer flow state during complex tasks ## Codespaces Integration ### Implementation Benefits - Reduced environment setup time from days to minutes - One-minute setup time for largest repository - Solved local environment issues (especially for Apple M1 machines) - Standardized development environments across teams ### Operational Improvements - Eliminated local environment troubleshooting - Provided quick environment reset capabilities - Enabled faster onboarding for new developers - Maintained customization options while ensuring standardization ## Custom API Integrations ### Development Process Improvements - Built custom GitHub bot implementation - Standardized workflows across repositories and projects - Enabled non-technical employees to make safe code changes - Implemented automated testing requirements before deployment ### Technical Architecture - Leveraged GitHub's API for custom functionality - Created Slack integrations for improved communication - Implemented automated quality checks - Built infrastructure for managing microservices architecture ## Results and Impact ### Quantitative Improvements - 67% decrease in median code review turnaround time - 70% increase in pull requests - One-minute setup time for largest repository - 25% overall increase in developer productivity ### Qualitative Benefits - Improved code consistency across repositories - Enhanced developer mobility between projects - Reduced cognitive load on developers - Better focus on complex business challenges - Streamlined onboarding process for new team members ## Best Practices and Lessons Learned ### Development Workflow - Standardized PR processes across repositories - Implemented automated quality checks - Created consistent engineering culture - Maintained flexibility while ensuring standards ### Tool Integration Strategy - Gradual adoption of new GitHub features - Custom development where needed using GitHub API - Focus on reducing context switching - Emphasis on maintaining developer flow state ### Environment Management - Standardized development environments - Quick reset capabilities for troubleshooting - Reduced local setup complexity - Cloud-based development environment standardization ## Future Considerations ### Scalability - Continued expansion of microservices architecture - Further automation of development processes - Enhanced integration between tools and services - Ongoing optimization of development workflows ### Developer Experience - Continued focus on reducing cognitive load - Enhancement of automated assistance tools - Further reduction in context switching - Ongoing improvement of development environment management