Read what ships.

01GitHub · architecture

The bridge to agent-scale Git infrastructure

GitHub faced infrastructure capacity limits and a 25ms cross-region network round trip when migrating repositories to Azure. The team changed the storage quorum from repository-wide locks to per-reference serialization and introduced ephemeral virtual replicas backed by object storage. This raised write throughput, prevented slow replicas from blocking traffic, and decoupled read scaling from the write quorum.

DEPTH ■■■■■8 min read4 days ago

Infrastructure

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02Fly.io · migration

SBD: We Have a Filesystem at Home

Fly.io replaced a custom JuiceFS-based filesystem for their Sprites infrastructure with the Sprites Block Device (SBD) to eliminate complex, out-of-sync metadata ledgers. SBD presents object storage as a block device over NBD, allowing standard ext4 to manage the filesystem and simplifying checkpoints and restores. The result is significantly improved reliability and a much simpler storage architecture.

DEPTH ■■■■■6 min read3 days ago

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03GitHub · architecture

From 2,500 requests to 24: Rebuilding star history without the stargazers

GitHub faced abuse and performance issues because tools were scraping user identities to reconstruct repository star history. The engineering team collaborated with maintainers to build a purpose-built aggregate endpoint that returns daily star counts without identities and filters by time before aggregation. This reduced request volume from thousands to a few dozen and eliminated heavy database scans.

DEPTH ■■■■■8 min read11 days ago

Microservices

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04GitHub · architecture

Building Git infrastructure for agent-scale development

GitHub is redesigning its Git infrastructure to handle the massive surge in concurrent read and write operations driven by agentic software development and high-volume workloads. The team is decoupling storage from compute by leveraging Azure Blob Storage for durability and lightweight compute workers for requests, which separates scaling dimensions. In internal benchmarks, this approach achieves up to 35 times higher write throughput and independent read scalability.

DEPTH ■■■■■9 min read2 weeks ago

Infrastructure

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05GitHub · architecture

Staying Evergreen: How we automated secure access and repo hygiene in a shared org

Manual repository housekeeping and access reviews for outside collaborators become difficult to maintain as organizations scale. The team built Evergreen, a GitHub App that automates quarterly access reviews and stale repository archiving using native platform features and IssueOps. This automation successfully enforces secure defaults by revoking unanswered access while maintaining a full audit trail in git history.

DEPTH ■■■■■8 min read10 days ago

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06GitHub · migration

Improving site performance by shipping more CSS

GitHub faced performance degradation on server-side rendering and initial page loads due to scaling issues with its CSS-in-JS solution. The engineering team executed a multi-year migration to CSS Modules and eliminated dynamic styling props using incremental rollouts, wrapper components, and automation tools. This resulted in significant performance gains, including 55% faster server-side rendering and the complete removal of styled-components.

DEPTH ■■■■■8 min read4 weeks ago

FrontendPerformance

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07GitHub · architecture

Rendering huge pull requests in the GitHub Copilot app

Rendering extremely large pull requests with millions of lines and hundreds of comments requires separating deterministic code row geometry from unpredictable dynamic comment blocks. The team implemented an idle-gated measurement scheduler, separate height indices, and identity-based scroll anchoring to maintain smooth scrolling. As a result, the pull request view remains performant and responsive even under extreme loads.

DEPTH ■■■■■14 min read4 weeks ago

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08GitHub · performance

How we make AI coding more cost efficient without sacrificing task quality

GitHub optimized GitHub Copilot to improve cost efficiency without sacrificing task quality by evaluating changes across the entire agentic workflow. The team implemented four targeted adjustments including selective output compression, removing unused line-number formatting, shortening prompts via meta-prompting with behavioral tests, and delivering background work completions directly. These modifications reduced token usage and model inference costs globally without causing task-success regressions.

DEPTH ■■■■■9 min read7 weeks ago

Machine Learning

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