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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02Grab · architecture

App stability at scale with a layered defense

To maintain app stability amidst rapid weekly releases and AI-generated code, Grab uses a layered defense strategy covering prevention, release gates, and incident response. This pipeline includes static analysis via custom lint rules, multiple tiers of automated and manual testing, a pre-production early access build, staged rollouts, and feature flags with instant kill switches. As a result, critical defects are caught early in the development lifecycle before reaching the wider production user base.

DEPTH ■■■■■16 min read4 days ago

MobileReliability

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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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04Grab · architecture

Powering AI-led research through simulation

The engineering team built a self-contained simulation platform called sim-rs to model marketplace dispatch dynamics and accelerate experimentation. By connecting automated software agents to this simulator with strict safety safeguards, the team successfully optimized strategy policies and simulator performance. This closed-loop lab environment allows agents to test ideas rapidly while ensuring that only valid, production-ready improvements are produced.

DEPTH ■■■■■10 min read2 weeks ago

Machine Learning

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05GitHub · 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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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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08Grab · architecture

Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs

Grab addressed runtime data reliability issues in its data mesh by implementing automated Data Production Issues (DPIs). The system uses an incident orchestrator to detect contract breaches, a Data Health API for cross-platform root cause analysis, and Hugo for automated remediation. This approach resolved 86.9 percent of incidents automatically and made mean time to resolve six times faster.

DEPTH ■■■■■12 min read8 weeks ago

DatabasesObservability

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09GitHub · 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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