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Deep, recent reads ranked by depth and freshness.

Infrastructureremove filterMicroservicesremove filterMachine Learningremove filterCloudflareremove filterGitHubremove filterClear all

  1. 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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  2. 02Cloudflare · performance

    Cutting our inference bill by 68% by being obsessive about optimization

    An engineering team investigated a steady rise in AI code review costs and found that cache misses and redundant model calls were driving up expenses. By restructuring system prompts to enable cross-repository prompt caching, eliminating wasted tool calls, and switching to more efficient models, the team cut inference costs by 68% while also significantly improving review latency.

    DEPTH ■■■■■15 min read6 days ago

    Machine Learning

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  3. 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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  4. 04Cloudflare · scaling

    Rewinding the exponential: How we keep hardware usage down

    Cloudflare tackled exponential traffic growth against linear hardware growth by continuously evolving their data pipeline architecture over five years. Key optimizations included breaking up monolithic schemas, switching to zstd compression, decoupling pipeline transit from database replication, decentralizing sampling, and moving aggregations earlier in the pipeline. These strategies significantly reduced backbone bandwidth usage, network congestion, and storage requirements while preserving data quality.

    DEPTH ■■■■■12 min read12 days ago

    Infrastructure

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  5. 05Cloudflare · architecture

    How Commerce.com and Cloudflare are getting storefronts ready for AI agents

    Commerce.com and Cloudflare collaborated to enable WebMCP at the edge for nearly 100,000 storefronts, allowing AI agents to interact reliably with e-commerce sites using structured tools rather than fragile HTML scraping. The team implemented edge-level configuration rules using custom hostname metadata to provide individual merchants with a seamless opt-out mechanism and added support for custom MCP paths. This integration allows agents to execute shopping tasks directly within the active browser session while ensuring fallback safety and zero extra latency for opted-out stores.

    DEPTH ■■■■■6 min read4 days ago

    Machine Learning

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  6. 06GitHub · 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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  7. 07GitHub · 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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