/ THE FEED /

Read what ships.

Deep, recent reads ranked by depth and freshness.

  1. 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. Beyond container image signing: How we enforce software supply chain policies at Datadog

    Container image signing alone cannot detect upstream security compromises occurring before the image is signed. To address this, the engineering team built an internal system called Turnstile that generates cryptographically signed attestations across an artifact's life cycle and evaluates them against environment-specific policies at deployment time. This approach enables the organization to enforce strict security and compliance requirements at scale without disrupting developer workflows.

    DEPTH ■■■■■15 min read5 days ago

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  3. Sentry JavaScript SDK v11 adds tracing for all JS platforms

    Sentry replaced its OpenTelemetry-based JavaScript SDK instrumentation with a source-code transformation approach using diagnostics channels in version 11. This change removes reliance on Node-specific module loader hooks, enabling reliable tracing across non-Node runtimes like Deno, Bun, and Cloudflare Workers, while also supporting bundled applications. The result is a simplified architecture that reduces bundle weight and fixes previous limitations with load order and immutable exports.

    DEPTH ■■■■■13 min read2 weeks ago

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  4. 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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  5. Reducing Android scope-sync overhead in Sentry Flutter

    The Sentry Flutter team investigated scope-sync overhead on Android and found redundant data normalization across isolates alongside unnecessary buffer copies during JSON encoding. By removing the duplicate normalization and encoding directly to UTF-8 bytes, the team reduced processing time by 27 to 30 percent across benchmarked payloads.

    DEPTH ■■■■■4 min read2 weeks ago

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  6. How we extended Apache DataFusion to execute one query across many machines

    Datadog needed to execute interactive queries across large datasets that exceeded single-node limits while maintaining low latency. To solve this, the team built Distributed DataFusion, an open-source framework extending Apache DataFusion across multiple machines using zero-copy streaming via Apache Arrow and Substrait. As a result, heavy queries complete up to 10 times faster without imposing distribution overhead on lightweight queries.

    DEPTH ■■■■■12 min read3 weeks ago

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  7. 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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  8. The Sentry Android SDK is now 20-30% faster!

    The Sentry team optimized the Android SDK to reduce initialization time by 20-30% and background CPU usage by up to 10%. Improvements were achieved by profiling with Perfetto, replacing slow runtime manifest parsing and DSN parsing with build-time bytecode writing and lightweight parsers, and reusing thread pools. Upgrading to the latest SDK version allows applications to realize these startup performance gains.

    DEPTH ■■■■■4 min read5 days ago

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  9. How we built an async-aware Python profiler

    Traditional Python profilers treat asyncio tasks as independent stacks, obscuring logical execution paths and relationships. The Datadog team built an async-aware profiling model that tracks task dependencies to reconstruct unified execution paths ("stacked stacks") while reducing profiling overhead by over 60 percent. This approach provides clearer performance visibility for asynchronous applications without excessive production latency.

    DEPTH ■■■■■19 min read3 weeks ago

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  10. 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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  11. Unreal MCP now speaks Sentry

    The Sentry team integrated their crash reporting plugin with Unreal Engine's new experimental Model Context Protocol (MCP) server, allowing coding agents to fully automate setup and debugging. By combining editor tools and Sentry MCP, an agent successfully configured the SDK, uploaded debug symbols, packaged a build, induced a crash, and traced the root cause without manual file editing. The integration streamlines crash reporting setup, though it currently suffers from occasional agent tool-discovery issues and server fragility across editor restarts.

    DEPTH ■■■■■7 min read2 weeks ago

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  12. 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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  13. Should you use a log or a metric?

    This post explains the distinction between logs and metrics in application monitoring, noting that logs capture what was true during specific events for debugging while metrics track ongoing numerical trends over time. The author demonstrates how to implement both using Sentry SDKs during a user signup flow and outlines criteria for choosing between them. The resulting takeaway helps engineers decide whether to log an event or track it as a metric based on the questions they need to answer.

    DEPTH ■■■■■12 min read3 days ago

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  14. 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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  15. How Etsy gets its mobile apps ready for peak traffic

    Etsy prepares its mobile apps for peak holiday traffic through advance planning, operational guardrails, and risk management rather than relying on capacity scaling or the ability to quickly revert binaries. The engineering team utilizes a phased code slush, dual crash reporting, remote feature flags, and custom error tagging to surface and mitigate issues effectively under heavy loads. By connecting crash rates to business metrics and automating manual triage workflows incrementally, the organization maintains high visibility and reliability when updates cannot be instantly rolled back.

    DEPTH ■■■■■11 min read3 weeks ago

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  16. Making Rust observability reliable at scale with OpenTelemetry

    Datadog engineers faced inconsistent tracing, broken spans, and context loss across Rust services due to fragmented tooling and unopinionated OpenTelemetry configurations. To fix this, they contributed upstream to OpenTelemetry to improve interoperability and built an opinionated Rust tracing library called dd-trace-rs. This reduced backend ingestion volume by 20x while increasing the rate of indexed spans per service by 3x.

    DEPTH ■■■■■10 min read7 weeks ago

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  17. 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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  18. Application Metrics caught my broken size estimator

    An engineer integrated application metrics into a client-side video conversion tool to monitor file size estimation accuracy without using invasive telemetry. By tracking key performance indicators and analyzing the metric distributions, they discovered that an AI-generated estimation formula was flawed and producing results off by up to 83 percent. Replacing the broken logic with a correct bitrate-based formula brought the size estimation accuracy to within five percent of real-world results.

    DEPTH ■■■■■15 min read7 weeks ago

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  19. Seer, the Sentry MCP and CLI, or your own coding agent: where each one fits

    Engineers often struggle to choose between native debugging agents, Model Context Protocols, and command-line tools when integrating error tracking into their workflows. Sentry demonstrates how its debugging assistant Seer, the Sentry MCP, the Sentry CLI, and various coding agent integrations complement each other rather than compete. The resulting setup allows teams to diagnose complex application errors and automatically generate pull requests through whichever surface they prefer.

    DEPTH ■■■■■11 min read3 weeks ago

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  20. Measuring the back/forward cache with Application Metrics

    Engineering teams often lack visibility into back/forward cache performance because successful cache hits leave no telemetry traces and browser heuristics frequently change. To solve this blind spot, the team introduced an opt-in application metrics integration in their JavaScript SDK that models browser bfcache signals into hits, specific miss reasons, and reload durations. As a result, developers can track, query, and fix silent navigation regressions using customizable dashboards.

    DEPTH ■■■■■6 min read4 weeks ago

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