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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02Datadog · architecture

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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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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04Kubernetes · migration

The Shift to cgroup v2 in Kubernetes: What You Need to Know

Kubernetes has deprecated cgroup v1 in favor of cgroup v2, with complete removal scheduled for future releases. The transition requires migrating Linux nodes, updating container runtimes, and verifying kernel compatibility. Adopting cgroup v2 enables advanced features such as Memory QoS, improved OOM handling, and pressure stall information metrics.

DEPTH ■■■■■12 min read2 weeks ago

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05Kubernetes · scaling

Scaling Kubernetes Workloads with Node Swap

Kubernetes memory limits often cap node density for bursty agentic AI workloads that sit idle with large memory footprints. To solve this, the engineering team benchmarked Kubernetes v1.34 node swap backed by fast NVMe Local SSDs across CI/CD builds, headless browsers, and Python sandboxes. The approach successfully increased pod density up to three times without significant latency costs.

DEPTH ■■■■■6 min read2 weeks ago

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06Datadog · architecture

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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07GitHub · 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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08Datadog · architecture

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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09Tailscale · tutorial

It's time to make your own RSS reader

This post walks through the process of self-hosting a personal RSS reader using FreshRSS and Docker, secured privately over a Tailscale network. The author details the configuration of environment variables, auth keys, and Docker compose deployment to avoid exposing the service to the open web. The result is a private, algorithm-free news reader accessible across multiple devices and integrable with external reading apps or AI agents.

DEPTH ■■■■■10 min read9 days ago

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10GitHub · 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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11Kubernetes · tooling

Why Is My Kubernetes Job Still Pending? Tracing Kueue Admission with Headlamp

Kubernetes jobs managed by Kueue can remain pending while waiting for admission checks and resource allocation. The team implemented a Kueue plugin for Headlamp to trace pending workloads through local queues, cluster queues, and resource flavors within a unified interface. This approach allows engineers to quickly determine whether a job is blocked by admission policies or standard pod scheduling.

DEPTH ■■■■■9 min read11 days ago

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12GitHub · 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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13Tailscale · architecture

Your agent, your network: How Meta’s Muse agent works with Tailscale

Meta integrated Tailscale into its Muse personal AI agent, allowing the agent to join a user's tailnet as a separate node and interact with self-hosted services and SSH sessions. By treating Muse like any standard tailnet node, users can apply Tailscale's access controls, grants, and tags to enforce security and adhere to the principle of least privilege.

DEPTH ■■■■■5 min read3 weeks ago

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14GitHub · 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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15Datadog · architecture

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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16GitHub · 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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