/ 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. 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. Evolving our calendar assistant Reclaim to be AI-native without starting over

    Dropbox engineers evolved their calendar assistant, Reclaim, to support natural-language AI requests by building a custom agent platform that integrates with existing scheduling logic. They utilized a shared action system and a Preview Mode backed by Redis to ensure AI suggestions are consistent and reviewable before affecting real calendars. This approach allows the product to adopt AI capabilities without fragmenting its core architecture or duplicating functionality.

    DEPTH ■■■■■7 min read3 weeks ago

    InfrastructureMachine Learning

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  5. 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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  6. 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. How Lufthansa Systems prototypes realistic maps for the cockpit with Figma Make

    Lufthansa Systems struggled to build realistic, dynamic map prototypes for cockpit navigation apps using traditional static design tools. The team experimented with Figma Make during an internal hackathon to integrate external data sources like Mapbox and the OpenSky Network into their designs. As a result, designers successfully tested interactive map features and complex flight traffic data with pilots prior to writing production code.

    DEPTH ■■■■■8 min read3 days ago

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  8. 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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  9. 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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  10. 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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  11. How to put your design system to work with the Figma agent

    This post explains how to provide the Figma agent with proper context through design libraries, explicit guidelines, practical examples, and reusable skills to produce accurate outputs. By connecting libraries and defining rules, teams can ensure the agent uses approved components and follows design patterns. Reviewing agent outputs helps teams identify missing context and refine their libraries and guidelines.

    DEPTH ■■■■■6 min read2 days ago

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  12. 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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  13. 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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  14. 4 team rituals that drive AI adoption

    To drive team-wide AI adoption beyond isolated power users, several companies introduced collaborative team rituals such as peer-led study groups, process-focused sharing forums, cross-disciplinary training, and prototype-first critiques. These forums helped bridge knowledge gaps, reduce intimidation, and build consistent habits. As a result, teams were able to move from individual experimentation to collective AI usage.

    DEPTH ■■■■■7 min read2 weeks ago

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  15. 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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  16. Workflow lab: Staying in the flow with Figma’s agent

    A fictional designer at Offtrail updates a permission settings flow using the Figma agent to handle repetitive maintenance, compliance checks, and component specs. By offloading busywork and context gathering to the agent, the designer focuses on feature logic and design decisions while keeping outputs connected in a shared canvas.

    DEPTH ■■■■■8 min read2 weeks ago

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  17. How to lighten design system upkeep with the Figma agent

    Design systems often suffer from maintenance overhead like documentation debt, naming inconsistencies, and drift between product screens and libraries. The Figma agent automates these upkeep tasks by drafting specs, auditing token and component usage, and bulk-replacing disconnected elements. This reduces manual toil and helps teams keep their design systems aligned with product work.

    DEPTH ■■■■■5 min read2 weeks ago

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  18. Behind the build: Generative plugins and shaders at Figma

    Figma developed generative plugins and shaders by replacing a prototype WebGL system with an enterprise-scale WebGPU pipeline, sandbox isolation, and custom UI kits. Through rapid prototyping and cross-functional collaboration, the team shipped the feature suite in two months for Config.

    DEPTH ■■■■■8 min read7 weeks ago

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  19. How do you actually measure the impact of AI on design?

    Figma conducted a multi-year research project to track the impact of AI on product design by building an AI sentiment index and running hundreds of AI-moderated interviews. To process the massive volume of qualitative data, the team engineered a custom thematic analysis tool and an internal dashboard to serve as a cross-functional source of truth. The results showed that AI's impact on workflows and collaboration nearly doubled over three years.

    DEPTH ■■■■■6 min read8 weeks ago

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