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Deep, recent reads ranked by depth and freshness.

  1. Shard design doesn't start from your tables

    Designing a sharded Postgres architecture requires grouping tables around shared query keys rather than starting from table structures alone. The post details how to configure shard groups, reference tables, SQL expressions in shard indexes, and global secondary indexes using Neki. This setup enables efficient single-shard queries and transactions while maintaining full Postgres semantics.

    DEPTH ■■■■■8 min read2 days ago

    PostgreSQL

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  2. 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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  3. The Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do

    Meta engineers reduced hallucinations and improved reliability in their data pipeline agents by replacing a single heavyweight LLM orchestrator with a thin deterministic router and fourteen specialized subagents. The thin orchestrator handles routing and reliability checks through a fixed protocol, while isolated subagents execute domain reasoning. This architecture reduced configuration preparation time from a day to under an hour.

    DEPTH ■■■■■7 min read6 days ago

    Machine Learning

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  4. I don't trust ON DELETE CASCADE

    Postgres ON DELETE CASCADE can cause production outages, replication failures, and performance degradation during large deletions or schema migrations due to long-running transactions holding locks and generating massive WAL. The author recommends using soft deletes with batched background cleanup instead to keep transactions short and bounded. This approach avoids table locks and simplifies data recovery.

    DEPTH ■■■■■5 min read4 days ago

    PostgreSQL

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  5. Inside Prometheus: Meta’s Journey to a Gigawatt-Scale Supercluster

    Meta built Prometheus, a gigawatt-scale AI supercluster designed to scale up to one million GPUs using multi-generation NVIDIA hardware and specialized backend Ethernet networks. The team achieved high goodput and significantly reduced system interruptions by implementing an in-rack sparing strategy, parallelism-aware fault tolerance, and close co-engineering with vendors. These infrastructure advancements allowed the team to successfully train frontier foundational models like Muse Wonder.

    DEPTH ■■■■■17 min read6 days ago

    InfrastructureMachine Learning

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  6. 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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  7. 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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  8. How Meta Built Forum With AI-Native Engineering

    Meta engineers built the Forum app using a human-on-the-loop AI agent workflow that automated planning, building, testing, and verification. By shifting focus from code generation to automated verification, tooling improvements, and architectural legibility, the team reached feature parity in eight weeks while catching systemic memory issues early.

    DEPTH ■■■■■8 min read10 days ago

    FrontendMobile

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  9. 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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  10. What gets TOASTed in Postgres

    This post examines how PostgreSQL uses TOAST to manage oversized rows and variable-length columns through compression and off-row storage. It compares the pglz and LZ4 compression algorithms across different workloads, highlighting trade-offs in search windows, file sizes, and CPU overhead. The analysis demonstrates how these mechanisms impact storage efficiency and read performance for large text and JSON data.

    DEPTH ■■■■■12 min read13 days ago

    PostgreSQL

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  11. Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI

    Meta developed GEM, a unified recommendation foundation model using an LLM-style architecture called Kunlun to handle heterogeneous ads data across multiple surfaces. The team implemented block attention, custom low-precision kernels, and post-training intelligence transfer techniques like LoopFM and Solaris to scale training efficiency and bypass real-time serving latency bottlenecks. The resulting system delivered an 18.9% conversion improvement and a 12% click-through rate increase across Meta ads surfaces.

    DEPTH ■■■■■15 min read12 days ago

    Machine Learning

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  12. 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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  13. 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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  14. TIN v1.0.6: 2-3x faster, with support for stemming

    Following competitive benchmarks that highlighted opportunities for optimization, the developers of the TIN full-text search index for Postgres released versions 1.0.4 and 1.0.6. These updates added Snowball stemming for 18 languages, optimized top-k sorting queries with deterministic tiebreakers, and improved overall query performance. As a result, benchmark tests show that TIN outperforms competing extensions across conjunction, disjunction, and phrase query workloads.

    DEPTH ■■■■■8 min read2 weeks ago

    PostgreSQL

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  15. OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks

    Meta shared its next-generation open networking infrastructure for AI at the OCP Summit 2026, including liquid-cooled 102.4T switch platforms, multiplane scale-out fabrics, and Ethernet-based scale-up solutions. The team also highlighted collaborative industry standards like ESUN and OSPA to unify hardware and transport layers. These open innovations aim to support massive gigawatt-scale AI clusters across diverse data center environments.

    DEPTH ■■■■■12 min read13 days ago

    Machine Learning

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  16. 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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  17. Building a faster, lazier router for Postgres

    To reduce overhead when routing queries across PostgreSQL shards, the Neki router avoids unnecessary parsing and decodes responses lazily. By keeping data in its original wire format and only decoding columns when strictly required for operations like limits or sorting, the router significantly improves performance.

    DEPTH ■■■■■16 min read3 weeks ago

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  18. 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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  19. GenRecs Tokenization: Transforming Ads Recommendation at Meta with Stable Model Vocabulary

    Meta implemented GenRecs Tokenization to replace dedicated item IDs with a stable semantic vocabulary for ads recommendation. Using a three-stage encoding and quantization pipeline, the system enables language models to reason about ad content and user preferences simultaneously. This approach resulted in a +0.35% lift in conversions and improved cold-start performance for new entities.

    DEPTH ■■■■■6 min read6 days ago

    Machine Learning

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  20. 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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