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Deep, recent reads ranked by depth and freshness.

Machine Learning Microservices Infrastructure Meta GitHub Clear all

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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02Meta · architecture
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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03Meta · architecture
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
Infrastructure Machine Learning
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04GitHub · 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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05Meta · architecture
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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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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07Meta · architecture
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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08Meta · architecture
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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09Meta · scaling
2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale
Meta's infrastructure handled record-breaking traffic during the 2026 World Cup, including 30 million messages per second on WhatsApp, by relying on automated load balancing, capacity buffers, and targeted pre-builds. Traffic patterns revealed that surges occurred primarily during breaks rather than gameplay, requiring region-specific forecasting rather than cross-region load shifting. The systems maintained full reliability and allowed normal continuous deployment schedules to continue without manual intervention or system freezes.
DEPTH ■■■■■5 min read10 days ago
Infrastructure
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10GitHub · 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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