Auto-aggregated global tech articles · 3,394 posts
How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC execution APIAuthors: Nilesh Mishra and Ajit KotiThis is the third entry of a multi-part blog series describing how we built a Real-Time Distributed Graph (RDG). In Part 1, we discussed the motivation for
Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timi
#660 — August 5, 2026 Web Version Lots of videos from this year's PGConf.dev are now on YouTube and I've rounded up some highlights at the end of the issue!__Your editor, Peter Cooper Postgres Weekly COUNT(DISTINCT) Too Slow? A Guide to Fast Approximations — A thorough, han
TL;DR We are enabling the next iteration of the borrow checker (coined Polonius Alpha) on nightly in preparation for stabilization in the next few months. Whaaaaaat? Yes! You heard it right! The next iteration of the Rust borrow checker is coming! Rust's first borrow checker ("AST borrowck") was ve
Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficienc
by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh SelverajNetflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created
Authors: Ying Li, Arjun Rao, Shradha SehgalIntroductionRecommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interac
How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought.Nestled into the lush hillside, this stunning modern retreat features striking natural wood architecture, terraced balconies, and a serene landscape.By: Rohit
How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time.By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie Moyerman, and Sanjeev
To understand what can influence win rates, we analyzed evidence packets from one million disputes over a 16-week period. Here’s what the data shows and what it means for how you mitigate disputes.
By AI Platform’s Model Runtime team and Inference teamIntroductionMost organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of t
Moving fast should not mean breaking things for your users. While testing on localhost is a good start, you need a near identical replica of production to catch any bugs before your code goes live. That is where a staging environment comes in. Heroku pipelines handle the infrastructure layout for yo
The Rust team has published a new point release of Rust, 1.97.1. Rust is a programming language that is empowering everyone to build reliable and efficient software. If you have a previous version of Rust installed via rustup, getting Rust 1.97.1 is as easy as: rustup update stable If you don't have
Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users’ inferred interests with the breadth of what advertisers
Training an LLM is the easy part. The hard part is designing experiments and evaluations that you can trust enough to know whether the new model is actually an improvement.By: Baharak SaberidokhtIntroductionShipping a production LLM system means iterating fast on improvements to something that is, b
By Parth Jain, Rakesh Sukumar, Yingwu Zhao, Renzo Sanchez-Silva & Nathan FisherA deep dive into the engineering challenges of building a real-time service dependency map at Netflix scale: from streaming architectures and distributed aggregation pipelines to time-travel queries and the methodolog
TL; DR At Meta’s scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance. When a Linux kernel upgrade risked regressing latency across Meta’s ad serving fleet, we turned to sched_ext — the upstream, BPF-based extensible scheduling framework — to bu
Another six months have passed since our last development update, and the crates.io team has been busy. Here's a summary of the most notable changes and improvements made to crates.io since then. Source Code Viewer Crate pages now have a "Code" tab that lets you browse the contents of published cra
3321–3340 / 3394
