Reading room.
Engineering blogs & AI research
A reading room from engineering teams and AI labs: titles, short feed excerpts, and links to the originals. For my own essays, explore the writing archive.
- 1359 articles tracked
- 37 sources
- Refresh scheduled daily
Sources
Skipper, Airbnb’s embedded workflow engine, is now open source
Durable workflows, running inside your service with no separate orchestration cluster. By : Ricardo Gamba , Andriy Sergiyenko Skipper , an embedded workflow engine used in production by teams all across Airbnb, is now av…
CASTLE: training natural language search before the first real query
How we addressed the cold-start problem for Airbnb’s natural language search by jointly generating synthetic training data — queries and relevance labels — from de-identified booking and search signals. By : Wendy Ran We…
Beyond synthetic testing: Capturing and replaying real database workloads at Airbnb
How we capture real production database traffic at Airbnb and replay it offline to load-test, plan capacity, and de-risk upgrades. By: Zuofei Wang , Erluo Li Introduction At Airbnb, MySQL-compatible databases are a criti…
Personalization without user identity
How Airbnb uses proximity signals to personalize without relying on individual user history. By : Wei Jiang , Bin Xu , Bharathi Thangamani , Weiwei Guo , Sundar Srinivasavaradhan , Tracy Yu , Huiji Gao , Swapnil Ghike ,…
The guest journey, updated in real time: extending Airbnb’s sequence recommender with Chronon
How two new Chronon capabilities, Push Mode and NRT Model Transform, allows us to provide more relevant search results instantly as a guest explores, rather than waiting for the next batch run. By : Pengyu Hou , Yuli Han…
Beyond the model: Engineering AI infra with scientific judgement
How Airbnb’s agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By : Wren Dougherty Ask a coding agent to analyze 100,00…
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize The data in Project Lighthouse is powered by privacy-preserving anonymization code. We’ve put this code into open source, and published two new techni…
How we knew COVID was over (and what our models had to unlearn)
When we retrain, when we rebuild, and when we leave a model alone. By: Harrison Katz A forecast that carries weight The Forecasting Data Science team at Airbnb produces many of the forecasts the rest of the company plans…
Flexible Authentication: Reimagining authentication for millions of users at Airbnb
Rebuilding login and signup surfaced product insights, not just technical challenges. Here’s how we designed Flexible Authentication at the intersection of product intuition and technical architecture. By : Jose Santos ,…
Eval-driven development: Lessons from evaluating GenAI at scale
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 strikin…
Personalizing Airbnb search by learning from the guest journey
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 ,…
From weeks to a day: how we made LLM evaluation fast enough to iterate on
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 Saberidokht Introduction Shipping…
Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world
How Airbnb’s data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into Homes, Experiences, and Services. By : Patrick Lam , Namrata Lamba , Jamie Stober…
Sitar-agent: Building a reliable dynamic configuration sidecar at scale
How Airbnb built a Kubernetes sidecar to deliver dynamic configuration reliably at scale. By : Bo Teng , Cosmo Qiu , Siyuan Zhou , Ankur Soni , Xin Huang , Willis Harvey Introduction In our previous post , we explored Ai…
When history fails you, borrow from geography
How Airbnb used sequential geographic recovery signals and prior propagation to generate reliable corridor-level forecasts when local data was scarce. By: Harrison Katz The problem with unprecedented shocks Almost every…
Scaling Airbnb’s identity graph with a unified knowledge graph infrastructure
How Airbnb shifts from PaaS to an internal knowledge graph infrastructure at scale. By: Lucen Zhao , Shukun Yang , Ashish Jain Knowledge graphs offer a natural and powerful way to represent relationships between entities…
Viaduct 1.0 and the future of Airbnb’s data mesh
Moving from an internal tool to a community-driven, production-ready data mesh. By : Ryan Tanner , Raymie Stata , Adam Miskiewicz Introduction We’re excited to announce the 1.0 release of the Viaduct. This release marks…
Monitoring reliably at scale
Designing monitoring that works when everything else doesn’t. By : Abdurrahman J. Allawala Introduction When an incident hits, teams lean on observability to answer the only questions that matter: what’s broken, and why?…
Skipper: Building Airbnb’s embedded workflow engine
How Airbnb built a lightweight workflow engine to solve durable execution. By : Ricardo Gamba , Andriy Sergiyenko Introduction: The durable execution problem Picture this hypothetical flow: A host submits an insurance cl…
Building a fault-tolerant metrics storage system at Airbnb
How we built a storage system that ingests 50 million samples per second and stores 2.5 petabytes of logical time series data. By : Rishabh Kumar Modern observability practice encourages instrumenting every meaningful co…
Privacy-first connections: Empowering social experiences at Airbnb
Discover how Airbnb prioritizes user privacy while building a more connected community, empowering guests to engage socially, connect confidently, and maintain control of their personal data. By: Joy Jing ✨ Building a mo…
Building a high-volume metrics pipeline with OpenTelemetry and vmagent
A production-tested approach for moving a large-scale metrics pipeline from StatsD to OpenTelemetry and Prometheus. By: Eugene Ma , Natasha Aleksandrova When migrating to a new monitoring system, you’ll want to frontload…