Engineering articles from Snap (Snapchat)
AI summaries and key learnings from Snap (Snapchat) engineering teams.
Spectacles - EyeConnect
The article discusses EyeConnect, a feature designed to facilitate shared augmented reality experiences by allowing users to connect their Spectacles through a novel motion tracking algorithm. Unlike...
Universal User Modeling (UUM): A Foundation Model for User Understanding at Snapchat
The article discusses Universal User Modeling (UUM) at Snapchat, a foundational model designed to enhance user understanding across various product surfaces. UUM captures user behaviors over time by...
From Monolith to Multicloud Micro-Services: Inside Snap’s Service Mesh - Snap Engineering
The article outlines Snap Engineering's transition from a monolithic application architecture to a microservices architecture deployed across multiple cloud providers, specifically AWS and Google...
Don't Rewrite Your App, Unless You Have To - Snap Engineering
The article discusses the Snapchat Engineering team's experience in rewriting their Android app to enhance performance and reduce bugs. It outlines the challenges faced due to the app's complexity...
Making The Most of a Rewrite - Snap Engineering
The article outlines the process and considerations involved in rewriting the Snapchat application, focusing on architectural improvements to enhance performance and maintainability. It emphasizes...
Device-Distributed Machine Learning - Snap Engineering
The article presents Device-Distributed Machine Learning (DDML), a framework developed by Snapchat that enables training machine learning models directly on client devices while preserving user...
Shipping Two Apps in One on Android - Snap Engineering
The article outlines the engineering challenges and solutions encountered by Snap in shipping two versions of the Snapchat app within a single APK. It discusses the need for A/B testing, the...
Privacy at Snap - Snap Engineering
The article outlines Snap's approach to privacy, emphasizing the importance of integrating privacy considerations into product design and development. It describes their four privacy pillars, which...
Measuring ‘Time to Camera ready’ - Snap Engineering
The article outlines Snap's approach to measuring and optimizing the 'Time to Camera Ready' for the Snapchat app, emphasizing the importance of minimizing startup latency to enhance user experience....
A Developer Ecosystem for Snapchat - Snap Engineering
The article outlines the Snap Kit ecosystem, which provides developers with tools to integrate Snapchat's features into their applications. It describes various kits such as Creative Kit, Login Kit,...
GPU Transcoding at Scale - Snap Engineering
The article explores the implementation of GPU transcoding at Snap, focusing on the optimization of video processing for the Snapchat platform. It highlights the trade-offs between quality,...
QUIC at Snapchat - Snap Engineering
The article discusses Snapchat's implementation of the QUIC protocol to improve network performance for its users. QUIC, developed by Google, serves as a replacement for the traditional TCP+TLS+HTTP2...
Build a Reliable System in a Microservices World at Snap - Snap Engineering
The article explores how Snap Engineering utilizes the Temporal open source project to orchestrate complex workflows across multiple microservices. It highlights the challenges of building reliable...
Applying GPU to Snap - Snap Engineering
The article discusses Snap's application of GPU technology to enhance machine learning model inference, emphasizing the importance of deep neural networks (DNN) in delivering personalized content to...
Improving Djinni - Snap Engineering
The article discusses the enhancements made to the Djinni project, a tool for generating bridging code between C++ and other programming languages, particularly for mobile applications. It highlights...
Building a Cross-Platform Mobile Messaging Experience
The article discusses Snap's approach to creating a consistent cross-platform mobile messaging experience by rewriting their messaging system in C++. It emphasizes the importance of consistent...
Machine Learning for Snapchat Ad Ranking
The article discusses the sophisticated machine learning framework employed by Snapchat for ad ranking, emphasizing the importance of delivering the right ad to the right user while maintaining user...
Training Large-Scale Recommendation Models with TPUs
The article discusses Snap's approach to training large-scale recommendation models using Google's Tensor Processing Units (TPUs). It highlights the computational challenges faced in training deep...
Bringing Locations to Life with AR
The article introduces the Custom Landmarkers feature in Lens Studio, enabling creators to anchor AR experiences to real-world locations using LiDAR technology. It explains the technical processes...
Modernizing the PlayCanvas Backend Infrastructure
The article outlines the modernization of PlayCanvas's backend infrastructure to enhance scalability and reliability, driven by the substantial growth of its user base. It details the transition from...