Tell a more complete data story with customized Looker charts and visualizations
Enhance data storytelling with customizable Looker charts and visualizations
European Conference on Computer Vision (ECCV) 2024
Discover Apple's involvement in ECCV 2024 showcasing advancements in ML and computer vision.
Cloudflare incident on September 17, 2024
An analysis of the 2024 Cloudflare incident due to an internal software error affecting Business plan websites, and the steps taken to prevent a similar issue in the future.
Removing uncertainty through "what-if" capacity planning
Modeling 'what-if' scenarios for capacity planning at Cloudflare: a look behind the curtain
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination
Exploring linguistic bias in ChatGPT's responses to different varieties of English and its implications on dialect discrimination
5 product lessons we learned from building Friend Streak
5 key insights for product managers from developing Friend Streak
Feature Caching for Recommender Systems w/ Cachelib
Optimizing recommender systems with feature caching using Cachelib by Meta Open Source.
How Cloudflare is helping domain owners with the upcoming Entrust CA distrust by Chrome and Mozilla
A technical blogpost discussing how Cloudflare is assisting domain owners affected by the upcoming distrust of Entrust CA certificates by Chrome and Mozilla
La Série entre amis : le nouveau moyen de se motiver à plusieurs
Discover how the new 'Série entre amis' feature on Duolingo enhances motivation through friend accountability.
The Practitioner's Guide to the Maximal Update Parameterization
A practical guide to implementing and leveraging the benefits of Maximal Update Parameterization (μP) for neural network training, offering insights into stable hyperparameters, reduced tuning costs, improved loss at large scales, stable training, and predictable scaling.
Fine-tuning Llama 3.1 with Long Sequences
Optimizing long sequence fine-tuning with Llama 3.1 for high-quality Retrieval Augmented Generation (RAG) and tool use systems
The Practitioner's Guide to the Maximal Update Parameterization
A practical guide to implementing the Maximal Update Parameterization for neural network training, offering benefits such as stable hyperparameters, reduced tuning costs, and improved training stability at large scales.