Ask Sendai

To unlock the full potential of virtual reality (VR) and augmented reality (AR) experiences, the technology needs to work anywhere, adapting to the spaces where people live and how they move within those real-world environments. When we developed Oculus Quest, the first all-in-one, completely wire-free VR gaming system, we knew we needed positional tracking that was precise, accurate, and available in real time — within the confines of a standalone headset, meaning it had to be compact and energy efficient. At last year’s Oculus Connect event we shared some details about Oculus Insight, the cutting-edge technology that powers both Quest and Rift S. Now that both of those products are available, we’re providing a deeper look at the AI systems and techniques that power this VR technology.

Ambassador uses Envoy Proxy as its core L7 routing engine. Envoy Proxy provides a configurable access logging mechanism. Ambassador uses the default format string for Envoy’s access logs. These access logs provide an extensive amount of information that can be used to troubleshoot issues. These provide additional details about the response or connection if any above and beyond the standard response code. Possible values for HTTP and TCP requests include UH (no healthy upstream hosts); UF(upstream connection failure); UO (upstream overflow); NR (no route configured); URX (rejected because of upstream retry limit or maximum connection attempts reached).

Early in 2017 we started exploring Presto for OLAP use cases and we realized the potential of this amazing query engine. It started as an adhoc querying tool for data engineers and analysts to run SQL in a faster way to prototype their queries, when compared to Apache Hive. A lot of internal dashboards were powered by AWS-Redshift back then and it had data storage and compute coupled together.

Classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty. The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.

We are excited to announce the Cilium 1.6 release. A total of 1408 commits have been contributed by the community with many developers contributing for the first time. Cilium 1.6 introduces several exciting new features: KVStore free operation: The addition of a new CRD-based backend for security identities now allows to operate Cilium entirely without a KVstore in the context of Kubernetes. (More details) KVStore free operation:

We’ve seen profound advances in technology, especially with the development of artificial intelligence and deep learning which are increasingly for voice assistants. This, in turn, promises to bring about huge changes in consumer behavior — what’s being called “voice commerce”. This is a new channel, governed by a new set of rules. Here, communication is key. These shopping assistants use AI and are changing the rules of the game when it comes to customers’ purchasing behavior; they can even make purchasing decisions for the customer if desired.

We are proud to introduce Maesh, Containous’ new service mesh designed from the ground up to be straightforward, easy to install and easy to use. Maesh allows for visibility and management of the traffic that flows inside your Kubernetes cluster, which is just as important as the ingress and egress traffic. Built on top of Traefik, Maesh is a simple, yet full-featured service mesh.

Zoncolan helps security engineers scale their work by using static analysis to examine code and detect security or privacy issues. Facebook’s web codebase currently contains more than 100 million lines of Hack code, and changes thousands of times per day. To handle the sheer volume of code, we build sophisticated systems that help our security engineers review code.

Natural language understanding (NLU) and language translation are key to a range of important applications, including identifying and removing harmful content at scale and connecting people across different languages worldwide. Although deep learning–based methods have accelerated progress in language processing in recent years, current systems are still limited when it comes to tasks for which large volumes of labeled training data are not readily available. Recently, Facebook AI has achieved impressive breakthroughs in NLP using semi-supervised and self-supervised learning techniques, which leverage unlabeled data to improve performance beyond purely supervised systems.

The Open Policy Agent Gatekeeper project can be leveraged to help enforce policies and strengthen governance in your Kubernetes environment. In this post, we will walk through the goals, history, and current state of the project. The following recordings from the Kubecon EU 2019 sessions are a great starting place in working with Gatekeeper: If your organization has been operating Kubernetes, you probably have been looking for ways to control what end-users can do on the cluster and ways to ensure that clusters are in compliance with company policies.