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Fastly’s QUIC and HTTP/3 beta is coming soon. Join the waitlist and discover how these two new protocols solve the modern internet’s problems. Source: fastly.com

A dive into the thriving black market of John Deere tractor hacking. To avoid the draconian locks that John Deere puts on the tractors they buy, farmers throughout America’s heartland have started hacking their equipment with firmware that’s cracked in Eastern Europe and traded on invite-only, paid online forums. Tractor hacking is growing increasingly popular because John Deere and other manufacturers have made it impossible to perform ‘unauthorized’ repair on farm equipment, which farmers see as an attack on their sovereignty and quite possibly an existential threat to their livelihood if their tractor breaks at an inopportune time.

As serverless technology increases in popularity, we examine how (and how much) serverless functions are being used in the real world. Serverless eliminates the need to provision and manage infrastructure components (e.g., servers, databases, queues, and even containers), allowing teams to focus on code while minimizing their operational overhead. This report will focus specifically on a subset of serverless known as functions-as-a-service (FaaS), which provides the same pay-as-you-go model that defines the public cloud, but at the level of “functions” rather than infrastructure components.

Rust is becoming a first class language in a variety of domains. At Discord, we’ve seen success with Rust on the client side and server side. For example, we use it on the client side for our video encoding pipeline for Go Live and on the server side for Elixir NIFs. Most recently, we drastically improved the performance of a service by switching its implementation from Go to Rust. This post explains why it made sense for us to reimplement the service, how it was done, and the resulting performance improvements.

According to the papers, the new memory exploits the quantum properties of a triple-barrier Resonant Tunneling (RT) structure to produce a nonvolatile memory that can be either read or written with low-voltages. This RT structure, illustrated in this post’s graphic, is produced by using elements from the 3rd and 5th columns in the periodic table, a combination called “III-V”, that is common in optical devices like LEDs and high-efficiency solar cells, and is less commonly used in microwave electronics.

Our backend infrastructure at Tinder relies on Redis-based caching to fulfill the requests generated by more than 2 billion uses of the Swipe® feature per day and hosts more than 30 billion matches to 190 countries globally. Most of our data operations are reads, which motivates the general data flow architecture of our backend microservices. Source: medium.com

A new study co-authored by an MIT economist shows that improved translation software can significantly boost international trade online — a notable case of machine learning having a clear impact on economic activity. The research finds that after eBay improved its automatic translation program in 2014, commerce shot up by 10.9 percent among pairs of countries where people could use the new system. To put the results in perspective, he adds, consider that physical distance is, by itself, also a significant barrier to global commerce.

This post provides the background on how Trip.com uses Cilium and what lead the team to standardize on Cilium as their networking and network security platform for the years to come. It is a summary with some commentary of the original trip.com blog post which provides extensive details into the decision-making process and experiences while running Cilium in production. Source: cilium.io

In November we released Envoy Mobile v0.2. In the accompanying blog post we detailed the features that the library supported and announced that we had replaced the networking libraries in Lyft’s alpha rider app with Envoy Mobile. In this blog post, I want to expand on the technical aspects of the v0.2 release and take a technical deep dive of Envoy Mobile’s current architecture. Source: lyft.com

We are standardizing OpenAI’s deep learning framework on PyTorch. In the past, we implemented projects in many frameworks depending on their relative strengths. We’ve now chosen to standardize to make it easier for our team to create and share optimized implementations of our models. As part of this move, we’ve just released a PyTorch-enabled version of Spinning Up in Deep RL, an open-source educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning.