System Architecture

Intel’s deepfake detector analyzes ‘blood flow’ in video pixels to return results in milliseconds with 96% accuracy.

This post is part of a series on Mastodon and the fediverse. We also have a post on why the fediverse will be great—if we don't screw it up, and more are on the way.With so many users migrating to Mastodon as their micro-blogging service of choice, a lot of questions are being raised about the...

Block 3rd-party trackers lurking in your apps with DuckDuckGo's App Tracking Protection, now in open beta!

How much computing power is needed at the edge? How much memory and storage are enough for AI at the edge?

Zero-knowledge proofs allow researchers to prove their knowledge without divulging the knowledge itself.

Learn the Fundamentals of Programming.

We build products and offer services that help protect organizations from software supply chain attacks and vulnerabilities.

A lightweight, ultra-fast tool for building observability pipelines

I am perpetually working on what I consider to be the essential API dimensions that ground how we talk about APIs. While having a common definition of the API lifecycle, as well as a clear list of roles and stakeholders involved across the API lifecycle is essential–these dimensions or focused on how we “see” and invest specifically in our APIs. In my experience, there are many ways we talk past each other and introduce friction into our discussions about how to do APIs well. While there are other dimensions not reflected here, these reflect on the areas I find myself talking with enterprises about the most, and that I am getting more opinionated on to help ground my conversations.

EIDF has combined Spectra Logic tape with Cerebras wafer-scale AI so archived datasets can be mined for insights.

Even talking about building a monolith today, is a bit taboo. It is all about microservices at the moment, and has been for a few years. But they aren’t a silver bullet…

Whether an API was built from scratch or integrated from a third-party source, securing it requires organized and active end-to-end visibility.

Companies need to consider both how fast they can put edge applications into action and update them, and how quickly they can process incoming data.

Today, I am publishing the Distributed Computing Manifesto, a canonical document from the early days of Amazon that transformed the architecture of Amazon's ecommerce platform. It highlights the challenges we were facing at the end of the 20th century, and hints at where we were headed.

Taking time to understand the tradeoffs could mean big savings for the organization and fewer surprises for teams that manage cloud infrastructure.

Why data consistency is a problem of promising semantics alongside dynamics.

Sapling is a source control system developed and used at Meta that places special emphasis on usability and scalability. Git and Mercurial users will find many of the basic concepts familiar, and that workflows like understanding your repository, working with stacks of commits, and recovering from mistakes, are substantially easier.

Lightstep's Austin Parker and Splunk's Morgan McClean share how the CNCF OpenTelemetry project created demo services to help the cloud native community better understand cloud native development practices and more.

In July, we launched the general availability of GitHub Projects, and now we are excited to bring you even more features designed to make it easier to plan and track in the same place you build!

This is a story of leveraging various tools to empower microservices architecture, how we use it at PerimeterX and when to use and not use

What is Confidential Computing? Confidential Computing encrypts sensitive data while in use in a public cloud. It is today’s gold standard for cloud security and a foundational “must-have” to continue the digital transformation journey with workloads that would...

Enarx: Confidential Computing with WebAssembly

Confidential Computing is a leading-edge security technology that encrypts sensitive data in use in a public cloud, private cloud or on-premises. It completes the data security triangle since data at rest (in storage) and in transit (across networks)...

Here’s a synopsis of what could go wrong in complex application environments and how you can contain your risk using tools and best practices.