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October 4, 2023

Why AIOps is Critical for Networks

Noutați, Software

Speaker 1: This is Techstrong TV. Mitch Ashley: With great pleasure of being joined by Andrew Colby. Andrew is VP of AIOps at Vitria. Welcome, Andrew. Andrew Colby: Good afternoon, Mitch. And thank you. Mitch Ashley: It’s a great topic. I’m excited to talk with you about it. We could go down the share war stories in telco experience, which really could be about 10 episodes of a different show, but today in the telco environment, or just in the business environment in general, the economic conditions, competitive pressures, looking for areas where we can get more for less, there are a lot of different parameters that have shifted or changed or maybe tightened that we’re currently working within. I’d love to get your perspective on that. Andrew Colby: Certainly, and thank you. Yeah, I’d say we see cautious optimism. Obviously, I’m based in the US in the DC Metro area, Maryland. And the US, the government entities and quasi-governmental entities have been tightening the economic structure in order to tame inflation. Fortunately, that has not driven our economy and had the potential recessionary effect that was feared, but people are still cautious, businesses are still cautious. That said, it’s hard to hire people and it’s really hard to hire technical people. So a lot of companies are continuing to look towards how to leverage technologies and automation to build efficiency so that they can do more with either the same number of people or re-task their people to higher value purposes, and let the technology do some of the more menial and mundane tasks. And we can explore this a little bit, especially in these new complex service delivery and network environments. It’s very difficult for me to imagine how an engineer who’s gone through anywhere from two to eight years of college education is going to really be happy going and spending their days collecting a lot of data across network, container management VM and other infrastructure systems to figure out what’s going on. I mean, really that’s where a lot of the automation provides a significant amount of value to let the engineers do the smart, difficult things that we want humans to do. Mitch Ashley: And a lot of pressures around meantime to recovery, even looking at resiliency, how do we stand up under a test-full situation, whether it be a security attack that might be going on or some unobserved condition that our systems and networks have never been under? Andrew Colby: Oh, there’s so much of that. So much is changing. It’s not just a person like you or me behind a smartphone that can actually report that there’s a problem, but it’s sensors and equipment that won’t necessarily report right away, so it needs to be detected. So that’s a whole nother additional dimension that service providers, large enterprise IT organizations are under, which is to be able to have this kind of real-time awareness of what’s going on. Whether the service is real time, like the video conference that we’re on or not, there really is a desire and expectation to have real time awareness of the service delivery to be able to detect what’s going on, react to it, address it before the user, whoever that is, the customer, the employee […]

October 4, 2023

What Is make_unique And How To Use With unique_ptr In C++

C++ Builder

Allocating memory and freeing it safely is hard if you are programming a very big application. In Modern C++ there are smart pointers that help avoid the mistake of incorrectly freeing the memory addressed by pointers. Smart pointers make it easier to define and manage pointers, they came with C++11. The most used types of C++ smart pointer are unique_ptr, auto_ptr, shared_ptr, and weak_ptr. In C++14, there is make_unique (std::make_unique) that can be used to allocate memory for the unique_ptr smart pointers. What is a smart pointer and what is unique_ptr in C++? In computer science, all data and operations during runtime are stored in the memory of our computation machines (computers, IoTs, or other microdevices). This memory is generally RAM (Random Access Memory) that allows data items to be read or written in almost the same amount of time, irrespective of the physical location of data inside the memory. Allocating memory and freeing it safely is really hard if you are programming a very big application. C and C++ were notorious for making this problem even more difficult since earlier coding styles and C++ standards had fewer ways of helping developers deal with pointers and it was fairly easy to make a mistake of manipulating a pointer which had become invalid. In Modern C++, there are smart pointers that help avoid the mistake of incorrectly freeing the memory addressed by pointers which was often the source of bugs in code which could often be difficult to track down. Smart pointers make it easier to manage pointers, they came with the release of the C++11 standard. The most used types of C++ smart pointer are unique_ptr, auto_ptr, shared_ptr, and weak_ptr. std::unique_ptr is a smart pointer that manages and owns another object with a pointer, when the unique_ptr goes out of scope C++ disposes of that object automatically. If you want to do some magic in memory operations use std::unique_ptr. Here are more details on how you can use unique_ptr in C++, What is std::make_unique in C++? The std::make_unique is a new feature that comes with C++14. It is used to allocate memory for the unique_ptr smart pointers, thus managing the lifetime of dynamically allocated objects. std::make_unique template constructs an array of the given dynamic size in memory, and these array elements are value-initialized. Since C++14 to C++20, the std::make_unique function is declared as a template as below, template unique_ptr make_unique( std::size_t size ); template unique_ptr make_unique( Args&&… args ); The std::make_unique function does not have an allocator-aware counterpart; it returns unique_ptr which would contain an allocator object and invoke both destroy and deallocate in its operator(). How to use std::make_unique with std::unique_ptr in C++? Let’s assume you have a simple class named myclass, We can create a unique pointer (a smart pointer) with this class as below, std::unique_ptr p = std::make_unique(); Or it can be used with struct as below: struct st_xy { int x,y; }; We can create a unique pointer as we illustrate in the example below. std::unique_ptr xy = std::make_unique(); We can use the auto keyword and we can define the maximum array elements like so: auto ui = std::make_unique(5); What are the advantages to use std::make_unique in C++? The std::make_unique function is useful when allocating smart pointer, because: We don’t need to think about new/delete or new[]/delete[] elisions. When […]

October 4, 2023

How To Use std::exchange In C++

C++ Builder

C++ is a very precise programming language that allows you to use memory operations in a wide variety of ways. Mastering efficient memory handling will improve your app performance at run time and can result in faster applications that have optimal memory usage. One of the many useful features that come with the C++14 standard is the std::exchange algorithm that is defined in the utility header. In this post, we explain how to use std::exchange in C++. What is std::exchange in C++? The std::exchange algorithm is defined in the header and it is a built-in function that comes with C++14. The std::exchange algorithm copies the new value to a given object and it will return the old value of that object. Here is the template definition syntax (since C++14, until C++20): template T exchange( T& object, U&& new_value ); How to use std::exchange in C++? We can use std::exchange to exchange variables values as below, and we can obtain old value from the return value of std::exchange. int x = 500; int y = std::exchange(x, 333); After these lines, x is now 333 and y is the old value of x , 500. We can use std::exchange to set values of object lists (i.e vectors) as shown below: std::vector vec; std::exchange( vec, { 10, 20, 30, 40, 50 } ); In addition, we can copy old values to another object list as we show in the following example: std::vector vec, vec0; vec0 = std::exchange( vec, { 10, 20, 30, 40, 50 } ); We can use std::exchange to change function definitions too, for example assume we have myf1() function, now we want to exchange myf() function to myf1() function, this is how we can do: void (*myf)(); std::exchange(myf, myf1); // myf is now myf1 myf(); Is there a full example of how to use std::exchange in C++? Here is a full example of how to use std::exchange in C++. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 #include #include void myf1() { std::cout

October 4, 2023

Open Sourcing IFC SDK for C++ Modules

C++ Builder

Open Sourcing IFC SDK for C++ Modules GDR October 3rd, 20234 3 Back with VS2019 version 16.10, we announced a complete implementation of C++ Modules (and, generally, of all C++20 features) across the MSVC compiler toolset, static analysis, IntelliSense, and debugger. Implementing Modules requires principled intermediate representation of C++ source programs. Today, we are thrilled to announce the availability of the IFC SDK, a Microsoft implementation of the IFC Specification. This is an open-source project under the Apache 2-with-LLVM-exception license. The IFC Specification formalizes C++ programs as data amenable to semantics-based manipulation. We are open sourcing the IFC SDK in the hope of accelerating widespread use and implementation of C++ Modules across the C++ community and the C++ tools ecosystem. What Is an IFC and What Is It Good for? A popular compilation strategy of a C++ Module interface (or header unit) source file is to translate the source file, exactly once, into an internal representation suitable for reuse in other source files. That intermediate form is generally referred to as a Built Module Interface (BMI). An IFC file is an implementation of the BMI concept. An IFC file is a persistent representation of all information pertaining to the semantics of a C++ program. In addition to direct use by compilers to implement C++ Modules, an IFC can also be inspected by non-compiler tools for semantics-based exploration of C++ source files. Such tools are useful for explorers in understanding how C++ source-level constructs can be represented in a form suitable for C++ Modules implementation. IFC SDK Scope The IFC SDK is currently an experimental project. It focuses on providing datatypes and source code supporting the read and write of IFC files. This SDK is the same tested implementation used in the MSVC compiler front-end to implement C++ Modules. Consequently, while the GitHub OSS repo is “experimental”, the code is far from it. Those datatypes can be memory-mapped directly for scalable and efficient processing. The project also features utilities for formatting or viewing IFC files. We welcome, and we are looking for, contributions that fix gaps between the SDK and the Specification, and for changes required to support C++ standards starting from C++20 and upwards. Call to Action We encourage you to check out the IFC SDK repo, build it, experiment with it, and contribute. We would like to hear from you. If you’re a C++ compiler writer interested in supporting the IFC file format, please reach out at the IFC Specification repo and share feedback, suggestions, or bug fixes. You can also reach us on Twitter (@VisualC), or via email at visualcpp@microsoft.com. GDR Software Architect, DevDiv Follow

October 4, 2023

Getting RCE in Chrome with incorrect side effect in the JIT compiler

GitHub Security

In this post, I’ll explain how to exploit CVE-2023-3420, a type confusion vulnerability in v8 (the Javascript engine of Chrome), that I reported in June 2023 as bug 1452137. The bug was fixed in version 114.0.5735.198/199. It allows remote code execution (RCE) in the renderer sandbox of Chrome by a single visit to a malicious site. Vulnerabilities like this are often the starting point for a “one-click” exploit, which compromise the victim’s device when they visit a malicious website. A renderer RCE in Chrome allows an attacker to compromise and execute arbitrary code in the Chrome renderer process. The renderer process has limited privilege though, so the attacker then needs to chain such a vulnerability with a second “sandbox escape” vulnerability: either another vulnerability in the Chrome browser process, or a vulnerability in the operating system to compromise either Chrome itself or the device. For example, a chain consisting of a renderer RCE (CVE-2022-3723), a Chrome sandbox escape (CVE-2022-4135), and a kernel bug (CVE-2022-38181) was discovered to be exploited in-the-wild in “Spyware vendors use 0-days and n-days against popular platforms” by Clement Lecigne of the Google Threat Analysis Group. While many of the most powerful and sophisticated “one-click” attacks are highly targeted and average users may be more at risk from less sophisticated attacks such as phishing, users should still keep Chrome up-to-date and enable automatic updates, as vulnerabilities in v8 can often be exploited relatively quickly by analyzing patches once these are released. The current vulnerability exists in the JIT compiler in Chrome, which optimizes Javascript functions based on previous knowledge of the input types (for example, number types, array types, etc.). This is called speculative optimization and care must be taken to make sure that these assumptions on the inputs are still valid when the optimized code is used. The complexity of the JIT engine has led to many security issues in the past and has been a popular target for attackers. The phrack article, “Exploiting Logic Bugs in JavaScript JIT Engines” by Samuel Groß is a very good introduction to the topic. The JIT compiler in Chrome The JIT compiler in Chrome’s v8 Javascript engine is called TurboFan. Javascript functions in Chrome are optimized according to how often they are used. When a Javascript function is first run, bytecode is generated by the interpreter. As the function is called repeatedly with different inputs, feedback about these inputs, such as their types (for example, are they integers, or objects, etc.), is collected. After the function is run enough times, TurboFan uses this feedback to compile optimized code for the function, where assumptions are made based on the feedback to optimize the bytecode. After this, the compiled optimized code is used to execute the function. If these assumptions become incorrect after the function is optimized (for example, new input is used with a type that is different to the feedback), then the function will be deoptimized, and the slower bytecode is used again. Readers can consult, for example, “An Introduction to Speculative Optimization in V8” by Benedikt Meurer for more details of how the compilation process works. TurboFan itself is a well-studied subject and there is a vast amount of literature out there documenting its inner workings, so I’ll only go through the background that is needed for this […]

October 4, 2023

Cybersecurity spotlight on bug bounty researcher @inspector-ambitious

GitHub Security

As we kick off Cybersecurity Awareness Month, the GitHub bug bounty team is excited to spotlight one of the top performing security researchers who participates in the GitHub Security Bug Bounty Program, @inspector-ambitious! As home to over 100 million developers and 372 million repositories, GitHub maintains a strong dedication to ensuring the security and reliability of the code that powers daily development activities. GitHub’s Bug Bounty Program continues to play a pivotal role in advancing the security of the software ecosystem, empowering developers to create and build confidently on our platform and with our products. We firmly believe that the foundation of a successful bug bounty program is built on collaboration with skilled security researchers. Since its inception nine years ago, our bug bounty program has been a fundamental component of GitHub’s security strategy. This dedication is manifested through live hacking events, the revamp of our VIP bounty program, limited disclosures on HackerOne, expanding bounty targets, over $3.8 million in total rewards via HackerOne since 2016, and much more! As we continue to explore opportunities to make our program more exciting for the researchers to hack on, we also heard the feedback from our community and launched the GitHub Bug Bounty Merch Shop earlier this year, so now every submission can potentially also receive a swag bonus along with the bounty! To celebrate Cybersecurity Awareness Month this October, we’re interviewing one of the top contributing researchers to our bug bounty program and learning more about their methodology, techniques and experiences hacking on GitHub. @inspector-ambitious specializes in application-level bugs and has found some unique and intricate issues throughout their research. Despite the intricacy of their submissions, they skillfully outline easily understandable reproduction steps, effectively streamlining the investigation process and consequently reducing the triage time. Can you share some insights into your journey as a bug bounty researcher? What motivated you to start and what has kept you coming back to it? I’ve been passionate about cybersecurity since the age of 10. During the 1990s, I didn’t see it as a viable career option, so I decided to shift to programming around age 16. I dedicated myself to coding until just a few months ago, when we underwent a two-day offensive security training at work. The trainer suggested that I explore bug bounty programs. A couple of weeks later, I joined GitHub’s Bug Bounty Program and was immediately hooked. There is nothing as cute as an Octocat. What do you enjoy doing when you aren’t hacking? Trying to be a good husband and dad is my top priority. When I have time left (it’s not that often), I try to improve my knowledge of mindfulness and Stoic philosophy. How do you keep up with and learn about vulnerability trends? I listen to the Critical Thinking – Bug Bounty Podcast by Justin Gardner (Rhynorater) and Joel Margolis (teknogeek); it’s an amazing podcast. I also check Twitter/X from time to time. What are your favorite classes of bugs to research and why? I would say I have been focusing mostly on application-level logic errors so far since my skill set is still fairly limited as I’m newer to bug hunting. What tools or techniques do you find most effective for discovering security vulnerabilities? I use Kali Linux and VSCode for code review. I don’t […]

October 2, 2023

Build Reliable and Secure C++ programs — Microsoft Learn

C++ Builder

Build Reliable and Secure C++ programs — Microsoft Learn Herb Sutter October 2nd, 20230 1 “The world is built on C and C++” is no overstatement — C and C++ are foundational languages for our global society and are always in the world’s top 10 most heavily used languages now and for the foreseeable future. Visual Studio has always supported many programming languages and we encourage new languages and experiments; diversity and innovation are healthy and help progress the state of the art in software engineering. In Visual Studio we also remain heavily invested long-term in providing the best C and C++ tools and continuing to actively participate in ISO C++ standardization, because we know that our internal and external customers are relying on these languages for their success, now and for many years to come. As cyberattacks and cybercrimes increase, we have to defend our software that is under attack. Malicious actors target many attack vectors, one of which is memory safety vulnerabilities. C and C++ do not guarantee memory safety by default, but you can build reliable and secure software in C++ using additional libraries and tools. And regardless of the programming languages your project uses, you need to know how to defend against the many other attack vectors besides memory safety that bad actors are using daily to attack software written in all languages. To that end, we’ve just published a new document on Microsoft Learn to help our customers know and use the tools we provide in Visual Studio, Azure DevOps, and GitHub to ship reliable and secure software. Most of the advice applies to all languages, but this document has a specific focus on C and C++. It was coauthored by many subject-matter experts in programming languages and software security from across Microsoft: Build reliable and secure C++ programs | Microsoft Learn This is a section-by-section companion to the United States government publication NISTIR 8397: Guidelines on Minimum Standards for Developer Verification of Software. NISTIR 8397 contains excellent guidance on how to build reliable and secure software in any programming language, arranged in 11 sections or topics. For each NISTIR 8397 section, this Learn document summarizes how to use Microsoft developer products for C++ and other languages to meet that section’s security needs, and provides guidance to get the most value in each area. Most of NISTIR 8397’s guidance applies to all software; for example, all software should protect its secrets, use the latest versions of tools, do automated testing, use CI/CD, verify its bill of materials, and so on. But for C++ memory safety specifically, see our Learn document’s information in sections 2.3, 2.5, and 2.9 for a detailed list of analyses (e.g., CodeQL, Binskim, /analyze), safe libraries (e.g., GSL, SafeInt), hardening compiler switches (e.g., /sdl, /GS, /guard, /W4, /WX, /Qspectre), and tools (e.g., Address Sanitizer, LibFuzzer) that your project can and should use regularly to harden your C++ programs. If your project uses C++ and you find items listed that you’re not using yet, don’t wait — start adding them to your project today! Safety and security are essential. We want to help all of our customers to know about and use the state-of-the-art tools we provide in Visual Studio, Azure DevOps, and GitHub for writing reliable and secure software in our device-and-cloud […]

October 2, 2023

A guide to designing and shipping AI developer tools

GitHub Products

After three-plus years of concepting, designing, and shipping AI-driven developer tools, GitHub is continuing to explore new ways to bring powerful AI models into the developer workflow. Along the way, we’ve learned that the most important aspect of designing AI-driven products is to focus extensively on the developer experience (DevEx). While it can now feel like there’s a new AI announcement from every company every week, we’re here to reflect on what it takes to build an AI product from scratch—not just to integrate an LLM into an existing product. In this article, we’ll share 10 tips for designing AI products and developer tools, and lessons we learned first-hand from designing, iterating, and extending GitHub Copilot. Let’s jump in. Tip 1: Build on the creative power of natural language “The hottest new design system is natural language,” reports the team designing GitHub Copilot. According to them, the most important tools to develop right now are ones that will allow people to describe, in their respective natural languages, what they want to create, and then get the output that they want. Leveraging the creative power of natural language in AI coding tools will shift the way developers write code and solve complex problems, fueling creativity and democratizing software development. Idan Gazit, Senior Director of Research for GitHub Next, identifies new modalities of interaction, or patterns in the way code is expressed to and written by developers. One of those is iteration, which is most often seen in chat functionalities. Developers can ask the model for an answer, and if it isn’t quite right, refine the suggestions through experimentation. He says, “When it comes to building AI applications today, the place to really distinguish the quality of one tool from another is through the tool’s DevEx.” To show how GitHub Copilot can help developers build more efficiently, here’s an example of a developer learning how to prompt the AI pair programmer to generate her desired result. A vague prompt like, “Draw an ice cream cone with ice cream using p5.js,” resulted in an image that looked like a bulls-eye target sitting on top of a stand: An example of GitHub Copilot responding to a vague prompt to draw an ice cream cone using p5.js and generating an image that looks like a bulls-eye target A revised prompt that specified details about the desired image, like “The ice cream cone will be a triangle with the point facing down, wider point at the top,” helped the developer to generate her intended result, and saved her from writing code from scratch: An example of a GitHub Copilot responding to a specific prompt to draw an ice cream cone using p5.js and generating the developer’s desired result Tip 2: Identify and define a developer’s pain points Designing for developers means placing their needs, preferences, and workflows at the forefront. Adrián Mato, who leads GitHub Copilot’s design team, explains, “It’s hard to design a good product if you don’t have an opinion. That’s why you need to ask questions, embrace user feedback, and do the research to fully understand the problem space you’re working in, and how developers think and operate.” Keeping devs in the flow For example, when designing GitHub Copilot, our designers had to make decisions about optionality, which is when an AI model […]

October 1, 2023

Senser Unveils AIOps Platform Using eBPF to Collect Data

Noutați, Software

Senser emerged from stealth this week to launch an artificial intelligence for IT operations (AIOps) platform that leverages extended Berkeley Packet Filter (eBPF) running in the microkernel of Linux operating systems to collect data from IT environments. Fresh from raising $9.5 million in funding, Senser CEO Amir Krayden said the company’s namesake platform then applies machine learning algorithms to that data to identify issues that could lead to outages. Those insights are surfaced using graph technology to make it simpler to both observe IT environments and triage issues at scale because the AIOps platform is running processes at the microkernel level rather than in user space. The approach provides IT teams with a more efficient and holistic approach to observability at a level of scale legacy platforms can’t achieve, said Krayden. The use of machine learning algorithms also reduces the cognitive load on DevOps teams because issues involving, for example, performance degradations are automatically surfaced, he added. In addition, the company is working toward adding generative AI capabilities to provide summaries that explain what IT events have occurred, noted Krayden. In effect, eBPF changes the way operating systems are designed because it enables networking, storage and observability software to scale at much higher levels of throughput since they are no longer running in user space. That’s especially critical for observability and AIOps platforms that need to dynamically process massive amounts of data in near-real-time. As the number of organizations running the latest versions of Linux continues to increase, more hands-on experience with eBPF will be gained. IT teams may not need to concern themselves with what is occurring in the microkernel of the operating systems, but they do need to understand how eBPF ultimately reduces the total cost of running IT at scale. AI and graph technology, in combination with eBPF, will fundamentally change how IT is implemented and managed. The current complexity of application environments is already exceeding the ability of IT teams to cost-effectively manage them at scale, so the need for a different approach is already apparent. Many IT environments are already too complex for IT personnel to manage without the help of some form of AI. It’s not clear precisely how much AI will automate the management of IT, but it’s not likely the need for humans to manage and supervise these environments will happen any time soon. However, the level of scale at which an IT environment can be effectively managed is changing as AI makes it easier to identify issues and understand their impact. Too often today, there are simply too many dependencies within an IT environment to keep track of using legacy monitoring tools that only track a set of pre-defined metrics. It may be a while before AI is pervasively employed across IT environments, but it’s now more a question of when rather than if. The issue now is determining where the interface between the humans and the machines that are jointly managing IT environments lies.

October 1, 2023

Raspberry Pi 5: Faster, Better, Stronger — Spendier

Noutați, Software

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters. In a cheeky extra post this week: Everyone’s favorite single-board ARM computer, the Raspberry Pi, has a new generation coming soon. Compared to the ’4, RPi5 has double the performance, quadruple the base RAM and far more capable I/O. Analysis: And you’ll even be able to buy one The pandemic completely messed up the Raspberry Pi Foundation’s supply chains, meaning they had to focus on supplying companies who’d forward-bought the devices. This time, Eben Upton’s crew are trying to get back to their roots, promising—for the first couple of months—to sell RPi5s only to individuals. What’s the story? Alaina Yee reports—“Raspberry Pi 5 just got announced”: “I can’t wait”Forget the holiday pie, this is what I want on my table for Thanksgiving. … It looks totally badass. … Not only does the Raspberry Pi 5 appear ready to deliver a sizable step up in performance compared to its 2019 predecessor, but its new silicon was designed in-house.…The Raspberry Pi 5 is leaning hard into high-octane mini-computing. … You can expect the Raspberry Pi 5 to be about two to three times faster. Memory bandwidth also doubles.…And … a new official first-party operating system will be launching … in mid-October. Called Raspberry Pi OS, it’s based on the Linux Debian distro, as well as the Raspbian derivative that’s existed for years. … I can’t wait. Speeds and feeds? Brad Linder’s got ’em—“Raspberry Pi 5 offers 2X the performance”: “4x ARM Cortex-A76”The new Raspberry Pi 5 is a single-board computer that’s a major upgrade over the Raspberry Pi 4 … in just about every way. … At launch, there will be two configurations available: a model with 4GB of RAM that sells for $60 and an 8GB version priced at $80. That means the starting model has twice as much RAM as a $35 Raspberry Pi 4.…At the heart of new computer is a new … 16nm chip featuring 4x ARM Cortex-A76 CPU cores @ 2.4 GHz, 512KB per-core L2 cache, 2MB L3 cache, VideoCore VII graphics with support for dual 4k/60 Hz HDMI displays. [It] also features 32-bit LPDDR4X 4267MT/s memory … 2x micro HDMI (4K/60Hz), 2x USB 3.0 Type-A, 2x USB 2.0 Type-A, 1x Gigabit Ethernet with PoE support, 1x USB-C power input, 1x microSD card reader. … There are also two 4-lane MIPI interfaces. Horse’s mouth? Eben Upton—“Introducing: Raspberry Pi 5!”: “We’re incredibly grateful”Virtually every aspect of the platform has been upgraded, delivering a no-compromises user experience. … And it’s the first Raspberry Pi computer to feature silicon designed in‑house here in Cambridge, UK. … Broadcom’s VideoCore VII [is also] developed here.…Like all flagship Raspberry Pi products [it’s] built at the Sony UK Technology Centre in Pencoed, South Wales. We have been working with Sony since the launch of the first Raspberry Pi … in 2012, and we’re firm believers in the benefits of manufacturing our products within a few hours’ drive of our engineering design centre in Cambridge.…We expect the first units to ship by the end of October. … We’re incredibly grateful to the community of makers and hackers who make Raspberry Pi what it is. [So,] we’re going to ringfence all of the Raspberry Pi 5s we sell until at least the end of […]

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