From the blog

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

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 […]

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Cybersecurity spotlight on bug bounty researcher @inspector-ambitious

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 […]

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Build Reliable and Secure C++ programs — Microsoft Learn

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 […]

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A guide to designing and shipping AI developer tools

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 […]

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Senser Unveils AIOps Platform Using eBPF to Collect Data

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.

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Raspberry Pi 5: Faster, Better, Stronger — Spendier

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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What Are The Relaxed Constexpr Restrictions In C++?

In C++, the constexpr specifier is used to declare a function or variable to evaluate the value of at compile time, which speeds up code during runtime. This useful property had some restrictions in C++11, these are relaxed in C++14 and this feature is known as Relaxed Constexpr Restrictions. In this post, we explain what are the relaxed constexpr restrictions in modern C++. What is the constexpr specifier in modern C++? The C++11 standard comes with the introduction of generalized constexpr functions. The constrexpr is used as a return type specifier of function and improves performance by computations done at compile time rather than run time. Return values of constexpr could be consumed by operations that require constant expressions, such as an integer template argument. Here is the syntax:   constexpr   Here is an example of how to use a constexpr in C++:   constexpr double sq(const double x) { return ( x*x ); }   and can be used as shown below.   constexpr double y = sq(13.3);   Note that here we used a constant variable 13.3, that is given in coding, thus the result y will be calculated during compilation as 176.89. Then, we can say, similarly this line will be compiled as we show below:   const double y = 176.89   Is there a simple constexpr specifier example in modern C++? Here we can sum all above in an example. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18   #include   // C++11, a simple constexpr example constexpr double sq(const double x) { return ( x*x ); }   int main() { constexpr double y = sq(13.3); std::cout

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MSVC ARM64 Optimizations in Visual Studio 2022 17.7

MSVC ARM64 Optimizations in Visual Studio 2022 17.7 Hongyon Suauthai (ARM) September 28th, 20230 2 In Visual Studio 2022 version 17.6 we added a host of new ARM64 optimizations. In this 2nd edition of our blog, we will highlight some of the performance improvements to MSVC ARM64 compiler backend, we will discuss key optimizations in the Visual Studio 2022 version 17.7 for both scalar ISA and SIMD ISA (NEON). We started introducing these performance optimizations in 17.6 release and we have landed them in 17.7 release. By element operation ARM64 supports by-element operation in several instructions such as fmul, fmla, fmls, etc.  This feature allows a SIMD operand to be computed directly by a SIMD element using an index to access it. In the example below where we multiply a scalar with an array, MSVC duplicated the vector element v0.s[0] into all lanes of a SIMD register, then multiplied that with another SIMD operand represented by array b.  This is not efficient because the dup instruction will add 2 more execution latency before executing the fmul instruction. To better understand this optimization let’s take the following sample code: void test(float * __restrict a, float * __restrict b, float c) {   for (int i = 0; i < 4; i++)        a[i] = b[i] * c; } Code generated by MSVC 17.6:        dup         v17.4s,v0.s[0]        ldr         q16,[x1]        fmul        v16.4s,v17.4s,v16.4s         str         q16,[x0] In Visual Studio 2022 17.7, we eliminated a duplicate instruction. It now can multiply by a SIMD element directly and the code generation is:        ldr         q16,[x1]        fmul        v16.4s,v16.4s,v0.s[0]         str         q16,[x0] Neon supports for shift right and accumulate immediate This instruction right-shifts a SIMD source by an immediate and accumulates the final result with the destination SIMD register. As mentioned earlier, we started working on this optimization in the 17.6 release and completed the feature in the 17.7 release. In the 17.5 release MSVC turned right shifts into left shifts using a negative shift amount. To implement it that way, it copied a constant -2 into a general register, and then duplicated a general register into the SIMD register before the left shift. We eliminated the duplicate instruction and used right shift with an immediate value in the 17.6 release. It was an improvement, but not yet good enough. We further improved the implementation in 17.7 by combining right shift and add by using usra for unsigned and ssra for signed operation. The final implementation is much more compact than the previous ones. To better understand how this optimization works let’s look at the sample code below: void test(unsigned long long * __restrict a, unsigned long long * __restrict b) {    for (int i = 0; i < 2; i++)        a[i] += b[i] >> 2; } Code generated by MSVC 17.5:        mov         x8,#-2        ldr         q16,[x1]        dup         v17.2d,x8        ldr         q18,[x0]        ushl        v17.2d,v16.2d,v17.2d        add         v18.2d,v17.2d,v18.2d         str         q18,[x0] Code generated by MSVC 17.6:        ldr         q16,[x1]        ushr        v17.2d,v16.2d,#2        ldr         q16,[x0]        add         v16.2d,v17.2d,v16.2d         str         q16,[x0] Code generated by MSVC 17.7:        ldr         q16,[x0]        ldr         q17,[x1]        usra        v16.2d,v17.2d,#2         str         q16,[x0] Neon right shift into cmp A right shift on a signed integer shifts the msb, with the result being either -1 or 0, and […]

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Microsoft C++ Team at CppCon 2023

Microsoft C++ Team at CppCon 2023 Sy Brand September 28th, 20230 2 As always our team will be at CppCon this year with a host of presentations. Many of us will also be present at our team’s booth in the main hall for the first two days of the conference. Come say hi and let us know if you have any questions about our talks, products, or anything else! You can also join the #visual_studio channel on the CppCon Discord to talk to us (note: to join, head to #directory channel first, and check the checkbox next to “Visual Studio” box). We’re also running a survey on the C++ ecosystem. If you have a moment, please take our survey. It’s quick and you could win a utility backpack or duffel bag. Here’s the lineup: Monday 2nd​ Lifetime Safety in C++ – Gabor Horvath – 11am ​ Informal Birds of a Feather for Cpp2/cppfront – Herb Sutter – 12:30pm​ Tuesday​ 3rd​ What’s New in Visual Studio – David Li & Mryam Girmay – 3:15pm​ ​Thursday 5th​​ Cooperative C++ Evolution: Towards a Typescript for C++ – Herb Sutter (Keynote) – 10:30am​ How Visual Studio Code Can Help You Develop More Efficiently in C++ – Alexandra Kemper & Sinem Akinci – 3:15pm​ Regular, Revisited – Victor Ciura – 3:15pm​ Friday 6th​​ Getting Started with C++ – Michael Price – 1:30pm   Sy Brand C++ Developer Advocate, C++ Team Follow

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Useful Features Of Modern C++ That Come With C++14

Hello fellow C++ Developers. This week we continue to explore features from the C++14 standard. One of the features that comes with C++14 is auto return type deduction,. We explain auto return type deduction with very simple examples including lambda and template examples. The Lambda Expression construct is introduced in C++ 11 and further developed in the C++14, C++17, and C++20 standards. In C++14, lambda expressions are improved by the generalized lambda (generic lambda) and we explain how you can use generalized lambda captures in modern C++. Our educational LearnCPlusPlus.org site has a broad selection of new and unique posts with examples suitable for everyone from beginners to professionals alike. It is growing well thanks to you, and we have many new readers, thanks to your support! The site features a plethora of posts that are great for learning the features of modern C++ compilers with very simple explanations and examples. RAD Studio’s C++ Builder, Delphi, and their free community editions C++ Builder CE, and Delphi CE are powerful tools for modern application development. Table of Contents Where I can I learn C++ and test these examples with a free C++ compiler? How to use modern C++ with C++ Builder? How to learn modern C++ for free using C++ Builder? Do you want to know some news about C++ Builder 12? Where I can I learn C++ and test these examples with a free C++ compiler? If you don’t know anything about C++ or the C++ Builder IDE, don’t worry, we have a lot of great, easy to understand examples on the LearnCPlusPlus.org website and they’re all completely free. Just visit this site and copy and paste any examples there into a new Console, VCL, or FMX project, depending on the type of post. We keep adding more C and C++ posts with sample code. In today’s round-up of recent posts on LearnCPlusPlus.org, we have new articles with very simple examples that can be used with: The free version of C++ Builder 11 CE Community Edition or a professional version of C++ Builder  or free BCC32C C++ Compiler and BCC32X C++ Compiler or the free Dev-C++ Read the FAQ notes on the CE license and then simply fill out the form to download C++ Builder 11 CE. How to use modern C++ with C++ Builder? C++11 allowed lambda functions to deduce the return type based on the type of the expression given to the return statement. The C++14 standard provides return type deduction in all functions, templates, and lambdas. C++14 allows return type deduction for functions that are not of the form return expressions. In this post, we explain the auto keyword, what is an auto type deduction, and how we can use it in functions, templates, and lambdas. In the first post, explain what is auto return type deduction. Lambda Expressions allow users to write an inline expression that can be used for short snippets of code in your C++ app which are not going to be reused and don’t require naming. The Lambda Expression construct is introduced in C++ 11 and further developed in the C++14, C++17, and C++20 standards. In C++14, lambda expressions are improved by the generalized lambda (generic lambda) or initialized lambda feature, and in the next post, we remember what lambda is and explain what a generic lambda is, and how we can use it. C++14 standard […]

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