February 26, 2019 / Paul Redmond Laravel 5.8 is now released and available to everyone. This release includes several new features along with the latest bug fixes and improvements to the framework’s core. Some of the new features include: PHP dotenv An integration with PHP dotenv 3.0 ships with Laravel 5.8 and includes the following new features through PHP dotenv 3.0: Check out our PHP dotenv 3.0 Released article for full details on the v3.0 updates. Carbon v2 Laravel 5.8 is capable of using either Carbon v1 or Carbon v2, including the ability to use CarbonImmutable, and even make CarbonImmutable the default. Localization has changed quite a bit in Carbon v2, with much better internationalization support offered than v1. You can learn more from our writeup Carbon Updates Coming to Laravel 5.8. Cache TTL Changes A significant change that might have a moderate to high impact is the Cache TTL Changes Coming to Laravel 5.8. Passing integers to cache methods represents seconds now instead of minutes. Check out my article if you want to change from integer values to a Carbon or \DateInterval instance during the migration process. Deprecated String and Array Helpers Don’t be too alarmed that String and Array helpers are deprecated in Laravel 5.8. They offer little value (asides from style) over using the class equivalents, and Laravel plans on releasing the helpers as an optional package if you want to keep using them. Automatic Policy Resolution Starting in Laravel 5.8, as long as policies and models are in conventional locations, you will not need to register them in the AuthServiceProvider class. If you prefer to...
As we all know with the increasing technological stacks and the increase in mobile app development ecosystem which is getting deeper into our day to day lives. With all these new things the emergence of Artificial Intelligence Technology is also equally a big point of attraction for all of us. AI is not ready to use technology, This is a multifaceted tech having a wide range of other technological stacks like Machine learning and deep learning along with NLP. Mobile app development services nowadays also implement the use of AI in it. This works at different levels, sometimes for building new apps and also for making some minor changes in the existing app. Many of the mobile apps in spite of their complex development and ample amount of content are capable of becoming more user engaging. AI is impacting more on developing mobile applications and has just started taking proper shape. With the use of AI in mobile app development, the app is getting updated and is trying to reach a great number of user and increase the business of the organization. Various aspects that explain how AI will change the mobile app development process. Artificial Intelligence has been especially effective for some applications in regard to driving client commitment and business development. Only a dormant calculation dependably isn’t powerful to draw in clients dependent on the client conduct. In this regard, AI helps to connect with clients dependent on the distinctive client and reaction designs. How AI truly helps client connection and commitment by fitting the application itself to the client requests? Indeed, here are a couple of...
Web Application Pentesting is a method of identifying, analyzing and Report the vulnerabilities which are existing in the Web application including buffer overflow, input validation, code Execution, Bypass Authentication, SQL Injection, CSRF, Cross-site scripting in the target web Application which is given for Penetration Testing. Repeatable Testing and Conduct a serious method One of the Best Method conduct Web Application Penetration Testing for all kind of web application vulnerabilities. Web Application Penetration Testing Checklist Information Gathering 1. Retrieve and Analyze the robot.txt files by using a tool called GNU Wget. 2. Examine the version of the software. database Details, the error technical component, bugs by the error codes by requesting invalid pages. 3. Implement techniques such as DNS inverse queries, DNS zone Transfers, web-based DNS Searches. 4. Perform Directory style Searching and vulnerability scanning, Probe for URLs, using tools such as NMAP and Nessus. 5. Identify the Entry point of the application using Burp Proxy, OWSAP ZAP, TemperIE, WebscarabTemper Data. 6. By using traditional Fingerprint Tool such as Nmap, Amap, perform TCP/ICMP and service Fingerprinting. 7.By Requesting Common File Extension such as.ASP,EXE, .HTML, .PHP ,Test for recognized file types/Extensions/Directories. 8. Examine the Sources code From the Accessing Pages of the Application front end. Authentication Testing 1. Check if it is possible to “reuse” the session after Logout.also check if the application automatically logs out a user has idle for a certain amount of time. 2. Check whether any sensitive information Remain Stored stored in browser cache. 3. Check and try to Reset the password, by social engineering crack secretive questions and guessing. 4.check if the “Remember my password” Mechanism...
This is the fourth in a in which we outline our experience with React Native and what is next for mobile at Airbnb.Where are we today? Although many teams relied on React Native and had planned on using it for the foreseeable future, we were ultimately unable to meet our original goals. In addition, there were a number of technical and organizational challenges that we were unable to overcome that would have made continuing to invest in React Native a challenge. As a result, moving forward, we are sunsetting React Native at Airbnb and reinvesting all of our efforts back into native. Failing to Reach Our Goals Move Faster When React Native worked as intended, engineers were able to move at an unparalleled speed. However, the numerous technical and organizational issues that we outlined in this series added frustrations and unexpected delays to many projects. Maintain the Quality Bar Recently, as React Native matured and we accumulated more expertise, we were able to accomplish a number of things that we weren’t sure were possible. We built shared element transitions, parallax, and were able to dramatically improve the performance of some screens that used to frequently drop frames. However, some technical challenges such as initialization and the async first render made meeting certain goals challenging. The lack of resources internally and externally made this even more difficult. Write Code Once Instead of Twice Even though code in React Native features was almost entirely shared across platforms, only a small percentage of our app was React Native. In addition, large amounts of bridging infrastructure were required to enable product engineers to work effectively. As...
The vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning. (For more background on AI, check out our first flowchart here.) Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on. In all of these instances, each platform is collecting as much data about you as possible—what genres you like watching, what links you are clicking, which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth. Frankly, this process is quite basic: find the pattern, apply the pattern. But it pretty much runs the world. That’s in big part thanks to an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning. Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to find—and amplify—even the smallest patterns. This technique is called a deep neural network—deep because it has many, many layers of simple computational nodes that work together to munch...
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