In SQL Server 2016, Microsoft introduced the concept of row-level security, which gives you fine-grained control over who gets access to what data, potentially down to the level of individual rows. Normally, SQL security grants coarse access to a whole table or view (SQL Server can also do it for columns), and anything more granular than that requires the use of views or stored procedures.
There is another way to do row-level security without changing the database clients, and that's with Gallium Data - a free database proxy that can change the network traffic between your database clients and your database servers.
Windows 11 is already here. Let's take a look at what Microsoft has changed in terms of security and privacy in its operating system. Does Windows 11 protect users’ data better than Windows 10? How does Windows 11 resist cyberattacks?
Windows 11 was released on October 4, 2021. Earlier it produced plenty of hype with its version for testing. Even a simulator was created. I would like to note that this article was prepared before the official release of the new OS version. Some functions could have been changed, removed, or improved as it happened for example, with the minimum system requirements. Initially, Windows 11 was not intended for everyone (at least 8th generation Intel processors were required). However, the Windows community found a way to bypass the installation lock. Microsoft decided that it would not take action to restrict installations. In its blog, the corporation disowned possible problems, including problems with drivers on old PCs.
At ForAllSecure, we’re all about fuzzing and making it easier for customers to quickly fuzz and secure their applications. That’s why we’ve gone ahead and compiled a catalog of fuzz targets intended for Mayhem that’s written and compiled using several different languages (and architectures) like C/C++, Python, Go, Rust, Java, and many others! We’ve also added several choices for specifying which fuzzer engine to use, whether that’s the popular libFuzzer, honggfuzz, AFL, or even our own Mayhem for Code fuzzer.
In essence, we’ve provided a swiss army knife of fuzzing options to not only serve as a quick-and-easy reference point for users to get started with fuzzing in general but to also showcase Mayhem’s versatility for fuzzing several languages and architectures in tandem with specific fuzzer engines. This catalog of tutorial fuzzing targets also emphasizes the recommended workflow for compiling targets using Docker and fuzzing such containerized applications in Mayhem.
You want to become an SRE. You’ve read the right books, taken the right classes, and earned the right certifications. You’re part of the way toward landing an SRE job.
But, you’ll also need the right SRE resume. And, while there are no universal rules to follow about creating the ideal SRE resume, following some key best practices will help you build a resume that sets you apart from other SRE job applicants.
The daily work of DevOps can be like a puzzle in the sense that the idea is to assemble several pieces in a logical order to create a structure understood by everyone. The different assembly steps are usually the same for any puzzle and therefore require learning to gain efficiency.
Efficient time management is probably what every DevOps engineer seeks to advance in his career. This is the skill that the top-performing engineers have improved over time and iteration. Time is what everyone needs to learn and test something new to become an expert in the area.
Have you ever found yourself in a situation where all your service mesh services are running in Kubernetes, and now you need to expose them to the outside world securely and reliably?
Ingress management is essential for your configuration and operations when exposing services outside of a cluster. You need to take care of the authentication, observability, encryption, and integration with other third-party vendors alongside other policies.
Has your entire career ever hinged on a single moment? For Darren Dillon, free beer in college set him on the path to a computer science degree and eventually a wildly successful career at Microsoft.
Today, as the CTO of Azure and AI at Microsoft Industry Solutions, Darren leads an impressive team of over 130 engineers and is at the forefront of cloud computing and AI technology.
Every day, the ProgrammableWeb team is busy, updating its three primary directories for APIs, clients (language-specific libraries or SDKs for consuming or providing APIs), and source code samples.
Do you need to add captions to your featured images in WordPress?
Captions are a great way to add context and background information, but many WordPress themes don’t display them properly. This can leave your images looking bare and your visitors missing out on valuable information.
In this article, we will show you how to easily display captions for your featured images in WordPress, enhancing your site’s visual appeal and user experience.
Why Add Captions to Featured Images in WordPress?
Do you ever feel like your featured images are missing something? Without captions, they can look bare and fail to convey the full story or context behind them.
Many WordPress themes don’t support captions for featured images, leaving your audience without the valuable background information that captions provide. This can be frustrating, especially when you have a powerful message or important details to share.
Adding captions to your featured image is a powerful way to provide context as to what the photo on your WordPress blog is about. There are a few reasons why you might consider adding captions:
Describing the featured image: Sometimes, it’s unclear what the featured image is about. For example, if you have a travel website that archives your outdoor adventures, you might want to add a caption of where your featured image was taken.
Enhanced accessibility: Adding captions can improve the user experience for those who may have visual impairments.
Dating the graphic: Sometimes, it’s a good idea to date the image. If your featured image shows a before and after photo, then you might want to include a date so readers know when it was taken.
Giving credit: If you’re taking someone else’s photo, especially a professional photographer, then it’s a good idea to provide a link to their website. These types of captions are often used in newspapers, publications, magazines, and popular blogs.
When you upload a featured image, you have the option to add a caption, along with other information, such as the image alt text.
Generally, almost all WordPress themes have built-in support for featured images and show them across many different areas of your WordPress website.
However, your theme may not show this caption to visitors.
That said, let’s see how you can add captions to featured images in WordPress using a plugin.
Adding Captions to Featured Images in WordPress
The best way to show featured image captions is by using the Featured Image Caption plugin.
First, you will need to install and activate the plugin. If you need help, then please see our guide on how to install a WordPress plugin.
All you have to do is head over to the blog post where you want to add a caption to your featured image. Go to Posts » All Posts and then find the blog post you want to make changes to.
Hover over the blog name, and then a few buttons will appear under the title. Click on ‘Edit.’
Once you land on the blog article, head over to the right panel. To make the panel appear, click on the panel button up top next to ‘Update’ or ‘Publish.’
Then scroll down to Featured Image and select ‘Set featured image.’
Then, a new window will appear, showing your existing media library.
Go ahead and select the photo you’d like to use for your featured image.
Next, scroll to the bottom of the screen. Click the ‘Set featured image’ button to make the photo appear as the main photo in your post.
Since you’ve downloaded the plugin, you should see a Featured Image Caption panel appear on the right, just below the photo you’ve uploaded.
Simply give your featured image the appropriate caption under ‘Caption Text.’
Feel free to add the source attribution for the featured image to give credit to the owner of the picture.
Once your caption is set, all that is left to do is hit the ‘Update’ or ‘Publish’ button.
This ensures you won’t lose your changes.
If you visit your blog post, you can see the caption in action. Notice how it appears right under the featured image.
There you have it! Now you can easily add captions to all of your featured images so that readers can better understand what the photo is about before they continue reading your blog.
Bonus: Adding Caption to Images in WordPress
Now that you know how to add captions to your featured images, you probably want also to add them to all images.
It’s a good way to add context, especially if you want to describe various images used throughout your blog post. You could explain screenshots, infographics, or even stock photos. It provides users with a better experience.
All you have to do is upload your image to the media library. You’ll see Alt Text, Title, Caption, and Description in the right-hand panel.
Under ‘Caption,’ type in the sentence or phrase to give readers additional context about the image. Then click ‘Select’ to insert the image.
NoSQL data sets arose in the latter part of the 2000s as the expense of storage drastically diminished. The days of expecting to create a complicated, hard to-oversee data model to avoid data replication were long gone and the primary expense of programming and development was now focused on the developers themselves, and hence NoSQL databases were brought into the picture to enhance their productivity.
As storage costs quickly diminished, the measure of data that applications expected to store increased, and the query expanded as well. This data was received in all shapes and sizes — organized, semi-organized, and polymorphic — and characterizing the schema ahead of time turned out to be almost incomprehensible. NoSQL databases permitted the developers to store colossal measures of unstructured data, providing them with a ton of flexibility.
I work at Appsmith, an open-source low code platform for developers to build internal tools and workflows.
At Appsmith, our developer users define business logic by writing any JS code in between {{ }} dynamic bindings almost anywhere in the app. They can use this while creating SQL queries, APIs, or triggering actions. This functionality lets you control how your app behaves with the least amount of configuration. Underneath the hood, the platform will evaluate all this code in an optimized manner to make sure the app remains performant yet responsive.
Viktor: You are known in the observability community and SRE community very well. I’ve followed your work for a while during my time at Confluent, so I’m super excited to speak with you. Can you please tell us a little bit about yourself? Like what do you do? And what are you up to these days?
Liz: Sure. So I’ve worked as a site reliability engineer for roughly 15 years, and I took this interesting pivot about five years ago. I switched from being a site reliability engineer on individual teams like Google Flights or Google Cloud Load Balancer to advocating for the wider SRE community. It turns out that there are more people outside of Google practicing SRE than there are inside of Google practicing SRE.
Any solution aimed at processing unstructured data (i.e., language, specifically text in most cases) is today based on one of two main approaches: Machine Learning and Symbolic. Both can be delivered in multiple ways (different algorithms in the case of ML, from shallow linguistics to semantic technology in the case of Symbolic), but not much has been done so far in the realm of hybrid approaches. While choosing one over the other is always going to present a compromise between advantages and drawbacks (higher accuracy coming from Symbolic, more flexibility derived from ML), Hybrid AI — or Hybrid NL — is a revolutionary path to solve linguistic challenges that can leverage the best of both worlds and, ultimately, make your NLP practices graduate to NLU (Natural Language Understanding). I won’t spend time explaining how ML or Symbolic work since there’s a ton of literature about that already, I’ll focus this page on Hybrid instead.
What is Going Hybrid?
To frame this conversation in a practical fashion, we must look at two aspects: development, and workflow. At the development stage, going hybrid means that a Symbolic solution will support the creation of a Machine Learning model in order to either reduce the effort or enhance its quality. On the other hand, at the production stage, our workflow can be supported by both ML and Symbolic to deliver a more precise outcome. In a project that considers the Machine Learning piece the pivot of the solution, the first type of integration places Symbolic at the top (before even creating a Machine Learning model), and the second one at the bottom (curating or enhancing the final output). Naturally, both of these hybrid ways can be present at the same time in a linguistic project.
