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It seems like the Coronavirus is dominating the news as of late. Along with major conferences being canceled, there is news about companies asking workers to go remote to prevent the virus from spreading. For large companies like Apple and Google, they likely have a lot of the tools in place for their employees to go remote since they have offices around the world.

For smaller organizations, employees may lack the expertise to know which tools will help their employees stay productive and stay in communication with each other. I’ve been doing some research around these tools over the past few weeks as a safety measure, so here are some apps for remote working.


Zoom is something I’ve been researching as of late as my school is making backup plans for distance learning if the situation arises. With Zoom, you can hold a large meeting for internal communication, easily display your screen, and then save the recordings offline. If you meet with customers, you can set up one on one meetings with ease. In my testing, I was quite impressed with the annotation tools that it offers.

File-Sharing Tools

If your organization doesn’t have a tool like Google Drive or OneDrive deployed for file sharing, you might check out Box or Dropbox. These tools will allow you to upload documents and securely share them internally or externally. A file sharing service is an essential app for remote working.


I’ve been using Spike as my primary email client for the past few weeks. I’ll have a full review coming up soon, but it’s helped turn my email into a messaging machine. It strips away all the fluff of email, and it makes it feel like an iMessage interface. It also includes voice and video chat, so if you don’t want to deploy something like Zoom, Spike can handle it all.


A very popular app for remote working is Basecamp. The folks behind it all work remotely, so it’s built for remote teams. They are also behind the Remote book

The Industrial Revolution’s “under one roof” model of conducting work is steadily declining as technology creates virtual workspaces that allow employees to provide their vital contribution without physically clustering together. Today, the new paradigm is “move work to the workers, rather than workers to the workplace.”

Slack / Microsoft Teams

Tools like Slack and Microsoft Teams have become very popular in recent years. They help eliminate emails and funnel everything into either group channels or direct messages. They support a wide variety of integrations as well. You can create channels for projects, groups of people, and more. They include mobile apps for staying in the loop while on the go as well.


Moving from in-office to a remote environment can be a challenge. Using a tool like Klokki, you can do time tracking with a native application. Tracking your time on the Mac is a great way to keep your employer informed of how you are spending your time while you are working from home. If you need a team based solution, check out Harvest.


If you need to use SFTP to connect to corporate servers, Transmit is going to be an essential for app for remote working.. I’ve been a customer for years. Transmit works with services like Backblaze B2, Box, Google Drive, DreamObjects, Dropbox, Microsoft Azure, and Rackspace Cloud Files. Of course, it works with FTP, SFTP, and S3 as well.


If your team doesn’t have a centralized password manager, now is the time to do so. If your team is going remote, a 1Password for Teams account is a great solution. You can create shared vaults, store corporate passwords, create secure notes, and more. You can manage your team from a single interface as well.

Wrap up on apps for remote working

If your office is preparing to start working remotely, I hope this list of apps will help you make the transition. It’s important to stay in communication and on top of projects, but in a way that works for all employees and employers. Thankfully, most of the tools that are subscription-based can be used on a month to month basis, so you aren’t committed for a long period. Is your office going to start working remotely? If so, do you have any other apps or services you recommend?

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Master Dimensionality Reduction With These 5 Must


Singular Value Decomposition (SVD) is a common dimensionality reduction technique in data science

We will discuss 5 must-know applications of SVD here and understand their role in data science

We will also see three different ways of implementing SVD in Python


“Another day has passed, and I still haven’t used y = mx + b.“

Sounds familiar? I often hear my school and college acquaintances complain that the algebra equations they spent so much time on are essentially useless in the real world.

Well – I can assure you that’s simply not true. Especially if you want to carve out a career in data science.

Linear algebra bridges the gap between theory and practical implementation of concepts. A healthy understanding of linear algebra opens doors to machine learning algorithms we thought were impossible to understand. And one such use of linear algebra is in Singular Value Decomposition (SVD) for dimensionality reduction.

You must have come across SVD a lot in data science. It’s everywhere, especially when we’re dealing with dimensionality reduction. But what is it? How does it work? And what are SVD’s applications?

I briefly mentioned SVD and its applications in my article on the Applications of Linear Algebra in Data Science. In fact, SVD is the foundation of Recommendation Systems that are at the heart of huge companies like Google, YouTube, Amazon, Facebook and many more.

We will look at five super useful applications of SVD in this article. But we won’t stop there – we will explore how we can use SVD in Python in three different ways as well.

And if you’re looking for a one-stop-shop to learn all machine learning concepts, we have put together one of the most comprehensive courses available anywhere. Make sure you check it out (and yes, SVD is in there as part of the dimensionality reduction module).

Table of Contents

Applications of Singular Value Decomposition (SVD)

Image Compression

Image Recovery


Spectral Clustering

Background Removal from Videos

What is Singular Value Decomposition?

Rank of a Matrix

Singular Value Decomposition

Why is SVD used in Dimensionality Reduction?

3 Ways to Perform SVD in Python

Applications of Singular Value Decomposition (SVD)

We are going to follow a top-down approach here and discuss the applications first. I have explained the math behind SVD after the applications for those interested in how it works underneath.

You just need to know four things to understand the applications:

SVD is the decomposition of a matrix A into 3 matrices – U, S, and V

S is the diagonal matrix of singular values. Think of singular values as the importance values of different features in the matrix

The rank of a matrix is a measure of the unique information stored in a matrix. Higher the rank, more the information

Eigenvectors of a matrix are directions of maximum spread or variance of data

In most of the applications, the basic principle of Dimensionality Reduction is used. You want to reduce a high-rank matrix to a low-rank matrix while preserving important information.

SVD for Image Compression

It minimizes the size of an image in bytes to an acceptable level of quality. This means that you are able to store more images in the same disk space as compared to before.

Here’s how you can code this in Python:

Python Code:


If you ask me, even the last image (with n_components = 100) is quite impressive. I would not have guessed that it was compressed if I did not have the other images for comparison.

SVD for Image Recovery

We’ll understand image recovery through the concept of matrix completion (and a cool Netflix example).

Matrix Completion is the process of filling in the missing entries in a partially observed matrix. The Netflix problem is a common example of this.

The basic fact that helps to solve this problem is that most users have a pattern in the movies they watch and in the ratings they give to these movies. So, the ratings-matrix has little unique information. This means that a low-rank matrix would be able to provide a good enough approximation for the matrix.

This is what we achieve with the help of SVD.

Where else do you see this property? Yes, in matrices of images! Since an image is contiguous, the values of most pixels depend on the pixels around them. So a low-rank matrix can be a good approximation of these images.

Here is a snapshot of the results:

Chen, Zihan. “Singular Value Decomposition and its Applications in Image Processing.”  ACM, 2023

SVD for Eigenfaces

The original paper Eigenfaces for Recognition came out in 1991. Before this, most of the approaches for facial recognition dealt with identifying individual features such as the eyes or the nose and developing a face model by the position, size, and relationships among these features.

The Eigenface approach sought to extract the relevant information in a face image, encode it as efficiently as possible, and compare one face encoding with a database of models encoded similarly.

The encoding is obtained by expressing each face as a linear combination of the selected eigenfaces in the new face space.

Let me break the approach down into five steps:

Collect a training set of faces as the training set

Find the most important features by finding the directions of maximum variance – the eigenvectors or the eigenfaces

Choose top M eigenfaces corresponding to the highest eigenvalues. These eigenfaces now define a new face space

Project all the data in this face space

For a new face, project it into the new face space, find the closest face(s) in the space, and classify the face as a known or an unknown face

You can find these eigenfaces using both PCA and SVD. Here is the first of several eigenfaces I obtained after performing SVD on the Labelled Faces in the Wild dataset:

As we can see, only the images in the first few rows look like actual faces. Others look noisy and hence I discarded them. I preserved a total of 120 eigenfaces and transformed the data into the new face space. Then I used the k-nearest neighbors classifier to predict the names based on the faces.

You can see the classification report below. Clearly, there is scope for improvement. You can try adjusting the number of eigenfaces to preserve and experiment with different classifiers:

Have a look at some of the predictions and their true labels:

You can find my attempt at Facial Recognition using Eigenfaces here.

SVD for Spectral Clustering

Clustering is the task of grouping similar objects together. It is an unsupervised machine learning technique. For most of us, clustering is synonymous with K-Means Clustering – a simple but powerful algorithm. However, it is not always the most accurate.

Consider the below case:

Clearly, there are 2 clusters in concentric circles. But KMeans with n_clusters = 2 gives the following clusters:

K-Means is definitely not the appropriate algorithm to use here. Spectral clustering is a technique that combats this. It has roots in Graph theory. These are the basic steps:

Start with the Affinity matrix (A) or the Adjacency matrix of the data. This represents how similar one object is to another. In a graph, this would represent if an edge existed between the points or not

Find the Laplacian (L) of the Affinity Matrix: L = A – D

Find the highest k eigenvectors of the Laplacian Matrix depending on their eigenvalues

Run k-means on these eigenvectors to cluster the objects into k classes

You can read about the complete algorithm and its math here. The implementation of Spectral Clustering in scikit-learn is similar to KMeans:

View the code on Gist.

You will obtain the below perfectly clustered data from the above code:

SVD for Removing Background from Videos

I have always been curious how all those TV commercials and programs manage to get a cool background behind the actors. While this can be done manually, why put in that much manual effort when you have machine learning?

Think of how you would distinguish the background of a video from its foreground. The background of a video is essentially static – it does not see a lot of movement. All the movement is seen in the foreground. This is the property that we exploit to separate the background from the foreground.

Here are the steps we can follow for implementing this approach:

Create matrix M from video – This is done by sampling image snapshots from the video at regular intervals, flattening these image matrices to arrays, and storing them as the columns of matrix M

We get the following plot for matrix M:

What do you think these horizontal and wavy lines represent? Take a moment to think about this.

The horizontal lines represent the pixel values that do not change throughout the video. So essentially, these represent the background in the video. The wavy lines show movement and represent the foreground.

We can, therefore, think of M as being the sum of two matrices – one representing the background and other the foreground

The background matrix does not see a variation in pixels and is thus redundant i.e. it does not have a lot of unique information. So, it is a low-rank matrix

So, a low-rank approximation of M is the background matrix. We use SVD in this step

We can obtain the foreground matrix by simply subtracting the background matrix from the matrix M

Here is a frame of the video after removing the background:

Pretty impressive, right?

We have discussed five very useful applications of SVD so far. But how does the math behind SVD actually work? And how useful is it for us as data scientists? Let’s understand these points in the next section.

What is Singular Value Decomposition (SVD)?

I have used the term rank a lot in this article. In fact, through all the literature on SVD and its applications, you will encounter the term “rank of a matrix” very frequently. So let us start by understanding what this is.

Rank of a Matrix

The rank of a matrix is the maximum number of linearly independent row (or column) vectors in the matrix. A vector r is said to be linearly independent of vectors r1 and r2 if it cannot be expressed as a linear combination of r1 and r2.

Consider the three matrices below:

In matrix A, row r2 is a multiple of r1, r2 = 2 r1, so it has only one independent row. Rank(A) = 1

In matrix B, row r3 is a sum of  r1 and r2, r3 = r1 + r2, but r1 and r2 are independent. Rank(B) = 2

In matrix C, all 3 rows are independent of each other. Rank(C) = 3

The rank of a matrix can be thought of as a representative of the amount of unique information represented by the matrix. Higher the rank, higher the information.

Singular Value Decomposition (SVD)

So where does SVD fit into the overall picture? SVD deals with decomposing a matrix into a product of 3 matrices as shown:

If the dimensions of A are m x n:

U is an m x m matrix of Left Singular Vectors

S is an m x n rectangular diagonal matrix of Singular Values arranged in decreasing order

V is an n x n matrix of Right Singular Vectors

Why is SVD used in Dimensionality Reduction?

You might be wondering why we should go through with this seemingly painstaking decomposition. The reason can be understood by an alternate representation of the decomposition. See the figure below:

The decomposition allows us to express our original matrix as a linear combination of low-rank matrices.

In a practical application, you will observe that only the first few, say k, singular values are large. The rest of the singular values approach zero. As a result, terms except the first few can be ignored without losing much of the information. See how the matrices are truncated in the figure below:

To summarize:

Using SVD, we are able to represent our large matrix A by 3 smaller matrices U, S and V

This is helpful in large computations

We can obtain a k-rank approximation of A. To do this, select the first k singular values and truncate the 3 matrices accordingly

3 Ways to Perform SVD in Python

We know what SVD is, how it works, and where it is used in the real world. But how can we implement SVD on our own?

The concept of SVD sounds complex enough. You might be wondering how to find the 3 matrices U, S, and V. It is a long process if we were to calculate these by hand.

Fortunately, we do not need to perform these calculations manually. We can implement SVD in Python in three simple ways.

SVD in NumPy

NumPy is the fundamental package for Scientific Computing in Python. It has useful Linear Algebra capabilities along with other applications.

You can obtain the complete matrices U, S, and V using SVD in numpy.linalg. Note that S is a diagonal matrix which means that most of its entries are zeros. This is called a sparse matrix. To save space, S is returned as a 1D array of singular values instead of the complete 2D matrix.

View the code on Gist.

Truncated SVD in scikit-learn

In most common applications, we do not want to find the complete matrices U, S and V. We saw this in dimensionality reduction and Latent Semantic Analysis, remember?

We are ultimately going to trim our matrices, so why find the complete matrices in the first place?

In such cases, it is better to use TruncatedSVD from sklearn.decomposition. You specify the number of features you want in the output as the n_components parameter. n_components should be strictly less than the number of features in the input matrix:

View the code on Gist.

Randomized SVD in scikit-learn

Randomized SVD gives the same results as Truncated SVD and has a faster computation time. While Truncated SVD uses an exact solver ARPACK, Randomized SVD uses approximation techniques.

View the code on Gist.

End Notes

I really feel Singular Value Decomposition is underrated. It is an important fundamental concept of Linear Algebra and its applications are so cool! Trust me, what we saw is just a fraction of SVD’s numerous uses.

I encourage you to check out this Comprehensive Guide to build Recommendation Engine from scratch to realize the power of SVD for yourself. Building this project will surely add value to your resume (and enhance your own skillset!).


Comment: Curved Screens Are A Stepping Stone Toward A High

This morning’s report that Apple is working on an iPhone with a curved display sounds like it will be a rather modest change from the iPhone X.

Apple is also developing iPhone displays that curve inward gradually from top to bottom, one of the people familiar with the situation said.

That seems to be referencing a rather subtle curve that may, like the very slight curve at the bottom of the iPhone X, be almost indistinguishable from a flat screen. Technically, of course, the iPhone X display is fully curved, as it folds back underneath itself to make possible the near-bezel-free design …

Apple doesn’t generally do things for novelty value. The curved edge on Samsung’s Galaxy Edge models looked cool when it was first launched, but those I know who own one say they rarely use the navigation feature it makes possible. Mostly the novelty quickly wears off.

Apple may find a more practical use for a curved screen, or it may simply be used as one of those subtle design touches that you barely notice but which do contribute to a great look and feel.

But for me, the really exciting prospect with curved screens is that they are a stepping stone toward a foldable iPhone.

We’ve seen a number of reports suggesting that Apple is working on this. Last October, a Korean site claimed that Apple was working with LG on a foldable display. Just last month, Bank of America Merrill Lynch reiterated that Apple was working with unnamed suppliers on a foldable iPhone. And there have been a number of Apple patents for the necessary technology.

An iPad in your pocket

The most obvious benefit to a foldable phone is that you can get a much larger screen in a pocket-sized device. If you imagine something like the iPhone X unfolding to a display twice the size, you’re effectively getting something like an iPad mini in an iPhone-sized casing.

The downside would seem to be that it would require a thicker device to accommodate what is effectively two screens when the phone is folded – but it doesn’t necessarily need to be much thicker.

Think MacBook designs. Most of the electronics are in the base, while the screen part of the clamshell is very much thinner. Apple could take a similar approach with a foldable iPhone: most of the electronics would be right where they are now, with a thin clamshell section opening up MacBook-style.

This approach also means Apple doesn’t necessarily need too many of the components to be flexible. As with MacBooks, most of the internal circuit boards and other parts could remain rigid, which simplifies production.

Most of the speculation so far as been around a ‘book’ design: the screen on the inside, being revealed as you open the device. An alternative would be a wraparound screen, where the display is on the outside, so you can use the folded phone just like you do today, and then unfold it when you want to use the entire display.

The latter seems to me less likely. Although it would have some usability benefits – especially for quick glances at notifications – it would be more technically complex. Perhaps more importantly, the screen would be much more vulnerable to damage, such as scratching.

Cannibalization of the iPad

One financial risk to Apple is that a foldable iPhone would cannibalize iPad sales. If you have a larger-screen device in your pocket, do you really need a tablet as well?

I don’t personally see that as a big concern for the company. Apple has always accepted that some of its products reduce demand for others. Some of those who buy an iPad Pro, for example, might otherwise have bought a MacBook. As Tim Cook and other execs have said on many occasions, if it isn’t willing to cannibalize its own sales, someone else will.

If we [fear cannibalizing our own product], somebody else will just cannibalize it, and so we never fear it. We know that iPhone has cannibalized some iPod business. It doesn’t worry us, but it’s done that. We know that iPad will cannibalize some Macs. That doesn’t worry us.

Phil Schiller went even further.

The iPhone has to become so great that you don’t know why you want an iPad. The iPad has to be so great that you don’t know why you want a notebook. The notebook has to be so great that you don’t know why you want a desktop. Each one’s job is to compete with the other ones.

And a foldable iPhone might replace an iPad mini, but that’s about it. It isn’t a substitute for a full-sized iPad. So no, Apple isn’t going to worry about this.

iPhone X style approach

Dates like 2023 and 2023 have been bandied around for the launch of a foldable iPhone. We’ve noted before that this sounds ambitious, but my suspicion is that Apple will take an iPhone X type approach: launch first in a high-end model.

Sales of the iPhone X may have been lower than Apple expected, but still appear to be at a level any other brand would kill for. And, crucially, it enabled Apple to begin the transition to a new form-factor without pricing out those unable or unwilling to pay a premium for the latest tech.

I can see the company doing exactly the same thing with a foldable iPhone. Launch a high-end foldable iPhone at a price premium alongside more conventional models, allowing consumers to choose whether to make the switch on day one or wait for the technology to become more affordable in subsequent years.

Concept image (actually for a rumored Microsoft product): David Breyer

Check out 9to5Mac on YouTube for more Apple news:

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Fifa World Cup Cryptocurrencies Are Soaring! These 10 Tokens Are Your Best Bet

Football fans are one of the reasons why these 10 FIFA world cup cryptocurrencies are soaring

Following the FTX exchange’s collapse on the 9th of November 2023, the crypto market experienced a crisis. Bitcoin (BTC) lost 25% of its value across three days, and the FTX token dropped by a staggering 90%. However, the crypto sphere didn’t just experience a financial crisis but a psychological one too. The loss of trust among many investors and the wider public has probably offset mass crypto adoption by years. The native token of the Chiliz blockchain (CHZ), which powers the largest sports fan token creator platform chúng tôi has surged 11% in the past 24 hours. Top world cup fan tokens for the national soccer teams of Portugal (POR) and Argentina (ARG), both major contenders to win the Cup, “will be the cherries on top during the World Cup,” the research team of crypto exchange Huobi wrote in a report. Here are the top 10 FIFA world cup cryptocurrencies soaring.

Flow (FLOW)

Flow is a Web3 platform powering the next generation of games, apps, and digital assets chosen for its combination of scalability and usability for consumers and developers. Flow is the only layer-one blockchain created by a team that has consistently delivered industry-leading consumer-scale Web3 experiences including CryptoKitties, Dapper, and NBA Top Shot. One of the best FIFA world cup tokens to check out.

Chiliz (CHZ)

Chiliz is a fintech platform for the sports industry to allow global fans to get closer to their favorite sports teams and clubs. Chiliz is a currency option for blockchain-backed products & services geared toward mainstream consumers. It is one of the fastest soaring FIFA world cup cryptocurrencies.

The Manchester City Fan Token (CITY)

The Manchester City Fan Token allows CITY fans to have a tokenized share of influence on club decisions, purchased through the consumer-facing platform, chúng tôi fans can engage in a wide variety of club decisions, for example, choosing a goal celebration song or deciding on team bus design, earn rewards and money can’t buy experiences. It is one of the top world cup fan tokens to check out.

Paris Saint-Germain Fan Token (PSG)

The PSG fan token is a secured utility token on the Chiliz Network, a proof-of-authority sidechain built on Ethereum. PSG fan tokens are finite, making them a priced commodity for users. There will be a total supply of 20,000,000 tokens minted with 2,500,000 rolling out per year for eight years. It is one of the fastest soaring FIFA world cup cryptocurrencies.

Atletico Madrid Fan Token (ATM)

It has a circulating supply of 0 ATM coins and a total supply of 10 Million. If you would like to know where to buy Atletico De Madrid Fan Token, the top cryptocurrency exchanges for trading in Atletico De Madrid Fan Token stock are currently Binance, chúng tôi BKEX, Paribu, and Hotbit.

The Lazio Fan Token (LAZIO)

The Lazio Fan Token is a BEP-20 utility token designed to revolutionize the fan experience for all S.S. Lazio supporters. The token empowers S.S. Lazio fans to participate in team voting polls, hunt digital collectibles, purchase NFTs, and enjoy gamification features that are tied with fan rewards or great experiences. It is one of the fastest soaring FIFA world cup cryptocurrencies.

FC Barcelona Fan Tokens (BAR)

FC Barcelona Fan Tokens are digital assets that can be purchased through the chúng tôi app, which will reward fans from all over the world for every activity they take on the app. They can climb leaderboards and earn reward points that can be used to purchase exclusive items and once-in-a-lifetime events.

Inter Milan Fan Token (INTER)

Inter Milan Fan Token has a circulating supply of 3 Million INTER coins and a total supply of 20 Million. If you are looking to buy or sell Inter Milan Fan Token, Paribu is currently the most active exchange. The INTER Fan Token allows Inter Milan fans to have a tokenized share of influence on club decisions, purchased through the consumer-facing platform, chúng tôi fans can engage in a wide variety of club decisions, for example, choosing a goal celebration song or deciding which MMA fighters should face off and in doing so, earn rewards and money can’t buy experiences.

dotmoovs (MOOV)

dotmoovs has a circulating supply of 800 Million MOOV coins and a total supply of 1 Billion. If you are looking to buy or sell dotmoovs, Uniswap (v2) is currently the most active exchange. dotmoovs is Gamifying sports in the ultimate Play2Earn platform powered by blockchain and a state-of-the-art AI system to analyze videos of players performing sports challenges.

Santos FC Fan Token (SANTOS)

These 8 Core Apps Are Changing In Windows 11

So far, Microsoft has shown off several Windows apps that it’s reworking for Windows 11, including some of the basics: Mail and Calendar, Paint, and even the lowly Clock app. Below, we’ll show you what to expect of these new apps within Windows 11 and how they’re evolving.

Sound Recorder

As of May 10, Microsoft began previewing the new Sound Recorder app within Windows 11. There’s a new visualization for audio within recording and playback, and new support for changing your recording device and file format from within the app, which Microsoft said was among the top requested features in Feedback Hub.

The update to the new Sound Recorder will replace the Voice Recorder app, Microsoft said.

Windows 11 Clock (Focus Sessions)

Perhaps the most unexpectedly interesting update to Windows 11’s suite of Windows apps is the lowly Clock app. Now, in addition to the usual suite of Timers, Alarms, a Stopwatch, and a World Clock, Microsoft has added Focus Sessions and Microsoft To-Do.

Microsoft has dramatically revamped its Windows 11 Clock app, adding Focus Sessions with Spotify integration.

Microsoft describes Focus Sessions as a major new feature, and it’s easy to see why. If you’re the type of person who concentrates best when music plays, you’ll love Focus Sessions and its integration with Spotify. Focus Sessions allows you to block out a period of time, with a literal stopwatch counting it down. During the Focus Session, you can connect your account to Spotify and ask it to play classical music, electronic, trance—whatever keeps you in the zone. (There’s a mute button, too, in case you receive a call.)

The Clock app also includes integration with To-Do, so you can accomplish tasks and check them off. Finally, you’ll even be able to configure “streaks”—a habit-building feature that’s part of Microsoft Rewards—to set a daily goal and then accomplish it, day after day.

Windows 11 Photos

Microsoft appears to be making some minor though interesting changes to the Photos app within Windows 11, which is currently in release to the Windows 11 Insider Dev Channel. (That’s possibly important, since the Dev Channel is the “future” branch of Microsoft’s beta program, and isn’t a commitment to releasing an updated app whe Windows 11 launches.)

Microsoft is redesigning the Photos app within Windows 11 with a new toolbar and a row of thumbnails.

Within the Photos toolbar at the top of the screen, Microsoft is adding shortcuts to other visual apps that you may already have on your machine, too. (Photos also continues to include the automatic, algorithmic “Enhance your photo” option, too.) Otherwise, Photos has added the familiar rounded corners and other visual elements of Windows 11.

Windows 11 Photos will also allow you to compare photographs.

Windows 11 Paint

Microsoft Paint has rolled successful saves against death many times over, surviving decisions to deprecate the beloved utility in 2023 as well as relegate it to a downloadable app. In 2023, Microsoft said Paint would remain a part of Windows 10 for now. 

The updated Windows 11 Paint app has a more intuitive interface.

The decision by Microsoft chief product officer Panos Panay to show off a new look for Paint in Windows 11 affirms that Paint has survived yet again. In a video, Panay revealed what looks more like a user-interface update than any major change in functionality. (One omission: The reference to Paint3D that currently exists with Windows 10’s Paint.) Still, updating the iconography as well as the drop-down functionality is a welcome step, and simply putting in the work shows that Microsoft remains committed to Paint as a whole.

Windows 11 Calculator

Calculator is a surprisingly powerful tool hidden within Windows 10, though most people probably use it merely for numerical calculations. Inside it is a graphing calculator (remember to expand the app’s window to use all of its functionality!), the ability to convert measurements and currencies, a scientific and programmer calculator, and more. None of that functionality appears to be changing for Windows 11, but the app will include a new theme setting. It’s also been rewritten in C#, which Microsoft did as a way to allow the public to contribute to the app on GitHub, code in new features, and update the app more frequently over time.

The Windows Calculator app (shown here within Windows 11) hides some powerful features like this graphing calculator.

Windows 11 Snipping Tool

“Both the classic Snipping Tool and Snip & Sketch apps have been replaced by a new Snipping Tool app that represents the best experiences of both apps in the next evolution of screen capture for Windows,” Microsoft’s Dave Grochocki wrote in a blog post. 

Panay showed off a revamped Snipping Tool that uses the Snip & Sketch shortcut (Win + Shift + S) but leaves the other snipping options unchanged. Aesthetically, the app now has the rounded corners and other visual cues of Windows 11—even dark mode. There will be some additional editing tools for annotations and improved cropping functionality, as well.

Windows 11 Mail and Calendar

Last but not least are Windows 11’s own Mail and Calendar apps, which eliminate a lot of the visual clutter within Outlook and provide a simplified, streamlined experience. Microsoft doesn’t seem like it will change anything here, simply giving the user interface the familiar rounded corners of Windows 11.

For now, Microsoft isn’t doing much with Mail and Calendar except for updating the UI.

For Windows 11 news, how-tos, guides, and more, check out PCWorld’s Windows 11 superguide.

This story was updated on May 12, 2023 with more detail on how Microsoft will update the Sound Recorder app and replace the Voice Recorder app.

These Are The Esports Games To Watch Out For In 2023

Clash Royale

Developed and published by Supercell, this real-time strategy game arrived on Android and iOS in March 2024. Clash Royale mashes multiple genres into one multiplayer game: Online battle arena, collectible card game, and tower defense. Players battle in one-on-one and two-on-two matches trying to destroy the highest number of opposing towers.

Supercell’s official esports league for 2023 consisted of 40 teams from Asia and Mainland China, Europe, Latin America, and North America. Each team had four to six players, three of which played in one-on-one and two-on-two games on match days. The best team in each region moved on to compete in the Clash Royale League World Finals. To become a pro team member for season one, you needed complete 20 wins in the CRL Challenge in March 2023.

Counter-Strike: Global Offensive

Few esports games have had as much impact as Counter Strike: Global Offensive. This first-person shooter developed by Valve and Hidden Path Entertainment launched in 2012 and became an esport the following year. Valve currently sponsors Major Championships (called Majors), in which 24 teams compete for a prize pool of $1 million. The list of hosts over the years include ELeague, Electronic Sports League (ESL), and Major League Gaming (MLG). The first Major of the year will be during Intel Extreme Masters XIII in Katowice hosted by ESL.

Valve changed the Majors format starting with Boston’s ELeague Major in early 2023. The company renamed all three stages, increased the overall team count to 24, and introduced stickers for all participating teams. ESL plans to tweak the Major format again before the Katowice tournament to implement the new Swiss system used in the Chicago Major in November. This pits teams against opponents with the same ELO rankings, rather than pairing teams with opponents of harder or weaker skill.

Dota 2

Just before Dota 2’s launch in 2013, Valve invited 16 Defense of the Ancients esports teams to play the unreleased game in a tournament during Gamecom 2011. Valve held a second tournament in 2012 during PAX Prime, followed by the official launch of The International at the Benaroya Hall in Seattle during 2013. The most recent International event took place in Vancouver, Canada in August 2023, where 18 teams compete for a prize pool $25 million.

Currently the second-most watched esports game on Twitch and YouTube, Dota 2 consists of two teams of five players with the goal of eliminating the opposing team’s Ancient. You can watch The International through Twitch, Steam Broadcasting, YouTube, China’s Gamefy, and in some cases traditional networks. Prize pool money stems from the purchase of a Battle Pass and related in-game items with a starting price of $10.

League of Legends

Originally launching in 2012, the League of Legends Championship Series (LCS) changed its format in 2024, bringing ten teams into Riot Games’ Los Angeles studios to compete live on Twitch and YouTube. The annual season consists of two local nine-week sessions, with the best three teams of each session moving on to compete in regional finals. After that, the winning team competes with other teams from across the globe in the League of Legends World Championship. Overall, 13 regions follow this or a similar format prior to the global showdown.

The 2023 World Championship saw 24 teams compete for a chunk of the $2.4 million prize pool and the tournament’s coveted trophy. The 2023 schedule started Feb. 2 here in North America and the local Spring Finals are scheduled for April 13 in St. Louis, Missouri. This year Riot Games chose to remove the third and fourth place matches, resulting in only two teams competing for the Spring Split Champion title and the chance to move on to the Mid-Season Invitational.

Both the European and North America leagues also rebranded for the 2023 season — the NALCS is now called the LCS, and the EULCS is now called the League European Championship (LEC).

According to the Esports Charts, the 2023 League of Legends World Championship was the most-viewed tournament of the year.

Mortal Kombat 11

Mortal Kombat 11 is the latest in the long-running fighting game series for consoles and PCs from developer NetherRealm. The game is also the center of a huge worldwide esports event, the Mortal Kombat Pro Kompetition 2023 tournament.

Players from around the world will compete in both offline and online tournaments throughout the year, and the top 16 players from those events will finally come together in Chicago for the final Pro Kompetition Championship in March 2023, to fight for a $250,000 prize pool. You can read the official rules here.

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