Trending February 2024 # Master The ‘Houseparty Lingo’ With These Tips # Suggested March 2024 # Top 7 Popular

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As the world strives to find a sense of normalcy during the ongoing COVID-19 pandemic, video calling applications have emerged as our sole, irrefutable lifelines. From Google Duo to Skype — the selection of traditional video calling applications is quite extensive. Sadly, none of these do enough to take your mind off mundane conversations and offer something a bit more engaging.

So, if you’re looking for a bit more fun and a little less worry, you’d be wise to take the road less taken and explore Houseparty — an app that has successfully fused video calling with interactive games.

Sure, there have been a few reports of compromised security, but none of them were conclusively proven. The company even held a press conference, clearing the doubt and assuring no data breach had occurred.

Now with that out of the way, let’s focus back on today’s discussion: getting you accustomed to the Houseparty language. We’ll share pretty much everything you need to know to get around the app and eventually make the most out of it.

Related: How to Use Houseparty Privately

What is different about Houseparty?

In an attempt to stand out from the crowd of conventional video calling applications, Houseparty uses innovative terms and phrases to denote even the simplest of tasks.

For newbies, mastering the language can be a little challenging, and that’s where we hope to come in. Check out the subsections below to learn about the many quirks of Houseparty.

Related: How to play Houseparty on PC

Houseparty Lingo: Tips to know

Passing a Note

In regular terms, it’s called sending a text message. Simply tap on one of your contacts and then hit ‘Pass a Note.’ After you’re satisfied with the message, hit send.

Ghosting

Houseparty, by default, notifies your friends when you log in to the app. And while that’s great for people you want to get in touch with, it can be a bit annoying if you’re looking to fly below the radar.

By Ghosting a specific contact, you could disable the notification we just mentioned for the individual, while still notifying your other important contacts. To do so, tap on the contact’s name, go to settings, and toggle on Ghosting.

House Party has changed ‘Ghosting’ in the latest update, making it more accessible for casual users. Instead of turning on ghosting, you’ll now need to toggle off ‘Send Notifications’ to ghost a contact. Similarly, you could toggle off ‘Get Notifications’ to stop getting alerts every time the user logs in to Houseparty.

Sneak into the house

This feature is quite similar to Ghosting but on a grander scale. While Ghosting allows you to evade a single individual, sneaking in lets you hide from all your friends. Do ‘Sneak into the house,’ press and hold the game icon, and tap on ‘Sneak into the house.’

Waving

This feature is identical to Facebook Messenger’s ‘Wave’ functionality. By waving, you notify a friend that you are available to talk. To wave, tap on a contact and hit ‘Say Hi.’

Send a Facemail

If you are familiar with Google Duo’s video messages, you’re going to feel right at home, here. Houseparty allows you to record short video messages — called a Facemail — and send it to as many contacts as you like.

Please note that the sender gets notified when the receiver watches their Facemail. So, it’s better to avoid reading Facemails when you’re sneaking in the house.

What is We Time?

Houseparty wishes to be the bridge between you and your contacts even you are thousands of miles apart. By offering this incentive called ‘We Time,’ the app encourages you to make more calls and stay within touching distance. This timer starts when you and your friend have been talking for 120 minutes.

Every minute you add beyond the 2-hour mark is added to your ‘We Time’ counter. Similar to the Snapchat streak, ‘We Time,’ too, needs regular maintenance. If you don’t talk with a contact for over 2 days, your We Time counter is reset and starts from 0.

Locking a Room

Houseparty allows your friends to see when you are online, who you are talking to, and whether you are available to chat. They can then simply join your room and start a video calling session. This feature makes Houseparty more accessible than other apps.

However, if you are looking for alone time with someone or don’t want to ruin your zen solo session, you could simply choose to lock your room. Friends would still be able to send a joining request, but you can easily ignore them. To lock your room, simply tap on the padlock button at the bottom of your screen.

If you want to learn deep dive into the world of Houseparty games, be sure to check out our dedicated article: “Cool Houseparty games to play right now.”

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Master Dimensionality Reduction With These 5 Must

Overview

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

Introduction

“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

Eigenfaces

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:



Output:

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!).

Related

Take Better Selfies With These Lighting And Angle Tips

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Selfies (or self-portraits) have a long and illustrious history. Renowned artists such as Van Gogh, Rembrandt, and Carvaggio frequently painted themselves—sometimes hundreds of times—and Robert Cornelius gave the format new life in 1839, when he shot the first photographic selfie in history. Our fascination with our own reflection is clearly nothing new—it’s just that our ability to depict it has never been more democratic.

And while being able to capture our own image as we’d like to be seen is a great thing, it sometimes makes a person’s lack of photographic know-how way more evident. Everyone wants to look their best, but not everyone has the skills to make themselves look their best. Don’t worry—we can fix that.

The classic modern selfie is most commonly shot with the front-facing camera of a smartphone, so let’s start there. The two things you need to nail are the lighting and the pose. The camera will do the rest.

Use good light

Even someone as gorgeous as me looks bad with a bright light directly overhead. Harry Guinness

The kind of light you use will determine how you look in your photos. It doesn’t matter if you’re a professional model with a perfect runway pose—you just won’t look your best in the wrong light.

Good light flatters people by highlighting their good features and smoothing over any spots or blemishes, while bad light emphasizes weird things. You can see it clearly in the photos above.

The good light (on the right) highlights my eyes and emphasizes my rock-solid jawline, while the bad light (on the left) casts a heavy shadow over my face and gives me a double chin (thankfully, mostly hidden by my luscious beard). Good light also smooths over the small blemishes and random pimples on my near-flawless skin, while bad light draws attention to them. On the right, my eyes and lips are the focus of the photo, while on the left, the bad lighting draws attention to my strong nose and shapely forehead.

The low-wattage bulb directly overhead in the photo on the left is a dire source of portrait light, while a big window, like the one I used for the photo on the right, is a great one. Unfortunately, even while we’re talking about good and bad light here, it’s not really a binary. You’ll find a wide range of more- and less-flattering kinds of lighting that fall between these two sources—you won’t always have perfect light, but you can always use the best light available.

For selfies, these are some places that have great lighting:

In the shade of a tree, alley, or building on a sunny day

In a doorway or under a bridge

In the open on an overcast day

In the golden hour—the moments just before and after sunrise and sunset.

Facing a big window when you’re indoors

On the other hand, here are some locations you should avoid when trying to find good light:

In the open on a bright, sunny day

Around midday on an overcast day

Under overhead, indoor lighting

In a dark bar or club

Ace that pose

Placing the camera slightly higher than eye level gives you a flattering angle for your selfie. Harry Guinness

Even the best light won’t make you look good if you’re glaring and over-pouting at the camera. It might seem obvious, but a good pose is essential to a good selfie. So before you hit the shutter button, keep in mind there are some simple things you can do to emphasize your good points.

First, check out our guide on how to pose for photos. Every tip in it applies doubly to selfies. By “squinching” and squaring your jaw you’ll draw more attention to where you actually want it—your face.

Next, you’ll need to position your smartphone. Ideally, you’ll want the camera just above your eye-line, angled slightly down. Too high, and you’ll look like a mid-2000s teenager posing for MySpace; too low, and you’ll just look weird.

How far away you hold your phone matters, too. Because of the laws of optics, the wide-angle camera on the front of your smartphone distorts your face a little, so the closer it is to you, the wider your face will look. This is the optical effect behind the “the camera adds 10 pounds” idea. On the other hand, you don’t want to look like you’re reaching. For most people, bending your arm to about 90 degrees will work best—but play around with it.

Once your phone is in position, remember the screen is not the camera. Use the screen to line up your shot, but when you’re ready to go, don’t just stare at yourself and make eye-contact with the camera instead.

Finally, while I’m fairly agnostic on the issue of filters and the like, you can always give your photo a finishing touch by trying an effect you like. Most smartphones or image platforms such as Instagram provide predetermined filters, so you can choose one there, or work on your own with image-editing apps such as Photoshop Express (available for Android and iOS) or Snapseed (available for Android and iOS).

But remember that filters cannot work magic, so no matter how many you use, they won’t fix a bad photo. Make sure you use good lighting and posing, so that when you’re done, you won’t even feel the need to browse for a filter.

Take some time to get creative

Taking a good selfie is obviously the main objective, but don’t forget to have fun with it. Harry Guinness

The best thing about selfies—and the reason so many artists have worked with this format over the years—is that you can take your time and get really creative. You don’t have to worry about paying models or photographers, making mistakes, or offending anyone. You can do whatever you like and see what works.

And since you’re already carrying a decent camera around most of the time, there’s really no need to upgrade your gear either. Sure, some of the more out-there options—like my levitation selfie above—are easier to do with a DSLR camera, a remote shutter release, and a tripod. But you can get the same results by propping your smartphone up and using the timer. The world is your oyster, and everything can be a prop or a tool.

Dig through your wardrobe, look on Instagram for inspiration, do something weird with your makeup, and have fun. It’s the best way to get photos of you being you.

3 Tips To Master A Minimalist Web Design

The world craves minimalism.

So much so, that whichever aspect of our lives you look at – whether it is the lifestyles we lead, homes we live in, or the products we consume – minimalism is present in every facet.

Even the smartphones in our pocket prove that minimalist design has more than caught on.

Why Minimalism?

Often confused with simplicity, the concept of minimalism entails reducing all elements to only include those that are essential.

This means that while minimalism is simple, simplicity, or using simple forms, does not necessarily translate to minimalism.

In the world of design, minimalism is used to directly convey the message without the unnecessary noise and obstruction of focus due to other distracting elements.

Seeing the benefits of using minimalism to swiftly and effectively convey the message, the minimalist approach has taken root in many branches of design.

From painting and sculptures to digital product design and web design, minimalism has managed to root itself and understandably so.

Designed with minimalism in mind, digital products and web designs are no less impressive.

Apple’s brand is one of the best examples of having a minimalist approach in mind when designing products.

The design itself is clean and sleek, and it puts an accent on every aspect of customer experience – from the first moment you hold an iPhone packaging in your hand, peeling off the foil of your screen, to finally using the smartphone.

Minimalist designs are visually appealing and user-friendly, so it’s really not surprising that so many businesses prefer to have a minimalist web design, as it helps them boost their company’s bottom line.

Taking Minimalist Approach to Web Design

Your company’s website is the best business card you have.

It tells your customers all they need to know about your business – from where to find you to what the business is all about.

Websites that are not functional, take a long time to load, have too many distracting elements or are not user-friendly will make your potential customers bounce in a heartbeat.

In essence, a business’ website tells customers all they need to know about the company, but if your visitors don’t stick around long enough to know who you are or what you do, they won’t actually get to know you or understand what you have to offer.

In minimalist design, gestalt takes precedence over all else.

This means that the elements that are connected must remain close to one another, whereas elements that lack connection should be further apart.

Every element on your website that is crucial in guiding the user should be bigger, bolded, or feature strong colors.

From these simple principles, you draw all other rules of minimalist approach in web design, whether typography, negative space, visual elements or overcrowding are in question.

Instead of overcrowding your website with elements that are not necessary, feature only the most essential ones.

This way, the most important information will quickly grab users’ attention, allowing them to easily solve their problems.

Choosing a minimalist approach for your company’s web design means making conscious decisions about the layout of the website.

This means that you ought to question every design decision, putting the user first.

Ask yourself:

Will the chosen typography be too off-putting for users?

Is the typography easily readable?

Does the chosen element distract the user from their path?

Are there too many images on the page?

Is the color scheme appropriate?

Are too many colors used?

Minimalist Web Design & SEO

SEO-friendly websites receive more traffic.

This means that when designing a website, you need to keep both the users and search engines in mind.

Questions of how to create the best user experience should keep you up at night.

Google said that when designing a website, you should aim to serve your users’ needs… but users aren’t limited to consumers – you have to consider search engines, too.

We all know that competition is fierce, and we need to do our best to keep users on their track when they visit the website.

We all want our websites to load quickly, and minimalist design entails lesser features, which means that there will be lesser features to load.

However, even if you have a single image on your webpage, it is still important to optimize it.

Great and memorable user experience is the basis of a minimalist approach.

It does not only aim to be visually appealing, but to keep the most important for the user in order to gain valuable information without unnecessary chaos surrounding it.

So, rather than losing your customers over a badly designed website, try a minimalist approach and create an experience on your site that is simple, but not one that they will be quick to forget.

Minimalist Web Design in Action

When it comes to creating a plan for a minimalist web design, here are three focus areas you should consider leveraging.

1. Visual Elements 

Every detail must be significant.

It should be functional and serve the purpose.

So when designing take each element into account, stop and ask yourself: Does this detail serve a function or is it just visual?

If it is only visual, then you must step back and decide if it belongs on your webpage or it clutters it.

Graphic Elements

All graphic elements need to be usable.

Think of the purpose the added element serves.

Does it have the role of shape divider?

Does it lead the eye, meaning it will help the user navigate easily?

Or, does it highlight the information that should be immediately noticeable to the user?

If it doesn’t serve a purpose, avoid using it.

Yes, illustrative detail might appear to be appealing to the eye.

However, every element must be added with a purpose in mind.

This applies to all types of visual elements, images, illustrations, and shapes.

A great example of how to incorporate graphic elements with minimalism can be found on plus63’s site:

Images & Videos

Having a single image featured on a homepage may seem monotonous, but think of it as highlighting a point.

Here’s an example by Measponte, a website that sells shoes, that features one simple image of a shoe with overlaid copy:

Instead of an image, you can always use a video or animation for the first impression.

Your website can still look clean and have minimal copy, but also be interactive and inform the user what they need to know.

Although sometimes perceived to be on the “heavy” side, videos – like images – can be optimized to ensure they do no hurt your page loading time.

Take a look at the website from My Switzerland, they’ve done a great job:

The most prominent type of minimalist web design focuses on having a hero image and hero text in focus, all the other elements, whether visual or written are featured on other pages.

Images vs. Words

A key element to keep in mind is the amount of copy your webpage uses.

Words are the quickest and most reliable way to get your message across.

However, they can be our biggest enemy.

If a user lands on a homepage with a ton of the copy, they are unlikely to stop and read.

What’s more likely to happen is that consumers will bounce off your site and search for information elsewhere.

So, as a rule of thumb, don’t let words overtake the webpage; use as little as possible and only include essential information.

Allow navigation pages on your site to be dedicated to describing a product, service, or mission, featuring more copy than your homepage.

Background Image

Choosing the right background image is important.

At times, the background image may be the only visual element on the page, over which you’ll have hero text displayed, similar to what KickPush did:

Just like other elements of minimalism, flat design and textures are featured in the background image.

By using flat design, you can employ a stripping concept, so that what’s left on the page seems deemphasized, allowing users to pay attention to the most important information on the webpage.

2. Colors & Contrasts

In minimalist design, colors must be used wisely.

Colors should create visual interest while also capturing and directing the users’ attention in a way that does not require additional elements or graphics.

The usage of colors should be limited – which is why on successful websites, you’ll see monochromatic colors or the use of two to three key colors on a website.

The commonly used monochromatic color palette (which uses several shades of a single color) tends to soothe the eye and not overshadow the important details.

However, contrasts still have value in minimalist web design, and many web designers make the use of white color, as it tends to contrast well with other elements on the page.

Remember that color has many purposes.

It can serve as a background of the webpage, similar to the design that We Are You has embraced.

But, it can also accentuate the important features on your webpage, serving as the functional element.

If you wish to highlight your product, this might be done best with a color that contrasts the rest of the palette.

It is wise to keep the psychological aspects that colors can tap into in mind.

Consider the purpose of your website and the message you want to convey to your visitors.

What would be the best color to use in order to set up the mood that aligns with your business?

For example, consider Huntington’s color choice.

They’re a financial company, therefore they leverage various colors of green, which psychologically signifies money.

Blue and purple hues can be calming, while yellows, oranges, and reds can be warm or exciting – all things to consider when making a color choice on your site.

3. White Space

Commonly referred to as negative space, this feature can be the most powerful trick up a web designer’s sleeve.

The white or negative space tends to open up the “room” and let it breathe, and it is quite contrary to the visual clutter that exists on many websites.

By increasing the negative space around the information you want the user to focus on, you can make particular content elements more noticeable.

White space has the power to naturally guide the eyes of the user toward key content.

If you opt for using the white space, make sure you steer away from having multiple focal points on one screen.

Keep in mind that users have vulnerable focus, and their attention can easily drift from one thing to another.

When faced with too many options, users may find it hard to figure out what to focus on.

Summing Up

Minimalist web design creates a win-win situation for both businesses and customers.

Clean, non-obstructive design with clear focus points allows the users to quickly find the information they need, while also allowing businesses to keep customers happy by quickly providing answers to key customer questions of who their business is and what they do.

The “less is more” approach appears simple to achieve, but there are many elements that need to be considered in the creative process.

We’ve all faced tough moments when all information feels too precious to let go of and cut out.

From choosing what colors and visual elements to use to deciding on the quantity of text you should display on each page and each screen, it’s important to keep one key item in mind:

What’s the purpose of the piece of information?

If you can’t answer that, then it shouldn’t be included in a minimalist web design.

More Resources:

Master Odor Removal With A Little Help From Science

This post has been updated. It was originally published on 11/09/18.

We don’t usually think of our noses as delicate chemical sensors, but that’s exactly what they are. And big honker or petite button, our sniffers hate when bad odors linger nearby. Luckily, science can help you root out that foul stench.

How smell works

Despite conventional wisdom giving all the credit to canine snoots, we humans also have an amazing sense of smell.

Any odor starts out as a volatile molecule—which means it can easily turn from liquid to gas—that’s looking for something to attach to. When you inhale, these molecules get inside your airways, and some get to the olfactory epithelia, a small patch of tissue called containing about six million olfactory sensory neurons. If the scent molecule is at least somewhat soluble in water and lipophilic (that is, it likes fats), it’ll attach to a light layer of mucus over these neurons, completing a connection that tells your brain just what’s in the air.

However, to humans, not all smells are created equal. We’re more likely to catch a scent from biological sources, such as organic matter, because our minds consider them more important as they may indicate disease. It’s not all bad, though—the smell of rain comes from bacteria in the soil doing nature’s trash-hauling by eating dead matter.

[Related: The five smells Americans hate most (and how to get rid of them)]

In another weird twist, because our olfactory system shares the same signaling channel as our sense of pain, we have particularly intense reactions to the malodorous. In other words, something can really smell so bad it hurts.

That said, scientists are still studying just why we scrunch up our faces at gym socks but follow our noses into the doughnut shop. To at least some degree, it appears our reaction to smells is not instinctive—we learn it. And with enough exposure, we can easily go “nose-blind” to even the worst stench.

But if smell comes from molecules entering our noses, why do some fragrances quickly dissipate while others stick around? That comes down to concentration.

Take cat pee, a notorious nasal horror. Felines boast incredibly efficient kidneys which even allow them to live on seawater if they need to, as they are capable of filtering salt. But that also means these furry pals fill their litter boxes with a highly concentrated solution, which includes a heavy dose of urea, a compound that forms when ammonia and carbon dioxide combine. The potent mix then wafts out of the litter box and assaults our faces.

Sometimes the liquids that transport smelly molecules can easily sink into all sorts of surfaces, which makes odors linger. Porous materials like wood and the fibers in carpets might hold onto fluids for months or even years. As long as ambient conditions let those volatile compounds turn to gases, the stink will stick.

Now that you know the science of smell, let’s apply it to that reeking part of your room.

Strike the source with an odor eliminator

First, a quick review. There are five categories of cleaning agents: surfactants, weak corrosive agents like vinegar, oxidizers, enzymatic cleaners, and solvents. Surfactant molecules, for example, have a hydrophilic and a hydrophobic side, so they can attach to and remove messy stains and smells at the same time. Enzymatic cleaners, meanwhile, use specially-engineered blends of enzymes to attack specific types of stains and the stinks that go with them. However, you won’t want to use oxidizers like OxyClean and bleach to get rid of a smelly contaminant. These solutions remove stains by knocking out chromophores (the parts that absorb and emit light), and in doing so, they have no effect on the stench.

[Related: Here’s why gym clothes smell so rank—and how to freshen them up]

In some cases—especially with food spills and pet accidents—bacteria might be feeding off the substance. You can blame those little pests for emitting the offending odor, and if this is the situation, you should use a cleaner that also disinfects. While you’re at it, consider an enzymatic cleaner designed to break down that specific stain, such as Nature’s Miracle for pet pee and poop.

If you’re tackling just the faint hint of a smell, or applying the cleaner to a non-porous surface, these tactics will probably get rid of the offending substance. But if the malodorous source persists, you may never entirely banish it from your floors and walls. That said, you can leave the substance in place while taking a few steps to prevent its scent from bedeviling your nose.

Neutralize the nasal noxiousness

If you fail to remove that smelly stuff, you can still neutralize it. Sometimes, when you’re dealing with a scent that’s stinky but not dangerous, this tactic may even be easier than scouring a stain. Odor compounds easily bond to other molecules, so certain substances will grab the offensive stuff out of the air before it reaches your schnozz. Take Febreeze, for example, one of the best-known odor neutralizers: it works by trapping scents in doughnut-shaped cyclodextrin molecules.

In an enclosed space, try baking soda, aka sodium bicarbonate. It became a back-of-the-fridge mainstay because it bonds with a lot of reek-causing chemicals, turning them from gross smells to less reactive salts.

Similarly, white vinegar can deodorize your entire house. Simply simmer the clear liquid for an hour, vaporizing the acetic acid it contains. Because acetic acid easily bonds with volatile molecules, a light mist of it will banish odor from your house. And the vinegar smell doesn’t stick around: Remember, it wants to bind with something, so it’ll get out of the air looking for it.

[Related: Can You Get A Smell Stuck In Your Head?]

For a real one-two punch, combine the white vinegar with baking soda. This makes it foam up, and you can use the reaction as a deodorizing cleaner.

Seal it off or exile it

If the smell lingers even after you’ve scrubbed the floors, simmered vinegar, and added enough carbon to create a black spot, then it’s time for drastic measures. You need to either seal the smell into the surface so it can’t escape or remove the odor-filled material altogether.

Sealing might be an option for floors, counters, and outdoor furniture. Look for a clear sealant or paint primer designed to prevent “off-gassing,” aka emitting rank vapors. These create an impenetrable molecular wall that locks smells inside the material they’ve already invaded.

But sealants may not be available for scent-saturated things like books and clothing. If your neutralization attempts fail, then you have two options: Put up with the smell or chuck the possession.

Odor doesn’t have to be a part of your life. With a little chemistry, and good old elbow grease, you can be completely free of it.

Chatgpt Updated With These 8 Cools Features

ChatGPT just received its biggest update, revolutionizing its capabilities by connecting in real time to the Internet. This groundbreaking development opens up a world of possibilities and empowers users with a multitude of new features and functionalities. Here are eight remarkable things that were not possible on ChatGPT before:

To unlock this feature, simply navigate to the settings and enable the Web Browsing option. By selecting the GPT-4 Web Browsing template, ChatGPT becomes a gateway to the vast expanse of knowledge on the web, ready to assist you in your inquiries.

ChatGPT becomes your ultimate news companion, capable of summarizing any news article within seconds. Just ask it to provide a concise summary of the latest United Nations climate change report released in 2023, and it will deliver a short paragraph along with a list of key points.

Say goodbye to endless hours spent analyzing academic documents. ChatGPT can now help you save time by summarizing the key findings of the latest research published in ‘Nature’ last week on exoplanet destruction. Expect a comprehensive answer presented in markdown format.

ChatGPT lends a helping hand to enhance your productivity in the remote work environment. Through recent online studies, it can generate a summary table outlining the top five tips to work more efficiently from home, helping you achieve your best.

Leverage real-time data and ChatGPT’s analytical abilities to gain insights into the stock market. Task ChatGPT with analyzing the latest financial results of tech companies, and it will provide predictions on how their shares may evolve in the coming weeks.

ChatGPT harnesses current information to break down complex events into easily understandable explanations. Discover the current price of gasoline in France and let ChatGPT explain to a 12-year-old why this price isn’t decreasing despite the declining price of oil.

Stay up to date with the latest coding practices. ChatGPT diligently searches for the most recent information to assist you in coding tasks. If you need help with the structure of a manifest file for a Chrome extension, ChatGPT will use the latest available documentation to guide you.

Moreover, ChatGPT can now provide you with links and references to enhance your essay writing experience. When composing an essay on the Napoleonic Wars, simply prompt ChatGPT to write an updated essay, citing sources with links, and even add titles in markdown to organize your work effectively.

The integration of internet connectivity has propelled ChatGPT into an even more indispensable tool, offering an array of functions previously unimaginable. Whether you seek concise news summaries, trend analysis, academic insights, productivity tips, stock market predictions, simplified explanations, coding support, or enriched essay writing, ChatGPT stands ready to assist you, armed with real-time data and the vast knowledge of the internet at its disposal. Harness the power of this upgraded AI assistant and unlock a world of limitless possibilities.

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