Trending February 2024 # Colouration Lets Jailbreakers Add A Custom Splash Of Color To Their Device # Suggested March 2024 # Top 3 Popular

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If you’re fortunate enough to have a jailbroken iPhone or iPad at your disposal, then that means you have the ability to make your device look completely different from anyone else’s by way of jailbreak tweaks. Whether you prefer subtle aesthetic changes or substantial aesthetic changes, there’s a little bit of something for everyone.

Today we’ll be giving you first-hand look at the newly released Colouration jailbreak tweak by iOS developer kanns. The name practically gives the tweak away, as it’s quite literally a system-wide colorization tweak that allows you to tint user interfaces and change system colors to be whatever you might prefer over Apple’s native selections.

Among the things Colouration can make color adjustments to are:

Status Bar colors

System-wide colors

Home Screen colors

Lock Screen colors

App colors

System colors

Slider colors

And more…

It’s worth noting that the Colouration tweak also supports presets, allowing users to set up their favorite values and save them for use another time if they ever decide to change things up and decide to walk their configurations back a notch.

Once installed, Colouration adds a dedicated preference pane to the Settings app where users may configure their iPhone or iPad’s native colors on demand. The preference pane is divided into several different sections, each of which we’ll showcase for you below:

Below, we’ll outline what each section provides to the end user:

Status Bar

In the Status Bar section, users may enable and configure custom colors for:

Status Bar

Status Bar body

Status Bar fill

Status Bar pin

Active Wi-Fi

Cellular

Time indicator

Activity loading indicator

System-wide

In the System-wide section, users may enable and configure different colors for:

Home Bar

Labels

Separator

Cell backgrounds

Touches

Scroll Bar

Cursor

Enabled toggle switches

Links

App Switcher background

Inner Control Center

Control Center background

Cover sheet background

Index

Search Bar

Tab Bar icons and labels

Segments

Alerts

Refresh background

UI tint

Home Screen

In the Home Screen section, users may enable and configure custom colors for:

App icon labels

Label backgrounds

Folder backgrounds

Spotlight interface

App Library search

Lock Screen

In the Lock Screen preference pane, users may enable and configure colors for:

Lock Screen background

Lock Screen icons

Lock Screen date background

Apps

In the Apps preference pane, users may enable and configure colors for:

Dates

Messages app top and bottom background gradients

Messages app recipient bubble top and bottom gradients

iMessage send button

SMS send button

Notes app color

System Colors

In the System Colors preference pane, users may customize system colors as such:

Change system blue colors to any color you want

Change system gray colors to any color you want

Change system background colors to any color you want

Change system red colors to any color you want

Change system orange colors to any color you want

Change system yellow colors to any color you want

Change system green colors to any color you want

Change system purple colors to any color you want

Change system pink colors to any color you want

Change system brown colors to any color you want

Change system teal colors to any color you want

Change system white colors to any color you want

Change system black colors to any color you want

Change system cyan colors to any color you want

Change system label colors to any color you want

Change system dark green colors to any color you want

Change system dark red colors to any color you want

Change system dark blue colors to any color you want

Change system dark orange colors to any color you want

Change system dark teal colors to any color you want

Change system dark pink colors to any color you want

Change system dark yellow colors yo any color you want

Sliders

In the Sliders preference pane, users may configure the following options:

Enable and configure a custom App Switcher blur effect

Enable and configure a custom App Switcher dimming effect

Enable and configure a custom App Switcher opacity effect

Enable and configure a custom Spotlight blur effect

Enable and configure a custom cover sheet blur effect

Enable and configure a custom Dock blur effect

Below the aforementioned preference panes are sections for saving and loading presets and accessing tweak settings. The latter contains an option to respring, reset the tweak’s options to their defaults, and a way to enter Safe Mode if needed. The developer also provides an Apply button at the top right of each preference pane for saving your changes — tapping it will result in a respring.

Colouration is a good way to get your hands dirty with system colorization, especially if you’re new to jailbreaking and might be interested in trying to make your handset unique from everyone else’s. The tweak is available for $0.99 from the Packix repository via any package manager and supports jailbroken iOS 13 and 14 devices.

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How To Add Custom Screensaver Images To Your Kobo E

Fortunately, if you’re unhappy with that out-of-the-box experience, a quick addition to your Kobo’s folder structure changes that. You can then add your own images, which will automatically rotate randomly through whatever assortment you copy over to the device.

You can use one of two methods to get custom screensavers, assuming your Kobo is running firmware version 4.13 or higher. Below are the instructions for the simpler method, which is fast to implement but disables showing current book covers in favor of your new screensaver images. For older Kobo readers or to have more control over what displays when your newer Kobo is in standby or powered off, you’ll need to head over to the MobileRead forums for a solution created by user frostschutz.

How to add screensaver images to your Kobo e-reader Step 1: Connect the Kobo to your PC.

Using the appropriate cable for your Kobo (e.g., USB-C), plug it into your computer.

Step 2: Allow your computer to manage your Kobo’s files Step 3: Open your Kobo’s files

On this PC, the Kobo is recognized as drive E:/.

PCWorld

Your Kobo e-reader will become accessible as a USB drive. Navigate to it using File Explorer. (The drive letter will vary, depending on how your PC is currently set up.)

Step 4 (Optional): Back up your Kobo

As a precaution, you may want to create a backup of your Kobo’s storage drive. (Kobo has not published official documentation on this process—rather, the knowledge lives primarily in the MobileRead user forums.)

This method for copying is slightly inelegant, but it avoids the possibility of accidentally individual folders.

PCWorld

Next, create a folder in the location you want to store the backup. Inside that folder, choose Paste. Wait for the file transfer to complete. Before moving on to the next step, spot check some files to verify the copy process was successful. (You can open EPUB files with a free program like Calibre.)

Step 5: Create a “screensaver” folder

Step 6: Add images

Typesetting photos are a fun, thematically appropriate screensaver, but you can use any PNG or JPG photo.

PCWorld / Unsplash

You can now start adding images to the screensaver folder. Every file you drop into this folder will become part of the set randomly displayed on your standby screen. If you only want one picture to show, copy just that single file into the folder.

Both JPG and PNG formats work. For the best experience, crop them to the resolution of your Kobo reader’s screen (e.g., 1264×1680 for the Libra 2). You can find the screen resolution of your Kobo by searching online. Otherwise, the file will display with proper proportions and not fill the screen completely.

Looking for good images? Try royalty free sites like Unsplash, Pexels, and Pixabay. As for editing apps to crop the files, check out our recommendations for the top five free alternatives to Photoshop.

Step 7: Eject your device

From the Windows system tray or File Explorer, eject your Kobo reader as a drive.

Step 8: Change your device settings Additional tips

As mentioned above, adding custom screensavers in this way overrides the display of book covers in standby mode. In order to see your book covers again, rename or delete your screensaver folder.

This trick also only works for standby mode. If you power off the Kobo, it will show the book cover for your current read. In order to toggle between custom screensavers and book covers in standby mode or to set different images for standby and power-off modes, you’ll need to follow these instructions on the MobileRead forums.

Social Composer Lets Jailbreakers Update Social Media And Share Messages From Anywhere In Ios

Social media has become such an integral part of peoples’ day to day lives that you’d be hard-pressed to find anyone without an app from this category on their smartphone or tablet. Be it Facebook, Instagram, Snapchat, Twitter, or something else not listed here, the simple fact is many make use of these online tools in an effort to keep in touch with one another when they’re otherwise out of touch.

While the messaging and sharing tools found in social media are incredibly convenient, one thing that some users may find cumbersome is that they have to launch the app pertaining to the platform they intend to use just to update their status or to send someone a message. That’s one superfluous step that a new jailbreak tweak dubbed Social Composer by iOS developer 0xKUJ attempts to eliminate from the user experience.

Briefly, Social Composer adds a grabber to the side of your display that can be expanded to display any number of your favorite social media or messaging apps. Upon launching the grabber, you can tap on the app you plan to use and begin composing your status or message from within the Social Composer interface without ever launching the app of the platform in question.

If you’ve been using an iPhone or iPad since the days of iOS 6, then you might recall a somewhat similar experience. At the time, Apple added native Facebook and Twitter integration through the implementation of widgets that could be tapped to share a status from anywhere besides the Facebook or Twitter app. Apple later removed these from the platform as the company chose to take a different approach to sharing going forward.

Social Composer takes me way back and reminds me of those days. While the interface is certainly much different from Apple’s antiquated iOS 6 sharing widgets, the composition sheet invokes some serious déjà vu. Below, you’ll find an animated GIF showcasing the tweak in action:

For those wondering, Social Composer currently supports all of the following different social media and messaging platforms:

Native Apps

Mail

Messages

Notes

Third Party Apps

Discord

Facebook

Facebook Messenger

Gmail

Instagram

Microsoft Teams

Pinterest

Snapchat

Telegram

Twitter

WhatsApp

The developer notes in the tweak depiction that he is open to adding support for additional apps and will take requests via Twitter @omrkujman. With that in mind, the list above is apt to grow as users request support for more in the future.

Once installed, Social Composer adds a preference pane to the Settings app where users can configure the tweak to their liking:

Here, you can:

Toggle Social Composer on or off on demand

Choose which of the supported apps that you’d like to have in the Social Composer grabber interface

Choose whether the grabber appears on the left or right side of your screen

Configure the grabber positioning via a slider

Select the grabber frame color

Select the grabber general color

Restore all settings to their defaults

Respring to save any changes you’ve made

And for a quick glance, this is what the supported app preference pane looks like:

Apps that aren’t installed on your device will appear grayed out, whereas apps that are present on your handset will appear in full color and can be interacted with.

Social Composer doesn’t necessarily make sharing things over social media or messaging platforms any easier than it would be if you were to simply open the pertaining app, but it does add a convenience component as you aren’t required to leave the interface that you’re already using. Depending on how much time you spend partaking in social media and messaging, this might be worth checking out.

Building A Custom Cnn Model: Identification Of Covid

This article was published as a part of the Data Science Blogathon

Dear readers,

In this blog, let’s build our own custom CNN(Convolutional Neural Network) model all from scratch by training and testing it with our custom image dataset. This is, of course, mostly considered a more impressive work rather than training a pre-trained CNN model to classify images. You can learn about it if you are interested, from one of my previous blogs here. We will be using the validation-set approach to train the model and thus divide our dataset into training, validation, and testing datasets.

By the end of the blog, you will be able to build your own custom CNN model for COVID-19 to perform multi-class image classification by training it with your own dataset! Besides, we will also evaluate the trained model thoroughly on both validation and testing datasets by getting its classification report and confusion matrix. Moreover, we will also create a beautiful and simple front-end using Streamlit and integrate our model with the web application.

I have used Google Colab for all the implementation. Also, I have uploaded my dataset to my google drive for the project. The Streamlit web application can be easily launched from Google Colab itself.

So, let’s begin!

Agenda

Introduction

Applications

Implementation

Conclusion

Introduction

In this blog, I will be explaining how to build a CNN model for COVID-19 using TensorFlow on one of the COVID multiclass datasets of CT scans. It can be directly downloaded from here. Now, pause and ensure that you download the dataset to follow along with the implementation.

The given Kaggle dataset consists of chest CT scan images of patients suffering from the novel COVID-19, other pulmonary disorders, and those of healthy patients. For each of these three categories, there is a number of patients and for each of them, there is a number of CT scan images correspondingly.

We will be using these CT scan images to train our CNN model to identify if a given CT scan is that of a COVID patient, a patient suffering from other pulmonary disorders except COVID, or that of a healthy patient. The problem includes 3 classes namely: COVID, healthy, and other pulmonary disorders, shortly referred to as ‘others’.

Applications

That’s where the project we are doing now can prove helpful for the medical community.

Implementation

Step-1: Image Pre-Processing

Step-2:Train-Test-Val split

Step-3: Model building

Step-4:Model evaluation

Step-5:Building the Streamlit Web Application

Firstly, let us import all the required packages as follows:

from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout,Conv2D,MaxPooling2D from tensorflow.keras.models import Model from tensorflow.keras.preprocessing import image from sklearn.metrics import accuracy_score,classification_report,confusion_matrix from tensorflow.keras.preprocessing.image import ImageDataGenerator from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential import numpy as np import pandas as pd import os import cv2 import matplotlib.pyplot as plt Image Pre-Processing

Whenever we deal with image data, image pre-processing is the very first step and also the most crucial step to be performed. Here, we just reshape all the images to the desired size(100×100 in this project) and divide them by 255 as a step of normalization.

According to the directory structure of our dataset, as discussed in the previous section, we must go through every image present in folder-2(patient’s folder) which is further present in folder-1(the category folder-COVID, healthy, or others). Hence, the code for the same goes this way:

# re-size all the images to this IMAGE_SIZE = (100,100) path="/content/drive/MyDrive/MLH Project/dataset" data=[] c=0 for folder in os.listdir(path): sub_path=path+"/"+folder for folder2 in os.listdir(sub_path): sub_path2=sub_path+"/"+folder2 for img in os.listdir(sub_path2): image_path=sub_path2+"/"+img img_arr=cv2.imread(image_path) try: img_arr=cv2.resize(img_arr,IMAGE_SIZE) data.append(img_arr) except: c+=1 continue print("Number of images skipped= ",c)

NOTE: Two images might be skipped here which happened in my case. We can ignore them because it’s just 2 images and not a large number of images that are being skipped!

The below code performs normalization of the images:

x=np.array(data)

x=x/255.0

Now, as our custom dataset has images in folders, how do we get the labels? This is achieved using ImageDataGenerator using the code below:

datagen = ImageDataGenerator(rescale = 1./255) dataset = datagen.flow_from_directory(path, target_size = IMAGE_SIZE, batch_size = 32, class_mode = 'sparse')

Further, to note the indices of the classes and assign these classes as labels, use the code below:

dataset.class_indices y=dataset.classes y.shape

Running the above code, you will observe that the following indices have been used for the corresponding classes:

Code 

Class

0

COVID

1

Healthy

2

Others

NOTE: At the end of this step,all the images will be resized to 100×100 and although they are CT scans,they have been provided as color images in the Kaggle dataset chosen.So,thats why we get 100x100x3 when we try to see the shapes of x_train,x_val and x_test in the next section.Here,3 refers to the color image(R-G-B)

Train-Test-Val split

In this step, we will divide our dataset into a training set, testing set, and validation set in order to use the validation set approach to training our model to classify among the CT scans of COVID, healthy, or others.

We can use the traditional sklearn to achieve the same.

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1) x_train,x_val,y_train,y_val=train_test_split(x_train,y_train,test_size=0.2)

Further, see the size of each of these datasets using the code below:

x_train.shape,y_train.shape x_val.shape,y_val.shape x_test.shape,y_test.shape

From the above code, you will observe that 3002 images belong to the train set, 751  images belong to the validation set and 418 images belong to the test set.

Step 3 Model Building

Now, we are all set to start coding our CNN model for COVID-19 from scratch. For this, we just need to keep adding layers, mostly Conv2D to extract features and MaxPooling2D to perform downsampling of the image. Besides, I have also used the BatchNormalization layer in order to improve the performance of the model in terms of its training as well as validation accuracies.

We can thus code our own CNN model as follows:

model=Sequential() #covolution layer model.add(Conv2D(32,(3,3),activation='relu',input_shape=(100,100,3))) #pooling layer model.add(MaxPooling2D(2,2)) model.add(BatchNormalization()) #covolution layer model.add(Conv2D(32,(3,3),activation='relu')) #pooling layer model.add(MaxPooling2D(2,2)) model.add(BatchNormalization()) #covolution layer model.add(Conv2D(64,(3,3),activation='relu')) #pooling layer model.add(MaxPooling2D(2,2)) model.add(BatchNormalization()) #covolution layer model.add(Conv2D(64,(3,3),activation='relu')) #pooling layer model.add(MaxPooling2D(2,2)) model.add(BatchNormalization()) #i/p layer model.add(Flatten()) #o/p layer model.add(Dense(3,activation='softmax')) model.summary()

A Convolutional Neural Network consists of several convolutional and pooling layers. I have added four Conv2D and MaxPooling layers. The first parameter of the Conv2D layer is where we must play a lot to arrive at the best possible model. You can read more about the syntax of Conv2D, MaxPooling2D, and BatchNormalization from the official Keras documentation.

After adding the convolution and max-pooling layers, I have included the BatchNormalization layers followed by which the input layer has been added using the Flatten() function.

There are no hidden layers here because they were not useful to improve the model’s performance during its training.

Finally, I have added the output layer which indeed gives us the output at the end! The Dense() function has been used for the same. It takes parameter 3 because we have 3 categories: COVID, healthy, and others. Also, the activation function used here is the softmax function because this is a multi-class problem.

This is just the model architecture. Now, before we train it, we must compile it as follows:

The optimizer used is the common adam optimizer. As the labels of the considered dataset are categorical and not one-hot-encoded, we must choose the sparse categorical cross-entropy loss function.

Early stopping is used to avoid overfitting. It stops training our model when it begins to overfit which in turn is identified through the sudden increase in the validation loss.

#compile model:

The optimizer used is the widely used and the most preferred adam optimizer. As the labels of the considered dataset are categorical and not one-hot-encoded, we must choose the sparse categorical cross-entropy loss function.

Early stopping can be used to avoid overfitting. This is done as we don’t know how many epochs our model must be trained for.

from tensorflow.keras.callbacks import EarlyStopping early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5) #Early stopping to avoid overfitting of model

Now, lets finally train our custom CNN model for, say,30 epochs:

history=model.fit(x_train,y_train,validation_data=(x_val,y_val),epochs=30,callbacks=[early_stop],shuffle=True)

Early stopping was encountered at the 16th epoch itself and thus model was trained only for 16 epochs, at the end of which it showed a training accuracy of 100% and a validation accuracy of 78.83%.

Model Evaluation

The best way to visualize our model training is by using the loss and accuracy graphs. The following codes can be used to get the loss and accuracy graphs for our trained model:

#loss graph plt.plot(history.history['loss'],label='train loss') plt.plot(history.history['val_loss'],label='val loss') plt.legend() plt.savefig('loss-graph.png') plt.show() # accuracies plt.plot(history.history['accuracy'], label='train acc') plt.plot(history.history['val_accuracy'], label='val acc') plt.legend() plt.savefig('acc-graph.png') plt.show()

The accuracy and loss graphs in my case are as follows:

Classification report and confusion matrix for the validation dataset:

y_val_pred=model.predict(x_val) y_val_pred=np.argmax(y_val_pred,axis=1) print(classification_report(y_val_pred,y_val))

confusion_matrix(y_val_pred,y_val)

Therefore, it can be clearly concluded that our CNN model for COVID CT scans is the best. It shows an average performance for the class of other pulmonary disorders. However, its performance is comparatively poor for healthy patients. Besides, our model shows an accuracy of 79% on the validation dataset.

Classification report and confusion matrix for the test dataset, which is completely new to our model:

y_pred=model.predict(x_test) y_pred=np.argmax(y_pred,axis=1) print(classification_report(y_pred,y_test)) confusion_matrix(y_pred,y_test)

It shows an accuracy of 75% on the test dataset with a similar performance to that of the validation dataset.

Overall, we can conclude that we have developed a realistic CNN model for COVID-19  all from scratch.

Lets us now save the model with the following code:

model.save('/content/drive/MyDrive/MLH Project/model-recent.h5') Building the Streamlit Web Application

To check which category our model predicted this image to be, we get the index corresponding to the maximum value using the np.argmax() function and thus conclude according to the codes of the labels discussed in the table of step 1.

Firstly, we must install Streamlit and import ngrok:

!pip install streamlit --quiet !pip install pyngrok==4.1.1 --quiet from pyngrok import ngrok

Then comes the actual code! Here, we mainly load the saved model-the h5 file and predict using it. The name of the model file is model-recent.h5.There is an option to upload an image directly from your local system and check its category-if the CT scan is that of COVID or healthy or other pulmonary disorders.

%%writefile app.py import streamlit as st import tensorflow as tf import numpy as np from PIL import Image # Strreamlit works with PIL library very easily for Images import cv2 model_path='/content/drive/MyDrive/MLH Project/model-recent.h5' st.title("COVID-19 Identification Using CT Scan") upload = st.file_uploader('Upload a CT scan image') if upload is not None: file_bytes = np.asarray(bytearray(upload.read()), dtype=np.uint8) opencv_image = cv2.imdecode(file_bytes, 1) opencv_image = cv2.cvtColor(opencv_image,cv2.COLOR_BGR2RGB) # Color from BGR to RGB img = Image.open(upload) st.image(img,caption='Uploaded Image',width=300) if(st.button('Predict')): model = tf.keras.models.load_model(model_path) x = cv2.resize(opencv_image,(100,100)) x = np.expand_dims(x,axis=0) y = model.predict(x) ans=np.argmax(y,axis=1) if(ans==0): st.title('COVID') elif(ans==1): st.title('Healthy') else: st.title('Other Pulmonary Disorder')

Finally, get the URL of your web application from:

!nohup streamlit run chúng tôi & url = ngrok.connect(port='8501') url

Paste this URL in the Chrome web browser to see our beautiful application.

Results Conclusion

Thus, we successfully built and trained our own CNN model for COVID-19 with our dataset! The same approach can be used for two or more classes. All you have to do is change the number of classes in the output layer or the last layer of the model architecture.

That’s it!

Hope you liked my blog and found it useful!

You can get the entire code from here.

Thanks for reading!

References About Me

I am Nithyashree V, a final year BTech Computer Science and Engineering student. I love learning such cool technologies and putting them into practice, especially observing how they help us solve society’s challenging problems. My areas of interest include Artificial Intelligence, Data Science, and Natural Language Processing.

Here is my LinkedIn profile: My LinkedIn

You can read my other articles on Analytics Vidhya from here.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

Related

The Etre Fivepoint Gloves Add A Touch Of Tech To Touch Tech

Inevitably, we all need to use our iPhones in the cold and our tiny little fingers hate the cold. Thankfully, technology entered the clothing world and taking off our gloves to use the phone is a thing of the past. Etre entered the scene after successfully establishing themselves as a web design company. Turning toward fashion, they promise “each piece is lovingly crafted in the British Isles, using responsibly sourced, time-honoured British materials.” There are many implementations of the “connected” glove and some companies get it right, others do not. Today, we take a look at the Etre Fivepoint gloves, and thankfully, these guys got it right….

Wrong

There are many more ways to do something wrong than right. Before describing the Fivepoint, I wanted to pause to complain about how other glove companies try to enable their handware. My biggest pet peeve regarding capacitive touch gloves is the inclusion of an actual hardware piece. There are a few retailers that sell regular gloves and they come with a type of push-pin or button looking add on. To operate, one must place the button additions onto the glove by pinning it on the inside. Once the user touches the inside piece, it conducts electricity through the hardware.

Wrong!

Wrong!

More often, companies have a separate material section on the thumb and pointer finger, as seen above. The material is different from the rest of the glove and are almost always awkwardly designed to draw unnecessary attention to the “e-tips,” or whatever dumb name has been created and registered as a trademark. Moreover, I have seen other people with these types of gloves and the special tips are not sewn properly or adhered correctly, consequently causing them to peel off.

Right

After a quick lesson on the wrong glove, let’s chat about the right glove. Etre Fivepoint gloves, for starters, are active on all five fingers across both hands. For the math challenged, that is ten total touch points for interacting with your devices. The inclusion of all fingers is especially helpful when using multitouch gestures on an iPad, or trackpad for that matter. Some companies solely worry about the right hand thumb and pointer finger, leaving lefties out in the cold and multitouch gestures in the dark.

The Fivepoint gloves boast capacitive thread, which is woven into each tip through the woolen fibers. As the thread is the conductor, there is no need for modified appliqués or button-like additions. While the tip thread is different from the rest of the glove, it is sewn directly into the existing glove base. This provides a continuous threaded material that has no seems at the connection point, further preventing the separation of the two pieces. It appears as one continuous threaded material, as seen below.

Otherwise, I find no trouble using the gloves or placing accurate taps. It takes a little getting used to, but eventually you can adjust. As with any wool gloves, there is a seam on the very tip of each finger, which sometimes gets in the way. Most importantly, make sure that you get the correct size and, if in doubt, get the smaller size. It is imperative your fingers completely fill the glove tips to ensure an accurate capacitive response.

Conclusion

Overall, I am a big fan of the Etre Fivepoint gloves. Sewn from a nice wool, the gloves have a high-end feel. As they are 100% wool, they do fuzz up a bit during normal use, but that is standard across the woolen glove family. If you are looking for a capacitive glove with a bit of class and style, these are the gloves for you. As they are located in England, you will have to shell out 30£/$46 (only $39 on Amazon) and we thank Etre for sending out a pair for review. Head over to the shop to find your own pair in Blue/Pear, Black/Charcoal, Victoria/Pearl, Blue Fairisle/Pearl, Sage/Charcoal, or Camel/Charcoal.

How To Choose A Branding Color Palette For Your Blog

Branding your blog or business is an essential first step when it comes to business creation.  Choosing a branding color palette for your blog is vital for its success. Branding represents your company’s identity online and makes it easily recognizable.  Here are a few examples of companies whose branding we recognize: Target and Amazon.

Target

Branding is important for your business because it is your online voice.  It is the way you communicate online.  Your blog or website needs to be visually appealing to attract an audience. Let’s discuss how to choose a branding color palette for your blog.

What Is A Color Palette?

A color palette is essential in branding your blog or website.  A color palette is a range of colors that a business uses as its online identity.  For example, I use four colors for my website chúng tôi  My main color is a blush, where my coordinating colors are a light gray and a dark gray.  My logo is black for simplicity and crispness.

An easy way to create a color palette for your business is by creating a mood board.  A mood board is a collage of various photos and shapes that show your favorite color schemes.  A mood board makes them easy to visualize.  Here is an example of a mood board I created:

Mood Board Example

You can also see other mood boards and color palettes HERE. 

How To Create A Mood Board

Mood boards can be created using software such as Canva, or you can create your own.  Canva is great because they already have templates ready to use.  They also have different example color palettes.  Scroll through the internet or Pinterest and find 20-30 pictures with colors that inspire you and save them to a file.  This will make it easy for you to choose your favorite colors that coordinate and create your mood board.

Here is an example color palette in Canva.  You can easily customize the premade palette with your photos.  They also have many designs to choose from.

Canva Color Palette

Color Choices For Branding

Before creating your brand color palette, you will also need to do a little research and figure out who your ideal audience is.  This can help you decide what colors will attract them to your blog or website.  Suppose your target audience, for example, is stay-at-home mothers who are raising small children. In that case, your color scheme might include bright, bold colors like pinks, blues, and yellows.

If your audience is an older group, then subdued simple colors, like black and white, might be a good choice.

How Do You Want Your Audience To Feel?

Another question to ask yourself is how you want your audience to feel when they visit your website or blog.   Colors can represent a different, unique mood. 

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For instance, beige could signify calm and simpleness.  Whereas dark blue can convey power and seriousness.  Keep these in mind when choosing your color palette.

A consistent color scheme will keep things consistent and help you create content more quickly.  From your blog or website to your social media platforms, a color scheme will save you time creating social media posts, pins, and even blog posts. 

Ready to start your very own website?  I can help you with branding your business.  Beautiful Showit designs that are easily customizable so that you can change colors to match your brand, which you can see HERE. 

I have also put together several feminine font pairings if you have no clue where to start with your site’s fonts.  You can see those HERE.  Or you can purchase your own fonts over on Creative Market (affiliate link).  They have tons of beautiful fonts to choose from.

Disclosure: This post contains an affiliate link to Creative Market. If you decide to purchase one of their products, I’ll receive a small commish at no cost to you… mama’s got to pay the bills so thanks for your support.

I hope this information will help give you the determination to start your very own blog!  And I will be here to assist you every step of the way.

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