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Streaming your content on the internet has become more popular nowadays. One of the main reasons for its growing popularity is the fact that streamers and viewers can interact with one another mid-stream.

However, managing the stream along with its various features and settings can sure feel a bit overwhelming, and much so if you are doing it through a single monitor. 

Streamers usually prefer setting up multiple monitors as that extra screen canvas does make the job a lot easier. Nevertheless, there is no limit on how many monitors you choose for the streaming purpose.

Although a single-screen stream setup makes it somewhat harder for you to manage your chats, notifications, and whatnot, it is still possible to achieve this feat efficiently. 

So, if you are an up-and-coming streamer or even if you are not new to the game, this guide shall help make the most out of your single monitor whilst the stream cam is on. So, before we waste any more time, let’s begin! 

Is It Possible to Stream With One Monitor?

To put it simply, it is possible to stream using only one monitor. However, the experience of doing so is comparatively worse than streaming with multiple screens. 

You as a streamer mainly need to manage your stream, the chatbox, and the in-app settings when you go live. Switching between windows to check your stream status is impossible and unprofessional. It breaks the engagement with the viewers while making the content feel clunky and mismanaged. 

However, there are various hacks and workarounds you can follow to manage your single monitor streaming service a bit more efficiently. 

How to Stream With One Monitor

The main difficulty while streaming with one monitor arises when you have to manage your stream status and read your viewers’ messages. So, that’s exactly what we are going to work on. Using the workarounds shown below, you can manage these things effectively to ultimately up to your game streaming.

Set Up Your Stream

Single screen streaming is a bit different than streaming via multiple monitors. So, the streaming application’s settings must be altered in a way that shall provide the most efficient output for a single monitor.

Hence, before we begin to understand how you can manage your stream on a single monitor, let’s skim past the ways to set up your stream on a single screen. So, we’ll use the OBS studio live streaming software as a reference. Here are the steps to set up your stream for a single monitor:

And voila! The stream has been set up and you are now ready to use some workarounds to make your single-screen streaming service more effective. 

Use Other Devices as a Secondary Screen

A second monitor sure does help. But since it’s out of the question, you can make use of any other devices lying around the house. 

Let’s take your phone for example. You can stream from your computer and also run your stream link to open the stream on your mobile phone. This way, you can use the mobile phone to read and reply to your viewers’ messages.

Additionally, through a secondary device, you can check the status of your stream and determine if it’s running well or not. Basically, stream as a first-person and view the status as a third person. So, you can use your laptop, mobile phone, or your tablet as a second screen. 

Run Your Applications on Windowed Mode

Some games and applications allow you to run it in windowed mode. That is, without going full-screen, you can part your screen to run the application on one side and the chatbot/donation alerts on the other. 

Keep in mind that this method doesn’t work with all applications and for the ones that do support this feature, it is a gamechanger. Although the chatbot might end up taking around a quarter of your screen space, you can still stream efficiently while staying active in your followers’ discussions.

So, run your application on windowed mode, pop out and resize the chatbox from your streaming platform, and screen record only the game window. 

Turn on Your Notifications

Another way you can make your single-screen streaming job a lot easier is by turning on the notifications.

These popped-up alerts provide you with a good idea regarding where the chat is headed towards. You can then read and respond to the message via the notifications on the side of your screen. Here’s how you can turn on the app notifications on your computer.

For Windows

For Mac

Use Third-party Applications

There are various third-party applications available online that can help you easily manage your streams via a single monitor. The core idea is to overlay your chats, donations, and other required windows and adjust their opacities to be able to view them whilst the stream cam is on.  

Although we never recommend using third-party applications, these applications sure do help. Out of infinite applications on the internet, we are going to talk briefly about a couple of them. Keep in mind that, from our research, we found these to be some of the best overlay applications available and none of them are our sponsorship deals. 


The StreamLabs Games Overlay application helps you view your chats and events on your screen while the streamed application is running in the background. So, to make use of StreamLabs,

You can now use the shortcut keys assigned earlier to bring up the chat and event windows while streaming. Keep in mind that some games may not support this feature while streaming in full-screen mode.

OBS Studio

The OBS studio has a feature called ‘Popout chat’ that lets you view the chatbox while you are streaming from the same window. So, to set this feature, 

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A Beginner’s Guide To Get Productive With Fastds!

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


Learn how to control your files and large dataset with a few lines of scripts. FastDS combines the power of Git and DVC to provide a hassle-free versioning experience.

Companies are always on the lookout for tools that can improve the productivity of their employees, as 92% of employees say productive tools that help them perform tasks efficiently improve work satisfaction. FastDS is for beginners, data science enthusiasts, and employers looking for a tool to improve the Git and data versioning experience.

In this blog, we will understand everything about FastDS. We will also use it to version file, dataset, and Urdu Automatic Speech Recognition model and push the project files to DagsHub remote.

What is FastDS?

FastDS (fds) is an open-source tool for all data professionals to version control code and datasets. fds at a high level uses Git and DVC to clone repositories, add changes, commit, and push. You can also use fds save for automating committing and pushing tasks.

 Conventional Vs. FastDS

FastDS provides shortcuts for Git, and DVC commands to minimise the chances of human error, automate repetitive tasks, and provide a beginner-friendly environment.

Key Features of FastDS

When I started using Git with DVC, I was making many mistakes in running commands. It took me a while to understand the mutual relation, and still, I make the same mistakes, which can cause delays during the project. The fds solve all that by automating repetitive tasks. In this section, we will learn about the various features of fds and how they can help us clone, monitor, and save project files.

Init: Initialize both Git and DVC repositories.

Status: Get the status of the Git and DVC repository.

Add: Add files/folders to Git and DVC repository.

Commit Commits changes to Git and DVC repository.

Clone: Clones from Git repository and pulls data from DVC remote.

Push: Push commits to Git and DVC remote.

Save: Add all the changes in the project, commit with a message, and push the changes to Git and DVC remote.

Automatic Speech Recognition

In this project, we will fine-tune a version of Facebook/wav2vec2-Xls-r-300m on the Urdu subset of the common_voice dataset under the CC-0 license. The wav2vec 2.0 model architecture has allowed data scientists to build a state-of-the-art speech-to-text application in more than 80 languages. Like Siri or Google, you can create your speech recognition in your native language.

I have followed XLSR-Wav2Vec2 for the low-resource ASR guide to fine-tune the model and used Wav2Vec2 with n-grams to boost the model performance. Combining a fine-tuned model checkpoint with a language model using Kenlm can improve the word error rate (WER) by 10 to 15 points.

In this project, we will learn:

To create a repository on DagsHub.

Clone the repository using fds.

Run the training script.

Monitor the results.

Add the Data and Model folder to DVC.

Add a language model.

Use one line to save and push changes to the DagsHub remote.

Setting up FastDS

First, we need to create a repository on DagsHub and add the required details. Your storage consists of Git, DVC, and MLflow remote links, and you can use them to upload files, datasets, and machine learning experiments.

Then, on your local machine, you can run the script below to clone the repository or if you are working on Colab, use `!` at the start of each script. The fds usually pull both Git and DVC files, but currently, we have an empty repository.

pip install fastds cd Urdu-ASR-SOTA dvc remote modify origin --local auth basic dvc remote modify origin --local user dvc remote modify origin --local password

Before running the training notebook, we need to move the dataset and required file to the local directory and then install all the packages required to fine-tune the model. You can check the list here.

Training FastDS

We won’t go deep into the training process. Instead, we will focus on generating model files and adding them to Git and DVC using fds. If you are interested in learning about the fine-tuning process, read XLSR-Wav2Vec2 for a low-resource ASR guide.

We will pull local audio data using the Hugging Face dataset library and use a transformer to download and finetune facebook/wav2vec2-xls-r-300m. We will also integrate DagHub logger and MLflow to track all the experiments.


After running the notebook and saving model checkpoints in a Model folder, we will use `fds add .` This will trigger an automatic response asking you to add large files/folders into DVC. After that, it will add those folders to .gitignore to avoid Git conflict.

You can now commit both Git and DVC and push the files to DagsHub remote storage by running the script below.

fds commit "first try" fds push Results

I ran multiple experiments; some failed, some inconclusive, and some gave optimal results. You can access all the experiments under the Experiment tab. In the end, I was able to produce the best results by training the model for 200 epochs on four V100S GPUs (OVH cloud). The model is significant, and it is impossible to run it on Google Colab.

Usually, on Google Colab, training a model on 30 epochs can take up to 14-27 hours, depending on the available GPU.

If you want to evaluate the results, run the below script in the base directory. It will generate both WER and CER with target and predicted TXT files.

python3 chúng tôi --model_id Model --dataset Data --config ur --split test --chunk_length_s 5.0 --stride_length_s 1.0 --log_outputs Language Model

To boost model performance, I have added a language model. If you are interested in learning about n-grams and want to add a language model to your ASR model, check out this tutorial: Boosting Wav2Vec2 with n-grams in Transformers.

Let’s save all the changes and push them to the remote by running the `fds save` script.

fds save "language model added"

We can rerun a model evaluation script to monitor the improvement. Combining the n-gram language model with Wav2Vec2 has improved WER 56 to 46.


The evaluation results with prediction are available in the Eval Results folder. The Eval Results folder has subfolders “With LM” and “Without LM”.

Quick Start

The project is open to the public under an open-source license, and if you are starting from zero, follow the steps mentioned in the code block to get started.

After that, you can run a prediction by running the Python script.

from datasets import load_dataset, Audio from transformers import pipeline model = "Model" data = load_dataset("Data", "ur", split="test", delimiter="t") def path_adjust(batch): batch["path"] = "Data/ur/clips/" + str(batch["path"]) return batch data = sample_iter = iter(data.cast_column("path", Audio(sampling_rate=16_000))) sample = next(sample_iter) asr = pipeline("automatic-speech-recognition", model=model) prediction = asr( sample["path"]["array"], chunk_length_s=5, stride_length_s=1) prediction


In this blog, we have learned about fds and how they can be used in a machine learning project. We have covered all the critical features with examples. The Urdu ASR project is open to contribution, and if you see a mistake in the code or have suggestions about the project, please create an issue. I made a Gradio demo and deployed it on Hugging Face spaces: Urdu-ASR-SOTA Gradio App.

Read more articles on FastDS here.

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


Beginner’s Guide To Image Gradient

So, the gradient helps us measure how the image changes and based on sharp changes in the intensity levels; it detects the presence of an edge. We will dive deep into it by manually computing the gradient in a moment.

Why do we need an image gradient?

Image gradient is used to extract information from an image. It is one of the fundamental building blocks in image processing and edge detection. The main application of image gradient is in edge detection. Many algorithms, such as Canny Edge Detection, use image gradients for detecting edges.

Mathematical Calculation of Image gradients

Enough talking about gradients, Let’s now look at how we compute gradients manually. Let’s take a 3*3 image and try to find an edge using an image gradient. We will start by taking a center pixel around which we want to detect the edge. We have 4 main neighbors of the center pixel, which are:

(iv) P(x,y+1) bottom pixel

We will subtract the pixels opposite to each other i.e. Pbottom – Ptop and Pright – Pleft , which will give us the change in intensity or the contrast in the level of intensity of the opposite the pixel.

Change of intensity in the X direction is given by:

Gradient in Y direction = PR - PL

Change of intensity in the Y direction is given by:

Gradient in Y direction = PB - PT

Gradient for the image function is given by:

𝛥I = [𝛿I/𝛿x, 𝛿I/𝛿y]

Let us find out the gradient for the given image function:

We can see from the image above that there is a change in intensity levels only in the horizontal direction and no change in the y direction. Let’s try to replicate the above image in a 3*3 image, creating it manually-

Let us now find out the change in intensity level for the image above

GX = PR - PL Gy = PB - PT GX = 0-255 = -255 Gy = 255 - 255 = 0

𝛥I = [ -255, 0]

Let us take another image to understand the process clearly.

Let us now try to replicate this image using a grid system and create a similar 3 * 3 image.

Now we can see that there is no change in the horizontal direction of the image

GX = PR - PL , Gy = PB - PT GX = 255 - 255 = 0 Gy = 0 - 255 = -255

𝛥I = [0, -255]

But what if there is a change in the intensity level in both the direction of the image. Let us take an example in which the image intensity is changing in both the direction

Let us now try replicating this image using a grid system and create a similar 3 * 3 image.

GX = PR - PL , Gy = PB - PT GX = 0 - 255 = -255 Gy = 0 - 255 = -255

𝛥I = [ -255, -255]

Now that we have found the gradient values, let me introduce you to two new terms:

Gradient magnitude

Gradient orientation

Gradient magnitude represents the strength of the change in the intensity level of the image. It is calculated by the given formula:

Gradient Magnitude: √((change in x)² +(change in Y)²)

The higher the Gradient magnitude, the stronger the change in the image intensity

Gradient Orientation represents the direction of the change of intensity levels in the image. We can find out gradient orientation by the formula given below:

Gradient Orientation: tan-¹( (𝛿I/𝛿y) / (𝛿I/𝛿x)) * (180/𝝅) Overview of Filters

We have learned to calculate gradients manually, but we can’t do that manually each time, especially with large images. We can find out the gradient of any image by convoluting a filter over the image. To find the orientation of the edge, we have to find the gradient in both X and Y directions and then find the resultant of both to get the very edge.

Different filters or kernels can be used for finding gradients, i.e., detecting edges.

3 filters that we will be working on in this article are

Roberts filter

Prewitt filter

Sobel filter

All the filters can be used for different purposes. All these filters are similar to each other but different in some properties. All these filters have horizontal and vertical edge detecting filters.

These filters differ in terms of the values orientation and size

Roberts Filter

Suppose we have this 4*4 image

Let us look at the computation

The gradient in x-direction =

Gx = 100 *1 + 200*0 + 150*0 - 35*1 Gx = 65

The gradient in y direction =

Gy = 100 *0 + 200*1 - 150*1 + 35*0 Gy = 50

Now that we have found out both these values, let us calculate gradient strength and gradient orientation.

Gradient magnitude = √(Gx)² + (Gy)² = √(65)² + (50)² = √6725 ≅ 82

We can use the arctan2 function of NumPy to find the tan-1 in order to find the gradient orientation

Gradient Orientation = np.arctan2( Gy / Gx) * (180/ 𝝅) = 37.5685 Prewitt Filter

Prewitt filter is a 3 * 3 filter and it is more sensitive to vertical and horizontal edges as compared to the Sobel filter. It detects two types of edges – vertical and horizontal. Edges are calculated by using the difference between corresponding pixel intensities of an image.

A working example of Prewitt filter

Suppose we have the same 4*4 image as earlier

Let us look at the computation

The gradient in x direction =

Gx = 100 *(-1) + 200*0 + 100*1 + 150*(-1) + 35*0 + 100*1 + 50*(-1) + 100*0 + 200*1 Gx = 100

The gradient in y direction =

Gy = 100 *1 + 200*1 + 200*1 + 150*0 + 35*0 +100*0 + 50*(-1) + 100*(-1) + 200*(-1) Gy = 150

Now that we have found both these values let us calculate gradient strength and gradient orientation.

Gradient magnitude = √(Gx)² + (Gy)² = √(100)² + (150)² = √32500 ≅ 180

We will use the arctan2 function of NumPy to find the gradient orientation

Gradient Orientation = np.arctan2( Gy / Gx) * (180/ 𝝅) = 56.3099 Sobel Filter

Sobel filter is the same as the Prewitt filter, and just the center 2 values are changed from 1 to 2 and -1 to -2 in both the filters used for horizontal and vertical edge detection.

A working example of Sobel filter

Suppose we have the same 4*4 image as earlier

Let us look at the computation

The gradient in x direction =

Gx = 100 *(-1) + 200*0 + 100*1 + 150*(-2) + 35*0 + 100*2 + 50*(-1) + 100*0 + 200*1 Gx = 50

The gradient in y direction =

Gy = 100 *1 + 200*2 + 100*1 + 150*0 + 35*0 +100*0 + 50*(-1) + 100*(-2) + 200*(-1) Gy = 150

Now that we have found out both these values, let us calculate gradient strength and gradient orientation.

Gradient magnitude = √(Gx)² + (Gy)² = √(50)² + (150)² = √ ≅ 58

Using the arctan2 function of NumPy to find the gradient orientation

Gradient Orientation = np.arctan2( Gy / Gx) * (180/ 𝝅) = 71.5650 Implementation using OpenCV

We will perform the program on a very famous image known as Lenna.

Let us start by installing the OpenCV package

#installing opencv !pip install cv2

After we have installed the package, let us import the package and other libraries

Using Roberts filter

Python Code:

Using Prewitt Filter #Converting image to grayscale gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #Creating Prewitt filter kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]]) kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]]) #Applying filter to the image in both x and y direction img_prewittx = cv2.filter2D(img, -1, kernelx) img_prewitty = cv2.filter2D(img, -1, kernely) # Taking root of squared sum(np.hypot) from both the direction and displaying the result prewitt = np.hypot(img_prewitty,img_prewittx) prewitt = prewitt[:,:,0] prewitt = prewitt.astype('int') plt.imshow(prewitt,cmap='gray')


Using Sobel filter gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) kernelx = np.array([[-1,0,1],[-2,0,2],[-1,0,1]]) kernely = np.array([[1, 2, 1],[0, 0, 0],[-1,-2,-1]]) img_x = cv2.filter2D(gray_img, -1, kernelx) img_y = cv2.filter2D(gray_img, -1, kernely) #taking root of squared sum and displaying result new=np.hypot(img_x,img_y) plt.imshow(new.astype('int'),cmap='gray')



This article taught us the basics of Image gradient and its application in edge detection. Image gradient is one of the fundamental building blocks of image processing. It is the directional change in the intensity of the image. The main application of image gradient is in edge detection. Finding the change in intensity can conclude that it can be a boundary of an object. We can compute the gradient, its magnitude, and the orientation of the gradient manually. We usually use filters, which are of many kinds and for different results and purposes. The filters discussed in this article are the Roberts filter, Prewitt filter, and Sobel filter. We implemented the code in OpenCV, using all these 3 filters for computing gradient and eventually finding the edges.

Some key points to be noted:

Image gradient is the building block of any edge detection algorithm.

We can manually find out the image gradient and the strength and orientation of the gradient.

We learned how to find the gradient and detect edges using different filters coded using OpenCV.

Beginner’s Guide To Using Nmap

nmap is a network scanning tool which can be used for a whole variety of network discovery tasks including port scanning, service enumeration and OS fingerprinting.

To install nmap on Ubuntu or Raspbian use:


apt-get install


For Linux versions that use yum, like Fedora, run this as root:

yum install


The simplest invocation is just to supply a hostname or IP address of a machine that you want to scan. nmap will then scan the machine to see which ports are open. For example:


All TCP/IP connections use a port number to uniquely identify each network service. For example, web browser connections are made on port 80; emails are sent on port 25 and downloaded on port 110; secure shell connections are made on port 22; and so on. When nmap does a port scan, it shows which ports are open and able to receive connections. In turn, this indicates which services are running on the remote machine.

From a security point of view, the less services which are running on a host, the more secure it is. This is because there are less “holes” that an attacker can use to try and access the machine. It is also a useful way to perform a preliminary check to see if a service is running (and accepting connections). A quick scan of my Ubuntu server looks like this:

To discover which software is providing the server behind each of the open ports use the -sV option:



Here are the results from a Raspberry Pi:

nmap has correctly discovered that the OpenSSH server is being used to provide a SSH service on the Pi. The tool also notes that the Pi is running Linux!




Here is the output from a scan performed against a Windows XP machine:

If you want to scan more than one host at a time, nmap allows you to specify multiple addresses or use address ranges. To scan more than one host just add extra addresses to the parameter list (with each one separated by a SPACE). For example to scan for open ports on, and, use:


To specify an address range use the dash symbol. For example to scan the first five hosts on your network you could use:



The output would look something like this:

The first host found is the router supplied by my Internet Service Provider (on address and the second one is my Raspberry Pi (on

Cookbook and summary

To check if a specific port is open use -p followed by the port number or the port name, for example:




It can be combined with the -sV flag to determine the version of the software attached to that port:





To discover which hosts are alive on your network use the -sn flag. This will just ping the hosts specified in the address range. For example:




As a closing word of warning, don’t run scans against hosts that you don’t control or have permission to scan. Excessive scanning can be interpreted as an attack or could disrupt services unnecessarily.

Image credit: fiber Network Server by BigStockPhoto

Gary Sims

Gary has been a technical writer, author and blogger since 2003. He is an expert in open source systems (including Linux), system administration, system security and networking protocols. He also knows several programming languages, as he was previously a software engineer for 10 years. He has a Bachelor of Science in business information systems from a UK University.

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The Beginner’s Guide To Smartphone Camera

But Why Not Get a Good Standalone Camera Instead?

The whole “smartphone vs. camera” debate has been raging ever since high-end back-lit CMOS cameras started appearing on regular phones. So, what’s better?

The answer is: It really doesn’t matter. All of it depends on the reasons you might want to get a phone with a good camera as opposed to a piece of hardware completely dedicated to taking photographs. People who are serious about photography might get a decent DSLR camera, but still might want to dump some cash on a smartphone with good optics simply because it’s less bulky. You might not be carrying your bulky rig with all its attachable lens around when a great photo opportunity presents itself. In those cases, it’s very useful to have a powerful camera in your pocket.

You just can’t drag all of this around every time you walk out of your house:

What Makes a Phone’s Camera “Good?”

If a phone doesn’t give you any information about its aperture or focal length, you have no way of telling whether it has a camera that meets your liking. Usually, phones that don’t show any indication in their specs other than the resolution are not putting any priority on their cameras.

Since you’re limited to whatever optical specifications the manufacturer provides for your lens, it’s not a bad idea to try to find something that suits your liking and provides the optical experience you are accustomed to taking pictures with.

For people who are not experienced with cameras, here are a few pointers:

A bigger focal length means that you’ll cover less area in the picture. The simplest way to describe focal length is by comparing it to zoom. The higher the focal length, the more “zoomed” the camera is. Smaller focal lengths mean you’ll have wider angles. Nikon has a decent guide on this if you’d like to know more in-depth information. Focal lengths are measured in millimeters. The typical optics on a phone have somewhere between 20 and 30 mm of focal length.

The aperture (focal ratio) determines how much light enters the camera. This ratio is notated with a fancy-looking italic lowercase “F”, known as an “f-number”. A higher f-number represents a smaller aperture, which captures more light. This is important for special shots that put objects in focus. For example, compare the two images below:

The top image is taken using a small aperture, and the bottom image is taken using a large one. On some phone cameras, the shutter will take care of this by moving ever so slightly just before taking a picture to modify the aperture. Similarly, focal length is also adjusted through optical zoom.

Ultimately, a good smartphone camera will have all these things. It will have the ability to zoom by moving the optics (adjusting the focal length) and change the aperture with a mechanical shutter. Since the cameras are digital, there has to be a decent on-board backlit CMOS sensor to construct these images with great accuracy. After all that, you can worry about resolution. But, anyway, a good camera will also have a decent resolution, althoug you shouldn’t make a big fuss about anything more than 5 megapixels.

Examples of Good Smartphone Cameras

The first thing that comes to mind as far as cameras are concerned is the Nokia Lumia 1020, with its brilliant 41-megapixel camera, its special software, and its spectacular CMOS and optics. There’s also the Samsung Galaxy S4 Zoom (a phone with an integrated full-blown PAS – point-and-shoot – camera), and the regular S4. The HTC One and iPhone 5S are close runners up.

Let’s Continue This Discussion!

Miguel Leiva-Gomez

Miguel has been a business growth and technology expert for more than a decade and has written software for even longer. From his little castle in Romania, he presents cold and analytical perspectives to things that affect the tech world.

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Gear Guide: How To Stream Like A Pro Gamer

We may earn revenue from the products available on this page and participate in affiliate programs. Learn more ›

During the broadcast of its championship match, League of Legends, one of the most played video games in the world, typically attracts more than 30 million viewers. That audience is more than triple the 8.3 million who tuned in to the MTV Video Music Awards last year. So it’s not surprising that chúng tôi the most popular video-streaming platform for gamers, attracts more than 1.2 million broadcasters per month.

This article originally appeared in the January 2024 issue of Popular Science under the headline “How To Stream Like A Pro Gamer”.

Siberia Elite Prism

Noise-isolating earcups and Dolby 7.1 simulation make these headphones a must-have for hours of streaming. $200

Asus PB287Q

This 28-inch 4K UHD monitor displays content at a 3840 x 2160 resolution. $565

Corsair Vengeance K95

Programmable LEDs beneath the keys give this keyboard 16.8 million possible looks. $189

Razer DeathAdder Chroma

Five programmable buttons make this mouse great for fast-paced games. $60

Elgato Game Capture HD

Flashback Recording allows you to retroactively capture gaming footage. $140

Falcon Northwest Tiki

With an optional NVidia GTX Titan Z GPU, this microtower PC is powerful enough to drive multiple 4K displays. $1,950

Origin EON17-SLX

This laptop supports dual-graphics cards and an Intel Core i7 quad-core processor. $2,008

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