Trending December 2023 # Mastering 3D Lighting In Blender # Suggested January 2024 # Top 20 Popular

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Making images in Blender 3D has a lot in common with photography. In fact, if you have any photographic skills, these will transfer nicely into 3D software like Blender.

In previous articles we have discussed how you light a scene on a basic level. But how can you use all the different kinds of lamp for something approaching real cinematography?

Types of Lamp

Cinematography is all about choosing the right lights. In the virtual world of a 3D program the lights are all computed rather than real, but they perform the same function as real world lights. To get good lighting in 3D graphics images, you need to have a grasp of lighting in the real world, so a good tip is to learn how to light photographs from photography tutorials out there on the Internet.

The basic types of lamps in Blender are as follows: Point, Sun, Spot, Hemi and Area.


These lights are a tiny ball of light which are omnidirectional – that is to say scattering light in all directions like a lightbulb. Shadows fan out from the source centre in radiating lines.


Sun lights emulate the light you get from the sun; the light comes down from the source in parallel lines. Shadows cast straight down from the source and are soft.


Spotlights have a point source, but they fan out at a particular angle set in the properties, and they have a soft transition from the middle to the outer radius, the same as a real spotlight. Shadows are hard-edged and follow the angle of the beam.


These lights are like spotlights, but the difference is the source is a half sphere and the light focusses in straight lines, like a lighting brolly. Shadows are hard-edged.


Area lights are flat planes which cast light like a softbox or light reflected from a large reflective surface. Shadows are sharp when the objects are close to a surface but softer when they are distant.

Emission Surfaces

Another kind of light you can have in Blender is to turn an object into a light by selecting a surface texture of Emission. The texture emits light, meaning you can make a ball, cube or plane be a light emitter. The light is soft and the shadows smooth.

You can turn objects into lights, the benefit being that you can see the lights. The standard lights in Blender are invisible to the camera, but lights which are objects can be seen. The only light sources in this scene are the objects themselves.

Basic Setup

The basic lighting setup taught by all photography courses is to have a key, fill and rim or edge light.

The key light is either a strong, sun-like light or spotlight shining on the front of the object being lit. This casts light on the front and top of the object and shadows on the surface over any undercuts. In this example we used a sun light above and to the right of the camera. Strength is set to 700.

The fill light is positioned opposite to the key light to fill in any shadows. In this example, an Area light is positioned below the camera and to the left pointing up at the object. Strength is set to 75.

The rim or edge light is positioned behind the object pointing towards the object and the camera to highlight the edge of the object to separate it from its background. In this example, a Hemi light is positioned above, to the left and behind the skull pointing forwards towards the camera. The Strength is set to 2.

And that is how you light something perfectly.

Lighting Tips

The main tip for setting up lights and even textures in Blender is to use a rendered viewport. This makes a draft-quality rendering of the light that you can see updated in real time to allow you to position lights and shadows perfectly while seeing the effects of your light positions live on the screen.


Learn as much as you can about real world lighting for photography and transfer that knowledge to the 3D virtual world of Blender for fantastic lighting.

Image Credit: Cole Harris

Phil South

Phil South has been writing about tech subjects for over 30 years. Starting out with Your Sinclair magazine in the 80s, and then MacUser and Computer Shopper. He’s designed user interfaces for groundbreaking music software, been the technical editor on film making and visual effects books for Elsevier, and helped create the MTE YouTube Channel. He lives and works in South Wales, UK.

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How To Create 3D Retro Effect In Photoshop

Creating 3D artwork using Photoshop is very interesting and adds interest to your work. In this post, we will show you how to create a 3D Retro effect in Photoshop to add a new spin to your 2D Images.

How to create 3D Retro effect in Photoshop

When you create 3D you add depth to images and this depth makes the image looks real. The 3D retro effect looks like what you would see if you looked at an old 3D movie without the 3D glasses. Learning how to create 3D Retro effect in Photoshop is quite easy to do. The steps are not complicated and you will learn some new tricks that you can do for other projects. Follow the steps below to turn any image into a 3d retro image.

Open and prepare Photoshop

Add the image to Photoshop

Duplicate the image

Name the duplicated image

Open the layer style for the duplicated image

Deselect the Green and Blue channels

Move the duplicated image

Crop the images


1] Open and prepare Photoshop 2] Add image to Photoshop

This is the image that will be used for the 3D effect.

This is the other image that will be used for the 3D effect, this one has a background and more colors. It will be used to show the cropping after the 3D effect is applied.

3] Duplicate the image 4] Name the duplicated image 5] Open the layer style for the duplicated image

This step requires that you open the Layer style for the duplicated image (3D).

6] Deselect the Green and Blue channels

In the Layer style window look for the Advanced blending category.

You will see the Channels, R, G, and B. R is for red, G is for green and B is for blue.

You will deselect the G and the B channels. After you have done this method for 3D, you can try the 3D by turning off other channels and seeing what it looks like.

7] Move the duplicated image

In this step you will create the 3D Retro effect by moving the duplicated 3D) image. make sure that the duplicated (3D) image is selected then use the right direction key tap  5 or 10 times. You can move the image further away to create a more colorful look.  As you tap you will see the images move apart and the colors start to show.

This is the image after 5 taps

This is the image after 10 taps

This is the image after 20 taps

This is the final image, the image had no background so you would not have to crop it as the other step below requires. However, if you notice hard lines at the edges of other objects in the image, you may have to crop.

8] Crop the images

This step is optional and dependent on the image that you used. If the image is a PNG image and has no background then you would not need to crop it. If the image that you used has a background then you would need to crop after the 3D effect. Cropping will remove the uneven edges that were created when you moved the images. This would only be visible when your images have a background.

Cropping the images

To crop the images, go to the left tools panel and select the Rectangular marquee tool. To crop the image use the Rectangular Marquee tool and select the inner parts of the image and leave the edges outside of the selection. Below you will see an image with a background used to create the 3D retro effect.

This is the Rectangular marquee selection around the image.

This is the image with the 3D retro effect before it is cropped. You may not notice the line that was created when both the original and the duplicates were moved. The line will be more noticeable in some images.

This is the image after it is cropped.

If you have a pair of Red, Cyan 3D glasses you will be able to see the 3D effect in the images.

How do you make retro 3d text in Photoshop?

You can add a retro 3D effect to text in Photoshop, below are the steps to do so.

Write the text in Photoshop

Duplicate the text by selecting it and then pressing Ctrl + J

In the Layers style uncheck the Green (G) and Blue (B) channels

Press Ok to close the Layer style window

Select the top image then press the left direction key to move it to the left by about 5 – 10 moves, you can move as far apart as you want.

How do you create a 3D Retro effect in Photoshop

Creating a 3D retro effect in Photoshop is quite easy. Follow the steps below to do it.

Place the images in Photoshop

Duplicate the image by pressing Ctrl + J

When the Layer style window appears turn off the blue and green channels

Press Ok to close the Layer style panel

Select the top image and using the left direction key, tap 5-10 times to the left. You will see the 3D effect as you tap. You can move the image further away to see the effect more.

Read: How to Posterize a Photo in Photoshop.

Spark: Lighting A Fire Under Hadoop

Also see: Hadoop and Big Data

Hadoop has come a long way since its introduction as an open source project from Yahoo. It is moving into production from pilot/test stages at many firms. And the ecosystem of companies supporting it in one way or another is growing daily.

It has some flaws, however, that are hampering the kinds of Big Data projects people can do with it. The Hadoop ecosystem uses a specialized distributed storage file system, called HDFS, to store large files across multiple servers and keep track of everything.

While this helps managed the terabytes of data, processing data at the speed of hard drives makes it prohibitively slow for handling anything exceptionally large or anything in real-time. Unless you were prepared to go to an all-SSD array – and who has that kind of money? – you were at the mercy of your 7,200 RPM hard drives.

The power of Hadoop is all centered around distributed computing, but Hadoop has primarily been used for batch processing. It uses the framework MapReduce to execute a batch process, oftentimes overnight, to get your answer. Because of this slow process, Big Data might have promised real-time analytics but it often couldn’t deliver.

Enter Spark. It moved the processing part of MapReduce to memory, giving Hadoop a massive speed boost. Developers claim it runs Hadoop up to 100 times faster in certain applications, and in the process opens up Hadoop to many more Big Data types of projects, due to the speed and potential for real-time processing.

Spark started as a project in the University of California, Berkeley AMPLab in 2009 and was donated as an open source project to the Apache Foundation in 2012. A company was spun out of AMPLab, called Databricks, to lead development of Spark.

Patrick Wendell, co-founder and engineering manager at Databricks, was a part of the team that made Spark at Berkeley. He says that Spark was focused on three things:

1) Speed: MapReduce was based on an old Google technology and is disk-based, while Spark runs in memory.

2) Ease of use: “MapReduce was really hard to program. Very few people wrote programs against it. Developers spent so much time trying to write their program in MapReduce and it was huge waste of time. Spark has a developer-friendly API,” he said. It supports eight different languages, including Phython, Java, and R.

3) Make something broadly compatible: Spark can run on Amazon EC2, Apache’s Mesos, and various cloud environments. It can read and write data to a variety of databases, like PostgreSQL, Oracle, MySQL and all Hadoop file formats.

“Many people have moved to Spark because they are performance-sensitive and time is money for them,” said Wendell. “So this is a key selling point. A lot of original Hadoop code was focused on off line batch processing, often run overnight. There, latency and performance don’t matter much.”

Because Spark is not a storage system, you can use your existing storage network and Spark will plug right into Hadoop and get going. Governance and security is taken care of. “We just speed up the actual crunching of what you are trying to do,” said Wendell. Of course, that’s also predicated on giving your distributed servers all the memory they will need to run everything in memory.

Prakash Nanduri, CEO of the analytics firm Paxata, said that Spark made Hadoop feasible for working in real time. “Now you have the ability to focus at real-time analytics as scale. The huge implication is suddenly you go from 10 use cases to 100 use cases and do it at a cost that is significantly lower than for traditional interactive analytic use cases,” he said.

Many of the cloud vendors that offer some kind of Hadoop solution, like Cloudera, Hortonworks, and MapR, are bundling Spark with Hadoop as a standard offering now, said Wendell.

At a recent Spark Summit, Toyota Motor offered an example of the speed Spark offers. It uses social media to watch for repair issues in addition to customer inquiries. The problem with the latter is people don’t care about surveys, so it shifted its emphasis to Twitter and Facebook. The company built an entire system on Spark to monitor social media to watch for keywords.

Its original customer experience app, done as a regular Hadoop batch job, would take 160 hours, or 6 days. The same job rewritten for Spark is completed in just four hours. The company also parsed the flood of input from social media and was able to filter out things like dealer promos, irrelevant material and incident reports involving Toyota products and reduced the amount of data to process by 50%.

Another use case is log processing and fraud detection, where speed is of the utmost, as banks, businesses and other financial and sales institutions need to move fast to catch fraudulent activity and act on the warnings.

“The business value you achieve is fundamentally derived through the apps. In the case of financial services, you need to be able to detect money laundering cases. You cannot find money laundering signals by running a batch process at night, it has to be in real time,” said Nanduri. “An app built on Spark can do the entire data set in real time and interactive speeds and get to the answer much faster.”

But Spark isn’t just about in-memory processing. Wendell said half of the performance gains come from running in memory and other half is from optimizations. “The other systems weren’t designed for latency so we improved on that a lot,” he said.

There is still more work to be done. Wendell said there is a big initiative underway with Databricks and Apache to further improve Spark performance, but he would not elaborate.

While it offers a standardized way to build highly distributed and interactive analytical apps, it still has a long way to go,” said Nanduri. “Spark lacks security and needs enhanced support for multiple concurrent users, so there is still some work to do.

Photo courtesy of Shutterstock.

Mastering Macos Mojave’s New Screenshot Tools

macOS Mojave significantly changed the way screenshots work on macOS. Review the changes made, discuss how to accomplish tasks previously accomplished in the new-defunct chúng tôi and describe how to use the new Screenshots application most effectively.

In the past Mac users could use the Command + Shift + 3 and Command + Shift + 4 to capture screenshots of the full screen and a region respectively. Those screenshot shortcuts are still available, so you don’t need to rewrite your muscle memory. But Command + Shift + 5 invokes the new Screenshots application which provides a screenshot GUI and more options, especially for post-capture editing.

The Screenshots Toolbar

Pressing Command + Shift + 5 will pull up the Screenshots toolbar.

The buttons on the toolbar perform the following actions, from left to right:

Capture Entire Screen: take a screenshot of everything on the screen.

Capture Selected Window: take a screenshot of only the foremost window.

Capture a Selected Area: drag a box around a region to capture.

Record Entire Screen: record a video of the entire screen.

Record Selected Area: record a video of the selected region.

The first three capture still images and relate to the keyboard shortcuts Command + Shift + 3, Command + Shift + 4 + Space, and Command + Shift + 4, respectively. The last two record videos, which are new features in Mojave. If you’ve ever used Quicktime to record a screenshot, you’ll recognize the functionality. It’s been essentially moved from Quicktime’s screen-recording functionality to the Screenshots toolbar.

To start a video, select either of the two record options and press the “Record” button in the toolbar. To stop the recording, either press the Stop button in the Touch Bar (if applicable) or in the menu bar.

Screenshot Options

The “Options” menu reveals more settings.

Long-time macOS screenshot pros may recognize these options. These screenshot options were once confined to Terminal commands. Now they can be set through this menu.

Save to

The first section allows you to select the target of your screenshot. By default, the screenshot won’t be saved there immediately. If you don’t interact with the screenshot thumbnail, the screenshot will be saved to this location.

“Clipboard” will copy the screenshot to the clipboard after capture. Use the “Paste” command (or the Command + V keyboard shortcut) to insert the screenshot into an editable field. Choosing an application (Mail, Messages, or Preview) will open the screenshot in that application immediately. “Other Location …” allows you to set a specific folder as the screenshot’s destination. If you select a location, it will be included in the destination menu later.


The timer functions as expected. There are presets for none, which captures the screenshot immediately. Five and ten seconds makes you wait that many seconds before capturing the screenshot.

Additional Options

At the bottom we have miscellaneous options. “Show Floating Thumbnail” controls the post-capture behavior. Keep the option checked, and macOS will display a temporary thumbnail after capture and before saving to disk. “Remember last selection” saves the region selection box used last, allowing for easier repeatable screen captures. For example, if you’re capturing the same window repeatedly, repeating the capture region is perfect. “Show mouse pointer” controls whether or not your cursor appears in the screenshot.

Editing Screenshots with Markup

In the Markup window you can use all of the annotation tools from Preview on your images. While the set might not approach the usefulness of professional annotation programs, they’re more than adequate for simple labeling or editing. Applying signatures or circling objects are both especially useful.


The new Screenshots tool in Mojave is a major upgrade from the previous tool. With the benefit of a graphical interface, taking a screenshot is easier, clearer, and more robust. While third-party tools like Snagit still offer significantly more editing and markup tools, Screenshots is a major upgrade for all Mac users.

Alexander Fox

Alexander Fox is a tech and science writer based in Philadelphia, PA with one cat, three Macs and more USB cables than he could ever use.

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Mastering Multiple Linear Regression: A Comprehensive Guide


Interesting in predictive analytics? Then research artificial intelligence, machine learning, and deep learning.

Let’s take a brief introduction to what linear regression sklearn is. Regression is the statistical method used to determine the strength and the relation between the independent and dependent variables. Generally, independent variables are those variables whose values are used to obtain output, and dependent variables are those whose values depend on the independent values. When discussing regression algorithms, you must know some of the multiple linear regression algorithms commonly used in python to train the machine learning model, like simple linear regression, lasso, ridge, etc.

In the following tutorial, we will talk about the multiple linear regression model or multilinear regression and understand how simple linear differs from multiple linear regression (MLR) in python.

Learning objectives

Understand the difference between simple linear regression and multiple linear regression in Python’s Scikit-learn library.

Learn how to read datasets and handle categorical variables for multiple linear regression using Scikit-learn.

Apply Scikit-learn’s linear regression algorithm to train a model for multiple linear regression.

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

Table of Contents What Is Machine Learning?

If you are on the path of learning data science, then you definitely have an understanding of what machine learning is. In today’s digital world, everyone knows what Machine Learning is because it is a trending digital technology across the world. Every step towards the adaptation of the future world is led by this current technology, which in turn, is led by data scientists like you and me.

Now, for those of you who don’t know what machine learning is, here’s a brief introduction:

Machine learning is the study of the algorithms of computers that improve automatically through experience and by the use of data. Its algorithm builds a model based on the data we provide during model building. This is the simple definition of machine learning, and when we go in deeper, we find that huge numbers of algorithms are used in model building. Generally, the most commonly used machine learning algorithms are based on the type of problem, such as regression, classification, etc. But today, we will only talk about sklearn linear regression algorithms.

Simple Linear Regression vs Multiple Linear Regression

Now, before moving ahead, let’s discuss the interaction behind the simple linear regression. Later, we will compare multiple and simple linear regression based on the intuition that we are solving our machine learning problem.

What Is Simple Linear Regression?

We considered a simple linear regression in any machine learning algorithm using an example.

Now, suppose we take a scenario of house prices where our x-axis is the size of the house, and the y-axis is the price of the house. In this example, we have two features – the first one is f1, and the second one is f2, where

 f1 refers to the size of the house and

f2 refers to the price of the house.

So, if f1 becomes the independent feature and f2 becomes the dependent feature, we usually know that whenever the size of the house increases, then the price also increases. Suppose we draw scatter points randomly. Through this scatter point, we try to find the best-fit line, which is given by the equation:

                            equation:   y = A + Bx

Suppose y is the price of the house, and x is the size of the house; then this equation seems like this:

When we discuss this equation, in which

In this equation, the intercept indicates what the base price of the house would be when the price of the house is 0. Meanwhile, the slope or coef (coefficient) indicates the unit increase in the slope, with the unit increase in size.

Now, how is it different when compared to multiple linear regression?

What Is Multiple Linear Regression?

Multiple Linear Regression (MLR) is basically indicating that we will have many features Such as f1, f2, f3, f4, and our output feature f5. If we take the same example as above we discussed, suppose:

f1 is the size of the house,

f2 is bad rooms in the house,

f3 is the locality of the house,

f4 is the condition of the house, and

f5 is our output feature, which is the price of the house.

Now, you can see that multiple independent features also make a huge impact on the price of the house, meaning the price can vary from feature to feature. When we are discussing multiple linear regression, then the equation of simple linear regression y=A+Bx is converted to something like:

                            equation:  y = A+B1x1+B2x2+B3x3+B4x4

“If we have one dependent feature and multiple independent features then basically call it a multiple linear regression.”

Now, our aim in using the multiple linear regression is that we have to compute A, which is an intercept. The key parameters B1, B2,  B3, and B4 are the slopes or coefficients concerning this independent feature. This basically indicates that if we increase the value of x1 by 1 unit, then B1 will tell you how much it will affect the price of the house. The others B2, B3, and B4, also work similarly.

So, this is a small theoretical description of multiple linear regression. Now we will use the scikit learn linear regression library to solve the multiple linear regression problem.

How to Train a Model for Multiple Linear Regression? Step 1: Reading the Dataset

Most of the datasets are in CSV file format; for reading this file, we use pandas library:

df = pd.read_csv('50_Startups.csv') df

Here you can see that there are 5 columns in the dataset where the state stores the categorical data points, and the rest are numerical features.

Now, we have to classify independent and dependent features.

Independent and Dependent Variables

There are total 5 features in the dataset, of which profit is our dependent feature, and the rest are our independent features.

Python Code:

Step 2: Handling Categorical Variables

In our dataset, there is one categorical column, State. We must handle the categorical values inside this column as part of data preprocessing. For that, we will use pandas’ get_dummies() function:

# handle categorical variable


# dropping extra column

x= x.drop(‘State’,axis=1)

# concatation of independent variables and new cateorical variable.



Step 3: Splitting the Data

Now, we have to split the data into training and test sets using the scikit-learn train_test_split() function.

# importing train_test_split from sklearn from sklearn.model_selection import train_test_split # splitting the data x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42) Step 4: Applying the Model

Now, we apply the linear regression model to our training data. First of all, we have to import linear regression from the scikit-learn library. Unlike linear regression, there is no other library to implement multiple linear regression.

# importing module from sklearn.linear_model import LinearRegression # creating an object of LinearRegression class LR = LinearRegression() # fitting the training data,y_train)

finally, if we execute this, then our model will be ready. Now we have x_test data, which we will use for the prediction of profit.

y_prediction =  LR.predict(x_test) y_prediction

Now, we have to compare the y_prediction values with the original values because we have to calculate the accuracy of our model, which was implemented by a concept called r2_score. Let’s briefly discuss r2_score:


It is a function inside sklearn. metrics module, where the value of r2_score varies between 0 and 100 percent,  we can say that it is closely related to MSE.

r2 is basically calculated by the formula given below:

                            formula:  r2 = 1 – (SSres  /SSmean )

now, when I say SSres, it means it is the sum of residuals, and SSmean refers to the sum of means.


y = original values

y^ = predicted values. and,

From this equation, we infer that the sum of means is always greater than the sum of residuals. If this condition is satisfied, our model is good for predictions. Its values range between 0.0 to 1.

”The proportion of the variance in the dependent variable or target variable that is predictable from the independent variable(s) or predictor.”

The best possible score is 1.0, which can be negative because the model can be arbitrarily worse. A constant model that always predicts the expected value of y, disregarding the input features, would get an R2 score of 0.0.

# importing r2_score module

from sklearn.metrics import r2_score

from sklearn.metrics import mean_squared_error

# predicting the accuracy score


print(‘r2 socre is ‘,score)

print(‘mean_sqrd_error is==’,mean_squared_error(y_test,y_prediction))

print(‘root_mean_squared error of is==’,np.sqrt(mean_squared_error(y_test,y_prediction)))

You can see that the accuracy score is greater than 0.8, which means we can use this model to solve multiple linear regression, and also mean squared error rate is also low.


Multiple Linear Regression is a statistical method used to study the linear relationship between a dependent variable and multiple independent variables. In the article above, we learned step-by-step how to implement MLR in Python using the Scikit-learn library. We used a simple example of predicting house prices to explain how simple linear regression works and then extended the example to multiple linear regression, which involves more than one independent variable. I hope now you have a better understanding of the topic.

Key Takeaways

Multiple linear regression is an extension of simple linear regression, where multiple independent variables are used to predict the dependent variable.

Scikit-learn, a machine learning library in Python, can be used to implement multiple linear regression models and to read, preprocess, and split data.

Categorical variables can be handled in multiple linear regression using one-hot encoding or label encoding.

Frequently Asked Questions Related

Nanoleaf Light Panels Versus Canvas: Homekit Smart Lighting

Nanoleaf HomeKit lights are great for decorating and entertaining all year — long after the Christmas tree is taken down and the holiday lights are all boxed up. Nanoleaf now makes two styles of smart lights that illuminate your walls: Light Panels and Canvas. Here’s how they compare:

Nanoleaf Light Panels ($229), originally called Aurora, are triangular tiles that connect using a modular system to create colorful light designs that you can control with Siri or an iOS app. My description from December 2023 still holds up:

Nanoleaf Aurora is like a beautiful screensaver for your wall.

Nanoleaf has since added a new music module accessory that makes Light Panels react to music and sound, and the Nanoleaf Remote adds to the ways you can trigger modes.

Nanoleaf Canvas ($249) launched earlier this month and reshapes what you can do with Nanoleaf HomeKit lights. Canvas tiles are a bit smaller and use squares instead of triangles to make different designs possible. The music module is also built-in so interactive sound scenes work out-of-the-box.

Both smart light systems let you create colorful scenes that you can trigger with HomeKit using Siri or the Home app. Just add a pre-configured scene from the Nanoleaf app or create your own, then the scene becomes available with HomeKit.

The blue/red/orange/yellow/white arrangement (above, left) is a rocket scene I created for Light Panels that doesn’t change colors. You can also easily switch to a color-shifting scene (and back again) as seen below:

Nanoleaf Canvas similarly supports color-shifting scenes. (Both scenes sped up 4x for demonstration purposes.)

The triangular Light Panels are a bit larger and let you easily create designs with angled corners like stars, trees, or even rocket ships. The smaller Canvas squares won’t cover as much surface area without more tiles, but you can design more traditional patterns with similar light effects.

Light Panels are powered by a controller module that includes a power toggle and a scene selector. Canvas is a bit more clever and integrates power, brightness, and mode switching in a special tile with labeled touch controls.

The other trick that Nanoleaf Canvas has up its sleeve is that each tile can react to touch. Nanoleaf Canvas supports special interactive scenes that let you play games with your tile arrangement with scenes including “Whack A Mole”, “Simon”, “Game Of Life”, “Memory”, and “PacMan.”

Nanoleaf HomeKit lights are already great for mood lighting and entertainment. Light “games” could just be a gimmick on their own, but I can definitely see a simple game of “Whack A Mole” or “PacMan” being a great party trick — especially with kids.

Personally, I really like Nanoleaf Light Panels. The triangular tiles cover more wall space and let you create designs that look a bit less 8-bit. I recommend Nanoleaf Canvas for most people though — especially over similar products.

The modular system is just as easy to use and the built-in support for music scenes and brightness control make it more polished — plus the touch-controlled games add to the entertainment.

Catch up on earlier HomeKit Weekly entries below:

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