Trending February 2024 # Greenshot: A Lightweight Yet Feature # Suggested March 2024 # Top 5 Popular

You are reading the article Greenshot: A Lightweight Yet Feature updated in February 2024 on the website Eastwest.edu.vn. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 Greenshot: A Lightweight Yet Feature

Introduction

Greenshot is a free to use desktop application compatible with Windows computers. While modern screenshot-capturing software come equipped with a plethora of “bonus” features such screencast recording and automatic uploading, Greenshot focuses on simplicity of usage and effectively taking screenshots the way you want. A tiny program, Greenshot installs quickly and silently runs in the background, operating only when you need it to. You can use it to instantly capture the entire computer screen, specific open app windows, or specific rectangular portions on your screen.

Usage

The Output tab in the settings window lets you control how your screenshots are treated. You can choose your screenshots to be copied directly to the clipboard, sent to your default image editor such as Paint, saved by prompting you for a name and location, saved using a predefined name and location, attached to an email, or sent to your printer. Greenshot lets you select multiple options thus letting you have full control over your screenshots.

Under this tab, you can also select the file format in which your screenshots are captured: JPEG, GIF, PNG, or BMP. In case you choose JPEG, you can select the quality of the output image; choose lower qualities for smaller file size.

If one of your options included sending your screenshots to the printer, you will want to adjust the default settings of Greenshot’s printing under the Printer tab. Here you can control whether or not print data and time with the image, rotate the screenshot, and similar relevant printing options.

With these controls set according to your needs, you can start taking screenshots. The program’s hotkeys include CTRL+PrintScreen for capturing the entire visible region on the screen, ALT+PrintScreen for capturing a particular open app window, and merely PrintScreen for capturing a specific rectangular region. Most users will be using the last option most.

Once your screenshot is taken, what happens to it depends on your selected preferences in the previously mentioned Settings window.

Conclusion

Greenshot is a lightweight screenshot capturing application for Windows computer. It is feature-rich and provides you full control over your captured screenshots. Unlike competing apps that try to incorporate non-screenshot features as bonuses, this app focus on its primary objective and excellently serves it. In other words, if you are a Windows user looking for a screenshot capturing software, Greenshot is where your search ends.

Greenshot

Hammad

Hammad is a Business student and computer geek who cover latest technology news and reviews at AppsDaily. Apart from that, I like to review web services and softwares which can be helpful for the readers.

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Ring Video Doorbell 4 Review: A Pricey But Feature

– Some features locked behind subscription – No Google Assistant compatibility – Not the cheapest smart doorbell around

Cons: – Some features locked behind subscription – No Google Assistant compatibility – Not the cheapest smart doorbell around

Ring’s fourth generation doorbell packs in the features while supplying high quality audio and video, but this comes at a higher than average price.

In my eyes, the doorbell was not a device that needed vast innovation. Walk up to the house, press a button and someone answers, that’s all you need right?! Well, clearly I was wrong with the smart doorbell market currently sitting at an overall valuation in the billions.

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As part of the ever-growing smart home market, there are plenty of brands now producing smart doorbells with the aim of making your life easier via automation, notifications and hands-free controls.

Colour me ever so slightly intrigued. With every missed package and timid knock at my door, my interest in a smart doorbell has been increasing. With this in mind, I spent some time using one of the leading products – the Amazon Ring 4 – to see if the hype was justified.

Setup

Whether the Ring 4 will be easy to set up or not will come down to which version you get. I tested the wireless version, but they also make one that needs to be plugged into the house’s electrical wiring.

While it’s more complicated, this option is going to be better in the long term as it saves you having to charge the Ring 4. If this all sounds like a job you don’t want to take on don’t worry, the wireless option saves a lot of hassle.

Amazon estimates the average wireless Ring doorbell will last up to six months on a single charge, this combined with an 8 hour charge time means over a year you’ll be charging for less than a full day.

In setting up the doorbell, the first step was as simple as downloading an app and scanning a QR code. Like all smart products these days, most of the setup process is simply entering mass amounts of data and selecting preferences.

The hard part of the process is getting the doorbell put up. Ring includes screws, wall plugs, a mount and a plethora of parts to get you sorted. It can be intimidating but there are easy instructions to follow.

If you are in a rental property or somewhere where drilling into the side of the house would be frowned upon, this will pose a challenge. You could always attach the device to a wall with sticky strips or some kind of temporary mount, but this won’t stop any potential thieves.

Design

The Ring doorbell is a lot of things, but it isn’t exactly the most aesthetically pleasing smart doorbell out there. It’s large and quite noticeable which, if you’re looking for a security deterrent, isn’t exactly a bad thing.

While brands like Arlo or Google’s Nest offer sleek doorbells, the Ring is a large rectangle that looks slightly dated. This is the same design that Ring has been offering for years across generations but, if it isn’t broken why fix it?

The large design is utilitarian in nature. Housing a powerful camera and an absolutely huge battery, the benefits outweigh the slight aesthetic issues.

The housing that covers most of the doorbell is plastic. It looks sleek and premium, but it is prone to scrapes and marks if you’re not careful with it. Obviously, you’re not moving it about much, but I did bump it while carrying something into the house leaving a noticeable mark.

App

Like most modern devices, when using the Ring doorbell the app will be your best friend. Through the Ring app you can access live feeds, change settings, rewatch old footage, set zones to scan and more.

In fact, the Ring doorbell can be somewhat overwhelming when you first boot up the app, offering a plethora of settings and preferences. However, day to day most of these options won’t be needed.

I mainly used the app to access the camera’s feed and to access push notifications when someone was at the door. However, it is also worth spending some time in the app messing with settings to get your perfect doorbell experience.

The app will also be used for any other Ring devices you invest in. This includes the optional Chime device, allowing you to hear your doorbell from other parts of your property.

King of the castle

So the doorbell is set up, the app is downloaded… what can it actually do? Yes, it can act as a doorbell, alerting you when someone rings the door but that is only one small part of the Ring’s functionality.

Whilst setting up the app, Ring requests a ‘Motion Zone’ be set up. This is the area that the camera will detect within, sending alerts and notifications if any human movement is detected in this range.

Depending on where you live, this zone is well worth setting up. I live on a busy road and would myself with upwards of 40 notifications in a day. Once I changed this to just focus on my entranceway, notifications dropped to a much more helpful quantity.

Motion detection can be edited even further. Adjustments can be made to the motion detection sensitivity to lower the frequency of notifications, and alerts can be sent when a package is left for you.

If the doorbell is rung while you’re out (or don’t want to have to socialise with the outside world) you can communicate via the doorbell’s microphone. It’s clear and loud, and so is the microphone that your postman will have to make awkward small talk with you through.

For even less socialising, automatic responses can be set up. These range from the simple ‘Hi! We’ll be right there’ to the blunter ‘Sorry, we’re not interested’ and the somewhat keen ‘Hi! Spring’s here and it looks like you are too! We’ll be right there’.

If you set up these automatic messages, people can leave messages, or you can still speak to them via the microphone once the message finishes.

The doorbell utilises a Full HD camera that operates in a 160 degree field of view. The video footage was mostly detailed and crisp, even when zooming in on a subject to get a clearer view.

Equally, the night vision camera is surprisingly accurate, although it is in black and white. It does have a habit of applying a somewhat sickly filter though which is not exactly going to provide the most flattering images of you as you come home late at night.

Sucked into the Amazon ecosystem

Something that isn’t exactly very upfront when you buy an Amazon Ring is how dependent it is on a ‘Ring Protect Plan’. You get a full month of this when you set the doorbell up, showing you the wonderful experience available, and then a host of features will be lost.

This price increases to £8 a month if you have multiple Ring devices that you want to be included in this plan. This will only cover Ring devices at one property and a second plan will need to be set up for any other property.

Another issue with being in Amazon’s ecosystem is that Ring isn’t compliant with Google device’s or Google Assistant. If you have a house full of Google speakers and devices, you’ll be much better off with a Nest doorbell.

Verdict

Ring has been making doorbells for a decade now, working on the design through iteration after iteration, so it is no surprise that the Ring 4 is a refined product, offering everything you will need from a doorbell.

 It is by no means the cheapest option around, but that money rewards you with a fantastic battery life, clear footage and audio, plus plenty of smart changeable features and more features than you’ll ever need.

However, with a lot of these features locked behind a subscription pay-wall, and a lack of Google assistant compatibility, those who aren’t already loyal Amazon customers could find brands like Arlo or Wyze better fit to needs or price brackets.

Alternatives Arlo Essential doorbell

Arlo’s Essential doorbell sits in a similar price bracket to the Ring Doorbell 4 and even has a similar list of features.

The big difference for Arlo is the more sleek design on offer. The brand’s Essential doorbell is slim and thin with the same layout as Ring of the camera being on top and the button below.

The camera can capture great video quality and offers a full length capture of anyone coming to your door.

Google Nest doorbell

The Google Nest doorbell looks very similar to the Arlo Essential above, slimming the design down to a long strip.

This device is mostly going to appeal to those who are deep into the Google ecosystem, filling their house with Google speakers, devices and possibly a Google smartphone in the pocket.

Where Ring requires you to set up a paid subscription to reap the full benefits of the doorbell, Nest includes it all for free.

Nest also offers facial recognition features so your camera knows when it is you or a friend at the door.

Eufy Security video doorbell

Eufy’s security doorbell is similar to its competitors in a lot of ways. The design is fairly similar, as is the list of features on offer. However, unlike some competitors that ask you to sign up for a subscription plan, Eufy instead stores your footage in a hub that comes with it. This does mean there is a delay to access photos and footage, but nothing that will be all that noticeable.

Across price, features and design, Eufy has one of the best smart doorbells available right now.

Read more reviews:

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Motorola One Power Review: A Decent Yet Flawed Budget Smartphone

Motorola has pulled up its sleeves to compete in the highly competitive mid-range segment in India and the newly launched Motorola One Power is what the company thinks will grab the attention of the users drooling over performance and longevity. The massive battery on the Motorola One Power, which besides Android One affiliation also gives the device its title, is set to generate sufficient attention for the brand and also help former Motorola enthusiasts and users live up to their love for the brand. So, is the new Motorola One Power, priced at Rs. 15,999 worth buying? Is the smartphone that will bring Motorola back to the fore in the Indian market? Let’s find out in our Motorola One Power review.

We shall see in the forthcoming sections whether the Motorola One Power lives up to these claims and the phonemaker’s confidence or not, so let me start with the specifications of the smartphone.

Motorola One Power Specifications

On paper, the Motorola One Power appears to house some solid specifications, including the tried-and-tested combination of the Snapdragon 636 along with 4GB RAM which is preferred by many other manufacturers. Here’s what the device offers in terms of its hardware:

Dimensions156 x 76 x 8.98 mm

Weight205 gm

Display6.2-inch FHD+ LCD with notch

ProcessorQualcomm Snapdragon 636

GPUAdreno 509

RAM4GB

Storage64GB

Primary Camera16MP f/1.8 + 5MP f/2.2 with PDAF

Secondary Camera12MP f/2.0

Battery5,000mAh

Operating SystemAndroid 8.1 Oreo (Android One)

SensorsFingerprint sensor (rear mounted), accelerometer, gyro, proximity, compass and e-compass

Connectivity802.11 a/b/g/n/ac WiFi, Bluetooth 5.0 EDR, A-GPS, GLONASS, and Galileo

What’s in the Box?

Inside the box, the Motorola One Power comes neatly rested, already clad in a protective TPU case. Besides the amazing quality of the cover, there’s nothing extraordinary in the package and this is what it contains:

Motorola One Power handset

18W TurboPower charging brick

USB-C cable

SIM ejector

Protective TPU case

User manuals and warranty literature

Design and Build

In a bid to revive its lost popularity, Motorola has attempted to check all the essential boxes and a good build quality is one of them. The device feels very durable and solidly built, with its metal casing promises a trustworthy design. But with that said, the design has been kept very minimal – presumably to keep the manufacturing woes away. Initially, the Motorola One Power feels noticeably heavier than most devices in its vicinity if you consider the price. However, over time that is something, you’ll get used to.

On the front, there’s a 6.2-inch display with curved corners that remind me of the edges on the Poco F1. Yes, you get a notch too which is almost as wide as the Mi 8 or the Poco because Motorola used the space to add a selfie flash. To protect the display, the company has covered the display under Gorilla Glass 3. Further, the edges of this display are not chamfered and the tapering end is curved to give an easy grip within the palm. The fact that Motorola has paid attention to small details like these reaffirms my faith in the brand.

The bottom edge houses a single speaker – although there are two grilles – along with the USB type-C jack. The addition of USB-C is a welcome addition at this price point. On the top, you get a noise-canceling microphone and Motorola has ensured complete user-friendliness by keeping the headphone jack. Lastly, on the left side, you get a three-slot SIM Tray which can accommodate two SIM cards and a Micro SD chip, all at once.

Motorola One Power lacks NFC and Wireless charging but that is not expected for this price either. Overall, the smartphone’s minimal and sturdy design adds a sense of durability and gives you assurance about its long life. The weight of the smartphone that is something that might batter down the experience for a few users but over the last one week of its usage, I’ve gotten much used to it.

Display

With the 6.2-inch Full HD+ display, Motorola has taken a leap over the smartphones it released earlier this year, including the Moto G6 series. This is because Motorola One Power is the first device from the company which comes with a notch and it’s great to see Moto start the trend with the budget segment itself.

The display is fairly responsive but one can easily observe the screen bleeding along the edges, as we see with other LCD and the recent culprit of this issue was the Poco F1.

Overall, the screen makes up for a palatable experience and I enjoy the large display over those on the Nokia 6.1 Plus and the archaic-looking Mi A2. I am not too fond of the super round corners but Motorola has done an appreciable job of optimizing the upper edge to cohere with the lower one and appear absolutely symmetric while gaming or watching videos.

Performance

The Motorola One Power draws its vigor from a Snapdragon 636 which is paired to 4GB of RAM. This setup is a common sight in the price segment and has been battered a lot of manufacturers by especially Xiaomi. This is also because the 636 is one of the chipsets which offer a balance between performance and power consumption with four high-frequency and four low-frequency cores. We’ve seen the same setup on many of this device’s competitors including Nokia 6.1 Plus, Redmi Note 5 Pro, and Asus Zenfone Max Pro M1.

In terms of benchmarks, Motorola One Power does have average scores and nothing more can actually be expected from the device at this price. Both AnTuTu and Geekbench scores are close to its closest rival spec-wise i.e. Redmi Note 5 Pro. Notably, the utilization of stock Android does not make any observable difference but I expect Android Pie update to improve these numbers.

Gaming

In terms of gaming, there’s nothing exceptional either. I was able to play moderately heavy games such as SBK16, Dead Trigger 2, Guns of Boom, War Wings, Nitro Nation 6 etc. without facing any lag. On graphics-heavy games such as Asphalt 9, the visuals take a hit because of the Adreno 509 GPU.

On PUBG Mobile, the default graphics are set to Low, but the game runs fine without any observable frame drop on Medium graphics. There is no option to choose higher graphics and honestly, you would anyway regret trying that on a device which packs in Snapdragon 636.

Security

Overall, I see no UI lag during daily operations or launching apps. However, there is a visible lag when unlocking the smartphone using the fingerprint and this raises a false alarm of misalignment with the sensor. This issue holds true only when you the display is not already on and the fingerprint unlocking is rather quicker when you’re on the lock screen. While I could not figure out the exact reason behind the issue, I was assured by the company’s Product Manager that this will be ironed out via an update.

Even though facial authentication at this price point is not very reliable, it is a handy feature and is becoming fairly popular among manufacturers making devices for this segment. For instance, Realme 1 came without a fingerprint scanner but it had face unlocking. However, Motorola has chosen to skip the face unlock option and this could be because it wants to maintain a nearly stock experience – although rival Mi A2 also has the feature despite also being on Android One program.

Speakers

Motorola One Power packs in an impressively loud bottom-firing speaker. Using the speaker on full inside public transport could earn you loathing if not a penalty. But its loudness will also allow you to be the center of attention for any party. Besides the loud speaker unit, the One Power also features a headphone jack over which the quality is good enough.

Surprisingly, the USB-C port does not support audio playback but there’s Bluetooth 5.0 which means you can get better connectivity. However, the smartphone neither supports the option to connect two Bluetooth audio devices at once.

HD Netflix Playback

Motorola took note of the issue that caused severe criticism for the Poco F1, especially in countries where over-the-top video watching is more popular in India. Since the Motorola One Power is certified for Widevine L1, which means you will be able to watch HD videos on apps like Netflix, Hulu, Amazon Prime Video, Google Play Movies, and other subscription-based services. This wasn’t expected from Motorola considering the price and the addition comes as a pleasant surprise.

Overall, I find the Motorola One Power living up to the expectations in terms of performance, looking at the price. I’m able to use most apps and play common games without any cutbacks in the entertainment experience. This is also because the notch is optimized for games and the only issue I wish Motorola would – and has promised to – fix is the speed of the fingerprint scanner.

Cameras

Daylight

In conditions of sufficient lights, the Motorola One Power picks up good colors and most often keeps them close to the natural shades. There a good balance between the details and the amount of exposure in the pictures when they’re snapped under daylight and this is even true when the weather is not very sunny. Here’s a look at the performance of the camera.

When it comes to HDR, the images turn warmer and closer to a yellow tint without any significant loss in the detailing of the image. The warmth also results in higher saturation in the images.

Indoors and Night Light

If you’re planning to invite the Motorola One Power back home for a coffee, beware to not be fooled by its macho image. While in under sufficient lighting indoor, the camera does pretty well but as the light falling on the sensor decreases, the image quality reduced significantly as well.

Portrait Mode

While I’m not very confident, I hope Motorola takes a serious look at the issue and fixes it.

Selfies

The Motorola One Power is surely a delightful proposition for those embracing self-love. Under daylight or sufficient indoor ambiance, selfies have good details and are mostly clear. If you look closely, however, you would be able to catch signs of noise which would not stop you from Instagramming these selfies.

The same low-light treatment can also be seen on selfies with a moderate amount of details and a lot of noise. To beat that you can try using the selfie flash which actually works as a flash and not as a torch.

Video

With the ability to capture videos at up to 4K resolution, capturing 30 frames every second, the Motorola One Power joins the league of mid-rangers with this facility. However, the lack of any form of image stabilization means that your 4K experience will be shaky. The colors are usually good but you might have trouble focusing on moving objects or have a shaky video if you’re moving. Motorola also gives you a post-record stabilization feature but it does not change things very much.

Here are two sample 4K videos captured with the Motorola One Power without and with the stabilization applied:

Comparison with Mi A2 and Redmi Note 5 Pro

I took the three smartphones for a trip to a nearby to capture the true colors of nature and found the Mi A2 to be outweighing the Motorola One Power and naturally, the Redmi Note 5 Pro.

Motorola takes a marginal lead in terms of the details in a portrait shot but the Mi A2 snatches it back in terms of the overall quality of the images.

Coming to selfies, Mi A2 is the leader here too with better details and crisper bokeh shots. While the Motorola One Power is better than the Redmi Note 5 Pro, it does not stand a chance against Mi A2.

You can learn more in our detailed camera review and comparison for Motorola One Power.

Software

As the first device under Google’s Android One affiliation program, the Motorola One Power get a fresh and bloat-free version of stock Android. The device flaunts Android Oreo 8.1 with the security patch from August. The only over-the-top software that I found on this device is for Moto’s popular gestures for the flashlight and the camera – doing a chopping gesture twice toggles the flashlight and twisting your wrist twice can open the camera.

Lastly, Motorola has promised that Android Pie will arrive later this year while an Android Q update is awaited to come next year. Besides this, users will receive Google security patches regularly for the next three years and get unlimited access to Google Photos cloud storage.

Battery

The battery on the Motorola One Power is among its leading highlights and has actually left a very good impression on me. With a 5,000mAh capacity, the battery can easily endure through the toughest and meanest of treatments and still manage to last longer than a day and a half, after being used strenuously.

What makes this proposition even better is the 18W Turbo charger bundled with the Motorola One Power. As we noted in our extensive battery test of the Motorola One Power, the charger takes slightly over two hours to charge the large battery fully and even 45 minutes of charge can give you a day’s worth of mileage.

Overall, this backup is far from average and is totally the compelling point for me to buy this device and the Turbo charging makes it even more attractive. If you travel a lot or have to visit remote areas frequently or are just too lazy to bend over and grab the charger, this is undoubtedly a device which you should consider buying – if you’re looking for flagship grade gameplay or performance.

Connectivity

In fact, the Motorola One Power cannot allow two SIM cards to have an active 4G connection simultaneously, which got me a little disappointed. For the same reason, fidgeting to find connectivity by switching SIMs in areas of poor network can be a hassle or a tiring task. This, however, helps when a bad 4G network is sabotaging your calls by coercing VoLTE on you, something that most of us have experienced.

Update: We were told by Motorola that dual 4G support is currently being tested and will be added in the Android Pie update. With this, users will also be able to receive VoLTE calls on both SIM cards simultaneously or use 4G data on one while being on call over the other.

Motorola One Power: Pros and Cons

Pros

Solid and reliable build quality

Promise of updates up to Android Q

Great mono speaker

Widevine L1 certification for on-demand HD playback

Actual flash for selfies

Mind-blowing battery and Turbo charging

Notch optimized for games and apps

Cons

Heavier than most smartphones

Crampy side buttons

Poor portrait shots

Laggy fingerprint (to be fixed in future updates)

No face unlock

No dual 4G or dual VoLTE

Cannot turn the notch off

Motorola One Power: Should You Buy it?

Now, coming to the all important questions. Should you buy the Motorola One Power? Is this the phone that will bring Motorola back into the limelight? Answering the first question, let me talk about the positives first. The Motorola One Power brings a decent display, decent cameras, great performance and some amazing battery life. So, it’s obvious that the One Power has a lot of things going for it, but there are a few things that the Motorola One Power doesn’t quite get right. There’s the bulky design, the mushy, cheap feeling buttons, the slowish fingerprint scanner, no dual 4G VoLTE support and the lack of features like face unlock and gestures.

Buy the Motorola One Power on Flipkart (Rs 15,999)

Introduction To Flair For Nlp: A Simple Yet Powerful State

Introduction

Last couple of years have been incredible for Natural Language Processing (NLP) as a domain! We have seen multiple breakthroughs – ULMFiT, ELMo, Facebook’s PyText, Google’s BERT, among many others. These have rapidly accelerated the state-of-the-art research in NLP (and language modeling, in particular).

We can now predict the next sentence, given a sequence of preceding words.

What’s even more important is that machines are now beginning to understand the key element that had eluded them for long.

Context! Understanding context has broken down barriers that had prevented NLP techniques making headway before. And today, we are going to talk about one such library – Flair.

Until now, the words were either represented as a sparse matrix or as word embeddings such as GLoVe, Bert and ELMo, and the results have been pretty impressive. But, there’s always room for improvement and Flair is willing to stand up to it.

In this article, we will first understand what Flair is and the concept behind it. Then we’ll dive into implementing NLP tasks using Flair. Get ready to be impressed by its accuracy!

Please note that this article assumes familiarity with NLP concepts. You can go through the below articles if you need a quick refresher:

Table of contents

What is ‘Flair’ Library?

What gives Flair the Edge

Introduction to Contextual String Embeddings for Sequence Labeling

Performing NLP Tasks in Python using Flair

What’s Next for Flair?

What is ‘Flair’ Library?

Flair is a simple natural language processing (NLP) library developed and open-sourced by Zalando Research. Flair’s framework builds directly on PyTorch, one of the best deep learning frameworks out there. The Zalando Research team has also released several pre-trained models for the following NLP tasks:

Name-Entity Recognition (NER): It can recognise whether a word represents a person, location or names in the text.

Parts-of-Speech Tagging (PoS): Tags all the words in the given text as to which “part of speech” they belong to.

Text Classification: Classifying text based on the criteria (labels)

Training Custom Models: Making our own custom models.

All of this looks promising. But what truly caught my attention was when I saw Flair outperforming several state-of-the-art results in NLP. Check out this table:

Note: F1 score is an evaluation metric primarily used for classification tasks. It’s often used in machine learning projects over the accuracy metric when evaluating models. The F1 score takes into consideration the distribution of the classes present.

What Gives Flair the Edge?

There are plenty of awesome features packaged into the Flair library. Here’s my pick of the most prominent ones:

It comprises of popular and state-of-the-art word embeddings, such as GloVe, BERT, ELMo, Character Embeddings, etc. There are very easy to use thanks to the Flair API

‘Flair Embedding’ is the signature embedding provided within the Flair library. It is powered by contextual string embeddings. We’ll understand this concept in detail in the next section

Flair supports a number of languages – and is always looking to add new ones

Introduction to Contextual String Embeddings for Sequence Labeling

Context is so vital when working on NLP tasks. Learning to predict the next character based on previous characters forms the basis of sequence modeling.

Contextual String Embeddings leverage the internal states of a trained character language model to produce a novel type of word embedding. In simple terms, it uses certain internal principles of a trained character model, such that words can have different meaning in different sentences.

Note: A language and character model is a probability distribution of Words / Characters such that every new word or character depends on the words or characters that came before it. Have a look here to know more about it.

There are two primary factors powering contextual string embeddings:

The words are trained as characters (without any notion of words). Aka, it works similar to character embeddings

The embeddings are contextualised by their surrounding text. This implies that the same word can have different embeddings depending on the context. Quite similar to natural human language, isn’t it? The same word may have different meanings in different situations

Let’s look at an example to understand this:

Case 1: Reading a

book

Case 2: Please

book

a train ticket

Explanation:

In case 1, book is an 

OBJECT

In case 2, book is a

VERB

Language is such a wonderful yet complex thing. You can read more about Contextual String Embeddings in this Research Paper.

Performing NLP Tasks in Python using Flair

It’s time to put Flair to the test! We’ve seen what this awesome library is all about. Now let’s see firsthand how it works on our machines.

We’ll use Flair to perform all the below NLP tasks in Python:

Text Classification using the Flair embeddings

Part of Speech Tagging (PoS) and comparison with the NLTK library

Setting up the Environment

We will be using Google Colaboratory for running our code. One of the best things about Colab is that it provides GPU support for free! It is pretty handy for training deep learning models.

Why use Colab?

Completely free

Comes with pretty decent hardware configuration

It’s on your web browser so even old machines with outdated hardware can run it

Connected to your Google Drive

Very well integrated with Github

All you need is a stable internet connection.

About the Dataset

We’ll be working on the Twitter Sentiment Analysis practice problem. Go ahead and download the dataset from there (you’ll need to register/log in first).

The problem statement posed by this challenge is:

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

1. Text Classification Using Flair Embeddings

Overview of steps:

Step 1: Import the data into the local Environment of Colab:

Step 2: Installing Flair

Step 3: Preparing text to work with Flair

Step 4: Word Embeddings with Flair

Step 5: Vectorizing the text

Step 6: Partitioning the data for Train and Test Sets

Step 7: Time for predictions!

     Step 1: Import the data into the local Environment of Colab:

# Install the PyDrive wrapper & import libraries.

# This only needs to be done once per notebook.

!pip install -U -q PyDrive

from chúng tôi import GoogleAuth

from pydrive.drive import GoogleDrive

from google.colab import auth

from oauth2client.client import GoogleCredentials

# Authenticate and create the PyDrive client.

# This only needs to be done once per notebook.

auth.authenticate_user()

gauth = GoogleAuth()

gauth.credentials = GoogleCredentials.get_application_default()

drive = GoogleDrive(gauth)

# Download a file based on its file ID.

# A file ID looks like: laggVyWshwcyP6kEI-y_W3P8D26sz

file_id = '1GhyH4k9C4uPRnMAMKhJYOqa-V9Tqt4q8' ### File ID ###

data = drive.CreateFile({'id': file_id})

#print('Downloaded content "{}"'.format(downloaded.GetContentString()))

You can find the file ID in the shareable link of the dataset file in the drive.

Importing the dataset into the Colab notebook:

import io

Import pandas as pd

data = pd.read_csv(io.StringIO(data.GetContentString()))

data.head()

All the emoticons and symbols have been removed from the data and the characters have been converted to lowercase. Additionally, our dataset has already been divided into train and test sets. You can download this clean dataset from here.

Step 2: Installing Flair

# download flair library #

import torch

!pip install flair

import flair

A Brief look at Flair Data Types

There are two types of objects central to this library – Sentence and Token objects. A Sentence holds a textual sentence and is essentially a list of Tokens:

from chúng tôi import Sentence

# create a sentence #

sentence = Sentence('Blogs of Analytics Vidhya are Awesome.')

# print the sentence to see what’s in it. #

print(Sentence)

Step 3: Preparing text to work with Flair

#extracting the tweet part#

text = data['tweet']

## txt is a list of tweets ##

txt = text.tolist() print(txt[:10])

Step 4: Word Embeddings with Flair

Feel free to first go through this article if you’re new to word embeddings: An Intuitive Understanding of Word Embeddings.

## Importing the Embeddings ##

from flair.embeddings import WordEmbeddings

from flair.embeddings import CharacterEmbeddings

from flair.embeddings import StackedEmbeddings

from flair.embeddings import FlairEmbeddings

from flair.embeddings import BertEmbeddings

from flair.embeddings import ELMoEmbeddings

from flair.embeddings import FlairEmbeddings

#glove_embedding = WordEmbeddings('glove')

#character_embeddings = CharacterEmbeddings()

flair_forward  = FlairEmbeddings('news-forward-fast')

flair_backward = FlairEmbeddings('news-backward-fast')

#bert_embedding = BertEmbedding()

#elmo_embedding = ElmoEmbedding()

stacked_embeddings = StackedEmbeddings(

embeddings = [

flair_forward-fast,

flair_backward-fast

])

Now you might be asking – What in the world are “Stacked Embeddings”? Here, we can combine multiple embeddings to build a powerful word representation model without much complexity. Quite like ensembling, isn’t it?

We are using the stacked embedding of Flair only for reducing the computational time in this article. Feel free to play around with this and other embeddings by using any combination you like.

Testing the stacked embeddings:

# create a sentence #

sentence = Sentence(‘ Analytics Vidhya blogs are Awesome .')

# embed words in sentence #

stacked.embeddings(sentence)

for token in sentence:

 print(token.embedding)

# data type and size of embedding #

print(type(token.embedding))

# storing size (length) #

z = token.embedding.size()[0]

Step 5: Vectorizing the text

We’ll be showcasing this using two approaches.

Mean of Word Embeddings within a Tweet

We will be calculating the following in this approach:

For each sentence:

Generate word embedding for each word

Calculate the mean of the embeddings of each word to obtain the embedding of the sentence

from tqdm import tqdm ## tracks progress of loop ##

# creating a tensor for storing sentence embeddings #

s = torch.zeros(0,z)

# iterating Sentence (tqdm tracks progress) #

for tweet in tqdm(txt):   

 # empty tensor for words #

 w = torch.zeros(0,z)   

 sentence = Sentence(tweet)

 stacked_embeddings.embed(sentence)

 # for every word #

 for token in sentence:

   # storing word Embeddings of each word in a sentence #

   w = torch.cat((w,token.embedding.view(-1,z)),0)

 # storing sentence Embeddings (mean of embeddings of all words)   #

 s = torch.cat((s, w.mean(dim = 0).view(-1, z)),0)

 Document Embedding: Vectorizing the entire Tweet

from flair.embeddings import DocumentPoolEmbeddings

### initialize the document embeddings, mode = mean ###

document_embeddings = DocumentPoolEmbeddings([

                                             flair_embedding_backward,

                                             flair_embedding_forward ])

# Storing Size of embedding #

z = sentence.embedding.size()[1]

### Vectorising text ###

# creating a tensor for storing sentence embeddings

s = torch.zeros(0,z)

# iterating Sentences #

for tweet in tqdm(txt):   

 sentence = Sentence(tweet)

 document_embeddings.embed(sentence)

 # Adding Document embeddings to list #

 s = torch.cat((s, sentence.embedding.view(-1,z)),0)

You can choose either approach for your model. Now that our text is vectorised, we can feed it to our machine learning model!

Step 6: Partitioning the data for Train and Test Sets

## tensor to numpy array ##

X = s.numpy()   

## Test set ##

test = X[31962:,:]

train = X[:31962,:]

# extracting labels of the training set #

target = data['label'][data['label'].isnull()==False].values

Step 6: Building the Model and Defining Custom Evaluator (for F1 Score)

Defining custom F1 evaluator for XGBoost

def custom_eval(preds, dtrain):

   labels = dtrain.get_label().astype(np.int)

   return [('f1_score', f1_score(labels, preds))]

Building the XGBoost model

import xgboost as xgb

from sklearn.model_selection import train_test_split

from sklearn.metrics import f1_score

### Splitting training set ###

x_train, x_valid, y_train, y_valid = train_test_split(train, target,  

                                                     random_state=42,

                                                         test_size=0.3)

### XGBoost compatible data ###

dtrain = xgb.DMatrix(x_train,y_train)         

dvalid = xgb.DMatrix(x_valid, label = y_valid)

### defining parameters ###

params = {

    'colsample': 0.9,

'colsample_bytree': 0.5,

'eta': 0.1,

'max_depth': 8,

'min_child_weight': 6,

'objective': 'binary:logistic',

'subsample': 0.9

}

### Training the model ###

xgb_model = xgb.train(

    params,

    dtrain,

    feval= custom_eval,

    num_boost_round= 1000,

    maximize=True,

    evals=[(dvalid, "Validation")],

    early_stopping_rounds=30

)

Our model has been trained and is ready for evaluation! Note: The parameters were taken from this Notebook.

Step 7: Time for predictions!

### Reformatting test set for XGB ###

dtest = xgb.DMatrix(test)

### Predicting ###

predict = xgb_model.predict(dtest) # predicting

I uploaded the predictions to the practice problem page with 0.2 as probability threshold:

Word Embedding

F1- Score

Glove

0.53

flair-forward -fast

0.45

flair-backward-fast

0.48

Stacked (flair-forward-fast + flair-backward-fast)

0.54

Note: According to Flair’s official documentation, stacking of the flair embedding with other embeddings often yields even better results, But, there is a catch..

It might take a VERY LONG time to compute on a CPU. I highly recommend leveraging a GPU for faster results. You can use the free one within Colab!

2. Part of Speech (POS) Tagging with Flair

We will be using a subset of the Conll-2003 dataset, is a pre-tagged dataset in English. Download the dataset from here.

Overview of steps:

Step 1: Importing the dataset

Step 2 : Extracting Sentences and PoS Tags from the dataset

Step 3: Tagging the text using NLTK and Flair

Step 4: Evaluating the PoS tags from NLTK and Flair against the tagged dataset

Step 1: Importing the dataset

### file was uploaded manually to local environment of Colab ###

data = open('pos-tagged_corpus.txt','r')

txt = data.read()

#print(txt)

The data file contains one word per line, with empty lines representing sentence boundaries.

Step 2 : Extracting Sentences and PoS Tags from the dataset

### converting text in form of list of (words with their tags) ###

txt = txt.split('n')

### removing DOCSTART (document header)

txt = [x for x in txt if x != '-DOCSTART- -X- -X- O']

### check ### for i in range(10):

 print(txt[i])

 print(‘-’*10)

### Extracting Sentences ###

# Initialize empty list for storing words

words = []

# initialize empty list for storing sentences #

corpus = []

for i in tqdm(txt):

 ## if blank sentence encountered ##

 if i =='':

   ## previous words form a sentence ##

   corpus.append(' '.join(words))

   ## Refresh Word list ##

   words = []

 else:

## word at index 0 ##

   words.append(i.split()[0])

  

# did it work? #

for i in range(10):

 print(corpus[i])

 print(‘-’*10)

### Extracting POS ###

# Initialize empty list for storing word pos

w_pos = []

#initialize empty list for storing sentence pos #

POS = []

for i in tqdm(txt):

 ## blank sentence = new line ##

 if i =='':

   ## previous words form a sentence POS ##

   POS.append(' '.join(w_pos))

   ## Refresh words list ##

   w_pos = []

 else:

## pos tag from index 1 ##

   w_pos.append(i.split()[1])

  

# did it work? #

for i in range(10):

 print(corpus[i])

 print(POS[i])

### Removing blanks form sentence and pos ###

corpus = [x for x in corpus if x!= '']

POS = [x for x in POS if x!= '']

### Check ###

For i in range(10):

 print(corpus[i])

 print(POS[i])

We have extracted the essentials aspects we require from the dataset. Let’s move on to step 3.

Step 3: Tagging the text using NLTK and Flair

Tagging using NLTK:

First, import the required libraries:

import nltk

nltk.download('tagsets')

nltk.download('punkt')

nltk.download('averaged_perceptron_tagger')

from nltk import word_tokenize

This will download all the necessary files to tag the text using NLTK.

### Tagging the corpus with NLTK ###

#for storing results#

nltk_pos = []

##for every sentence ##

for i in tqdm(corpus):

 # Tokenize sentence #

 text = word_tokenize(i)

 #tag Words#

 z = nltk.pos_tag(text)

 # store #

 nltk_pos.append(z)

The PoS tags are in this format:

[(‘token_1’, ‘tag_1’), ………….. , (‘token_n’, ‘tag_n’)]

Lets extract PoS from this:

### Extracting final pos by nltk in a list ###

tmp = []

nltk_result = []

## every tagged sentence ##

for i in tqdm(nltk_pos):

 tmp = []

## every word ##

 for j in i:

   ## append tag (from index 1) ##

   tmp.append(j[1])

 # join the tags of every sentence #

 nltk_result.append(' '.join(tmp))

### check ### for i in range(10):

 print(nltk_result[i])

 print(corpus[i])

The NLTK tags are ready for business.

Turning our attention to Flair now

Importing the libraries first:

!pip install flair

from chúng tôi import Sentence

from flair.models import SequenceTagger

Tagging using Flair

# initiating object #

pos = SequenceTagger.load('pos-fast')

#for storing pos tagged string#

f_pos = []

## for every sentence ##

for i in tqdm(corpus):

 sentence = Sentence(i)

 pos.predict(sentence)

## append tagged sentence ##

 f_pos.append(sentence.to_tagged_string())

###check ###

for i in range(10):

 print(f_pos[i])

 print(corpus[i])

The result is in the below format:

Note: We can use different taggers available within the Flair library. Feel free to tinker around and experiment. You can find the list here.

Extract the sentence-wise tags as we did in NLTK

Import re

### Extracting POS tags ###

## in every sentence by index ##

for i in tqdm(range(len(f_pos))):

 ## for every words ith sentence ##

 for j in corpus[i].split():

   ## replace that word from ith sentence in f_pos ##

   f_pos[i] = str(f_pos[i]).replace(j,"",1)

   f_pos[i] = str(f_pos[i]).replace(j,"")

   ## removing redundant spaces ##

   f_pos[i] = re.sub(' +', ' ', str(f_pos[i]))

   f_pos[i] = str(f_pos[i]).lstrip()

### check ###

for i in range(10):

 print(f_pos[i])

 print(corpus[i])

Aha! We have finally tagged the corpus and extracted them sentence-wise. We are free to remove all the punctuation and special symbols.

### Removing Symbols and redundant space ###

## in every sentence by index ## for i in tqdm(range(len(corpus))):

 # Removing Symbols #

 corpus[i] = re.sub('[^a-zA-Z]', ' ', str(corpus[i]))

 POS[i] = re.sub('[^a-zA-Z]', ' ', str(POS[i]))

 f_pos[i] = re.sub('[^a-zA-Z]', ' ', str(f_pos[i]))

 nltk_result[i] = re.sub('[^a-zA-Z]', ' ', str(nltk_result[i]))

 ## Removing HYPH SYM (they are for symbols) ##

 f_pos[i] = str(f_pos[i]).replace('HYPH',"")

 f_pos[i] = str(f_pos[i]).replace('SYM',"")

 POS[i] = str(POS[i]).replace('SYM',"")

 POS[i] = str(POS[i]).replace('HYPH',"")

 nltk_result[i] = str(nltk_result[i].replace('HYPH',''))

 nltk_result[i] = str(nltk_result[i].replace('SYM',''))    

                 

 ## Removing redundant space ##

 POS[i] = re.sub(' +', ' ', str(POS[i]))

 f_pos[i] = re.sub(' +', ' ', str(f_pos[i]))

 corpus[i] = re.sub(' +', ' ', str(corpus[i]))

 nltk_result[i] = re.sub(' +', ' ', str(nltk_result[i]))

 

We have tagged the corpus using NLTK and Flair, extracted and removed all the unnecessary elements. Let’s see it for ourselves:

for i in range(1000):

 print('corpus   '+corpus[i])

 print('actual   '+POS[i])

 print('nltk     '+nltk_result[i])

 print('flair    '+f_pos[i])

 print('-'*50)

OUTPUT:

flair        NNP NNP NNP NNP CD

That looks convincing!

Step 4: Evaluating the PoS tags from NLTK and Flair against the tagged dataset

Here, we are doing word-wise evaluation of the tags with the help of a custom-made evaluator.

flair        NNP NN NNP NNP VBD DT JJ JJ NN VBD JJ IN PRP

Note that in the example above, the actual POS tags contain redundancy compared to NLTK and flair tags as shown (in bold). Therefore we will not be considering the POS tagged sentences where the sentences are of unequal length.

### EVALUATION FUNCTION ###

def eval(x,y):

 # correct match #

 count = 0

 #Total comparisons made#

 comp = 0

 ## for every sentence index in dataset ##

 for i in range(len(x)):

   ## if the sentence length match ##

   if len(x[i].split()) == len(y[i].split()):

     ## compare each word ##

     for j in range(len(x[i].split())):

       if x[i][j] == y[i][j] :

         ## Match! ## count = count+1

         comp = comp + 1

       else:

         comp = comp + 1

 return (count/comp)*100

Finally we evaluate the POS tags of NLTK and Flair against the POS tags provided by the dataset.

print("nltk Score ", eval2(POS,nltk_result)) print("Flair Score ", eval2(POS,f_pos))

Our Result:

NLTK Score: 85.38654023442645

Flair Score: 90.96172124773179

Well, well, well. I can see why Flair has been getting so much attention in the NLP community.

End Notes

Flair clearly provides an edge in word embeddings and stacked word embeddings. These can be implemented without much hassle due to its high level API. The Flair embedding is something to keep an eye on in the near future.

I love that the Flair library supports multiple languages. The developers are additionally currently working on “Frame Detection” using flair. The future looks really bright for this library.

Related

What Iphone Apps Support Facetime Shareplay Feature? Here’s A Complete List!

If FaceTime suddenly seems like a compelling alternative to video conferencing services like Zoom and Google Meet, it could be because of two notable feature additions – screen-sharing and SharePlay. As is the case with almost any new feature, app developers are taking their sweet time to make their apps compatible with SharePlay in FaceTime, which is designed to offer an enhanced co-watching experience. If you have upgraded your iPhone to iOS 15, you might be wondering what all apps support SharePlay and can be broadcasted to other users over FaceTime. Well, if that’s the case, we have you covered with an exhaustive list of apps compatible with FaceTime’s SharePlay feature on iPhone.

Best FaceTime SharePlay Compatible Apps in 2023 (Regularly Updated)

First things first, FaceTime’s SharePlay is much more than a nifty feature for setting up a virtual watch party with your friends. Well, you can use this new feature to listen to your favorite tracks with your buddies, invite friends to sweat it out with you to lose that pandemic fat, or even have fun gaming nights with the family. It is just the tip of the iceberg, so take a look at the complete list of SharePlay supported apps for Phone.

Best SharePlay Supported Apps for Video Streaming

To kickstart this roundup, let’s first check out the SharePlay apps that let you watch videos with your friends.

Apple TV+

As you might have already guessed, Apple TV+ supports SharePlay right off the bat. That means you can use Apple’s video streaming app to watch movies and TV shows together with friends and loved ones over FaceTime on your iPhone. You can watch Apple TV+ shows like Ted Lasso, Dickinson, The Morning Show, and others over FaceTime using SharePlay.

Disney+

Disney+ is one of the few third-party video streaming apps that supports FaceTime’s SharePlay feature. With a massive library of movies and exclusive originals from the creators of Pixar, Marvel, Star Wars, National Geographic, and more, Disney+ has got a boatload of rich content for you to binge with friends.

Install: Free, subscription starts at $7.99/month 

HBO Max

HBO Max has long been a popular video-streaming app, and well, it’s one of the few to support FaceTime’s SharePlay feature from the get-go. With an ever-growing collection of blockbuster movies, TV shows, and the exclusive Max Originals, it has enough variety of content to warrant your attention. 

Install: Free, subscription starts at $9.99/month 

Hulu

Install: Limited free trial, $6.99/month 

MUBI: Curated Cinema

Install: Limited free trial, $10.99/month 

SHOWTIME

Showtime is a one-stop platform for streaming blockbuster movies, award-winning documentaries, comedies, and even sports. Notably, it frequently adds new titles, so you will always have something new to stream during your virtual watch parties.

TikTok

Vertical short videos have become a fascinating trend these days, and TikTok deserves credit for its immense popularity. A platform with millions of creators, the short-video platform lets you explore a variety of videos from dancing and comedy to DIY. Thanks to the support for SharePlay in Apple’s FaceTime video calling app, you can now easily watch funny clips with your friends.

Install: Free

Digital Concert Hall

If you are a fan of concerts, you shouldn’t miss out on Digital Concert Hall. With this app, you can watch more than 40 live broadcasts every season. With support for SharePlay, Digital Concert Hall also lets you relish the beauty of live and on-demand concerts with your friends at home during the pandemic. 

Install: 7-day free trial, $19.99/month

Cameo

Install: Limited free version, starts at $0.99 for Cameo item 

MasterClass

Looking to learn a new skill with your friends? Well then, you should check out MasterClass, an app that offers you access to tutorials and lectures from experts in a variety of fields, randing from best-selling authors, celebrities, and musicians to scholars, politicians, as well as fashion designers, among others. So, you can get an exorbitant subscription for MasterClass and watch the tutorials with friends using the FaceTime app’s SharePlay feature.

Install: Free, All-Access pass starts at $179.99

Twitch

If you are a gaming enthusiast and would like to watch live streams for your favorite FPS shooter game or MOBA games together on Twitch, it is now possible to do so. The Twitch app on iPhone has added support for FaceTime’s SharePlay feature and allows you to watch streams together in sync and share playback controls.

Best SharePlay Supported Apps for Live Sports

If you are a sports lover and can’t live without catching up with the latest live event, you would like to have these SharePlay-supported apps at your disposal.

Paramount+

For sports lovers, Paramount+ is a go-to option for streaming live events. With this app, you can catch up with live sports, breaking news related to sports, and even stream over 30,000 episodes from hit series, including NCIS, SpongeBob SquarePlants, Survivor, and more. 

Install: Limited free trial, $4.99/month

ESPN+

When there is a discussion about the best apps to follow sports news and live events, you can’t keep ESPN out of the equation. Apart from letting you watch NFL, NBA, MLB, golf, soccer, Tennis, the app also lets you stream exclusive originals. And with support for SharePlay, you can stream all of its with friends over FaceTime.

NBA: Live Games & Scores

While NBA (official app) may not be as popular as ESPN and Paramount+, it is exactly what you should choose to keep track of the latest scores of NBA basketball matches. What’s more, it also lets you keep track of the latest news and watch in-game and post-game highlights along with friends over FaceTime.

Install: Limited free version, $9.99 for NBA conference finals

Best SharePlay Compatible Apps for Music and Podcast

There are quite a few music and podcast apps that support SharePlay.

Apple Music

Despite the stiff competition from the likes of Spotify and YouTube Music, Apple Music is one of the most popular music-streaming apps in the world. And if you are deep into the Apple ecosystem, you would be glad to know that Apple Music supports SharePlay out of the box.

Spotify

If you are on the other side of the court and use Spotify for your music streaming needs, you will be delighted to know that it supports the SharePlay feature during FaceTime calls. You can listen to music from a catalog of over 75 million songs and share playback controls with ease over FaceTime.

Install: Free, Premium subscription starts at $7.99/month

SoundCloud

Boasting a huge library of over 200 million tracks, SoundCloud is the world’s largest audio streaming platform. So, whether you like to tune into podcasts, audiobooks, jazz, or other trendy tracks, it has the variety to win you over and enjoy some amazing tracks with friends using FaceTime.

Install: Limited free version, premium starts at $5.99/month

Moon FM

Install: Paid, $4.99

Vinyls

If you are looking for a good free music player that lets you listen to your favorite songs with friends and loved ones, make sure to check out Vinyls. What makes this app worthy of your attention is the beautiful and fully customizable UI. Moreover, this app also offers cool widgets and supports AirPlay, as well as SharePlay, to enhance your music listening experience.

Install: Paid, $4.99

BetterSleep

BetterSleep (formerly Relax Melodies) can help you relieve stress and improve your sleep. With more than 200 relaxing sounds, bedtime stories, and meditation exercises, the app is designed to make you fall in love with sleep. Or, for that matter, get the better of stress – now with friends or loved ones over FaceTime.

Best SharePlay Supported Apps for Fitness and Workout

With SharePlay, you can also organize a FaceTime workout party to sweat out in sync with your buddies to get rid of the pandemic fat that we have accumulated being cooped up at home.

Apple Fitness+

Whether you are a fitness freak or a health-conscious person who wants expert tips and guides to achieve the desired goals, Apple Fitness+ could be the right option for you. Featuring a ton of workout types, including strength, pilates, HIIT, and more, it’s has enough variety to fit your specific needs. Moreover, it also offers guided mediation as well as video and audio sessions that you can follow over FaceTime using SharePlay.

Install: Free app, $9.99/month subscription

SmartGym

SmartGym is what you should pick to create fully personalized workouts and keep track of your progress. The app has curated collections of workouts based on different equipment and goals. So, whether you wish to lose a lot of pounds or strengthen your muscle, this app can live up to your expectation.

Mapless Walking Directions

Here is a pretty simple navigation app that shows which direction you need to go and how far away your destination is. And thanks to its SharePlay integration, you and your friends can comfortably walk on your path without having to check the turn-by-turn directions again and again.

Install: Limited free version, $0.99/month

Workout Plan Bot

If you are on the lookout for a smart workout tracker, I recommend trying out the Workout Plan Bot. With this app, you can create workout routines based on your goal and keep an eye on your progress with ease. Moreover, it automatically syncs your data with Apple Health and also provides the flexibility to share your workout routines with friends over SharePlay.

Install: Limited free version, $0.99/month

BetterMe: Health Coaching

Install: Limited free version, $9.99/week

Best SharePlay Supported Apps for Games

Nnot just movies or music, you can also play games together with friends over FaceTime using the SharePlay feature. Here are some cool games that support SharePlay and you would enjoy playing with friends:

SharePlay Guessing Game

If you are fond of fun-loving guessing games, chances are you would enjoy playing the SharePlay Guessing Game. The gameplay is pretty easy and requires you to answer questions through voice. Hence, you won’t have to fiddle with the keyboard to input answers during this FaceTime party game.

Install: Limited free version, $2.99 for extra pack

Shhh!

Install: Limited free version, $2.99 for full version

Install: Paid, $1.99

Kahoot! Play & Create Quizzes

As someone who never gets tired of playing quiz-based games, I’m very fond of Kahoot! The best part about quiz games is that they are not only plenty of fun but offer a learning experience as well. And that’s why Kahoot! may appeal to everyone at the FaceTime SharePlay game party.

Piano with Friends 

If quizzes and word guessing games are not your cup of tea, you could try your hand at music creation using the Piano with Friends app. It comes with SharePlay support and allows you to share a resizable 88-key keyboard with friends over Facetime on your iPhone.

Install: Paid, $1.99

Best SharePlay Apps for Tasks and Reminders

With these task and reminder apps, you will have an enhanced collaboration experience over SharePlay with your friends or colleagues.

Bluebird – Focus Timer & Tasks

Time management is key for enhancing productivity. And for this exact purpose, Bluebird can be a great asset for you. By combining task management with focus mode, this iPhone app allows you to take control of shortcomings and prevent hurdles from coming in the way of your tasks. You can use it for virtual group study sessions with SharePlay over FaceTime calls.

Doneit: Reminders, To Do List

Doneit is an efficient to-do list and task manager app. With the help of this app, you can create as many tasks as you need and keep track of the progress with timely reminders. What’s more, thanks to SharePlay support, the Doneit app allows you to securely share and collaborate on tasks with your friends over FaceTime on iPhone.

Install: Limited free version, $1.99/month for the premium version

Flow: Sketch, Draw, Take Notes

Apart from being a super handy app for drawing and sketching, Flow can also double up as a useful note-taking app. As it supports Apple Pencil, you can use the digital pen to create stunning works of art and also take notes with precision.

Install: Limited free version, $1.99/month for the premium version

Other Notable SharePlay Apps You Should Check Out Night Sky

Install: Limited free version, $4.99/month for the premium version

Carrot Weather

While there is no dearth of iPhone weather apps in the App Store, Carrot Weather stands out in terms of accuracy and rich interface. So, if you are hunting for a powerful weather app for your Apple Device, make sure to check out this 2023 Apple Design Award winner. But, why would you want to screen share a weather app over FaceTime is beyond me.

Install: Limited free version, $4.99/month for the premium version

Apollo for Reddit

Apollo has long been my favorite Reddit app on iPhone, so it’s good to see the app getting onboard with the SharePlay integration. If you are a Redditor, you may already be familiar with the app’s beautiful interface and fully customizable gestures. And well, you can now use Apollo for Reddit to browse through memes together with friends.

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Install: Limited free version, $9.99/month for the premium version

List of Apps that Support SharePlay on Apple’s FaceTime

What’s Your Favorite Ipad Feature?

I can still remember a few things about owning the original iPad. It was definitely a spur-of-the-moment purchase at the time, a piece of technology that had absolutely no place in my daily routine at the time. But I sure did love that ling. It was big and kinda bulky, and yet still portable. It was mainly a great way to play some mobile games and watch stuff.

Here we are all these years later, so many different versions of the iPad later, and not much has changed in that department. Except basically everything. Sure, there are still some folks out there who believe the iPad –no matter the variant– is still nothing more than a glorified iPhone with a bigger screen, so therefore it’s nothing more than a tool to play some games and watch some stuff. And, honestly, that’s probably true for a lot of iPad owners — and there’s nothing wrong with that!

The iPad mini, both in its previous design and its current “all-screen” option, is a great way to just sit somewhere and peruse the internet. It’s an especially great way to play a lot of different iOS/iPadOS games. And, yeah, if you’re wanting to watch something it can help with that, too. But the iPad mini, especially now, is also a great way to take notes and read books. (Still probably not a direct replacement for an eReader, for fans of eReaders, though.)

I don’t necessarily think we, or Apple, need to have so many different iPad variations. But I can’t argue with the fact that they definitely fill certain use cases better than others. If anything, I think maybe Apple could live with dropping the price of the iPad Air and replacing the standard iPad altogether.

I do want to hear what your favorite iPad model is, but, mostly, I want to know about features. I want to hear from you about the feature, or features, that won you over in the first place. What about the iPad that you currently own, and use on a regular basis, won you over in the first place? And, what keeps you coming back? Is it those initial points of interest, or did something else about the iPad surprise you at some point later, after using it for some time?

I’m one of those crazy people that think an iPad is an incredibly powerful tool. I think it’s a great second screen for getting stuff done — even before there were apps to help with that, and even before Apple built the feature into the software. I think an iPad can be a remarkably useful product, for a range of needs, and it can also be something that just lets me play games and watch movies, too.

The new iPad mini is probably my favorite iPad right now. I love the portability (which is definitely a feature), even if after using a 12.9-inch iPad Pro I miss that thing’s screen and display quality. But I love how handy the iPad mini is for taking notes on the fly, and working as my eReader so I don’t have to constantly use my phone. I wish Apple would engineer a keyboard case for the thing, though. That sounds crazy, I know, but I’d at least give it a shot.

I think my favorite feature is Scribble, though. I love being able to just write in an open text area and watch as the iPad automatically translates the written content to typed words. It’s really handy, especially with the new Quick Note feature, to just get something written down and have it saved automatically. It’s a feature I find myself using almost every single day.

But, what about you? What are your favorite iPad features?

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