You are reading the article Combine Multiple Bookmarklets And Use It From Any Computer updated in November 2023 on the website Hatcungthantuong.com. 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 December 2023 Combine Multiple Bookmarklets And Use It From Any ComputerHow to Combine Multiple Browser Bookmarklets
2. Go to the Bookmarklet Combiner website. You will see the following interface:
Copy the entire code shown in the “Location” field of the boomarklet.
Soumen is the founder/author for Ampercent, a tech blog that writes on computer tricks, free online tools & software guides.
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Do you need a quick and easy way to mash up multiple RSS feeds into a single feed? There are many reasons why you may need to combine multiple RSS feeds, such as:
You may write for or own more than one blog and you want to make it easy for readers to subscribe to all of your RSS feeds at once.
You may want to combine multiple blog feeds into a single RSS feed for better organization, and to make them easier to read in your favorite RSS feed reader or by email.
There are various tools available for this task, but the best and easiest is ChimpFeedr. It’s a simple tool that combines the RSS feeds that you choose into a single feed.
Head over to the ChimpFeedr website and begin adding your RSS feeds one-by-one.
2. You can choose to resize the images in your feeds if you’d like. You can scale down images larger than the width that you choose (in pixels).
4. You’ll be given a URL for your aggregator RSS feeds, which you can use as you please. A good RSS to email tool would be great if you want to read your feed by email.
You may even consider IFTTT, the awesome Web automation tool. You can create a recipe that will send your ChimpFeedr RSS feed to your email, or even to another service like Evernote, Pocket, Dropbox, and more.
Combining your RSS feeds into a single feed is as easy as that!
Charnita has been a Freelance Writer & Professional Blogger since 2008. As an early adopter she loves trying out new apps and services. As a Windows, Mac, Linux and iOS user, she has a great love for bleeding edge technology. You can connect with her on Facebook, Twitter, Google+, and LinkedIn.
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This article was published as a part of the Data Science Blogathon
Since the initial breakthrough in Computer Vision achieved by A. Krizhevsky et al. (2012) and their AlexNet Network, we have definitely come a long way. Computer Vision has since been making its way into day-to-day human lives without even us knowing about it. The one thing Deep Learning Algorithms need is data, and with the progress in portable camera technology in our mobile devices, we have it. A lot more, and a lot better. With great data, comes great responsibility. Data Scientists and Vision Engineers have been using data to create value in the form of awesome Vision applications.
Computer Vision has found applications in very diverse and challenging fields and these algorithms have been able to assist, and in some cases, outperform human beings. Be it Medical Diagnosis (Biology), Production Automation (Industry), Recommender Systems (Marketing), or everyday activities like driving or even shopping, Vision Systems are everywhere around us. In this blog, I am going to discuss some applications of computer vision, and how companies are implementing scalable vision systems to solve problems and generate value for their customers.
A timeline of some seminal computer vision papers against milestone events in computer vision product applicationsSelf Driving Vehicles
Tesla uses 8 cameras on the vehicle to feed their models, and the models do pretty much everything that can be done using video data, to guide the vehicle. The granular sub-applications that Tesla Autopilot needs to function are:
Detection and Recognition of Objects (Road Signs, Crosswalks, Traffic Lights, Curbs, Road Markings, Moving Objects, Static Objects) (Object Detection)
Following the Car Ahead (Object Tracking)
Differentiating between Lanes/ Lanes and Sidewalk / Switching Lanes (Semantic Segmentation)
Identifying Specific Objects (Instance Segmentation)
Responding to events (Action Recognition)
Smart Summon (Road Edge Detection)
Evidently, this is an extremely multitasked setting, where there is a need to know a lot about the scene at once. That is why the tech stack is designed in such a way that there are multiple outputs for a given input sequence of images. The way it is implemented is that for a set of similar tasks, there is a shared backbone, with a set of tasks, at the end, all of which give a specific output.
Some tasks require features from specific camera feeds to make a prediction, so each camera has its own HydraNet trained for camera-specific tasks. But there are more complicated tasks like steering the wheel, depth estimation, or estimating road layout, which might need information from multiple cameras, and therefore, features from multiple HydraNets at the same time to make a prediction. Many of these complicated tasks can be recurrent, adding another layer of complexity to the network.
Summing it up, Tesla’s Network consists of 8 HydraNets (for 8 cameras), each responsible for specific tasks. In addition to that, the features from these HydraNets go into another run of processing which requires camera interactions with each other, and spread over time, to derive meaningful insights and is responsible for more complex tasks.
According to Tesla, there are nearly a hundred such tasks. This modular approach has many benefits for Tesla’s specific use case:
It allows the network to be specifically trained for specific tasks. The network is subsampled for that specific task and is then trained for it.
It drastically reduces the overall number of trainable parameters, thus amortizing the process.
It allows certain tasks to be run in shadow mode while the overall system performs as usual.
It allows for quicker improvements to the overall network, as updates can be installed in parts rather than overall.
What Tesla has done well, and many other efforts at autonomous driving failed to achieve is data generation. By giving more and more products in the hands of consumers, Tesla now has a large source of quality data. They are able to capture disagreements between the Human and the Autopilot by deploying models in live mode as well as shadow mode. In this way, they have been able to improve their models by inference capabilities on real-world data, capturing disagreements and mistakes made by both, the Human and the Autopilot. As long as they receive well-labeled data, their models keep on improving with minimal effort.Medical Imaging
Arterys is one of such leading players, reducing subjectivity and variability in medical diagnosis. They have used Computer Vision to reduce the downtime to image blood flow in the heart, which took hours initially, to minutes. This allowed cardiologists to not only visualize, but also quantify blood flow in the heart and cardiovascular vessels, thus improving the medical assessment from an educated guess to directed treatment. It allowed cardiologists as well as AI to diagnose heart diseases and defects within minutes of MRI.
But why did it take hours for scans to generate flows in the first place? Let’s break this down.
Multiple in vivo scans are done to capture 3D Volume cycles over various cardiac phases and breathing cycles.
Iterative Reconstruction Methods on MRI data to evaluate flow increases reconstruction times automatically.
Along with 4D flow generation, object detection algorithms (Fast R-CNN(2023), R-FCN(2023)) in Arterys’ tech stack help to identify unidentifiable abnormalities and contours in the heart, lungs, brain, and chest scans. It automatically indexes and measures the size of the lesions in 2D and 3D space. Image Classification Networks help to identify pathologies like fracture, dislocation, and pulmonary opacity. Arterys trained its CardioAI network, which can process CT and MRI scans, on NVIDIA TITAN X GPUs running locally and on Tesla GPU accelerators running in Google Cloud Platform. Both were supported by the Keras and TensorFlow deep learning libraries. Inference occurs on Tesla GPUs running in the Amazon cloud.
Though these insights are very important for the medical professional, their availability to the medical professional can cause bias in the medical professional’s assessment of the case. Arterys mitigates this problem by flagging certain cases for attention but not specifying the exact location of the abnormality in the scan. These can be accessed once the specialist has made an unbiased assessment of the case.
Cloud-based deployment of its stack has allowed Arterys to provide reconstructions as well as invaluable visual and quantifiable analysis to its customers on a zero-footprint web-based portal in real-time. Computer Vision’s biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. The high-value applications are in rural and medically underdeveloped areas where physicians or specialists are hard to come by.Visual Search
Visual Search is a search based on images rather than text. It heavily depends on computer vision algorithms to detect features that are difficult to put into words or need cumbersome filters. Many online marketplaces, as well as search tools, have been quick to adopt this technology and consumer feedback for the same has been strongly positive. Forbes has forecasted that early adopters of visual search are projected to increase their digital revenue by 30%. Let us talk about a few early adopters and even late adopters to the visual search technology and how they have gone about implementing it.
Pinterest’s Visual Lens added a unique value to their customers’ experience wherein they could search for something difficult to put in words. Essentially, Pinterest is a giant Bipartite Graph. On one side are objects, which are pinned by the users, and on the other side are boards, where mutually coherent objects are present. An edge represents pinning an object (with its link on the internet) on a board that contains similar objects. This structure is the basis of Pinterest’s data and this is how Lens can provide high-quality references of the object in similar as well as richly varied contexts. As an example, if Lens detects Apple as an object, it can recommend Apple Pie Recipe and Apple farming techniques to the user, which belong to very separate domains. To implement this functionality, Pinterest has separated Lens’ architecture into two separate components.
In the first component, a query understanding layer has been implemented. Here, certain visual features are generated like lighting conditions and image quality. Basic object detection and colour features are also implemented as a part of the query understanding layer. Image Classification algorithms are also used to generate annotations and categories for the queried images.
In the second component, results from many models are blended to generate a continuous feed of results relevant to the queried image. Visual search is one of these models which returns visually similar results where the object and its context are strongly maintained. Another one would be an object search model which gives results that have the given object in the results. The third model uses the generated categories and annotations from the query understanding layer to do a textual image search to get the results. The blender does a very good job of dynamically changing the blending ratios as the user scrolls through the search results. Confidence thresholds are also implemented such that low confidence results from the query understanding layer are skipped while generating the final search results. Evidently, the basic technology supporting Pinterest Lens is object detection. It supports Visual Search, Object Search, and Image Search. Let’s understand in detail how object detection is done at Pinterest.
In step 1, the input image is fed into an already trained CNN, which identifies regions that might contain objects, and converts the image into a feature map. Once the feature map is generated, in step 2, A Region proposal network is used to extract sub-mappings of various sizes and aspect ratios from the original feature map. These sub-mappings are fed into a binary softmax classifier which predicts whether a given sub-region contains an object. If a promising object is found, it is indexed into a list of possible objects, along with the region bounded sub-mappings, which are then classified into object categories by an object detection network.
This is how Lens has been able to use Computer Vision and Pinterest’s bipartite graph structure to generate highly relevant and diverse results for visual search.Gaming
Eye-tracking is a technology that makes it possible for a computer system to know where a person is looking. An eye-tracking system can detect and quantify the presence, attention, and focus of the user. Eye-tracking systems were primarily developed for gaming analysis to quantify the performance of top gamers; but since then, these systems have found utility in various devices like consumer and business computers.
Tobii is the world leader in eye-tracking tech and the applications they support have moved from gaming to gesture control and VR. Data acquisition by Tobii is done via a custom-designed sensor which is pre-set on the device where eye-tracking information is needed. The system consists of projectors and customised image sensors as well as custom pre-processing with embedded algorithms. Projectors are used to create an infrared light-map on the eyeballs. The camera takes high-resolution images of the eyeballs to capture the movement pattern. Computer Vision algorithms are then used to map the movement of eyeballs from the images onto a point on the screen, thus generating the final gaze point. The stream of temporal data thus obtained is used to determine the attention and focus of the subject.
The human and economic cost of workplace injuries around the world is a staggering $250 billion per year. With AI-enabled intelligent hazard detection systems, workplaces prone to a high level of onsite injuries are realizing the decrease in number as well as the severity of injuries. Imagine a resource that works 24/7 without fatigue and keeps a watchful eye on whether safety regulations are being followed in the workplace!
Intenseye is an AI-powered employee health and safety software platform that helps the world’s largest enterprises to scale employee health and safety across their facility footprints. With real-time 24/7 monitoring of safety procedures, they can detect unsafe practices in the workplace, flag them and generate employee level safety scores along with live safety-norm violation notifications. Assistance is also provided in operationalising the response procedures, thus helping the employer in being compliant with the safety norms. Along with normal compliance procedures, they have also developed Covid-19 compliance features which help in tracking whether covid appropriate norms like masking and social distancing are being followed in the workplace.
The product is implemented on two levels. The basic driver for the product is Computer Vision. A big challenge for the org was to implement real-time predictions from live video streams. This is inherently a slow process and requires parallelisation and GPU computation to achieve due to the nature of the data pipeline. The vision systems employed range from anomaly detection to object as well as activity detection. Finally, the predictions generated are aggregated to rapidly create analysis, scores, and alerts on the suite available with the EHS professionals in the workplace who can ensure compliance from the workers.
Intenseye has developed general-purpose suites for workplaces, like PPE Detection, Area Controls, Vehicle Controls, Housekeeping, Behavioural Safety, and Pandemic Control measures. With their AI-based inspection system, Intenseye has been able to add a lot of value to the businesses they support. Along with saved lives, there have been decreased costs in damages, a boost in productivity, improved morale, and gain in goodwill for their clients.Retail Stores
In-aisle innovation is shifting how we perceive the future of retail, opening the possibilities of what can be done to shape customer experiences. Computer vision is posed to tackle many retail store pain points and can potentially transform both customer and employee experiences.
Amazon opened the doors on its first AmazonGo store in Seattle in January 2023 after a year of testing its Just Walk Out technology on its employees at its headquarters. This concept creates an active shopping session, links the shopping activity to the amazon app, and allows the customer to have a truly hassle-free experience. It eliminates the need to group with other buyers at checkout points to make the purchase, thus creating a unique value proposition in the current pandemic stricken world.
How does Amazon do it? The process which is primarily driven by Computer Vision can be divided into a few parts:
Data Acquisition: Along with an array of application-specific sensors (pressure, weight, RFID), the majority of data is visual data extracted from several cameras. Visual data ranges from images to videos. Other data is also available to the algorithm, like amazon user history, customer metadata, etc.
Data Processing: Computer Vision algorithms are used to perform a wide array of tasks that capture the customer’s activity and add events to the current shopping session in real-time. These tasks include activity detection (eg. article picked up by customer), object detection (number of articles present in cart), image classification (eg. customer has a given product in the cart). Along with tracking customer activity, visual data is used to assist the staff in other store-specific operations like inventory management (object detection), store layout optimisation(customer heat maps), etc.
Charging the customer: As soon as the customer has made their purchase and moves to the store’s transition area, a virtual bill is generated on the items present in the virtual cart for the customer’s current shopping session. Once the system detects that the customer has left the store, their Amazon account is charged with that purchase.
AmazonGo is a truly revolutionary use of computer vision and AI. It solves a very inherent problem for the in-store retail customers, assisting staff, improving overall productivity, and generating useful insights from data in the process. Though, it still needs to make economic sense in the post-pandemic world. There are privacy concerns that also need to be addressed before this form of retail shopping is adopted by the larger world.Industrial Quality Control
The ability of Computer Vision to distinguish between different characteristics of products makes it a useful tool for object classification and quality evaluation. Vision applications can sort and grade materials by different features, such as size, shape, colour, and texture so that the losses incurred during harvesting, production, and marketing can be minimized.
The involvement of humans introduces a lot of subjectivity, fatigue, delay, and irregularity in the quality control process for a modern-day manufacturing line/sorting line. Machines are able to sort unacceptable items better than humans 24/7 and with high consistency. The only requirement is a robust computer vision system. Vision systems are being implemented for the same on a large scale to push products in the refurbished market or improve on manufacturing shortcomings.
Let’s solve the problem of detecting cracks in smartphone displays. The system needs a few important things to function as intended.
Data: Quality data is imperative for the success of any machine learning system. Here, we require a camera system that captures the screen at various angles in different lighting conditions.
Labels: We might want to eliminate human shortcomings in the process, but quality labels generated by humans under normal working conditions are crucial for the success of a vision system. For a robust, large-scale process, it is best to employ multiple professional labellers and let them agree on different labelling results, thus eliminating subjectivity from the process.
Modelling: Models must be designed and deployed keeping in mind the throughput required for a given sorting line. Simple classification/object detection models are enough for detecting cracks. The main focus should be on prediction time which will be different for different sorting lines.
Inference and Monitoring: Models can be initially deployed in shadow mode where their performance is evaluated against human workers on live data. They can be deployed if the performance is acceptable, otherwise, another modelling iteration can be adopted. Data Drift must be monitored manually / automatically along with model performance when data drift is high. Retraining should be done when results are not acceptable.
Many manufacturing lines have implemented automated control systems for screen quality, be it televisions, phones, tablets, or other smart devices. Companies like Intel are also providing services to develop such systems which provide great value to many businesses.
Another quality control application using vision systems has been launched by food technology specialist, OAL, who have developed a vision system, April Eye, for automatic date code verification. The CV-based system achieves full automation of the date-verification process, removing the need for a human operator. They have reduced the risk of product recalls and emergency product withdrawals (EPWs) which are majorly caused by human error on packaging lines. The product is aimed at solving mistakes that cost food manufacturers £60-80 million a year. The system has managed to increase throughput substantially to 300 correct packs a minute. The precision of the system is also highly acceptable. A neural network trained on acceptable and unacceptable product date codes is used to generate predictions on live date codes in real-time. The system ensures that no incorrect labels can be released into the supply chain, thus protecting consumers, margins, and brands.
Computer Vision adds value to quality control processes in a number of ways. It adds automation to the process, thus making it productive, efficient, precise, and cost-effective.
With a deeper understanding of how scalable vision-based systems are implemented at leading product-based organisations, you are now perfectly equipped to disrupt yet another industry using computer vision. May the accuracy be with you.References
April EyeAbout me
For immediate exchange of thoughts, please write to me at [email protected]
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What to know
Midjourney Remaster is a new feature that enhances the quality of old images using a new algorithm that focuses on coherence and detail.
Remaster option can be accessed when creating images on older versions of Midjourney, i.e., v3 or older (at the time of writing).
You can either remaster one of the generated images or create an image using the experimental parameter “–test –creative” manually.
When you enter your ideas on Midjourney, the AI tool creates different samples of images for you to select. Based on the results generated, you can upscale or make variations to one of the images or refresh the whole bunch with a brand-new set of images. In addition to these tools, Midjourney also offers a Remaster function that lets you rework an image created by running it through more algorithms.
In this post, we’ll explain what Remaster on Midjourney is all about and how you can use this feature.
Related: Midjourney Cheat Sheet: Become a Pro at Using Midjourney!
What is Midjourney Remaster?
Midjourney Remaster is a new feature that allows users to enhance the quality of their old images, especially those that were created with older versions of Midjourney. It accomplishes this by employing a new algorithm that is more attentive to coherence and detail.
Remaster can take your old images and make them look like new. It can sharpen the details, remove noise, and correct colors. It can even add new details, like hair or fur.
Related: Midjourney V5: How to Use It
The Remaster function only works when you create images on Midjourney’s older versions. At the time of writing, Midjourney runs on version 4; so if you created images using v3, or older models, you will be able to use the Remaster option to generate an enhanced version of the original image. Being an experimental feature, the remastered image may either look more refined or may entirely change the elements present in the original image.
Related: Can Midjourney Make NSFW?
How to use Remaster on Midjourney
There are two ways you can use the Remaster function inside Midjourney – one is using the Remaster button that will be accessible when you upscale your preferred image and another is by entering certain prompts inside Midjourney.
Method 1: Using Remaster option
The option to remaster images you generate on Midjourney is only available when you create them using an older version of the AI tool. This is because Remaster runs the work created on the older version and processes it through the algorithms of the current version in order to rework it. So, to access the Remaster option, you can use a prompt that looks like this:
/imagine [art description] --v 3
Notice the “–v 3” prompt we added at the end? This is to make sure Midjourney is using version 3 of its AI model instead of the current version (v4, at the time of writing). You can use older models as well to generate your desired set of images.
You can then expand the upscaled remastered image and see how it compares to the original version of the image. Here’s an example of the remaster option we used when creating “chromolithography of Aglais lo” (Aglais lo is a rare species of butterfly).
Related: Can Midjourney Images Be Used Commercially? Midjourney License Explained
Method 2: Using prompts to remaster manually
If you don’t wish to use Midjourney’s older version to remaster images, you can directly use the remaster function using additional prompts that you’ll have to enter manually when tying your input prompt. Remastered images can be generated using the “–test –creative” prompt that you can enter alongside the input. For reference, you can follow the syntax below to generate a remastered image of your concept:
/imagine [art description] --test --creative
The upscaled image should now show up on the screen. You can expand it and save it on your device from here.
If you want Midjourney to rework your idea once again, you can repeat the same prompt as input, and upon each run, you should see different iterations of your concept. You can also add other experimental parameters like “–beta” and “–testp” to get more variations to the image you want to generate.
Related: 3 Ways to Upload an Image to Midjourney
I cannot access the Remaster option in Midjourney. Why? And how to fix
The Remaster option on Midjourney is an experimental feature, meaning it may not work best every time you use it, or on some occasions, won’t even show up as an option. If you’re unable to access the Remaster button:
Make sure your input prompt includes the parameter “–[version number]”; for eg. “–v 3”. This is important because Midjourney can only remaster those images that were created using its older versions. If you don’t include this parameter at the end of your input prompt, images will be created using Midjourney’s current version and these images cannot be remastered as they have already been processed through the newest version’s algorithms.
Some images/art simply won’t show the Remaster option. This could be because Midjourney wasn’t able to create or process another iteration of the concept you entered.
If you entered the “–test –creative” parameters manually, Remaster wouldn’t show up as an option as these parameters themselves are creating remastered images on Midjourney.
That’s all you need to know about Midjourney’s Remaster option.
In the not-so-distant past, fully-featured videoconferencing was merely a pipe dream for small businesses and startups. Services from the likes of Polycom and LifeSize can cost tens of thousands of dollars per month, which is well beyond the budget of most SMBs. Luckily—as Bob Dylan so aptly put it—the times they are a-changin’. Now, offerings such as Google Hangout deliver video chat completely for free. And for companies that need a more fleshed out—and yet still affordable—option, there’s the Tely Labs TelyHD, a $499 hardware solution that is a simple as it is feature-packed. (A more basic consumer model is available for $249.)
On the surface, the TelyHD looks like a hefty webcam with a sleek and simple design. The cylindrical unit measures about a foot long and a couple of inches in diameter; it fit right in attached to the top of our 50-inch HDTV. Two grille-covered speakers on either end provide more than adequate volume during calls. The back of the device features an SD card slot, a USB port, an Ethernet port, a Mini HDMI port, and a power input. The box also includes the necessary telyHD remote, a simple affair with a five-way control, a mute button, and an end call key. The Android-based interface has some quirks, but is relatively straightforward.
As evidenced by the simple design, setting up the telyHD could not be easier. All in all, it took us about 10 minutes to get the unit up and running—no computer required. Simply attach the the power cord to an outlet, connect the HDMI cable to any HD TV, follow the oncreen setup prompts and you’re good to go. Although it’s not necessary due to the camera’s built-in Wi-Fi, we’d also recommend using a direct Ethernet connection if your TV is close enough to your router (details on why below). In addition, the UI was confusing at times, with the main button not always taking us back to the main menu, an inconsistency that was a minor annoyance.
The TelyHD Business Edition includes a whole host of features. Namely, six parties in varying locations can join in on a video call, whereas most competitors only allow between two to four remote participants. That makes it a better solution than the close-up meetings that smaller webcams and consumer conferencing apps enable. And of course—like its consumer-centric brother—the system allows for desktop sharing and document collaboration. It also creates an Internet-connected TV, so you can browse the Web and share information with participants. In addition, it’s Skype-certified, so not only will it automatically import all of your contacts, but anyone using a Skype-enabled device can connect with you via your TelyHD. Finally, the system is built on the Android OS foundation, which leaves it open to potential updates and third-party development in the future.
We put the TelyHD to the test by conferencing in three contacts in total. The 720p HD video was apparent, with all participants showing up crisp and clear. One thing to be aware of, though, is your subjects’ lighting and distance from the camera , although this is a concern with any videoconferencing units we’ve used. The one notable issue we ran into was so-so audio with some dropouts when we depended on using the Wi-Fi connection. Switching to Ethernet seemed to solve the problem, and that is recommended.Bottom line
At the end of the day, the TelyHD Business Edition is a solid solution for SMBs on a limited budget. Of course, it’s not going to cost just $500. For one, most offices will require more than one unit. Also, there is a $199 annual subscription fee that goes into effect after the first year. Still, given the ease of use, we think this a reasonable price to pay to keep your business moving.
Microsoft Sway has been available for years, but remains one of Microsoft’s best-kept secrets. The digital storytelling app provides a quick way to create beautiful, animated presentations that are automatically tailored for different devices.
Unlike PowerPoint, there’s not much of a learning curve to Sway. Think of Microsoft Sway as PowerPoint for people who don’t want to learn PowerPoint. In fact, Sway doesn’t even want you to call them “presentations.” You’ll be creating “Sways.”
Table of ContentsIs Microsoft Sway Free?
Microsoft Sway is a web app that’s free for anyone with a Microsoft account. Go to Sway on your browser and login with your Microsoft account. If you’re using Sway as part of Microsoft 365, you’ll have access to a few extra features that people using a free account won’t have, like removing the footer and adding password protection to your Sway presentation.
Microsoft 365 users enjoy higher limits to the number of Sway elements they can use in each Sway they create.
Again, these limits are per Sway presentation. The free account will likely suffice for most users.How You Could Use Sway
A presentation for work
A newsletter for clients
A slideshow of embarrassing photos for a friend’s Zoom birthday party
A compelling story on any topic you wish
A good first step is to look through the templates that Sway provides or “Get inspired by a featured Sway” and view some great examples of what you can do with the app. Alternatively, you can search for a topic, and Sway will create an outline for you to follow. Don’t you wish PowerPoint would do that for you?How to Create and Design a Sway
The Sway workspace is divided into two tabs: Storyline and Design.
Since your final Sway isn’t likely to be a series of slides (although you will have that option), but rather a single, flowing web page that you’ll navigate through by scrolling (either top to bottom or left to right), think of your presentation as a trip you’ll be taking viewers on from start to finish.Sway’s Storyline Workspace
Select Create New to begin a Sway from scratch, or select Start from topic to let Sway create an outline for you. Alternatively, you can begin by uploading a PDF, Word, or PowerPoint document, and Sway will use it as a template.
In this case, we’ll search for a topic and select the Create outline button. Sway will create the framework of your presentation for you.
Sway has automatically given the Sway a title and content cards which you can edit at any time. Delete any card by selecting the trash icon on the card you want to remove.
You add content to your Sway by adding cards to the Storyline, and you can rearrange cards at any time with Sway’s drag-and-drop controls.
Another way to add content to your Sway is by searching for content on your computer or on the web. From the menu bar, select Insert.
From there you can search a variety of sources for content to add to your Sway.
Select a content source and then type a word or phrase into the field marked Search sources. Finally, select the magnifying glass icon or press Enter. Check the Creative Commons Only box to restrict the results to content that doesn’t require a license to use.Card Options
Cards in the Storyline workspace offer a number of options depending on what type of content they hold. Image cards allow you to format the text of the image’s caption, choose the Focus Points on your image, and choose how much you want to emphasize that card.
Setting focus points is important because it helps Sway choose how to position the image. Select the most important part(s) of the image, and Sway will determine the best position for the image depending on your device and the style you choose.
You can see previews of how your content will look on a computer screen or a mobile device.
Text cards also provide options for text formatting, linking, and emphasis.Sway’s Design Workspace
The Design workspace is where you can control the look and feel of your Sway. Select the Design tab from the menu.
Then select Styles.
You’ll always see a preview of how your Sway will appear to others in the Design workspace.
If you’re feeling uninspired, select the Remix button to let Sway choose the design and layout for you.
Select the Play button to get the full experience.How to Share Your Sway
The Share button gives you several ways to share your Sway.
You can generate a view or edit link or share to Facebook, Twitter, or LinkedIn, or you can get the code to embed the Sway on a website.Save Time and Impress Others with Microsoft Sway
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