I sure do love little reminders about HTML semantics, particularly semantics that are tougher to commit to memory. Scott has a great one, beginning with this markup:
<p>I am a paragraph.</p>
<span>I am also a paragraph.</span>
<div>You might hate it, but I'm a paragraph too.</div>
<ul>
<li>Even I am a paragraph.</li>
<li>Though I'm a list item as well.</li>
</ul>
<p>I might trick you</p>
<address>Guess who? A paragraph!</address>
You may look at that markup and say “Hey! You can’t fool me, only the
elements are “real” paragraphs!
You might even call out such elements as divs or spans being used as “paragraphs” a WCAG failure.
But, if you’re thinking those sorts of things, then maybe you’re not aware that those are actually all “paragraphs”.
It’s easy to forget this since many of those non-paragraph elements are not allowed in between paragraph tags and it usually gets all sorted out anyway when HTML is parsed.
The accessibility bits are what I always come to Scott’s writing for:
Those examples I provided at the start of this post? macOS VoiceOver, NVDA and JAWS treat them all as paragraphs ([asterisks] for NVDA, read on…). […] The point being that screen readers are in step with HTML, and understand that “paragraphs” are more than just the p element.
This won’t be easy. Tumblr hosts over half a billion blogs. We’re talking about one of the largest technical migrations in internet history. Some people think it’s impossible. But we say, “challenge accepted.”
Half a billion blogs. Considering that WordPress already powers somewhere around 40% of all websites (which is much, much higher than 500m) this’ll certainly push that figure even further.
I’m sure there’s at least one suspicious nose out there catching whiffs of marketing smoke though I’m amicable to the possibility that this is a genuine move to enhance a beloved platform that’s largely seen as a past relic of the Flickr era. I loved Tumblr back then. It really embraced the whole idea that a blog can help facilitate better writing with a variety of post formats. (Post formats, fwiw, are something I always wished would be a WordPress first-class citizen but they never made it out of being an opt-in theme feature). Tumblr was the first time I was able to see blogging as more than a linear chain of content organized in reverse chronological order. Blog posts are more about what you write and how you write it than they are when they’re written.
I was about to say this “could” be a neat opportunity, but nay, it’s a super interesting and exciting opportunity, one where your work is touching two of the most influential blogging platforms on the planet. I remember interviewing Alex Hollender and Jon Robson after they shipped a design update to Wikipedia and thinking how much fun and learning would come out of a project like that. This has that same vibe to me. Buuuut, make no illusions about it: it’ll be tough.
Generative Pre-Trained Transformer models are game-changing technology in artificial intelligence, more so in natural language processing. OpenAI develops models aimed at understanding and generating human-like text, taking meaning from the previous context words to predict the next word in the sequence.
The underlying architecture of GPT models is a transformer that scales to deep models and huge datasets for achieving extreme levels of language understanding and generation. It all began with GPT-1, followed by more advanced versions like GPT-2 and GPT-3.
They were drastically improving the model size, training data, and performance capabilities with each new iteration. They have been trained on diverse, large datasets—books, articles, websites, and so on—so a lot of things become possible, such as the completion of texts, translations, summarization, conversational AI, and so on.
Advantages of Building GPT Models
Versatility
Among the primary advantages of Generative AI models is their flexibility. Their ability to fine-tune their performance in various tasks across huge fields is second to none. Be it chatbots, content data generation and analysis, or customer services, the GPT models find applications in each such domain with ease. This flexibility makes it an asset for businesses and a developer looking to milk AI in several areas.
Scalability
GPT models are designed for large datasets and complex queries. Scalability in them provides for the handling of large volumes of data, with sustained performance under heavy loads, making them very suitable for large-scale applications that require robust and reliable language processing capabilities.
Efficiency
GPT models automate the generation of high-quality text and would hence drastically bring down the time and effort involved in content creation. This efficiency spells improved productivity and savings of money for any business. Be it drafting emails, generating reports, or even marketing-related content, GPT models are there at your rescue by automating these tasks and saving time for higher-order tasks.
Personalization
Models can become attuned to expert domains or user preferences. Further training the models on domain-specific data will enable businesses to increase the relevance and quality of generated content. Such personalization will ensure that AI output remains very close to what has been specified in style, tone, and matter of the desired output. Coupling this with the coherent generation ability of GPT models ensures a much more engaging and effective user experience.
How to Choose the Right GPT Model for Your Use Case?
Attention to some factors will ensure the proper selection of a GPT model that suits a given requirement and objectives of use.
Purpose
First, mention what the GPT model shall be used for. Identify the case of use and specify the tasks that one desires the model to be applied to. In the case of developing chatbots for customer support, for instance, it has to understand and respond with accurate answers to common customer queries. By keeping the purpose in mind, one will be able to better choose a model that fits the intended application.
Scale
Consider the size of your dataset and decide upon a model that it can support. Large models with increasing parameters could more easily process large amounts of data to produce excellent-quality results. On the other hand, they require more computational resources; hence, there needs to be a balance between the capabilities of the model and what your infrastructure can support.
Budget
Estimate the cost of the various models, then choose one that is not going to be too costly; that is, choose one within your budget constraint. The costs for training and deployment of GPT models vary tremendously with factors such as model size, computational requirements, and licensing fees. There is a need to be very clear about the costs involved and see that the chosen model has good value for money.
Customization
Understand how much customization your app might need. Whereas some models are in critical need of fine-tuning to meet special needs, others can perform well with minimal adjustment. If your application requires a high degree of specialization, then use models that give you ease of customization and fine-tuning.
How Much Does It Cost to Use GPT Models?
The use of GPT models can range from very cheap to very expensive based on things like model size, computational resources, data storage, and licensing fees.
Model Size
The larger the models, the more expensive it is to train and deploy. GPT-3 has about 175 billion parameters, so it requires a lot of computational power and storage resources. The small models could be more cost-effective in less complex applications or for high performance.
Computational Resources
Training and running GPT models are very intensive, most of the time requiring either a high-performance GPU or TPU. These resources can be computationally costly, especially when projects involve large numbers of parameters. One should, therefore, consider the cost involved in acquiring and maintaining hardware infrastructure.
Data Storage
One has to pay extra for storing large datasets used for training. Since the size of training data directly impacts the storage requirements, it impacts associated costs as well. This brings along the requirement to engage cloud-based storage solutions for their scalability and convenience. However, one needs to be aware of the long-term costs of storing and fetching data.
Licensing Fees
In the case of some providers, there will be licensing fees for the use of the pre-trained models or to access the cloud-based services. The licensing fees vary based on the volume usage and other conditions per the license agreement. It is, therefore, important to go through the terms of licensing with a fine comb and understand the financial implications before deploying a certain model or service.
How to Create a GPT Model? A Step-by-Step Guide
It includes steps from the formulation of the goal to data collection, training, and deployment. The following is a step-by-step approach to creating a GPT model:
Step 1: Define the Objective
First and foremost, there must be a clear definition of the purpose and objectives of your GPT model. This means knowing what tasks should be done and what the result should look like. Having this spelt out will help drive the next steps and ensure that the characteristics of your model are better aligned with the desired goals.
Step 2: Data Collection and Pre-processing
Data Collection
Get a large, diverse dataset that is relevant for your application. This can be based on text from books, articles, websites, etc. Be sure the dataset is wide and representative of the language and context in which it is used.
Data Cleaning
Remove any irrelevant or redundant information from a dataset. This step is of the essence to ensure the data is tidy and in good order. Cleaning a dataset may entail duplicate removal, error correction, and format standardization.
Tokenization
This means converting the text into tokens—that is, smaller units like words or sub-worlds—which the model can understand. It will break down the text into manageable pieces so that the model processes and analyses them effectively. For this, tools such as the BERT tokenizer or GPT-2 tokenizer are available.
Step 3: Model Selection
Choose a Pre-Trained Model
First, use a pre-trained GPT model from providers such as OpenAI. Using a pre-trained model saves computational time and resources since it was already trained on a large corpus of text data, which is always a good foundation to be fine-tuned and tailored to the intended application.
Fine-Tuning
It fine-tunes the model pre-trained on your dataset. This provides parameter tuning in the model towards an application at hand. This step shall enable the model to learn nuances and contexts in your data and become better at the tasks targeted.
Step 4: Training the Model
Environment Setup
Set up any computational environment that may be required, including GPUs and TPUs, and install relevant libraries like Tensor Flow and Py Torch. Be sure that an environment is engineered to train a large-scale language model.
Training Process
Now, train the model by passing the tokenized dataset to it. As it trains, track its progress. Make changes in the hyperparameters for better performance. The training of a GPT model is computationally intensive and may take time depending on the size of the dataset and model.
Step 5: Evaluation
Validation
Evaluate the performance of your model with a validation set. This step can check, for instance, the accuracy, coherence, and relevance of the generated text. The validation finds the pitfalls or what is to be improved before the model is rolled out.
Adjustments
Tune the model based on the evaluation results. It could be further fine-tuning, hyperparameter tweaking, or dataset refinement. The step is to ensure that the model meets the desired performance standards so that high-quality output will be issued.
Step 6: Deployment
Integration
Implement the fitted model in an application or system. This can be an API for real-time interaction or integration into larger software. Be sure to integrate it, to the best of your abilities, as seamlessly and functionally as possible.
Scalability
Make sure that the model scales well under different loads while ensuring performance. Scalability is most important where there might be surges or drops in application usage or even fast turnaround of vast data. Apply resource usage optimization techniques that guarantee uniform performance.
Things to Consider While Building a GPT Model
Ethical Considerations
Avoid AI text that is generated to sound malevolent. Avoid content creation that is offensive and vulgar. Safeguards must be included to allow monitoring and mitigation if bias generation occurs because of training data or output.
Data Privacy
Be sure to protect the privacy of any sensitive data used in training the model. It is realized by providing relevant security measures for the protection of the data and in compliance with relevant privacy regulations. Data privacy can help avoid the loss of user trust and future legal problems.
Performance Monitoring
Check Continually: Monitor the performance of the deployed model to meet the desired standards. The output of the model is checked regularly and tuned for accuracy and relevance. Monitoring performance ensures that any issues are noted early and ensures that the model continues to yield high-quality deliverables.
Cost Management
Keep all training and deployment costs in a record for your model while optimizing the use of resources within the budget. Cost control brings about balancing computational time resources, data storage, and licensing fees against the effective performance of the model.
Conclusion
GPT model creation includes a long chain of strict procedures: from the definition of objectives and data collection to training and model deployment. Understanding the advantages that come with GPT models and what affects their cost and performance is critical to making informed decisions. Moving on a structured line and considering ethical and practical considerations will let you create an effective GPT model that will suit all your needs for AI-based text generation in business or any project.
FAQs
Is creating custom GPT free?
Customization of GPT models is not free. Where there exists basic access to GPT, mostly requiring customization of the models calls for paid subscriptions to certain platforms that have usage costs associated with them.
How to create a custom GPT prompt?
To create a custom GPT prompt, the context or task needs to be described clearly, provide specific instructions, and include examples that would lead the model to generate the desired response.
Can I train GPT on my data?
Yes, you can fine-tune GPT models on your data using OpenAI and similar platforms, but usually, this is a task that requires technical expertise and access to certain tools or APIs.
Does ChatGPT require coding?
No, not at all if you’re just using ChatGPT. However, to personalize and integrate it with applications, it might require some coding, especially when fine-tuning or using the APIs.
Is ChatGPT replacing coders?
While ChatGPT alone might not be replacing coders, in general, it is increasing their work. Sure, it can automate tasks and provide aid in coding, but skilled developers are still needed on complex projects.
Visual elements in email campaigns are more than just eye-candy. They grab attention and deliver messages effectively.
The right email designs can make your email newsletters stand out in a crowded inbox, quickly conveying your message and driving engagement. High-quality visuals also influence actions, making them critical to any effective email campaign.
Now, as more Artificial Intelligence (AI) tools infiltrate email marketing, the big question for email marketers is: how can Generative AI help create the visual content that drives these email campaigns?
In this post, we’ll explore how to leverage AI for image generation in email marketing. We will dive into the tools and best practices to automate the more tedious aspects of image creation while still focusing on creative control where you want it.
Understanding The Role Of Gen AI In Email Image Generation
Generative AI is gradually making its mark in email marketing, particularly image generation.
From 2023 to 2024, its usage increased from 2% to 9%. Although this may seem minor compared to the 34% adoption rate of AI-generated written content, it’s a clear sign of a growing trend.
Written content is the easier choice for AI due to its accessibility. You don’t need specialized skills to type a prompt into a large multimodal model (LMM) and get decent results.
Image generation, however, is more complex. It requires a solid understanding of graphic design to craft prompts that produce visuals that are both on-brand and up to standard.
As Chad S. White, Head of Research at Oracle Digital Experience Agency, notes, “Generative AI is mostly about saving time, not necessarily improving performance—at least for now.”
Despite the learning curve, Generative AI offers substantial benefits for email marketers. Bartosz Foltyn, Art Director at GetResponse, explains that AI-generated designs often serve as a starting point. While these images usually need tweaking to meet specific needs, AI still significantly reduces the time spent on image production.
The takeaway? Generative AI is a powerful tool but is not a one-stop solution. You may need to refine AI-generated email designs, regenerate outputs, or experiment with different prompts to get the desired results.
How AI Image Generation Works
AI image generation tools use advanced algorithms to create visuals based on the text you provide. These tools are trained on millions of images and their descriptions from across the internet. This training helps the AI learn what different things look like and how they are described.
When you enter a prompt, the AI draws on this vast library of knowledge to generate an image that matches your description. The AI doesn’t just copy existing images—it uses pieces of what it has learned to create something new and unique that fits your needs.
AI For Visual Images: Advantages and Drawbacks
Integrating Gen AI tools into email marketing for image generation brings both advantages and drawbacks. Understanding these can help you play to AI’s strengths while being aware of its limitations.
Advantages of AI-generated Images in Email Marketing
Time Efficiency
AI tools for image generation tools streamline the email content creation workflow. It allows marketers to generate high-quality images efficiently and at scale. This means less time and money spent on traditional graphic design processes.
Resource Saving
Forget about sifting through stock photo websites. Gen AI produces high-quality visuals in a wide range of styles in minutes. Plus, it lowers the costs associated with traditional design methods.
Image Upscaling
Have a low-resolution image that needs to be higher quality? AI upscalers can enhance these images, boosting their resolution up to 4K. This technology allows you to take existing visuals and elevate their quality, making them crisp and professional, even if they started out with lower resolution.
Creating Textures
Generative AI can drastically reduce the time it takes to create detailed textures for 3D models. Instead of spending days on this task, you can quickly generate a specific texture, like a “close-up of a cracked stone surface.” This approach is faster and cheaper and allows designers to maintain creative control while bypassing the tedious work of sourcing or creating textures manually.
Brand Consistency
GenAI can be set up to stick to your brand’s visual identity and style guide, ensuring that your colors, design elements, and overall look are always on point. This means your visuals will stay consistent and recognizable across all email marketing campaigns.
Drawbacks of AI-generated Images in Email Marketing
Imperfect Quality
While AI can produce impressive images, there can still be times when the generated images don’t quite match your vision, making it necessary to tweak and refine them. Getting the best results requires understanding how to craft the right prompts and adjust settings.
Copyright Risks
Generative AI tools come with significant copyright concerns. Since these AI models are trained on vast numbers of images, including many copyright-protected ones, lawsuits over potential infringement have already been filed.
It’s likely that users of these tools could face legal challenges from artists, photographers, and other copyright holders. To minimize this risk, avoid using AI to create images that resemble real people, especially celebrities, or that imitate specific logos, designs, and visual elements owned by companies or organizations.
Risk of Errors
Despite its capabilities, GenAI isn’t immune to mistakes. It can sometimes produce images with incorrect proportions or add or omit elements. These errors can affect the quality and accuracy of your visuals, requiring additional manipulation by the designer.
The Best AI Image Generation Tools For Email Marketing
DALL-E 3
DALL-E 3 from OpenAI is a powerful AI tool for generating unique images. Unlike typical image generators, DALL-E 3 excels at creatively mixing and matching visual elements, helping your email visuals stand out.
Using DALL-E 3 is straightforward. You type in a detailed prompt, and the tool generates one or two images that match your description. If the initial image isn’t quite right, you can give feedback, and DALL-E 3 will adjust and refine the image. It also handles text well, allowing you to add product names or slogans directly to your images.
Pros
Understands and processes detailed prompts, making creating the exact image you want easy.
Great at producing unique and engaging images that grab attention.
Allows for easy tweaks and adjustments to get the perfect image.
Cons
Sometimes, the realistic images might not look quite right.
Generating images can take a bit of time, especially for detailed prompts.
Access to DALL-E 3 comes with a $20 monthly subscription to ChatGPT Plus.
Canva
Canva’s AI image generator is a versatile and user-friendly tool perfect for those who prefer working with design templates or need assistance during the design process. For marketing teams pressed for time, Canva AI offers a quick and efficient way to generate high-quality images and text, making it an invaluable asset.
Key Features
Canva’s Magic Studio, a suite of AI-powered tools, automates and simplifies many design tasks. The Magic Design tool can generate complete presentations—outlines, slides, and content—from just a single text prompt.
Other features, such as Magic Eraser, Magic Switch, and Magic Morph, allow for easy manipulation and transformation of design elements, speeding up the creation of high-quality visual assets.
Magic Studio also includes an AI editor, text generator, and various imagery options, enabling the creation of diverse content types, including images, text, and even basic video clips. These tools are conveniently grouped together in the software’s “Magic Studio” section, making them easy to access and use.
Pros
Canva AI is beginner-friendly and accessible to anyone.
Offers tools for text-to-image, video editing, and more.
Competitive pricing for individuals and teams.
Access to millions of ready-to-use templates and design elements.
Cons
Lacks advanced features for professional designers.
Complex projects may take time to master.
Midjourney
Midjourney is an innovative AI image generation tool that is highly praised for its ability to create images that are so clear and detailed that you might mistake them for real photographs.
Accessed primarily through Discord, Midjourney is a favorite among creatives, including marketing professionals. It can produce visually stunning content that can elevate any email campaign.
Key Features
Midjourney produces images with such crystal-clear detail that they often resemble real photographs. The tool provides detailed control over image output, allowing users to fine-tune their prompts for the best results.
Users interact with Midjourney through Discord, where they can generate images, share prompts, draw inspiration, and collaborate with others in the community.
Pros
Produces near-photorealistic images with exceptional detail.
Engaging the Discord community for inspiration and support.
Offers detailed control over image generation.
Cons
Available only through Discord, which may be inconvenient.
Generated images are public by default, raising privacy concerns.
Requires a subscription to use, as free trials are currently unavailable.
Adobe Firefly
Adobe Firefly is an AI image generation tool developed by Adobe, a leader in creative software for professionals. Given Adobe’s long history of innovation, it’s no surprise that Firefly stands out as an impressive tool in the world of AI-generated visuals. Whether you’re a marketer or a designer, Firefly offers a simple yet powerful way to create stunning images for your email campaigns.
The feature Structure Reference allows you to input an existing image as a template, which the AI uses to generate a new image with the same layout and composition. It’s perfect for maintaining consistency in your visuals.
With Style Reference, you can guide the AI to create new images in the same style as a reference image, ensuring your designs stay on-brand.
Another standout feature is that Adobe Firefly was trained on Adobe Stock images, openly licensed content, and public domain content, making all generated images safe for use in commercial projects.
Pros
Excels in producing diverse and detailed artistic images.
Offers good control over image adjustments, allowing you to edit specific details.
Generates images quickly, saving time on projects.
Cons
May struggle to produce realistic images, particularly with complex queries.
It sometimes has difficulty accurately depicting interactions between elements in an image.
The tool can falter when handling more complex image requests.
How To Choose The Right AI Image Tool For Email Marketing
Selecting the right AI image tool can elevate your email marketing efforts. Here’s what to consider:
Ease of Use
Pick a tool that matches your team’s skill level, ensuring a smooth workflow and quick adoption.
Customization
Look for tools that offer strong customization options to tailor images to your brand’s style.
Scalability
Ensure the tool can handle the volume of images required for your campaigns as they grow.
Integration
Ensure the tool integrates seamlessly with your existing design and email marketing platforms.
Cost-Efficiency
Evaluate the cost per image and consider the potential return on investment through improved engagement and conversion rates.
Best Practices for Crafting Perfect AI Prompts For Image Generation
Be Specific with Details
Include important elements like mood, setting, color palette, and style. This guides the AI to produce images that align with your vision.
Keep It Balanced
Provide enough context for clarity without overwhelming the AI with excessive details. Aim for a prompt that is one to six sentences long, depending on how much control you want over the result.
Use AI for Initial Drafts
Start with AI to generate the first draft of your images, then refine and polish them in post-production to fix any imperfections.
Leverage ChatGPT for Help
Use ChatGPT to help write your prompts. By uploading a reference image, ChatGPT can help craft more accurate and effective prompts.
Avoid Vague Instructions
Clear and precise communication is essential. Ambiguous prompts can lead to irrelevant or unwanted results.
Don’t Rely Solely on AI
While AI is a powerful tool, don’t depend on it exclusively. Combine AI capabilities with human creativity to avoid generic outputs.
Provide Context
Explain not just what you want but why it matters. This helps the AI understand the purpose behind the image, ensuring it resonates with your audience.
Wrapping Up
AI-generated images are revolutionizing email marketing. By quickly and affordably producing high-quality visuals, generative AI tools offer marketers a wealth of creative possibilities. They help maintain brand consistency, deliver personalized content, and resonate with your audience.
While these tools are powerful, remember that they’re not infallible. Always ensure your AI-generated images adhere to ethical standards and avoid relying solely on AI.
Craft clear, concise prompts and combine AI capabilities with human creativity for optimal results. Embrace Gen AI as a valuable asset in your marketing toolkit, but remember it’s a complement to, not a replacement for, human insight and creativity.
Developers suffer in the great multitudes whom their sacred block-based websites cannot reach.
Johannes Gutenberg (probably)
Long time WordPresser, first time Gutenberger here. I’m a fan even though I’m still anchored to a classic/block hybrid setup. I believe Johanes himself would be, too, trading feather pens for blocks. He was a forward-thinking 15th-century inventor, after all.
My enthusiasm for Gutenberg-ness is curbed at the theming level. I’ll sling blocks all day long in the Block Editor, but please, oh please, let me keep my classic PHP templates and the Template Hierarchy that comes with it. The separation between theming and editing is one I cherish. It’s not that the Site Editor and its full-site editing capabilities scare me. It’s more that I fail to see the architectural connection between the Site and Block Editors. There’s a connection for sure, so the failure of not understanding it is more on me than WordPress.
The WP Minute published a guide that clearly — and succinctly — describes the relationships between WordPress blocks, patterns, and templates. There are plenty of other places that do the same, but this guide is organized nicely in that it starts with the blocks as the lowest-level common denominator, then builds on top of it to show how patterns are comprised of blocks used for content layout, synced patterns are the same but are one of many that are edited together, and templates are full page layouts cobbled from different patterns and a sprinkle of other “theme blocks” that are the equivalent of global components in a design system, say a main nav or a post loop.
The guide outlines it much better, of course:
Gutenberg Blocks: The smallest unit of content
Patterns: Collections of blocks for reuse across your site
Synced Patterns: Creating “master patterns” for site-wide updates
Synced Pattern Overrides: Locking patterns while allowing specific edits
Templates: The structural framework of your WordPress site
That “synced pattern overrides” is new to me. I’m familiar with synced patterns (with a giant nod to Ganesh Dahal) but must’ve missed that in the WordPress 6.6 release earlier this summer.
I’m not sure when or if I’ll ever go with a truly modern WordPress full-site editing setup wholesale, out-of-the-box. I don’t feel pressured to, and I believe WordPress doesn’t care one way or another. WordPress’s ultimate selling point has always been its flexibility (driven, of course, by the massive and supportive open-source community behind it). It’s still the “right” tool for many types of projects and likely will remain so as long as it maintains its support for classic, block, and hybrid architectures.
JavaScript comes with a lot of built-in functions that allow you to carry out so many different operations. One of these built-in functions is the Math.random() method, which generates a random floating-point number that can then be manipulated into integers.
However, if you wish to generate a series of unique random numbers and create more random effects in your code, you will need to come up with a custom solution for yourself because the Math.random() method on its own cannot do that for you.
In this article, we’re going to be learning how to circumvent this issue and generate a series of unique random numbers using the Set object in JavaScript, which we can then use to create more randomized effects in our code.
Note: This article assumes that you know how to generate random numbers in JavaScript, as well as how to work with sets and arrays.
Generating a Unique Series of Random Numbers
One of the ways to generate a unique series of random numbers in JavaScript is by using Set objects. The reason why we’re making use of sets is because the elements of a set are unique. We can iteratively generate and insert random integers into sets until we get the number of integers we want.
And since sets do not allow duplicate elements, they are going to serve as a filter to remove all of the duplicate numbers that are generated and inserted into them so that we get a set of unique integers.
Here’s how we are going to approach the work:
Create a Set object.
Define how many random numbers to produce and what range of numbers to use.
Generate each random number and immediately insert the numbers into the Set until the Set is filled with a certain number of them.
The following is a quick example of how the code comes together:
function generateRandomNumbers(count, min, max) {
// 1: Create a Set object
let uniqueNumbers = new Set();
while (uniqueNumbers.size < count) {
// 2: Generate each random number
uniqueNumbers.add(Math.floor(Math.random() * (max - min + 1)) + min);
}
// 3: Immediately insert them numbers into the Set...
return Array.from(uniqueNumbers);
}
// ...set how many numbers to generate from a given range
console.log(generateRandomNumbers(5, 5, 10));
What the code does is create a new Set object and then generate and add the random numbers to the set until our desired number of integers has been included in the set. The reason why we’re returning an array is because they are easier to work with.
One thing to note, however, is that the number of integers you want to generate (represented by count in the code) should be less than the upper limit of your range plus one (represented by max + 1 in the code). Otherwise, the code will run forever. You can add an if statement to the code to ensure that this is always the case:
function generateRandomNumbers(count, min, max) {
// if statement checks that count is less than max + 1
if (count > max + 1) {
return "count cannot be greater than the upper limit of range";
} else {
let uniqueNumbers = new Set();
while (uniqueNumbers.size < count) {
uniqueNumbers.add(Math.floor(Math.random() * (max - min + 1)) + min);
}
return Array.from(uniqueNumbers);
}
}
console.log(generateRandomNumbers(5, 5, 10));
Using the Series of Unique Random Numbers as Array Indexes
It is one thing to generate a series of random numbers. It’s another thing to use them.
Being able to use a series of random numbers with arrays unlocks so many possibilities: you can use them in shuffling playlists in a music app, randomly sampling data for analysis, or, as I did, shuffling the tiles in a memory game.
Let’s take the code from the last example and work off of it to return random letters of the alphabet. First, we’ll construct an array of letters:
In the original code, the generateRandomNumbers() function is logged to the console. This time, we’ll construct a new variable that calls the function so it can be consumed by randomAlphabets:
And, when we put the generateRandomNumbers`()` function definition back in, we get the final code:
const englishAlphabets = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
];
function generateRandomNumbers(count, min, max) {
if (count > max + 1) {
return "count cannot be greater than the upper limit of range";
} else {
let uniqueNumbers = new Set();
while (uniqueNumbers.size < count) {
uniqueNumbers.add(Math.floor(Math.random() * (max - min + 1)) + min);
}
return Array.from(uniqueNumbers);
}
}
const randomIndexes = generateRandomNumbers(5, 0, 25);
const randomAlphabets = randomIndexes.map((index) => englishAlphabets[index]);
console.log(randomAlphabets);
So, in this example, we created a new array of alphabets by randomly selecting some letters in our englishAlphabets array.
You can pass in a count argument of englishAlphabets.length to the generateRandomNumbers function if you desire to shuffle the elements in the englishAlphabets array instead. This is what I mean:
In this article, we’ve discussed how to create randomization in JavaScript by covering how to generate a series of unique random numbers, how to use these random numbers as indexes for arrays, and also some practical applications of randomization.
The best way to learn anything in software development is by consuming content and reinforcing whatever knowledge you’ve gotten from that content by practicing. So, don’t stop here. Run the examples in this tutorial (if you haven’t done so), play around with them, come up with your own unique solutions, and also don’t forget to share your good work. Ciao!
If you want your business to be safe and secure, 2FA Live is one of the most useful tools available. With malicious cyber attacks increasing at an alarming rate, what better way to secure your business than to use 2FA Live. The following is a guide to achieve just that.
What is 2FA Live?
Before stepping into the implementation part, be aware of what 2FA Live is. 2FA Live is the next upgrade level of 2-factor authentication. Along with the traditional methods of identity confirmation through usernames or passwords, the platform also utilizes real-time verification methods like one-time password or biometric data to ensure that the business information is safe in the hands of the right users.
Assessing Your Business Needs
The first step in getting the 2FA Live system operational is to determine which systems, applications and information require a second layer of protection. If you scan your business, you will probably conclude that the systems to add this second layer of security include employee email accounts, client database, financial information, proprietary software and other information specific to your business.
Choosing the Right 2FA Live Solution
After identifying your needs, carefully select the right 2FA Live solution for you. There is an array of providers out there offering a myriad of functionalities. Choose one that complements your existing IT infrastructure and that supports multiple methods of authentication. Also, ensure your provider offers excellent customer care and posts updates as new threats emerge.
Setting Up 2FA-Live
Now that you’ve elected to go with a 2FA Live configuration, you can install the system. Nearly all vendors provide detailed instructions or installation by a professional for them. Generally, you’ll need to:
Register your business and create an admin account.
Integrate the 2FA Live API with your existing systems and applications.
Configure authentication methods, such as SMS OTP, email OTP, or biometric verification.
Test the setup to ensure it works correctly across all platforms.
Training Your Team
Your team will also need to know how to work with2fa live. Run some training sessions to explain the need for 2FA and how it works, including which employees to request it from and how to do so properly. Create guides and documentation to make it easier for employees to find this information on an ongoing basis. Also, ensure everyone knows what to do when stuff happens – a device getting lost, authentication failure, etc.
Monitoring and Management
After 2FA-Live is deployed, it needs to be monitored continuously. Check authentication logs periodically for unusual events. Update 2FA methods and policies as needed to counter new threats and ensure that a team or individual is dedicated to the management of 2FA.
Implementing Two-Factor Authentication (2FA) for Your Business: A Comprehensive Guide
Indeed — in the modern digital era, there is nothing more vital than ensuring cybersecurity for your business. One of the best ways to improve security is two-factor authentication (2FA). This is a security measure that would require users to prove they are the original owner of an account by two forms before logging in. We will also understand it in the preceding paragraphs, and finally, we are going to discuss how you can implement a two-factor authentication system for your enterprise.
The Significance of Two-Factor Authentication
With cyberattacks growing more sophisticated every day, you simply cannot depend on passwords to safeguard your business. Passwords that are easily compromised due to phishing attacks, brute force, and data breaches. Essentially, 2FA is a way to further secure yourself by adding that second step of authentication making it much more difficult for unauthorized users to access something.
If implemented, then 2FA secures against Not allowing malicious identity theft or disclosing any sensitive information and believes adherence to the rules of changing nature. In addition, businesses that deal with sensitive data — such as customer info or financial records — absolutely must employ 2FA to ensure they can prevent what could be very expensive breaches and remain trustworthy in the eyes of their customers.
Common 2FA Methods
There are several 2FA methods, and each of these has its unique limitations and advantages. We have an in-depth understanding as to which is the best fit to meet your business requirements.
1. SMS-Based Authentication
One-Time Passcode (OTP) is sent to the user on his mobile via SMS as a message. The user will then need to enter this code in their password field.
Pro: Simplicity to implement and well popularized.
What is bad: The weakness of SIM-swapping and communication interception. Not the most secure method.
2. Authenticator Apps
Time-based OTPs are generated by external authentication apps like Google Authenticator, Authy, or Microsoft Authenticator after the password is entered. No-cybernetic query, credential codes that regenerate every 30 seconds, and don’t rely on the pathological disenchantment of internet accessibility.
Pros: More secure than SMS, does not rely on mobile networks.
Cons: Requires to install and manage additional app
3. Hardware Tokens
Hardware Tokens: These are physical devices that generate OTPs. The memory cards would be housed in devices connected to a computer via USB plugins.
Pro: this is very secure and can be attacked online.
Cons: Expensive to develop and support; danger of physical device being lost or damaged.
4. Biometric Authentication
Biometric 2FA — in this mode, real-time measurements of facial recognition and voice into the system extract more personal characteristics used to verify who they are.
Pros: Highly secure — user-convenient.
Cons: Needs special hardware, privacy, and data storage issues
5. Email-Based Authentication
This method sends an OTP or verification link to a user-registered Email Address. The user has to authenticate by clicking the link or submitting an OTP.
PROS: Simple to use and implement.
Email: Checks and balances; Use of a compromised email service could lead to insecurity; Emails lost in transmission run the risk of delays.
6. Push Notifications
The delayed push notification is sent to the user’s smartphone asking for approval/denial of login. One common approach is to go with an authenticator app.
Advantages: Easy to use and safe; instant notifications.
Negatives: You have to own a smartphone and you also need an active internet connection.
How To Add 2FA For Your Organization
Next up, let’s get you set up using 2FA inside your workplace since by now we’ve covered the essentials of what a 2nd-factor is. Here is a step-by-step guide to help you out.
1. Evaluate Your Business Needs
Step 1: Analyze your Business Security Needs – GO FOR 2FA Account for your data sensitivity, the type of regulatory compliance environment you work in, and very real risks that may haunt your business. This will encourage you to choose which 2FA method is best for your company.
2. Use The Appropriate 2FA(s)
From your assessment, decide on the 2FA method(s) that will provide value to your business. You could use text messages for 2FA with regular accounts, and bump up the security to hardware tokens or biometrics for super important things.
3. Select a 2FA Provider
Many 2-factor authentication providers exist with various features and prices in the market. Duo Security, Google Authenticator (2FA), Microsoft Authenticator, and Yubikey are some of the popular Biometric & Perfecto alternatives Some things to take a look at are how easy it is to integrate, scalability, and customer support.
4. Employ Two-factor Authentication in Your Systems
After you choose a 2FA provider, the next step is to start using 2FA for your business. Setting up these systems may involve cooperation with your IT team or a third-party consultant to assist in easing the implementation. Nearly all 2FA providers offer APIs and plugins, making them quite
Conclusion
2FA-Live is an essential component to help secure your business. Taking the time to understand your needs, choosing the right solution, deploying properly and training your team will help ensure that your business data is secure.
For companies who are looking for a trust-worthy 2FA-Live product, LogMeOnce has a whole suite of security tools that can protect against the latest cyber threats such as phishing, credential stuffing, malware and ransomware.
Giant kudos to Scott Jehl on releasing his new Web Components De-Mystified online course! Eight full hours of training from one of the best in the business.
