Traditional sales organizations used the concept of a sales “funnel” to describe the process through which potential customers move, ending up with sales at the end. Winners today have abandoned that way of thinking in favor of building flywheels — business models in which every element reinforces every other.

Ah, the marketing funnel…

Prospective clients go through a predictable set of experiences, students learn in business school marketing classes. It looks like this:

Understanding the funnel helps evaluate sales success indicators. Gail Goodwin, former CEO of small business direct mail provider Constant Contact, said managing the pipeline was key to escaping the sluggish SaaS ramp of death.

Like the funnel concept. To predict how well your business will do, measure how many potential clients are aware of it (awareness) and how many take the next step. If 1,000 people heard about your offering and 10% showed interest, you'd have 100 at that point. If 50% of these people made buyer-like noises, you'd know how many were, etc. It helped model buying trends.

TV, magazine, and radio advertising are pricey for B2C enterprises. Traditional B2B marketing involved armies of sales reps, which was expensive and a barrier to entry.

Cracks in the funnel model

Digital has exposed the funnel's limitations. Hubspot was born at a time when buyers and sellers had huge knowledge asymmetries, according to co-founder Brian Halligan. Those selling a product could use the buyer's lack of information to become a trusted partner.

As the world went digital, getting information and comparing offerings became faster, easier, and cheaper. Buyers didn't need a seller to move through a funnel. Interactions replaced transactions, and the relationship didn't end with a sale.

Instead, buyers and sellers interacted in a constant flow. In many modern models, the sale is midway through the process (particularly true with subscription and software-as-a-service models). Example:

You're creating a winding journey with many touch points, not a funnel (and lots of opportunities for customers to get lost).

From winding journey to flywheel

Beyond this revised view of an interactive customer journey, a company can create what Jim Collins famously called a flywheel. Imagine rolling a heavy disc on its axis. The first few times you roll it, you put in a lot of effort for a small response. The same effort yields faster turns as it gains speed. Over time, the flywheel gains momentum and turns without your help.

Modern digital organizations have created flywheel business models, in which any additional force multiplies throughout the business. The flywheel becomes a force multiplier, according to Collins.

Amazon is a famous flywheel example. Collins explained the concept to Amazon CEO Jeff Bezos at a corporate retreat in 2001. In The Everything Store, Brad Stone describes in his book The Everything Store how he immediately understood Amazon's levers.

The result (drawn on a napkin):

Low prices and a large selection of products attracted customers, while a focus on customer service kept them coming back, increasing traffic. Third-party sellers then increased selection. Low-cost structure supports low-price commitment. It's brilliant! Every wheel turn creates acceleration.

Where from here?

Flywheel over sales funnel! Consider these business terms.

As a marketer, I joined Twitter on August 31, 2022 to use it.

Growth has been volatile, causing up-and-down engagements. 500 followers in 11 days.

I met amazing content creators, marketers, and people.

Those who use Twitter may know that one-liners win the algorithm, especially if they're funny or humorous, but as a marketer I can't risk posting content that my audience won't like.

I researched, learned some strategies, and A/B tested; some worked, some didn't.

In this article, I share what worked for me so you can do the same.

Thanks for reading!

Let's check my Twitter stats.

• Tweets: how many tweets I sent in the first 28 days.

• A user may be presented with a Tweet in their timeline or in search results.

• In-person visits how many times my Twitter profile was viewed in the first 28 days.

• Mentions: the number of times a tweet has mentioned my name.

• Number of followers: People who were following me

Getting 500 Twitter followers isn't difficult.

Not easy, but doable.

Follow these steps to begin:

Determine your content pillars in step 1.

My formula is Growth = Content + Marketing + Community.

I discuss growth strategies.

My concept for growth is : 1. Content = creating / writing + sharing content in my niche. 2. Marketing = Marketing everything in business + I share my everyday learnings in business, marketing & entrepreneurship. 3. Community = Building community of like minded individuals (Also,I share how to’s) + supporting marketers to build & grow through community building.

Identify content pillars to create content for your audience.

2. Make your profile better

Create a profile picture. Your recognition factor is this.

Professional headshots are worthwhile.

This tool can help you create a free, eye-catching profile pic.

Use a niche-appropriate avatar if you don't want to show your face.

2. Create a bio that converts well mainly because first impressions count.

what you're sharing + why + +social proof what are you making

Be brief and precise. (155 characters)

3. Configure your banner

Banners complement profile pictures.

Use this space to explain what you do and how Twitter followers can benefit.

Canva's Twitter header maker is free.

Birdy can test multiple photo, bio, and banner combinations to optimize your profile.

• Versions A and B of your profile should be completed.

• Find the version that converts the best.

• Use the profile that converts the best.

4. Special handle

If your username/handle is related to your niche, it will help you build authority and presence among your audience. Mine on Twitter is @marketershruti.

5. Participate expertly

Proficiently engage while you'll have no audience at first. Borrow your dream audience for free.

Steps:

• Find a creator who has the audience you want.

• Activate their post notifications and follow them.

• Add a valuable comment first.

6. Create fantastic content

Use:

• Medium (Read articles about your topic.)

• Podcasts (Listen to experts on your topics)

• YouTube (Follow channels in your niche)

Tweet what?

• Listicle ( Hacks, Books, Tools, Podcasts)

• Lessons (Teach your audience how to do 1 thing)

• Inspirational (Inspire people to take action)

Consistent writing?

• You MUST plan ahead and schedule your Tweets.

• Use a scheduling tool that is effective for you; hypefury is mine.

Lastly, consistency is everything that attracts growth. After optimizing your profile, stay active to gain followers, engagements, and clients.

If you found this helpful, please like and comment below.

Unleash the power of persuasion and captivate your audience with compelling headlines.

As the old adage goes, "You never get a second chance to make a first impression."

In the world of content creation and social ads, headlines and titles play a critical role in making that first impression.

A well-crafted headline can make the difference between an article being read or ignored, a video being clicked on or bypassed, or a product being purchased or passed over.

To make an impact with your headlines, mastering the art of rhetoric is essential. In this post, we'll explore various rhetorical devices and techniques that can help you create headlines that captivate your audience and drive engagement.

tl;dr : Headline Magician will help you craft the ultimate headline titles powered by rhetoric devices

Example with a high-end luxury organic zero-waste skincare brand

✍️ The Power of Alliteration

Alliteration is the repetition of the same consonant sound at the beginning of words in close proximity. This rhetorical device lends itself well to headlines, as it creates a memorable, rhythmic quality that can catch a reader's attention.

By using alliteration, you can make your headlines more engaging and easier to remember.

Examples:

"Crafting Compelling Content: A Comprehensive Course"

"Mastering the Art of Memorable Marketing"

🔁 The Appeal of Anaphora

Anaphora is the repetition of a word or phrase at the beginning of successive clauses. This rhetorical device emphasizes a particular idea or theme, making it more memorable and persuasive.

In headlines, anaphora can be used to create a sense of unity and coherence, which can draw readers in and pique their interest.

Examples:

"Create, Curate, Captivate: Your Guide to Social Media Success"

"Innovation, Inspiration, and Insight: The Future of AI"

🔄 The Intrigue of Inversion

Inversion is a rhetorical device where the normal order of words is reversed, often to create an emphasis or achieve a specific effect.

In headlines, inversion can generate curiosity and surprise, compelling readers to explore further.

Examples:

"Beneath the Surface: A Deep Dive into Ocean Conservation"

"Beyond the Stars: The Quest for Extraterrestrial Life"

⚖️ The Persuasive Power of Parallelism

Parallelism is a rhetorical device that involves using similar grammatical structures or patterns to create a sense of balance and symmetry.

In headlines, parallelism can make your message more memorable and impactful, as it creates a pleasing rhythm and flow that can resonate with readers.

Examples:

"Eat Well, Live Well, Be Well: The Ultimate Guide to Wellness"

"Learn, Lead, and Launch: A Blueprint for Entrepreneurial Success"

⏭️ The Emphasis of Ellipsis

Ellipsis is the omission of words, typically indicated by three periods (...), which suggests that there is more to the story.

In headlines, ellipses can create a sense of mystery and intrigue, enticing readers to click and discover what lies behind the headline.

Examples:

"The Secret to Success... Revealed"

"Unlocking the Power of Your Mind... A Step-by-Step Guide"

🎭 The Drama of Hyperbole

Hyperbole is a rhetorical device that involves exaggeration for emphasis or effect.

In headlines, hyperbole can grab the reader's attention by making bold, provocative claims that stand out from the competition. Be cautious with hyperbole, however, as overuse or excessive exaggeration can damage your credibility.

Examples:

"The Ultimate Guide to Mastering Any Skill in Record Time"

"Discover the Revolutionary Technique That Will Transform Your Life"

❓The Curiosity of Questions

Posing questions in your headlines can be an effective way to pique the reader's curiosity and encourage engagement.

Questions compel the reader to seek answers, making them more likely to click on your content. Additionally, questions can create a sense of connection between the content creator and the audience, fostering a sense of dialogue and discussion.

Examples:

"Are You Making These Common Mistakes in Your Marketing Strategy?"

"What's the Secret to Unlocking Your Creative Potential?"

💥 The Impact of Imperatives

Imperatives are commands or instructions that urge the reader to take action. By using imperatives in your headlines, you can create a sense of urgency and importance, making your content more compelling and actionable.

Examples:

"Master Your Time Management Skills Today"

"Transform Your Business with These Innovative Strategies"

💢 The Emotion of Exclamations

Exclamations are powerful rhetorical devices that can evoke strong emotions and convey a sense of excitement or urgency.

Including exclamations in your headlines can make them more attention-grabbing and shareable, increasing the chances of your content being read and circulated.

Examples:

"Unlock Your True Potential: Find Your Passion and Thrive!"

"Experience the Adventure of a Lifetime: Travel the World on a Budget!"

🎀 The Effectiveness of Euphemisms

Euphemisms are polite or indirect expressions used in place of harsher, more direct language.

In headlines, euphemisms can make your message more appealing and relatable, helping to soften potentially controversial or sensitive topics.

Examples:

"Navigating the Challenges of Modern Parenting"

"Redefining Success in a Fast-Paced World"

⚡Antithesis: The Power of Opposites

Antithesis involves placing two opposite words side-by-side, emphasizing their contrasts. This device can create a sense of tension and intrigue in headlines.

Examples:

"Once a day. Every day"

"Soft on skin. Kill germs"

"Mega power. Mini size."

To utilize antithesis, identify two opposing concepts related to your content and present them in a balanced manner.

🎨 Scesis Onomaton: The Art of Verbless Copy

Scesis onomaton is a rhetorical device that involves writing verbless copy, which quickens the pace and adds emphasis.

Example:

"7 days. 7 dollars. Full access."

To use scesis onomaton, remove verbs and focus on the essential elements of your headline.

🌟 Polyptoton: The Charm of Shared Roots

Polyptoton is the repeated use of words that share the same root, bewitching words into memorable phrases.

Examples:

"Real bread isn't made in factories. It's baked in bakeries"

"Lose your knack for losing things."

To employ polyptoton, identify words with shared roots that are relevant to your content.

✨ Asyndeton: The Elegance of Omission

Asyndeton involves the intentional omission of conjunctions, adding crispness, conviction, and elegance to your headlines.

Examples:

"You, Me, Sushi?"

"All the latte art, none of the environmental impact."

To use asyndeton, eliminate conjunctions and focus on the core message of your headline.

🔮 Tricolon: The Magic of Threes

Tricolon is a rhetorical device that uses the power of three, creating memorable and impactful headlines.

Examples:

"Show it, say it, send it"

"Eat Well, Live Well, Be Well."

To use tricolon, craft a headline with three key elements that emphasize your content's main message.

🔔 Epistrophe: The Chime of Repetition

Epistrophe involves the repetition of words or phrases at the end of successive clauses, adding a chime to your headlines.

Examples:

"Catch it. Bin it. Kill it."

"Joint friendly. Climate friendly. Family friendly."

To employ epistrophe, repeat a key phrase or word at the end of each clause.

Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Let’s get started.

What It Does

Stable Diffusion uses numerous components:

• A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).

• An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).

• A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).

This figure shows all data flow:

The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.

Install

Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults conda

Install the source and prepare the environment:

git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgrade

Download the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.

Running the optimized version

Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:

python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).

Running Stable Diffusion without GPU

If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().

• Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.

• Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().

Run the script again.

Testing

Test the model. Text-to-image is the first choice. Test the command line example again:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:

Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:

Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):

I can create an image from this drawing:

python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8

It was far better than my initial drawing:

I hope readers understand and experiment.

Stable Diffusion UI

Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:

• Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).

• Start the script.

Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:

V2.1 of Stable Diffusion

I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:

• alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.

• a new depth model that may be used to the output of image-to-image generation.

• a revolutionary upscaling technique that can quadruple the resolution of an image.

• Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.

The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:

conda deactivate  
conda env remove -n ldm  # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldm

Hugging Face offers a new weights ckpt file.

The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:

It looks different from v1, but it functions and has a higher resolution.

The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):

python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckpt

This code allows the web browser UI to select the image to upscale:

The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:

Stable Diffusion Limitations

When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:

V1:

V2.1:

The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.

I can also ask the model to draw a gorgeous woman:

V1:

V2.1:

The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.

If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:

V1:

V2.1:

Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:

V1:

V2.1: improved but not perfect.

V1 produces a fun cartoon flying mouse if I want something more abstract:

I tried multiple times with V2.1 but only received this:

The image is OK, but the first version is closer to the request.

Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:

V1:

V2.1:

Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:

I typed "abstract oil painting of people dancing" and got this:

V1:

V2.1:

It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.

The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:

This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.

I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).

Conclusion

The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).

Is Generative AI a game-changer? My humble experience tells me:

• I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.

• Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.

• It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).

• When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.

Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

We may never tap again in ten years.

I discussed styling websites and web apps on smartwatches in my earlier article on W3C standardization.

The Parallax Chronicles

Section containing examples and flying objects

Another intriguing Working Draft I found applies to all devices, including smartphones.

These pages may have something intriguing. Take your time. Return after scrolling:

• Exhibit 1

• Exhibit 2

• Exhibit 3

What connects these three pages?

JustinWick at English Wikipedia • CC-BY-SA-3.0

Scroll-linked animation, commonly called parallax, is the effect.

WordPress theme developers' quick setup and low-code tools made the effect popular around 2014.

Parallax: Why Designers Love It

The chapter that your designer shouldn't read

Online video playback required searching, scrolling, and clicking ten years ago. Scroll and click four years ago.

Some video sites let you swipe to autoplay the next video from an endless list.

UI designers create scrollable pages and apps to accommodate the behavioral change.

Web interactivity used to be mouse-based. Clicking a button opened a help drawer, and hovering animated it.

However, a large page with more material requires fewer buttons and less interactiveness.

Designers choose scroll-based effects. Design and frontend developers must fight the trend but prepare for the worst.

How to Create Parallax

The component that you might want to show the designer

JavaScript-based effects track page scrolling and apply animations.

Javascript libraries like lax.js simplify it.

Using it needs a lot of human mathematical and physical computations.

Your asset library must also be prepared to display your website on a laptop, television, smartphone, tablet, foldable smartphone, and possibly even a microwave.

Overall, scroll-based animations can be solved better.

CSS Scroll-linked Animations

CSS makes sense since it's presentational. A Working Draft has been laying the groundwork for the next generation of interactiveness.

The new CSS property scroll-timeline powers the feature, which MDN describes well.

Before testing it, you should realize it is poorly supported:

Firefox 103 currently supports it.

There is also a polyfill, with some demo examples to explore.

Summary

Web design was a protracted process. Started with pages with static backdrop images and scrollable text. Artists and designers may use the scroll-based animation CSS API to completely revamp our web experience.

It's a promising frontier. This post may attract a future scrollable web designer.

Ps. I have created flashcards for HTML, Javascript etc. Check them out!

If I could tell my younger self a few rules, they would boost my career.

1. Treat design like medicine; don't get attached.

If it doesn't help, you won't be angry, but you'll try to improve it. Designers blame others if they don't like the design, but the rule is the same: we solve users' problems. You're not your design, and neither are they. Be humble with your work because your assumptions will often be wrong and users will behave differently.

2. Consider your design flawed.

Disagree with yourself, then defend your ideas. Most designers forget to dig deeper into a pattern, screen, button, or copywriting. If someone asked, "Have you considered alternatives? How does this design stack up? Here's a functional UX checklist to help you make design decisions.

3. Codeable solutions.

If your design requires more developer time, consider whether it's worth spending more money to code something with a small UX impact. Overthinking problems and designing abstract patterns is easy. Sometimes you see something on dribbble or bechance and try to recreate it, but it's not worth it. Here's my article on it.

4. Communication changes careers

Designers often talk with users, clients, companies, developers, and other designers. How you talk and present yourself can land you a job. Like driving or swimming, practice it. Success requires being outgoing and friendly. If I hadn't said "hello" to a few people, I wouldn't be where I am now.

5. Ignorance of the law is not an excuse.

Copyright, taxation How often have you used an icon without checking its license? If you use someone else's work in your project, the owner can cause you a lot of problems — paying a lot of money isn't worth it. Spend a few hours reading about copyrights, client agreements, and taxes.

6. Always test your design

If nobody has seen or used my design, it's not finished. Ask friends about prototypes. Testing reveals how wrong your assumptions were. Steve Krug, one of the authorities on this topic will tell you more about how to do testing.

7. Run workshops

A UX designer's job involves talking to people and figuring out what they need, which is difficult because they usually don't know. Organizing teamwork sessions is a powerful skill, but you must also be a good listener. Your job is to help a quiet, introverted developer express his solution and control the group. AJ Smart has more on workshops here.