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I’ll set out my stall and let you know I am still an AI skeptic. Heck, I still wrap “AI” in quotes a lot of the time I talk about it. I am, however, skeptical of the present, rather than the future. I wouldn’t say I’m positive or even excited about where AI is going, but there’s an inevitability that in development circles, it will be further engrained in our work.

We joke in the industry that the suggestions that AI gives us are more often than not, terrible, but that will only improve in time. A good basis for that theory is how fast generative AI has improved with image and video generation. Sure, generated images still have that “shrink-wrapped” look about them, and generated images of people have extra… um… limbs, but consider how much generated AI images have improved, even in the last 12 months.

There’s also the case that VC money is seemingly exclusively being invested in AI, industry-wide. Pair that with a continuously turbulent tech recruitment situation, with endless major layoffs and even a skeptic like myself can see the writing on the wall with how our jobs as developers are going to be affected.

The biggest risk factor I can foresee is that if your sole responsibility is to write code, your job is almost certainly at risk. I don’t think this is an imminent risk in a lot of cases, but as generative AI improves its code output — just like it has for images and video — it’s only a matter of time before it becomes a redundancy risk for actual human developers.

Do I think this is right? Absolutely not. Do I think it’s time to panic? Not yet, but I do see a lot of value in evolving your skillset beyond writing code. I especially see the value in improving your soft skills.

What are soft skills?

A good way to think of soft skills is that they are life skills. Soft skills include:

• communicating with others,
• organizing yourself and others,
• making decisions, and
• adapting to difficult situations.

I believe so much in soft skills that I call them core skills and for the rest of this article, I’ll refer to them as core skills, to underline their importance.

The path to becoming a truly great developer is down to more than just coding. It comes down to how you approach everything else, like communication, giving and receiving feedback, finding a pragmatic solution, planning — and even thinking like a web developer.

I’ve been working with CSS for over 15 years at this point and a lot has changed in its capabilities. What hasn’t changed though, is the core skills — often called “soft skills” — that are required to push you to the next level. I’ve spent a large chunk of those 15 years as a consultant, helping organizations — both global corporations and small startups — write better CSS. In almost every single case, an improvement of the organization’s core skills was the overarching difference.

The main reason for this is a lot of the time, the organizations I worked with coded themselves into a corner. They’d done that because they just plowed through — Jira ticket after Jira ticket — rather than step back and question, “is our approach actually working?” By focusing on their team’s core skills, we were often — and very quickly — able to identify problem areas and come up with pragmatic solutions that were almost never development solutions. These solutions were instead:

• Improving communication and collaboration between design and development teams
• Reducing design “hand-off” and instead, making the web-based output the source of truth
• Moving slowly and methodically to move fast
• Putting a sharp focus on planning and collaboration between developers and designers, way in advance of production work being started
• Changing the mindset of “plow on” to taking a step back, thoroughly evaluating the problem, and then developing a collaborative and by proxy, much simpler solution

Will improving my core skills actually help?

One thing AI cannot do — and (hopefully) never will be able to do — is be human. Core skills — especially communication skills — are very difficult for AI to recreate well because the way we communicate is uniquely human.

I’ve been doing this job a long time and something that’s certainly propelled my career is the fact I’ve always been versatile. Having a multifaceted skillset — like in my case, learning CSS and HTML to improve my design work — will only benefit you. It opens up other opportunities for you too, which is especially important with the way the tech industry currently is.

If you’re wondering how to get started on improving your core skills, I’ve got you. I produced a course called Complete CSS this year but it’s a slight rug-pull because it’s actually a core skills course that uses CSS as a context. You get to learn some iron-clad CSS skills alongside those core skills too, as a bonus. It’s definitely worth checking out if you are interested in developing your core skills, especially so if you receive a training budget from your employer.

Wrapping up

The main message I want to get across is developing your core skills is as important — if not more important — than keeping up to date with the latest CSS or JavaScript thing. It might be uncomfortable for you to do that, but trust me, being able to stand yourself out over AI is only going to be a good thing, and improving your core skills is a sure-fire way to do exactly that.

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The Importance of Investing in Soft Skills in the Age of AI originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

Advanced UX goes beyond Nielsen’s principles, focusing on cognitive load, workflow continuity, and customization to empower expert users. These pro-level heuristics enhance efficiency and resilience in high-performance environments.

In 2024, Artificial Intelligence (AI) hit the limelight with major advancements. The problem with reaching common knowledge and so much public attention so quickly is that the term becomes ambiguous. While we all have an approximation of what it means to “use AI” in something, it’s not widely understood what infrastructure having AI in your project, product, or feature entails.

So, let’s break down the concepts that make AI tick. How is data stored and correlated, and how are the relationships built in order for an algorithm to learn how to interpret that data? As with most data-oriented architectures, it all starts with a database.

Data As Coordinates

Creating intelligence, whether artificial or natural, works in a very similar way. We store chunks of information, and we then connect them. Multiple visualization tools and metaphors show this in a 3-dimensional space with dots connected by lines on a graph. Those connections and their intersection are what make up for intelligence. For example, we put together “chocolate is sweet and nice” and “drinking hot milk makes you warm”, and we make “hot chocolate”.

We, as human beings, don’t worry too much about making sure the connections land at the right point. Our brain just works that way, declaratively. However, for building AI, we need to be more explicit. So think of it as a map. In order for a plane to leave CountryA and arrive at CountryB it requires a precise system: we have coordinates, we have 2 axis in our maps, and they can be represented as a vector: [28.3772, 81.5707].

For our intelligence, we need a more complex system; 2 dimensions will not suffice; we need thousands. That’s what vector databases are. Our intelligence can now correlate terms based on the distance and/or angle between them, create cross-references, and establish patterns in which every term occurs.

A specialized database that stores and manages data as high-dimensional vectors. It enables efficient similarity searches and semantic matching.

Querying Per Approximation

As stated in the last session, matching the search terms (your prompt) to the data is the exercise of semantic matching (it establishes the pattern in which keywords in your prompt are used within its own data), and the similarity search, the distance (angular or linear) between each entry. That’s actually a roughly accurate representation. What a similarity search does is define each of the numbers in a vector (that’s thousands of coordinates long), a point in this weird multi-dimensional space. Finally, to establish similarity between each of these points, the distance and/or angles between them are measured.

This is one of the reasons why AI isn’t deterministic — we also aren’t — for the same prompt, the search may produce different outputs based on how the scores are defined at that moment. If you’re building an AI system, there are algorithms you can use to establish how your data will be evaluated.

This can produce more precise and accurate results depending on the type of data. The main algorithms used are 3, and Each one of them performs better for a certain kind of data, so understanding the shape of the data and how each of these concepts will correlate is important to choosing the correct one. In a very hand-wavy way, here’s the rule-of-thumb to offer you a clue for each:

• Cosine Similarity
  Measures angle between vectors. So if the magnitude (the actual number) is less important. It’s great for text/semantic similarity
• Dot Product
  Captures linear correlation and alignment. It’s great for establishing relationships between multiple points/features.
• Euclidean Distance
  Calculates straight-line distance. It’s good for dense numerical spaces since it highlights the spatial distance.

INFO

When working with non-structured data (like text entries: your tweets, a book, multiple recipes, your product’s documentation), cosine similarity is the way to go.

Now that we understand how the data bulk is stored and the relationships are built, we can start talking about how the intelligence works — let the training begin!

Language Models

A language model is a system trained to understand, predict, and finally generate human-like text by learning statistical patterns and relationships between words and phrases in large text datasets. For such a system, language is represented as probabilistic sequences.

In that way, a language model is immediately capable of efficient completion (hence the quote stating that 90% of the code in Google is written by AI — auto-completion), translation, and conversation. Those tasks are the low-hanging fruits of AI because they depend on estimating the likelihood of word combinations and improve by reaffirming and adjusting the patterns based on usage feedback (rebalancing the similarity scores).

As of now, we understand what a language model is, and we can start classifying them as large and small.

Large Language Models (LLMs)

As the name says, use large-scale datasets &mdash with billions of parameters, like up to 70 billion. This allows them to be diverse and capable of creating human-like text across different knowledge domains.
Think of them as big generalists. This makes them not only versatile but extremely powerful. And as a consequence, training them demands a lot of computational work.

Small Language Models (SLMs)

With a smaller dataset, with numbers ranging from 100 million to 3 billion parameters. They take significantly less computational effort, which makes them less versatile and better suited for specific tasks with more defined constraints. SLMs can also be deployed more efficiently and have a faster inference when processing user input.

Fine-Tunning

Fine-tuning an LLM consists of adjusting the model’s weights through additional specialized training on a specific (high-quality) dataset. Basically, adapting a pre-trained model to perform better in a particular domain or task.

As training iterates through the heuristics within the model, it enables a more nuanced understanding. This leads to more accurate and context-specific outputs without creating a custom language model for each task. On each training iteration, developers will tune the learning rate, weights, and batch-size while providing a dataset tailored for that particular knowledge area. Of course, each iteration depends also on appropriately benchmarking the output performance of the model.

As mentioned above, fine-tuning is particularly useful for applying a determined task with a niche knowledge area, for example, creating summaries of nutritional scientific articles, correlating symptoms with a subset of possible conditions, etc.

Fine-tuning is not something that can be done frequently or fast, requiring numerous iterations, and it isn’t intended for factual information, especially if dependent on current events or streamed information.

Enhancing Context With Information

Most conversations we have are directly dependent on context; with AI, it isn’t so much different. While there are definitely use cases that don’t entirely depend on current events (translations, summarization, data analysis, etc.), many others do. However, it isn’t quite feasible yet to have LLMs (or even SLMs) being trained on a daily basis.

For this, a new technique can help: Retrieve-Augmented Generation (RAG). It consists of injecting a smaller dataset into the LLMs in order to provide it with more specific (and/or current) information. With a RAG, the LLM isn’t better trained; it still has all the generalistic training it had before — but now, before it generates the output, it receives an ingest of new information to be used.

INFO

RAG enhances the LLM’s context, providing it with a more comprehensive understanding of the topic.

For an RAG to work well, data must be prepared/formatted in a way that the LLM can properly digest it. Setting it up is a multi-step process:

1. Retrieval
   Query external data (such as web pages, knowledge bases, and databases).
2. Pre-Processing
   Information undergoes pre-processing, including tokenization, stemming, and removal of stop words.
3. Grounded Generation
   The pre-processed retrieved information is then seamlessly incorporated into the pre-trained LLM.

RAG first retrieves relevant information from a database using a query generated by the LLM. Integrating an RAG to an LLM enhances its context, providing it with a more comprehensive understanding of the topic. This augmented context enables the LLM to generate more precise, informative, and engaging responses.

Since it provides access to fresh information via easy-to-update database records, this approach is mostly for data-driven responses. Because this data is context-focused, it also provides more accuracy to facts. Think of a RAG as a tool to turn your LLM from a generalist into a specialist.

Enhancing an LLM context through RAG is particularly useful for chatbots, assistants, agents, or other usages where the output quality is directly connected to domain knowledge. But, while RAG is the strategy to collect and “inject” data into the language model’s context, this data requires input, and that is why it also requires meaning embedded.

Embedding

To make data digestible by the LLM, we need to capture each entry’s semantic meaning so the language model can form the patterns and establish the relationships. This process is called embedding, and it works by creating a static vector representation of the data. Different language models have different levels of precision embedding. For example, you can have embeddings from 384 dimensions all the way to 3072.

In other words, in comparison to our cartesian coordinates in a map (e.g., [28.3772, 81.5707]) with only two dimensions, an embedded entry for an LLM has from 384 to 3072 dimensions.

Let’s Build

I hope this helped you better understand what those terms mean and the processes which encompass the term “AI”. This merely scratches the surface of complexity, though. We still need to talk about AI Agents and how all these approaches intertwine to create richer experiences. Perhaps we can do that in a later article — let me know in the comments if you’d like that!

Meanwhile, let me know your thoughts and what you build with this!

Further Reading on SmashingMag

• “Using AI For Neurodiversity And Building Inclusive Tools,” Pratik Joglekar
• “How To Design Effective Conversational AI Experiences: A Comprehensive Guide,” Yinjian Huang
• “When Words Cannot Describe: Designing For AI Beyond Conversational Interfaces,” Maximillian Piras
• “AI’s Transformative Impact On Web Design: Supercharging Productivity Across The Industry,” Paul Boag

Product Hunt, once a hub for diverse and innovative products, has become oversaturated with repetitive AI tools, crowding out other types of creativity and frustrating users. To regain its appeal, the platform needs to balance its focus on AI with better curation and support for a wider range of product categories.

Web designer salaries vary globally, depending on experience and location. Beginners typically earn modest wages while building foundational skills, while advanced designers command higher salaries by specializing in UX/UI or leading projects. This article explores salary ranges across countries, providing insights for designers planning their careers.

Whether your law firm is new with a handful of clients or you have been around for twenty years, your marketing strategy should include search engine optimization or SEO. 

Combined with the best of 2024’s marketing tools and software, SEO can help you attract the right clients for your expertise. It can also help you build a completely effective online presence.

However, it is important to note that SEO for law firms does not mean adding a couple of legal-sounding words to your website. This is the first and biggest mistake most law firms make when trying to incorporate SEO into their marketing efforts. 

Here are five more mistakes you must avoid if you want to get more clients and improve your online visibility.

Not Using Local SEO Strategies

People looking for legal services tend to look in their local area first. Your clients will not want to drive for miles whenever they need to consult with your firm. This is why local SEO strategies are crucial. If you overlook or ignore local SEO, your online presence will be incomplete. 

Moreover, your Google Business Profile will not work as effectively. Your website will be missing critical location-specific keywords. 

You can avoid this issue by updating your Google Business Profile. If you do not have a listing, claim your profile on Google and fill out your listing with details about your law firm. Include your expertise, services, phone number, address, and business hours. 

Encourage your existing clients to leave reviews on your profile and add photos of your location. The more visible you make your profile, the easier it will be for potential clients to find you. 

Most importantly, add local SEO keywords to your profile and website. For example, if your law firm is in New York, add the location to keywords relevant to your firm’s services. You can target potential clients near New York by adding keyphrases like ‘car accident lawyer near New York.’ 

Local SEO strategies also include adding business listings to local business directories. Ensure your name, address, and phone numbers are correct and consistent in all directories. 

Not Optimizing Your Website for Mobile Use

More than half of all web traffic comes from mobile devices. Your law firm cannot afford not to have a mobile-friendly website. If you do not optimize your website for mobile use, you will lose clients and hurt your law firm’s SEO ranking. A mobile-friendly website means more people can find your law firm, especially if your SEO is on point. 

The problem is that a non-optimized website will load incredibly slowly on a mobile device. If your website does not load within seconds, visitors will move on, and you will not have a chance of getting their business in the future. 

If your website is not optimized, the content will also be difficult to read, even if it does load quickly. The interface may not be as intuitive as you intended it to be. This makes for a very frustrating user experience. 

In 2024 and beyond, you must have a responsive website. This means the website will adjust automatically to the screen on which it is opened. Responsive design also means images are compressed, which reduces loading speed. Furthermore, depending on the website builder you use, responsive design can also mean that you do not need dozens of plugins to keep your website going. 

Using Outdated SEO Techniques

There is no excuse for using outdated SEO techniques in 2024. Search engine algorithms continue to be upgraded to deliver better search results for online users. This means that outdated SEO strategies will not work and might get your website or Google listing suspended. 

Do not buy backlinks from low-authority websites. Backlinks are important for SEO, but low-authority websites will not give you the necessary value. It’s also a bad idea to use hidden text or links to try and fool the search engine algorithm. 

Instead of trying to take shortcuts, focus on creating quality content. Add useful SEO keywords naturally into your content. Never overload your website with keywords to make your content seem relevant; SEO simply does not work this way. 

Once you start working on a backlink strategy, make sure you get and earn your links from reputable sources only. This may include trustworthy legal publications and reputable local news outlets. 

Not Having a Content Marketing Strategy

The goal of SEO is to get potential clients to your website. Once they get to your website, you must provide valuable information about how your law firm can help them. Low-quality content is not going to help your firm or your clients. Neither is neglecting to update outdated articles and web pages. 

Instead of posting content randomly and hoping for the best, take the time to create a content calendar. Plan your content weeks ahead of time so you get enough time to do research and create high-quality content. 

If you are not a writer, hire a professional to create your content. All your website and social media content must be unique and diverse. This means using original content. It also means creating different types of content like videos, infographics, blogs, and podcasts. 

Never Tracking Your SEO Performance

Once you’ve implemented SEO strategies, it does not mean you can sit back and forget about them. You have to keep track of each strategy’s performance. If you do not do this, you will never know what works and what doesn’t. 

Google Analytics and Search Console are valuable tools to help you track key performance metrics like bounce and click-through rates. If you do not understand metrics like organic traffic and keyword rankings, get a law firm SEO specialist to guide you. 

A legal marketing agency or specialist can track all the necessary metrics and do A/B testing to see what drives more conversions. They can also implement all your SEO strategies in the first place. 

SEO Can Mean the Difference Between Gaining and Losing Clients

SEO strategies are not a one-off effort. You need to keep at it to see ongoing results. But the reward is well worth it. Your law firm will be highly visible online to local and surrounding clients. Your search rankings will go up, and your website traffic will increase. Ultimately, this will mean more clients and more success for your business.

Featured image by Sebastian Pichler on Unsplash

The post 5 Common SEO Mistakes Your Law Firm Must Avoid appeared first on noupe.

Thanks to the internet and a growing raft of online, AI-powered tools, it’s easier than ever to start a business. The downside of this entrepreneurial free-for-all? It’s harder than ever to make that business stand out. 90% of businesses say their industry has gotten more competitive in recent years.

Every new business should be aiming for a strong brand name, but in saturated markets such as beauty, fashion, food, and tech it’s even more important to build your brand on a powerful, memorable name. Still, these markets offer subtle opportunities for brands too, which can be capitalized on with the right name — one which differentiates you from established competitors, and emotionally resonates with an underserved niche market.

We live in a noisy world. Here’s how to name your new brand to be heard.

How Your Name Eases Entry into a Competitive Market

Your name is your first connection to your customers, and your first point of differentiation from your competitors. Here’s how a great name is a platform for success in a saturated market.

Your Name Gains Instant Recognition

A memorable name contributes to top-of-mind awareness, or brand recall: the ability of customers to remember your brand, without being offered options. This makes you the first choice for customers, like how the feeling of thirst is accompanied by a craving for Coca-Cola in many individuals. In crowded markets, top-of-mind awareness is key to breaking through.

Your Name Emphasizes Differentiation

With many similar products or services available in busy markets, differentiating your offering becomes increasingly important. Your name is your first opportunity to highlight how you’re different from your competitors. It could exude cutting-edge innovation with metaphor or visual imagery, or highlight your unique selling proposition.

Your Name Counters Consumer Skepticism

In saturated markets, consumers are skeptical of new entrants, questioning the need for another option in an already crowded space. A strong name makes an emotional connection with consumers by hinting at your values and mission. This builds trust and the perception of authenticity, bringing consumers on board with your brand.

Naming Strategies for Saturated Markets

So how do you craft a name that not only stands out but signals to your audience that you’re different from all the rest? Here are some naming strategies for brands entering highly competitive markets.

1. Create a Unique Word

Inventing a unique word for your brand name can make it instantly memorable and distinct. Consider using linguistic techniques such as blending words, altering spellings, or creating portmanteaus to develop a name that doesn’t exist in the dictionary. This approach allows you to establish a strong brand identity while ensuring domain availability and trademark protection.

Challenger soda brand Olipop is now outperforming Pepsi in some retailers, an extraordinary achievement in the highly competitive carbonated beverage market. Olipop is a clean-sounding and memorable unique name, around which this up-and-coming business is building a strong brand.

2. Use Metaphors

Metaphors can infuse your brand name with deeper meaning and emotional resonance. They evoke imagery and associations that align with your brand’s values and purpose and form connections in the minds of customers that reinforce memorability.

Brands entering into crowded markets can particularly benefit from metaphors that emphasize their innovation, and help differentiate them from pre-existing options. Tesla, as a leading electric vehicle manufacturer, has leaned on innovation and technology in its brand positioning. The name, which recalls inventor Nikolai Tesla is a powerful metaphor for these attributes.

3. Incorporate Visual Imagery

A name that conjures vivid imagery can captivate consumer attention and enhance brand recognition. Consider names that evoke a specific visual concept or a scene related to your product or service. This technique creates a mental picture that sticks in the consumer’s mind.

Red Bull is a great example of a brand name utilizing the power of imagery. What’s more, when they launched in 1987, the carbonated drink had two players, Coca-Cola and Pepsi, so they really had to stand out. With powerful visual imagery that evokes energy, daring, and even aggression, Red Bull has grown into the third most valuable soft drink brand in the world.

4. Create an Emotional Connection

Names that invoke positive emotions activate a part of the brain called the amygdala. This enhances the memorability of a name as it becomes hard-coded in consumers’ minds.  Focus on how your offering makes consumers feel rather than solely highlighting functional attributes, and choose a name that taps into consumer aspirations and desires.

D2C kitchenware brand Our Place creates an immediate sense of home and community for anyone hearing their name. When consumers purchase a frying pan or chopping board, they’re not simply buying a practical item, but they’re buying into the idea that cooking is an exercise in building connections with family and friends. By emphasizing emotion in your name, you can create a more personalized and engaging brand experience.

5. Raise an Eyebrow

In competitive markets, bucking trends is one way to be noticed. A name that courts controversy or emphasizes your difference will have an immediate impact on your customers and, if you back up the words with action, create a powerful brand that instantly challenges the traditional industry leaders.

Who Gives a Crap, the ethical toilet paper brand, is flush with success on this point. The (mildly) blue language, the very literal translation and the hint at an underlying mission have helped it grow to become the third-largest toilet roll brand in the UK, taking on the likes of Charmin and Kleenex.

Leveraging Name for Opportunity in Saturated Markets 

Saturated or highly competitive markets present obvious challenges for new brands, but there are also opportunities for new brands to make a big impact. You might be entering a saturated market based on your personal passion for the product or service you can offer — or maybe you’ve got a game-changing innovation that helps you stand out. Whatever the reason, choose your name strategically. 

• Reaching Underserved Segments

Saturated markets often have underserved niches or segments that established brands overlook. By identifying, analyzing and targeting these niches, entrepreneurs can name their brands to resonate with and reach this audience.

• Leveraging Innovation

Saturated markets are by no means static markets. Innovations, particularly in the age of AI, can allow brands to engage in ‘challenger mode’ and outmaneuver existing brands. Nor does innovation always have to mean new technology — it could be updated sustainable practices, or offering personalized experiences that set the brand apart. 77% of consumers associate creative brand names with innovative offerings, so choose a name that emphasizes your innovative brand position when entering new markets with an updated offering.

• Building Authentic Connections

While existing brands have a headstart in brand equity, they’re often slow to react to changed consumer expectations. In today’s world, consumers, particularly younger audiences, value authenticity and transparency and 88% of consumers say authenticity guides their brand choices. If the big hitters in your industry are positioning themselves as traditional, reliable and dull, then new and upcoming brands like yours can connect with their audience on a personal level. Choose a name that builds relationships based on shared values and relatable storytelling. 

Layers of Meaning: The Best Names Combine Strategies

The best names work on multiple levels. Who Gives a Crap combines literal and metaphorical meanings and Feeld, a dating app taking on the big three of Bumble, Hinge and Tinder, created a unique word that hints at ‘playing the field’ as well as ‘feelings’.

Any brand entering a saturated market needs to go to extra lengths to make themselves heard. Understanding your overall strategy — whether it’s your customer segmentation, innovative offering or authentic approach that customers will love — can guide you to an impactful business name.

Featured image by l ch on Unsplash

The post Naming in a Noisy World: How to Stand Out in Saturated Markets appeared first on noupe.

Incognito mode, often marketed as a private browsing tool, offers minimal privacy by only preventing local history and cookies from being saved. It fails to protect users from being tracked by ISPs, employers, websites, or government surveillance, creating a false sense of security.