Credits : Ciol

This article presents a hypothesis on what the (not too far in the future) world of AI assisted Software Development will look like. In a line, it’ll read something like this; concepts governing software creation will stay the same, but the pipeline is going to look incredibly different. At almost every stage, AI will assist humans and make the process more efficient, effective and enjoyable.

Our hypothesis is supported by predictions that, the AI industry’s revenue will reach $1.2 trillion by the end of this year, up 70% from a year ago. Further, AI-derived business value is expected to reach $3.9 trillion by 2022. We have also factored in observation of three main themes over the last decade; compute power, data and sophisticated developer tools.

More Compute Power: Easy access to elastic compute power and public clouds have empowereddevelopers, enterprises and tool creators to quickly run heavier analysis workloads, through parallelization. According to IDC, cloud-based infrastructure spends will reach 60% of all IT infrastructure by 2020.

More Data: Improved processing power will see digital leaders investing in better collection and utilization of data – 90% of the world’s data was created last year but, utilization is at 1%. It’s slated to grow to 3% or 4% by 2020

Integration and Distribution of Systems: The integration of disconnected systems using APIs coupled with microservices pattern enables the distribution of previously monolithic systems. This leads to a powerful mix that leverages tools and processes (required for software development) composed of multiple systems, running in different places.

The software creation process consists of 3 phases. They can be further split into 9 different task categories. Interestingly, only some of these categories have seen more investment in AI powered tooling than others. In the course of this article, let’s discuss some of the instances where AI will assist technologists in software development by taking over data analysis and prediction capabilities. Such an evolution will permit technologists to have more time to focus on judgement and creativity related tasks that machines can’t take on.

There is an increasing presence for what we call Intelligent Development Tools. We believe this turn of events is because of the 3 themes, and the growing clout of developers, that have caused dozens of startups to offer developer-focused services such as automated refactoring, testing and code generation. The evolution of these tools can be compartmentalized into 3 levels of sophistication.

The Levels of Sophistication

The first focused on the automation of manual tasks that increased reliability and efficiency of software creation. For example, the test automation reduced cycle time through parallelizing which shortened feedback loops. The deployment automation improved reliability using repeatable scripts. However, it’s still been humans who analyzed and acted on the feedback.

The next level of sophistication covered tools that permitted machines to take decisions based on fixed rules. Auto-scaling infrastructure is a good example of this. Machines could now determine the required compute power to service loads being handled by an application, while humans configured the bounds and steps that the compute power could scale.

The final level of sophistication will enable machines to evolve without human intervention – analyzing data and learning from it, will empower tools to mutate or augment rules that allow them to take increasingly complex decisions. We wanted to share a few ideas of how AI can augment the software development cycle.

The Software Development Cycle

One of the most common approaches to building AI use cases is leveraging the neural network; a computer system modelled on the human brain and nervous system. The popular approach involves developing a single algorithm that encompasses the intermediate processing steps of multiple neural net layers, leading to direct output from the input data. This process is successful and provides very good results when large samples of labelled data is available. The challenge with this method is that the internal processing of learning is not clearly explainable and sometimes gets difficult to troubleshoot for accuracy.

Ideation – Analysis of usage data to find anomalies/unexpected behaviour.

Prototyping – Low / no-code tools to create clickable prototypes from hand-drawn sketches.

Validation – Leverage past usage data to test new designs/ideas.

Development – Automated code generation and refactoring.

Requirements Breakdown – Generation of positive and negative acceptance criteria based on past requirements.

Testing – Automating test creation and maintenance.

Deploy – Ensure zero impact deployments by predicting right time to deploy and rate of the roll-out.

Monitoring – Use Telemetry Data to predict hardware/system failure.

Maintenance – Automate identification and removal of unused features.

One of the most common approaches to building AI use cases is leveraging the neural network; a computer system modelled on the human brain and nervous system. The popular approach involves developing a single algorithm that encompasses the intermediate processing steps of multiple neural net layers, leading to a direct output from the input data. This process is successful and provides very good results when large samples of labelled data is available. The challenge with this method is that the internal processing of learning is not clearly explainable and sometimes gets difficult to troubleshoot for accuracy.

AI Assistance

Ideation Augmented: Take the example of an e-commerce website. Here, people analyze data to find where users drop-off during an ordering funnel and come up with ideas to improve conversion. In the future, we could have machines that blend usage analytics with performance data to derive if slow transactions are the cause for drop-offs. Additionally, these machines could also identify faulty code that when fixed, will improve performance.

Testing Augmented: Writing tests for legacy systems, even with documentation, is very hard. Automated test creation tools that leverage AI to map out the application’s functionality, using usage and code analytics, allow teams to quickly build a safety net around such legacy systems. This allows technologists to make changes without breaking existing functionality.

Maintenance Augmented: A large part of maintenance-related costs, today, are spent on managing redundant features. Identification of these redundancies is a complex error-prone process because people have to correlate data with multiple sources. Allowing AI tools to take up this role of connecting and referencing data across sources will automate marking of unessential features and associated code.

Given the nature of evolution in the dynamic software development world, here’s our recommendation for how to prepare and focus efforts –

1. Recognize and leverage elastic infrastructure which ensures the ability to add and remove resources ‘on the go’ to handle the load variation

2. Equip your teams to strategically collect and process data, an invaluable asset whose volume will only increase given the prevalence of emerging tech like voice, gesture etc.

Include a stream within investment strategies that grow AI assisted software creation – rule based intelligent tools and self-learning tools.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Searcherp.techtarget

Information security risks in supply chain software are becoming increasingly prevalent, particularly as global companies have become more dependent on third-party vendors.

According to Symantec, more and more attackers are injecting malware into the supply chain to infiltrate organizations. In fact, there was a 200% increase in these attacks in 2017 — one every month compared to four attacks annually in previous years.

Supply chain software offers a new arena to threat actors intent on penetrating enterprise networks, said Peter Nilsson, vice president of strategic initiatives at MP Objects, a provider of supply chain orchestration software in Boston.

“Previously, people had their ERPs behind their very tight firewalls, and no one from the outside could get in without being monitored by the hawk eyes of the IT department,” he said. “Now, enterprises are saying, ‘We need to collaborate with our partners and we have to open up our ERP and let them in.'”

But if those third parties don’t have adequate security, attackers can infiltrate their systems to attack the enterprise.

Any time an enterprise introduces software into the mix of its supply chain, it runs the risk of cybersecurity issues, said Justin Bateh, supply chain expert and professor of business at Florida State College in Jacksonville, Fla. Most risks are caused by not having the proper controls in place for third-party vendors.

“There are many low-tier suppliers that will have weak information security practices, and not having clean and limited guidelines for these providers about security expectations will pose a significant threat,” he said.

Credits : Datanami

Application development has become a cloud-focused initiative in many enterprises. Consequently, the languages, tools, and platforms needed to support today’s development initiatives are rapidly evolving.

Application development is also a discipline in which data science is assuming a greater role. To support the growing range of development projects with artificial intelligence (AI) at their core, enterprises are having to continually transform DevOps workflows to support continual building, training, and iteration of deep learning, machine learning, and other statistical models for deployment into production cloud environments.

As we look ahead to 2019, we expect to see the following dominant trends in enterprise application development:

• Open development ecosystems will be at the heart of every tool vendor’s go-to-market strategy: Practically every vendor—large and small, established and startup—has pinned its future on its participating in the open-source community. Some have taken that open commitment even further in 2018. In the year gone by, Microsoft took on a special status in the open-source world with its acquisition of GitHub, the foremost DevOps platform in the open-source ecosystem. In 2019, Microsoft will continue to abide by its express commitment to allow

• GitHub to operate in vendor-agnostic fashion in support of any language, license, tool, platform, or cloud that developers wish to use. In addition, Microsoft is likely to open-source more of its software projects and to refrain from asserting IP claims on a wider range of its IP patents, consistent with its recent joining of the Open Invention Network. The vendor is likely to assume a more proactive role in the open-source community as an evangelist for the new era of post-proprietary software development.
• Serverless will dominate new cloud-native application development: Cloud application developers flocked to functional programming, also known as serverless, in a big way in 2018. This trend shows no signs of slowing down, as evidenced by the growing range of serverless tools, interfaces, projects, and other initiatives that have come to market this year. It’s also evident in the eagerness with which developers are adopting these offerings. In 2019, we’re likely to see the open-source Knative serverless project implemented by many vendors beyond its core developers Google, Pivotal, IBM, Red Hat and SAP, with Microsoft, AWS, and Oracle likely to come on board during the year. In addition, it’s very likely that Knative will be submitted to CNCF for development and governance under its growing cloud-native stack.

Developers will build hybrid serverless and containerized cloud applications: Hybrid clouds are becoming common in many enterprise IT strategies. At the application level, more developers are building hybridized cloud applications that incorporate data, workloads, and other resources that span public and private clouds. In 2019, we’ll see more development tools that enable hybridization of heterogeneous containerization and serverless environments.

• Adoption of the emerging Knative project will accelerate the creation of hybridized serverless applications that run over federated Kubernetes multiclouds.
• Transactional applications will shift toward the cloud’s edges: Conversational commerce, Alexa style, is the harbinger of the more pervasive edge-commerce future that awaits us all. In 2019, developers will increasingly build transactional applications that are designed to operate over and entirely distributed IoT, edge, mesh, and other cloud fabrics. To support these radically decentralized environments, more enterprises will use blockchains and smart contracts to provide immutable logs, enable edge-to-edge transactional integrity, and ensure full transparency and accountability. However, it will take still 2-3 years, at least, for all necessary technological, commercial, regulatory, and other standard practices to coalesce into a new edge-based transactional backplane for any-to-any e-commerce.
• Data-science workbenches will adopt standardized cloud-native DevOps: AI is the heart of modern applications. Developing AI applications for the cloud increasingly requires the building of containerized microservices that are orchestrated within and across Kubernetes clusters over DevOps workflows. In the past year, the AI community has developed an open-source project called Kubeflow that provides a framework-agnostic pipeline for making AI

• microservices production-ready across multi-framework, multi-cloud computing environments. Early adopters of Kubeflow include Agile Stacks, Alibaba Cloud, Amazon Web Services, Google, H20.ai, IBM, NVIDIA, and Weaveworks. In 2019, we’ll see the project mature and be implemented more broadly in commercial AI DevOps toolchain solutions. In this way, more enterprise app-development teams will be able to align their DevOps processes across teams working on AI and other cloud-native development projects.
• Python, Kotlin, and Rust will become core languages for building new applications: Mobile application developers will continue to rely on JavaScript, Java, Objective-C, and PHP. In 2019, other languages will grow in importance in developer toolkits to address the requirements of many hot new applications. Most importantly, Python has become the go-to language for AI, Internet of Things (IoT), Web, mobile, and gaming apps, owing to the fact that it’s easy to learn and use on practically any platform. Kotlin’s superior flexibility may enable it to replace Java at some point in the standard Android developer’s repertoire, while Swift’s compact, clear syntax is building momentum among iOS developers. Rust’s support for memory-safe concurrency gives it a leg up on other languages for IoT, embedded, and other applications that require always-on 24×7 robustness.
• Client-side AI frameworks will transform Web application development: JavaScript frameworks such as React are the heart of rich application development for Web, mobile, and other client-side edge application platforms. In 2019, more developers will build edge applications in JavaScript frameworks that enable richly interactive browser-based experiences, platform-native performance parity, and AI-powered client-side intelligence. GPU-accelerated client-side AI will become the heart of edge applications, as adoption of such open-source frameworks as js,Brain.js, and TensorFire continues to grow.

• Advances in GPUs will stimulate innovation in immersive applications: Users are adopting augmented, mixed, and virtual reality applications in a wider range of industrial, business, scientific, and consumer uses. Gaming, in particular, has been a huge growth area for these immersive applications, owing in part to the availability of high-performance, low-cost GPUs on more client platforms. In 2019, we’ll see this trend accelerate as the new Nvidia Turing GPUs, with their lightning-fast real-time raytracing, come to market in support of next-generation immersive apps that combine photorealistic visuals with AI-driven contextual intelligence. Developers will build a new generation of GPU-aware smart camera applications that leverage the client-side AI frameworks, such as TensorFlow.js, to support fluidly continuous immersive visuals even in disconnected and intermittently connected usage scenarios.
• Robotic process automation will become a principal development platform for AI-driven apps: Robotic process automation has been one of chief growth sectors in the software market over the past year. As an enabler for developing automation apps that emulate how people carry out myriad tasks, RPA has become a principal use case for AI in the workplace. Though traditionally used in RPA to infer application logic from artifacts that are externally accessible, AI’s role has expanded to enable creation of intelligent bots for business process automation. In 2019, we’ll see a growing role for AI in RPA to enable development of bots that can be orchestrated as microservices across Kubernetes environments. Through the adoption of cloud-native interfaces, RPA vendors will be able to address more IoT, edge, and multicloud opportunities.
• AI-augmented programming tools will make developers more productive: Software developers have long used automated code generation tools to lighten the load. Augmented programming refers to the next-generation of “no code,” “low code,” and other approaches for automating coding and other development tasks. In 2019, we expect to see more of these tools incorporate abstraction layers that allow

  developers to write declarative business logic that is then translated by tools into procedural programming code. In addition, more augmented programming tools will incorporate AI to generate code, by means of machine learning algorithms that have been trained on human-developed codebases maintained in GitHub and other repositories. More of these AI-augmented programming tools will rely on embedded graph models and leverage reinforcement learning to compile declarative specifications into code modules that are automatically built, trained, and refined to achieve the intended programming outcomes.

• Conversational user interfaces will grow less chatty but more useful: Chatbots have been a growing focus for application developers over the past several years. They’ve entered the consumer IoT and mobility arenas through Amazon Alexa, Google Assistant, and similar voice-activated appliance initiatives, while also finding their way into bot-powered text chat features in more enterprise applications. In 2019, we’ll see developers tap into sophisticated AI-powered digital assistant platforms such as Google Duplex to enable chatbots to automate more tasks predictively, thereby becoming paradoxically less chatty but more productive.

• Digital wellness will become a key mobile-app usability criterion: Users’ growing dependency on devices is undeniable, and it’s beginning to impact how developers approach building mobile applications. Though no one seriously believes that the average user will rely on their devices any less in the future, there is a growing repertoire of mobile application features—such as predictive automation of routine tasks and context-adaptive suppression of distracting notifications–that can help users unglue their frantic eyeballs from their smartphones now and then. Google’s emphasis on “digital wellness” features in its new Android 9 Pie operating system signals that we’ve entered a new era in mobile application development. In 2019, mobile application developers will leverage the predictive, adaptive, contextual and other usability features in this and other mobile platforms to help users stay sane, focused, and productive amid the growing glut of mobile devices in their lives.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Phoronix

While Windows users last week were greeted by the Radeon Software Adrenalin 2019 driver on the Linux side was the Radeon Software for Linux 18.50 release. The only listed public change for this 18.50 Linux hybrid driver build was RHEL 7.6 support, but I’ve since been able to test and confirm that the Radeon RX 590 is working with this new Linux driver package. As a result, here is a look at the Radeon RX 590 performance from this “AMDGPU-PRO” driver build compared to the latest open-source driver stack in the form of Linux 4.20 with Mesa 19.0-devel.

This article is offering an initial look at how the Radeon RX 590 graphics card performs between these two different AMD Linux graphics driver options. Radeon Software for Linux 18.50 is the first release with this RX 590 support due to the few AMDGPU kernel patches needed for getting this newest Polaris variant working out on Linux. Those RX 590 AMDGPU patches are in the process of landing for the Linux 4.20 mainline kernel.

When benchmarking the “PRO” 18.50 OpenGL/Vulkan driver components to the fully open-source alternative, Mesa 19.0-devel was used by the Padoka PPA from this Xubuntu 18.04 test box. No hardware changes were made between the different test driver configurations.

Using the Phoronix Test Suite, a variety of OpenGL and Vulkan Linux gaming benchmarks were carried out with the Sapphire Radeon RX 590 on both of the drivers.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Windpowerengineering

AnalySwift, a provider of efficient high-fidelity modeling software for composites and other advanced materials, announced the launch of its Academic Partner Program, through which it will offer universities no-cost licenses for academic research.

“We have always been close to the academic community, where both the SwiftComp and VABS software programs originated,” said Allan Wood, President & CEO of AnalySwift. “Our Academic Partner Program honors that tradition and broadens university access to cutting-edge simulation tools.”

Academic licenses of VABS and SwiftComp have always been available to universities for purchase, but the new program offers the licenses at no cost.

“Engineering faculty and students can benefit greatly from the full versions of the programs,” said Dr. Wenbin Yu, CTO of AnalySwift. “These are tools being used in industry to model complex, real composites including wind turbine and helicopter rotor blades, deployable space structures made from high-strain composites (HSC).”

The composite simulation programs are typically used in aerospace and mechanical engineering programs, such as for wind-turbine blades, with emerging applications in other areas.

“Since 2014, VABS has become our method of choice for rotor-blade structural design and optimization at our institute,” explained PhD student Tobias Pflumm at the Technical University of Munich. “With its help, we have successfully designed, tested and manufactured the rotor blades of our Autonomous Rotorcraft for Extreme Altitudes or AREA. We are currently using VABS extensively within a multi-disciplinary design environment to quantify uncertainties in the rotor blade design process.”

Inaugural members of the Academic Partner Program include the University of British Columbia (Composites Research Network), Technical University of Munich (Institute of Helicopter Technology), and Carleton University (Rotorcraft Research Group).

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Laravel-news

Yesterday the PHP team released PHP 7.3.0 for general availability (GA) and marked the third feature update to PHP 7. You can download the latest version from the official PHP downloads page. You can also get all the nitty-gritty details about PHP 7.3 by reading the PHP 7 changelog on the official site.

While today marks the day of the stable release, you will have to wait a bit longer for the migration guide, which should be available shortly.

If you haven’t read much on PHP 7.3 yet, here are the highlight features coming to PHP 7.3:

• Trailing Commas in function calls
• JSON_THROW_ON_ERROR flag for json_encode() and json_decode()
• Flexible Heredoc and Nowdoc syntax
• An is_countable() function
• list() reference assignment

Besides the flagship PHP 7.3 announcement, December 6th included five total PHP releases.

Among the five releases, PHP 5.6.39 and PHP 7.0.33 are both security releases considered to be the last release in their respective branches. You should view these versions as the final releases unless an unforeseen security issue warrants another release.

credits : Theserverside

The most popular articles on TheServerSide tend to be the highly technical ones that hit directly at the heart of the enterprise Java developer. While we report important news, discuss interesting trends and cover key conferences, the most-read articles tend to be the ones that deal directly with development. For 2018, our most popular articles were Java developer tutorials on new APIs, frameworks such as Spring or the tools and techniques server-side developers use to move code into production.

The most popular articles on TheServerSide tend to be the highly technical ones that hit directly at the heart of the enterprise Java developer. While we report important news, discuss interesting trends and cover key conferences, the most-read articles tend to be the ones that deal directly with development. For 2018, our most popular articles were Java developer tutorials on new APIs, frameworks such as Spring or the tools and techniques server-side developers use to move code into production.

Here is some of our best and essential coverage for developers on the front lines.

Credits : Zdnet

The rise of collaborative robots (cobots) is bringing robots and humans closer together than ever before, but robots still lack some important social graces. Researchers at Yale University have developed a robotic system that helps robots be more polite (and more importantly, more useful). They are teaching robots to respect ownership of objects.

“As robots begin to be used in our homes, schools, and workplaces, it is important that they be able to understand the social conventions that we use every day,” researcher Brian Scassellati explains.

Scassellati, along with Xuan Tan and Jake Brawer, developed a system to help robots distinguish between tools that they own and tools that other people or robots own (or are temporarily using).

Scassellati says, “I want my robot at home to understand that it is allowed to clear the dishes from the table when we have finished eating, but not before. I want my robot at work to know that it can borrow the screwdriver that I’m not using, but that it cannot borrow my coffee cup. Knowing how to work side-by-side with people is a skill that many robots will need.”

To accomplish this, the researchers combined two different kinds of machine learning representation: one that uses explicit rules, and another that uses experiences to predict an object’s likely owner.

They used a technique called Bayesian inference, which Scassellati explains is a statistical technique that the robot uses to keep track of how certain it is about a particular fact or idea in such a way that it can update that certainty as more information becomes available.

The research is pre-published on arXiv. According to the paper, “Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects.”

The researchers used Baxter robot (from now defunct Rethink Robotics) to demonstrate how their software system works, but the system itself could be used on other robots. Through both simulated and real-world experiments, they demonstrated that robots could use their system to complete tasks while respecting ownership rules.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Technotification

There’s so much hype about frontend technologies like Vue.js, AngularJS, ReactJS in the web development domain, it gets easy to ignore the backend. Normally, users are more likely to notice what’s served before their eyes and not take note of what goes behind the scenes. Backend web development involves the part of coding which is not seen by the user. Most often, backend web developers are responsible for the code behind juggling data to and from the frontend.

Needless to say, backend web development does deserve a lot of attention. As per the task, the right programming language should be chosen. Here are some interesting server-side web development programming languages worth considering:

1. PHP

PHP starts the list on basis of its popularity and widespread use. Not many people agree to love PHP, but chances are that like it or not, you may have to come across it someday. Almost all active web users have come across some sort of implementation of PHP code, considering about 75% of websites use PHP. PHP has come a long way since its creation way back in 1994. It has grown massively, and it is now being used on a multitude of servers and can be integrated with a large number of frameworks and templates.

It is also compatible with a good number of Content Management Systems. Interesting frameworks include Laravel, Symphony and CakePHP. Assuming prior knowledge with a scripting language, a newbie will find it very easy to learn PHP. With a huge community of developers, beginners have lots of resources to choose from. Little processing power demands make PHP fast.  Recently, the latest version, PHP7 came out, leaving many anticipating the next release.

2. Python

Gone are the days when Python was restricted to desktop programming. Now, in the realm of backend web development, python is rising among the ranks. Most server-side programmers nowadays have adopted Python as a substitute for PHP. It may have been around for decades, but it keeps on dominating and getting better with time. Python is popular, easy, with a very low learning curve.

Python is flexible and seen as some as the gateway programming language to learn other programming languages. We can’t speak of python web frameworks without mentioning Django. It is robust and capable of catering to all your backend needs. Feel free to check out these best Python frameworks.

3. Java

Just like Python, Java has risen to be a multipurpose language. It’s used for desktop software development, android development and most importantly, for web development. As much as one may love the newer technologies such as Node.js, Java has been around for a long time, which makes it stable and ultimately safer. If you’re going to venture into server-side development with Java, the Spring framework is a very versatile framework.

4. Ruby

Ruby has an active community, which translates to awesome documentation and impressive open-sourced dependencies. To add to the fun, ruby’s code is simple and also expansive. With the Ruby on Rails framework, a developer can get a RESTful API up and running and serving CRUD resources in hours. Not just prototypes, but functioning APIs with security, unit tests, functional tests and databases.

Somehonourable mentions include Perl, Scala, .NET and JavaScript (Node.js). Of course, this list may come across as opinionated to many. That being said, there are a lot of other options which many will like to see or some which should be expunged. Feel free to mention this in the comment section.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.

Credits : Entrepreneur

A successful brand can get you customers, and enable you to keep them at the same time. Also, by keeping your buyers satisfied with your products or services, they will remain loyal. The first thing you should do, however, is to tell them what your brand is all about. You can also build your brand through web design.

Here are several tips on how web design can help build a brand suitable for your audience.

Choose the right color

Colors are important because they can evoke people’s emotions and their subconscious. For example, what comes to mind when you see the color green? You are probably thinking of the environment or your health. Seeing it may also keep you calm. For this reason, many hospital walls are now painted with pale green.

Meanwhile, black is a serious color since it can represent luxury. Think of brands like Louis Vuitton or Rolex watches. Orange, however, is less suitable for serious brands. When people see this color, they see it bursting with youthful energy.

If you want to build excitement or interest when people see your brand, find out which colors can do that. You should also consider that various cultures have different interpretations of the same colors. If you’re catering to an international market, use only colors that are universally favored if possible.

Inject some personality

People like brands that have human-like attributions because it helps them identify with it. Psychologists call it anthropomorphism.

A fine example of this is Twitter with its little blue bird. Once people see this winged mascot, they think of Twitter. It’s easy to recall a brand that has some human-like familiarity.

Stir your audience’s feelings

If they see your brand, what emotions do you want them to experience? Using the latest designs isn’t going to cut it anymore. To make your brand popular, associate it with pleasant emotions and vibes that you can include in the web design.

With our site, we used the color orange and short video clips to showcase our brand as a creative web design agency. Upon landing on the page, the images aim to bring positive feelings to the audience.

Keep the design consistent

Brands are successful because people care and remember them. So how do you make your brand memorable? The answer is consistency.

This means you need to be consistent in your web design. In every page, use the same colors, formatting, graphics, personality, and emotions. People should see a uniform image throughout your website.

This will also make your website perform better. Since you’re reusing content such as graphics, your site will load faster. If someone has visited the site before, your images are easily displayed from the browser’s cache. For example, if you’re using the same navigation bar in all web pages, the browser only needs to download the code once.

Display your logo properly

You can get creative with your logo design but there’s only one place where it should be located. Always put your logo on the upper left corner of the website because that’s where most people will look for it. You should also link the logo’s image to the home page.

Logo size is also important. So make sure that the logo is big enough to get noticed immediately by your visitors.

Present value proposition

First-time visitors will have several questions upon landing on your site. What products or services are you offering? Can it provide solutions to their problems? This is where your value proposition comes in.

A value proposition is a short statement that visitors can easily see on your page.  It should be placed high up on the page just after the logo and menu.

You can only afford yourself a few words to tell what benefits the visitors will get from your site. So make sure the value proposition is:

• Clear
• Short
• Concise
• Answer the question what and why

Use the appropriate voice

The language or tone you’re going to use should support the brand’s personality and emotions. If your audience is millennials, the tone could be informal. If you’re catering to stock market investors, a formal tone is more appropriate.

If you’re going to say the same message to different audiences just remember this:

You don’t change what you say but change how you say it.

For example, you can use both formal and informal words to convey the same meaning. In other words, you need to use a tone that your audience will respond to.

Here is a simple example on how you can define what a car is in two different tones:

Layman’s version: It’s a metal thing that lets you travel to faraway places.

Scientific version: It’s a machine that uses energy in the form of fuel that gets converted to mechanical energy to move the wheels and gears.

By using different tones, you can spread your brand’s message to a correct audience.

Make your brand unique

Using all these elements is not enough. To truly set your brand apart from the rest, you have to make it unique.

The colors, personality or voice doesn’t matter if your website looks the same as your competitors. How could prospective buyers distinguish? Yes, it will take a bit of effort to create a brand that’s different from the others. That hard work, however, will pay off since your visitors will likely remember you. There’s also a greater chance that they will come back again once they are satisfied with your products or services.

Creating a brand makes sense even for small businesses as well as personal websites and blogs. In the face of fierce competition, having a good brand will help you get noticed. So in designing a website, these tips will help your brand get a head start from your competitors.

This article is shared by www.itechscripts.com | A leading resource of inspired clone scripts. It offers hundreds of popular scripts that are used by thousands of small and medium enterprises.