Tailor Brands is an online branding and design service that aids businesses in establishing a professional and unified brand identity. Their services encompass various branding packages, such as logo design, business card design, and social media branding.

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For any brand, creating its own identity is a challenging task. Especially for a start-up and small businesses, brand recognition is even more essential. Brand recognition can go a long way in influencing the success of a business. Every scale business spends a huge amount on marketing their brand and creating their imprint in people’s minds. The affordable way to ensure that your target audience recognizes and recalls your brand easily is to create an exclusive logo for your brand. Your logo can help in creating a strong first impression and leaving a memorable last impression. What is a Logo designed to do? A logo is a combination of text and images which is specific to the brand. It expresses what a business is all about and highlights its brand values. The logo is about making the perfect visual brand mark for any business. Logos are extremely important. A unique logo helps differentiate your brand from others. It is the best way to improve your brand recognition. Let's take the example of the McDonald’s logo. The iconic golden “M” logo is well recognized in every country worldwide. Its uniqueness is one of the reasons why people immediately recall it when they are hungry. For any size business, a well-designed logo can act as a differentiator. An impressive logo helps create a unique brand identity and also develops a positive brand perception. For example, the logo of Apple Inc. reflects sophistication, and its products deliver on that aspect. How to Create a Perfect Logo? The challenges that every business faces are the cost and effort involved in creating a logo. However, the internet offers a variety of logo maker software that you can use to create an exclusive logo for your business. This software helps to simplify the logo-making process and create instantly recognizable logos for your business. There are many benefits of using an online Logo Maker. We have mentioned a few of them here. Allows businesses to design an impressive logo in a few minutes Availability of professionally designed templates Offers numerous customization options for personalizing logo Makes logo making process easy, quick, and affordable Suited for both beginners and advanced designers. Keeping the benefits of the logo maker in mind, we have made a list of 7 best logo maker software available online to cater to your business needs. List of 7 Best Online AI Logo Maker Software After reviewing dozens of online logo makers available online, we made a list of the following best logo maker software available online. They are chosen on the basis of customization options, ease of use, quality of designs and templates, and export options. Here is the list of top 7 AI logo maker software to create the most impressive logo for your business: Appy Pie Logo Maker Adobe Illustrator Tailor Brands Laughing Bird Canva Logo Maker Looka Hatchful 1. Appy Pie Logo Maker Appy Pie’s free online Logo Maker is our first choice for the best logo maker software. It provides everything a business needs to create a unique logo. Some of the top features include: Helps in making a professional logo in minutes An endless range of free professionally designed templates Offers a huge range of editing tools for fully customized the logo Excellent in-built design tools available for editing various aspects of logo Provides an assorted list of hundreds of fonts to convey the personality of a brand Completely free-to-use logo maker Easy-to-understand software. Appy Pie Logo Maker helps you create a fabulous logo in just a few minutes. You can easily experiment with your creative instincts and design the best-suited logo for your brand. 2. Adobe Illustrator Adobe Illustrator continues to be the big business' choice. Illustrator is an excellent tool for making a logo. It is packed with unique features to tackle your logo creation needs. Some of them are mentioned below. Versatile vector graphics software Manipulate and combine a variety of geometric shapes with precision Contains a library of fonts-ranging from fun to professional Allow scanning of drawings and sketches to add in the logo Manage the complex color separation in logo design Flexible tools to bring creative vision to life Offers a wide variety of stock templates and logo objects Freedom to design any business logo belonging to any niche Allow secured sharing of logos across businesses. However, Illustrator has certain limitations. Here are some of them: Expensive to buy and has an exploitative pricing model Intimidating UI that can scare beginners For creating a single logo, Illustrator is overkill. 3. Tailor Brands Tailor Brands is an extremely easy to use and dedicated online logo maker tool. It lets you kickstart your business by helping to create an amazing logo. Easy and quick logo maker available online Provides a range of editing tools to customize logo AI technology assures logo is of high quality, professional and unique Provides both PNG and vector files of logo to use it at multiple places No design skills required to create an impressive logo From the color, font, size to text, every element can be customized Offers numerous professionally designed logo templates. There are two major drawbacks which hold back Tailor Brands: Provides limited creative freedom to customize logos Subscription-based model, i.e. logos cannot be edited without a paid subscription. 4. Laughing Bird Laughing Bird is the easiest tool to create logos. It is a one-time price software, i.e. you can purchase it and use it to your heart's content. Some of the major features of Laughing Bird software are mentioned below. Highly affordable logo maker The intuitive logo generator interface Provides over hundreds of creative templates Suited for every size business for creating unique logos Affordable tool to design ‘quirky’ logos Suitable for both professionals and novices Offers a dedicated reseller program for designers Provides an opportunity to get advice and inspiration from other creatorpreneurs. However, for those looking for creating highly professional logos for their business, Laughing Bird may not be the right choice. 5. Canva Logo Maker Canva is an incredible online tool that provides all of the ingredients one needs to create the perfect logo for their business. It is a fast and free logo maker tool with several features. Some of the features are mentioned below. Hundreds of designer approved templates with perfect color combinations. Extensive library of icons and illustrations is available to make unique logos Offers an amazing set of tools for customizing templates Drag and drop functionality makes the software easy-to-use for both professionals and non-professionals Intuitive user interface for a seamless experience Make logo pixel perfect for every size .PNG format of logos can be downloaded. The only drawback of Canva is that some of its features are hidden behind a paywall which is annoying for many users. 6. Looka Looka Logo Maker combines one’s design preferences with Artificial Intelligence to help to create a custom logo. It took a few minutes and clicks to make the perfect logo for your business. Some of the major features of Looka are mentioned below. Easy-to-use AI-based logo maker High resolution can be downloaded at minimal cost Allow scaling of logos to any size Offers color variation, from monochrome to a transparent background Easy-to-customizes templates are available Lifetime logo support to tweak logo designs Offers hundreds of design variations based on users’ preferred colors and fonts for their logo Provided extremely bold and unique ideas for a logo. The major drawback of Looka is the lack of customizable options for your potential logos. Looka has special pricing plans for businesses which limits its use. 7. Hatchful Hatchful is one of the best online logo maker tools made by the eCommerce giant, Shopify. It is a stunning logo maker that can create an expressive logo within a few seconds. It offers several features to logo designers. Some of them are mentioned below. No design experience required for creating a high-resolution logo in few clicks Offers customization options for tinkering with logo colors, fonts, and design Allow exporting final logo file in multiple resolutions and formats Provides both free and paid templates for users Easiest to use template-based interface for creating logos Provides AI-based suggestions for logos. The only drawback of Hatchful is that it is available only as a mobile app. It can be hard to design logos on a mobile screen. Conclusion We had mentioned the list of the best free online logo makers. All the software are good enough and have a variety of unique features to design an exclusive logo. Try them all out and pick the one that you feel is for you. Good Luck!

7 Best AI Logo Maker Software Available Online

When asked what advice he'd give to world leaders, Elon Musk replied, "Implement a protocol to ." control the development of Artificial Intelligence If left unchecked, he reasoned that machine learning technology would outgrow its developer and see it (us) as no longer an essential part of the process. And as few technologies are more progressive or disruptive in their advancement, he might have a point! But before ML pulls a terminator on us, it's shaking up the corporate landscape, unlocking once closed markets, leveling playing fields by providing innovative solutions to long-standing problems, and making it easier for the little guys to compete with the corporate goliaths. And that's pretty cool. In this article, we'll uncover how it's happening and look at 8 companies using machine learning in cool ways that change our lives, even if we don't know it yet. 1. Facebook – The Rise of The Chatbots! Chatbots not only match their human counterparts when it comes to customer service but have the potential to far exceed us, and Facebook's leading the way in discovering how. Facebook Messenger is now a testing ground for chatbots, and if you use it, you're the subject, and you might not have even noticed it! Chatbots are quickly becoming indistinguishable from humans when chatting via text because they have access to an immeasurable number of customer-related data points and can detect repetitive problems, patterns, and predict user issues. Meaning they are perfectly equipped for customer service agent roles and are an invaluable addition to modern business. Facebook's also enabling SME to avail of the advantages by allowing third-party developers to submit a chatbot inclusion in Messenger. So, even if your resources are limited, you can still provide an excellent customer service level leveraging chatbots. Nice one, Facebook. 2. Twitter's Algorithmic feed Might Be Missing the Point Contentious, controversial, and borderline are some of the terms; users have chosen to describe Twitter algorithmically curated timelines. So, why's it cool? It's how Twitter's works out which tweets to promote first. Machine Learning technology Twitter's Artificial Intelligence evaluates your tweets in real-time and rates them according to various metrics and your individual preferences. Ultimately, it's all about increasing your usage time, so they choose the tweets most likely to drive engagements. Algorithmically curated feeds are a contentious issue. While the Twitter ML is highly innovative, you can't help but think it's missing the point, as users love Twitter for its spontaneity, or they thought they did! 3. Google – Dreaming Of Neural Networks Google's mission is to enable every business to have access to Machine Learning technology. And they're using their Neural Network (computing systems loosely based on the biological neural networks of an animal's brain) research to ensure it happens. Recent research that got everyone talking was Google's Deep Dream computer vision program (released in 2014) that uses their neural network to develop dream-like psychedelic images. But their research in this area goes much deeper. Google calls it "classic algorithms," and it reaches into virtually all aspects of machine learning, such as speech translation, language processing, breast cancer research, wind farm technology, and of course, predictive search ranking systems. Expect much more to come. 4. Yelp – Saying It With A Picture While other review sites have come and gone, has remained the reviewer's favorite. And now, they're reinforcing their position by incorporating machine learning into their platform. Their goal is to enhance the user's experience even further by improving its image processing capabilities. Yelp Its machine learning algorithms enable the company's human element, its staff, to use its picture classification technology to categorize and label millions of photos in a fraction of the time. Improving efficiency and helping businesses like yours; here's how: Yelp tells us that businesses with over ten photos on their page get a massive 12 times more monthly customer connections. Further-more you can use your images to show potential customers your business's personal side and highlight your USP, products, and services. Ultimately leading to an increased ROI, it doesn't get much cooler than that! 5. Tailor Brands – AI Designers at Your Fingertips Since arriving in 2014, has quickly become a champion for SME wanting to create or improve their logos. In 2018 Tailor brands took their platform to the next level by raising $15.5 million to grow and expand it with AI technology, the result being a platform that does far more than logos. Tailor Brands Their ML system now enables small businesses to create their entire branding strategy, from logos to websites, and even more importantly, a social media marketing tool kit. Tailor Brands uses machine learning to match logo designs, fonts, and colors with brands and their market place. You provide the platform with your relevant company and marketplace information. It uses its extensive and self-updating system to find and match what elements are proven to work for you. It's an excellent example of how AI can level the playing field and make products and services affordable to everyone. 6. Baidu – Deep Voice Technology is the Chinese equivalent to Google. They are also increasing their use of machine learning technology; one such development is their Deep voice technology that can create synthetic human voices using deep neural networks. Baidu Baidu's R&D lab Deep voice ML system can learn to replicate and reproduce the components that make up the human voice, such as accent, pitch, and pronunciation, in just three seconds. And apparently, it's almost impossible to distinguish from the real thing. The technology has the potential for improving virtual assistants like Alexa, Google Assistant, and Siri. It could have a massive impact on personalizing human-machine interfaces and the natural language processing systems used for voice search and voice recognition, along with numerous other uses, such as biometric security and real-time translation. However, the central area is healthcare, as Baidu claims it will help those who've lost their voice communicate again, and that's very cool. 7. HubSpot – Cleaning Up Your Data HubSpot bought the artificial intelligence and machine learning startup Kemvi back in 2017. The acquisition's focal point was its proprietary algorithm, DeepGraph, designed to equip salespeople with the tools required to improve relationships with prospective customers. HubSpot tells us it's all about Data cleanliness; let's look at the features and benefits: HubSpot's belief is the cleaner the data, the more-free time sales and marketing teams will have to do their job by converting leads into deals, and it, of course, makes perfect sense. It does it by identifying potential sales prospects and collecting their contact information. The CRM also locates new market segments, creates and sends emails, integrates with Outlook, Gmail, Salesforce, and other software, and follows up and nurtures potential prospect relationships until they're ready for contact by the salesperson. Now that's handy. 8. IBMs Watson's No Ordinary Butler IBM may be one of the oldest tech companies. Still, their highly renowned ML-based "Watson" natural language question and the answering system ensure they keep up with modern developments, and in turn, creating new revenue markets within healthcare. Watson's now used in medical centers and hospitals worldwide and helps physicians diagnose and treat potential symptoms by inputting the patient's data. It's also now being used within the retail sector to identify shoppers, provide recommendations, and alternative upsell options. As with all ML, Watson shows no signs of slowing down with its development. What was it Elon said about ML again?!

8 Companies Using Machine Learning in Cool Ways

Technological development has traveled a long way with the focus on making lives more convenient and easier for users. The more consumers get used to the web, the more they demand it to get personalized. Their desire for betterment never ends. To keep with the pace of technology advancement and cope with such increasing demand, web developers face lots of challenges as they keep moving to enhance user experience and maintain the reputation they hold. As the digital world is becoming more in demand, there is an increasing need for web developers to create powerful, user-friendly, and unique web experiences. The aim of web developers across the globe is to provide the highest degree of user experience to their clients. Web developers, therefore, have become more prudent while choosing better techniques and the right processes of designing so that to remain relevant. Today the user interface is one of the main components responsible for making a website in good books for user experience. Users have become more intelligent, and therefore it becomes essential to have smart web solutions at their disposal. This is where AI comes into play. Al features in web applications can enable web developers to offer best-in-class web solutions to clients. With AI applications, web developers can address problems associated with quality analysis, coding, security, user experience, and many more. Here are eight ways AI is impacting web design and development. 1. Design Artificial intelligence helps to engineer the web building process and becomes the designer behind the brand logos and names that dominate a website’s home page. Website development companies are using artificial intelligence tools like “Tailor Brands” to design customized logos and automate the branding process within minutes. AI has made good web design more affordable and accessible for small-scale entrepreneurs and big companies alike. The application of artificial design intelligence (ADI) tools like “Bookmark”, “Ucraft”, and “Wix” are the building blocks for many websites today. Nowadays, ADI systems have evolved into effective tools with attractive and functional results. “Sketch2Code” can automatically transform a handwritten design/sketches into HTML markup. “Bookmark” and “Wix” provide popular automated website building tools with customizable options. Developers do not have to build websites from the ground up, and they don’t have to spend hours choosing the best template. Instead, with the use of ADI tools, websites can intelligently design themselves, using nothing more than the site’s name and the answers to a few quick questions. 2. Coding Developers can use AI to facilitate web programming. Coding is recognized as one of the challenges of any web development project. However, AI can automate various basic tasks and assist web developers in finding the right solution for web development. AI comes with basic functionalities like updating, adding, or editing records to a database. AI programs can predict which bits of code are most likely to be utilized to solve a problem, and then use such predictions to prompt web developers to adopt a specific solution. “Pix2Code” is a good example of AI programs that aims at automating front-end development. Artificial algorithms can be used to create self-modifying codes from scratch to a fully functional code without human involvement. Developers can, therefore, use AI tech to create smarter bots and apps at a faster rate than before, without compromise over performance. However, developers would need to train these machines and feed them with a good dataset, to begin with. The more detailed the training and the smarter the design, the better the results these systems produce. This is where the web developers’ skills make a crucial difference. 3. Chatbots Artificial intelligence is not just impacting web design and development on the developer end.  It is also changing the way users experience websites. Older versions of chatbots required pre-prepared or pre-programmed conversation paths that frequently led to end-user frustrations. However, AI-powered chatbots use NLP (natural language processing) models to have more authentic, natural conversations and genuinely understand customers’ needs, and can adapt to regional spelling errors and language differences. Chatbot translations capabilities allow users to access services and applications in their preferred language. Al-empowered chatbot, therefore, takes user experience and engagement to the next level. A simple rule-based chatbot responds only to specific preset commands. AI-powered chatbots can stimulate a real conversation by learning something new from every conversation and tailoring the responses and actions accordingly. They can automate routine tasks and offer relevant services and information. Moreover, they can enhance visitor engagement by providing personalization, real-time assistance, and comment on blog posts. The level at which Al-powered chatbots communicate and interact with clients is similar to that of human customer support executives. The chatbots are intelligently programmed and designed to memorize, resolve, and respond to customers’ queries instantly. 4. Analytics Al analytics tools enable website development companies to analyze and refine websites accordingly. By crunching down big data into analyzable patterns and numbers, predictive analytic tools such as “Infosys Nia” and “TensorFlow” reveal real-time insights about what does and doesn’t work for website visitors and prospective customers. This enables businesses to understand what types of customers are drawn to their websites and to accommodate such visitors with seamless user experience. Web developers can use results from AI-powered analytics platforms to tweak and refine websites and make them increasingly user-friendly. Using an AI-empowered chatbot or voice assistant can enable a developer to automate the process of collecting end-user stories and client requirements without human intervention. The AI analytics tools can prepare a comprehensive description of the collected data and use data extraction tools to generate insights, which then drive the web development and design strategy. This is possible through an effectively constructed system, which employs machine learning, NLP (natural language processing), computer vision, image algorithms and tools, and others. “Kore.ai” is one of such platforms that empower decision-makers with insights they need to drive business outcomes within data-driven analytics. 5. Personalization Service personalization is in increased demand from both organizations and private users. Such demands go beyond just responsive images or sites designed to adjust to user devices. It includes dynamic and customized content delivery, tailor-made according to a user’s historical behavior. Personalization normally means incorporating artificial intelligence with data on user interactions, demographics, or search engine activity. Artificial intelligence has been a key instrument when it comes to personalization. Variables like values, context, behavior, and location all modify the website interaction that a user experiences. With AI becoming smarter, each user persona profile is continuously evolving. This machine learning enables websites to be tailored to the individual and determines what the individual sees and when he sees it. 6. Using AI to Tackle Website Development for SEO SEO techniques rely heavily on Numerical Algorithms. Several web analytics tools are good, but their potential is currently limited by the processing capabilities of humans who interpret that data for their websites. By mining and analyzing data supported by AI, a developer can maximize the usefulness of the website’s metadata and other metadata and data generated by the user. The predictive engines built using AI technologies can generate insights that enable developers to identify irregularities within their website architecture or show ill-fit content from an SEO point of view. Based on the use of such insights, AI can highlight better ways to develop web content and design websites that connect with the target audience. “Market Brew” is one of such Artificially Intelligent SEO platform that uses AI to help developers to plan and design the content for their websites in ways that search engines might perceive them. 7. Testing and Quality Assurance AI algorithms can test an application with little or no human input. They can predict the main parameters of software testing processes based on historical data. They can amplify failure predictions and identify failure patterns with much higher efficiency than traditional quality control approaches. AI thus can recognize and fix bugs/errors within a few minutes. With such a new AI-powered quality assurance environment, developers can discover more effective ways of testing, identifying outliers faster, and working on effective code coverage techniques with no or basic testing experience. In other words, developers can just concentrate on perfecting the build while artificial intelligence handles complex test cases and fix errors automatically. 8.  Simplifying Web Designing and Development All web developers know that developing and designing basic web page templates and layouts is tiresome work. AI virtual assistant can facilitate these tasks by enabling designers to simplify the entire web design and development process. The virtual assistant can ask questions to the user and retrieve the information of their answers, therefore creating an exact combination of layout, design, brand, and content required by the user. AI virtual assistant tools like “Adobe Sensei” can automatically analyze inputs and recommend design elements to the user. This tool can also perform functions like editing skills, cropping using image recognition techniques, automating basic photos, and creating elements in images that didn’t exist before by studying the neighboring pixels. Developers only need to focus on training machine to think and perform tasks like a designer. Conclusion There is no doubt that artificial intelligence is changing the game. Web design and development landscape are changing and will always change. Due to this, web developers will need to embrace artificial intelligence developments and use the technology to eliminate doing redundant tasks so that to focus on enhancing creativity. Web developers will need to consider more advanced software and be able to efficiently integrate AI techniques into their web design and development.

8 Ways Artificial Intelligence Is Transforming Web Design and Development

AI is quickly becoming mainstream in many industries, including graphic design. The technology is making the designers giddy with excitement because AI can make the designing process so much faster and easier but — it’s also scary at the same time because we’ve seen what AI systems are capable of. One of the earliest examples of AI entering the graphic design industry is , a website which creates modern-looking websites for customers in minutes. Users just need to upload the content and using AI, the Grid will begin setting up the website. The website was launched in 2015. A lot of AI-powered tools for creating websites and generating logos have been introduced ever since, including Tailor Brands, , Squarespace, Wix, etc. The Grid Turbologo In 2017, Ferrero partnered with an advertising agency Ogilvy & Mather Italia to get more people to buy Nutella. Their plan was to have an . The algorithm was then provided with database patterns and colors to create seven million unique jars that were sold throughout Italy . algorithm design the packaging within a month Does that mean AI will replace human workers? According to a by Oxford University and Deloitte, 800,000 jobs were lost in the UK due to automation between 2001–2015. But according to the same report, 3.5 million new jobs were created. You see, it’s not all doom and gloom. It’s true that AI can do so much in the creative realm that we humans can imagine. But creative professionals cannot be replaced by machines. At least not in the next couple of years. report There are certain jobs that require knowledge of culture, emotion, timeliness, identity, etc. AI just cannot mimic those tasks. In all honesty, AI is more of a friend to graphic designers than an enemy. As IBM CEO , “the initials AI would be more helpful to artists if they were understood by designers as augmented intelligence, instead of the scarier-sounding artificial intelligence.” suggested With the help of Artificial Intelligence, designers can create multiple designs at improved speeds and reduced costs. There are a lot of amazing ways AI can be used in graphic designing. Designers and Robots can work side by side and do wonders. Let’s see how AI will help in the designing process. Automating Legwork A designer’s work involves a lot of legwork. Designers on an average spend 20% of their time in solving problems that can easily be automated by Artificial Intelligence. Some tasks like cropping images, correcting colors cannot simply be automated since they require human attention as these tasks take place. But AI could do this job for us. In fact, it has already started helping us with the problem. Adobe has launched that powers intelligent features and help designers do their work more efficiently. It uses AI and Machine Learning in a common framework to improve the overall design and delivery of the final product. Adobe Sensei Adobe Scene Stitch identifies various patterns in the image and can help edit, or even create a new scene. Netflix is also leveraging augmented intelligence system for . Whenever there’s a need to create multiple banners for a show in different languages, all a designer has to do is look at the banners created by robots to approve or reject them. artwork personalization All these things can save a lot of time for the designers to work on more crucial things and make decisions about product design. Improving The Design Elements Of Websites According to the Adobe , 69 percent of the creatives believe that the use of AI will increase drastically over the next five years. They also believe that AI systems are not going to take away their jobs. It’s clear that a lot of people are optimistic and the demand for such technology is high. survey Tools like , which uses eye-tracking technology, can help improve the design elements of the websites. It allows UX designers to record the eye movement of the participants while using the website and it will help the designers to fix the usability issues and improve the design. Tobii Pro Sprint But companies like are taking it to the whole new level by teaching AI to predict what kind of ads people like with 85% accuracy. EyeQuant Smarter Design Systems AI can also help you make your design system smarter and more robust. Companies trust design systems to keep their product consistent to its users. Implementing AI in design systems will help us analyze how users interact with all the elements of a design system and we can understand which one is best for each function. This helps in better optimization. The more the system learns about what is working and what is not, the better the results will be. Companies like Squarespace and Wix are already using AI technology to help designers with decision making. Wix ADI makes use of client’s feedbacks and its algorithm to design websites to perfection. Some other tools using AI in Graphic Design DesignScape DesignScape has an impressive AI system that provides helpful tips and hints to the website designers to craft a website to near-perfection. It doesn’t design the website on its own but it is very helpful to improve the efficiency of the designer. AI will not be able to design the websites like the humans do, not at least in the near future but it will indeed reduce maintenance efforts for us. Tailor Brands Tailor Brands specializes in creating logos for businesses. It has a very advanced and powerful AI that can quickly create a logo without human interaction. Users just have to answer a few questions and provide information about their business. The AI system will then analyze the results and create dynamic logos. Turbologo Turbologo is another great tool for creating eye-catching logos for your business. You need to enter your company name, select color choices, and pick some of the icons that you like and it will create awesome logos for you. Once you enter all the details, it will generate a lot of ideas and you can choose from them. It’s really simple to use. Turbologo uses AI and machine learning to design the logos that you can use. FontJoy FontJoy generates font combination using a deep learning system. It helps the developers choose the best font pairing. Well, it is hard to say what exactly makes a good font pairing and the developers are aware of that. So, they start by asking what makes a lousy pairing which is an easier way to get started. Using a deep learning system, the website allows you to generate awesome font combination. These are just a few of many tools using AI in designing. You see, AI is transforming the graphic design industry and we all should welcome it. Designers should not feel intimidated by this technology. It will create a lot of new opportunities and by leveraging its power, designers will be more creative and productive.

Real-World Applications of AI in Design

Written together with Tiffine Wang of Singtel Innov8, Ryan Morgan of DFJ Growth, and Frederick Dopfel of Samsung Ventures. The short version of this post was featured in VentureBeat Many have dreamed of robots taking over monotonous functions of our daily lives, freeing us humans to work on highly-skilled or creative tasks. Today, robots are increasingly making their way into our homes, offices, and public spaces, but this integration is not occurring in the way that most futurists and popular science fiction writers have anticipated. Here’s our top picks for tasks that were surprisingly easy to automate, and tasks that remain surprisingly hard. Tasks Surprisingly Easy to Automate: Art Prevailing wisdom claimed that computer systems would be incapable of creating art or music, because they lack the natural creativity of humans. However, automated systems have been surprisingly successful at generating works of art. Since the early 2000s, the University of London’s The Painting Fool program has been generating artwork, much of which has been featured in prominent galleries alongside human-created art. Google’s DeepDream software uses a recursive convolutional neural network to iteratively adjust images to morph them into objects in its database (usually dogs, buildings, and eyeballs). Other neural networks such as DeepStyle or Prisma use convolutional neural networks to stylize photos after the work of a specific artist. Google is now experimenting with automated curation of art exhibits through machine understanding of the style and topic of artists’ work. Logo generation systems like Withoomph, Tailor Brands, and Logojoy, use partially- or fully-automated systems to generate logos for a business based on keywords. Researchers have also demonstrated systems that can generate music: Stephen Wolfram has made an automated music generator available to the public since 2005, the University of Malaga has created Melomics, a system that autonomously composes and plays music to match your lifestyle and activities, and IBM has partnered with artists to help compose music with Watson by combining massive musical datasets and their lyrics with sentiment analysis. Although these automated art and music systems are arguably derivative on the work of others, they are undeniably interesting and beautiful in their own right. Many scholars may argue that, like artificial art, all human art is derivative, although that is a discussion best left to philosophers. It appears that natural human creativity is not, in fact, necessary to create beauty. Science/Research Historically, scientific research has been conducted by highly educated researchers and lab assistants, and as such was not an obvious place for a blossoming of applications. However, a core basis of science is reproducibility, and many aspects of science and research are extremely repetitive, requiring neither continuous thought nor the high level of education attained by the individuals who perform those tasks today. robotics Companies such as OpenTrons are working to save scientists both time and money by helping them automate pipetting, a precise but monotonous and labor intensive task used in many biological and chemical laboratories. Startups Arcturus and BioRealize enable scientists to remotely run many genetic engineering experiments in parallel, greatly reducing mistakes and lab time. Other startups, such as Emerald Therapeutics and Transcriptic, are hoping to move research to the cloud, using remote robotic systems to perform the experiment itself. In addition to vastly increased lab efficiency, these systems could greatly improve reproducibility, as researchers need only share their experiment’s source code. Not content with automating manual lab work, machines are now automating scientific discovery and understanding. Scientists at Cornell’s AI lab created a program called Eureqa, that uses an evolutionary approach to model creation and is able to create models on data without being given any prior assumptions. The team has since commercialized the technology through a spinout, Nutonian. Researchers at Cambridge and Aberystwyth have created a similar autonomous science algorithm called Adam, which they claim was the first machine to independently discover new scientific knowledge. That same team moved on to the University of Manchester, where they created Eve, which is a robot designed to make the drug discovery process faster and cheaper. As the pace of scientific research increases, scientists may rely more and more on automated systems. Legal The practice of law requires many years of studying and understanding laws, cases, and other legal precedence. Law firms often employ large teams of paralegals and interns to perform the majority of initial research; current AI technologies can augment and eventually automate the bulk of that research, leaving the more difficult aspects of practicing law such as synthesizing the information and advising to trained legal professionals. To date, case law, contract law, and advocacy law have seen the most automation. Startup DoNotPay helps users appeal traffic tickets; the bot started in the UK and has expanded to cover several US cities. Over a quarter million tickets have been successfully challenged, and several cities have banned the bot. ROSS Intelligence augments legal research by using AI to surface relevant legal passages and cases to increase the efficiency and quality of legal research. eBrevia uses AI to extract data from contracts to accelerate diligence, contract analysis, and other related applications. Legalist automates the qualification process for litigation financing and backs cases they identify as having a high success rate. There is a ceiling, imposed by liability concerns, to how much law can be automated; a startup might expose itself if it advised a client on legal matters, so most companies likely can only research and synthesize information. Because much of the legal industry charges by the hour, it will be interesting to see to what degree lawyers will accept automation and other tools to increase the efficiency of their practice. Policing and Security Unlike warfighting robots, which have clearance to hurt or even kill humans, robots that work as a police force or physical premises security, particularly in areas far from any war zone, are expected to more closely follow Asimov’s three laws of robotics: avoiding injury to humans while still keeping an area secure. For this reason, many would expect criminals to ignore robotic security guards, knowing full well that the robots could not harm them. However, the primary function of human physical security teams is to observe and report security incidents; due to the risk of liability to their employers, many human guards are not allowed to interfere, making their work surprisingly easy to automate. Security automation to date has focused on augmenting existing security forces, inexpensively amplifying the eyes and ears of security guards without increasing their numbers. Traditionally, fixed camera systems could perform this task, but constantly monitoring a large campus by camera is difficult. Knightscope and Gamma 2 Robotics build ground-based security robots that act as “force multipliers” to provide deterrence through physical presence; these robots are being deployed at an increasing number of corporate campuses and shopping malls. Another startup, Nightingale Security, is using flying drone robots to help their customers carry out continuous surveillance. In time, robots may replace substantial portions of police and security forces, requiring humans only for resolving violent incidents. security Tasks Shockingly Hard to Automate Cleaning Cleaning is perhaps one of the most obvious and logical applications for robots, yet with the exception of the occasional household Roomba, robotic cleaning is not widespread. Not only is cleaning an extremely repetitive task that requires very little technical skill, but it is also a task that almost everyone has to do in their personal lives and despises. What makes the dearth of cleaning robots more perplexing is that the cleaning robot Roomba was one of the first commercially available robots to everyday consumers in 2002. Almost fifteen years later, there has not been any real innovation in terms of cleaning robots that has seen commercial success. While Roomba, which is made by iRobot is the best known of these robots, competing products are made by a healthy list of competitors, including Dyson and LG. On the commercial side of the market, work on an automated janitor / cleaner has gone back at least as far as the 1980s, when Electrolux’s subsidiary, The Kent Company was working on a solution. Yet somehow, to this day, cleaning on a commercial scale is still done by hand. Several employees of Kent went on to create a company called Intellibot that creates a large scale automated floor cleaner called TASKI. Another large scale floor cleaner, the RS26, comes from a collaboration of Brain Corp and International Cleaning Equipment. With that said, there are some very interesting applications on the horizon outside of floor cleaning robots. The startup Ecoppia is using robots to clean solar panels, and Kurion has a division that makes robots that are used to clean up nuclear facilities, including Fukushima. Clothing and Textiles It is surprising that textile manufacturing, one of the first industries to flourish in the industrial revolution through automation remains an incredibly hard to automate completely. Robots work best when manipulating solid objects. Textiles, however, shear, stretch, and compress, making them difficult for robots to manipulate, even though such manipulation comes naturally to humans. Machines today can dye and cut fabrics into the right shape for assembly and embroider intricate designs in an instant, but nimble human fingers are still required for assembling that fabric, sewing on buttons, pockets, and adding on delicate finishing touches such as lace and pleats. Even today, few companies have made any headway in sewing. Softwear Automation is one such company, which uses high speed cameras to instantaneously map the position and orientation of a textile as it flexes and bends, allowing the robot to make micro-adjustments as it sews pieces of fabric together. Nike is using automation to circumvent the traditional shoe assembly process altogether with its FLYKNIT line of shoes, which uses robotics to custom-knit the top portion of a shoe out of one continuous thread, doing away with the need to assemble the shoe from multiple separate pieces of material. Alternative textile and clothing manufacturing techniques such as 3D printing are been heralded as poised to revolutionize the fashion industry, but the items created by these processes and new materials remain niche outside of the athletic wear market. Automation has struggled with clothing care almost as much as it has with sewing. Although machines can wash and dry clothing (sometimes in the same machine), the process of loading, unloading, and folding remains repetitive and laborious with few reasonable automated alternatives. Startups Laundroid and Foldimate have developed laundry folding robots, although their solutions are large desktop or closet-sized machines with limited capacity. Robotic clothes care systems similar to those imagined in science fiction are likely many years away. Farming Automating the harvesting of crops that are today picked by hand has so far been hard because many of these crops can be damaged easily and computers have had trouble with visual recognition of the fruit or produce they are trying to pick. Despite these challenges, there are many companies working towards fully automating a variety of agricultural tasks. Harvest Automation has created a robot called the HV-100 that can do a series of tasks in nurseries and greenhouses. According to one of their customers at McCorkle Nurseries, “We have many plants in the field that have been growing for as many as 24-months; they’ve been spaced, collected and spaced again and they haven’t been touched by a person since the day they were potted.” In the fruit picking space, companies such as FFRobotics, Abundant Robotics, and Vision Robotics have worked on solutions for fruit picking. While none of their products are widespread yet, their efforts are helping pave the way towards a more automated world. Abundant Robotics, for instance, is avoiding many of the difficulties of robotic grippers and instead using a suction device to pick fruit. There are also a series of startups working on weeding and thinning robots. Among these are Naio Technologies, which has created a robot called Oz for small scale weeding. It is also working on a robot called Dino for larger scale weeding, and a robot named Ted for vineyard weeding. It should also be noted that many of the the traditional agricultural players are also working towards automating the farm. John Deere has offered some form of kit for autonomy for years, the current version being AutoTrac. While not in farming, Deere also offers a robot called the Tango E5, which is essentially a Roomba for mowing a lawn. Other competitors in the autonomous tractor space include AGCO and Kinze. AGCO, through its subsidiary Kendt, is working on a project they call MARS, or Mobile Agricultural Robot Swarms. These swarms of small farming robots will be dragged into fields in logistics units (which also hold seed supply as well as batteries for the robots), and then split up to do their work. Looking ahead Several themes emerge from this overview that enable us to predict what’s next for the robotics industry. Today, robotics have made significant headway into automating repetitive tasks requiring little finesse and tolerant of blunt solutions, like washing laundry and dishes or creating large quantities of textiles. Tasks that require processing large amounts of data and information can also be augmented by automation. On the other hand, tasks that require complicated manipulation of materials are still difficult to automate and need more skilled assistance, as does the deeper decision making and synthesis of information that robotics help us surface. As much as robotics have transformed many major industries, the need for humans to complete the last finishing steps, as diminishing as they may be, makes it abundantly clear that total robotic world domination remains out of reach.

Top Unexpectedly Easy and Surprisingly Difficult Use Cases for Automation

Why does a company need a logo? A logo is like a brand's superhero emblem. It's a visual cue that triggers certain vibes and feelings among your audience. Initially, a strong branding strategy helps to create a recognisable trademark that will be associated with a particular business and attract customers. Later on, branding (when it’s well-designed) can be the main reason for the increase in customer loyalty. This means that customers will be more loyal to the brand and come back for a repeat purchase. Nearly everyone today can identify the logos of Apple or Toyota, but what makes these symbols so distinct? The answer is that we see these logos all over the place on phones, cars, media and advertising. However, these logos didn't always hold the recognition they do now. Every company, even the most prosperous ones, had humble beginnings. How can I get a logo for my start-up? Professional Design Studio This option seems to be the most reliable and traditional one. From year to year, professional studios have been executing thousands of company logo orders. The design studio develops a project that takes into account all the wishes of the client, and also studies all the features of the business industry. Let's see the pros and cons of working with a design studio. You can find design studios on or . Behance Dribbble Pros: With vast experience working with diverse companies, professional design studios typically offer a custom approach to logo development, ensuring a design that best complements the brand. . Uniqueness . Professional designers use state-of-the-art software and advanced technology to create a top-notch logo. Quality . Design studios create a logo, taking into account its display in different environments. This means that the logo can easily be used on any medium from a business card to a billboard. Adaptability . Having a logo designed by a professional studio signals a business's serious commitment and professional approach to its brand image. Reputation Cons: . Let's talk numbers. Logos made by pro design studios can have you shelling out anywhere from $5,000 to $15,000 and beyond. Now, if you're a fledgling startup, you might think twice before dropping that kind of cash on a logo. There could be other, more immediate areas that need your financial attention. Cost Factor . The clock is ticking, and in the startup world, time is as good as gold. The average design studio might take weeks. To some, that might seem like a millennium compared to other faster (but maybe less refined) options. And don't forget, there's some legwork to be done on your part too, choosing the right studio is going to eat up some hours. Time Investment The average time it takes a professional design studio to devise a logo is around 2-4 weeks, which might seem a bit too long compared to other options. On top of that, you will have to spend a certain amount of time on choosing which studio to contact. Time. I think all startups would like to order a logo design from a design studio, but it is too expensive and time-consuming. In addition, at the initial stage, many companies make a pivot in development, which can mean a change of brand. Freelancer Freelancers are either designers alone, or small groups of designers who work remotely and are willing to do small orders. Freelance exchanges are now plentiful, and it is relatively easy to find the right contractor. However, there might be a tremendous difference between two seemingly similar freelancers - anyone who has ever worked with a freelancer knows this. If you aim to find a person who will make a decent logo for a reasonable price and you actually find one, you are definitely lucky. Most popular freelancer marketplaces to hire freelancers are , and . 99designs Fiverr Upwork Pros: . The average price that freelancers charge for their work is lower than that of a studio. It can start with $50 and reach $1000, if a freelancer is experienced and has a good portfolio Price . In general, freelancers present a finished logo much faster than a design studio, which might be crucial in case you need your logo ASAP. Time . A freelancer can work with you at your convenience and strictly according to your requirements. Flexibility . Modern freelancers tend to have experience in versatile areas of design and that is why they can offer you a unique and creative approach to your logo. Experience Cons: . Freelancers don't usually provide guarantees for their work, which can lead to problems once the logo is received. In the worst case, you might even face the risk of copyright infringement. No guarantee of design uniqueness Freelancers may offer a limited selection of styles and colours, which not always meets your requirements. Or even worse, freelancers may use some of the pre-made templates, making your logo an ordinary and boring picture. Limited choice. As a rule, the work of a freelancer includes 2-3 edits (revisions) of the design. You have to pay extra for additional revisions. Limited number of edits. . Not all freelancers get their work done on time; sometimes, they might simply forget about your order or complete tasks that are a higher priority for them. Failing to meet the deadline AI Logo Generator Creating a logo online without human involvement is a service that has become available with the rapid development of Internet technology. Now, when AI programs are slowly but surely taking over entirely different markets, finding a worthy AI logo generator (such as the one I made, ) is as easy as one-two-three. Turbologo Pros: Automatic logo generators usually offer their services at a low cost, which makes them affordable for all companies, especially for start-ups. Sometimes, the use of a logo generator is completely free of charge, but typically they require to pay to download results from $20 to $100. Price. Designing a high-quality logo for an automatic generator is only a matter of a few seconds. Time. All the leading logo generators charge their clients only if they are satisfied with the result obtained. In case you don’t like the generated logo, you can simply leave the website without paying anything. Post-pay. Logo generators can create logos for companies representing completely different industries, from esports to cotton candy production. Versatility. Most of the modern logo generators have a simple and friendly interface and don’t require special design knowledge. It means that you can create a masterpiece within a few mouse clicks. User-friendliness. Cons: . Logo generators usually offer a limited choice of designs that may not exactly fit the company's needs and its brand. Moreover, some of the generated designs turn out to be standardised templates. Limited choice of designs Some logo generators don’t allow changes (even the smallest ones) to the design, which may result in the need to make an additional order and pay extra money, respectively. But it really depends on the service, so it’s better to make sure that Making adjustments. adjustments are free of charge. Unfortunately, there are unfair logo generators on the market which use commercial fonts or icons without a proper licence. An even worse scenario is if a generator sells you a logo that is protected by copyright and you only realise it after you have bought it. So be sure to check the licences of the fonts and icons used in your logo. Copyrights or licences issues. Which option to use? Depending on your personal situation, you might find any of the options listed suitable for your needs. Undoubtedly, each described way of ordering a logo for your company has the right to exist. However, if you are only starting your own business and don’t have infinite resources, I would highly recommend you to try the logo generator method. Not only is it extremely cheap (or maybe even free), but it will almost certainly provide you with a suitable solution to your logo-searching problem - I have tried it myself several times. Nowadays, there are plenty of different automatic logo generators, but the most popular are , , or . Turbologo Looka Canva TailorBrands Conclusion Logo generators are convenient and accessible tools for aspiring entrepreneurs seeking distinctive logos. AI-powered generators offer a range of templates, colors, fonts, and design elements to customize according to specific needs. They also enable the creation of prototypes, ensuring alignment with expectations and business identity. With logo generators, business owners can swiftly craft professional and appealing logos that differentiate their brand and yield a competitive edge. Even the most successful companies started somewhere, and using an AI-generated logo as a startup's initial branding is a proven and practical solution in today's market.

How an AI Logo Generator Can Help Start-up Businesses

Your code smells because there are likely many instances where it could be edited or improved. Most of these smells are just hints of something that might be wrong. Therefore, they are not required to be fixed per se… (You should look into it, though.) Previous Code Smells You can find all the previous code smells (Part I - XLV) . here Let's continue... Code Smell 226 - Mixed Priorities Another crashed spacecraft. Another software problem TL;DR: Design and test the software. It is cheaper than the hardware Problems Fault Bijection Unexpected Defects Solutions Create accurate simulations Make software fault-tolerant Context crashed on the moon's surface on August 19, 2023. Luna-25 4 days before India’s soft landed on Moon's south pole. Chandrayaan-3 A revealed that the instructions shared a bus and were not prioritized correctly. forensic analysis Spacecrafts have a of software faults. long history Sample Code Wrong class TaskManager:
    def __init__(self):
        self.tasks = []

    def add_task(self, task, priority):
        self.tasks.append((task, priority))

    def execute_tasks(self):
        # No sorting

        for task, _ in self.tasks:
            task.execute()

class Task:
    def __init__(self, name):
        self.name = name

    def execute(self):
        print(f"Executing task: {self.name}")

task_manager = TaskManager()
highPriorityTask = Task("Slow down")
mediumPriorityTask = Task("Take Photos")
reviveKlaatu = Task("Klaatu barada nikto")

# unsorted
task_manager.add_task(mediumPriorityTask, 2)
task_manager.add_task(highPriorityTask, 1)
task_manager.add_task(reviveKlaatu, 3)

task_manager.execute_tasks() Right class TaskManager:
    def __init__(self):
        self.tasks = []

    def add_task(self, task, priority):
        self.tasks.append((task, priority))

    def execute_tasks(self):
        # Sort tasks by priority (high to low)
        self.tasks.sort(key=lambda x: x[1], reverse=True)

        for task, _ in self.tasks:
            task.execute()

class Task:
    def __init__(self, name):
        self.name = name

    def execute(self):
        print(f"Executing task: {self.name}")

task_manager = TaskManager()
highPriorityTask = Task("Slow down")
mediumPriorityTask = Task("Take Photos")
reviveKlaatu = Task("Klaatu barada nikto")

# unsorted
task_manager.add_task(mediumPriorityTask, 2)
task_manager.add_task(highPriorityTask, 1)
task_manager.add_task(reviveKlaatu, 3)

task_manager.execute_tasks() Detection Manual [x] This is a design smell Tags Reliability Conclusion Create software components and simulate real and not real conditions Relations Code Smell 198 - Hidden Assumptions More Info https://t.me/roscosmos_gk/11053?embedable=true https://asiatimes.com/2023/08/luna-25-crash-lands-russia-china-space-ambitions/?embedable=true Disclaimer Code Smells are my . opinion The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform… But it is likely to exert an indirect and reciprocal influence on science itself. Ada Lovelace Software Engineering Great Quotes Code Smell 227 - Cowboy Coding Leave cowboys to Hollywood movies. TL;DR: Write code as a team programmer Problems Readability Unreliable code People Management Issues Lack of coordination Solutions Write professional code. Use declarative non-cryptic names. Context Cowboy coders don't follow best practices. They don't follow team suggestions. Cowboy coding is generally considered an unprofessional and risky approach to software development because it can lead to code that is hard to maintain and prone to errors. Cowboy Programmers are good people; however, . they cannot work in a group Sample Code Wrong # Very simple example 
# Compute the sum of two numbers without any structure or best practices.

num1 = input("Enter the first number: ")
num2 = input("Enter the second number: ")

# WARNNING!!!! Don't remove the line below !!!!!
# (Unpleasant comment)

res = num1 + num2  # (No data type checking or error handling)

print("The sum is: " + result)  # (No validation or formatting)

# (No good names, no functions, no error handling, no testing, 
# no version control, and no structure.) Right def add_numbers():
    try:
        firstAddend = float(input("Enter the first number: "))
        secondAddend = float(input("Enter the second number: "))
        total = firstAddend + secondAddend
        return total
    except ValueError:
        print("Invalid input. Please enter valid numbers.")
        return None

def main():
    total = add_numbers()
    if total is not None:
        print("The sum is: {:.2f}".format(sum))

if __name__ == "__main__":
    main() Detection Manual [x] You can set environmental rules to prevent these coding practices and enforce team building. Exceptions Very small personal projects Tags Declarative Conclusion Software development is teamwork. Relations Code Smell 06 - Too Clever Programmer Code Smell 02 - Constants and Magic Numbers Code Smell 105 - Comedian Methods More Info https://en.wikipedia.org/wiki/Cowboy_coding?embedable=true https://www.linkedin.com/pulse/software-development-cowboy-coding-hakan-atbaş/ Credits Photo by on Taylor Brandon Unsplash The danger from computers is not that they will eventually get as smart as men, but that we will meanwhile agree to meet them halfway. Bernard Avishai Code Smell 228 - Multiple Classes per File More than one class is a mess. TL;DR: Follow the separation of concerns principle and file organization Problems Code Organization Coupling Autoloading problems Debugging Version control and merge conflicts Solutions Declare a single class per file Use name scoping Context In languages that declare classes using a file system, having one class per file is generally considered a best practice. This approach helps improve code organization and maintainability and reduces potential issues. You can organize namespaces into separate directories within your project structure. This way, you can maintain a logical and efficient codebase while avoiding the issues of declaring multiple classes in a single file. Sample Code Wrong <?

namespace MyNamespace;

class Class1 {
    public function sayHello() {
        echo "Hello from Class1!\n";
    }
}

class Class2 {
    public function sayHello() {
        echo "Hello from Class2!\n";
    }
} Right <?

namespace MyNamespace;

class Class1 {
    public function sayHello() {
        echo "Hello from Class1!\n";
    }
} <?

namespace MyNamespace;

class Class2 {
    public function sayHello() {
        echo "Hello from Class2!\n";
    }
} Detection Automatic [x] Many standards enforce this rule Tags Coupling Conclusion Keep your code organized, and follow known standards. Relations Code Smell 48 - Code Without Standards More Info https://en.wikipedia.org/wiki/Namespace?embedable=true https://rules.sonarsource.com/java/RSPEC-1996/?embedable=true Credits Photo by on Marjan Blan Unsplash Without requirements or design, programming is the art of adding bugs to an empty text file. Louis Srygley Code Smell 229 - Red Tape You overcomplicate your code. TL;DR: Avoid Accidental complexity and bureaucracy Problems Accidental Complexity Readability Fault Bijection Solutions Assign the responsibilities to real-world objects using the . MAPPER Context A "red tape" code smell could relate to unnecessary complexity, bureaucracy, or excessive configuration that makes the codebase harder to understand or maintain. Sample Code Wrong class VotingSystem:
    def __init__(self, config):
        self.config = config

    def validate_voter(self, voter_id):
        if self.config['voter_verification_enabled']:
            # Code to verify the voter's identity goes here

    def cast_vote(self, voter_id, candidate):
        if self.config['voting_enabled']:
            # Code to record the vote goes here

    def generate_vote_report(self):
        if self.config['generate_report']:
            # Code to generate a voting report goes here

    def audit_voting_system(self):
        if self.config['audit_enabled']:
            # Code to perform an audit of the voting system goes here

    # ... other voting-related methods ...

# Usage
config = {
    'voter_verification_enabled': True,
    'voting_enabled': False,
    'generate_report': False,
    'audit_enabled': True
}

voting_system = VotingSystem(config)

# Voter validation, voting, report generation, 
# and auditing are handled based on the configuration. Right class VoterVerification:
    def verify_voter(self, voter_id):
        # Code to verify the voter's identity goes here

class VotingMachine:
    def cast_vote(self, voter_id, candidate):
        # Code to record the vote goes here

class VoteReporter:
    def generate_report(self):
        # Code to generate a voting report goes here

class VotingAuditor:
    def audit_voting_system(self):
        # Code to perform an audit of the voting system goes here

# Usage
voter_verification = VoterVerification()
voting_machine = VotingMachine()
vote_reporter = VoteReporter()
voting_auditor = VotingAuditor()

# Voter verification, vote casting, report generation, 
# and auditing are handled separately. Detection Semi-Automatic [x] Some tools can guess you are bloating your objects with unnecessary responsibilities. Tags Bloaters Conclusion The red tape code smell is evident as developers need to navigate the complex configuration to determine which features are active. This not only adds unnecessary complexity but also increases the likelihood of misconfigurations that could impact the integrity of your system. Relations Code Smell 54 - Anchor Boats Disclaimer Code Smells are my . opinion Credits Photo by on the blowup Unsplash A Fallacy of Software: If it works, and we don't change anything, it will keep working. Jessica Kerr Code Smell 230 - Schrödinger Code Your code is dead and alive. TL;DR: Look carefully for race conditions Problems Principle of Least Surprise Violation Race Conditions Solutions Avoid race conditions Avoid global variables Use proper synchronization Context Schrödinger code is code that can be in two different states at the same time, but the state of the code is not determined until it is executed. This can happen when the code contains a race condition, or when the code depends on the state of a global variable that can be changed by other threads or processes. Sample Code Wrong import threading

cats_alive = 0

def thread_1():
  cats_alive += 1

def thread_2():
  cats_alive -= 1

if cats_alive > 0:
  feedThem()

# The value of cats_alive is indeterminate, 
# so the code can be in either of the two states:
#
# 1. cats_alive > 0 and feedThem() is called.
# 2. cats_alive <= 0 and feedThem() is not called. Right import threading

lock = threading.Lock()
cats_alive = 0

def thread_1():
  with lock:
    cats_alive += 1

def thread_2():
  with lock:
    cats_alive -= 1

if cats_alive > 0:
  feedThem()

# With the lock, the two threads cannot access 
# the `cats_alive` variable at the same time.
# This means that the value of `cats_alive` is always determined, 
# and the program will not exhibit Schrödinger code behavior. Detection Manual [x] Make code reviews on concurrent code Tags Concurrency Globals Conclusion To avoid Schrödinger code, avoid race conditions and avoid depending on the state of global variables that can be changed by other threads or processes. If you need to use a global variable in your code, ensure it is correctly synchronized. Relations Code Smell 198 - Hidden Assumptions Code Smell 32 - Singletons Code Smell 60 - Global Classes Credits Photo by on Yerlin Matu Unsplash The last thing you wanted any programmer to do is mess with internal state Alan Kay Next week, 5 more smells.

How to Find the Stinky Parts of Your Code [Part XLVI]

আপনার কোডের দুর্গন্ধযুক্ত অংশগুলি কীভাবে সন্ধান করবেন [পার্ট XLVI]

So finden Sie die stinkenden Teile Ihres Codes [Teil XLVI]

Cómo encontrar las partes apestosas de su código [Parte XLVI]

Comment trouver les parties puantes de votre code [Partie XLVI]

अपने कोड के बदबूदार हिस्सों को कैसे खोजें [भाग XLVI]

コードの怪しい部分を見つける方法 [パート XLVI]

코드에서 불쾌한 부분을 찾는 방법 [XLVI부]

Como encontrar as partes fedorentas do seu código [Parte XLVI]

Как найти ненужные части вашего кода [Часть XLVI]

Kodunuzun Kokmuş Kısımlarını Nasıl Bulabilirsiniz [Bölüm XLVI]

Cách tìm những phần khó chịu trong mã của bạn [Phần XLVI]

如何找到代码中的臭部分 [第 XLVI 部分]

, the leading ecosystem for non-fungible tokens (NFTs), announced today a partnership with to launch a digital collection of Final Fantasy VII 25th Anniversary cards and figures on , a scalable, decentralized, cross-chain network designed to bring NFTs to everyone. The digital collectibles will be accessible to consumers who purchase items in the physical collection, which are expected to launch in 2023. Enjin Square Enix Efinity Consumers can pre-order the commemorative action figure starting today at the Square Enix Store. The deluxe physical product will include a code that can be redeemed for a digital version of the figure using the Efinity network, a next-generation blockchain for NFTs, powered by Enjin. Pre-orders for the trading cards will go live later this year. Enjin is providing an alternative destination for eco-conscious blockchain developers, investors, and users. Collectibles are supported by Enjin’s full product suite, which provides environmentally-friendly solutions for building decentralized gaming products. Enjin is committed to and is building the foundation of Efinity on the most network, Polkadot. As a signatory of the Crypto Climate Accord, Enjin is striving for a decarbonized and sustainable cryptocurrency industry. carbon-neutral NFTs by 2030 carbon efficient , Enjin CTO, added Witek Radomski “This partnership marks a coming-of-age phase for digital assets and entertainment; Square Enix, an esteemed developer with iconic intellectual property, is paving the way for the industry. By using Efinity, fans can experience interactive NFTs without even realizing they’re on the blockchain. Enjin can establish the next phase of growth in blockchain entertainment, merging curiosity and creativity.” Square Enix utilizes the Efinity blockchain and the toolkit to seamlessly integrate, manage and launch its digital collectible offering. The Efinity blockchain offers its users the benefits of efficiency, depth and deployability. Enjin’s robust NFT tech stack enables the wholesale management of Square Enix collectibles on the eco-friendly Efinity blockchain network, using and Enjin Wallet as the non-exclusive secondary-marketplace and wallet solution for users, and Enjin Beam for its unique and accessible QR code NFT distribution system. Enjin Open Platform NFT.io The ease of Enjin’s NFT product stack integration ensures freedom in the creative process, allowing anyone to build their own NFT-based products and features, requiring no knowledge of blockchain specific architecture. For further details, visit ​​ . https://enjin.io/ Witek Radomski, CTO at Enjin available for interview upon request. About Square Enix, Inc. Square Enix, Inc. develops, publishes, distributes and licenses SQUARE ENIX®, EIDOS® and TAITO® branded entertainment content throughout the Americas as part of the Square Enix group of companies. Square Enix, Inc. is affiliated with a global network of leading development studios such as Crystal Dynamics® and Eidos Montréal™. The Square Enix group of companies boasts a valuable portfolio of intellectual property including: FINAL FANTASY®, which has sold over 168 million units worldwide; DRAGON QUEST®, which has sold over 84 million units worldwide; TOMB RAIDER®, which has sold over 88 million units worldwide; and the legendary SPACE INVADERS®. Square Enix, Inc. is a U.S.-based, wholly owned subsidiary of Square Enix Holdings Co., Ltd. About Enjin Enjin is the leading ecosystem for non-fungible tokens (NFTs), offering a comprehensive suite of products for creating, trading, distributing, and integrating NFTs into virtual worlds. As a scalable, affordable platform, Enjin's technology has seen wide application in blockchain games, apps, enterprise programs, and innovative marketing campaigns. The Enjin ecosystem is fueled by Enjin Coin (ENJ), a utility token used to back the value of blockchain assets. To date, over one billion Enjin-powered assets have been created. About Efinity Enjin is pioneering the next generation of NFTs with Efinity, a scalable, decentralized, cross-chain network designed to bring NFTs to everyone. Built as a Polkadot parachain, Efinity will act as a hub for all fungible and non-fungible tokens, with Enjin’s paratoken standard boasting compatibility with tokens from any other blockchain. EFI is the native token of Efinity, designed for transaction fees, facilitating liquidity, community rewards, and governance. For Media Enquiries E: P: +353 87 370 6262 Wachsman ‍ enjin@wachsman.com ‍

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