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ManulGuide.com, a leader in Health and Wellness SEO, is now offering a Free Website Audit Service specifically designed for professionals in

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MSCI Inc. (NYSE: MSCI), a leading provider of critical decision support tools and services for the global investment community, announced today that the MSCI Nigeria Indexes will be reclassified from Frontier Markets to Standalone Markets status in one step coinciding with the February 2024 Index Review.

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Miquido gathers experts in Mobile App Development and AI. We deliver Native and Cross-platform Mobile Apps, take care of Web Development, Data Science, Artificial Intelligence, R&D, Product Design and Strategy. We’re certified by Google, awarded by Deloitte and covered by Forbes. Let’s meet!

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Photo by Jens Johnsson on Unsplash So, you’d like to spice things up in your current business strategy and add an extra layer of high technology with a sophisticated AI solution. Let me just ask you one simple question first: Are you sure it is the best move? If your eyes sparkle with excitement as you think “Yes!”, you can skip this article and go find your perfect AI vendor. If, however, you have even a shade of doubt — stick around. I promise that you won’t regret it! What I have for you is answered by Miquido’s Head of Innovation that should or financial suicide for your business. Ok, no more waiting — here are the questions: 10 questions help anyone understand whether adopting AI technology is the right move Can you clearly? state your problem Can you ? solve your challenge without AI What do you understand by “ ”? doing your task right Do you accept that ? your solution will never be perfect Do to get started? you possess enough data Is your ? data relevant Will you have ? continuous access to new data Can you l ? abel your data correctly or is accuracy enough? Do you need an interpretable model Do you have to keep the project going? enough resources Sound simple enough? Well, it gets a bit more complicated. So we’ll look into each of them in more detail in just a second. Get comfy, grab something to take notes with, and go along with this article, or simply save it for later. Let’s dive in! 1. Can you state the problem clearly? A clear vision of what you want to achieve is a must when it comes to applying AI in business. You need to have a specific problem that requires high-tech solutions that can be solved with Artificial Intelligence and/or Machine Learning. Introducing AI for the sake of bragging about having it won’t fly here. To help you answer this question, try thinking about specific challenges you want to address and always have your target audience in mind. Ask yourself: Which repetitive tasks are you trying to automate? Who will benefit from this automation? Will it be worth the investment? Pro tip Its ultimate goal is to help you get inspiration and once your idea is stated, you can come back to Machine Learning. Focus on what you can have right there and right now — the cornerstone of Artificial Intelligence, data. If you’re not sure where to start — start with data mining. And then ask yourself the next question: 2. Can you solve your challenge without AI? Artificial Intelligence is great because it offers seemingly simple solutions to complex problems. The only issue is that, in fact, it’s far more demanding than it appears. So the ugly truth about the development of AI technology is that if you can get on without it — just do it. Consider these questions: Is your problem complex enough to engage machine learning? Could it be represented by a mathematical equation? Can you think of a step-by-step recipe for an output? If you answered “yes” to the last two questions — congratulations, you need artificial intelligence! don’t If, however, you couldn’t quite wrap your mind around the correct patterns, advanced machine learning algorithms might be your only chance to succeed. Pro tip It’s quite simple, really: . if you can recognise the pattern yourself, you don’t need Machine Learning to do that for you By now, you should have a pretty good understanding of whether you actually AI in your strategy or if you simply want to follow the trends. Let’s see if Artificial Intelligence actually help you out. need can 3. What does it mean to do your task right? First of all, you’ll have to define what “right” and “wrong” mean to you and your business. The world of technology is still pretty binary, and if something isn’t “true”, it false by definition. is So, before investing in any solution, . make sure you understand what it is you hope to gain from it Artificial intelligence is, first and foremost, a complicated algorithm. And in order for it to learn, you need to be able to evaluate its performance. Think along the lines of: How will you evaluate that a task is done correctly? Which mistakes will be more harmful than others? How many mistakes per 1000 results can you afford to have? That’s another big revelation about working with AI: . mistakes are unavoidable Pro tip Be careful not to aim too high with accuracy. . Placing an excessively high bar may result in you missing some profitable opportunities That brings us straight to question #4: 4. Do you accept that your solution will never be perfect? If your reply to this question is “no”, I’ve got some bad news for you: you’re not ready to work with machine learning. Sometimes more, sometimes less, but there’s no chance in the world for your solution to run error-free. Even if you have Elon Musk on your team. There will be mistakes. At this point, think about yourself, your mental health, and the daily struggle you’ll have to accept from now on in your business: Can you live with occasional mistakes in your model? What are the actual consequences of such mistakes? What does it all mean from an ethical standpoint? Once again, there are two potential outcomes here: You’ve evaluated the risks correctly, talked yourself into accepting them, and are on your merry way to hiring an AI software development company. You’ve reached the conclusion that the stakes are too high, so you can either give up on the idea of working with machine learning altogether. Pro tip To minimise the chance of costly and dangerous mistakes, simply . This is known as a “human-in-the-loop approach” and can save you some headaches. have someone who will double-check the results With that in mind, you must have a pretty good idea of whether or not the AI onion is worth peeling. Now it’s time to see if you’ll be able to actually build your model. 5. Do you have enough data? The most common and broadly discussed limitation of Artificial Intelligence is its heavy dependency on datasets. There is simply no machine learning without data. To put it bluntly: if you don’t have the data to keep your project running, your chances of launching it in the first place are slim. Some questions that might help you think are: Do potentially useful inputs even exist? Can you gain access to them (e.g. build them, buy them, etc.)?Do you have enough examples? Pro tip It usually takes at least 10 thousand samples when training the model from scratch. Yet, , and don’t we all thrive for perfection? the more examples you have the more reliable your AI model will be In data science, however, it’s both quality quantity that matter, so if you do have enough materials to proceed with, let’s check if they hold any value. and 6. Is your data relevant? As you could’ve guessed from the previous point, without having a well-thought-through plan for data collection, you’ll stumble across multiple serious issues with your AI solution pretty quickly. The best way to ensure it doesn’t happen is to double-check the actual relevance of your data. There’s no point in having 1000+ features with no practical use, they’ll only eat up your precious storage space. The important questions to keep in mind to ensure that you’ll only work with the highest quality data points are: Are your data points significant? Is all the data clean enough? Is the data you have relevant to your target audience? Is it free of bias? Pro tip . In order for everything to work in an orderly fashion, you need to ensure your sample data is well-balanced, clean and free of inconsistencies. To learn from examples, AI needs good examples to learn from And if you’ve got that covered, it’s time to think about the further evolution of your AI solution. 7. Will you be able to access new data continuously? We are almost done with data questions, I promise! But while we’re on the subject, let’s think about the scalability of your project. Having an accurate initial model might suffice for obtaining reliable predictions for a relatively short period of time. Yet, you’ll soon notice there are numerous factors that may negatively affect its performance. These threats include social events, seasonal changes, shifts in demographics, the geographical location of your users, etc. When there are factors that can influence your target audience over time, . your AI model needs to be constantly retrained Here, you will have to face new challenges and try to answer questions like: How sensitive is your dataset to changes? Do or will you have access to new data continuously in order to update it? A great example of such unexpected threats is Covid-19. It has pretty much rendered a massive amount of data obsolete, as people’s behaviour has changed drastically. Pro tip Or, in case of a disastrous setback, like a worldwide pandemic, at least can be easily updated. Make sure that your AI solution will be weather-proof. That brings us to the final question about data. 8. Can you label your data properly? We’ve covered some paramount issues like obtaining, updating, and navigating Big Data, so now it’s time to talk about data management. . Some datasets, such as image recognition systems, inherently contain labels based on the logged user actions. However, if your plan is to build a classification from the ground up, you’ll need to come up with a system for correct data labelling. In order for your AI solution to correctly understand the data, it requires proper labelling The important questions to think about here are: Do you to label your dataset? need Can you get a human to do it? How much time and money will that process require? Pro tip When working with advanced data, such as ECG signals or medical images, before your model could learn from examples. hire experts to correctly classify each case With that, we’re ready to move on to the next question! 9. Does your model have to be interpretable? is one of the primary issues with machine learning. But what does it even mean? Interpretability In the simplest terms, the higher the , the easier it makes it for a human to understand the processes behind the algorithm’s decision making. interpretability of an AI training model It is crucial for some businesses to fully understand the flow due to external policies and regulations. However, more often than not, . having a model that is accurate yet not entirely interpretable, is enough to get your AI project up and running So, the two major questions that arise here are: Are there any regulations governing your model’s interpretability? What is your trade-off between accuracy and interpretability? Pro tip Testing helps to launch many projects regardless of their complexity. If you can’t explain how something works, run as many tests as needed to prove that it does work. Looks like it’s time for the final and the most important question you should ask yourself before committing to an AI project: 10. Do you have enough resources to implement & maintain your AI solution? As you may already know, machine learning projects aren’t particularly cheap, are not that easy to implement, and require an experienced team behind the wheel. So before you jump into your next idea headfirst, try to evaluate with great care whether you have enough resources to really pull this off. Some of the questions that might help you at this stage are: How much does building and updating the database cost? Do you have access to enough processing power? Are you aware of the costs of training and retraining your model? Are the expected benefits higher than the estimated costs? Depending on the complexity of your model, all these numbers can vary drastically. Pro tip G , as these solutions generally tend to be much more cost-effective than self-hosted solutions. o with Cloud Final remarks With all that in mind, you should be perfectly ready to make a final decision regarding your AI project. If you’re still up for the challenge — that’s great news! All there’s left to do is to find the right vendor to deliver your AI solution. And if you’ve discovered it may not be the best time for one just yet — don’t worry! The world of technology is evolving rapidly, so while something might have been too complicated yesterday, it may as well become the new normal tomorrow. Just remember — as long as you can get on Machine Learning, there’s really no pressing need to spend resources on it. without About The Author This article was written by Anna Kvasnevska, and has been posted on her behalf, with her permission. Anna is a Millennial, an ex-pat, a writer, a marketer — in that particular order. On a path to understanding the world through personal & spiritual growth.

10+ Questions you Should ask Yourself Before Developing an AI Solution

Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries. the visualization by McKinsey Global Institute As businesses realize these technologies are a boon, the demand for machine learning consulting companies is skyrocketing, which predictably influences the supply. In such a way, today’s IT market is literally larded with ML vendors tackling projects of any scope and budget. To help you make your way through this enormous and diverse offering, we assembled a list of today’s most trustworthy vendors of machine learning consulting and development services. Our IT market analysts assessed an extensive range of companies according to the following objective criteria: Proven machine learning expertise Prior projects: size and complexity Diversity of business domains served Client references Speed and quality of customer service As a result, we got a list of 30 companies worthy of attention. For readers’ convenience, we present the vendors in descending order. 1. Itransition A recognized custom software development company, Itransition has delivered 1,500 projects over the span of its 20-year market presence. With more than 2,000 IT experts on board, the company has served 810 clients from 30 countries. Among the ML services the company provides there are data mining, natural language processing, computer vision, robotic process automation, and machine learning consulting. To deliver its ML solutions, Itransition uses such technologies as Python, R, Azure, Google Cloud, and AWS. Location: Denver, CO Established: 1998 Project budgets start at: $10,000 Key clients: Expedia, Xerox, Toyota, eBay, PayPal, Shell, adidas Other, non-ML services: custom software development, CRM consulting, ecommerce development Website: https://www.itransition.com/ 2. Tooploox EU-based Tooploox has around 100 skilled experts on board. For seven years of the company’s operation, they delivered 150+ successful projects. Tooploox is a machine learning consulting company using this technology to deliver computer and 3D vision, sensor fusion, deep learning, natural language processing, reinforcement learning, and other ML-related services. The company has expertise with other emerging technologies, such as blockchain. Location: Wrocław, Poland Established: 2012 Project budgets start at: $25,000 Key clients: CoinDesk, DTS, ING, Spire Health, Group Nine Other, non-ML services: mobile app development, product design Website: https://www.tooploox.com/ 3. Skim Technologies London-based Skim Technologies is an internationally recognized machine learning consulting company delivering a wide range of related services. The company’s data scientists offer chatbot development, custom ML model creation, and NLP engineering. Skim Technologies also delivers AI workshops as well as ML and AI consulting services. Location: London, United Kingdom Established: 2014 Project budgets start at: $10,000 Key clients: Jones Lang-Lasalle, Breast Cancer Care, Quant Insight Other, non-ML services: business intelligence consulting, custom software development Website: https://www.skimtechnologies.com/ 4. ThirdEye Data US-based ThirdEye Data has been delivering its ML development services for nine years. The company specializes in chatbot development, custom ML algorithms, and machine learning consulting. Among the data science technologies the company works with, there are Spark, TensorFlow, Caffe, and others. Location: Santa Clara, CA Established: 2010 Project budgets start at: $25,000 Key clients: Microsoft, Amazon, Nokia, Ford Direct, Getty Images, Amgen Other, non-ML services: business intelligence consulting, cloud consulting Website: https://thirdeyedata.io/ 5. Chatbots.Studio As its name implies, Chatbots.Studio is a company focusing on AI-powered chatbot development. It builds multichannel conversational platforms, automates business processes, and designs conversations. The company’s technology stack includes React, Redux, Angular 6, Node.js, Express.js, Botkit, and others. Location: Lviv, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Visa, Unibot, Aldi, Digital Flex Other, non-ML services: custom software development Website: https://chatbots.studio/ 6. Azati Software Since 2001, Azati's experts have cooperated with 330 clients globally. Among the ML competencies of the company there are supervised and unsupervised learning, distributed computing, natural language processing, digital signal processing, and machine learning consulting. Azati’s tech toolkit includes CuDNN, Scikit Learn for Python, and Tensor Flow. The company mainly targets clients in the Insurance domain. Location: Livingston, NJ Established: 2001 Project budgets start at: $1,000 Key clients: MedPro Group, SequenceBase, Casio, Thomson Reuters Other, non-ML services: mobile app development, web development Website: https://azati.ai/ 7. Hidden Brains Since its foundation in 2003, Hidden Brains has cooperated with clients from 107 countries. The company delivers AI, ML, deep learning, and data science services. It also specializes in developing natural language processing software. Hidden Brains also delivers chatbots, including those for Facebook, Telegram, WhatsApp, and Slack. Location: Ahmedabad, India Established: 2003 Project budgets start at: $10,000 Key clients: McAfee, Raymond, Maxtor, SanDisk Other, non-ML services: mobile app development, web development Website: https://www.hiddenbrains.com/ 8. InData Labs Established in 2014, InData Labs houses 50+ data scientists, engineers, and analysts. The company develops ML algorithms and natural language processing solutions. Besides, InData Labs specializes in computer vision and predictive modeling. The company's stack includes Python, SpaCy, TensorFlow, Keras, and more proven languages and frameworks. Location: Minsk, Belarus Established: 2014 Project budgets start at: $5,000 Key clients: Captiv8, Flo Health, Wargaming Other, non-ML services: business intelligence consulting Website: https://indatalabs.com/ 9. Arcanys Since 2010, Philippines-based Arcanys has been delivering its ML and AI services to clients from all over the world. The company specializes in sentiment analysis, recommender systems, and image recognition. As one of its cooperation models, the company provides dedicated teams of highly experienced data scientists and developers. Location: Cebu City, Philippines Established: 2010 Project budgets start at: $100,000 Key clients: General Electric Korea, Enfo, Payment Logic, L’Oreal, Spinetix Other, non-ML services: custom software development, mobile app development Website: https://www.arcanys.com/ 10. MobiDev Since 2009, MobiDev’s IT experts have delivered 350+ software solutions. While headquartered in the US, the company provides its services globally. MobiDev uses the following machine learning technologies: Python, R, C++, Go, TensorFlow, ONNX, and Keras. MobiDev’s mobile ML toolkit includes Core ML, TensorFlow Lite, and Cafe2Go. On top of that, the company provides machine learning consulting. Location: Atlanta, GA Established: 2009 Project budgets start at: $10,000 Key clients: Verizon Media, Comcash, Quality Bridge, SmartTab, SMS Group Other, non-ML services: IoT development, web development Website: https://mobidev.biz 11. Spire Digital For over 20 years that Spire Digital has been providing IT services, it has cooperated with 600+ global customers. Among the ML and AI development services the company offers, there are natural language processing, supervised and unsupervised learning, and sentiment analysis. Also, Spire Digital creates chatbots for Facebook Messenger, Slack, Skype, and Cisco WebEx. Location: Denver, CO Established: 1998 Project budgets start at: Undisclosed Key clients: TomTom, Lockheed Martin, US Navy, USDA, Western Union Other, non-ML services: custom software development, mobile app development Website: https://www.spiredigital.com/ 12. Icreon New York-based Icreon is a digital innovation agency mostly targeting medium-sized businesses. The company uses AI and ML technologies to create innovative applications, platforms, and ecosystems. The company’s technological expertise in ML includes Amazon Web Services, Azure, Caffe2, Google Cloud, Keras, and more. Location: New York, NY Established: 2000 Project budgets start at: $25,000 Key clients: National Geographic Channel, FOX Movies, Harry Winston, Nokia Other, non-ML services: custom software development Website: https://www.icreon.us/ 13. Miquido For eight years, Miquido's team has successfully completed more than 100 projects, including in machine learning. The company offers solutions for AI-based personalization, predictive and behavioral analytics, and churn prediction. Miquido also develops chatbots. Recognized by Deloitte as one the fastest growing companies in Europe, Miquido also offers UI/UX design creation and mobile apps development. Location: Kraków, Poland Established: 2011 Project budgets start at: $25,000 Key clients: Skyscanner, Abbey Road Studios, HelloFresh Other, non-ML services: mobile app development, web development Website: https://www.miquido.com/ 14. Neoteriс Founded in 2005, Europe-based Neoteric offers a wide range of ML solutions, including predictive analytics, recommendation engines, NLP, robotic process automation, and machine learning consulting. The company also specializes in such AI platforms as SaaS Manager, SoapBox, AppOrchid, and RapidSOS. Location: Gdańsk, Poland Established: 2005 Project budgets start at: $10,000 Key clients: LunchBadger, Arvata, IT Trans Other, non-ML services: custom software development, web development Website: https://neoteric.eu/ 15. Unicsoft Established in 2005, Unicsoft employs more than 100 specialists in emerging technologies and software development. Within its ML tech stack, the company offers development of predictive and behavioral analytics solutions, neural networks, and recommendation engines, among other ML applications. The company also delivers consulting services, and maintenance of existing ML platforms. Location: Kiev, Ukraine Established: 2005 Project budgets start at: $5,000 Key Clients: Hoodifood, Executive Eye, GOFORIT, Intellica Group Other, non-ML services: blockchain Website: https://unicsoft.com/ 16. Abto Software With 12 years of its market presence, Abto Software’s has worked with 200+ global customers. Specializing in AI and ML, the company offers custom enterprise solutions for advanced and predictive analytics, virtual assistants, recommendation systems, and intelligent business process automation. Abto Software works with the following tools and frameworks, among others: TensorFlow, Keras, Caffe, OpenCV, and AWS. Location: New York, NY Established: 2007 Project budgets start at: Undisclosed Key clients: Just Answer, Skybow, Medikit, Up Great Other, non-ML services: web development, enterprise app modernization Website: https://www.abtosoftware.com/ 17. Accubits Technologies, Inc. US-located Accubits started in 2012 and has already cooperated with 140+ clients. The company’s ML capabilities include development of conversational and classification tools, video, image and text analytics solutions, as well as predictive analytics software. Besides, Accubits offers machine learning consulting. The company’s experts work with NumPy, PHP, Python, SpaCy, Spark, and several other platforms. Location: Vienna, VA Established: 2012 Project budgets start at: $5,000 Key сlients: Dubai Land Department, Smart Dubai, Eduvalue, Landmark Other, non-ML services: blockchain, custom software development Website: https://accubits.com/ 18. BotsCrew Established in 2016, BotsCrew is a global leader in chatbot development, delivering to mid-sized companies and startups. BotsCrew has its own enterprise chatbot platform that is ready to be customized and optimized according to customers' needs. Besides the proprietary platform, the company offers development of voice assistants and IoT bots. Location: London, United Kingdom Established: 2016 Project budgets start at: $10,000 Key clients: Ailira, IVA, ReVA, Dr. Dean Other, non-ML services: None Website: https://botscrew.com/ 19. STX Next Founded in 2005, STX Next is a custom software development company that specializes in streamlining IT solutions with AI and ML technologies. The company’s main technological advantage is in its exceptionally skilled Python developers. For 14 years on the market, STX Next has delivered over 250 successful projects. Location: Poznań, Poland Established: 2005 Project budgets start at: $25,000 Key clients: Seerene, Verve, Hogarth WW, Pixomondo, Unity Technologies Other, non-ML services: web development, custom software development Website: https://stxnext.com/ 20. Dogtown Media California-based Dogtown Media specializes in delivering mobile applications, enhanced with ML and AI technologies. Besides ML-powered mobile apps, the company provides machine learning consulting. For the last eight years, Dogtown Media has brought to life at least 200 applications. Location: Venice Beach, CA Established: 2011 Project budgets start at: $25,000 Key clients: Google, Lexus, United Nations, Citibank, Red Bull, YouTube Other, non-ML services: mobile app development, IoT development Website: https://www.dogtownmedia.com/ 21. Sigmoidal Sigmoidal is a New-York-based consultancy specializing in machine learning and data science, mostly for companies in Finance, Trading, and Pharma. The company integrates these technologies into web and mobile applications and APIs. It also offers solutions for NLP, computer vision, and predictive analytics. Location: New York, NY Established: 2016 Project budgets start at: $1,000 Key clients: Undisclosed Other, non-ML services: business intelligence consulting Website: https://sigmoidal.io/ 22. Cyber Infrastructure Established in 2003, Cyber Infrastructure is a custom software development company with 100+ clients worldwide. Its ML expertise includes development of virtual assistants and chatbots for a variety of domains. Cyber Infrastructure also provides machine learning consulting and employs Python, Java, C++, Lisp, and R as its primary ML and AI stack. Location: San Francisco, CA Established: 2003 Project budgets start at: $5,000 Key clients: Mannatech, Bell Other, non-ML services: custom software development, web development Website: https://www.cisin.com/ 23. Azilen Technologies US-headquartered Azilen Technologies has 250+ IT experts on board distributed across four offices worldwide. They employ ML technologies to deliver customer service, survey, and informational bots. On top of that, the company delivers facial recognition and predictive analytics solutions. Location: West Bloomfield, MI Established: 2009 Project budgets start at: $25,000 Key clients: Samsung, Pizza Hut, Sodexo, ICICI Bank Other, non-ML services: custom software development Website: https://www.azilen.com/ 24. Stermedia Stermedia has been delivering its ML and data science services since 2007. The company’s dedicated teams of data scientists and software architects design ML algorithms and models for extracting knowledge from big data sets. The company also provides NLP solutions and uses such technologies as TensorFlow and Keras, to name just a few. Location: Wroclaw, Poland Established: 2007 Project budgets start at: $10,000 Key Clients: Family Fabric, WorkService, ISOK Other, non-ML services: custom software development, web development Website: https://stermedia.ai/ 25. Belatrix Having five offices with 600+ professionals all around the world, Belatrix is one of the most fast-evolving machine learning consulting companies on the market today. Its machine learning experts have a proven track record of building various solutions, from predictive modeling to communication automation. Location: Mendoza, Argentina Established: 2003 Project budgets start at: $5,000 Key clients: Disney, Adobe, AOL, FIS, Chatman Financial Other, non-ML services: custom software development, mobile app development Website: https://www.belatrixsf.com/ 26. DataRoot Labs DataRoot Labs is an agency focusing on ML and AI development. It delivers full-cycle ML software development, including project evaluation, machine learning consulting, strategy development, architecture, integration, and support. Besides, DataRoot Labs helps enterprises with digital transformation with AI at the core. Location: Kiev, Ukraine Established: 2016 Project budgets start at: $5,000 Key clients: Servers.com, Everad, ABM Cloud, Cargofy Other, non-ML services: custom software development, mobile app development Website: https://datarootlabs.com/ 27. Softengi Since 1995, Softengi has completed 900+ projects for clients across six continents. The company’s ML services include chatbot engineering and NLP algorithms. For these purposes, Softengi uses R, Hadoop, and Python, among other tools. Location: Kiev, Ukraine Established: 1995 Project budgets start at: $10,000 Key clients: Boeing, Zeppelin, Enviance, OneRoof Energy Other, non-ML services: custom software development, AR/VR development, IoT development Website: http://softengi.com/ 28. Waverley With 27 years of software development experience, Waverley is a company housing 370K talented engineers. Among other services, they offer ML-centric solutions such as data clustering and unsupervised algorithms. On top of that, the company offers NLP and computer vision solutions and chatbots. To deliver ML products, Waverley’s engineers use Java, Python, Scala, and R. Location: Palo Alto, CA Established: 1992 Project budgets start at: $50,000 Key clients: Mozilla, Qualcomm, Swisscom, American Express Other, non-ML services: custom software development, mobile app development, IoT development Website: https://waverleysoftware.com/ 29. Imaginovation Founded in 2011, Imaginovation is a software development agency that has successfully completed 300+ IT projects of various complexity. As for ML and AI, the company develops recommendation and effectiveness assessment engines as well as solutions for speech and text processing automation. Location: Cary, NC Established: 2011 Project budgets start at: $10,000 Key clients: MetLife, Nestle, UNC Pembroke, Contractor Score Other, non-ML services: custom software development, mobile app development, web development Website: https://www.imaginovation.net/ 30. E2logy Established in 2005, E2logy has delivered 300+ projects to businesses of various sizes. Being a leading Amazon Web Services and Azure AI vendor, the company develops ML algorithms and chatbots. They also offer machine learning consulting. In addition to their ML services, E2Logy brings custom IoT solutions to its global customers. Location: Ahmedabad, India Established: 2005 Project budgets start at: $5,000 Key clients: McMillan Publishers, Polaris Industries, Vodafone Other, non-ML services: custom software development, web development Website: https://e2logy.com/ This was a full list of the top 30 machine learning consulting companies in 2019. If you cooperated with any of the presented vendors, please do get in touch and share your story.

Top 30 Machine Learning Consulting Companies

Since Flutter early alpha was first announced at Google’s I/O in May 2017, it has gradually become one of the fastest cross-platform frameworks, gaining worldwide popularity and improving its stability in time (version 1.0 was released in December 2018). Today, 2 years after Flutter alpha was announced, the framework is used by Google and developers around the world to deliver beautiful native apps on iOS and Android. But how could you tell if a company offering Flutter development in its services is reliable? Below, I have created a shortlist of Top Flutter development companies, based on 4 key factors: their experience, successful Flutter projects, contribution to Flutter community, and official approval from Google. Miquido While some companies adopted Flutter no sooner than it hit 1.0, was one of the first early adopters of its alpha — with fairly spectacular results. The company earned the position of mobile development leaders with in Flutter and React. Miquido a strong portfolio Topline, the app that they created for Abbey Road Studios in 10 weeks (working on Flutter alpha) was appreciated by Google and officially featured during Google I/O in May 2018 and Flutter Live 2018. It’s also and their series of . showcased by Google on the flutter.dev website Flutter Developer Stories The solution for Abbey Road Studios is not the only application written in Flutter by the Miquido team. The company created i.a — an intuitive platform for work time tracking and reporting, available on the App Store and Google Play. Quidlo Timesheets Miquido team also hosts and participates in a number of Flutter community events. Their expertise earned a top position among Flutter development companies globally. GeekyAnts are one of the earliest adopters of Flutter, experimenting with it since its early release. Their work has also been recognised by the core Flutter team at Google, and they have been registered service suppliers for Google LLC since 2017. They also have a major presence in many Flutter community events and have their own Flutter Marketplace. GeekyAnts Alibaba , one of the world’s biggest online commerce companies, used Flutter to create Xianyu app, which has 50M+ downloads and is featured in Google’s series of Flutter Developer Stories. Alibaba Very Good Ventures and The Yakka Formly Posse, which was featured in a series of Flutter Developer Stories next to Topline app by Miquido, Very Good Ventures in NYC and in SF are both major contributors to the Flutter community. is behind the development of the Hamilton App as well as the KENKEN Flutter Web app demoed at Google IO 2019. The Yakka maintains the popular Fluro routing library. The Yakka Very Good Ventures AppInventiv After announcing Flutter Beta on Mobile World Congress 2018, helped a few of their clients port their React Native apps into cross-platform Flutter apps. AppInventiv Mindinventory offers app development using Flutter framework. The team is full of Flutter enthusiasts, the participants of Flutter meetups and community — they also created Mindinventory some of their projects with Flutter SDK. DISCLAIMER: The list above covers Flutter companies that, in my individual opinion, are the most serious players in the world of Flutter, considering their expertise, community engagement, and approval from Google. Obviously, there are still a lot of those, that develop their interest in Flutter and will probably join the list soon. If you can think of any company that should be added to the list, feel free to paste some link to their apps on Google Play or the Apps Store in a comment.

Top 6 Flutter Development Companies in 2019

I summed up the most important reasons why Google Assistant is soon going to change the game for good. Now let me take you a step deeper into the rabbit hole, and explain what it takes to integrate with it, and what business opportunities it opens for you. In the last article, Assistant is everywhere — use it to reach your customers There are already over 1 billion Assistant-enabled devices — including smartphones, smart speakers, headphones, TVs, smartwatches, smart home appliances, and more! Every day brings news of new ones being released. Integrating with Google assistant means your business will be there to assist your customers whenever and wherever they need it. Ordering products, finding stores, scheduling appointments, giving instructions, accessing content — it is only a fraction of what can be done with Google Assistant. Whatever the focus of your business is, I am fairly sure Google Assistant can help you boost it in a way you might not have thought of yet. Below, I will give you plenty of such examples. But first, I will briefly explain how you can write your own Google Assistant extension. Actions on Google — Cloud platform of the future Actions on Google is a platform that allows you to extend the functionality of Google Assistant with your own applications and deploy them for all Assistant-enabled devices. It is a cloud-based platform which means that the apps you are writing are never really installed on any of the end devices. This makes it extremely convenient for both the developers and the users. First of all, you don’t have to worry about many things that are big issues for mobile or desktop apps, like supporting older versions of the hardware, running updates, etc. Moreover, users don’t have to install your actions to use them — all that is required to open an action is to say: “Ok Google, talk to…” and name the action you want to invoke. When it comes to the development effort, actions are far easier to implement than any mobile or web application, and their user interface is simple and standardised, thus eliminating much of the standard UI work. What it takes to create an action for Google Assistant What is really great about actions development is that Google’s tools and machine learning algorithms do most of the heavy lifting for you. They handle both speech-to-text and text-to-speech, and most importantly, they can cleverly guess user’s intents and choose the best path of the conversation. Don’t worry, you don’t have to provide every possible combination of user’s words for the algorithms to work — they manage to correctly infer intents even if a user doesn’t use the exact words you provide in the console. The most important of these tools is definitely Dialogflow. It is a natural language understanding tool, where most of this magic happens. The biggest challenge during the creation process of an action is to design the conversational experience that is both efficient and pleasant for its users. This requires an in-depth knowledge of the capabilities, restrictions and Google’s guidelines for the platform. Another challenge is to provide a business logic that will deliver content for the action, perform operations and integrate with external services. This logic is handled by a backend application called a fulfillment service. A fulfillment service is basically a piece of software that receives requests from the Assistant, processes them and responds. It can be written in any technology you prefer, as long as it can communicate with Google’s API. There are also Node.js and Java/Kotlin client libraries that further simplify the implementation. Possible use cases for integration with Google Assistant I do not intend to cover all possible use cases here. I just want to point out a few popular scenarios that might apply to your business, or at least will move your imagination. – The most straightforward scenario is an intelligent FAQ bot, which can share its “knowledge” and answer questions about your service or products. FAQ Examples: Visa Guru, Pringles. – Going further, a virtual assistant can aid your call centre, staff or even banking system in some of their duties. Customer service Examples: Bank of America, Mr. Spring, AGL, U. S. Bank. – Ordering a cab, coffee, pizza or even flowers? You can do all of that with Google Assistant in a matter of seconds! Ordering Examples: Uber, Starbucks, Domino’s Pizza, 1–800-Flowers. – From groceries through tickets to pretty much everything… You can buy them just by using your voice or screen interface. Purchases Examples: Asda, Fred Meyer, Ticketmaster, Fandango, eBay. – Checking flights, air quality, the weather or movie recommendations can be done on the go using voice interface. Information Examples: Singapore Airlines, World Air Quality Index, AccuWeather, Itcher. – You can listen to and control your music and video streaming services. There are also use cases for audiobooks, children stories background music or even meditation! Multimedia Examples: Spotify, Netflix, YouTube, Google Play, Disney, StoryCastle, Headspace. – Shopping lists, work trackers, notes — Assistant can be the fastest and most natural way to interact with those. Lists, tasks and notes Examples: Todoist, Evernote, Trello, Timesheets Companion. – You can give your users instructions on pretty much everything. The most common scenarios would probably involve cooking and workouts. But assistant could be easily used to make quick, hands-free guides for things like hair colouring or newborn bathing. Instructions Examples: Tasty, Fitness Buddy, Clairol Color Expert, Johnson’s. – Assistant can be a quick way to send and receive messages and emails, add posts on your social app, or even find a date._Examples: WhatsApp, Twitter, Viber, Sonus Mail, Only One._ Communication & Social media – Finding nearest branch offices, restaurants, dental providers, events, or dogs to adopt — you can write an action that will direct clients to your business. Location Examples: OCBC Bank, Hipster Cat, Sunfish Events, Petfinder, MetLife. – Dining, activities, beauty and fitness — the Reserve with Google service allows your clients to book appointments with Google Assistant. There are also many existing services that provide appointment scheduling actions. Using one of them will allow you to reach your clients even without writing your own action. Reservations Examples: Appointy, Booksy, MINDBODY, Tiqets, TripAdvisor. – Once you try it, voice becomes the most natural interface to control your smart home appliances like light, entertainment systems, heating, cleaning robots and more. But it doesn’t really stop with home smart devices there. Many car brands like Lexus, Mercedes or Volvo provided integrations with Google Assistant that let their users check fuel level or turn on heating remotely. Smart home Examples: Philips Hue, Harmony, Nest, Roomba, Volvo on call, Mercedes me. These are just a taste of what you can do with Google Assistant. Basically, if you can think of any fast and frequently used scenario within your service where you interact with its users, it is a likely candidate for an action! How to make your action profitable? There are a few ways to monetise your action directly. If it is an extension or another interface of your premium service, you can introduce account linking to allow for authorisation. There is also an option to integrate payments via Google Pay, enabling users to pay for access to premium features directly from Google Assistant. Purchasing is another example. With the Google Pay integration mentioned above, the checkout process can be pretty seamless. But if you don’t want to integrate Google Pay, you can as easily implement only part of the flow in Google Assistant, and when the user is ready to check out, ask him to use their phone (if one isn’t already using one), and continue the process in a browser opened directly from your action. But the business value can also be gained through different ways than online payment. There are countless use cases which prove that assistant can bring profit by allowing users to connect to your service easily, find your business locations, or just by increasing your brand recognition. From Zyrtec’s allergy forecasts to Johnson’s newborn bathing instructions — there are many actions that are not only useful but are actually very smart marketing campaigns. Wrap-up Google Assistant can provide a powerful interface for your business to connect with your clients, and creating an action is simpler and faster than writing an app for other platforms like mobile, web and desktop. Start small, think of a simple scenario, and release your first app to the Actions Directory. It won’t cost you much, and the results can exceed your expectations! Want to develop an action? If you’re interested in writing a Google Assistant application for your business, reach out to — . And if you have any questions about Google Assistant, drop me a note at . Miquido Actions on Google development experts jerzy.biernacki@miquido.com What’s next? I’m far from done when it comes to Google Assistant and actions. The next piece in the series is going to be about Google’s guidelines and tips on how to design a high-quality action for the pleasure of its users and Google’s reviewers. Stay tuned and follow ! Miquido’s blog Read more: How Google Assistant is about to disrupt your business Miquido joins an exclusive group of Actions on Google experts

How to boost your business with Actions on Google

MAHE, Seychelles, May 16th, 2023/Chainwire/-- , the first layer-1 blockchain to create a DeFi Ecosystem for Liquid Staking Derivatives (LSDs), has joined forces with , a leading blockchain infrastructure provider, to increase adoption of LSDs across all blockchains, including those where liquid staking derivatives are currently not available yet. Tenet Ankr As part of the partnership, Tenet will have discounted pricing on Ankr’s LSD integration services, which allows Proof of Stake Blockchain’s to easily set up liquid staking for their assets with Ankr. In addition, Tenet will help cover some of the integration costs for its partners in exchange for the new Blockchain’s committing a certain amount of LSD assets to Tenet’s network. Proof of Stake networks today dominate the blockchain landscape, with the vast majority of alternative layer-one chains and application-specific blockchains (“appchains”) each having their own staking token. Staking is key in securing the network to protect it from economic attacks which means that DeFi use cases that generate alternate sources of yield will potentially reduce the security of the network. By adopting liquid staking derivatives, which allow using staked assets in DeFi, the conundrum is effectively solved — increasing the security of the network as it scales. Most LSD liquidity today comes from Lido, which means that smaller blockchains may need to wait sometime before they see their chain integrated. Ankr, on the other hand, has created a white-label system that allows any project to quickly set up its own LSD. By working with Tenet, projects will benefit from both the cheaper LSD integration services, as well as the Tenet layer-1 LSD ecosystem, which allows users to earn a maximum yield on their assets by supplying them to various DeFi protocols on its network. In exchange, Tenet partners will need to commit a certain amount of LSD total value locked to Tenet Validators, which boosts its security model. Tenet has a unique model called Diversified Proof of Stake (DiPoS), which supports staking with any LSD asset from other chains, such as ETH, BNB, ATOM, SOL, and ADA. This allows Tenet to be partially secured by all its supported networks, dramatically improving security compared to standard single-token Proof of Stake. "We're on a mission to bring LSDs to all the top Layer-1 blockchains and make Tenet the defacto place for them to safely earn yield and utility," said Greg Gopman, CEO of Tenet and former CMO of Ankr. “During my time at Ankr, I was inspired by all the amazing LSD products the company launched, but disappointed by their ultimate lack of use. With Tenet, we aim to change this.” As part of the deal, Tenet will commit to using Ankr for its RPC services, as well as other integrations that make sense for the network’s infrastructure. The two companies will also put together a Liquid Staking Conference (side event) at in Paris in July 2023. EthCC Tenet is currently live on testnet and will launch Mainnet and its token generation event on . May 25 About Tenet Tenet is a DeFi-focused Layer-1 ecosystem, providing liquidity and yield opportunities for liquid staking derivatives (LSDs). Co-founded by Greg Gopman, former CMO of Ankr, and Dan Peterson, former head of revenue operations at Blockdaemon, Tenet aims to unlock the potential of the rapidly growing LSD market, which is currently valued at over $17 billion. Tenet takes a unique approach by sharing security through Diversified Proof of Stake, which allows LSDs to stake to its network and earn yield on their liquid staking assets, and participate in the platform's governance. About Ankr Ankr is an all-in-one portal built to support the growth of Web3. They provide multi-chain API connections, dApp development tools, and crypto staking solutions with a global node infrastructure powering it all over 30+ blockchains. As the pioneer of liquid staking, Ankr creates next-generation solutions for supporting validation on several proof-of-stake chains with an industry-leading node delegation system and security. With a Total Value Locked of over $500M, Ankr is the 3rd largest Ethereum staker and go-to staking infrastructure provider for ecosystems like the BNB Smart Chain, Polygon, and more. Contact Press team@tenet.org This story was distributed as a release by Chainwire under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

Tenet and Ankr Partner Are Bringing Liquid Staking Derivatives to More Blockchains: Here's How

Tenet y Ankr Partner están llevando derivados líquidos de staking a más cadenas de bloques: así es como

Tenet et Ankr Partner apportent des dérivés de jalonnement liquide à plus de chaînes de blocs : voici comment

टेनेट और अंकर पार्टनर लिक्विड स्टेकिंग डेरिवेटिव्स को अधिक ब्लॉकचेन में ला रहे हैं: यहां बताया गया है कि कैसे

Tenet と Ankr パートナーは、リキッドステーキングデリバティブをより多くのブロックチェーンに導入しています: その方法は次のとおりです

Tenet e Ankr Partner estão trazendo derivativos líquidos para mais blockchains: veja como

Đối tác Tenet và Ankr đang mang các công cụ phái sinh đặt cược thanh khoản cho nhiều chuỗi khối hơn: Đây là cách

Tenet 和 Ankr Partner 正在将 Liquid Staking 衍生品引入更多区块链：方法如下

The crypto market is what is on everyone’s mind right now, above the surface that is. Below the surface, there is something serious happening in the mining industry. Mining hashrate has come to a crossroads once again. Whether the most shutdown products will come back to the main stage is probably not the major concern of all the miners. At this moment, the good news is that there are still some old miners of high hashrate power running. While the bad news is that the miners who entered the field with high coin prices or owning several low hashrate ones were forced to shut down or have a bunch of machines but no suitable hashrate power. The former has already gained profits back, in the case of a big drop in coin prices, laying flat has a good return. The latter, at least for now, is the receiver of the high end of the market. Is this the ending of the mining industry? And, is it true that buying cheap miners at is the smartest way of becoming profitable right now? What are those pioneers and mining machine manufacturers doing? Some questions: Water-cooled products, which are like modified cars in the past, are being accepted and recognized by mainstream manufacturers. So what exactly does it mean? The truth may be scarier than thought. An era of high hashrate power and super hashrate power is about to come. The year 2022 will be the first year of the third hashrate revolution. This article is close to 5000 words and is divided into seven dimensions. You can browse in sections. This article was written by Mark Zhou, originally published . It has been republished with permission Note: here 1. An important hashrate generation we are experiencing An important hashrate generation we are experiencing The individual & geek era spanned 4 years, from 2009 to 2012 where the ultimate core was still CPU and GPU mining. First, it was the M (Mega, MH/s), then G (Giga, HG/s). During these rapidly changing years, the G as the leading individuals or companies have earned a lot of profits day to day, no one has foreseen the upcoming of the TeraHash era (Tera, TG/s). We bid farewell to the past with no signs. No goodbye, only farewell. The professional miner (ASIC) air-cooled era: From the first ASIC mining machine which appeared in 2013 to now, as the chip is made smaller and smaller, the hashrate power is getting higher and higher which increased from the beginning 10G of each unit to the maximum 140T today. According to today’s best energy consumption ratio, ASIC miners basically have approached or reached the physical limit of ASIC air-cooled products. Currently, Intel's ASIC air-cooled 138T, has almost the same hashrate as Antminer S19XP. Therefore, the next step is not to make further changes to chips but to achieve exponential growth in ASIC product’s hashrate. The professional miners (ASIC) of the water-cooled era: It has been exactly a decade since these have been operating (2012). This decade the mining industry has made a large number of people, firm believers, mining machine manufacturers, power resource consolidators, power equipment suppliers, mining machine traders, mine builders, mine owners, etc. Ten years in the mining area equal decades in traditional industries. This year Bitmain has released mass production of Antminer ASIC water-cooled miners. Here, we can't help but ask will ASIC miners encounter the third revolution of the mining industry shortly? This question is like asking whether new energy cars will replace traditional fuel cars. No need to ask, because it has already come. It's just that our industry is different from the traditional car industry. It will only grow faster than we think. Just like ASIC replacing the CPU, GPU mining. No goodbye, only farewell. 2. Several logics of liquid-cooled becoming mainstream Several logics of liquid-cooled becoming mainstream The electricity cost ratio will only get higher in future hashrate products unless a more desirable power consumption ratio can be obtained. For example, Antminer XP Hyd has a power consumption ratio of 20.5 which is more than double the power consumption ratio of current running miners. Use limited energy to pursue the best power consumption ratio: Decentralized professional mining sites are not all located in the mountains or next to reservoirs, but are more likely to be in industrial areas or close to residential areas. noise pollution, related to greenhouse gas emissions indicators, is likely to become a prerequisite for limiting the existence or opening of scaled sites. Scaled pollution sources: As for the air-cooled systems, because of the dissipative, physical level of heat dissipation, coupled with the high-intensity operation of hardware products and environmental corrosion, not only the problems of the machine occur frequently and but also its life expectancy is limited. The cooling efficiency of the wind-touch chip is only about 8%. For the immersed oil cooling, it is a good way to dissipate heat because of the full immersion of the body. The oil flows to take away the heat from the heatsink and achieve heat dissipation for the chip, but the oil cannot reach the chip directly. The projected area of its heatsink is only about 15%, and the flow rate is slow, so its heat dissipation efficiency is only about 20%~35%. While the liquid-cooled is different due to the perfect match between the water-cooled plate and the chip, the specific heat capacity of water is better than oil and the density is 10,000 times more than air, making the rapid flow of water can take away 99% of the thermal energy. The best heat dissipation of liquid-cooled systems has given rise to a "large" market: Taking actual data of running series as an example, at the same frequency, the average chip temperature is 70-75 degrees of air-cooling, 65-70 degrees of oil-cooling while only around 60 degrees of liquid-cooled. Therefore it is obvious that the heat dissipation of liquid-cooled is way better than air-cooling and oil cooling, which makes the future an era of large power supply, and large computing power. From the technical point of view, because of the heat dissipation of liquid-cooled, 6000, 10000, and 15000W power supply is possible. For TSMC's 5nm or 3nm chips, under the premise of an adequate number of chips, solve the problem of power consumption and chip temperature. In 2023, the emergence of 300-400T miners is also very possible. In addition, the power consumption ratio may also decrease to 20.5% or lower. Antminer 19 **The soaring rise of hashrate has not come:**Is the online overall hashrate we saw real? No, not exactly. The key point of hashrate is energy. How many miners are on their way to global migration or have already reached their destination, waiting for the accomplishment of infrastructure. The real hashrate may probably break through 300E with the help of energy protection. In this case, can we say the revenue goes down 40% if the hashrate increases by 40%? Probably not, because the base electricity price remains the same. So for miners, how much revenue decline will occur? Let’s take as an example, according to the base electricity price of 7.5 cents (0.5 RMB), the electricity bill share is 51.9%, with a daily gain of 75.16. But if the hashrate rises 40% to 300E, will the revenue go to 45(75.16*0.6)? It is not. At this time, electricity costs will account for 81% of net income of $6.1, a 78% drop in revenue. Of course, the logic of the measurement lies in the currency price remaining unchanged. And we know in this industry, we can’t predict the short-term, and no need to predict the long-term. Let’s see the performance of water-cooled XP. When the hashrate reaches 300E and the S19 electricity share is 86%, the XP liquid-cooled electricity share is 53% and the gain is 57.4 per day. The single T gain is nearly 3 times higher than S19 and the overall gain is 9.5 times higher. Of course, if this is the number and the coin price remains the same, XP liquid-cooled payback time is about 6 years. OMG! S19 Of course, some people will say that the above calculation is not rigorous. When the hashrate rose 40%, the priority shutdown are low-hashrate machines. If they are shut down, the hashrate will accordingly drop. This is true, but we must not underestimate the manufacturers' ability to produce, and the number of high-hashrate machines already stored in the warehouse. According to the ability of several major manufacturers to sell futures, we can only say that it is exaggeratedly unbelievable. Therefore, we must believe that the future does not need to be predicted. Otherwise, why do we still mine? 3. ‘Crisis’of Revolutionary Crisis of Revolutionary Shortly, the cheap miners you buy may become scrap metal. It is just like buying CPU and GPU miners in the ASIC era. The change in the technology upgrade is revolutionary. In a sense, in the bear market of air-cooled ASIC, buying miners at the bottom is actually a good investment. Picking up low-hashrate miners during the storm of liquid-cooled technology is nothing but only committing to the beliefs. The price of electricity determines the value of the mining farm. No matter how close a relationship you have with energy sites, the majority of profits will go away without competitive prices. Industrial electricity, high electricity price countries, high energy tax countries, etc. have nothing to do with resources, but with the viability of the business. The upcoming liquid-cooled era and the improvement of the industry infrastructure make us believe in the power of the leading force. The arrival of the "cloud storage", is the result of education by the giant. Today "cloud storage" is everywhere. Bitmain, accounting for 80% of the industry, has targeted 80% of all the products to be liquid-cooled in the 2023 annual plan. It will certainly lead the industry from the air-cooled infrastructure to the gradual change of water-cooled infrastructure. Traditional TSMC 5nm miners, such as the S19 series, are highly likely to be upgraded and their performance can be greatly improved. The sooner they enter the liquid-cooled era, the more industry dividends they will get. We don’t need temporary winning. Whoever survives in this battle will be the winner. Risk: Risk: Opportunity: Opportunity: 4. Why TSMC chips have good performance improvement? Why do TSMC chips have good performance improvement? Here, let’s analyze the competing logic between those big manufacturers. Any general leader, if they are not fully committed to the market and their followers and do not pay 100% or 120% effort, their chances of surviving in this industry will almost become zero. Only second-rate players will directly go all out in the face of a master just like martial arts combat. While in the mining world, the chip manufacturer is the most powerful one. Bitmain’s purpose is to make sure a long-term supply plan of chips so that its business logic is all about how to use more TSMC chips instead of maximizing its technology. If they turn their focus to chip technology, the large number of miners that they need to produce will become a big challenge. So in a word, as for Bitmain, everything is all about the large quantities of chips rather than maximizing the performance of TSMC chips to the extreme. In this case, the stock market has better chances now. Have you ever thought about transforming a 5nm chip with a liquid cooling system? It is not a golden opportunity that manufacturers left you, but the supply chain of chips. Therefore with enough improvement space for hardware, the right upgrade method of software, and adequate infrastructure support, air-cooling ASICs will have an access to upgrade into the brand-new liquid-cooled with lower cost. 5. Intel VS big manufacturers, a generation difference in products Intel VS big manufacturers, a generation difference of products We are not talking about technology, but concepts, acknowledgment, and understanding. There is no doubt that Intel can definitely get the world’s best chips and supply system. But acknowledging is a magic thing. When Intel decides to only provide three mining listed companies in North America with prototype production, the arrogance will leave them at least a generation difference in products. Is Facebook’s technical ability worse than TikTok's? No, it’s not. But it is the inadequate acknowledgment of short videos that makes them miss a whole generation. Today Intel is highly likely to lose in this liquid-cooled war if they are putting such great effort into air-cooling. Mining products are not consumer ones. The competitiveness doesn’t depend on the scale of the company. They are investment products that pursue the best power consumption ratio, the best revenue ratio, and the best use of time. ‘Where you start decides where you end.’ but if you run 10,000 meters to the left, and suddenly you just found that the race is supposed to be to the right. It’s OK. The night is long and we are always waiting for you. 6. Is liquid-cooled the ultimate form of hashrate product? In the short-term known technology (not to mention quantum computing) and from the history of mining development, people hesitated between air, oil, and liquid cool. The core limitation of single miner hashrate is ultimately about dissipation and temperature. From the perspective of cost, pollution, and convenience, water can be recycled and non-polluting compared to the annoying noise brought by the wind. It is hard to find another alternative cooling medium. Is liquid-cooled the ultimate form of hashrate product? 7. What are the differences between the future liquid-cooled market and today’s market? What are the differences between the future liquid-cooled market and today’s market? The infrastructure cost will increase; however, compared with investing in mining products the combined cost is less than 5% of the hashrate cost. For an investment of 95% to buy hashrate. The number of machines will be reduced; the number of machines in water-cooled sites will be, on average, at least 40% less than those in air-cooled sites, and up to 50% less. That is, where a 10 MW site used to require about 3,000 machines, today only 1,850 machines, or 1,500 machines. The requirements for land and environment have become lower. **Reduce the generation of electronic waste:**because of the reduction of machine space, with the same 10 MW, the deployment of machines becomes less, which is equal to the possibility of reducing the generation of electronic waste, in addition to reducing the land area then the cost of land. Machine chip heat dissipation is enhanced and hashrate is increased, which is to enhance its service life. with the gradual upgrading of the industry, the competitiveness of air-cooled sites will become increasingly weak, and will be replaced by water-cooled sites. More and more new sites will consider a hybrid site solution (air-cooled + water-cooled), or water-cooled only (Bing as representing). In the water-cooled infrastructure (water-cooled mining container) branded, standardized, globalized water-cooled infrastructure service providers will appear. Branded liquid-cooled infrastructure service providers aim not only to the product, and the manufacturer services but also serve the whole industry, multi-brand, covering a variety of models. Basic service providers appear: **Trade flow is still strong:**liquid-cooled products are not only in the world of institutions and large scale clients, there will be miniaturized liquid-cooled equipment suppliers, providing infrastructure to more small-scale new entrants, lowering the current industry threshold of tens of millions to million. The mining machine market will continue to thrive. **Technical service providers appear:**Some miners owning the S19 series and TSMC 5-nanometer chip machines will upgrade their stocks and catch the bonus first. In the case where the stock of upgradable machines exceeds one million units, there will be specialized upgrading service providers. The upgrade from air cooling to liquid-cooled will become a standardized product entering the market, and one or more large-scale liquid-cooled technology upgrade companies will emerge in stages. **Super-hash rate products:**Due to the introduction of air plugs and liquid-cooled, liquid-cooled power supplies will appear with products of more than 10,000W. With super power supplies, there will be mining machines with ultra-large computing power exceeding 400T. The market really can't be analyzed, inside the bear market, the analysis is pessimistic. Why do we still say liquid-cooled is the third hashrate revolution, in the mining industry if you can call it a strategic opportunity, there are two main ones. In the bear market, firm belief in the coin, or the valley bottom held a low-cost machine. Most of the profiteers in the current round of the bull market, are resistant to the cold winter of 312, the sting of 519, somehow embraced the era of institutional entry, and witnessed the ascension of $60,000 bitcoin. Breaking ground in a technological revolution situation. The head mining machine manufacturers that are still active today, all made their debut in the air-cooled ASIC era. The big miners and institutional miners with "money" capabilities also broke ground in this era. These pioneers, because of the fear of technology and faith, deserve to be the shareholder of the dividends of the times. What about the future? What kind of story will happen in the upcoming water-cooled era of big hashrate, and will more sovereign nations embrace Bitcoin? Will it fall from grace amidst traditional skepticism, or will it reach new heights in consensus and faith? Will great companies still be born in the liquid-cooled era? Will there be other disruptive technologies other than water cooling? The blockchain industry, at any time, is staging miracles and has infinite possibilities. Whether individually or collectively, you are in it, you are a force to change the times, and it is the unknown situations of the future that will make us look forward to it together. Two friends were walking in a forest, both without shoes. But one of them kept his shoes around his neck, suddenly they met a bear. One could not run fast as he had no shoes, and the other immediately put his shoes back on. Then when the bear came up to gnaw on his partner, he ran away. The moral of the story is; that bears in the forest, we will certainly encounter, but shoes are not to let you run faster than the bear, it is to make you run a little faster than your friend. As long as you can run faster than your friend, you can survive and finally see the sea through the forest. Here, in addition to technology and outlook, for all miners, there is a small story that is encouraging. The air-cooling revolution is just like the shoes around our neck. Are you ready to start your journey with shoes around your neck? See more information about the miner at . Meta-Luban

Will Liquid-Cooled Miners Dominate the 3rd Hashrate Revolution?

¿Dominarán los mineros enfriados por líquido la tercera revolución de hashrate?

Imagine a future where your digital assets are not just a static investment but a living, breathing entity that works for you around the clock. Jonny Huxtable, a visionary in the blockchain space, takes us on a journey through the intricate world of Stake.link, where he's redefining what it means to 'stake' in the ecosystem of Chainlink. From his early days mining BTC to his pivotal role in liquid staking, Jonny's story is not just about the ascent of LinkPool but also the potential of decentralized finance to empower the individual. As we unpack the mechanics behind stake.link and its mission to infuse liquidity with security, we stand on the cusp of a new chapter in blockchain's narrative—a chapter where every participant holds the power to shape the network. Unlocking Chainlink's Potential: Stake.link's Strategic Vision for a Liquid Future Hi Jonny, great to have you here for our "Behind the Startup" series. Please tell us about your background? Ishan Pandey: Great to speak. For me, I got into the space like most people would do. I had some BTC in 2014/2015, but I really got into things before the 2017 cycle, and I became interested in Chainlink as the token offering took place in 2017. I joined the Chainlink slack channel, started speaking to everyone about the project, and I came to understand the premise of the project. I saw promise in Chainlink. It was that promise that made me want to get more hands-on in the space, and that’s when I started LinkPool. Jonny Huxtable: LinkPool began as sort of a bedroom project I worked on the side of my full-time job. It was one of the first third parties to build on top of Chainlink, run infrastructure, and carry it on from there. LinkPool was one of the first three node operators that went live on Ethereum mainnet in the Summer of 2019, and we’ve played a significant role in not just breaking down the barriers to participation in the Chainlink Ecosystem, but also developing the tools and the infrastructure for node operators on the network. Stake.link: The First-Mover Liquid Staking Protocol in the Chainlink Ecosystem Stake.link is described as the "first-mover liquid staking protocol in the Chainlink Ecosystem." What motivated the creation of stake.link, and what unique value does it offer to users? Ishan Pandey: To answer this question, it was the original vision and drive of a pooling system for Chainlink in 2017 that drove the vision to build stake.link but on a much grander scale. With the explosion of LSTs with the rise of Ethereum staking protocols, it became clear that with the launch of Chainlink Staking it would also greatly benefit from a liquid staking protocol that has a cohort of the very best Node Operators that participate in the Chainlink network. That’s what stake.link is, the premier staking protocol for Chainlink backed by the very best. Jonny Huxtable: The Concept of Liquid Staking and Stake.link Protocol Could you explain the concept of liquid staking and how it works within the stake.link protocol? Ishan Pandey: Liquid staking is a concept that allows users to stake their tokens in a way that they remain liquid and tradable, rather than being locked up in a staking contract. Jonny Huxtable: Traditional staking typically involves locking up tokens in a network's smart contract to support its operations and earn rewards. These tokens are usually illiquid and can't be readily traded or used elsewhere. Liquid staking, on the other hand, enables users to stake their tokens while still having the freedom to trade or utilize them for other purposes. It's made possible through liquid staking tokens, LSTs, representing the staked assets' value, which can be traded or used in DeFi applications. These are very visible in the Ethereum network, and we’re working to make it just as visible for the Chainlink Ecosystem. This is currently the case with General Access Staking in the Chainlink Community Staking Pool, and the stake.link protocol addresses the issue of locked staked tokens. At the core of this is the withdrawal capability feature. This feature provides a liquidity buffer in the protocol, allowing users to withdraw their staked LINK from the protocol by depositing their receipt tokens that they are given after they’ve staked LINK: this is the stLINK which represents the staked amount of LINK. User Journey on Stake.link Could you elaborate on the user journey on stake.link, from connecting a Web3 wallet to earning rewards and withdrawing assets? Ishan Pandey: Sure thing. The protocol is built to be as seamless as possible even for people who haven’t been involved in DeFi or moved their tokens off of exchanges in the past. Jonny Huxtable: Right now, Chainlink is at the end of its version 0.1 of Staking and all space is taken up. Chainlink Labs who builds the infrastructure for the entire Ecosystem has announced that Staking v0.2 is coming in Q4 2023, so between now and the end of the year. This version of staking entails an increase to the amount of tokens that can be staked from 25M in v0.1 to 45M in v0.2. To start the user journey on stake.link, a user would first need to connect their Web3 wallet containing LINK tokens to the platform, and deposit LINK tokens in the “holding zone” that we call the Priority Pool. Once Staking v0.2 arrives and General Access opens to the public, the LINK deposited in the Priority Pool will be staked in the General Access Community Pool using Chainlink Automation contracts. That means we’ve crafted smart contracts that recognize down to the second when the General Access Pool opens up to the public, and they will immediately begin auto staking Priority Pool LINK so that the users don’t have to. Once a user’s LINK is staked, they’re then able to claim their staked LINK receipt token, stLINK to their wallet and decide for themselves what they’d like to do with it. Users will then earn rewards for staking their LINK tokens. Node operators, who provide services in the Chainlink Ecosystem, earn rewards in the form of a delegation fee through stake.link’s native SDL token. Community stakers, who supply LINK collateral to secure the network and act as alerters if they notice the ETH/USD price feed has not updated within a set time frame, receive rewards after delegation and core contributor fees to the protocol. The rewards earned depend on the staking strategy and the capacity of the staked pool. At launch, stake.link enabled a "Node Operator" strategy pool, and in 2023, the pool capacity will be increasing for node operators. We’re excited to bring liquid staking to a portion of the Community Pool. The combination of these two strategies results in a blended reward rate. Engaging with the Stake.link User Community How does stake.link engage with its user community, and what channels do you primarily use for client outreach? Ishan Pandey: Our community includes LINK holders encompassing retail users to institutional, banking users, and everyone in between seeking seamless access to staking the LINK token. Client outreach primarily takes place on LinkedIn, Twitter across the profiles of stake.link node operators, multiple Telegram channels, and a dedicated Discord for the stake.link protocol. Jonny Huxtable: One of the core reasons we’re able to deliver for the community is because of open dialogue and constructive feedback that they provide in our Telegram and Discord channels, and this is certainly the case on Twitter. We listen to that feedback every day and actively incorporate it into the protocol to make for a better overall staking experience. Key Challenges and Opportunities in Liquid Staking In your view, what are the key challenges and opportunities in the liquid staking space, and how does stake.link plan to address them? Ishan Pandey: Two of the big things that stand out in the Liquid Staking space are security and centralisation. Jonny Huxtable: Stake.link consists of 15 Chainlink node operators. 15 different teams, different companies, different people that are some of the most experienced and professional teams not just in the Chainlink Ecosystem, but across all of Web3, and this consortium directly addresses the dilemma of centralisation vs decentralisation. As for security, this is something everyone in the industry can relate to and we see it like clockwork: protocols, dApps, bridges being exploited for tens of millions, hundreds of millions of dollars. It’s a serious problem because not only are people losing their money, but that money is now in the hands of bad actors who can do real harm with that capital. That’s why we take rigorous security measures and work with some of the most prominent smart contract auditors in Web3. Our ethos is straightforward: audit, audit, audit, and then audit again, as well as work closely with our smart contract auditing firms to address every vulnerability – no matter how severe or seemingly insignificant. AI-Driven Analytics and Data Insights AI-driven analytics and data insights are becoming crucial for decision-making in the crypto and blockchain space. How does stake.link plan to incorporate data-driven insights for its users? Ishan Pandey: It’s amazing to see the explosion of adoption and real-world use of AI technology in the last 11 months. Jonny Huxtable: In September 2023, we launched our first major protocol upgrade that included the rollout of our Chainlink-inspired chatbot called SergAI – named in honor of the Co-Founder and CEO of Chainlink Labs, Sergey Nazarov. You can think of SergAI as a massive repository of all things Chainlink Staking and stake.link. Everyone has lives outside of the industry. They can’t be online 24/7. That’s why SergAI is important: people are able to ask questions and get straightforward answers. We view SergAI as a significant step forward and a net positive for our overall user experience, and without revealing too much, it’s just the beginning of the direction we want to take in incorporating AI technology into the platform. Stake.link's Role in DeFi and NFT : With the growth of decentralized finance (DeFi) and non-fungible tokens (NFTs), how does stake.link envision its role in supporting these emerging ecosystems within the broader Web3 framework? Ishan Pandey : We’re excited for the future of onchain finance and we’re proud to have played a part in its success in even basic form as we saw in Summer 2020. Enabling decentralised applications to get accurate, up-to-date, precise reference data changed the game and enabled the sector to flourish as it did in DeFi Summer when applications were dealing with flash loan attacks due to the use of centralised price feeds. Jonny Huxtable The work that Chainlink node operators played providing reliable reference data in DeFi Summer can be considered as a sort of proof of concept for what can be done in the future for DeFi. Chainlink has rolled out, and the node operators have scaled the use of other Web3 services such as smart contract automation, Verifiable Randomness (VRF), and recently Data Streams where applications no longer have large deviation thresholds for price feeds – price feeds can now be updated at a hyperfast rate compared to the previous 0.5% deviation threshold as was the standard up until recently and in DeFi Summer. Data Feeds has immense potential and was needed in order to cater to larger players in the broader financial industry that require hyperfast, reliable reference data with little to no deviation threshold in order to operate properly onchain. This will only grow and enable further adoption. NFTs are another sector of Web3 ripe for adoption, and much like DeFi, can benefit immensely from Chainlink Web3 services. VRF is a critical one, ensuring that NFT mints are verifiably random and fair for all participating users – regardless if a user is retail, enterprise, or anything in between. We also see a lot of potential in dynamic NFTs (dNFT) too. These are NFTs with encoded smart contract logic that enables them to automatically change its metadata, characteristics based on external conditions and events. The market has already spoken in that it wants NFTs that update based on external events. The simplest form of this is a basketball card, for example. If someone owns a tokenized basketball player’s card (NFT), their statistics will change over time as the player continues their career. NFTs need the ability to update their characteristics, and node operators as those in stake.link enable just that. Going beyond the very basic use-cases of something like a basketball card, though, is something like tokenized real estate – An NFT representing a property that could reflect numerous factors such as maintenance history, age, market value, and more. Tokenizing these everchanging assets requires NFTs that have the ability to update with changing metadata. We enable that and can provide that reference from the offchain world, and bring it onchain. Regarding both NFTs and onchain finance / DeFi, it’s still very early for both sectors of the Web3 economy. These categories are going to play a significant role in how humans, businesses, industries conduct commerce, exchange data and value, and above all instill a layer of confidence in the capital markets industry. Don’t forget to like and share the story! : Vested Interest Disclosure This author is an independent contributor publishing via our . Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR brand-as-author program

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