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McEwen Mining Inc. (NYSE:MUX) (TSX:MUX) is pleased to report production for Q3 2023 was 38,600 gold equivalent ounces ("GEOs") and 104,800 GEOs for the nine months ended

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Panama's President Laurentino Cortizo on Friday announced a ban on new metal mining concessions, but maintained a contract with a Canadian company that has sparked massive protests in the country.

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Peru's government is planning to cut red tape for the mining sector, part of a package of measures expected to be unveiled next week to arrest a deepening recession, Economy and Finance Minister Alex Contreras told Reuters on Thursday.

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The Binding Term Sheet agreement gives Slam Exploration the option to earn 51% of the Notre-Dame Niobium Critical Minerals Project by making the following cash payments and share issuances to St-Georges: Issuance of 500,000 common shares of Slam Exploration to St-Georges upon regulatory approval from the TSX Venture Exchange.

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Teck holds a 60% stake in Quebrada Blanca, while 30% is jointly controlled by Sumitomo Metal Mining and Sumitomo Corp. Chilean state-run Enami holds the remaining 10% stake. Reporting by Fabian Cambero; Editing by Sarah Morland and Anthony Esposito Our ...

Miner Teck to re-do environmental permit for Quebrada Blanca expansion in Chile

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Nucor

nucor

Web3 hiring culture. What can we improve?

ওয়েব 3 নিয়োগের সংস্কৃতি। আমরা কি উন্নতি করতে পারি?

Einstellungskultur bei Web3. Was können wir verbessern?

Cultura de contratación Web3. ¿Qué podemos mejorar?

Culture d'embauche Web3. Que pouvons-nous améliorer ?

वेब3 भर्ती संस्कृति। हम क्या सुधार कर सकते हैं?

Web3 の採用文化。何を改善できるでしょうか?

Web3 채용 문화. 무엇을 개선할 수 있나요?

Cultura de contratação da Web3. O que podemos melhorar?

Культура найма Web3. Что мы можем улучшить?

Web3 işe alma kültürü. Neyi geliştirebiliriz?

Văn hóa tuyển dụng Web3. Chúng ta có thể cải thiện điều gì?

Web3 招聘文化。我们可以改进什么？

In , the design of test cases is fundamental to ensuring the reliability and robustness of apps. Combinatorial test design techniques, such as k-way testing and pairwise testing, aim to test the interactions between different input variables. However, it becomes apparent that challenges and pitfalls can hinder their effectiveness in uncovering critical errors. software testing This short article focuses on specific issues that could be encountered during combinatorial test design, exploring the nuances that demand careful consideration. Let’s delve into instances of incorrect input values, the importance of robust oracles, the significance of often-overlooked variable combinations, and the crucial understanding of how variables interact. ncorrect Identification of Input Values I A k-way test set comprises all possible combinations of values for k variables. For instance, applying the Allpairs Algorithm results in a 2-way test set, encompassing all possible combinations of pairs of variable values. Consequently, a k-way fault refers to an error resulting from the interaction of values of k variables. Examining two systems, SYS1 and SYS2, revealed errors after their production releases. Both systems underwent k-way testing with k values of 2, 3, 4, and 5+. The table displays the outcomes of k-way testing for the two systems. Fault type SYS1 SYS2 2-way 30 29 3-way 4 12 4-way 7 1 > 4-way 7 3 Not Found 34 43 Sequential checks were conducted for each k-way set. Figure 2.1 indicates that errors arising from the interaction of two or fewer input variables were and . Errors from the interaction of three variables were and . For interactions involving four variables, errors were and . Errors from interactions with more than four variables were and . Notably, errors were not found in SYS1, and errors were not found in SYS2. 29 in SYS1 30 in SYS2 4*(30+4) in SYS2 12*(29+12) in SYS1 7*(30+4+7) in SYS2 1*(29+12+1) in SYS1 3*(29+12+1+3) in SYS1 7*(30+4+7+7) in SYS2 43 34 In this example, the most significant errors belonged to the category. Further investigations revealed that most of the undetected errors were 1-way faults, meaning a specific value of one variable led to the error independently of other variable values. Pairwise testing should have detected these errors, but for some reason, they remained undiscovered. These errors are primarily 1-way faults that went unnoticed because certain input values were either not selected or chosen incorrectly. Not Found Not Found lies in the fact that any errors made before the application of the Allpairs Algorithm will persist. This implies that if previous test design techniques were incorrectly applied or input values were incorrect, errors in the application will persist regardless of using the Allpairs Algorithm or Orthogonal Array Testing. The essence of the problem Weakness in Defining Expected Results: Uncertainty in Thest Outcomes As an example, let’s consider an application module with numerous options (something that looks like this form) and, consequently, a large number of input data combinations. In the module, let’s say that there are possible combinations. Here, the challenge is not in choosing incorrect input values, as all possible variable values can be exhaustively tested using the Allpairs algorithm. However, will this uncover all critical errors? Likely not, due to issues with expected results. After manipulating each combination of options in such kind of a software module, one would need to use the system for some time to verify that the options worked correctly and nothing else broke after applying the chosen options. In this case, there is no guarantee that there is no subtle, non-obvious problem easily missed. 2^12 * 3 After each test, one may need to thoroughly assess the core functions of the system. The point here is that serious defects are often not as apparent as one would like them to be, and accounting for everything in expected results becomes practically impossible. Inattention to the Most Probable Combinations Out of the combinations (as we suggested in the example above), there is likely one combination of module options that will occur much more frequently than all others - the setting. If 95% of people will never change the options from the default settings for this module, there should be good coverage of options. Testing all variations with a deviation from the default configuration in one option would result in a 2-digit number of tests. If testing all combinations of options with a possible deviation in two settings from the default, it would be around a hundred test cases. Testing with these test cases and all-pair test cases may yield better results than ignoring the existence of the default option. However, the Allpairs algorithm forces the tester to overlook the most probable and commonly used variable combinations. 2^12 * 3 default almost/near-default Unknown Variable Interactions The key factor for the success or failure of pairwise testing lies in understanding how the combination of input variables affects output data. Applying pairwise testing to two different tested applications may yield significantly different results. Some applications use fewer input data to produce output data, while others use more. As an example, consider the program, which has three input variables (X, Y, Z) and three possible output data (1, 2, 3). The arrows indicate which variables should interact to produce a specific result. To obtain a result of 1, variables Y and X must interact; to obtain a result of 2, variables Z and X must interact. For this application, applying pairwise testing would be a suitable choice, and positive results are likely. However, in cases where there are scenarios where output data result from the interaction of more than two input variables, there is a high likelihood that pairwise testing will fail to identify errors. Simply applying pairwise testing increases the risk of overlooking critical errors in the tested application. As a QA professional, understanding these nuances is important. While theoretical in some cases, these insights underscore the need for a holistic testing approach that goes beyond the surface, ensuring the reliability and robustness of your apps. Understanding the complexity of combinatorial test design allows QA professionals to effectively test the complicated business logic of apps and deliver high-quality software in to their users.

Software QA: Solving Issues with Combinatorial Test Design

সফ্টওয়্যার QA: কম্বিনেটরিয়াল টেস্ট ডিজাইনের মাধ্যমে সমস্যার সমাধান করা

Software-QA: Probleme mit kombinatorischem Testdesign lösen

Control de calidad del software: resolución de problemas con el diseño de pruebas combinatorias

Assurance qualité logicielle : résoudre les problèmes liés à la conception de tests combinatoires

सॉफ़्टवेयर QA: कॉम्बिनेटोरियल टेस्ट डिज़ाइन के साथ समस्याओं का समाधान

ソフトウェア QA: 組み合わせテスト設計の問題の解決

소프트웨어 QA: 조합 테스트 설계 문제 해결

Controle de qualidade de software: resolvendo problemas com design de teste combinatório

Контроль качества программного обеспечения: решение проблем с помощью комбинаторного проектирования тестов

Yazılım Kalite Güvencesi: Kombinatoryal Test Tasarımıyla Sorunları Çözme

QA phần mềm: Giải quyết vấn đề với thiết kế thử nghiệm tổ hợp

软件 QA：通过组合测试设计解决问题

I am pretty sure you all are familiar with full-stack, MERN stack, and many similar tech stacks related to development. But I will not talk about it because lots of content are already there. I am going to tell you about the a new trend in tech and how you will master this new tech by involving in related communities. "No-Code /Low-Code stack" What is Low-Code / No-Code Stack? This tech stack is nothing but a complete knowledge of Low-Code / No-Code tools and platforms for different stages or processes of software development. Like planning, designing, building, deploying, and launching a fully functional product. It's a new trend, many developers are working in this field and new platforms are coming around low code like Notion or Zapier. If you are not from a tech background and do not want to go deep with coding, You can consider learning this low-code / no-code tech stack and make wonders happen. If you don't know about low-code / no-code. Read my article about it. Here If you want to know about some low-code / no-code tools and platforms. Read my article about it. Here Explore Low-Code / No-Code Stack by Involving in These Communities As you know communities are very important in the tech world, as it helps in many ways. Communities are the best place to find the right solution, You will learn about the true meaning of networking, which goes beyond just following each other on Twitter and connecting on LinkedIn. Communities also help in boosting your capacity for technological proficiency and many more good things. Like finding jobs and helping you in choosing the right step. Communities can be free or paid. Let's see some good low-code / no-code communities. 1. 100 days of No Code 100 Days of No Code is a global collective of no-code learners. People from many backgrounds have gathered in this location to learn, connect, and share their knowledge. It has more than 5,000 learners who are bringing their ideas to life. In just 30 minutes a day, you can learn how to use no-code from scratch with the help of some entertaining and helpful bite-sized courses. Both free & paid memberships are available. 2. NoCodeDevs The largest collection of no-code tools, applications, and resources available. In the NoCodeDevs community, business owners may find fresh tips and methods for implementing their ideas without writing code and connect with other makers to assist one another in the creation process. Join this community of 20,000+ non-tech founders, 300+ tool directories, and 150+ tutorials. Live workshops and events are also organized. 3. Makerpad Ben Tossell founded the Makerpad community in 2019, and it is dedicated to providing no-code education for creating applications and websites, managing online businesses, and automating operations without writing code. Makerpad is free to sign up for, but in order to access premium features, you must buy a membership plan. Members of the Makerpad community can also make their professional profiles public to draw clients looking for specialized assistance with their no-code projects. 4. NoCodeFounders This is a well-known online network for innovators, makers, solopreneurs, and business people without technical backgrounds. More than 13,000 people are involved. Members take part in online gatherings and activities where they make new relationships and get knowledge about the best no-code platforms. You must pay a one-time cost of $99 to sign up as a premium user. Access to the Slack community, filter unlocks, exclusive tutorials and benefits with unmatched insights into the no-code landscape are all included with membership. 5. Webflow Community Connect with the 75,000+ members who are developing the future of visual development and contributing to Webflow. Connect with entrepreneurs, product and project managers, business owners, designers, founders, marketers, and developers from businesses of all sizes and sectors. Webflow is a low-code website builder. 6. Nucode Nucode is a community of makers and inventors. There are almost 7000 members in total. You can learn to use tools like Webflow, Glide, and Bubble for nothing! Together, anyone can grow and learn in this amazing space. Your fellow creators can offer you suggestions based on their experience as you submit your knowledge. You can set a challenge for other non-coders to create apps for challenging situations in order to promote a sense of competition in addition to learning. 7. No-Code Alley Citizens who are no-code developers can connect on a discussion board set up by No-code Alley to solve their business issues. You can discover the top no-code products, their applications, and helpful hints. Additionally, there are specific forums like Stack Overflow where you can post specific queries and get responses from other non-coders. 8. Nocode HQ The Nocode HQ community provides a network where developers can talk about problems facing the industry and work together to find innovative solutions. The learning environment provided by the platform includes step-by-step lessons, templates, and expert sessions. You can connect with people who are creating similar items to gain assistance and feedback by hosting a network with social media capabilities. Joining is free, and it gives you access to a few resources and a place in the community. In addition, you can choose premium membership by paying a monthly charge of $29 for it. You get access to limitless tutorials, premium templates, and cost-free Nocode sessions with a premium membership. 9. NoCodeList The tools and resources needed to build your own no-code product are all collected in one place by No Code List. No Code List also includes No Code Agencies that can assist you in creating no-code goods or services. Numerous other no-code forums, channels, blogs, tutorials, courses, websites, etc. are included in their Resources area. If you sign up, four new tools will be delivered to your inbox every Friday. 10. Codem4p Clients looking to hire specialists, companies, and independent freelancers can connect through this network. If you are a no-coder looking to work for respected businesses, this is the place for you. You can also interact with your peers, present your work, learn, and get feedback. You also gain access to premium events, professional resources, expert sessions, and discussions about various tools. Hire the best personnel for automation, no-code, low-code, and remote work. Develop software up to 10 times faster, automate tasks, and launch it in a few of weeks by learning no-code. I hope you find this article helpful! If you know some other communities, let me know on . Twitter If You ❤️ My Content! Connect Me on or Supports Me By Twitter Buying Me A Coffee☕ Also Published Here

Get Involved in These Communities to Learn and Explore the Low-Code / No-Code Stack

I've recently become obsessed with , a word puzzle game created by Brooklyn-based software engineer for his word game-loving partner. As a homage to Josh, and just-for-fun, I created a version of the game that can be played via text message. Read on to learn how to build an SMS word game using the , , the , , and , and play Twordle yourself: ! Wordle Josh Wardle Dictionary API Twilio Functions Twilio Serverless Toolkit Twilio Assets cookies in Twilio Runtime text a 5-letter word or "?" to +12155156567 Prerequisites A Twilio account - sign up for a free one here and receive an extra $10 if you upgrade through this link A Twilio phone number with SMS capabilities - configure one here Node.js installed - download it here Get Started with the Twilio Serverless Toolkit The Serverless Toolkit is CLI tooling that helps you develop locally and deploy to . The best way to work with the Serverless Toolkit is through the . If you don't have the Twilio CLI installed yet, run the following commands on the command line to install it and the Serverless Toolkit: Twilio Runtime Twilio CLI npm install twilio-cli -g
twilio login
twilio plugins:install @twilio-labs/plugin-serverless Create your new project and install our lone requirement , an HTTP client library to make HTTP requests in Node.js, by running: got twilio serverless:init twordle
cd twordle
npm install got Add a Static Text File to Twilio Assets Twilio Assets is a static file hosting service that allows developers to quickly upload and serve the files needed to support their applications. We want our Twilio Asset to be private–this means it will not be accessible by URL or exposed to the web; rather, it will be packaged with our Twilio Function at build time. . For more information on Private, Public, and Protected Assets, check out this page Copy this and add it to our folder as . We will read the file from our Twilio Function and generate a random word from it that will be used for each Wordle game. The word will be different for each person, and each person can play multiple times a day. GitHub file containing five-letter words from the English dictionary Assets words.private.text Write the Word Game Logic with JavaScript into the directory and make a new file called game.js containing the following code to import the module, read the file from Twilio Assets, create a function to return a random word from the Asset, and initialize two constants (the user always has five chances to guess the word, and all the words are five letters): cd \functions got words.txt randomWord const got = require('got');
let words = Runtime.getAssets()['/words.txt'].open().toString().split("\n");
const randomWord = () => {
    return words[words.length * Math.random() | 0];
}
const maxGuesses = 5;
const wordLength = 5; Next we have the meaty function that takes in a parameter (an object representing each player), and a guess (the word they text in as a guess.) We make a score card which will contain the boxes we return based on how close the user's guess is to the generated random word. In a block, we make an HTTP request with to the dictionary API using : if the page exists, the guess is a word, and we increment the attribute of the player object. For each letter in the guess, we check if it is in the goal word: if a letter is in the same spot, that spot in the score card will contain a green square (🟩). If a letter in the guess is not in the same index as the generated word for the player but the letter is in the generated word, the score card will contain a yellow square (🟨). Else, the score card will contain a black square (⬛). If our HTTP request is unsuccessful, our score card will be a string telling the user to try again. handleGuess player try got guess guessesAttempted const handleGuess = async (player, guess) => {
  let newScoreCard = [];
  try {
    const response = await got(`https://api.dictionaryapi.dev/api/v2/entries/en/${guess}`).json();
    if (response.statusCode !== 404) {
      player.guessesAttempted+=1;
      for (let i = 0; i < guess.length; i++) {
        if (guess.charAt(i) == player.randWord.charAt(i)) {
          if (player.dupLetters[i] != null) {
            player.numCorrectLetters+=1;
          }
          player.dupLetters[i] = null;
          newScoreCard.push('🟩');
        } else if (guess.charAt(i) != player.randWord.charAt(i) && player.randWord.includes(guess.charAt(i))) {
          newScoreCard.push('🟨');
        } else {
          if (!player.incorrectLettersArr.includes(guess.charAt(i))); {
            player.incorrectLettersArr.push(guess.charAt(i));
          }
          newScoreCard.push('⬛');
        }
      }
      console.log(`newScoreCard ${newScoreCard}`);
      return newScoreCard;
    } 
    else { //404 word not in dict
      newScoreCard = "word not found in dictionary! try again!";
      console.log('Word not found!');
      return newScoreCard;
    }
  }
  catch (err) {
    newScoreCard = "word not found in dictionary! try again!";
    console.log('Word not found!');
    return newScoreCard;
  }  
} After our function to handle each guess, let's make a function to check if the game is over. For parameters it accepts the object and . If the number of attempted guesses for the player is greater than or equal to five (the most number of guesses a player can have), the number of correct letters guessed is equal to the word length (five), or the score card contains five green squares, the game is over and returns true. Else, the game continues and returns false. player scoreCard endFunc const endFunc = (player, scoreCard) => {
  if (player.guessesAttempted >= maxGuesses) { 
    console.log(`guessesAttempted >= maxGuesses`);
    return true;
  }
  else if (player.numCorrectLetters == wordLength) {
    console.log("in numCorrect");
    return true;
  }
  else if(scoreCard == `🟩,🟩,🟩,🟩,🟩`) {
    console.log(`scorecard = 🟩,🟩,🟩,🟩,🟩`);
    return true;
  }
  else {
    console.log(`game still going`);
    return false;
  }
} Call Game Logic in Twilio Functions' Handler Method The handler method is like the entry point to your app, similar to a function in Java or in Python. In this tutorial, it will run each time someone texts our Twilio number. . main() __init__ For more information on Function invocation and execution, read this page First in the method, we initialize a Twilio Messaging Response object to respond to the player's guess text message, a variable which is whatever the player texted in, a string as empty text that we will append to depending on the guess, create a Twilio Response object to handle memory management with cookies,and a object whose attributes we will initialize based on the guess. guess responseText player exports.handler = async function(context, event, callback) {
  let twiml = new Twilio.twiml.MessagingResponse();
  let responseText = '';
  let guess = event.Body.toLowerCase().trim();
  let response = new Twilio.Response();
  let player; If the player texts in a question mark, we return a message about Josh Wardle who made the game as well as instructions on how to play the game. if (guess == "?") {
    twiml.message(`Wordle was made by Josh Wardle, a Brooklyn-based software engineer, for his partner who loves word games. You guess a 5-letter word and the responding tiles reflect how close your guess was to the goal word. 🟩 means a letter was in the right spot, 🟨 means the letter was correct but in the wrong spot, and ⬛️ means the letter is not in the goal word.`)
    return callback(null, twiml); //no need for cookies
  } Then we check if the player has texted in before. If the player does not exist, we generate a new word for them and initialize a new player object with the random word, the guesses attempted (none so far), number of correct letters (none so far), an array of duplicate letters, and an array of incorrect letters guessed (currently empty.) If the player does exist, we pull data off the cookie to get the player state and make that the object. with cookies player if (!event.request.cookies.player) { //any guesses attempted? -> new player
    let randWord = randomWord(); //new random word
    player = { //init new player
      randWord: randWord, 
      guessesAttempted: 0,
      numCorrectLetters: 0,
      dupLetters: [...randWord],
      incorrectLettersArr: []
    }
  } else { //else pull data off cookie to get player state
    player = JSON.parse(event.request.cookies.player);
  } We check the length of the guess and if it's five letters, we run the method and pass it and from above. We then check if the game is over and if it was a win, we send a congratulatory response; otherwise if it's a loss, we send a more apologetic message. Under both conditions, we remove the from cookie memory to start the player over using . handleGuess player guess player response.removeCookie("player"); If the game is not over, the response message is the scorecard with squares and we save the game state with the object with . It's in that we also set a four-hour time limit so the user has four hours to guess before the game state is lost–the default time limit for cookies in a Twilio Function is one hour. Lastly, if the guess is not five letters-long, we tell the player to send a five-letter word. player response.setCookie setCookie if (guess.length == wordLength) { //5 letters
    let scoreCard = await handleGuess(player, guess); //guessesAttempted increments
    console.log(`scoreCard ${scoreCard}`);
    if(endFunc(player, scoreCard)) { //over, win
      if(guess == player.randWord) {
        responseText += `Nice🔥! You guessed the right word in ${player.guessesAttempted}/${maxGuesses} guesses. You can play again by sending a 5-letter word to guess a new random word 👀 \nThanks for playing our SMS Twordle game. Do head to https://www.powerlanguage.co.uk/wordle for web-based word fun! Original Wordle creator Josh Wardle: as fellow builders we salute you and thank you for inspiring us to create our SMS experiment`
        response.removeCookie("player");
      }
      else if (guess != player.randWord) { //over, lose
        responseText += `Game over 🙈\nThe correct word was ${player.randWord}. Send a 5-letter guess to play again! \nThanks for playing our SMS Twordle game. Do head to https://www.powerlanguage.co.uk/wordle for web-based word fun! Original Wordle creator Josh Wardle: as fellow builders we salute you and thank you for inspiring us to create our SMS experiment`;
        response.removeCookie("player");
      }
    }
    else { //keep guessing, not over
      responseText += `${scoreCard.toString()} \n${player.guessesAttempted}/${maxGuesses} guesses`;
      response.setCookie("player", JSON.stringify(player), [
        'Max-Age=14400' //4 hour time-limit
      ]);
    }
  }
  else { //not 5 letters
    responseText += `"${guess}" is not valid. Please send a word in the dictionary that is 5 letters to get started!`;
    // twiml.message(`"${guess}" is not valid. Please send a word in the dictionary that is 5 letters to get started!`);
    console.log(`randWord ${player.randWord} in invalid `);
  } At the bottom of the handler method we append header, add information to our response about playing if the player has only guessed one time, send our in , and add twiml to return to our Twilio Response to both send our response text message to the player as well as update the object in cookie memory. responseText twiml.message player response.appendHeader('Content-Type', 'text/xml');
  // see if player.guessesAttempted == 1
  if (player.guessesAttempted == 1) {
    responseText += `\nText "?" for help on how to play`
  }
  twiml.message(responseText);
  response.setBody(twiml.toString());
  return callback(null, response); Wow that was a lot! The complete handler method is below. exports.handler = async function(context, event, callback) {
  let twiml = new Twilio.twiml.MessagingResponse();
  let responseText = '';
  let guess = event.Body.toLowerCase().trim();
  let response = new Twilio.Response();
  let player;
  if (guess == "?") {
    twiml.message(`Wordle was made by Josh Wardle, a Brooklyn-based software engineer, for his partner who loves word games. You guess a 5-letter word and the responding tiles reflect how close your guess was to the goal word. 🟩 means a letter was in the right spot, 🟨 means the letter was correct but in the wrong spot, and ⬛️ means the letter is not in the goal word.`)
    return callback(null, twiml); //no need for cookies
  }
  
  if (!event.request.cookies.player) { //any guesses attempted? -> new player
    let randWord = randomWord(); //new random word
    player = { //init new player
      randWord: randWord, 
      guessesAttempted: 0,
      numCorrectLetters: 0,
      dupLetters: [...randWord],
      incorrectLettersArr: []
    }
  } else { //else pull data off cookie to get player state
    player = JSON.parse(event.request.cookies.player);
  }
  
  if (guess.length == wordLength) { //5 letters
    let scoreCard = await handleGuess(player, guess); //guessesAttempted increments
    console.log(`scoreCard ${scoreCard}`);
    if(endFunc(player, scoreCard)) { //over, win
      if(guess == player.randWord) {
        responseText += `Nice🔥! You guessed the right word in ${player.guessesAttempted}/${maxGuesses} guesses. You can play again by sending a 5-letter word to guess a new random word 👀 \nThanks for playing our SMS Twordle game. Do head to https://www.powerlanguage.co.uk/wordle for web-based word fun! Original Wordle creator Josh Wardle: as fellow builders we salute you and thank you for inspiring us to create our SMS experiment`
        response.removeCookie("player");
      }
      else if (guess != player.randWord) { //over, lose
        responseText += `Game over 🙈\nThe correct word was ${player.randWord}. Send a 5-letter guess to play again! \nThanks for playing our SMS Twordle game. Do head to https://www.powerlanguage.co.uk/wordle for web-based word fun! Original Wordle creator Josh Wardle: as fellow builders we salute you and thank you for inspiring us to create our SMS experiment`;
        response.removeCookie("player");
      }
    }
    else { //keep guessing, not over
      responseText += `${scoreCard.toString()} \n${player.guessesAttempted}/${maxGuesses} guesses`;
      console.log(`randWord in not over ${player.randWord}`);
      response.setCookie("player", JSON.stringify(player), [
        'Max-Age=14400' //4 hour time-limit
      ]);
    }
  }
  else { //not 5 letters
    responseText += `"${guess}" is not valid. Please send a word in the dictionary that is 5 letters to get started!`;
    // twiml.message(`"${guess}" is not valid. Please send a word in the dictionary that is 5 letters to get started!`);
    console.log(`randWord ${player.randWord} in invalid `);
  }
  response.appendHeader('Content-Type', 'text/xml');
  // see if player.guessesAttempted == 1
  if (player.guessesAttempted == 1) {
    responseText += `\nText "?" for help on how to play`
  }
    // Add something to responseText that says: "Text 'HELP' for help" or whatever
  twiml.message(responseText);
  response.setBody(twiml.toString());
  return callback(null, response);
}; You can . view the complete code on GitHub here Configure the Function with a Twilio Phone Number To open up our app to the web with a public-facing URL, go back to the <em>twordle</em> root directory and run . Grab the link ending in . In the phone numbers section of your Twilio Console, select a purchased Twilio phone number and scroll down to the <em>Messaging</em> section. Under , change to and then under select , for select , and then for select . twilio serverless:deploy /game A MESSAGE COMES IN Webhook Function Service Twordle Environment dev-environment Function Path /game Click the button below and tada🎉! You can now text your Twilio number a 5-letter word to get started playing Twordle! Save What's Next for Twilio Serverless, Assets, and Word Games? Twilio's Serverless Toolkit makes it possible to deploy web apps quickly, and Twilio Runtime seamlessly handles servers for you. Let me know online what you're building with Serverless and what your current Wordle streak is! Mine is Wordle 208 5/6 ⬛⬛⬛⬛⬛ 🟧🟧⬛⬛⬛ 🟧🟧⬛⬛⬛ 🟧🟧⬛🟧⬛ 🟧🟧🟧🟧🟧 Twitter: @lizziepika GitHub: elizabethsiegle Email: lsiegle@twilio.com Livestreams: lizziepikachu

Build a Wordle-like SMS game with Twilio Serverless in JavaScript

I have developed a fascinating — a list of users, installed applications, user gender, and statistics on the gender distribution for apps. dataset DOI: /KAGGLE/DSV/ 10.34740 2309388 For a successful advertising campaign, working with a segment is vital, and the gender of the user simplifies the work of selecting segments at times. I will tell you how collecting statistics on applications allow ML to predict a user’s gender. Data Two new files have been added to the dataset: — A list of users with their most likely gender and a list of several installed applications. users.csv — Gender distribution of users in the application. bundles_gender.csv Pay attention to the field — it shows the number of users who have this app installed, whose gender we know, and, accordingly, we can collect statistics regarding the app. This field can also be used as a measure of confidence in information about this application. cnt EDA First of all, it is interesting to look at how gender is distributed among devices. One might expect the devices to be roughly equally divided, but this has not happened. Therefore, I hypothesize that women are less likely to indicate their gender in the app’s settings. Perhaps this is influenced by the fact that the number of applications that are made exclusively for the female audience is less than for the male audience. The following picture indirectly confirms this. Let’s look at the histogram. The first one will be without additional filters. Almost nothing is visible except for symmetrical and pronounced peaks. Let’s take a closer look at one of the peaks. genders_df[
    (genders_df[ ]>= ) & 
    (genders_df[ ]<= )
].describe() 'F' 0.3325 'F' 0.3375 If you ignore the outlier, you can see that most of the applications from this subsample are extremely rare, which leads to a large number of the same values. Let’s try to keep only those applications that are encountered more than 10 times. Peaks are still visible but not so clear. Increasing the threshold to 50 almost eliminates the peaks. The graph clearly shows that there are fewer applications with a female audience. New Features Let’s create a few more additional features that will show additional information. It can be assumed that the number of installed applications can be helpful. users_df[ ] = users_df[ ].apply(len)
users_df.groupby( )[ ].describe() 'apps_count' 'ids' 'gend' 'apps_count' You can see that women, on average, install more apps on their devices. I have data about users, their gender and installed apps, and information about the distribution of gender for these applications. Is there a correlation between this data? It is logical to assume that there is, but how strong is this correlation? g_dict = genders_df[ ].to_dict()
users_df[ ] = users_df[ ].apply( x: np.mean(
        list(filter( .__ne__, list(map(g_dict.get, x))))
    )
) 'F' 'F_prob' 'ids' lambda None Instead of the average, you can use more complex methods, but for the initial analysis, this is quite enough. np.corrcoef(
    users_df[ ],
    users_df[ ].astype( ).cat.codes
)[ , ] 'F_prob' 'gend' 'category' 0 1 The correlation turned out to be very significant. -0.46602945129982887 The histogram shows that users are well divided into two groups. Baseline For conclusions and assessments, I need a baseline model. Therefore, I choose the simplest approach. print( )
print( ) f"Accuracy: \ " {accuracy_score(users_df[ ].astype( ).cat.codes, users_df[ ]< )} 'gend' 'category' 'F_prob' 0.5 f"AUC: \ " { - roc_auc_score(users_df[ ].astype( ).cat.codes, users_df[ ])} 1 'gend' 'category' 'F_prob' Even such a naive approach gives a good result, but let’s try to improve it further. Accuracy: AUC     : 0.740925288445762 0.7793767183917958 Train and Test Since the dataset with users is large, I can select a subset on which the models will be checked and compared. train, test = train_test_split(
    users_df, train_size= ,
    random_state= , stratify=users_df[ ]) 0.7 0 'gend' Logistic Regression First, I’ll try the simplest and most common method — logistic regression. But for this, we need numeric features instead of lists with id. Again, I can use the simplest method — binarization. But there is an obvious problem — the number of unique ids. 109186 But the fact that the resulting binarized data will be sparse allows the use of . sparse matrices mlb = MultiLabelBinarizer(sparse_output= )
mlb.fit(users_df[ ])
train_mlb = mlb.transform(train[ ])
test_mlb = mlb.transform(test[ ]) True 'ids' 'ids' 'ids' I use the approach to obtain reliable results and reduce the influence of randomness when dividing into training and validation subsamples. I don’t use third-party libraries and wrote a simple function. Please note that splitting the dataset into folds must be stratified. OOF (Out-of-Fold) ntrain = x_train.shape[ ]
    ntest = x_test.shape[ ]  
        
    oof_train = np.zeros((len(seeds), ntrain, ))
    oof_test = np.zeros((ntest, ))
    oof_test_skf = np.empty((len(seeds), n_folds, ntest, ))
    models = {} iseed, seed enumerate(seeds):
        kf = StratifiedKFold(
            n_splits=n_folds,
            shuffle= ,
            random_state=seed) i, (tr_i, t_i) enumerate(kf.split(x_train, y)):
            print( )
            x_tr = x_train[tr_i, :]
            y_tr = y[tr_i]
            x_te = x_train[t_i, :]
            y_te = y[t_i]
            model = LogisticRegression(
                random_state=seed,
                max_iter = ,
                verbose= ,
                n_jobs= )
            model.fit(x_tr, y_tr)
            oof_train[iseed, t_i, :] = \
                model.predict_proba(x_te)
            print( )
            oof_test_skf[iseed, i, :, :] = \
                model.predict_proba(x_test)
            models[(seed, i)] = model
    oof_test[:, :] = oof_test_skf.mean(axis= ).mean(axis= )
    oof_train = oof_train.mean(axis= ) oof_train, oof_test, models : def get_oof_lr (n_folds, x_train, y, x_test, seeds) 0 0 2 2 2 for in True for in f'\nSeed , Fold ' {seed} {i} 10000 1 20 f"AUC: " {roc_auc_score(y_te, oof_train[iseed, t_i, :][:, ])} 1 1 0 0 return AUC: Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : Seed , Fold : 0 0 0.8752592302937795 0 1 0.8741272807694727 0 2 0.8754404425783484 0 3 0.8750862228494931 0 4 0.8767777821454008 42 0 0.876839970445301 42 1 0.8771914077769174 42 2 0.8762049208242458 42 3 0.8725705419477277 42 4 0.8731672122759209 888 0 0.8752996641300741 888 1 0.8749304780764804 888 2 0.8762614986655877 888 3 0.8765240184267109 888 4 0.8725618258459555 Let’s check the prediction on the test subsample. Accuracy: AUC     : 0.8208932240918818 0.8798990678456793 I would say that the difference is big compared to the baseline. I will assume that the quality can be increase by tuning the hyperparameters, let it be the reader’s homework. CatBoost #1 When I look at the feature, I see a list of tokens. Why not try working with this data like plain text? ids I chose as the free library for the model. CatBoost is a high-performance, open-source library for gradient boosting on decision trees. From release 0.19.1, it supports text features for classification on GPU out-of-the-box. The main advantage is that CatBoost can include categorical functions and text functions in your data without additional preprocessing. You can find more detail about text features in the article . CatBoost Unconventional Sentiment Analysis: BERT vs. Catboost !pip install catboost Let’s write a function to initialize and train the model. model = CatBoostClassifier(
        task_type= ,
        iterations= ,
        eval_metric= ,
        od_type= ,
        od_wait= ,
        learning_rate= ,
        **kwargs
    ) model.fit(
        train_pool,
        eval_set=test_pool,
        verbose= ,
        plot= ,
        use_best_model= ) : def fit_model (train_pool, test_pool, **kwargs) 'GPU' 10000 'AUC' 'Iter' 1000 0.1 return 1000 False True Unfortunately, in the current version of CatBoost, it is impossible to use a list of already prepared tokens. Therefore, let’s do a little trick — turn the feature into text and use it to create a model. users_df[ ] = \
    users_df[ ].apply( x: .join([str(i) i x ])) 'ids_txt' 'ids' lambda " " for in As with logistic regression, I make an OOF prediction. columns = [ , ]
oof_train_cb, oof_test_cb, models_cb = get_oof_cb(
    n_folds= ,
    x_train=train[columns],
    y=train[ ].values,
    x_test=test[columns],
    text_features=[ ],
    seeds=[ , , ]
) 'ids_txt' 'apps_count' 5 'gend' 'ids_txt' 0 42 888 Model quality metrics in the test subsample show better quality than when using logistic regression. Accuracy: AUC     : 0.8218224490121011 0.8856101448105046 Interestingly, two completely different approaches give very similar results. In such a situation, it is logical to assume that combining methods will give a synergistic effect. CatBoost #2 As a new feature, I’ve added OOF predictions from a logistic regression model. In addition, do not forget about the feature, which worked well for the base model. F_prob columns = [ , , , ]
oof_train_cb_2, oof_test_cb_2, models_cb_2 = get_oof(
    n_folds= ,
    x_train=train_2[columns],
    y=train_2[ ].values,
    x_test=test_2[columns],
    text_features=[ ],
    seeds=[ , , ]
) 'ids_txt' 'F_prob' 'lr' 'apps_count' 5 'gend' 'ids_txt' 0 42 888 I can say that the model almost ideally predicts the gender of the user using only information about the installed applications on the device. Accuracy: AUC     : 0.836950230713273 0.9010077023800467 Summary In this story, I: Introduced a new free dataset; Did exploratory data analysis; Created several new features; Created several models for predicting the gender of a user of a mobile device. All this required the accumulation of certain statistical information about applications users use and information about the distribution of gender among users for the applications themselves. The code from the article can be viewed . here

Gender Prediction Using Mobile App Data

The Hitchhiker's Guide to pySpark DataFrames

Q&As, tips, and resources packed into one article. We recently launched our SaaS offering on Product Hunt. Obviously, we did a bit of research to increase the odds of having a successful launch. I encountered a couple dozen articles with elaborate stories of success/failure. Valuable, but it took me some time to comb through. Shipright Eventually you’re just looking for the answers to your questions and the actionable tips, right? Therefore; I decided to create a list answering most of the questions you might have for your Product Hunt launch in more of a Q&A style / a bulleted list of actionable tips. With the help of the following makers that ended up in the top 5 of the day on Product Hunt as well, I tried to make it as all-encompassing as possible. So feel free to say hi and thank from , from , from , and (several projects, check his profile) as well 🙌 Fletcher Halp Joe NuCode Sammy Lito Peter If you’re curious about how we did with Shipright, and how we could have done better, read the end of this post. Since you’re here for the list, here we go — feel free to cmd + F to search through it: ☝️ General What’s Product Hunt? <a href="https://medium.com/media/106c4475726559b3378d34a0ff562317/href">https://medium.com/media/106c4475726559b3378d34a0ff562317/href</a> You most likely wouldn’t have jumped into this article if you hadn’t heard of Product Hunt. Let’s skip this one 😉 **How useful is launching on Product Hunt?**Before diving to all of the prep-work, ask yourself who your target audience is, and whether that fits the Product Hunt audience ( techies, product enthusiasts, founders, investors, reporters, engineers, designers, marketers and early adopters from small startups to fortune 500 companies). typically If that’s a fit, give it a go. Think about your goals though. E.g. Do you mainly want to learn fast about your problem/solution? Or have you already validated crucial parts of your business, and you’re mainly looking for a spark in traction and customers? Get the expectations right. **Is my product ready? I’m afraid I’m launching too early…**That’s how everyone feels. And as Reid Hofmann said: ‘If you’re not embarrassed by the first version of your product, you’ve launched too late’. Know that you can always re-submit and launch again. Either because you’ve overhauled a huge part, or because you’re introducing substantial additions –that’s what the Product Hunt FAQ said. They give you two examples: “the launch of a mobile app” or “complete product redesign with new functionality.” After all, a lot of companies use Product Hunt to introduce major updates / new features on the platform, so you can too. What can I expect from a Product Hunt Launch? A significant amount of traffic and sign-ups (this varies wildly, depending on the type of product). It will have its long-tail as well — so you can expect a bit of traffic as time passes, since quite a bunch people search for tools on Product Hunt Possibly: some investors reaching out (don’t be all too distracted by it) Possibly: press, which might lead to additional traffic. A lot of feedback from people trying/signing up for your product (small tip and plug: ) to learn from manage that feedback properly 💡 Other tips: Set launch goals for your Product Hunt launch. When will your launch be a success? Are you looking at sign-ups? Active usage/retention (‘x’ days and/or weeks after your launch)? Conversions? Putting a line in the sand helps you celebrate and evaluate. Have fun with it! the Product Hunt community is one that loves new products, supporting the people that built them, and celebrating launches. Use this as a chance to celebrate with your team a bit as well 🎉 😬 Pre-launch **Is my product good enough to be on Product Hunt?**If you care about learning fast on whether a product or idea might work for a set of people: the answer is almost always yes (given that your target audience is present). Carefully track for whom the solution works and for whom it doesn’t though, so you’ll learn more about your target user. exact If you’re looking for traction/customers; you obviously increase your odds of success by validating parts of your business upfront (e.g. do cold outreach to your assumed target audience and see whether the problem/solution resonates. Let them use it. See if they convert/keep using it. You know the drill.). A few things to keep in mind regarding the product you’ll launch: _Have a simple offering_An easy-to-understand value prop. and product. If things looks good and the branding is on-point, this definitely helps to get attention. It doesn’t say a whole lot about whether your product will be successful though. _Have solid onboarding_What’s the primary value users get from your product? Make it clear and lead them to it. Tip: think it through as a core part of your product, don’t see it as an ‘after-the-fact’ thingy. Read . this _Have tracking in place_Make sure you track user behavior (e.g. use ), and have a way of tracking all the qualitative product feedback. It’ll help you evaluate and learn from your launch. Mixpanel **Does it matter who’s ‘hunting’ my product?**Kind of. Product Hunt seems to take a lot of things into account when ranking products. The advantage of an influential Product Hunt user hunting your product is that all of his/her followers will get a notification on the platform. This will provide you with more exposure. it’s also said that when influential hunters hunt your product, they end up on the front page of Product Hunt straight away (which is crucial, because no presence on the homepage = no upvotes = no eyeballs = no signups = nothing to learn from). I’m unsure on whether that’s actually true… In some articles Who’s a suitable Hunter for my product? Someone with a big following. Someone that maps with the type of product/business you’re building (e.g. we had Chris Messina — former product designer at Google, hunt Shipright. Our tool is helping software businesses track product feedback, so that felt like a good match). This helps since your product will resonate more when you ask for a hunt + chances are their followers also relate better to your offering. **How do I find my Hunter?**Use to find a top 5 to reach out to. . Make it personal and follow up a few times if they don’t respond (they probably have a lot of other things on their plate). If Chris Messina is a match for you: ask him to hunt you . this list (top 500 hunters) Don’t spam them with a vanilla message right here 💡_Tip_: use a tool like or anything else that allows you to use e-mail templates and sequences, so follow-ups can be scheduled and will go out automatically (there are also a bunch of Chrome extensions that let you that with Gmail). Hubspot **When do I contact these potential hunters?**Get in touch with a few hunters you’re planning to launch to gauge interest. If they agree, with the necessary info. Confirm whether they’ve scheduled your product . at least 1 month before follow up two weeks before your launch a few days before **What do I need for the Product Hunt post itself?** (use it as a template, if you like). You can share something like that with your hunter as well. Use something like Dropbox Paper / Google Docs to share the post details (like the ) and add a link to a Dropbox / Google Drive folder for your assets. It’ll allow you to tweak things later on without the hassle of sending your hunter new assets/info. This example You’ll find some additional tips within that I linked up as well. the template **Do I need an audience before my launch?**I suggest you do. The main reason(s): Having an audience to support you will get you some initial traction on the Product Hunt platform, which leads to more/new people discovering your product. Additionally: this will help you validate problem/solution fit upfront. It depends on the stage you’re at and your goals. Having validated parts will increase the odds of gaining more customers. If not; Product Hunt could be a great catalyst to learn more about that really fast. Just keep it in mind when setting launch goals and expectations. **How do I build an audience before my launch?**This can be a post all by itself. We mostly did cold outreach and a bunch of surveys to learn about our problem space and then re-connect to test (parts of) a solution(s). This allowed us to build up a first user/customer-base, learn things (what works/doesn’t work), and have an audience to reach out to for our launch. Look into for gaining beta users. Keep them in the loop on your progress and reach out when you’re about to launch. Product Hunt Ship Try a tool like (or create a process yourself, like we did initially) to get in touch with a set of people in your target audience for an interview / survey / testing. Often, a sub-set of the people you talk to, might be interested in trying your product. Growbots Engage with others by commenting on Product Hunt products and Product Hunt Ask. Follow relevant people and engage with their hunts (you could even share interesting products on Twitter and tag the maker). Find your target user in the bunch of Slack groups out there, and start a quick chat to solicit feedback. Ask them whether they’d like to try it. Keep them in the loop and reach out when you launch. Start blogging (personal / business) for your target audience, bring value, and build your audience/following while building your product/business (I haven’t, really, and regret it. If you’re planning to stay in a similar space, it’ll definitely help you. It’s just not a short-term tactic) It basically comes down to evaluating all the possible channels that you’d use to build a more sustainable customer acquisition strategy for your business anyway. Use (based on your type of business/product), it’s amazing. Julian Shapiro’s guide on evaluating these channels What channels and means can I use to promote my Product Hunt post on launch day? A lot. Make sure you prep and pre-schedule most of it upfront (and add the exact Product Hunt post URL to it on launch day), and/or create a promo schedule for things you have to do on the day itself. if you have done some major updates before the launch. Ask if they’d like ‘pre-launch access’. Schedule an e-mail again and . Use tools like (with templates/sequences), MailChimp, or Intercom to schedule things. E-mail people who trialed before a day before launch on the day itself Hubspot Use tools like Hubspot (with templates/sequences), MailChimp, or Intercom to schedule things. E-mail other users. E-mail/message your mentors / advisors / informal investors. E-mail/(LinkedIn)message your (founder) friends / relatives to message beta sign-ups Use Product Hunt Ship I suggest you request access & connect way before your launch. E.g. ask for feedback on your early versions. Use to find the ones relevant to you (at least use the ones focused on founders/startups). Keep an eye on the rules for each of these groups. It isn’t allowed to share ‘promotional’ content in every group. Use Slack groups. this handy site Request access & connect way before your launchA few you can use (depends on your product, obviously):Keep an eye on the rules for each of these groups. It isn’t allowed to share ‘promotional’ content in every group. Use Facebook groups. Request access & connect way before your launchA few you can use (depends on your product, obviously):Keep an eye on the rules for each of these groups. It isn’t allowed to share ‘promotional’ content in every group. Use LinkedIn groups. , , , . Find the the ones that are relevant to you, and focus on gaining feedback. Use Reddit r/startup r/entrepreneur r/sideproject r/trymything to share things pre-launch and give them a heads-up on launch day. You probably have Twitter, LinkedIn, Facebook, Instagram etc. Schedule your content by using something like Buffer for all your social channels. Tweet your upvote milestones (100, 200 etc.) in a fun and creative way additionally. The more fun and worthwhile content you can get out, the better (without it feeling like spamming the same message over and over). You never know how many you’ll get, but it might be worth prepping a few visuals for the first 4 milestones. Use your personal social accounts Don’t direct them to your Product Hunt post, direct them to your homepage, and make sure you have a widget (like this ) ready that allows for a vote straight away. Whether that traffic just ends up in a few sign-ups and/or votes; it’s fine either way. Prepare Facebook, Instagram, and Twitter ads. Product Hunt Intercom widget E.g. include a one-liner that allows the person to Tweet (make a pre-defined Tweet for lower friction) on the page after they signed up (thank you page / you received your activation mail) Stimulate word of mouth during sign-up 👆Pre-defined Tweet with a single click, after sign-up 💡 Additional tips: Check out similar products to yours and learn about their launch strategies. Spy on titles, taglines, logos, screenshots, comments and general wording. Create a special offer for Product Hunters and add it to the landing page. This stimulates people to sign up. Also state this in your ‘Maker comment’ on your Product Hunt post. See , where I included a Maker comment with our offering. this template If you have a Chrome extension or app, make sure to try and get some reviews from your first set of users. For us, a Chrome extension was a core part of the product and onboarding. To stimulate quick installs, it helps to have some reviews / decent ratings, since this is often shown when the user is about to install the extension / app. Work on a press list and release, and to cover your launch. find relevant journalists 🚀 Launch **What day and time should I launch on Product Hunt?**From what I’ve read in multiple articles: Tuesday, and Wednesday work best. We’ve heard Thursday as well. More important is: don’t launch when Apple, Google, or any other huge company launches something new. Double check it. Time to post: it’s best to post 12:01AM PST. If you’re located in Europe, or get a lot of support from folks in Europe e.g., this will give you head-start. You’ll be higher up the list before people in the US start to wake up and are ready to take a peek. What should my schedule look like on launch day? If you’re multiple co-founders; have some roles in place. We’re with 4, so we had someone dealing with outreach/marketing inviting/onboarding users and doing support 2 ‘firefighters’ in case we’d run into any crucial issues (we didn’t, fortunately, thanks to a period of testing pre-launch) 1. 2. 3. Schedule out your day upfront. Use it for promo during your launch day and things that were hard to schedule upfront, so you can stick to a rough plan / time-line during a (few) day(s) of chaos. 💡 Additional tips: Aim for the top 5. You’ll end up in the Product Hunt daily (newsletter), going out to all Product Hunt users. Refer your tech-savvy friends to the homepage of Product Hunt for your product on launch day, if you’re featured in the top posts. Engagement from the direct URL seems to have less impact on the ranking algorithm compared to users accessing via the homepage (this is a hunch but ) confirmed by others Make sure you reply on comments on your Product Hunt post Look up the Twitter handles of people that upvoted you on Product Hunt. Most of the time, the Product Hunt handle of a user, is similar to their Twitter handle. You could at least thank them for supporting you (with a funny GIF e.g.). This might get you some additional retweets which spreads the word to their followers. 🙌 Post-launch **What happens after launch-day?**You’ll still get quite a bit of traffic after day one. This lasts about a week. It’s important to keep a close eye on your users and support them where needed. While connecting, try to solicit some feedback by asking how they like your product. What are the likes and dislikes? What are they missing? Does the product live up to their expectations? Why/why not? 💡 Additional tips: Write something about your experiences and lessons learned that can be of value to others. Give it a bit of a unique twist. Like this article 😉 📊 How we did ourselves: #5 of the day with , a B2B SaaS offering for tracking product feedback to help teams build better products and increase customer satisfaction/loyalty. Shipright Shipright is more of a team tool than a simple one-man-band utility. The latter & B2C oriented tech tend to perform a bit better (from what I’ve seen and studied). ~300 upvotes on launch day, 370 over the week 14 reviews ( 💝) and they were pretty awesome ~1000 visits on our website on launch day, ~700 the day after, then 500, 300 etc. ~100 teams signed up during the week after launch Investor interest (a hand full) 🧐 What we could have done better: We had experienced a bot continuously signing up for Shipright just a few days before launch. We quickly added a reCAPTCHA but didn’t extensively test it. Turned out it gave trouble on iOS Safari browsers. We discovered it when launch day was almost over 🤯 (luckily most people came through desktop, so we probably just missed out on a few) Double checked our sign up flow I had given some people a heads-up a week before, and others weren’t informed upfront at all. We had people support us, but also a lot of people responding the day after I sent them an e-mail or (LinkedIn ) message. Given a heads-up to our support network the day before launch day 📚 Articles referenced / useful reads: The Ultimate Playbook to blowing your Product Hunt Launch out of the water Favorize — The step-by-step guide to totally blow your startup’s Product Hunt launch eFounders — What we learned about Product Hunt after 4 startup launches and more than 4000 upvotes Haystack — How to launch (and not launch) a product on Product Hunt How to Launch on Product Hunt by Product Hunt Station — Do yourself a favor, nail your Product Hunt launch Waydev — How to get in TOP #5 products on Product Hunt and get in their newsletter

All you need to know for prepping your Product Hunt Launch

1) HyperLedger HyperLedger is an open source collaborative effort between enterprise institutions and corporations to support the development of , to date Hyperledger has including . HyperLedger’s goal is to form a consortium with the intention of supporting the development of enterprise-grade and cross-industry platforms to utilize open distributed ledger technology. Hyperledger can be thought of as an umbrella project that intends to supply the necessary framework, standards, and support necessary to build open sourced blockchains that become widely adopted. blockchain technology over 250 members Accenture, Deloitte, Fujitsu, Hitachi, Huawei, Intel, IBM, Moscow Exchange, SAP, Samsung SDS, Xiaomi, Cisco, and more 2) Ethereum Ethereum is a decentralized project backed by a consortium known as the Ethereum Enterprise Alliance (EEA) that is joined by corporations and institutions such as _Accenture, BlockApps, BNY Mellon, CME Group, ConsenSys, IC3, Intel, J.P. Morgan, Microsoft, Nuco, Banco Santande_r and many more. Since EEA’s inception in 2017, the consortium has added into their fray. EEA’s goal is to connect Fortune 500 companies, startups, and government institutions with Ethereum developers and experts, today the group has over . Mitsubishi UFJ, DTCC, Deloitte, Samsung SDS, Infosys, and the National Bank of Canada 300 members 3) Stellar Stellar is a blockchain project dedicated to connecting payment providers, banks, and people together. Backed by a strategic partnership with IBM, Stellar has become a popular rival to Ripple, another leading bank and financial services related blockchain project. The team behind Stellar have been busy and have collaborated with over to date including: . Stripe is Stellars most interesting partnerships as the payment system provides support to over 100,000 businesses including OpenTable and Warby Parker. expanding their clientele 48 organization SHIFT Markets, Lightyear, STRIPE, and Wanxiang Group 4) Ripple Ripple is a blockchain project designed to connect banks, payment providers, digital exchanges and corporations together. Ripple is the leading project to collaborate and form strategic partnerships with numerous banks around the world such as; . Focusing on growing a clientele of banks, Ripple has also begun to establish strong ties with payment processors, exchanges and remittance services such as Western Union. Commonwealth Bank of Australia, CBW Bank, Cross River Bank, Santander, UniCredit, UBS, ReiseBank, CIBC, National Bank of Abu Dhabi(NBAD), Standard Chartered, National Australia Bank (NAB), Siam Commercial Bank and Shanghai Huarui Bank 5) Aergo An upcoming blockchain platform Aergo aims to provide an enterprise-ready solution for business, large corporations, and institutions alike. Aergo is backed by Blocko — a commercial blockchain startup — recognized as South Korea’s #1 commercial private blockchain systems provider that has built an impressive list of partnerships over time. It’s important to note that companies are not merely testing blockchain technology but have actually implemented and are currently utilizing it in their operations, therefore Blocko is considered to be one of the most active and advanced enterprise blockchain solution providers in this space. 6) Vechain Vechain is a blockchain-as-a-service (BaaS) platform that focuses on alleviating problems in supply-chain management. Vechain has been in development since 2015 and has managed to team up with that include the likes of . The project has also established a partnership with China’s largest insurance company, the People’s Insurance Company of China (PICC). Here is a complete 24 companies Deloitte, Microsoft, Renault, PricewaterhouseCooper, DB Schenker, TCL communication, China Unicom, BMW and LV China list of VeChain’s partnerships 7) ICON A blockchain project based from South Korea that plans to hyper-connect private and public institutions by developing a decentralized network of communities that consists of Financial institutions, insurance, universities & hospitals. ICON, WAN and AION announced in December 2017 that the three independent projects will come together to , a advocacy group centered around promoting interoperability between the disparate networks. Up to date, ICON’s biggest partnership is with Line, the popular Japanese messaging app that has over 200 million users signed up for the social platform form the BIA 8) Qtum Qtum has established a strategic partnership with China’s Amazon Web Services (AWS) division. The partnership will allow AWS users to develop and launch smart contracts using an Amazon Machine Image (AMI). This strategic partnership between QTUM and AWS will allow blockchain startups to utilize Amazon’s hosting service to develop smart contracts. QTUM will be able to access Amazon’s database thus sharing important data between startups such as marketing, sales data, and technical resources. 9) OmiseGo Headquartered in Thailand, OmiseGo is a company that plans on providing an open source payment platform with a decentralized exchange that is built on top of Ethereum. Essentially, companies establishing a partnership with Omise, the parent company of OmiseGo will provide payment gateways for Thailand, Japan, and Singapore. Omise and OmiseGo are separate entities, however due to the strong establishment of the parent company OmiseGo has a long slew of merchants who are interested in utilizing their blockchain-based payment service. 10) Brave Brave software is a web browser that plans on revolutionizing the internet with an ad-blocking private browser that also rewards readers and content publishers. On April 18, 2018, the Dow Jones Media Group announced a strategic partnership with Brave. Popular sites such as the and became verified publishers that intend to utilize Brave’s Basic Attention Token (BAT) platform, a blockchain-based system that allows consumers and eventually, advertisers to pay publishers directly for their content. Barron’s MarketWatch 11) PowerLedger Power Ledger, a renewable energy-trading platform, has partnered with BCPG, a Thailand based electric utility company, to allow users to trade renewable energy from solar panels via use of a secure banking interface. The project has announced that it will also be collaborating with _Silicon Valley Power, City of Santa Clara, Helpanswers, and American PowerNe_t. This green-initiative has also received backing from the Australian government and was recently endorsed by billionaire, Richard Branson when the team won the 2018 Extreme Tech Challenge that was held on Branson’s Necker Island. If you know of any further projects that have strong enterprise partnerships and collaborations, feel free to leave a comment below so I can add them onto the list!

11 Blockchain Projects Flaunting High-Profile Partnerships & Collaborations

What is BABB? BABB, a company that describes itself as the World Bank for the Micro-Economy, stands for Bank Account Based Blockchain. It is a decentralised banking system on the ERC-20 token Ethereum network. Their vision is to increase businesses on a global scale by improving financial support in communities, improve living conditions, and have an overall impact on countries and the global economy as a whole. They plan on attaining these goals by offering the world access to UK regulated peer-to-peer financial services through the use of biometrics and the blockchain. Useful DYOR Information About Babb: May 21, 2018 Research’s Date : CoinMarketCap URL : https://coinmarketcap.com/currencies/babb/ Webpage: https://getbabb.com BABB (BAX) Coin Name : 50,000,000,000 Total Supply : 20,950,010,000 Circulating Supply : $50,156,628 USD Market Cap : 0.00090000 Pre Ico Price : 1 BAX = $0.002394 USD Ico Price : $10,000,000 during pre-ico / $19,300,000 during crowd sale Tokens/Money Raised Through ICO : UTC-01:00 Time Zone : White Paper : https://resources.getbabb.com/whitepapers/en/babb-whitepaper.pdf ERC20 Token Type : Potential Clients: BABB is targeting the current ‘unbanked’ world market of approximately 2 billion people and represents a $380 billion dollar untapped industry. Primarily these are countries that consist of low per capita income, unstable education, unstable health sectors and constant political strife. In an increasingly connected world, 30% of the world population is still disconnected from the global economy. Breakdown: The system is made of three main parts; The mobile banking app, Social KYC, and the Black Card. To begin, users need only download the smartphone app, verify their identity via KYC and BABB will connect them to traditional banking services effectively plugging into the economic world system. Once an account is made, users have access to peer to peer financial services such as personal payments, loans, trade, issuance of digital currencies, and raise funds for business ventures as well as other features. Users will also receive the Black Card, which acts as the physical connection to their digital assets. Let’s get into a little more detail for each. Mobile Application: Everything will be done via the BABB Mobile Application. This will allow end users complete control of their money from anywhere in the world as long as they have internet connection which includes SEPA and IBAN. Social KYC: This project believes in trust and utilizes a ‘Social KYC’ strategy. Any user who is fully KYC compliant through verified documentation can invite other users to join the platform. This method only requires a valid I.D. such as your passport, and does not require a UK address or credit history. A passphrase and biometric data is collected to ensure identity. This fluid interaction will allow wide scale seamless adoption of BABB. The Black Card: With this payment card, you are provided a direct link with your BABB bank account via a QR code or NFC (Near Field Communication). It acts as debit card or a prepaid card for friends and family. Due to being a QR (Quick Response) code, it does not have a chip and therefore cuts out the requirement of being a Visa or Mastercard. There is no personal data other than your code which decreases security risks. The card is simply a hardware version of your banking access. Integration of Central Banks: A detail worth mentioning is BAPP services not only individuals, but Central Banks (CB). Several CB’s have already alluded to their intention to begin blockchain adoption and its seems assured that a Central Bank Digital Currency (CBDC) will be utilized. This integration potential will act as further token utility. BAPP provides the infrastructure for banks to piggyback on their system and allow for the launching of CBDC. BAPP will merge both banks and individuals which is a huge market. The Tech Specs: The technical aspect of BABB is designed on a Federated blockchain which translates to centralised. They will have nodes governed by the company which run on smart contracts. FIAT is stored in the BABB bank reserves and represented as BAX. Privacy is kept top priority by the private key always being kept solely on the users device. Moreover, they utilize data encryption and cryptographic hashing as well as biometric identification e.g. face and voice recognition. Use cases and Services: BABB offers many flexible services. A further advantage is to make extra cash. This is accomplished by users acting as a bank utilizing BAX. This includes: Peer-to-peer Cash allows for withdrawal and deposit by engaging with other users or agents. Exchanging of different currencies is simple on the app. Peer-to-peer borrowing and lending with clearly defined terms and rates are available using smart contracts making the service streamlined. Three Peer-to-peer fundraising methods: , which is invite only; which is available to friends and family; and is for global fundraising allowing direct positive impact such as disaster relief and community infrastructure projects as well as personal projects. Private Personal Public BABB uses BAX tokens with the advantage of no currency conversion. Zero middlemen. Finally, a huge benefit is creating a payroll service which allows companies to easily automate payroll process for employees. On top of all these features comes a fully authorized payment institution (API) certification from the FCA. They are also currently in the process of applying for a banking license which will have massive end result potential. While BABB is only in its infancy, the token use is widespread in capability and therefore definitely a currency that will be in high demand. Finally, there will also be a token burn of 50% of all tokens utilised for payment of platform use. This will occur until the total amount of tokens in circulation is 20% of current total. The remaining tokens will be frozen on the platform reserve so there are negligible effects on market price. Social Media Information : Facebook URL :https://www.facebook.com/getbabb/ Likes :8492 Likes 8,855 Followers How Many Posts / Updates In The Last 7 Days : 9 Recent Updates Twitter URL :https://twitter.com/getbabb Followers : 8,900 Followers How Many Tweets / Retweets In The Last 7 Days : 2 Recent Tweets/Retweets Telegram URL : https://t.me/babb_official Users : 13,882 Members Telegram Admins User Name : @CryptoTeddy User Name :@Ani_BABB User Name :@CryptoTeddy User Name :@Mig_AmaZix Reddit URL :https://www.reddit.com/r/getBABB/ Readers : 1181 Readers Youtube URL :https://www.youtube.com/channel/UCQtkZd7sfzbEugz7VdFhv4Q/featured Subscribers : 1564 Subscribers Medium URL :https://medium.com/@BABB Followers : 2K Followers How Many Updates In The Last 7 Days : 1 Recent Update LinkedIn URL : https://www.linkedin.com/company/babb Followers : 859 Followers How Many Updates In The Last 7 Days :- Extra If you find more social media platforms or chats or other links please put them here. Staff / Team Info : Name :RUSHD AVERROES Title :Founder / CEO Bio : Rushd is founder and CEO of BABB, leading a diverse team of industry veterans to make his vision a reality. He is a financial inclusion specialist and has an MA from University of Greenwich in Microfinance and Financial Inclusion. Before that, he attended the Ecole Polytechnique de Lausanne and was awarded an IT Science Degree in 2006. Rushd has managed a highly successful Authorised Payment Institution (API) in the UK — Wowpaymobi. LinkedIn :https://www.linkedin.com/in/rushd-averro%C3%ABs/ Name :Jorge Pereira Title :CTO Bio : Jorge is technologist and serial entrepreneur, with extensive experience in developing technology projects in a variety of areas, for local and global markets. He founded Seegno in 2008, a world-class web application development firm, where he remains as an Advisor. In early 2014 Jorge joined Uphold, where as CTO he was responsible for the design and architecture of the whole technology stack, as well as UI and UX of its products. Ever since, Jorge has focused intensively on Fintech, developing products and technology related to Cryptocurrencies, Distributed Ledgers, Regulatory Compliance, KYC/AML, Fraud Prevention, Risk Mitigation, Trading, Hedging, Bank Integrations and other areas related to fintech platforms and applications. In late 2017 Jorge founded FintechServer, which helps fintech companies bring products to market faster and more securely, and also co-founded fintech startups Wayfex and LoyalChain. LinkedIn :https://www.linkedin.com/in/jorgerpereira/ Extra Links :- Name :Adam Haeems Title :COO Bio : Adam is responsible for the daily operations of BABB. He has a Master of Finance from Cambridge University (Judge Business School) and joins BABB fresh from an accomplished trading career at Bank of America Merrill Lynch and a $1bn global macro hedge fund (QCM). He is passionate about financial inclusion and improving market efficiency within the microeconomy. LinkedIn :https://www.linkedin.com/in/adam-haeems-8a457813/ Name :Paul Johnson Title :CIO Bio : Paul is a technological evangelist and has held CIO and COO positions in companies such as Aldermore Bank, Lloyds Bank and GMAC FS. In addition to his executive experience in banking and payments, Paul is an iNED on the Board of a foreign bank and has served in the Royal Navy. He also enjoys sport and travelling with his wife and six children. LinkedIn :https://www.linkedin.com/in/paul-johnson-719a0b6 Name :Dean Refaat Title :Head of Research Bio : Dean’s goal is to launch BABB in the UK and then penetrate markets in the MENA region. He gained his experience in banking regulation and economic risk mitigation as a banking inspector at the Central Bank of Egypt. He then became an independent researcher and has received his master’s degree in ‘Philosophy and Public Policy’ from the LSE. LinkedIn :https://www.linkedin.com/in/nour-el-dean-refaat-a58944136/ Name :Ani Alexander Title :Head of Marketing Bio : Ani is a marketing professional specialising in brand architecture, with experience in banking, telecom, media agencies and charities. She loves building, launching and growing brands from scratch using storytelling, new media content and social media. When not working, she writes novels and records podcasts. LinkedIn :https://www.linkedin.com/in/anialexander/ Name :Annabel Mellor Title :Head of Communications Bio : Annabel shapes BABB’s content and communications, to ensure the company delivers a clear, distinct and inspiring message across all channels. Annabel has helped various successful consumer-facing fintech startups, including Chip and Nutmeg, to grow rapidly and create an engaged community. LinkedIn :https://www.linkedin.com/in/annie-mellor-26104ab1/ Name :Leonard Seelig Title :NE Director Bio : Leonard is supporting BABB in implementing best practices on both market-facing and operational processes. He has twenty years of banking experience, including in the technology sector, and in helping lead both private and public companies. Leonard has recently retired from his role as chairman of APC Technology Group PLC, and Board member of Defenx PLC, a security software company listed on the AIM London stock market. Name :Luís Fraga Title :Lead Product Manager Bio : Fraga is Head of Fintech Products at Seegno, and is responsible for Product management at BABB. During his 16-year career, Luís worked as project manager, product owner and creative director, defining end-2-end experiences in multiple areas such as Telco, Retail, Banking, FinTech, Entertainment, Music streaming, Games, Safety and Fashion. Some of the major clients he worked with include Microsoft, Sesame Workshop, Bungie, Vodafone and has also taken part in European research projects. Fraga worked at MobiComp, Microsoft, iMobileMagic, Smith Micro, Uphold and did his internship at Philips Research. LinkedIn :https://www.linkedin.com/in/lu%C3%ADs-fraga-543728b3/ Name :Luís Oliveira Title :Creative Director Bio : Luís is Creative Director at Seegno, and is responsible for creative, web and mobile design at BABB. He is an award-winning, obsessively oriented, diversely experienced creative director, as well as art director, designer, manager and leader. With 16 years of experience in design and technology, he has had the opportunity to collaborate with companies such as Microsoft, Millennium BCP, Active Bank and Ikea. Luís is a friend, lover, musician and sportsman, laying his energy and passion in whatever he does, never afraid to go the extra mile to get things done. LinkedIn :https://www.linkedin.com/in/luisoliveira/ Name :Nuno Rocha Title :Head of Engineering Bio : Nuno is the Head of Engineering at Seegno, leading both the FintechServer and BABB’s Engineering teams. He is a leader at heart, and a fullstack developer with years of experience in both frontend and backend technology, and in the last few years has worked increasingly with blockchain technology. In the last few years, Nuno has worked on projects such as VRI Direct, Uphold, Wayfex, LoyalChain, FintechServer, and also software for PSD2 banking infrastructure. LinkedIn :https://www.linkedin.com/in/nunorafaelrocha Name :João Gonçalves Title :Senior Software Engineer Bio : João is a Senior Engineer at Seegno, and part of BABB’s development team. His focus lies on building software that bridges the gap between blockchains and legacy systems, particularly focused on KYC and data processing. In the last few years, João has worked on projects such as Uphold, Wayfex, LoyalChain, FintechServer, and also software for PSD2 banking infrastructure. LinkedIn :https://www.linkedin.com/in/joao-feio-goncalves/ Name :Jingwei Li Title :Researcher Bio : Jingwei is a Researcher at BABB with a passion for understanding how Blockchain could disrupt the future of Banking. After spending a year with the Bank of England, Jingwei built a solid understanding of the central banking framework by analaysing a portfolio of regulatory approvals for international banks. When not working, Jingwei is studying towards his MSc in Finance at Imperial College LinkedIn :https://www.linkedin.com/in/jingweili93/ Name :Mai Abdel-Aziz Title :Middle East Communication Manager Bio : Mai is BABB’s Arabic content writer. She worked as a journalist in Cairo before moving to London to tutor BBC journalists in Arabic as well as providing translation for their Middle East focused work. In addition, she worked as a Research Assistant for the British Council through Lancaster University, providing translation services. LinkedIn :https://www.linkedin.com/in/mai-abdelaziz-637283126/ Name :David Devia Title :Content Creator Bio : David is a videographer, director and content creator with over five years’ experience. He has done an internship with Level 39, the largest technology accelerator in Europe, assisting with content strategy and stock footage creation. David is also part of the team who record and edit Fintech Insider, the most popular business podcast in the UK. LinkedIn :https://www.linkedin.com/in/dvddevia/ Level 39 One Canada Square Canary Wharf London, E14 5AB, UK Company Address :

BABB: Bank Account Based Blockchain, We Are Our Own Banks

Why you should be cautious with neural networks for trading So I built a Deep Neural Network to predict the price of Bitcoin — and it’s astonishingly accurate. Curious? See the prediction results for yourself. Looks pretty accurate, doesn’t it? And before you ask: Yes, the above evaluation was performed on unseen test data — only prior data was used to train the model (more details later). So this is a money-making machine I can use to get rich! Right? In fact, I am giving you the for the above model so that you can use it yourself… code Ok, . Don’t do it. stop right there I repeat: Do not use it for trading. Don’t do it! Don’t be fooled. There is something utterly deceptive about these results. Let me explain. Too Good to be True During the last couple of weeks and months I’ve encountered many articles that take a similar approach to the one presented here and that show graphs of cryptocurrency price predictions that look like the one above. The seemingly stunning accuracy of price predictions should immediately set off alarm bells. These results are obviously too good to be true. “When something looks too good to be true, it usually is.” — Emmy Rossum In the following, I want to demonstrate why this is the case. Don’t get me wrong — my intention is not to undermine the work put into those articles. They are good and deserve the claps they received. In fact, many of those approaches are very accurate — . technically speaking The goal of this article is to bring out why those models are, fallacious and why their predictions are not necessarily suitable for usage in actual trading. in practice, So why exactly is this the case? Let’s take a close look. Predicting the Price of Bitcoin using LSTMs To explain, let me walk you through an example of building a multidimensional neural network to predict the price of Bitcoin that yields the prediction results you saw above. Long Short Term Memory (LSTM) LSTMs are a special kind of , that are particularly suitable for time series problems. Hence, they have become popular when trying to forecast cryptocurrency prices, as well as stock markets. Recurrent Neural Networks (RNN) For in-depth introductions to LSTMs I recommend and article. this this For the present implementation of the LSTM, I used Python and . Keras (You can find the corresponding Jupyter Notebook with the complete code on my Github .) 1. Getting the Data First, I fetched historic Bitcoin price data (you can do this for any other cryptocurrency as well). To do so I used the API from : cryptocompare import jsonimport requestsimport pandas as pd endpoint = 'https://min-api.cryptocompare.com/data/histoday'res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=2000')hist = pd.DataFrame(json.loads(res.content)['Data'])hist = hist.set_index('time')hist.index = pd.to_datetime(hist.index, unit='s')hist.head() A snapshot of historic Bitcoin price data. Voilà, historic daily BTC data for the last days, from until . 2000 2012–10–10 2018–04–04 2. Train-Test Split Then, I split the data into a and a set. I used the last of the data for testing, which splits the data on the All data before this date was used for training, all data from this date on was used to test the trained model. Below, I plotted the column of our DataFrame, which is the daily closing price I intended to predict. training test 10% 2017–09–14. close def train_test_split(df, test_size=0.1):split_row = len(df) - int(test_size * len(df))train_data = df.iloc[:split_row]test_data = df.iloc[split_row:]return train_data, test_data def line_plot(line1, line2, label1=None, label2=None, title=''):fig, ax = plt.subplots(1, figsize=(16, 9))ax.plot(line1, label=label1, linewidth=2)ax.plot(line2, label=label2, linewidth=2)ax.set_ylabel('price [USD]', fontsize=14)ax.set_title(title, fontsize=18)ax.legend(loc='best', fontsize=18) train, test = train_test_split(hist, test_size=0.1)line_plot(train.close, test.close, 'training', 'test', 'BTC') Train-test split of historic Bitcoin price data 3. Building the Model For training the LSTM, the data was split into windows of days (this number is arbitrary, I simply chose a week here) and within each window I normalised the data to , i.e. the first entry of each window is and all other values represent the change with respect to the first value. Hence, I am predicting price , rather than absolute price. 7 zero base 0 changes def normalise_zero_base(df):""" Normalise dataframe column-wise to reflect changes withrespect to first entry."""return df / df.iloc[0] - 1 def extract_window_data(df, window=7, zero_base=True):""" Convert dataframe to overlapping sequences/windows oflength `window`."""window_data = []for idx in range(len(df) - window):tmp = df[idx: (idx + window)].copy()if zero_base:tmp = normalise_zero_base(tmp)window_data.append(tmp.values)return np.array(window_data) def prepare_data(df, window=7, zero_base=True, test_size=0.1):""" Prepare data for LSTM. """# train test splittrain_data, test_data = train_test_split(df, test_size) # extract window data  
X\_train = extract\_window\_data(train\_data, window, zero\_base)  
X\_test = extract\_window\_data(test\_data, window, zero\_base)  
  
# extract targets  
y\_train = train\_data.close\[window:\].values  
y\_test = test\_data.close\[window:\].values  
if zero\_base:  
    y\_train = y\_train / train\_data.close\[:-window\].values - 1  
    y\_test = y\_test / test\_data.close\[:-window\].values - 1

return train\_data, test\_data, X\_train, X\_test, y\_train, y\_test train, test, X_train, X_test, y_train, y_test = prepare_data(hist) I used a simple neural network with a single LSTM layer consisting of neurons, a dropout factor of , and a Dense layer with a single activation function. In addition, I used as loss function and the optimiser. 20 0.25 linear Mean Absolute Error (MAE) Adam I trained the network for epochs with a batch size of . 50 4 Note: The choice of the network architecture and all parameters is arbitrary and I didn’t optimise for any them, as this is not the focus of this article. def build_lstm_model(input_data, output_size, neurons=20,activ_func='linear', dropout=0.25,loss='mae', optimizer='adam'):model = Sequential() model.add(LSTM(neurons, input\_shape=(  
          input\_data.shape\[1\], input\_data.shape\[2\])))  
model.add(Dropout(dropout))  
model.add(Dense(units=output\_size))  
model.add(Activation(activ\_func))

model.compile(loss=loss, optimizer=optimizer)  
return model model = build_lstm_model(X_train, output_size=1)history = model.fit(X_train, y_train, epochs=50, batch_size=4) 4. Results Using the trained model to predict on the left-out test set, we obtain the graph shown in the beginning of this article. So what exactly is wrong with these results? Why shouldn’t we use this model for actual trading? Let’s take a closer look and zoom into the last days of the plot. 30 targets = test[target_col][window:]preds = model.predict(X_test).squeeze()# convert change predictions back to actual pricepreds = test.close.values[:-window] * (preds + 1)preds = pd.Series(index=targets.index, data=preds) n = 30 line_plot(targets[-n:], preds[-n:], 'actual', 'prediction') See that? You might have already correctly guessed that the fundamental flaw with this model is that for the prediction of a particular day, it is mostly using the value of the previous day. The prediction line doesn’t seem to be much more than a shifted version of the actual price. In fact, if we adjust the predictions and shift them by a day, this observation becomes even more obvious. line_plot(targets[-n:][:-1], preds[-n:].shift(-1)) As you can see, we suddenly observe an almost perfect match between actual data and predictions, indicating that the model is essentially the price at the previous day. learning These results are exactly what I’ve been seeing in many of the examples using single-point predictions with LSTMs. To make this point clearer, let’s compute the expected as predicted by the model and compare those with the actual returns. returns actual_returns = targets.pct_change()[1:]predicted_returns = preds.pct_change()[1:] Looking at the actual and predicted returns, both in their original form as well as with the applied to them, we obtain the same observation. 1-day-shift Actual and predicted returns. In the left plot predictions are adjusted by a day. Actually, if we compute the correlation between actual and predicted returns both for the original predictions as well as for those adjusted by a day, we can make the following observation: fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9)) # actual correlationcorr = np.corrcoef(actual_returns, predicted_returns)[0][1]ax1.scatter(actual_returns, predicted_returns, color='k')ax1.set_title('r = {:.2f}'.format(corr), fontsize=18) # shifted correlationshifted_actual = actual_returns[:-1]shifted_predicted = predicted_returns.shift(-1).dropna()corr = np.corrcoef(shifted_actual, shifted_predicted)[0][1]ax2.scatter(shifted_actual, shifted_predicted, color='k')ax2.set_title('r = {:.2f}'.format(corr)); As you can see from the plots above, actual and predicted returns are uncorrelated. Only after applying the on the predictions we obtain highly correlated returns that resemble the returns of the actual bitcoin data. 1-day-shift Summary The goal of the this blogpost was to address the many examples of predictions of cryptocurrency and stock market prices using deep neural networks that I have encountered in the past couple of months — these take a similar approach as the one employed here: Implementing an LSTM using historic price data to predict outcomes. I have demonstrated why these models might not be necessarily viable for actual trading. future Yes, the network is effectively able to learn. But it ends up using a strategy in which predicting a value close to the previous one turns out to be successful in terms of minimising the mean absolute error. However, no matter how accurate the predictions are in terms of the loss error — in practice, the results of single-point prediction models based on , as the one showcased here, remain hard to accomplish and are not particularly useful for trading. historic price data alone Needless to say that more sophisticated approaches of implementing useful LSTMs for price predictions potentially do exist. Using more data, as well as optimising network architecture and hyperparameters are a start. In my opinion, however, there is more potential in incorporating data and features that go beyond historic prices alone. After all, the finance world has already known for long that “ ”. past performance is not an indicator for future outcomes And the same might also hold for cryptocurrencies. Disclaimer: This is not financial advice. The article and the presented model are for educational purposes only. Do not use it for trading or making investment decisions.

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