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The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 10 of 18. DEFENDANTS’ ACTUAL AND CONSTRUCTIVE KNOWLEDGE OF THEIR VIOLATIONS 105. The OpenAI Defendants have acknowledged that use of copyright-protected works to train ChatGPT requires a license to that content. and, in some instances. Recognizing that obligation, the OpenAI Defendants have entered into agreements with large copyright owners such as Associated Press, the Atlantic, Axel Springer, Dotdash Meredith, Financial Times, News Corp, and Vox Media to obtain licenses to include those entities’ copyright-protected works in Defendants’ LLM training data. 106. The OpenAI Defendants are also in licensing talks with other copyright owners in the news industry, but have offered no compensation to Plaintiff. 107. In a May 29, 2024 interview, OpenAI’s Chief of Intellectual Property and Content, Tom Rubin, stated that these deals focus on “the display of news content and use of the tools and tech,” and are thus “largely not” about training.[15] This admission, while qualified, confirms that these deals involve training, at least in part. 108. The OpenAI Defendants created tools in late 2023 to allow copyright owners to block their work from being incorporated into training sets. This further corroborates that the OpenAI Defendants had reason to know that use of copyrighted material in their training sets is copyright infringement. 109. The creation of such tools also corroborates that the OpenAI Defendants had reason to know that their copyright infringement is enabled, facilitated, and concealed by their removal of author, title, copyright, and terms of use information from their training sets. 110. Defendants had reason to know that the removal of author, title, copyright notice, and terms of use information from copyright-protected works and their use in training ChatGPT would result in ChatGPT providing responses to ChatGPT users that abridged or regurgitated material verbatim from copyrighted works in creating responses to users, without revealing that those works were subject to Plaintiff’s copyrights. This is at least because Defendants were aware that ChatGPT responses are the product of its training sets and that ChatGPT generally would not know any author, title, copyright notice, and terms of use information that was not included in training sets. 111. Upon information and belief, Defendants had reason to know that the removal of author, copyright notice, and terms of use information from copyright-protected works used in synthetic searching would result in ChatGPT or Copilot providing responses to ChatGPT users that abridged or regurgitated material verbatim from copyrighted works in creating responses to users, without revealing that those works were subject to Plaintiff’s copyrights. This is at least because Defendants were aware that Copilot’s and later versions of ChatGPT’s responses to prompts are the product of the articles encoded in their computer memory, from which, upon information and belief, Defendants removed author, copyright notice, and terms of use information. 112. Defendants had reason to know that users of ChatGPT would further distribute the results of ChatGPT responses. This is at least because Defendants promote ChatGPT as a tool that can be used by a user to generate content for a further audience. 113. Defendants had reason to know that users of ChatGPT would be less likely to distribute ChatGPT responses if they were made aware of the author, title, copyright notice, and terms of use information applicable to the material used to generate those responses. This is at least because Defendants were aware that at least some likely users of ChatGPT respect the copyrights of others or fear liability for copyright infringement. 114. Defendants had reason to know that ChatGPT would be less popular and would generate less revenue if users believed that ChatGPT responses violated third-party copyrights or if users were otherwise concerned about further distributing ChatGPT responses. This is at least because Defendants were aware that Defendants derive revenue from user subscriptions, that at least some likely users of ChatGPT respect the copyrights of others or fear liability for copyright infringement, and that such users would not pay to use a product that might result in copyright liability or did not respect the copyrights of others. 115. If a commercial user of Defendants’ ChatGPT and Copilot products is sued for copyright infringement, Defendants have committed to paying the user’s costs in defending against the infringement claim, and to indemnifying the user for an adverse judgment or settlement. These commitments apply only if the user uses the product as advertised. In particular, Microsoft’s “Copilot Copyright Commitment” applies only if the user “used the guardrails and content filters we have built into our products,”[16] and OpenAI’s “Copyright Shield” does not apply if the user “disabled, ignored, or did not use any relevant citation, filtering or safety features or restrictions provided by OpenAI.”[17] Thus, Defendants know or have reason to know that ChatGPT and Copilot users are capable of infringing and likely to infringe copyright even when used according to terms specified by Defendants. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. [15] Charlotte Tobitt, OpenAI content boss: ‘Incumbent’ on us to help small publishers, not just the giants, PressGazette (May 30, 2024), https://pressgazette.co.uk/platforms/openai-tom-rubinpublishers-news/. [16] https://www.microsoft.com/en-us/licensing/news/microsoft-copilot-copyright-commitment. [17] https://openai.com/policies/service-terms/. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 10 of 18. The Center for Investigative Reporting Inc. v. OpenAI Court Filing, retrieved on June 27, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 10 of 18. HackerNoon’s Legal PDF Series here DEFENDANTS’ ACTUAL AND CONSTRUCTIVE KNOWLEDGE OF THEIR VIOLATIONS 105. The OpenAI Defendants have acknowledged that use of copyright-protected works to train ChatGPT requires a license to that content. and, in some instances. Recognizing that obligation, the OpenAI Defendants have entered into agreements with large copyright owners such as Associated Press, the Atlantic, Axel Springer, Dotdash Meredith, Financial Times, News Corp, and Vox Media to obtain licenses to include those entities’ copyright-protected works in Defendants’ LLM training data. 106. The OpenAI Defendants are also in licensing talks with other copyright owners in the news industry, but have offered no compensation to Plaintiff. 107. In a May 29, 2024 interview, OpenAI’s Chief of Intellectual Property and Content, Tom Rubin, stated that these deals focus on “the display of news content and use of the tools and tech,” and are thus “largely not” about training.[15] This admission, while qualified, confirms that these deals involve training, at least in part. 108. The OpenAI Defendants created tools in late 2023 to allow copyright owners to block their work from being incorporated into training sets. This further corroborates that the OpenAI Defendants had reason to know that use of copyrighted material in their training sets is copyright infringement. 109. The creation of such tools also corroborates that the OpenAI Defendants had reason to know that their copyright infringement is enabled, facilitated, and concealed by their removal of author, title, copyright, and terms of use information from their training sets. 110. Defendants had reason to know that the removal of author, title, copyright notice, and terms of use information from copyright-protected works and their use in training ChatGPT would result in ChatGPT providing responses to ChatGPT users that abridged or regurgitated material verbatim from copyrighted works in creating responses to users, without revealing that those works were subject to Plaintiff’s copyrights. This is at least because Defendants were aware that ChatGPT responses are the product of its training sets and that ChatGPT generally would not know any author, title, copyright notice, and terms of use information that was not included in training sets. 111. Upon information and belief, Defendants had reason to know that the removal of author, copyright notice, and terms of use information from copyright-protected works used in synthetic searching would result in ChatGPT or Copilot providing responses to ChatGPT users that abridged or regurgitated material verbatim from copyrighted works in creating responses to users, without revealing that those works were subject to Plaintiff’s copyrights. This is at least because Defendants were aware that Copilot’s and later versions of ChatGPT’s responses to prompts are the product of the articles encoded in their computer memory, from which, upon information and belief, Defendants removed author, copyright notice, and terms of use information. 112. Defendants had reason to know that users of ChatGPT would further distribute the results of ChatGPT responses. This is at least because Defendants promote ChatGPT as a tool that can be used by a user to generate content for a further audience. 113. Defendants had reason to know that users of ChatGPT would be less likely to distribute ChatGPT responses if they were made aware of the author, title, copyright notice, and terms of use information applicable to the material used to generate those responses. This is at least because Defendants were aware that at least some likely users of ChatGPT respect the copyrights of others or fear liability for copyright infringement. 114. Defendants had reason to know that ChatGPT would be less popular and would generate less revenue if users believed that ChatGPT responses violated third-party copyrights or if users were otherwise concerned about further distributing ChatGPT responses. This is at least because Defendants were aware that Defendants derive revenue from user subscriptions, that at least some likely users of ChatGPT respect the copyrights of others or fear liability for copyright infringement, and that such users would not pay to use a product that might result in copyright liability or did not respect the copyrights of others. 115. If a commercial user of Defendants’ ChatGPT and Copilot products is sued for copyright infringement, Defendants have committed to paying the user’s costs in defending against the infringement claim, and to indemnifying the user for an adverse judgment or settlement. These commitments apply only if the user uses the product as advertised. In particular, Microsoft’s “Copilot Copyright Commitment” applies only if the user “used the guardrails and content filters we have built into our products,”[16] and OpenAI’s “Copyright Shield” does not apply if the user “disabled, ignored, or did not use any relevant citation, filtering or safety features or restrictions provided by OpenAI.”[17] Thus, Defendants know or have reason to know that ChatGPT and Copilot users are capable of infringing and likely to infringe copyright even when used according to terms specified by Defendants. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 27, 2024, motherjones.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. motherjones.com [15] Charlotte Tobitt, OpenAI content boss: ‘Incumbent’ on us to help small publishers, not just the giants, PressGazette (May 30, 2024), https://pressgazette.co.uk/platforms/openai-tom-rubinpublishers-news/. [16] https://www.microsoft.com/en-us/licensing/news/microsoft-copilot-copyright-commitment. [17] https://openai.com/policies/service-terms/.

Microsoft and OpenAI Accused of Copyright Theft

What can thirty-seven million dollars do for you? Or rather, how will you spend thirty-seven million dollars on a marketing spree? Last week, Crypto Twitter was buzzing after Polkadot released its 2024 first-half Treasury Report, highlighting how funds were spent during the period and detailing the protocol's assets and liabilities. An estimate of 250,000 dollars is spent monthly on salaries, but what caught everyone's attention was the $37 million spent on attracting new users to Polkadot, which grew from over 300% when compared side by side with the marketing budget for H1 of 2023. While this may seem very significant, it might be justified if the cost of acquiring users is favorable relative to the amount spent. Lessons Must Be Learned How Polkadot spent $37m for Outreach You can't be the only one wondering where the $37M for Polkadot’s outreach went to. Here are some receipts 👇🏽 Taxi and Uber ad in Berlin. Sponsored the Indy500, an annual automobile race, with Conor Daly to raise awareness about blockchain. The Polkadot logo was placed on a racing car. 10k monthly salary for ambassadors. Influencer marketing consumed almost $5 million. $6.8 million on a sports sponsorship deal with Inter Miami CF a soccer club. Private jet fleet branding for 6 months at $180k. Sponsored community building by Chainwire for $100k. More ‘community projects’ with top crypto parasites for $208k. The list is endless, find the bulk of receipts here. Is Polkadot Doing Badly? As per Dotlake’s report on X: Polkadot witnessed a 33% increase in monthly signed transactions in June, last month. Number of unique accounts remained stable at 7 million. Through a gaming development approach by Mythos, 800,000 new accounts came in. 853 million DOT tokens were staked. There has been a 66% month-over-month increase in the number of governance proposals, indicating a significant rise in activity or interest in governance matters. The current total number of proposals, including ongoing ones, is 151. Out of the total proposals, 43% have been approved. 17% of the proposals have been rejected. The total value of assets in Polkadot’s treasury totals 167 million dollars which is estimated to keep the organization running for the next two years. Maybe, Just Maybe, Polkadot Still Won 🏆 The Way Forward It shouldn't sound new to you that millions of dollars are squandered in the crypto space for “marketing aesthetics.” So, Polkadot is neither going to be the first nor the last. 📌 If the budget for outreach is substantial, it could be a red flag. Why spend so much to acquire users and pay influencers to promote your project if the product has a real use case? The CEO of Celestia Labs is one of the few who also thinks it's cringe-worthy to hire influencers for shilling. The importance of doing your research by examining the use cases of Web3 projects before investing, rather than relying on hype, can not be overemphasized. 📌 For a business-to-developer model like Polkadot, the focus should be on building products that developers find user-friendly, rather than targeting KOLs with non-technical audiences or spending on physical ads where the target audience may not be found. Hosting hackathons is one effective way to target and engage developers. 📌 Marketing entails not just getting new users but retaining them. A closer look at Polkadot suggests some users lost trust in the project after getting hacked. When trust is lost, it is difficult to get it back, and splashing money around will be like pouring water on the rock. 📌 Polkadot's claim of being a fully decentralized autonomous organization (DAO) is marred by corrupt practices in pushing proposals, which contradicts a decentralized process. Notably, a screenshot attached above shows an X user being asked to become an ambassador with the assurance, 'We can work behind the scenes to make sure you get elected.' 📌 There’s no one-size-fits-all for stunt marketing. Printing your logo on the largest billboard is risky, and you must weigh the cost against the potential for generating new users. Experiments are okay in marketing, but don’t be frivolous with spending. Ensure you're taking big shots where your audience can be found. What can thirty-seven million dollars do for you? Or rather, how will you spend thirty-seven million dollars on a marketing spree? Last week, Crypto Twitter was buzzing after Polkadot released its 2024 first-half Treasury Report, highlighting how funds were spent during the period and detailing the protocol's assets and liabilities. An estimate of 250,000 dollars is spent monthly on salaries, but what caught everyone's attention was the $37 million spent on attracting new users to Polkadot, which grew from over 300% when compared side by side with the marketing budget for H1 of 2023. While this may seem very significant, it might be justified if the cost of acquiring users is favorable relative to the amount spent. favorable Lessons Must Be Learned Lessons Must Be Learned How Polkadot spent $37m for Outreach You can't be the only one wondering where the $37M for Polkadot’s outreach went to. Here are some receipts 👇🏽 Taxi and Uber ad in Berlin. Taxi and Uber ad in Berlin. Sponsored the Indy500, an annual automobile race, with Conor Daly to raise awareness about blockchain. The Polkadot logo was placed on a racing car. Sponsored the Indy500 , an annual automobile race, with Conor Daly to raise awareness about blockchain. The Polkadot logo was placed on a racing car. Indy500 Conor Daly 10k monthly salary for ambassadors. 10k monthly salary for ambassadors. Influencer marketing consumed almost $5 million. Influencer marketing consumed almost $5 million. $6.8 million on a sports sponsorship deal with Inter Miami CF a soccer club. $6.8 million on a sports sponsorship deal with Inter Miami CF a soccer club. Private jet fleet branding for 6 months at $180k. Private jet fleet branding for 6 months at $180k. Sponsored community building by Chainwire for $100k. Sponsored community building by Chainwire for $100k. More ‘community projects’ with top crypto parasites for $208k. More ‘community projects’ with top crypto parasites for $208k. The list is endless, find the bulk of receipts here . here here Is Polkadot Doing Badly? As per Dotlake’s report on X: Dotlake’s Polkadot witnessed a 33% increase in monthly signed transactions in June, last month. Polkadot witnessed a 33% increase in monthly signed transactions in June, last month. Number of unique accounts remained stable at 7 million. Number of unique accounts remained stable at 7 million. Through a gaming development approach by Mythos, 800,000 new accounts came in. Through a gaming development approach by Mythos , 800,000 new accounts came in. Mythos 853 million DOT tokens were staked. 853 million DOT tokens were staked. There has been a 66% month-over-month increase in the number of governance proposals, indicating a significant rise in activity or interest in governance matters. There has been a 66% month-over-month increase in the number of governance proposals, indicating a significant rise in activity or interest in governance matters. The current total number of proposals, including ongoing ones, is 151. Out of the total proposals, 43% have been approved. 17% of the proposals have been rejected. The current total number of proposals, including ongoing ones, is 151. Out of the total proposals, 43% have been approved. 17% of the proposals have been rejected. The total value of assets in Polkadot’s treasury totals 167 million dollars which is estimated to keep the organization running for the next two years. The total value of assets in Polkadot’s treasury totals 167 million dollars which is estimated to keep the organization running for the next two years. Maybe, Just Maybe, Polkadot Still Won 🏆 The Way Forward It shouldn't sound new to you that millions of dollars are squandered in the crypto space for “marketing aesthetics.” So, Polkadot is neither going to be the first nor the last. 📌 If the budget for outreach is substantial, it could be a red flag. Why spend so much to acquire users and pay influencers to promote your project if the product has a real use case? 📌 If the budget for outreach is substantial, it could be a red flag. Why spend so much to acquire users and pay influencers to promote your project if the product has a real use case? The CEO of Celestia Labs is one of the few who also thinks it's cringe-worthy to hire influencers for shilling. The importance of doing your research by examining the use cases of Web3 projects before investing, rather than relying on hype, can not be overemphasized. 📌 For a business-to-developer model like Polkadot, the focus should be on building products that developers find user-friendly, rather than targeting KOLs with non-technical audiences or spending on physical ads where the target audience may not be found. Hosting hackathons is one effective way to target and engage developers. 📌 Marketing entails not just getting new users but retaining them. A closer look at Polkadot suggests some users lost trust in the project after getting hacked. When trust is lost, it is difficult to get it back, and splashing money around will be like pouring water on the rock. 📌 Polkadot's claim of being a fully decentralized autonomous organization (DAO) is marred by corrupt practices in pushing proposals, which contradicts a decentralized process. Notably, a screenshot attached above shows an X user being asked to become an ambassador with the assurance, 'We can work behind the scenes to make sure you get elected.' 'We can work behind the scenes to make sure you get elected.' 📌 There’s no one-size-fits-all for stunt marketing. Printing your logo on the largest billboard is risky, and you must weigh the cost against the potential for generating new users. Experiments are okay in marketing, but don’t be frivolous with spending. Ensure you're taking big shots where your audience can be found.

Polkadot’s $37M Marketing Saga: What Really Went Wrong (Receipts Attached)

Async/await in C# is a framework used for writing asynchronous C# code that is both readable and maintainable. These tips will help you to integrate async/await programming more effectively in the # projects: 1. ValueTask for Lightweight Operations Use ValueTask instead of Task for asynchronous methods that often complete synchronously, reducing the allocation overhead. public async ValueTask<int> GetResultAsync()
    {
        if (cachedResult != null)
            return cachedResult;
        int result = await ComputeResultAsync();
        cachedResult = result;
        return result;
    } 2. ConfigureAwait for Library Code Use ConfigureAwait(false) in the library code to avoid deadlocks by not capturing the synchronization context. public async Task SomeLibraryMethodAsync()
    {
        await SomeAsyncOperation().ConfigureAwait(false);
    } 3. Avoiding async void Prefer async Task over async void except for event handlers, as async void can lead to unhandled exceptions and is harder to test. public async Task EventHandlerAsync(object sender, EventArgs e)
    {
        await PerformOperationAsync();
    } 4. Using IAsyncDisposable For asynchronous cleanup, implement IAsyncDisposable and use await to ensure resources are released properly. public async ValueTask DisposeAsync()
    {
        await DisposeAsyncCore();
    }
    
    private async ValueTask DisposeAsyncCore()
    {
        if (resource != null)
        {
            await resource.DisposeAsync();
        }
    } 5. Efficiently Combine Tasks Use Task.WhenAll for running multiple tasks in parallel and waiting for all to complete, which is more efficient than awaiting each task sequentially public async Task ProcessTasksAsync()
    {
        Task task1 = DoTask1Async();
        Task task2 = DoTask2Async();
        await Task.WhenAll(task1, task2);
    } 6. Cancellation Support Support cancellation in asynchronous methods using CancellationToken. public async Task DoOperationAsync(CancellationToken cancellationToken)
    {
        await LongRunningOperationAsync(cancellationToken);
        cancellationToken.ThrowIfCancellationRequested();
    } 7. State Machine Optimization For performance-critical code, consider structuring your async methods to minimize the creation of state machines by separating synchronous and asynchronous paths. public async Task<int> FastPathAsync()
    {
        if (TryGetCachedResult(out int result))
        {
            return result;
        }
        return await ComputeResultAsync();
    } 8. Avoid Blocking Calls Avoid blocking on async code with .Result or .Wait(). Instead, use asynchronous waiting through the stack. public async Task WrapperMethodAsync()
    {
        int result = await GetResultAsync();
    } 9. Eliding Async/Await In simple passthrough scenarios or when returning a task directly, you can elide the async and await keywords for slightly improved performance. public Task<int> GetResultAsync() => ComputeResultAsync(); 10. Custom Task Schedulers For advanced scenarios, like limiting concurrency or capturing synchronization contexts, consider implementing a custom TaskScheduler. public sealed class LimitedConcurrencyLevelTaskScheduler : TaskScheduler
    {
        // Implement the scheduler logic here.
    } 11. Using Asynchronous Streams Leverage asynchronous streams with IAsyncEnumerable<T> for processing sequences of data asynchronously, introduced in C# 8.0. public async IAsyncEnumerable<int> GetNumbersAsync()
    {
        for (int i = 0; i < 10; i++)
        {
            await Task.Delay(100); // Simulate async work
            yield return i;
        }
    } 12. Avoid Async Lambdas in Hot Paths Async lambdas can introduce overhead. In performance-critical paths, consider refactoring them into separate async methods. // Before optimization
    Func<Task> asyncLambda = async () => await DoWorkAsync();
    
    // After optimization
    public async Task DoWorkMethodAsync()
    {
        await DoWorkAsync();
    } 13. Use SemaphoreSlim for Async Coordination SemaphoreSlim can be used for async coordination, such as limiting access to a resource in a thread-safe manner. private readonly SemaphoreSlim semaphore = new SemaphoreSlim(1, 1);
    
    public async Task UseResourceAsync()
    {
        await semaphore.WaitAsync();
        try
        {
            // Access the resource
        }
        finally
        {
            semaphore.Release();
        }
    } 14. Task.Yield for UI Responsiveness Use await Task.Yield() in UI applications to ensure the UI remains responsive by allowing other operations to process. public async Task LoadDataAsync()
    {
        await Task.Yield(); // Return control to the UI thread
        // Load data here
    } 15. Asynchronous Lazy Initialization Use Lazy<Task<T>> for asynchronous lazy initialization, ensuring the initialization logic runs only once and is thread-safe. private readonly Lazy<Task<MyObject>> lazyObject = new Lazy<Task<MyObject>>(async () =>
    {
        return await InitializeAsync();
    });
    
    public async Task<MyObject> GetObjectAsync() => await lazyObject.Value; 16. Combining async and LINQ Be cautious when combining async methods with LINQ queries; consider using asynchronous streams or explicitly unwrapping tasks when necessary. public async Task<IEnumerable<int>> ProcessDataAsync()
    {
        var data = await GetDataAsync(); // Assume this returns Task<List<int>>
        return data.Where(x => x > 10);
    } 17. Error Handling in Async Streams Handle errors gracefully in asynchronous streams by encapsulating the yielding loop in a try-catch block. public async IAsyncEnumerable<int> GetNumbersWithErrorsAsync()
    {
        try
        {
            for (int i = 0; i < 10; i++)
            {
                if (i == 5) throw new InvalidOperationException("Test error");
                yield return i;
            }
        }
        catch (Exception ex)
        {
            // Handle or log the error
        }
    } 18. Use Parallel.ForEachAsync for Asynchronous Parallel Loops Utilize Parallel.ForEachAsync in .NET 6 and later for running asynchronous operations in parallel, providing a more efficient way to handle CPU-bound and I/O-bound operations concurrently. await Parallel.ForEachAsync(data, async (item, cancellationToken) =>
    {
        await ProcessItemAsync(item, cancellationToken);
    }); 22. Avoid Excessive Async/Await in High-Performance Code In performance-critical sections, minimize the use of async/await. Instead, consider using Task.ContinueWith with caution or redesigning the workflow to reduce asynchronous calls. Task<int> task = ComputeAsync();
    task.ContinueWith(t => Process(t.Result)); 23. Async Main Method Utilize the async entry point for console applications introduced in C# 7.1 to simplify initialization code. public static async Task Main(string[] args)
    {
        await StartApplicationAsync();
    } 24. Optimize Async Loops For loops performing asynchronous operations, consider batching or parallelizing tasks to improve throughput. var tasks = new List<Task>();
    for (int i = 0; i < items.Length; i++)
    {
        tasks.Add(ProcessItemAsync(items[i]));
    }
    await Task.WhenAll(tasks); More Cheatsheets Cheat Sheets - .Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: | | | Visit our other platforms: More content at Youtube X LinkedIn Dev.to GitHub C# Programming Also published . here

Async/Await For Basic to Advanced C# Developers: 24 Essential Tips

Introduction Remember when Femtech was a brand-new term nobody knew? It feels like we have been using this word forever, but it has been around for less than a decade! Yet the history of products, technologies, and services addressing health issues specific or prevalent to women started long ago, ages before this umbrella term was first used in 2016 to address this rapidly evolving market. Since then, this industry has had major plot twists, from Maven Clinic becoming the first unicorn in 2021 to venture capital infusions shaking up the game; the sector is sizzling hot! Femtech And let us give a thumbs-up to the growing acceptance and issues. It is no longer a niche but a booming market, with its size estimated at over $55 billion in 2022 and . de-stigmatization of women’s health expected to surpass $100 billion by 2030 Why is it important to me? I am a digital tech specialist coming from the Artificial Intelligence/ Machine Learning field with a rich background in building and successfully deploying voice recognition, AI cameras, and virtual assistant products catering to millions. Lately, I have shifted my focus, stepping into the intriguing world of Femtech and healthcare drawn by the promise of AI to simplify and enhance healthcare solutions, changing stagnant approaches to women’s healthcare through daring initiatives. With this article providing a thoughtful inquiry into how advanced technologies like LLMs shape modern healthcare and Femtech, I invite you to join me on this exciting journey. Together, we will take a look at how boosts the Femtech market and sift through the new directions and strategies reshaping this burgeoning sector. Our aim will be not only to learn about the tendencies of the industry but also to identify key patterns that could offer thrilling opportunities. And so, if you are curious like me about how novel technologies improve healthcare experiences for women and what strategic footholds exist in the Femtech market, let us dive in and explore. AI/ML Current trends and thrilling developments in Femtech In the last few years, the Femtech sector has been undergoing a transformative shift. Gone are the days when Femtech products predominantly focused on menstrual cycle tracking and fertility planning. Today, the industry is blossoming with more specialized solutions, from advanced home-testing kits to cutting-edge wearables. Technological advances have attracted a range of strong players in a multitude of niche markets, each aiming to revolutionize different aspects of women’s health. 1. From Basic Tracking to Comprehensive Health Solutions Take Sonio, a Paris-based Femtech firm. They recently received FDA clearance for their AI product, Sonio Detect, which aims to redefine prenatal ultrasounds. What sets Sonio apart is its manufacturer-independent tool that simplifies the screening process while raising its efficiency and accuracy, offering an easy-to-use yet powerful solution. San Francisco startup Mira is pioneering another monumental shift in Femtech by embedding lab technology into a handheld fertility monitor. With a user base exceeding 76,000, Mira’s advanced fluorescent detector goes beyond traditional ovulation kits. The technology allows for daily health checks and has accumulated an impressive amount of data — over 8.5 million hormone data points and analysis of more than 670,000 menstrual cycles. With AI-driven insights into hormone fluctuations, Mira is offering more personalized and accurate predictions than ever before, quite literally putting a mini hormone lab in the palms of its users. Recently, The Inc. placed Mira at number 437 on its 2023 list of fastest-growing private companies in the US, ranking it the highest among Femtech companies. More complex products that require significant medical research are also quickly gaining momentum. Teal Health exemplifies this with its recent $8.8 million seed funding round, backed by prominent investors like Emerson Collective and Serena Ventures. The funds aim to build a comprehensive platform, starting with the crucial issue of cervical cancer. women’s telemedicine 2. The Rise of Wearable Innovations Wearable technology is another frontier where Femtech is making significant strides. One famous example is Bloomlife, a clinically validated diagnostic device for pregnant women that allows future mothers to track contractions, giving them a better awareness of their progress. Movano Health has captured attention with its Evie Ring — a wearable gadget currently under examination by the FDA. Upon receiving FDA clearance, the Evie Ring will have the potential to set new standards in women’s health monitoring, offering an integrated, round-the-clock health monitoring and tracking solution right at the user’s fingertips. Ava, a fertility and pregnancy tracking bracelet with an accuracy rate of over 90%, has already been cleared by the FDA. Established in 2014, well before we even started using the term ‘Femtech,’ Ava is a prime example of a successful Femtech startup. Having attracted over $42 million in funding, the company reported to have helped over 70 thousand women in 36 countries to get pregnant by 2020. 3. The Expansion to Different Age Groups One of the noteworthy trends in Femtech recently is the surge in startups focusing on menopause. According to Female Insights, over 30% of companies in this sector were founded in the last three years alone. This uptick is accompanied by increasing diversity in the types of solutions being offered to tackle menopausal symptoms — from mobile apps that track hormones and symptoms and platforms facilitating user interaction and community building to consumer goods and therapeutic medications. According to Pubmed, by 2030, up to 1.2 billion women will be in or approaching menopause globally, with 47 million new women joining every year. By 2025, it is estimated that over 1 billion people worldwide—about 12% of the population—will be going through menopause. Financially, the menopause product market was valued at $15 billion in 2020 and is projected to skyrocket to $23 billion by 2028. These figures illustrate a maturing market with significant financial potential, underscoring why it has become a magnet for innovation and investment. An Irish startup, IdentifyHer, offers menopausal women sensor solutions. Controlled through a smartphone app, the sensor passively gathers information on menopause symptoms without interfering with the wearer's daily activities. The app also makes use of the data gathered to track progress and assess the effectiveness of lifestyle changes. The collected data is presented in periodic reports and can be used by medical professionals to diagnose and treat menopausal symptoms. AI-driven Midday is another customized menopause product developed by US-based startup Lisa Health that offers comprehensive management of the menopause journey. Midday’s solution, including a wearable device, an AI-based symptom tracking system, and a decision support tool for hormone therapy, uses advanced tracking to determine the menopause stage and anticipate symptoms. The app is used by doctors to manage the entire menopause process and provide decision support, while users are provided with a variety of therapeutic options and informational resources to give them personalized insights and get them ready for every stage of menopause. 4. Going Beyond Just Health Although Femtech is considered, first of all, a rising star in the Medtech industry, the possibilities of women-oriented digital technology products are not limited to healthcare alone. Recently, the term started to see new applications, one of which is Women’s Safety. Indian startup Shakti Wearables manufactures Shakti, a women’s defensive wearable device that increases women's safety in dangerous situations. When the wearer activates the button on the device, a small, non-lethal current is released, electrifying an attacker, while the internal insulation of the bangle keeps the wearer safe; this gives women a chance to flee their attackers and call for assistance. Additionally, Shakti sends the wearer's most important three contacts a WhatsApp message with their most recent live location. Another interesting personal safety app is being developed by Australian startup Safely, offering women peer-to-peer support in tense or frightening circumstances. With their contacts, users of the app can create private groups or circles for internal communication and location sharing. In case of emergency, users can also switch on the high-alert mode and notify each of their designated guardians. The app then uses live monitoring, records live video and audio, sends GPS updates, and saves all of this data to the cloud for later use as evidence. Additionally, users receive safety alerts from locations of interest to help them plan their commute or travel safely. Expanding healthcare’s reach with new technologies The world of healthcare is rapidly evolving, especially when it comes to Femtech. This dynamic transformation is powered by modern innovative technologies that are reshaping how women engage with healthcare and how healthcare providers offer services tailored to them. 1. The Personalization Revolution Fueled by LLMs One of the most disruptive advancements in this domain is the rise of Large Language Models (LLMs) like ChatGPT and GPT-4. Far from being a mere tech fad, LLMs are revolutionizing healthcare, with Femtech embracing this new technology. LLMs have the potential to serve as virtual nursing assistants, supporting busy doctors in responding to patients' basic queries about medications or health conditions. As you will see from the examples below, LLMs are proving to be instrumental in facilitating the creation of highly promising Femtech products, from personalized consulting products to innovative community engagement tools. One of the most intriguing examples of the implementation of this technology in Femtech is Soula, a Cyprus-based startup that provides women with support during pregnancy, childbirth, and motherhood with its "AI-powered" assistant. Powered by Med-PaLM, an LLM developed by Google to generate answers to medical questions, the app is expected to be able to respond to inquiries about pregnancies and maternity issues and provide information on a range of health-related concerns. It will also be able to answer questions about diet, sleep, physical activity, and emotional self-care, as well as track fetal movement and kick frequency. Mexican startup Kuna utilizes an LLM-powered chatbot to give women a secure way to access information and advice on parenting and sexual health. The startup's , Kuna Chat, offers details on sex, reproduction, pregnancy, and parenthood. The startup also provides online consultations with experts for more complex or difficult questions. In this way, Kuna encourages sex education for the uneducated while offering trustworthy, practical, and concrete answers to users' queries. AI-powered chatbot 2. Cost-Effective Health Management An extraordinarily important aspect of building successful medical products is to make services affordable to the customer, reaching broader but less solvent user segments and geographies. In this vein, Femtech offers not only personalized but also cost-effective healthcare solutions. Apps often provide free basic versions, with premium services available through subscription fees. Quite obviously, the use of LLMs trained on medical data sets and specialized in specific product cases can provide a boost to the more affordable cost of the products themselves. For example, the Ava fertility bracelet mentioned in the previous section of this article costs around $249 in the U.S., and the accompanying app is free with the bracelet purchase. When considering that the average cost of infertility treatment in the U.S. is around $11,000—often not covered by basic healthcare plans—Femtech presents a more affordable starting point for many women. 3. Breaking Barriers in Remote and Rural Areas Digital technology in Femtech is also crucial for improving healthcare access in remote and rural areas, especially in low- or middle-income countries in Africa and Asia — two regions with the highest female populations. Femtech provides innovative solutions such as virtual prenatal visits and remote monitoring, to manage their health more cost-effectively, regardless of their location. empowering women One successful example of such a product is M♡THer – a virtual care product facilitating the diagnosis of gestational diabetes mellitus by measuring blood glucose levels, blood pressure, weight, and more. GDM affects up to 20% of pregnant women, leading to various birth complications, and M♡THer, developed by the Australia E-Health Research Centre, has already benefited thousands of women in remote areas of the region. 4. A Multifaceted Approach to Chronic Diseases Femtech is not solely about reproductive health; it also targets female-specific chronic diseases through a mix of prevention, societal change, and technology. Mobile apps and wearable devices make it easier to manage and monitor conditions over time, a vital component of tackling chronic diseases that affect women disproportionately. To name an example of a health issue specific to women, it is estimated that 1 in 10 women have endometriosis – the growth of endometrial tissue outside of the uterus that can cause significant problems from pelvic pain to infertility. DotLab, a US-based medical diagnostics company, pioneers medical tests in this area, applying machine learning mechanisms to diagnose and assess this currently incurable disease. On the other side of the Atlantic Ocean, Kheiron Medical Technologies, a UK Medtech startup, combines data science and radiology to help with the diagnosis of breast cancer, another terrible disease affecting predominantly women. According to published clinical trial results, the company’s breakthrough technology achieves previously unseen heights in malignancy detection in breast cancer screening. Applied Conclusions Recent years have shown rapid exponential growth in the number of Femtech companies and products, both through the use of innovative technologies and through the discovery of more and more new applications and solutions. With the Femtech market estimated to reach an imposing $100 billion globally by 2030, it is one of the areas of the modern technology economy that is expected to provide maximum lucrative opportunities for new bright ideas and prosperous businesses in the coming years. While deciding whether to follow this attractive route, aspiring Femtech entrepreneurs should consider the following market characteristics. While there are highly competitive niches in the Femtech market, specifically well-established fields like menstrual cycle and pregnancy tracking, there are also areas that are more attractive for entry. Examples of such areas include app-controlled wearables and menopause management. The largest share of the global women’s health market surprisingly pertains to chronic women’s health conditions (forecast to reach $218 billion by 2027), and only 1 percent of existing Femtech companies address this area. Did you hear that? That is an opportunity knocking. AI and specifically Large Language Models can be a powerful boost for Femtech products as (a) with proper customization, LLMs assist in scaling the product or service itself and strengthen the core part of the product, and (b) they provide magnificent tools for community development, and the community is said to be the most important part of a product! Organic communities built around products are both a way of monetization and a tool that increases retention, helping build durable community-led growth. Taking into account the fact that the Femtech market is rapidly developing with a simultaneous increase in competition, and given the general market conditions (in particular, subscription applications revenues are currently declining; even if positive dynamics have remained in the Medtech segment, it has become more difficult to attract and retain users), companies need to be cost-efficient and provide a highly personalized, secure service to survive and rise above the competitors. A relevant and extremely effective tool employed by more and more technology startups for achieving these goals, again, is AI technologies. Whether it's addressing questions about menstrual health, pregnancy, or chronic conditions, LLMs can fill the information gap quickly and efficiently. Statistically, only 6% of Femtech companies used AI in 2021, mostly in diagnostics and data analytics, and while this share has seen stable growth over the years, there is enough room for new inspired ideas and success stories in this area. All in all, the advent of heralds an exciting era for Femtech entrepreneurs. LLMs have the potential to revolutionize user engagement and content delivery, supporting busy healthcare professionals by handling preliminary patient communication, serving as virtual nursing assistants or health advisors, and simply bringing a personalized touch to technology. With cutting-edge AI technologies charging Femtech products, humanity has the chance to drastically improve the efficiency of the entire healthcare system catering to women. Finally, there is a chance to overcome the millennia-old barriers plaguing this area of medicine, such as high costs, gender bias, stigmatization of women's health-related topics, and a lack of resources. Large Language Models Powered by the latest technological advancements, Femtech waits for its next hero — and it may be you!

Beyond Wellness: How Femtech is Shaping the Future of Women's Healthcare

সুস্থতার বাইরে: কীভাবে ফেমটেক মহিলাদের স্বাস্থ্যসেবার ভবিষ্যত গঠন করছে

Beyond Wellness: Wie Femtech die Zukunft der Frauengesundheit gestaltet

Más allá del bienestar: cómo Femtech está dando forma al futuro de la atención médica de las mujeres

Au-delà du bien-être : comment Femtech façonne l'avenir des soins de santé des femmes

कल्याण से परे: कैसे फेमटेक महिलाओं के स्वास्थ्य देखभाल के भविष्य को आकार दे रहा है

ウェルネスを超えて: フェムテックが女性のヘルスケアの未来をどのように形作るか

웰니스를 넘어서: Femtech가 여성 건강 관리의 미래를 형성하는 방법

Além do bem-estar: como a Femtech está moldando o futuro da saúde feminina

За пределами здоровья: как Femtech формирует будущее женского здравоохранения

Wellness'ın Ötesinde: Femtech Kadın Sağlığı Hizmetlerinin Geleceğini Nasıl Şekillendiriyor?

Ngoài sức khỏe: Femtech đang định hình tương lai của ngành chăm sóc sức khỏe phụ nữ như thế nào

超越健康：Femtech 如何塑造女性医疗保健的未来

's Goroutines make it easy to run code concurrently. We can simply add the keyword “go” in front of a function call to make it run in a separate routine, or asynchronously. Golang However, Go routines on their own don't offer much benefit unless we use primitives to help us either share data between routines or make routines coordinate with each other. In this blog post, we will look at a few handy primitives provided by the sync package. Mutex Sometimes two threads might attempt to access the same data simultaneously. The most representative example of this is when two routines are manipulating a variable holding your bank balance. The two routines will perform the following operations: Read the balance to a local variable; Add 100; Write the local variable to the shared variable. Now when this operation is performed by two routines interleaving the write after the read operation, it can produce a wrong result. A simple way to prevent this issue is using a . A Mutex, once it is locked, ensures that no other routine can lock the same Mutex until it is unlocked. Mutex is one of the most basic primitives and is used in the implementation of most other synchronization primitives that will be listed below. Mutex Note: in Golang it is common to use the defer keyword with mutex.Unlock(). You will see the following code at the beginning of a function: defer mu.Unlock() mu.Lock() Adding this at the top of a function ensures that only one routine can access the function at a time. Calling unlock with defer is also convenient since it ensures that the mutex is unlocked in the event of an error or unexpected return from the function. Channels Channels are by far the most practical synchronization primitive offered by the sync package. We can use channels to handle most of the synchronizing we need. If you encounter a synchronization problem that is difficult to solve, it can likely be restructured to make use of channels. So let's first look at how a channel works and the different ways it can be used. Mental Model Channels allow us to enable communication between two goroutines. One way to think about a channel is as a queue data structure that supports only the enqueue and dequeue methods. If we were to implement a queue data structure we would have to handle the below cases: What happens when we call dequeue on an empty queue; What happens when we call enqueue on a queue that is full. In a typical queue data structure, dequeue can be handled by returning nil or an error while enqueue can be handled by returning an error or more elaborately by resizing the queue. Channels handle these scenarios by blocking the routine until the condition is no longer true. Specifically: Calling dequeue on an empty channel blocks the calling routine and switches to another routine (i.e. a context switch). Once the channel is no longer empty, Golang returns to the calling routine and the function returns an available value; Calling enqueue on a full channel blocks the calling routing and switches to another routine (i.e a context switch). Once the channel is not full, Golang returns to the calling routine and returns from successfully enqueuing data to the channel. So, enqueuing on a full channel and dequeuing on an empty channel are necessary conditions because they determine when a routine will block (or when a context switch will occur). This makes the second parameter, size, to the make function quite important. make(chan int, 10) creates a channel of size 10 make(chan int) creates a channel with a default size of zero For a channel of size zero, these two conditions are true for each enqueue and dequeue operation, i.e. the queue is simultaneously full and empty. In other words, any attempt to read or write from a channel of size 0 will cause the routine to block immediately unless there is a reader or writer blocked awaiting such an operation on the other side of the channel. This can therefore be used as a signalling mechanism.Channels are a reference type and therefore can be passed between functions without the need for a pointer. Non-Blocking Writes In Go, we can use the select statement to perform non-blocking operations on channels. This can be useful when we don't want a routine to get blocked while attempting to read from or write to a channel. To do a non-blocking write, we can use a select statement with a default case. This way if the channel is full the routine will not get blocked. select { }
} // define a channel of size 2, which can buffer a maximum two numbers numbers := make(chan int, 2 ) data := []int{ 1 , 2 , 3 , 4 } for x := range data { case numbers <- x: fmt.Println( "wrote value" , x) default : fmt.Println( "didn't write since channel is full" ) /* Output: wrote value 0 wrote value 1 didn't write since channel is full didn't write since channel is full */ Non-Blocking Reads Sometimes it's important for our routine to attempt to read a value if one exists or proceed to do something else. That is a non-blocking enqueue function. This can also be achieved via the select function: ​ select { channel := make(chan int) // Attempt a non-blocking read from our channel case val := <-channel: fmt.Println("value in chan is ", val) default: fmt.Println("No value in channel") } ​ // Output: // No value in channel As you can see the calling function will not block and will hit the default case in the select statement. We may spawn N workers and check if any one of them passed back an error, which may or may not exist. Reading from Multiple Channels The Select statement can also be used to read from multiple channels. We can use a select statement around two channels to block until either one produces a value. We can also add a default to the above code to make the select statement non-blocking, in which case we will attempt to get a value from channel A; if it’s unavailable, we then attempt to get a value from channel B, and if unavailable, hit the default case. ​ select { fmt.Println("value from ChannelA", valA)
   case valB := <-channelB:
      fmt.Println("value from ChannelB", valB)
   }
}()
​
channelA <- 1
time.Sleep(1) channelA := make(chan int) channelB := make(chan int) // Attempt a non-blocking read from our channel go func ( ) { case valA := <-channelA: Channel Closing Closing a channel is a great way to indicate that we are done sending data as well as the workers listening on these channels, and stop listening to them. Below we will look at an example of reading from channels manually and by using a range loop. if !ok {
          break
      }
      fmt.Println("1: read value from channel val", val)
   }
   fmt.Println("1: channel is closed")
}
​
func forRangeConsumer(channel chan int) {
   for val := range channel {
      fmt.Println("2: read value from channel val", val)
   }
   fmt.Println("2: channel is closed")
}
​
func main() {
   numbers := make(chan int)
​
   // Spawn two consumers
   go manualConsumer(numbers)
   go forRangeConsumer(numbers)
​
   // write all data into channel
   data := int{1, 2, 3, 4, 5}
   for x := range data {
      numbers <- x
   }
​
   // close channel using go provided 'close' function 
   // to indicate the end of data to consumers.
   close(numbers)
​
   // Give some time for consumer routines to exit
   // we can do this more beautifully with use of waitGroups 
   // but for simplicity sake use sleep
   time.Sleep(1)
} func manualConsumer ( channel chan int ) { for { val, ok := <-channel A few points to note here: Once a channel is closed it can't be closed again. Otherwise, this will cause a panic. When reading from a closed channel, all data pushed into the channel is first returned. Once all data is read, subsequent reads will return the default value of channel type as the first parameter and false as the second parameter. Data can’t be pushed into a closed channel. Wait Groups Wait Groups are a very simple synchronization primitive. Their primary use case is to wait for all threads to complete before proceeding to the next step in the program. Mental Model A wait group is nothing more than a class with an integer. The class provides three functions: Add, Done, and Wait. These functions manipulate the integer internally in a thread-safe manner (you can think of this as an atomic int or an integer with a lock). Let me expand a bit on the 3 main functions: Add(delta int) adds  the "delta"  value to the atomic integer; Done() decrements the atomic integer value by one; Wait() blocks the caller until the atomic integer value is zero. The Done function cannot be called more times than the delta value. In other words, the internal integer value cannot be zero. When passing a to a function, pass it by reference to avoid making a copy of the WaitGroup (i.e is not a reference type). WaitGroup WaitGroup is commonly used in situations where the main thread should wait for workers to complete. WaitGroup wg.Done()
}
​ go worker(&wg)
   }
​ wg.Wait()
} func worker ( wg *sync.WaitGroup ) { fmt.Println( "working ..." ) func main ( ) { wg := sync.WaitGroup{} for i := 0 ; i < 5 ; i++ { wg.Add( 1 ) fmt.Println( "I am doing some work" ) Condition Variables Condition variables allow multiple routines to coordinate with each other. A condition variable has the following functions: : This is a helper function or constructor used to create a new condition variable; NewCond(mu Locker) blocks a routine until a different routine calls Broadcast or Signal. This function also ensures that the Locker is released upon blocking and reacquired before returning from the function; Wait() notifies all blocked routines to start; Broadcast() notifies a single blocked routine to start; Signal() The that is passed when constructing the object is public and accessible via the field. Locker L Note that we can mimic the signal function above with the use of channels. i.e. by executing a non-blocking write to a channel with a buffer size of 0. If a routine is blocked on reading from the channel, it will get unblocked. However, it's difficult to implement the broadcast operation with the use of channels. You might feel like it's possible to implement a broadcast by calling close on a channel, which in turn would unblock all routines that are blocked on attempting to read the channel. This is true, but the channel cannot be reused for a second broadcast which a condition variable can do. To better understand the usefulness of condition variables, let's look at an example where a group of workers are working on two tasks, A and B. Task B is dependent on Task A, meaning all workers must finish working on Task A before they can start working on Task B. This is essentially the implementation of a barrier. By using a condition variable, we can easily implement this synchronization logic and ensure that all workers complete Task A before moving on to Task B. }
​ cond.L.Lock()
   defer cond.L.Unlock() *sharedCounter--
​ cond.Broadcast() } cond.Wait()
}
​ ​ ​ notifyAllOrWaitForOthers(workerID, sharedCounter, cond)
​ ​
   wg.Done()
}
​ mu            = &sync.Mutex{}
      cond          = sync.NewCond(mu)
      wg            = &sync.WaitGroup{} sharedCounter = &numWorkers
   )
​
   wg.Add(numWorkers) go worker(i, &activelyWorking, cond, wg)
   }
   wg.Wait()
} func doTask ( workerID int, taskName string ) { fmt.Printf( "worker %d is doing task %s\n" , workerID, taskName) taskDuration := time.Duration(rand.Intn( 5 )) time.Sleep(taskDuration) // represents work workerID doing taskName func notifyOrWaitForOthers ( workerID int, sharedCounter *int, cond *sync.Cond ) { // Worker has finished his job so decrements the shared counter // You are the last to complete the task, best notify the rest of the workers. if *sharedCounter == 0 { fmt.Printf( "worker %d is the last to complete work\n" , workerID) fmt.Printf( "last worker signals all workers that they can now proceed with next task\n" ) return // You finished early, let's wait till the rest are done. /* Wait is a special function. Three things will happen upon calling it 1. cond.L.Unlock will be called 2. scheduler will switch to a different routine and will run it 3. Before the wait call returns, cond.L.Lock will be called. */ func worker ( workerID int, sharedCounter *int, cond *sync.Cond, wg *sync.WaitGroup ) { // All workers start on task A doTask(workerID, "A" ) // Once task A is done, you either wait for others or if your the last // to complete the task notify the rest they can start working on task B // All workers start on task B doTask(workerID, "B" ) func main ( ) { var ( numWorkers    = 3 for i := 0 ; i < numWorkers; i++ { You might feel like it is possible to implement the broadcast by calling close on a channel since this would unblock all routines blocked on attempting to read the channel. Although it is possible, the channel cannot be reused for a second broadcast, unlike a condition variable that can be used repeatedly. The example shown above demonstrates how condition variables can be used to coordinate multiple routines working on two tasks A and B. However, this concept can be extended to handle more complex scenarios where the result of the work done in the previous task is important to complete the task that follows. Sources The Go Standard Library Documentation: https://pkg.go.dev/sync An Introduction to Programming in Go, Chapter 10, Concurrency: https://www.golang-book.com/books/intro/10 Luke Champine, The Pros of Cond’s: https://lukechampine.com/cond.html The featured image was generated with Kadinsky 2 Prompt: Illustrate a screen displaying lines of code.

Mastering Synchronization Primitives in Go 

“‘Better social media’ is the new category.” Okay, sounds like some far-fetched idea from a Silicon Valley CEO that is about to pour millions of dollars into the next , even though they are so removed from the average user experience they wouldn’t understand why an edit button is needed. Twitter Wait, that was a 16-year-old that hasn’t finished high school yet!? When I sat down for in October to talk to Spencer and Sophia Rascoff, a father and daughter duo that mixes an impressive resume with impressive ideas, I didn’t know what I was in for. Success Story I assumed it would be Spencer driving the conversation — after all, he is a co-founder of Zillow, Hotwire, and a handful of other successful startups. But it was Sophia, his incredibly articulate daughter that had me scribbling quotes and advice. They have recently launched which is what they call a “vertical social media network.” That’s something focused on a single space (in this case, food), instead of a broad reach, and it just might be the next evolution of how we interact online. Recon Food, Teenaged success story “I’ve had a few lemonade stands.” That’s Sophia again with a perfect one-liner to explain her experience at such a young age. She’s not your average 16-year-old. In middle school, she decided to start an entrepreneurship program based on the kinds that are in high schools and colleges. She is a recipient of the President’s Volunteer Service Award, a leader in the Latin American/Hispanic Student Organization, and has taught herself software development on the side. She’s not even a senior yet. It helps to have a dad with such a strong resume. Spencer went from Goldman Sachs to private equity, founding Hotwire, working for Expedia, launching Zillow, and on and on. He created (and taught) the Harvard Business School course , and now hosts his successful podcast on the dot.LA network (which he also founded). Managing Tech Ventures If you are thinking that Sophia might take over the world, you’re right. But after talking to her for a little while, there might not be any better hands to put it in. Don’t mix your cupcakes with climate change So what does it mean to evolve social media? The Rascoffs aren’t trying to create a new . They aren’t trying to outdo . While those platforms chase down broad engagement and encourage outrage, she just wants to share photos of her favorite food. Instagram Facebook It’s not that simple, but it kind of is, too. I’ll let Spencer explain: “We baked cupcakes this weekend, right? And posted them on Recon Food. But Instagram was pretty heavy on Sunday. When you were making those cupcakes, there were a lot of terrible things happening in the world. Recon Food is meant to be a respite from all of that, and bring social media back to when it wasn’t the noisy, scary place that it is today.” If that seems like pie-in-the-sky thinking (sorry for the pun), it’s not. It’s what teenagers, like Sophia and her friends, are looking for. When internal documents from Facebook were leaked, an internal research presentation “Teens blame Instagram for increases in the rate of anxiety and depression. This reaction was unprompted and consistent across all groups.” included a slide that stated The data continued to show how much the big platforms were harming young people (young girls especially), as they felt immense pressure to post regular content and fight for likes. Stressors, not depressors When Spencer explained that posts on Instagram to do with climate change, the Ukraine war, or gun violence in schools depresses him, Sophia jumped on to identify a key generational gap that is being overlooked. “I describe it as more of a stressor than a depressor. It makes me sad, I guess, but it makes me way more stressed than sad because it’s about the future. Something that weighs on me is this idea of having to fix these problems, or at least having my generation fix these problems.” It’s something that I hadn’t ever considered. How differently a 15 or 16-year-old kid would feel about something like climate change, knowing that — as Spencer tragically put it — “if their generation doesn’t fix it, we’re all doomed as a planet.” For so many users that are arguing on social media today, there isn’t the built-in pressure of having to fix the problem. It can just be a topic to yell about or regurgitate talking points within your echo chamber. Vertical-driven socials So what is the answer? In their minds, it’s to “reduce the noise” and to think of social media as a vertical, instead of horizontal, platform. Focus on a specific category or topic and don’t let it spread beyond that. In their case it is food, but they were quick to point out some other examples. LinkedIn has built a vertical platform based solely on career growth. Strava is one based on running and cycling. Alltrails, for hiking. This is why it can be called evolution, not necessarily an outlier. The next wave of platforms is already on its way, Sophia and Spencer are just leading the charge. If that’s the case, then there will be readers of this article that want to know: how do you do it? How do you build that next evolution and successfully roll it out? Well, they were kind enough to share some wisdom. Building community Everything always comes back to community. Every startup, every brand, and every marketplace always needs to create loyalty and investment from its users. For Recon Food, that means four key methods Iteration There is nothing worse than a stagnant service. If you are trying to build a community, you have to serve your existing users first. That means frequently iterating on your product or service, whether it’s adding new features, fixing bugs, or simply changing the design. The key is to show that you are constantly listening to feedback and trying to improve the user experience. By having their suggestions heard and implemented, you’ll create evangelists for your product — ones who will go out and spread the gospel to others. PR Listen, even in today’s age you still need a good PR game. That’s why I could book the Rascoffs for Success Story, and why you’ll probably see them in some other publications going forward. If you’re not constantly selling your story, then you’re not doing enough. Make sure everyone on your team is aware of your community efforts and can speak to them confidently. In-person events But even if you are the co-founder of a billion-dollar company, there’s still room for showing up at a trade show and shaking some hands. When it comes to community, you need to put a face to the name. That’s why in-person events are so important for startups. It could be as simple as attending a meetup or throwing an event. But whatever you do, make sure you show up and represent your company well. Convert existing groups There are already probably thousands upon thousands of people in the world that are using a group chat to share pictures of food. They’ve created their own small-scale social media platform and housed it in another service. If you can target those people, and show them that there is someone out there building something that specifically meets their needs, you can poach them from the existing group and get them invested in your own. Doing this will also give you access to their network, which could be full of other potential users for your product or service. Interestingly, Sophia also pointed out an unexpected factor that has inhibited Recon’s growth. People like anonymity on social media, at least when they are so used to the angry shouting matches on other platforms. It sometimes leads to users consciously not sharing it with their friends, as they would rather get feedback on their recipes and photos from someone they didn’t know. To get around this, you’ll have to build trust over the long haul and show them that it’s safe to bring people they know into the sphere — there won’t be any political arguments here. Topic-specific features “Instagram’s never going to build a run-tracking feature because that’s not what their app is for. But that is what Strava is for.” Next on the list after building community is creating features that won’t be found elsewhere. These are topic-specific things that don’t belong on other platforms but can add value to the vertical you are chasing. For Recon, it’s recipes, restaurant maps, and food collections. The possibilities here are endless because so are the topics. Cater to your audience, not the whole world. Optimize for enjoyment While the broad social media apps are just trying their best to keep you from moving on, the next era will be focused on giving you the best experience. Sophia, again, puts it perfectly: “It’s such a powerful tool to connect people across borders, across countries, across worlds. There’s such potential there, but the way that it’s currently being implemented and executed is not it. It’s not maximized for user success and enjoyment. It’s maximized for clicks, likes, views, and time spent on app.” Make them love to be there, not scared to leave. Audience first, monetization later In terms of building it into a business, Spencer is clear about one thing. Don’t worry about monetization until you’ve secured your audience. There will be ways to make money down the line, but if you’re building something with that in mind from the start, you’ll fall into the same habits that so many others have. Final thoughts That’s enough to fill an entire podcast but I touched on so much more with Spencer and Sophia. The challenges of balancing school and business, the importance of continued education, the benefits (and challenges) of working with a family member, and what characteristics make the best startup CEOs. If you want to hear the entire discussion, head over to the and marvel at one of the next generation of business leaders. Success Story YouTube channel That’s it for this week, talk soon! Also published here.

Optimizing for Positivity: Niches and the Future of Social Media

Microtransactions have been a big part of the gaming industry over the last few years. They can come in all shapes and sizes, from the most common cosmetic effects, such as skins for your characters, all sorts of accessories and housing if that’s something on the game, all the way to the often controversial pay-to-win - or pay-to-fast - transactions. It is a business model that not only offers revenue to sustain free-to-play game developers, but is also used on huge titles from AAA companies, and all combinations in between. The point of this text is not to discuss whether or not microtransactions should be a thing in gaming, if they should be used only in ways that do not interfere with gaming mechanics, which companies should use them, nor the particularities in legislation and the fact that some countries have banned certain types of microtransactions. All that is intended is to provide some context and certain ideas and stay open to the possibility and thoughts brought by those reading it, after all, a paradigm change rarely, if ever, happens with a single person's point of view. Valve and Epic: Battle Pass Perspectives on DOTA 2 and Fortnite It's almost impossible to discuss business models in gaming without talking about the great behemoth casting its dark shadow over us all. And then there's Valve on top of it. Jokes aside, Gaben Newell's company did something remarkable almost two decades ago when Steam was released. The way Steam was able to bring together developers and players, creating opportunities for new games to be discovered and almost extinguishing physical media for PC games was something else. For years Valve reigned alone, and indie developers or massive companies had to follow their will to sell their games, or else. Of course, some tried to create other solutions and substitutes for Steam, but it wasn't until 15 years later, in December 2018, that a real alternative appeared when Epic Games released its Epic Store. Let's not talk numbers right now, it's a fact that Steam is still way bigger and holds a huge amount of games titles more than Epic, but for the first time, some developers have an alternative, with Epic's better rates in sales and even more than that when studios used their proprietary gaming engine, Unreal. The fact that these two companies have a and , makes them, in my opinion, perfect for observing small decision changes that impact their community. That takes me to two of the crown jewels of these companies: Valve's DOTA 2 and Epic Games' Fortnite. Store Side Game Development Side It's important to observe that the two games have hugely different demographics, mechanics, and universes. For that reason, I will not talk about Epic's borderline magic abilities to create skins in partnership with any company and/or personality. The focus will be on the differences and similarities between the companies' Battle Passes, and just a bit on their general cosmetics store. Fortnite: Seasonal Battle Pass Fortnite's Battle Pass was reworked in Chapter 2: Season 7 (June, 2021-September, 2021), and since then, it follows this formula: 100 levels, divided into 10 pages, each with 10 rewards. After the 100 levels, there are 100 more levels where players can get Bonuses Rewards, usually alternate styles for already owned skins. Check below: Seasons on Fortnite usually last around 3 months, with some lasting slightly less or more. They are wrapped around the lore of Fortnite and have quests that earn players experience points (XP). There are all kinds of quests, some explore the lore of the game, others are "daily" or "weekly" quests that are just mechanical stuff players have to do in their matches. All levels need the same amount of XP, players can buy levels in order to get their rewards faster if they so want, but it's important to note that playing Fortnite and doing the quests daily for around 30min to 1 hour and a half, during the season, will surely get you all the rewards from the Battle Pass and likely a lot, if not all, of the Bonuses as well. Trust me, I'm not even good at the game. In April 2022, sportskeeda published an showing how much would be needed to get all the rewards of the Battle Pass in Fortnite. Since each level costs around 150 V-Bucks (Fortnite Currency), in order to get all 100 levels one would need to get 14,850 V-Bucks, since after purchasing the Battle Pass, for 950 V-Bucks, you already unlock level 1. Considering the rates of the currency, one would spend close to $100 to get all the rewards. If you add the 100 levels from the Bonuses Rewards, it goes up to around $200. article DOTA 2: The International Battle Pass Dota's Battle Pass, formerly known as The International Compendium, was released for the first time in 2013 as a way to reward players and members of the community and also increase the prize pool of The International (TI), basically Dota's World Cup and also the highest paying esports event in the world. This collaborative way of increasing the prize pool for TI was a huge success, with prizes going from $1,600,000 on the first edition to a whopping $40,018,195 last year alone. One can figure then that Battle Pass in DOTA was a huge deal. It was released once a year, with all sorts of quests, special game modes, cosmetics, skins, special effects, etc. While at some point most of the rewards were already completed, if players kept leveling up their Battle Pass, they would get some unique rewards on the highest of levels, such as even a real - TI's trophy. miniature of the Aegis of Champions The release of the Compendium was that big moment when players felt that not only they were participating in the game they loved so much, but they also were rewarded because of it. The first three Compendiums were not based on levels; instead, they were focused on goals for the International Prize Pool. Only the first one didn't reach all the goals, the other two did it with ease, and the last one went 3.5 million dollars more than the highest tier Valve created. From 2016 onward, the Compendium became the Battle Pass, focusing on levels that rewarded the players with all sorts of rare/unique skins that would, for the most part, only be available then. Some could be traded or gifted later, but would never be in the game store. It was an interesting change, that felt welcomed by the community for the most part. Battle Passes had more than 2000 levels, but the biggest rewards were around level 200~300, and going beyond that were special cosmetic effects that would vanish after a time period. At some point though, around 2019, Valve decided that Arcanas - DOTA's most sought skins that completely change the hero's model, voice, and skins - would be released on the Battle Pass as well, and if one couldn't get them at that point it would never be able to. As if that was not problematic enough, the huge bummer for DOTA's community was the fact that it was virtually impossible to get the Arcana on the Battle Pass without paying for it with real money. The number of levels one could get without it, only playing the game, would never be enough. Before that, the next hero to receive an Arcana was voted by the community on the Compendium and the prized skin would come later at the game store, for everyone to purchase at any time, for the standard price of $34.99. With this 'update' on their decision-making, Arcanas would be prized around level 330 of the Battle Pass, but not spending money players could get only around 111 levels. This number is not precise though, because there was a random reward that could get players from a quarter of a level up to 10 levels in one go, so if you're lucky enough... The values to get the Arcanas on the last two Battle Passes, which were in fact Seasonal, not contributing to The International prize pool or any other improvement to the pro scene, were quite similar to Fortnite's price to get all the rewards, , depending on if the player wants to get the prize right away or get the max number of levels for free before spending their money. around $75 to $150 Do game characters dream of virtual clothing? I hope readers are still with me after this not-so-small exposure. My point from here on out, is to bring some facts and some conjectures, that I hope are good enough ideas that can make the microtransaction market a bit fairer with its real users, the gamers. A big element of both Fortnite and DOTA's Battle Pass is FOMO (Fear Of Missing Out), since players are aware that the only chance they will have to get some skins is " ", they go crazy in order to obtain the skins they want. I'm not personally a big fan of this model, but I can for sure claim that it works, my wallet is a sad witness of it, and I would bet an Arcana that some readers have suffered with it as well. now It's important to note that there's a big difference, at this point in time, between Steam and Epic, and that is Steam's Community Market. For those uninitiated, the Community Market is where Steam users can sell their skins to other users for "real money". It's indeed real money, but you can only use it on the Steam Store, and more than that, every and each transaction on the Market has a fee that Valve takes. It's not possible to trade, sell, or gift Fortnite skins that you already own (it is possible to gift to a friend skins that are on the game store). But in DOTA there's a whole economy that occurs in the Community Market and even on third-party sites where people trade their skins for real money outside of Steam, a practice that is not permitted but it's highly overlooked. But maybe another strategy could work out in order for these huge companies still earn obscene amounts of money. And that is . Epic and Steam have their on blockchain gaming at this moment, Valve is seemingly against it on their platform (though Mir4 is still there), and Epic claims to welcome games on their store, but not a single one is there yet. blockchain own perspective Blockchain Gaming Play-to-Earn games, built on the blockchain, have a strong claim: to give ownership of game resources back to their players. That is a double-edged blade since many started to see them as investments instead of games, but that is something I would prefer to get in-depth in another article, what should be noted here is that just like cryptocurrencies, P2E games are going downhill in their value and players recently. Even though shows that the activity in this first quarter of the year has grown 2000% compared with last year, and investments are still happening, one can notice that few are the projects that are capable of keeping their players for more than a few months. DappRadar Report This is not to say that they will all fail, that the projects are not interesting, and that the games aren't fun. The point is that if all projects are following more or less the same model, then maybe they should start to think differently. Blockchain is not a "new" technology, but it is new in regard to its presence in day-to-day life. Like all new technologies, it takes a while to learn what are the best applications to it, most of the time the first idea is not the best idea. The Titan's Embrace But what if companies like Valve and Epic, instead of rejecting or welcoming this new technology, embrace it? What if, for instance, Valve develops its blockchain, transforming game skins in NFTs? They don't need to do this with ALL their skins, they can do this for the ones with limited marketability, special ones, as the P2E game do. Blankos The implications of such a decision, at a first glance, could be that of players feeling to have real ownership of their stuff, knowing that if at some point they need to leave their gaming community, all the investment they did would not be lost unless they went after shady marketplaces that negotiate accounts. Another point is that players will continue to be aware of skins scarcity. These big companies would still be able to use the FOMO strategy, skins will only be available during their Battle Passes period, the number will not get any higher after that time, so it's as exclusive as before, but people will be able to sell it if they want to. For Valve this would mean staying a pioneer in the game industry, being the bastion they so much like to be, and showing how to use blockchain in gaming legitimately. They would also be able to continue to get its chunk out of transactions by applying as many gas fees as they wanted to. For Epic Games this would mean opening up a new universe of possibilities for their already immense skin portfolio on Fortnite. They would not only keep producing their skins, but be able to show other developers that they really stand by their claims of supporting blockchain games, and will continue to earn from skins of past Battle Passes as they are traded on their chain. I would love to see a game like those portrayed on Korean Webtoons, where the game currency can easily become real-world money and people can decide if they want to work as an IT tech or as a blacksmith in a VR game. I know that I would craft the best items around. But maybe it's not the time to put finances and games together, maybe we should address certain social issues in the physical world first. Regardless of that, I believe that "simply" mixing successful, well-known, current games from great industries, not intending on transforming them into an investment but simply by giving players the awareness that their money is respected and they are purchasing a real asset, could be the first step into P2E right now and rebranding NFTs as something useful and good, not a scam.

Paradigm Change: Microtransactions

The Journey Of The CTO (Podcast Transcript)

An In-Depth Look Into the World's Most Popular Dating Apps

Bitfinex, , has more than 180 open-source repositories in GitHub–from a to . The most sophisticated trading toolkit developed by Bitfinex, though, is Honey Framework with many useful packages such as , , , and . one of the largest cryptocurrency digital exchanges Tezos JS library UI goodies bfx-hf-ui bfx-hf-strategy bfx-hf-indicators bfx-hf-strategy-exec In this tutorial, we'll use the Honey Framework ecosystem to define and backtest a trend trading strategy on historic data for the past 500 days. Requirements Let's install the libraries we're going to use. First, run the following command to install Honey Framework libraries. npm install bfx-hf-strategy bfx-api- bfx-hf-util bfx-hf-indicators node -models We'll also need the following packages to display the trades on the command line. npm install cli- colors table Eventually, install library as well to retrieve the data from Bitfinex API. node-fetch npm install node -fetch Strategy We use a simple trend trading strategy based on the following logic: position if the price is between -1 SD and -2 SD Bollinger Bands® and short SMA is below the long SMA. Open short position if the candle is green and more than 75% of its body crossed above the -1 SD band. Close short position if the price is between +1 SD and +2 SD bands and the short simple moving average is above the long simple moving average. Open long position if the candle is red and more than 75% of its body crossed below +1 SD band. Close long The logic behind the strategy is fairly straightforward: if the price is between  -1 SD and +1 SD bands (No Man's Land), we close our position. If the price is in the sell-zone (between the lower bands) and the market trend is bearish (short SMA is below long SMA), we open a short position. On the other hand, if the price is between upper bands and the market is bullish, we open a long position (in other words, we only trade in direction of the overall trend). Check to find more information about trend trading based on Bollinger Bands®. this article from Investopedia Code Let's get our hands dirty. First, create that defines a dummy strategy. strategy.js { SYMBOLS, TIME_FRAMES } = ( ); { BollingerBands, SMA } = ( ); HFS = ( ); .exports = HFS.define({ : , : ,
    symbol,
    tf, : { : BollingerBands([ , ]), : BollingerBands([ , ]), : SMA([ ]), : SMA([ ]),
    },
  }); // strategy.js const require "bfx-hf-util" const require "bfx-hf-indicators" const require "bfx-hf-strategy" module ( ) => { symbol = SYMBOLS.BTC_USD, tf = TIME_FRAMES.ONE_DAY } id "bbands" name "bbands" indicators bbands2 new 20 2 bbands1 new 20 1 smaShort new 25 smaLong new 100 Let's break it down. We define the default symbol (BTCUSD) and timeframe (one day). Then, we define the indicators we're going to use. We need two Bollinger Band® bands, both in the period of 20 days. We give a multiplier of 2 to the first (±2 standard deviations) and a multiplier of 1 to the second one  (±1 standard deviations). In the end, we add two SMAs, one in a 25-day period (short SMA) and the other in a 100-day period (long SMA). to find a list of all indicators available in Bitfinex Honey Framework. Check this We can enter the market now. The strategy we defined accepts five handler methods to update positions and make trades on each event: - called when no position is open onEnter - called when a long position is open onUpdateLong - called when a short position is open onUpdateShort - called when any position is open onUpdate - called on every tick onPriceUpdate Event handlers receive two parameters, (the strategy context) and (point-in-time data), they look for trade opportunities and update if there's a trade to be made. state update state Enter Into the Market Let's define the strategy to enter the market. First, add to encapsulate the reusable logic. Define in it to extract Bollinger Band® bands values from the state. helper.js getBBands HFS = ( ); getBBands = { indicatorValues = HFS.indicatorValues(state); { bbands1, bbands2 } = indicatorValues; { : plusOne, : minusOne } = bbands1; { : plusTwo, : minusTwo } = bbands2; {
    plusOne,
    plusTwo,
    minusOne,
    minusTwo,
  };
}; .exports = {
  getBBands
} // helpers.js const require "bfx-hf-strategy" const ( ) => state const const const top bottom const top bottom return module returns a map of all indicators defined in the strategy. We then extract the Bollinger Bands objects from them, which include three values: top, bottom, and middle. Eventually, the method returns ±1 and ±2 SD bands. indicatorValues Define another method to extract uptrend/downtrend data from the state. checkIfIsUptrend = { indicatorValues = HFS.indicatorValues(state); { smaShort, smaLong } = indicatorValues; isUpTrend = smaShort > smaLong; isUpTrend;
}; .exports = {
  getBBands,
  checkIfIsUptrend,
} // helpers.js /*
getBBands here
*/ const ( ) => state const const const return module The method simply checks if short SMA is above long SMA and infers the trend from it. Add . We'll use it to find trade opportunities. look_for_trade.js HFS = ( ); { getBBands, checkIfIsUptrend } = ( ); .exports = (state = {}, update = {}) => { { price, : timestamp } = update; orderParams = { : timestamp, : ,
    price,
  }; bands = getBBands(state); isBuyZone = bands.plusTwo >= price && price >= bands.plusOne; isSellZone = bands.minusTwo <= price && price <= bands.minusOne; isUptrend = checkIfIsUptrend(state); (isSellZone && !isUptrend) { HFS.openShortPositionMarket(state, orderParams);
  } (isBuyZone && isUptrend) { HFS.openLongPositionMarket(state, orderParams);
  } state;
}; // look_for_trade.js const require "bfx-hf-strategy" const require "./helpers" module async const mts const mtsCreate amount 1 const // between +1 SD and +2 SD from mean, the trend is up const // between -1 SD and -2 SD from mean, the trend is down const const if return if return return The method checks if the price is in the trade zone (between +1 and +2 SD or -1 SD and -2 SD bands). If the price is in the sell zone and the trend is bullish, it opens a short position, and if the price is in the sell zone and the trend is bearish, it opens a long position. If none is true, it simply returns the previous state. Notice that we use Honey Framework helper functions to open positions. You can find the list of all helper functions . Add to use the function we just defined to handle onEnter events. here on_enter.js lookForTrade = ( ); .exports = (state = {}, update = {}) => {
  newState = lookForTrade(state, update); newState;
}; // on_enter.js const require "./look_for_trade" module async await return Now add the handler to the strategy. on_enter = ( ); .exports = HFS.define({ onEnter: on_enter,
  }); // strategy.js /* other imports */ //  handlers const require "./on_enter" module ( ) => { symbol = SYMBOLS.BTC_USD, tf = TIME_FRAMES.ONE_DAY } /*
    other strategy properties
    */ Update Positions Well done! We've just exposed ourselves to the market. But we should add other handlers to react to market updates while we have an open position. Define another method in to check if 75% or more of the candle body crossed the inner bands. helpers.js HFS = ( ); checkIfShouldClose = { candleBody = .abs(close - open); closeToNoMansLandDist =
    position == ? close - bands.minusOne : bands.plusOne - close; shouldClose = closeToNoMansLandDist / candleBody >= ; shouldClose;
}; .exports = {
  getBBands,
  checkIfIsUptrend,
  checkIfShouldClose,
}; // helpers.js const require "bfx-hf-strategy" /*
 Other methods
*/ const ( ) => close, open, bands, position const Math const "short" // check if 75% of candle body entered to the No Man's Land. // see: https://www.investopedia.com/thmb/AtmrjGx96Tbz8f1cfnGOGzpqHqU=/2378x0/filters:no_upscale():max_bytes(150000):strip_icc():format(webp)/dotdash_Final_Using_Bollinger_Bands_to_Gauge_Trends_Oct_2020-02-f76c639116734ccfb8493dce32ed149a.jpg const 0.75 return module calculates the candle body height by finding the distance between open and close values. Then, it checks how much of the candle entered the No Man's Land. If more than 75% of the candle body is in between two inner bands, it closes the position. checkIfShouldClose It's time to create . on_long.js HFS = ( ); lookForTrade = ( ); { getBBands, checkIfShouldClose } = ( ); .exports = (state = {}, update = {}) => { { candle, : timestamp, price } = update; { open, close } = candle; orderParams = { : timestamp, : ,
    price,
  }; bands = getBBands(state); isCandleGreen = open <= close; (isCandleGreen) state; isCloseAbove1Std = bands.plusOne < close; (isCloseAbove1Std) state; shouldClose = checkIfShouldClose(close, open, bands, ); (!shouldClose) state; newState = HFS.closePositionMarket(state, orderParams); newState = lookForTrade(newState, update); newState;
}; // on_long.js const require "bfx-hf-strategy" const require "./look_for_trade" const require "./helpers" module async const mts const const mtsCreate amount 1 const const if return const if return //  check if 75% of candle body is below 1 std const "long" if return let await //  look if should open short await return The method we just defined checks if: The candle is green. The candle is above the No Man's Land (i.e., the closing value is above the +1 SD band). Less than 75% of the candle is in the No Man's Land. And if any of these are correct, it doesn't touch the position. Otherwise, it closes the long position using helper method and then calls to check if a short position should be opened. Now, let's define our strategy while shorting. closePositionMarket lookForTrade HFS = ( ); lookForTrade = ( ); { getBBands, checkIfShouldClose } = ( ); .exports = (state = {}, update = {}) => { { candle, : timestamp, price } = update; { open, close } = candle; orderParams = { : timestamp, : ,
    price,
  }; bands = getBBands(state); isCandleRed = open >= close; (isCandleRed) state; isCloseBelowStd = bands.minusOne > close; (isCloseBelowStd) state; shouldClose = checkIfShouldClose(close, open, bands, ); (!shouldClose) state; newState = HFS.closePositionMarket(state, orderParams); newState = lookForTrade(newState, update); newState;
}; // on_short.js const require "bfx-hf-strategy" const require "./look_for_trade" const require "./helpers" module async const mts const const mtsCreate amount 1 const const if return const if return //  check if 75% of candle body is below 1 std const "short" if return let await //  look if should open long await return checks if: on_short.js The candle is red. The candle is below the No Man's Land (i.e., the closing value is below the -1 SD band). Less than 75% of the candle is in the No Man's Land. And if all above are false, it closes the short position and looks for new trade opportunities. That's it. We've finished defining the strategy. Add the new event handlers to . Then we can start testing the algorithm. strategy.js on_enter = ( ); on_long = ( ); on_short = ( ); .exports = HFS.define({ onEnter: on_enter, : on_long, : on_short,
  }); // strategy.js /* other imports */ //  handlers const require "./on_enter" const require "./on_long" const require "./on_short" module ( ) => { symbol = SYMBOLS.BTC_USD, tf = TIME_FRAMES.ONE_DAY } /* 
      Other strategy properties
    */ onUpdateLong onUpdateShort Test on Historical Data Add to retrieve historic data from Bitfinex API. get_data.js ; fetch = ( ); url = ; get_500_days = { date = (); end = date.getTime();

  date.setDate(date.getDate() - ); start = date.getTime(); { start, end };
}; .exports = { { { start, end } = get_500_days(); pathParams = ; queryParams = ; req = fetch( ); response = req.json(); response;
  } (err) { .log(err);
  }
}; // get_data.js "use strict" const require "node-fetch" const "https://api-pub.bitfinex.com/v2/" const => () let new Date const 500 const return module async ( ) function getData { symbol, tf } try const const `candles/trade: : /hist` ${tf} ${symbol} const `start= &end= &limit=500` ${start} ${end} const await ` / ? ` ${url} ${pathParams} ${queryParams} const await return catch console fetches OHLC data of the past 500 days, given a symbol and a timeframe.  You can modify the parameters to meet your needs. Find more information about Bitfinex API's public endpoint for candle data . getData here Add to define the execution logic. exec.js process.env.DEBUG = ; { Candle } = ( ); { SYMBOLS, TIME_FRAMES } = ( ); logTrades = ( ); HFS = ( ); getData = ( ); BBandsStrategy = ( ); market = { : SYMBOLS.BTC_USD, : TIME_FRAMES.ONE_DAY,
}; getCandles = () => { rawCandleData = getData(market); candles = rawCandleData
    .sort( a[ ] - b[ ])
    .map( ({
      ...new Candle(candle).toJS(),
      ...market,
    })); candles;
}; run = () => { candles = getCandles(); strategyState = BBandsStrategy(market); ( i = ; i < candles.length; i += ) {
    strategyState = HFS.onCandle(strategyState, candles[i]);
  }

  logTrades(strategyState);
}; {
  run();
} (e) { .error(e);
} // exec.js "*" const require "bfx-api-node-models" const require "bfx-hf-util" const require "bfx-hf-strategy/lib/debug/log_trades" const require "bfx-hf-strategy" const require "./get_data" const require "./strategy" const symbol tf const async const await // attach market data const ( ) => a, b 0 0 ( ) => candle return const async const await let for let 0 1 await try catch console method retrieves the data and attaches market information to them. During live execution, we may receive data from multiple sources for different symbols and different timeframes, thus, it's necessary to attach this information to raw data. getCandles Then, gets candle data and the strategy and passes them to helper method that orchestrates relevant strategy event handlers for the new price action data. Eventually, logs the results using function. run onCandle run logTrades Let's test the algorithm. Open the command line in the project's directory and run the following command. node exec The result will be something like this: You can find a list of all positions along with the amount, price, fee, and profit/loss figure of each one. At the bottom, general information about the strategy is logged. The bot has made 37 trades, opened 19 positions, and gained 3960.91$. You can modify and change how it displays the results. helper function log_trades.js That's it! We've defined and backtested a simple trading strategy using Bitfinex Honey Framework toolkit. The example pieces of code in the article are available . here

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