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How to Use Swift for Web Development

ওয়েব ডেভেলপমেন্টের জন্য কীভাবে সুইফট ব্যবহার করবেন

So verwenden Sie Swift für die Webentwicklung

Cómo usar Swift para el desarrollo web

Comment utiliser Swift pour le développement Web

वेब डेवलपमेंट के लिए स्विफ्ट का उपयोग कैसे करें

Swift を Web 開発に使用する方法

웹 개발에 Swift를 사용하는 방법

Como usar o Swift para desenvolvimento web

Web Geliştirme için Swift Nasıl Kullanılır?

Cách sử dụng Swift để phát triển web

如何使用 Swift 进行 Web 开发

The urge to delete Social media and messaging apps have created new avenues for people to enter our lives. You might feel the urge to delete someone for various reasons – annoyance, personal growth, or just a shift in content preferences. In a world where change is constant, the desire to curate our online circles reflects this innate need for control and authenticity. Personally, I’ll admit this urge is like an automatic response. Most of the time, it feels cathartic, a satisfying release, and a digital refresh. Other times, it’s how my fear expresses itself, and it helps me feel protected from a perceived threat. Our patterns of behaviour Research into deleting phenomena unveils a tapestry of human behaviour. Deleting certain individuals may lead to the emergence of hidden aspects of ourselves while fostering deeper connections with others. Though deleting generally refers to removing someone from your friends list or connections, it varies depending on the platform. Subtle differences between notifications and accessibility reflect how we engage with each other on these platforms. For example, Facebook carries more emotional weight due to stronger personal connections, while LinkedIn is a professional disconnect. Instagram is more towards social exclusion, while Twitter ranges from negligible to significant, sometimes leading to being silenced within an online community. Over time we’ve learned to sculpt a more intricate inner world, rich with nuance and layers. This creates a maze around our identity where fragments are selectively revealed — a new freedom that also questions our accountability. The power of perception Attention is currency, so the act of deleting carries weight beyond the digital realm. The energy behind our gaze and thoughts can shape the way we perceive and interact with others online. Deleting someone from our virtual sphere doesn’t diminish the impact of our attention, it redirects it. As connections add up over time they weave a complex web of relationships. Being perceived by this network is a novel phenomenon, expanding beyond our immediate physical surroundings to include individuals from anywhere. Engaging with this vast array of minds, each with their own belief systems presents a challenge that transcends our ability to fully comprehend. Tip: Try to visualise your network, what kind of groups are there? What shapes emerge? Express this in a way that feels natural to you, draw it out or write about it. Navigating conflicting emotions Deleting can stir a mix of conflicting emotions – jealousy, loneliness, and misunderstanding. Leaning into these feelings can help you learn about yourself. Perhaps jealousy points you to something you want to do in your life. Feeling misunderstood by your current group of friends might motivate you to take the leap into new groups. Then there’s the possibility of the deleted individual finding out, would they care? Would you care? Try to simulate meeting them in real life, how would you feel and what would you say to them? I’ve found it quite freeing to admit to people about deleting. It takes courage and humor, but it also opens up an opportunity to talk about digital spaces and our personal needs. Remember: digital etiquette is different for everyone, and by acknowledging the complexities of human interaction we can make it more enjoyable. Exploring your digital maze The act of deleting people is both a reflection of our evolving selves and a tool for self-preservation. Growth and change are normal for us, yet our need to hold on accumulates an unnatural web of connections. By embracing the nuance of digital relationships and remaining mindful of our intentions, we can navigate the intricate maze of human connection with a bit more clarity and compassion. Stay tuned for more posts and future webinars, shedding light on the intimate quirks of our digital existence. Much love ₊˚⊹♡ The urge to delete The urge to delete Social media and messaging apps have created new avenues for people to enter our lives. You might feel the urge to delete someone for various reasons – annoyance, personal growth, or just a shift in content preferences. In a world where change is constant, the desire to curate our online circles reflects this innate need for control and authenticity. Personally, I’ll admit this urge is like an automatic response. Most of the time, it feels cathartic, a satisfying release, and a digital refresh. Other times, it’s how my fear expresses itself, and it helps me feel protected from a perceived threat. Our patterns of behaviour Our patterns of behaviour Research into deleting phenomena unveils a tapestry of human behaviour. Deleting certain individuals may lead to the emergence of hidden aspects of ourselves while fostering deeper connections with others. Though deleting generally refers to removing someone from your friends list or connections, it varies depending on the platform. Subtle differences between notifications and accessibility reflect how we engage with each other on these platforms. For example, Facebook carries more emotional weight due to stronger personal connections, while LinkedIn is a professional disconnect. Instagram is more towards social exclusion, while Twitter ranges from negligible to significant, sometimes leading to being silenced within an online community. Over time we’ve learned to sculpt a more intricate inner world, rich with nuance and layers. This creates a maze around our identity where fragments are selectively revealed — a new freedom that also questions our accountability. The power of perception The power of perception Attention is currency, so the act of deleting carries weight beyond the digital realm. The energy behind our gaze and thoughts can shape the way we perceive and interact with others online. Deleting someone from our virtual sphere doesn’t diminish the impact of our attention, it redirects it. As connections add up over time they weave a complex web of relationships. Being perceived by this network is a novel phenomenon, expanding beyond our immediate physical surroundings to include individuals from anywhere. Engaging with this vast array of minds, each with their own belief systems presents a challenge that transcends our ability to fully comprehend. Tip: Try to visualise your network, what kind of groups are there? What shapes emerge? Express this in a way that feels natural to you, draw it out or write about it. Navigating conflicting emotions Navigating conflicting emotions Deleting can stir a mix of conflicting emotions – jealousy, loneliness, and misunderstanding. Leaning into these feelings can help you learn about yourself. Perhaps jealousy points you to something you want to do in your life. Feeling misunderstood by your current group of friends might motivate you to take the leap into new groups. Then there’s the possibility of the deleted individual finding out, would they care? Would you care? Try to simulate meeting them in real life, how would you feel and what would you say to them? I’ve found it quite freeing to admit to people about deleting. It takes courage and humor, but it also opens up an opportunity to talk about digital spaces and our personal needs. Remember: digital etiquette is different for everyone, and by acknowledging the complexities of human interaction we can make it more enjoyable. Exploring your digital maze Exploring your digital maze The act of deleting people is both a reflection of our evolving selves and a tool for self-preservation. Growth and change are normal for us, yet our need to hold on accumulates an unnatural web of connections. By embracing the nuance of digital relationships and remaining mindful of our intentions, we can navigate the intricate maze of human connection with a bit more clarity and compassion. Stay tuned for more posts and future webinars, shedding light on the intimate quirks of our digital existence. Much love ₊˚⊹♡

Deleting People Online: Liberating and Conflicting

Authors: (1) LI LI, Beihang University, China; (2) XIANG GAO, Beihang University, China; (3) HAILONG SUN, Beihang University, China; (4) CHUNMING HU, Beihang University, China; (5) XIAOYU SUN, The Australian National University, Australia; (6) HAOYU WANG, Huazhong University of Science and Technology, China; (7) HAIPENG CAI, Washington State University, Pullman, USA; (8) TING SU, East China Normal University, China; (9) XIAPU LUO, The Hong Kong Polytechnic University, China; (10) TEGAWENDÉ F. BISSYANDÉ, University of Luxembourg, Luxembourg; (11) JACQUES KLEIN, University of Luxembourg, Luxembourg; (12) JOHN GRUNDY, Monash University, Australia; (13) TAO XIE, Peking University, China; (14) HAIBO CHEN, Shanghai Jiao Tong University, China; (15) HUAIMIN WANG, National University of Defense Technology, China. Table of Links Introduction Background of OpenHarmony The State Of OpenHarmony Ecosystem Overview Of Mobile Software Engineering The Research Roadmap Discussion Related Work Conclusion & References 3 THE STATE OF OPENHARMONY ECOSYSTEM As revealed in the previous literature review, despite Mobile Software Engineering has been a longstanding and hot topic, the efforts spent by our fellow researchers for exploring OpenHarmony have been limited. Indeed, there is almost no contribution made to OpenHarmony in the current MSE community. Therefore, as our initial attempt towards bringing OpenHarmony research to the Mobile Software Engineering (MSE) community, we summarize the current achievements of OpenHarmony to help readers better understand the state of the OpenHarmony ecosystem. Specifically, in this section, we first briefly introduce the OpenAtom OpenHarmony initiative and subsequently highlight the existing toolchains and datasets available in the community. After that, we go one step further to also summarize other existing resources that may not be directly related to OpenHarmony but could still be beneficial to grow the ecosystem of OpenHarmony. 3.1 The OpenAtom OpenHarmony Initiative Recall that OpenHarmony is now a fully open-sourced project, which is currently incubated by the OpenAtom Foundation. At the moment, the OpenHarmony project is run by an operating committee, and its technical part is mainly run by a Technical Steering Committee (TSC). To support the technical success of OpenHarmony, the TSC has further set up various domain-specific Technical Supporting Groups (TSG). At the moment, there are six TSGs and the number is growing. The current supporting groups cover the following six domains: (1) Program Language, (2) Crossplatform App Development Framework, (3) Security and Privacy Computing, (4) Web3 Standard, (5) Robot, and (6) Integrated Development Environment (IDE). These domain-specific TSGs are responsible for understanding the domain-related requirements, summarizing the technical map of the domain, and preparing the fundamental technicals for helping domain-specific technical-related decision-making and eventually supporting the overall success of OpenHarmony. 3.2 Existing Toolchains We then look at the existing toolchains offered by the official OpenHarmony framework to support app developments and these toolchains are considered important and essential. Indeed, these tools could provide fundamental capabilities to support the implementation of more advanced OpenHarmony-specific toolchains. Ideally, these toolchains should cover the full lifecycle of app development, including development, build, testing, debugging, code review, and publishing. Table 1 summarizes some of the tools provided by OpenHarmony. The second column demonstrates the software engineering phase that the tool intends to support. At the moment, these toolchains have covered almost all the aforementioned lifecycle phases, e.g., including app development-related ones (e.g., IDE, Emulator, Device Manager), app building tools (e.g., hvigor), app testing tools (e.g., jsunit, uitest), debugging tools (e.g., HiLog, profiler), code reviewing tools (e.g., Code Linter), command line tools (e.g., hdc), and package management tool (e.g., ohpm). The only exception is the phase of publishing. At the moment, there is no such tool offered for OpenHarmony. It is nonetheless understandable as there is no app market available for hosting OpenHarmony apps yet. We believe such a tool will be provided once a dedicated app market is offered. It is worth noting that, at the moment, we only conducted a high-level overview and did not check in detail to what extent are the required functions in each phase covered by these tools. For example, there is a tool called Monkey in Android that supports random exploration of Android apps, it is not clear to us if the existing toolchains of OpenHarmony provide equivalent functions. As for our future work, we plan to have a more detailed look at these tools and provide to the community a clearer overview of these toolchains. 3.3 Existing Datasets As shown in Section 4, the datasets targeted by our MSE community can be mainly divided into four types: (1) Mobile apps (including both open-sourced and closed-sourced apps), (2) Mobile App Development Framework, (3) Third-party Libraries, (4) App Store Info (including app reviews). We now respectively summarize the current situation of these types of datasets in OpenHarmony, respectively. We further go one step deeper to harvest the relevant datasets, if possible, and make them publicly available for supporting our fellow researchers to conduct OpenHarmony-related software engineering research. Recall that OpenHarmony is a fully open-sourced system, its app development framework is open-sourced. The framework is the first gate that OpenHarmony apps need to interact with before running into the system. The interaction is mainly through OpenHarmony Framework. APIs provided by the app development SDK, as part of the OpenHarmony framework. Some of the meta-data of the OpenHarmony framework are shown in Table 2. The current framework is open-sourced at the interface_sdk-js repository[4] on Gitee and it currently has 105 branches, 30 tags, 1,400 forks, 57 stars, 7,833 commits, and 627 contributors. As a comparison, the last column of Table 2 shows the meta-data of the Android framework repository, respectively. It is obvious that OpenHarmony has a big step to go in order to catch up with Android, which poses lots of opportunities for our MSE community to mitigate the gap between the OpenHarmony framework and the Android framework We further look into the number of APIs offered by the OpenHarmony framework. Since there is no such information directly provided on the web, we decide to write a parser to directly harvest that from the open-source repository. We select the latest version (i.e., OpenHarmony 4.0) and only count the number of functions (including static and non-static functions). In the latest version, there are 10,435 APIs. This number is also significantly smaller than that of the Android framework, which already has over 30,000 APIs in 2018 (i.e., API version 28 [66]). Nonetheless, as illustrated in Fig. 5, the number of APIs (again, any functions are considered) is continuously increasing, showing that the capabilities of OpenHarmony are keeping maturing. We believe as time goes by, such a difference between the APIs of Android and OpenHarmony will be much smaller. One of the most important reasons that make mobile software engineering (especially for the Android community) a longstanding hot topic is due to the existence of a large number of mobile apps [35, 49]. Indeed, there are over 2 million Android apps (there is a similar number for the iOS community) available on the official Google Play store. In the famous AndroZoo dataset [67], there are over 23 million Android apps collected from various sources (e.g., the official Google Play store and over 10 third-party markets such as PlayDrone, AppChina, etc.) spanning various years. Liu et al. have subsequently harvested the open-sourced Android apps and formed them as a dataset called AndroZooOpen [72]. This dataset is also made publicly available to the software engineering community and has been demonstrated to be useful in supporting Android research tasks. Inspired by this, we hypothesize that OpenHarmony apps will be one of the most important resources for supporting OpenHarmony research. We, therefore, take our initial attempt to harvest existing OpenHarmony apps. Since there is no app market available for OpenHarmony yet, we solely focus on open-sourced OpenHarmony apps. Specifically, we take OpenHarmony as well as HarmonyOS as the search keyword and apply it to two famous cloud-based software version control websites, namely GitHub and Gitee, which are the most famous sites of such in the world and in China, respectively. OpenHarmony Apps. Our initial search results in 3,804 repositories[5] , for which 910 of them are from GitHub while the remaining 2,894 from Gitee. We remind the readers that these identified repositories may not always be OpenHarmony apps. Therefore, we resort to a Shell script (with manually identified features of OpenHarmony apps considered) to select such repositories that indeed contain OpenHarmony apps. Our experiment has eventually discovered 174 such repositories, with 147 and 27 from Gitee and Github, respectively. To facilitate further research, we have also made this list publicly available on the same site.[6] Libraries We remind the readers that OpenHarmony takes a newly introduced language called ArkTS to support app implementations. In this work, we also look at the existing third-party libraries that are available for supporting the development of OpenHarmony apps. Specifically, we would like to understand to what extent are ArkTS-based libraries available in our community and what are they designed for. In OpenHarmony, the official team has introduced a tool called ohpm (as also shown in Table 1) for managing all the third-party libraries designed for developing OpenHarmony apps. In the current central registry[7] , there are already 96 libraries and the number is growing. The functions of these libraries can be divided into 10 categories. Table 3 enumerates some of the representative ones for each category. Generally, we show one or two libraries for each category. The two are randomly chosen if there are more libraries available for the given category. Third-party Furthermore, as mentioned previously, ArkTS is not entirely new. It actually extends Typescript, which has been a popular programming language for more than 10 years. Typescript is Javascript with syntax for types, i.e., adding static typing with optional type annotations to Javascript. Theoretically, existing Typescript code (as well as Javascript code) can be directly reused for developing OpenHarmony apps. Those Typescript/Javascript implementations could be regarded as third-party libraries as well. By taking Typescript and Javascript as the search keyword, Github returns 513,000 and 1.7 million repositories for Typescript and Javascript, respectively. Such a large number of repositories (despite not all of them being code-related repositories) indicates that there are already a lot of potential third-party libraries available for OpenHarmony.[8] Those libraries could be leveraged (either directly or with additional efforts contributed by our fellow researchers) to facilitate the development of OpenHarmony apps and its broad ecosystem. . Our software engineering researchers have leveraged app store info (such as the app’s author information, description, user rating, user reviews, etc.) to support various studies. For example, Gorla et al. [43] have leveraged the app’s description to check against the app’s behavior. Obie et al. [83] have leveraged the app’s review data to investigate the violation of honesty in mobile apps. To the best of our knowledge, there is barely any app store hosting OpenHarmony apps at the moment. Therefore, there is no such dataset that can be collected so far. Nonetheless, the OpenHarmony version of a given app will also share much of such information as that available in Android or iOS. This information could also be helpful when mining OpenHarmony-specific app store information. App Store Info 3.4 Existing OpenHarmony Research As our initial attempt towards building the research roadmap for guiding our software engineering researchers to contribute to OpenHarmony, we start by conducting a lightweight literature review about OpenHarmony. Our method is straightforward. We use OpenHarmony and HarmonyOS as the search keywords and we apply them separately to search for relevant publications on both Google Scholar and DBLP, respectively. At this step, when applied to Google Scholar, we will only consider the top 100 results. Table 4 enumerates the list of OpenHarmony-related publications. In total, we only found 8 papers and among which only one (i.e., the one published at the APWeb conference) can be found on DBLP, while all of them can be found on Google Scholar. At this step, we only consider a paper relevant if and only if it directly contributes to the OpenHarmony project or if it takes OpenHarmony as its dataset to evaluate their approaches. There are several other papers that are not included in this review although they do involve OpenHarmony/HarmonyOS systems. They are excluded because they do not contribute anything to OpenHarmony as they only involve running their approaches on OpenHarmony/HarmonyOS systems. For example, the work proposed by Qiu et al. [87] is not included in this paper because it only leverages HarmonyOS to support their model implementation about supporting distributed user interfaces to be dynamically configured on multiple IoT devices based on user preferences. As shown in Table 4, there are only eight OpenHarmony-related papers published in the community. The efforts could be neglected if compared to those for Android, where there are over 7,000 papers published as recorded in DBLP (searching by taking Android as the keyword). This evidence confirms our previous argument that there is still a huge gap between OpenHarmony and Android. This, however, also demonstrates that there are huge opportunities open for our community. Ideally, the research methods applied to Android or iOS could also be applied to OpenHarmony. Despite there being only eight papers published, it is motivating to find that the number of relevant papers keeps growing. The venues where the current papers are published are generally not in reputed journals or conferences. Indeed, among the eight papers, only four of them are published at venues recorded by CORE and only three of them are ranked. We would hope that our community could spend more effort in developing software engineering approaches for OpenHarmony and publish more papers at mainstream venues. [4] We remind the readers that the framework and the SDK are not exactly the same as the framework may contain more capabilities that are reserved for system apps while SDK is only supposed to be used by third-party apps. For simplicity, in this work, we will not differentiate this as there is no direct repository provided for hosting the framework code of OpenHarmony. [5]The full list is made available on GitHub (https://github.com/SMAT-Lab/SE4OpenHarmony). [6] Our further investigation finds that most of these apps are not comprehensive ones (i.e., might be toy apps or demonstrating the usability of certain libraries). We hence commit to keep updating this list toward forming a more useful dataset for supporting OpenHarmony-based software engineering research. [7]https://ohpm.openharmony.cn [8] We hypothesize that this is one of the major reasons why ArkTS is proposed as the default programming language for developing OpenHarmony apps. This paper is under CC 4.0 license. available on arxiv

The State of OpenHarmony Ecosystem: Initiatives, Toolchains, and Datasets

⚠️⚠️Risk Warning and Conflict of Interest Statement: Degen projects carry significant risks, such as potential rug pulls, hacked contracts, failed operations, etc., which could even lead to the total loss of your principal! I am not your financial advisor. All content is for entertainment and educational purposes only, not financial advice. Always DYOR! Also, I hold some or all of the crypto assets mentioned in this article. By continuing to read, you acknowledge this statement. ⚠️⚠️ A month ago, I published an article: . I posted it on Medium, Hackernoon, and Twitter, but it was largely ignored. The BTC Ecosystem: The New Wild West for Blockchain Developers As we all know, in crypto, alpha often lies in information and knowledge gaps. I immediately realized this was a niche area not many people were paying attention to yet, full of money-making opportunities. So, on December 8th, I published another tutorial on How to Mint $cook Regrettably, this high-value tutorial was also ignored. People probably did not realize what they had missed. Because today, the leading BRC-20 protocol tokens, $ordi, and $sats, have already exceeded $1 billion in market cap. As the first implementation of the Rune protocol (Rune Alpha), the value of $cook is self-evident. On December 8th, when I minted this token, although it was a fair mint for free, Bitcoin network gas fees were still very high, with gas for minting once (1000 $cook) reaching $20, plus $2 collected by the project team. So the cost of minting one rune (1000 $cook) was about $22 back then. Each $cook costs around $0.022. Today (December 14th), although the project team has not opened trading on markets yet, I checked some OTC WeChat and Discord groups. The current trading price has reached $0.5 per $cook. Once the team enables market trading, I believe it will quickly reach around $1 (50X). Even at that price, $cook's market cap would still be less than $200 million. Compared to the market caps of $ordi and $sats, the two leading BRC-20 protocol tokens, it's not exaggerated at all. But please note, I'm not publishing this today telling you to buy $cook after market trading opens. Because there are so many opportunities in this market now, you really don't need to FOMO. Actually, today, I want to discuss some projects with even higher upside potential with you. Main Problems in the Current Bitcoin Ecosystem and Possible Solutions If you pay close attention, you’ll notice we’ve already entered an alternative DeFi summer. On the Bitcoin network, gas fees are abnormally high almost every day, like today. For a simple transfer transaction, if your gas fee is below $30, it will likely get stuck unconfirmed. This is because people are minting BRC-20 tokens, Rune tokens, and tokens of other protocols you’ve probably never even heard of on the Bitcoin network. If you find these concepts confusing and have no idea what’s going on, you can refer to my . intro article This may feel familiar because we saw something similar on the ETH network back in 2017 when issuing tokens first emerged as a major use case. Back then, countless competing protocols (blockchains) sprang up from ETH, filled with scammers and adventurers, and many get-rich-quick myths emerged. Together with Bitcoin’s halving, this led to the great bull market that year. Today, in 2023, 7 years later, I seem to be seeing the same landscape again. But in my previous article, I also mentioned what I believe to be the Bitcoin ecosystem’s main problem compared to the ETH ecosystem — Bitcoin’s scripting language is not Turing complete, so it cannot be used to write smart contracts. Bitcoin does not have an EVM like ETH, nor does it have smart contracts, so naturally, it cannot maintain state on-chain. If you are a developer, you surely understand what I mean. Not being able to maintain an on-chain state means there is no way to use token standards and interfaces like ERC20, let alone build various DeFi applications based on smart contracts. So, how can we enter a DeFi summer? Also, in that article, I mentioned some possible solutions — building decentralized oracles on the Bitcoin network (a field Chainlink has not yet ventured into), distributed and open-sourced Indexing service solutions, and integrating both to form a state management framework compliant with relevant fungible token protocols. I did not come up with these solutions out of nowhere. They are based on existing solutions in the market, plans of institutions in this field, and some projects they are working on. In that article, I provided the links to the . L1 Foundation and BRC-20 documentation I believe most people, even those hyping $ordi and $sats now, did not actually read these documents carefully. The indexing section of the BRC-20 doc clearly identifies some existing indexing providers. In addition, it explicitly tells everyone that Alex Lab is leading the creation of a , “In short, this intends to validate the indexer’s global state against each other every block, and attest their coordination on-chain via Stacks.” Bitcoin oracle Analysis of Related Projects To make an imperfect analogy, in this Bitcoin DeFi summer, if , who created Ordinals, is Vitalik, then people like and are like the earliest Ethereum core developers. They are pushing the entire Bitcoin ecosystem towards explosion based on Ordinals theory.🚀🚀🚀 Casey Domo Benny If the current inscription craze is akin to the ICO craze sparked by the emergence of the ERC20 standard in the 2017 bull market, then perhaps next year, we will see a real DeFi summer in the Bitcoin ecosystem. When we think about what happened between the 2017 ICO craze, the 2021 DeFi summer, and the 2022 metaverse explosion, it’s not hard to see that it was the development and improvement of various infrastructures in the Ethereum ecosystem that led to this dramatic change. (Readers who truly understand this paragraph, I believe its value to you definitely exceeds $1 million.)🔮🔮🔮 This field is still exploding and developing rapidly, but some situations are becoming clear. L1 Foundation has become the largest startup gang in this space, with Domo as their godfather. This camp contains the two largest tokens in BRC-20 and even the entire Bitcoin ecosystem right now — $Ordi and $Sats (their market caps have exceeded $1 billion). From the Brc-20 documentation, we can see other core and peripheral members of this startup gang. You can even find all of them on the L1 Foundation forum. For example, Best in Slot, Unisat, Benny, and Alex Lab. These teams all have more or less connections and collaborations with L1 Foundation. I believe they are the most powerful group of people in the Bitcoin ecosystem’s wild westward movement right now.👨‍💻👨‍💻👨‍💻 Let’s go through these projects one by one🏆🏆 🏆 Best in Slot currently has no token or plans announced. If it continues to have no token later, I’m inclined to believe it has joined Unisat’s ecosystem and will use $Sats. Unisat is strong, but $Sats already has a $1B+ market cap. For me, I may prefer higher odds, and smaller market cap projects. OKX has its own token $OKB, with a very large market cap. It’s a typical CEX token, a bit like $BNB, not a native Ordinals inscription or BRC-20 token. I don’t think it belongs to this ecosystem. Hiro is a bit like Alchemy on the Bitcoin network, actually a developer platform. But like Best in Slot, it currently has no token either. $TRAC is Benny’s project. In addition to indexing services, it has built a huge ecosystem, including its own token protocol and exchange. In the Bennyverse, besides $TRAC there is also $PIPE and $-TAP, the only cursed inscription in the Ordinals ecosystem. Alex Lab has its own token $ALEX, which surged rapidly in price recently as more investors realized its value. It has been corrected in the past couple of days. Of these projects above, the ones I must obtain and enter quickly are Benny’s projects and Alex Lab’s projects. Because they both have the potential to become super infrastructures and behemoths like Chainlink in the Bitcoin ecosystem; after all, we are still in a period similar to the 2017 ICO craze; the real DeFi summer has not arrived yet! Some may argue Unisat is strong, but $Sats already has a $1B+ market cap. For me, I may prefer higher potential returns and smaller market cap projects.💎💎💎 $TRAC currently only has a $120 million market cap, $ALEX less than $200 million. Compared to $Sats’ over $1 billion market cap, their potential returns are obviously much higher with the same upside. Meanwhile, I don’t think the probability of success for Benny’s projects and Alex Lab’s projects is necessarily that low either. They are also not mutually exclusive relationships. Chainlink reached an astonishing peak market cap of $22 billion last bull market. I think all these early projects still have a long way to go. 😁 Since you’ve read this far, please give me a like for the insights and thoughts! If this gets enough likes, I will share more #gems💎 in the future.👍👍👍

The Bitcoin Ecosystem Explosion and Hidden Gems in the Inscription Craze🤑🤑🤑

Everything You Need To Know About Managing Go Processes

On-chain asset tokenization represents a long-term trend with the potential to shake up the existing financial landscape. Within this arena, tokenized government bonds make up an increasingly important segment. The segment experienced a stellar growth of nearly seven-fold in 2023 and is quickly recovering from a brief slump at the end of 2023. The article dives into the current situation and some exciting trends in tokenized government bonds and the tokenized real-world assets (RWA) sector as a whole. Current Situation Tokenized government bonds and other tokenized real-world assets (RWA) have witnessed significant growth, propelled by rising interest rates joining forces with relatively low DeFi yields. This development has reflected investors’ appetite for asset classes that offer more stable, predictable returns. More and more investors, especially those seeking to strike a balance between the traditional financial market and the cryptocurrency market, are favoring these tokenized RWA products. A estimates that tokenization of global illiquid assets will reach $16 trillion, or 10% of global GDP by 2030. This includes both on-chain asset tokenization and traditional asset fractionalization (e.g. Exchange Traded Funds (ETFs) and Real Estate Investment Trusts (REITs)). Considering the potential market size, even capturing a small portion of this vast market would signify significant growth opportunities for the crypto industry. Boston Consulting Group (BCG) report Players in the RWA Ecosystem RWA Infrastructure Infrastructure providers in the RWA ecosystem, also known as RWA rails, provide the regulatory, technical, and operational infrastructure upon which RWAs are brought into crypto. They serve as the foundation for the entire ecosystem, and the development of RAW rails is vital to the sustainable growth of the RWA sector. Asset Providers Additionally, there are asset providers that focus on originating and creating demand for RWAs across various asset classes， including real estate, fixed income, equities, and others, contributing to the sector’s diversity and breadth. Market Size RWAs are the 11th largest sector in DeFi based on protocols tracked by , with a combined TVL of over $4 billion. According to a , the number of holders of tokens associated with RWA protocols reached over 67,000 on the Ethereum blockchain, more than doubling in the last 12 months. Within this sector, the total value of the tokenized Treasury segment increased to $719 million as of Mar 20, 2024, from around $379 million a year earlier, according to RWA monitoring platform . The average Yield-to-Maturity was at 5%. DeFi Llama Dune Analytics dashboard by @j1002 RWA.xyz That said, the combined TVL of RWAs has been slumping for the last several months after peaking at $6.271B last October. The value of the tokenized Treasury segment also dropped from $771 million at the end of last November, but it has moved back to an uptrend since February this year. As the broader crypto market recovers from the bear market, it seems that some people are pivoting to higher-yielding markets like DeFi lending and liquid restaking. User Profiles and Behaviors We’ve attempted to analyze the profiles and behaviors of RWA users to understand the underlying user demand. We observed that most RWA users are native to the cryptocurrency ecosystem, indicating that the early adopters did not come from traditional investment avenues. One of the facts that can demonstrate this observation is that the average creation date of RWA token holders’ wallet addresses predates the time when RWAs were tokenized on the blockchain. This means that the RWA userbase is a subgroup that evolved from the broader crypto community rather than a community that rose from influxes of traditional investors. RWA users often have a strong grasp of blockchain technology and many have entered DeFi long before RWAs become buzzed. We used the number of RWA token holders as a proxy to gauge RWA adoption. Both the number of RWA token holders and the unique interactions with RMA tokens have been on a continuous rise despite the recent drop in RWA protocols’ combined TVL. We believe this is a sign of the RWA sector’s increased awareness and positive expectations among investors. With a time-series analysis of the RWA holders’ wallet addresses, we’ve found that most of the addresses have been in use for a long time and have participated in various blockchain activities. This means that the RWA sector is largely participated by a healthy group of long-term crypto users. That said, we did notice growing participation from new wallet addresses as well. This probably means the RWA sector is gradually permeating into more traditional user groups as more traditional financial institutions, including Franklin Templeton and Wisdom Tree, embrace RWAs. RWAs are bridging the divide between crypto and traditional finance, but they also carry inherent risks and limitations from the real world. Most RWA interactions require checks from traditional financial systems, such as KYC/AML, credit checks, and minimum balance requirements. These prerequisites limit RWAs’ potential to extend financial inclusivity. Competitive Landscape We conducted an in-depth comparison of different projects in terms of market share, growth rates, pricing, and user experience to assess the competitive landscape. In the tokenized government bond sector, Franklin Templeton, leveraging its extensive accumulation and brand advantage in traditional finance, holds the largest market share. Ondo Finance and Matrixdock have different user demographics due to their different marketing strategies. Emerging companies like Hashnote and Superstate, while having smaller market shares, are experiencing rapid growth, demonstrating market vitality and diversity. With respect to pricing, different projects have different management fee rates, reflecting their strategies for market positioning and cost control. For example, Franklin Templeton and Ondo Finance have lower management fee rates, which help attract large institutional investors. On the other hand, Matrixdock and Maple Finance have relatively higher management fee rates, possibly to compensate for revenue pressures stemming from their smaller market shares. In terms of user experience, providing clear, intuitive, and user-friendly interfaces is crucial for attracting and retaining users due to the nature and complexity of tokenized government bond products. Additionally, with intensified market competition, enhancing service quality and customer support will become key differentiators for each project. To summarize, tokenized government bond projects are in a phase of rapid development, with significant differences among projects in market share, growth rate, pricing strategy, and user experience. Their prospects will be greatly influenced by the global economic environment, regulatory policies, and relevant technological innovations. To remain competitive, they need to continuously optimize their product features, enhance user experience, and adapt to evolving market and regulatory conditions. Major Players in Tokenized Government Bond Products Franklin Templeton Franklin Templeton is a US-based global asset manager with over $1.3 trillion in assets under management. As a traditional investment firm actively exploring the blockchain industry, Franklin Templeton has launched several blockchain-focused venture capital and equity funds in the past. Market Share: 48.6% Government Bond Product’s Market Value: $350,077,608 Management Fee: 0.15% Market Share Change in the Past Quarter: -5.51 percentage points Ondo Finance Ondo Finance launched three tokenized bond products by investing in multi-billion dollar, highly liquid, exchange-traded funds managed by the world’s preeminent bond managers, including BlackRock and Pacific Investment Management Company (PIMCO). They are the U.S. Government Bond Fund (OUSG), Short-Term Investment Grade Bond Fund (OSTB), and High-Yield Corporate Bond Fund (OHYG). Market Share: 17.01% Government Bond Product’s Market Value: $122,511,877 Management Fee: 0.15% Market Share Change in the Past Quarter: -6.69 percentage points Matrixdock Matrixdock is an on-chain treasury bond platform launched by Matrixport, a Singaporean asset management platform. STBT is Matrixdock’s first product, introducing a risk-free rate based on US government bonds. Market Share: 10.78% Government Bond Product’s Market Value: $77,674,902 Management Fee: 0.3% Market Share Change in the Past Quarter: -2.11 percentage points Backed Finance Backed is a Swiss start-up bridging real-world assets on-chain. The company has issued blB01, a tokenized short-term U.S. government bond ETF, on Coinbase’s Layer2 Base. Market Share: 6.73% Government Bond Product’s Market Value: $48,485,006 Management Fee: 0% Market Share Change in the Past Quarter: -0.4 percentage points Hashnote Hashnote is a regulated company under the oversight of the U.S. Commodity Futures Trading Commission (CFTC) and the Cayman Islands Monetary Authority (CIMA). It is a fully regulated, institutional-grade DeFi asset management platform, offering comprehensive service support, zero counterparty risk, and customizable risk/reward strategies to consumers. Market Share: 5.92% Government Bond Product’s Market Value: $42,613,396 Management Fee: 0% Market Share Change in the Past Quarter: +0.2 percentage points Superstate Superstate is a blockchain-based asset-management firm. It raised $14 million in venture capital to develop regulated, on-chain funds that U.S. investors can access last November. In February 2024, it debuted its first tokenized U.S. Treasury fund offering on the Ethereum blockchain. Market Share: 4.16% Government Bond Product’s Market Value: $29,985,969 Management Fee: 0.15% Market Share Change in the Past Quarter: +4.16 percentage points Open Eden Open Eden is a crypto startup established in early 2022. It was co-founded by Jeremy Ng and Eugene Ng, Gemini’s ex-Asia-Pacific head and former Head of Business Development for the region. Market Share: 3.24% Government Bond Product’s Market Value: $23,328,886 Management Fee: 0% Market Share Change in the Past Quarter: -1.12 percentage points Maple Finance Maple Finance is an institutional capital marketplace that provides credit experts with tools to run an on-chain lending business connecting institutional lenders and borrowers. It has been a market leader in private credit. In April 2023, it introduced a new cash management pool allowing non-U.S. accredited investors and entities to participate directly in U.S. Treasury investments using USDC. Market Share: 1.45% Sovereign Debt Product Market Value: $10,428,616 Management Fee: 0.5% Market Share Change in the Past Quarter: -3.23 percentage points Predicting Future Developments We believe six trends will drive the development of the tokenized government bond segment of the RWA market. These trends will transform the market landscape and provide investors with new opportunities. The tokenized government bond segment will witness a deeper integration of on-chain technology with traditional financial assets. This trend will enable more efficient asset tokenization processes and more timely synchronization of on-chain and off-chain data, significantly improving the efficiency and transparency of transactions. This will, in turn, lead to faster and more trustworthy trading experiences for investors, potentially attracting more capital into the area. Deeper Integration of On-chain and Off-chain Mechanics With increased regulatory attention around digital assets, tokenized government bond projects will be forced to strengthen their compliance. We believe more blockchain-based compliance solutions, such as those using smart contracts for compliance checks, will emerge to meet increasingly stringent regulatory requirements. This will boost investor confidence and encourage more institutions to participate in this market. Increased Compliance with Advances in RegTech Solutions Advancements in cross-chain technologies will enable seamless interoperability of tokenized government bond products on various blockchain platforms, enhancing liquidity and accessibility across the entire market. This will provide investors with more choices and flexibility and reduce market risks. Additionally, increased cross-chain interoperability will promote the convergence of international markets, creating more global investment opportunities. Increased Cross-Chain Interoperability We expect projects to introduce more customized and personalized products to meet the specific needs of different investor groups. These products will include those that precisely target an investor’s risk preference and investment objectives, enabling them to achieve their financial goals better. This movement, in general, will allow investors to have a more diversified range of investment choices. More Customized and Personalized Products Projects are expected to place greater emphasis on transparency and security, leveraging blockchain technology’s traceability and tamper-proof nature to enhance investor trust and confidence. This will attract more risk-sensitive investors to participate in the market and help mitigate market manipulation risks. Enhanced Transparency and Security The application of artificial intelligence for market analysis and predictions, as well as the generation of automated investment strategies, will be a significant innovation direction for the Sovereign Debt RWA market in the future. With these innovative developments, investors will be better informed and empowered to seize market opportunities. Integration of Cutting-Edge Financial Technologies like AI Promising Project Types Based on the above understanding, we believe the following five types of projects will be worth paying attention to. Amid more and more stringent regulatory requirements, projects that have a high level of compliance and offer high transparency are more likely to gain favor in the market. These projects will be able to establish a trustworthy reputation and attract more investors. High Compliance Projects Projects that fully leverage cutting-edge technologies such as blockchain, artificial intelligence, and big data, particularly those capable of providing efficient trading and investment strategies, are poised to secure a place in the market. These projects will bring faster and smarter investment experiences. Technology-Driven Innovative Projects Projects that boast seamless interoperability across different blockchain platforms, especially those providing a broader selection of assets with optimized liquidity, will have a clear competitive advantage. Cross-Chain Compatible Projects Platforms that are easy to use and provide comprehensive customer support and educational resources will be more popular, especially among non-professional investors. User-Friendly Platforms Projects that excel in cost control and operational efficiency, such as those offering low management fees and highly automated services, will be able to win out in the competitive market. Low-Cost, High-Efficiency Solutions In conclusion, investors are more inclined to seek higher-yield investment opportunities under a hiking Federal Reserve. The growth of the RWA market indicates that despite macroeconomic pressures, its potential for high returns has attracted a significant amount of capital. With regulatory improvements and technological advancements, we expect the tokenized government bond market to continue to grow, especially given the increasing adoption of digital currencies like stablecoins and CBDCs. That said, tokenized government bond projects may face stricter regulatory requirements in the future, particularly in anti-money laundering (AML) and Know Your Customer (KYC). As the regulatory environment becomes more stable, these projects are expected to integrate more seamlessly into the traditional financial system while providing investors with new investment channels and risk management tools. To mitigate the inherent risks associated with RWA products, we believe tokenized government bond issuers should adopt decentralized governance decision-making mechanisms, such as the DAO framework used by many issuers in the private credit category. Such mechanisms enhance transparency, allow for better risk assessment and mitigation and also help ease regulatory compliance. Also published . here

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Ugh! To think that this article started from a rant about my K-drama search results. SMH. I’m thrilled that it did because it led me down the rabbit hole of Algorithms, Deep Personalization, Search optimization, and…wait for it… ! That site is to my brain what a pâtisserie is to my eyeballs and belly. I digress, but seriously, if you haven’t checked it out, you should. Netflix’s Research Library It is due to my curiosity that I decided to write this article regarding optimizing Netflix's ML ranker outcomes for a more efficient and accurate deep personalization experience of subscribers’ long and short-term viewing preferences — to address both Fetch and Explore intents. I found writing this article very interesting because it takes the business and customer angles, as well as the infrastructure, into consideration. Context Netflix seems to focus more on home-page recommendations that enhance users’ pre-query experience and less on post-search experience. To some extent, this is great because if it were to be the other way around from the beginning, we’d all have dropped Netflix years ago. After all, the user experience (UX) would have been a hot mess! According to Das et al. (2022) in their paper on , even though most of the content discovery happens on the home page, a large amount still comes from Search. So, I can’t help but wonder, if this is the case and Netflix has solidified its , how come members still don’t have some control over their experience, especially during Search? As a Product Manager, I understand focussing on features that affect more users, but sometimes, paying attention to the seemingly insignificant 20% that don’t seem like urgent user needs could lead to 80% of our desired product outcomes. query-facet mapping and its applications UX dominance amongst its competitors There’s been some recent about Netflix’s customer satisfaction ratings in the US/Canada region, and the data shows that there seems to be quite some churn in this region despite Netflix’s growing presence in . Nonetheless, it can be argued that that region was the primary market for the product. Thus, it’s a more mature market. Therefore, it is highly likely that most other markets will follow the same trajectory as the product/markets mature and other market players arise in the mid to long term. buzz other regions The way I see it, as stated in a tweet, “…Real value from AI always hinges on solving the hard 20%- and this almost always requires an entirely different set of approaches.” thus, Netflix must start to look into the features that they don’t focus much on — based on the evidence, that’d be query-related recommendations and Search. What if the “different set of approaches” is simple? Alex Ratner Let’s be real: Netflix’s recommender systems are SUPREME (All Hail!), and the recommendations on the homepage are great a lot of the time, but it becomes more stressful when I need to find films within a genre or category that falls within Netflix’s “movie genome” (“This show is …” e.g., Swoonworthy) because . From my research, which included an admittedly ‘leading’ and informal Twitter with a small sample size, interview sessions, and scouring tons of forums on the internet for reasons why people don’t like Netflix, recommendations and search relevancy seem to be recurring customer issues. These hangups don’t stop the users from clocking their “Hours spent watching…” metric because the recommendations are decent enough, but from the responses I got, it’s almost like a nagging but dull pain point that subscribers have. They know it can be better, but it works fine to accomplish their primary goal, and they’ve used Netflix for so long that it’s a pain that they have learned to deal with. the recommendations on the homepage are finite poll I want to be able to find more movies that meet my specific taste so that my recommendations are more accurately tailored to my preferences. As a subscriber, what do I want? According to their Research website, they want me, the subscriber, to spend less time searching and more time watching what I like. What does Netflix want? So, what if I’m able to input search components once in a blue moon, and my filters are so detailed that the queries finetune the recommender systems to provide better in-session pre-query recommendations during Fetch and Explore user intent actions such as idly scrolling/hovering through recommendations lists and typing to search respectively? For instance, this has happened to me too many times than I can count and apparently so many others experience the same; Scenario: Uchenna prefers to watch Korean TV shows in the romance genre that are swoonworthy, comedy and heartfelt. It doesn’t mean that if Netflix recommends a Chinese TV show that is in the romance genre with genome tags for ‘Bittersweet, Heartfelt and Emotional’, she wouldn’t watch it. She most likely would or she would have to scroll for several minutes looking for something that meets her current mood and preference. Netflix’s Problems In my humble opinion, these are Netflix’s problems; Firstly, some of its core metrics are extrinsically motivated, quite presumptive, and based on the premise that success is in the completion of the main goal, which is, watching something on the platform in video format. In a perfect product world with business KPIs at the forefront of decision-making, yes, this is right, but in a world with flawed humans with varying moods, preferences, and intents, this is a flawed and unempathetic approach — it does not take into account that I might enjoy watching something that was recommended to me, but it doesn’t mean that it is what I would usually go for or it’s what my mood calls for at the point of selection. Because Netflix is in the growth stage of the product lifecycle, it seems the current metrics are based on customer acquisition and retention without much focus on customer satisfaction. However, the bottom line is that I’m generally happy, but my need at that moment hasn’t been met. In addition, in a scenario where I have to find films with labels that are close to what I have in mind, it’d take longer for me to find something, which then explains why most people are likely to watch something that’s been recommended to them by a friend instead of searching or always following the recommendations. Secondly, another recurring complaint on the internet pertains to the tags. The genre tags are inconsistent and do not follow regular genres. In addition, the genome tags and categories seem to be inconsistent and disorganized as well. With inconsistent and scattered labeling of video content, the likelihood of the recommender systems producing pre-query and post-query recommendations that are exactly like the ‘previously watched’ content and/or the probability of accurately predicting the subscriber’s taste becomes diminished. Thus, Netflix has a great recommender systems infrastructure, but the labels aren’t organized properly enough to help the customer decide as well as help the systems make fully optimized anticipatory decisions. For instance, last night, I started watching a TV show with genome tags ‘Swoonworthy, comedy, quirky,’ and there’s absolutely nothing comedic about this show. It’s quirky and romantic but definitely not comedy—wrong labeling. Lastly, e-commerce, content-sharing, and video tech companies are beginning to optimize their Search function despite having great algorithms that predict content based on user preferences but Netflix’s search function barely even has any filters. I found lots of comments online during my research, but one of them seems to encapsulate everything I know is wrong with Netflix. Recommendations Option 1: Optimization of Search Filters and Creation of ‘Saved Search Labels Applying optimized search filters and member pre-selected ‘saved search’ results could help improve Netflix’s recommender systems’ outcomes for pre-query in-session and long-term recommendations. Using a saved search to establish a baseline for behavior and user interest. User interactions with the baseline recommendation results can then help predict user preferences within the confines of that saved search, e.g., a Saved search named Quirky K-Drama can show more related results as the user interacts with the recommendation results — ‘Because you saved Quirky K-Drama.’ Below is a design of the Advanced Search/Saved Search feature recommendation. Option 2: Creating Expandable Pre-Query Recommendations Carousel Lists ‘Because you watched…’ and ‘Because you liked…’ rows/ recommendations are finite, which puts a limit on the subscriber’s explore intent. Currently, the recommender system provides a carousel row for each and then more carousel rows for movies with similar genome tags to what you’ve watched in the past. This creates quite a lot of clutter and diminishes the user’s experience because they have to scroll down to view films within each genome tag. Instead of creating too many lists/carousels that make the user continuously scroll downwards and sideways to find videos within mislabeled genome tags, Netflix can optimize the ‘Because you liked…’ and ‘Because you watched…’ carousels by opening them up to become expendable. This way, there’s not much of a break in the user’s exploratory actions, thus creating a better experience for them. Option 3: Creating Genome Tags Ratings to Improve Labelling and Algorithmic Outcomes I’m thinking out loud (on paper), but just keep up with me, and hopefully, this written soliloquy will be coherent enough for you to understand the point that I’m trying to make. There’s no way that humans can possibly watch every video in the Netflix library to judge the genome tags and relabel them correctly. Besides, if this were possible, the labels might even be more disorganized because the perception of a video is, to some extent, subjective. Bringing that bias into the mix might make the data more inaccurate. But could there be a way for subscribers to rate a video’s genome tag accuracy? If this happens, the recommender systems can use the data to properly label the video content. For instance, Grammarly does something similar with its text tone detector ratings. I believe these ratings feed into their algorithm that helps predict and detect the tone of text. The drawback to this is that Netflix might have to introduce a more detailed rating system for videos, which is a feature that was discontinued many years ago. However, if there’s a way to introduce the genome tag rating system even for three months, I believe that predictions will be better. Open Beta testing provides the perfect environment and timeframe for Netflix to allow subscribers to help train the model for more accurate predictions. Potential Limitations in Implementing My Recommendations It is vital to figure out whether the cost of implementing these ‘delightful’ feature recommendations can justify the required investment because, at the end of the day, Netflix is a business, and cost-benefit analysis is a given. There was not much Netflix-specific data to analyze; thus, the cost-benefit analysis outcome is unknown, and the feasibility of implementing these suggestions cannot be determined with any certainty. Cost-Benefit Dilemma: Qualitative research for this article was done using a random sampling approach on a very small cumulative sample size of about 30 people. Other feedback gathered from different online forums might also be indicative of a recurring customer pain point. However, Netflix has over 200 million subscribers; thus, even though it may seem like these are widespread customer complaints, they might not necessarily be representative of the broader subscriber population size as the small sample size comes with the probability of a sampling error (Phew! What a long sentence). Anyway, only Netflix’s data can truly confirm the validity and importance of these issues to their users. Regardless, Hypothesis testing and further research would be required to know if these recommendations are worth implementing. Without testing, implementing these would be a shot in the dark. Risk of Sampling Error: These suggestions are presumptive that the features haven’t already come up on the Netflix Product team’s backlog or brainstorming board, and they either haven’t been prioritized or have been canned because they aren’t feasible. Potentially Erroneous Assumption of Priority: Recommendation Option 3 assumes that genome tags are determined by Netflix’s AI, however, there's a possibility that in reality, genome tags are added manually during the uploading process e.g. Think: How labels and categories are added to products in a POS system. If that's the case, then we are back to square one — human error due to subjectivity — this might account for discrepancies. However, on second thought, despite this change in scenario, I believe that the validity and viability of the genome tags ratings recommendation (Option 3) could very well still stand. Just think about it for a second. Unfortunately, unless you work at Netflix, this information is unknown, so let’s just stick with our imagination and inferences, okay? :) Reality-Hypothesis Scenario Divergence: Conclusion Netflix arguably has the best Recommender systems in the game; its subscriber growth rate is increasing at a healthy pace due to expansion strategies, and its UX is probably the best in the competitive landscape as well. I envisage that they will continue to hold on to the majority of the market share for at least the next five years. However, what happens if they eventually manage to capture the streaming markets in most target countries? What happens when they can't compete on price with local and international competitors? What happens when, like the US, most of these markets mature, and customers in these countries begin to look for better customer experiences with their video streaming? What happens when innovation in other streaming service companies, like Hulu, AppleTV, and HBO, outpace Netflix's holy grail recommender systems? Ultimately, in the words of Steven Van Belleghem , “What if customers want more than excellent service?” I'm sure the amazingly talented people of Netflix are already thinking about these potential issues. These are long-term questions, but I believe that in the meantime, it's time to tweak Netflix's recommender systems to harness its optimum potential for this much-loved service to provide much better customer satisfaction. Customer experience (CX) is currently Netflix’s strength; however, customer satisfaction is slipping. A great product can have amazing CX, but if the customers want more than just excellent service to stay satisfied, more will need to be done. This is where these recommendations come in. According to Former VP of Product at Netflix, Gibson Biddle, the Netflix product team is not just focused on understanding the customer’s current wants and needs but instead on ‘inventing and delivering on unanticipated future needs.’ I also came across an article in titled ‘Stop Trying to Delight Your Customers’, and one statement caught my attention, “According to conventional wisdom, customers are more loyal to firms that go above and beyond. But our research shows that .” Harvard Business Review exceeding their expectations during service interactions makes customers than simply meeting their needs only marginally more loyal Source: (Former VP of Product at Netflix ) Gibson Biddle Therefore, typical metrics for customer satisfaction, such as NPS, might not capture the sentiments observed during my research. Focus groups and interviews are the best research methods for figuring out such “nagging-but-dull” pain points that customers have, as they don't cause churn per se. However, like a toothache, over time, the dull pain points eventually might be the reason for customers to migrate to a different service provider. Thus, this is the reason why I strongly agree with Dixon et al. (2010) in their HBR article that states that Customer Satisfaction and Customer Retention/Loyalty are more likely to be captured using Customer Effort Score as opposed to Net Promoter Score or CSAT and that’s because you learn more when asking the main question in CES — “How much effort did it take the customer to achieve their desired goal.” I believe that if Netflix asks this question often, they will find the reason why customer experience (CX) is great but customer satisfaction (CS) and loyalty (especially in the US) are disproportionately lower, and the results would help deepen personalization efforts on the platform. In the context of Netflix, Deep personalization stands at the intersection between Customer Satisfaction, Customer Experience, Customer Loyalty, and an optimized AI system. Supercharge Deep Personalization using features based on insights from research methods such as CES, the ML ranker becomes more efficient in determining user preferences. The hypotheses in this article address business problems and user problems and encourage revisiting the current product metrics at Netflix. It’s a win-win for all, right? Well, it’s up to you to decide. In a nutshell, there’s still a lot that the Netflix ML/AI systems are capable of. There’s still a lot that Netflix can do better to acquire and retain subscribers if they ask the right questions and implement features that reduce the users’ effort, create a better experience, and enhance the efficiency of the recommender systems. Thus far, they have done a great job, but it can certainly be better. The question is whether the cost of enhancement is commensurate with the benefit of a more efficient platform. What are your thoughts on this subject? Do the pain points resonate with you? Also published . here

Enhancing Netflix's Deep Personalization: The Full Potential Of Its Current AI Recommender Systems

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