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Startups of The Year 2023 Winners: Europe

The Restaurant Association of Ireland (RAI) has launched a universal gift voucher, redeemable in up to 3000 participating member restaurants and the first known voucher to be powered by Blockchain technology. The platform was developed by eosDublin and is powered by Europechain, which offers a level of trust needed to ensure users and their customers funds are safe and secure. The Gift Voucher platform is an industry first, utilizing the latest blockchain technology to secure the unique voucher codes to the Europechain Blockchain. Blockchain is a technology which is changing the world, as the world is going digital, blockchain will enable data immutability, security and transparency. “ ," Adrian Cummins - CEO RAI The RAI is proud to offer a nationwide restaurant voucher for all participating members. We were excited to hear that the platform was being built using the latest class leading technology and are happy to be part of this initiative. We look forward to building on our initial success for the years to come “ ,” adds Adrian Cummings, chief executive of the RAI. .” Currently it can be difficult for individual restaurants to offer gift vouchers. They can be tricky to track, manage and process “In addition, when giving a gift of a meal, it is fantastic to offer a choice of restaurant, so your recipient can make the choice of dining The allows users to see participating restaurants by location broken down by province. Vouchers can be purchased online with instant email verification. RAI Listings site The voucher system includes a sales dashboard for members with real time sales, drill-down monthly reports and an ability to forecast incoming revenue. The system can also be used to track performance, redemption and refund ratios, as well as breakdown of sales by member. Adam Bouktila, of Europechain said “ At the moment, the system is super easy to use, restaurants can add their logos, have one click profile creation and also offer individual promotions and offers.” The RAI voucher platform consists of a listing website with a voucher purchase link using for simple integration of payment processing. The platform is integrated with the to protect against fraud and double spending. Blockchain technology ensures transparency and immutability – no voucher need ever be lost, double spent or unaccounted for again. Stripe Europechain blockchain " We at eosDublin and Europechain are very proud to have worked with the RAI to develop such a progressive voucher platform which will deliver huge benefits to members and diners. This year, we will be exploring further enhancements to the voucher tool using blockchain technology such as NFT (non-Fungible Token) voucher creation, Escrow Smart Contracts and Incentive models. 2020 is going to be exciting,” he says.

World's 1st Restaurant Voucher Platform Using Blockchain Launched In Ireland

Authors:
(1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu);
(2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu);
(3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Table of Links Abstract and 1. Introduction 2. Problem Formulation 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3.2 Assumptions and induced strategies 3.3 Constraints given by phase-II experiments 3.4 Persuasion ratio and the optimal signaling structure 3.5 Comparison with classical Bayesian persuasion strategies 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4.2 Determined versus sender-designed experiments 4.3 Multi-phase model and classical Bayesian persuasion and References 3.4 Persuasion ratio and the optimal signaling structure Careful readers may notice that if we multiply the persuasion ratio with the prior, the value will be the (maximum) expected utility the sender can achieve from the given set of strategies. Since the sender’s expected utility is monotone increasing in the prior p regardless of which set of strategies the sender adopts, the persuasion ratio under a given prior can be viewed as the relative utility gain this set of strategies can offer to the sender. Hence, given a specific prior, if a set of strategies has a higher persuasion ratio with respect to another set of strategies, the sender should use a strategy in the former instead of the latter. According to this discussion, we can draw a persuasion ratio curve for each set of strategies as the prior is varied in [0, 1]. Abusing notation, we represent the persuasion ratio curve by P R(S). It may appear that an optimization needs to be carried out for each value of the prior. However, structural insights presented in the following two lemmas considerably simplify the analysis. Properties presented in Lemma 3 narrow down the space where the sender needs to search for the optimal signaling strategy. This allows us to depict persuasion ratio curves P R(S) for some basic strategies. On top of that, Lemma 4 provides a systematic approach to derive persuasion ratio curves for all types of strategies. Lemma 3. Given a type of strategy S and a prior p, there exists a (sender’s) optimal strategy S ∈ S which satisfies one of the following two conditions: At least one IC requirement of the constituent induced strategies is tight;


There is a signal that will be sent with probability 1 under S. Before discussing the simplifications that Lemma 3 yields in terms of the key properties for solving the problem, we give an intuitive outline of the proof of Lemma 3. When the IC requirements of the two induced strategies are not tight and both signals are sent with non-zero probability in a strategy S, the sender can increase her expected utility by slightly raising the probability of the signal with a higher persuasion ratio (and adjust the probability of the other signal to respect the prior) to form a strategy S+. The sender can keep doing this ‘slight’ modification of her strategies until either one of the IC conditions is satisfied or the signal is sent with probability one. Lemma 5. The persuasion ratio curve of the optimal signaling strategy P R∗ (p) is the upper envelope of the different types of strategies’ persuasion curves. Further, the optimal signaling strategy (under a given prior) is the strategy that reaches the frontier of the persuasion ratio curve (at that prior). For a two-phase trial or to solve the last two phases of a trial with more than two phases studied in Section 4, the following corollary can further simplify the sender’s optimization procedure. With Corollary 1, the Equation (3) in Lemma 4 reduces to a linear equation. Hence, the comparison in Lemma 5 and the computation in Lemma 4 can be reduced to a comparison of the (unique) corresponding IC strategies (if one exists) under the interim belief listed in Corollary1 for different types of strategies. This paper is available on arxiv under CC 4.0 license. Authors: (1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu); (2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu); (3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Authors: Authors: (1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu); (2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu); (3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Problem Formulation 2. Problem Formulation 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3.2 Assumptions and induced strategies 3.2 Assumptions and induced strategies 3.3 Constraints given by phase-II experiments 3.3 Constraints given by phase-II experiments 3.4 Persuasion ratio and the optimal signaling structure 3.4 Persuasion ratio and the optimal signaling structure 3.5 Comparison with classical Bayesian persuasion strategies 3.5 Comparison with classical Bayesian persuasion strategies 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4.2 Determined versus sender-designed experiments 4.2 Determined versus sender-designed experiments 4.3 Multi-phase model and classical Bayesian persuasion and References 4.3 Multi-phase model and classical Bayesian persuasion and References 3.4 Persuasion ratio and the optimal signaling structure Careful readers may notice that if we multiply the persuasion ratio with the prior, the value will be the (maximum) expected utility the sender can achieve from the given set of strategies. Since the sender’s expected utility is monotone increasing in the prior p regardless of which set of strategies the sender adopts, the persuasion ratio under a given prior can be viewed as the relative utility gain this set of strategies can offer to the sender. Hence, given a specific prior, if a set of strategies has a higher persuasion ratio with respect to another set of strategies, the sender should use a strategy in the former instead of the latter. According to this discussion, we can draw a persuasion ratio curve for each set of strategies as the prior is varied in [0, 1]. Abusing notation, we represent the persuasion ratio curve by P R(S). It may appear that an optimization needs to be carried out for each value of the prior. However, structural insights presented in the following two lemmas considerably simplify the analysis. Properties presented in Lemma 3 narrow down the space where the sender needs to search for the optimal signaling strategy. This allows us to depict persuasion ratio curves P R(S) for some basic strategies. On top of that, Lemma 4 provides a systematic approach to derive persuasion ratio curves for all types of strategies. Lemma 3. Given a type of strategy S and a prior p, there exists a (sender’s) optimal strategy S ∈ S which satisfies one of the following two conditions: Lemma 3. Given a type of strategy S and a prior p, there exists a (sender’s) optimal strategy S ∈ S which satisfies one of the following two conditions: At least one IC requirement of the constituent induced strategies is tight; There is a signal that will be sent with probability 1 under S. At least one IC requirement of the constituent induced strategies is tight; At least one IC requirement of the constituent induced strategies is tight; At least one IC requirement of the constituent induced strategies is tight; There is a signal that will be sent with probability 1 under S. There is a signal that will be sent with probability 1 under S. There is a signal that will be sent with probability 1 under S. Before discussing the simplifications that Lemma 3 yields in terms of the key properties for solving the problem, we give an intuitive outline of the proof of Lemma 3. When the IC requirements of the two induced strategies are not tight and both signals are sent with non-zero probability in a strategy S, the sender can increase her expected utility by slightly raising the probability of the signal with a higher persuasion ratio (and adjust the probability of the other signal to respect the prior) to form a strategy S+. The sender can keep doing this ‘slight’ modification of her strategies until either one of the IC conditions is satisfied or the signal is sent with probability one. Lemma 5. The persuasion ratio curve of the optimal signaling strategy P R∗ (p) is the upper envelope of the different types of strategies’ persuasion curves. Further, the optimal signaling strategy (under a given prior) is the strategy that reaches the frontier of the persuasion ratio curve (at that prior). Lemma 5. The persuasion ratio curve of the optimal signaling strategy P R∗ (p) is the upper envelope of the different types of strategies’ persuasion curves. Further, the optimal signaling strategy (under a given prior) is the strategy that reaches the frontier of the persuasion ratio curve (at that prior). For a two-phase trial or to solve the last two phases of a trial with more than two phases studied in Section 4, the following corollary can further simplify the sender’s optimization procedure. With Corollary 1, the Equation (3) in Lemma 4 reduces to a linear equation. Hence, the comparison in Lemma 5 and the computation in Lemma 4 can be reduced to a comparison of the (unique) corresponding IC strategies (if one exists) under the interim belief listed in Corollary1 for different types of strategies. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How to Maximize Persuasion Ratios in Two-Phase Trials

Golden Celestial Ratio (GCR): A Comprehensive Guide to the Divine Ecosystem

গোল্ডেন সেলেস্টিয়াল রেশিও (GCR): ডিভাইন ইকোসিস্টেমের একটি ব্যাপক নির্দেশিকা

Golden Celestial Ratio (GCR): Ein umfassender Leitfaden zum göttlichen Ökosystem

Proporción áurea celestial (GCR): una guía completa del ecosistema divino

Ratio Céleste Doré (GCR) : Un guide complet de l'écosystème divin

स्वर्णिम आकाशीय अनुपात (जीसीआर): दिव्य पारिस्थितिकी तंत्र के लिए एक व्यापक मार्गदर्शिका

黄金比（GCR）：神の生態系への総合ガイド

황금 천체 비율(GCR): 신성한 생태계에 대한 종합 가이드

Proporção Celestial Áurea (GCR): Um Guia Abrangente para o Ecossistema Divino

Золотое небесное сечение (GCR): подробное руководство по божественной экосистеме

Altın Gök Oranı (GCR): İlahi Ekosistem İçin Kapsamlı Bir Kılavuz

Tỷ lệ thiên thể vàng (GCR): Hướng dẫn toàn diện về hệ sinh thái thần thánh

黄金天体比例（GCR）：神圣生态系统的综合指南

Covalent (CQT), the leading structured modular data infrastructure layer, is excited to announce that the CQT staking ratio on Ethereum has now exceeded the all-time high staking ratio previously achieved on Moonbeam. In less than a month since migrating the staking contract to Ethereum, over 13% of the total supply is now locked in the staking contract with a current historical APY of around 18%. This milestone highlights the growing confidence and engagement within the Covalent network, underscoring Covalent’s ongoing efforts to enhance its staking infrastructure and make the process more efficient and accessible for all users. Migration to Ethereum Boosts Staking Participation The migration process encouraged stakers to unstake and bridge their tokens through Wormhole onto Ethereum. Utilizing Covalent's intuitive staking dashboard, CQT holders staked their now unstaked tokens into the new Ethereum staking contract. As a result, the current staking amount now stands at approximately 13% of the total supply—surpassing the 11% previously staked on Moonbeam. The increase in the staking ratio signifies more than just a successful migration; it reflects the growing trust in Covalent's long-term vision from both existing and new stakeholders. If all 11% of the previously staked CQT on Moonbeam transitioned to Ethereum, the rise to 13% suggests additional stakeholders have joined, showcasing enhanced confidence in the staking process on Ethereum, and possibly excitement around the Ethereum Wayback Machine launch set to bring Long-Term Data Availability to Ethereum. Previously, staking CQT on Moonbeam required both GLMR and ETH for gas operations. The migration to Ethereum has simplified this by requiring only ETH, significantly improving the user experience and reducing complexity for less advanced stakers. The new staking dashboard further streamlines the process, allowing existing stakers to migrate and new users to stake with just a few clicks. These improvements are likely major contributors to the increased participation and influx of new stakers. Conclusion The migration to Ethereum and the subsequent increase in the CQT staking ratio underscore Covalent's commitment to providing a seamless and efficient staking experience for its community. Stakers that participate earlier are poised to benefit from a higher APY, which currently stands at approximately 18%. This rate will gradually adjust to around 12% as Covalent approaches its target of having 20% of the total CQT supply staked on Ethereum. Looking ahead, Covalent is committed to deploying a variety of strategies and updates to further strengthen the network, ensuring a more secure, efficient, and scalable infrastructure for all users. Token holders can expect continuous improvements and enhancements as Covalent works towards creating a robust and decentralized ecosystem. Call to Action For those who have yet to stake their CQT or are considering joining the Covalent community, now is the perfect time to get involved. Head over to our staking dashboard and start staking your CQT on Ethereum today! About Covalent Covalent (CQT) is shipping data infrastructure for the decentralized ecosystem, solving Long-Term Data Availability for Ethereum—infrastructure that can re-execute blobs and shape AI. With CQT staking now back on Ethereum, The New Dawn of network expansion towards the Ethereum Wayback Machine commences. Today, this means AI, DeFi, and other use cases have unfettered access to structured onchain data from over +225 blockchains and growing. Learn about the EWM: Technical | Non-Technical. For more information, check out Covalent’s: Official Website | Twitter/X | Linktree This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Covalent (CQT), the leading structured modular data infrastructure layer, is excited to announce that the CQT staking ratio on Ethereum has now exceeded the all-time high staking ratio previously achieved on Moonbeam. In less than a month since migrating the staking contract to Ethereum, over 13% of the total supply is now locked in the staking contract with a current historical APY of around 18%. This milestone highlights the growing confidence and engagement within the Covalent network, underscoring Covalent’s ongoing efforts to enhance its staking infrastructure and make the process more efficient and accessible for all users. Migration to Ethereum Boosts Staking Participation The migration process encouraged stakers to unstake and bridge their tokens through Wormhole onto Ethereum. Utilizing Covalent's intuitive staking dashboard, CQT holders staked their now unstaked tokens into the new Ethereum staking contract. As a result, the current staking amount now stands at approximately 13% of the total supply—surpassing the 11% previously staked on Moonbeam. The increase in the staking ratio signifies more than just a successful migration; it reflects the growing trust in Covalent's long-term vision from both existing and new stakeholders. If all 11% of the previously staked CQT on Moonbeam transitioned to Ethereum, the rise to 13% suggests additional stakeholders have joined, showcasing enhanced confidence in the staking process on Ethereum, and possibly excitement around the Ethereum Wayback Machine launch set to bring Long-Term Data Availability to Ethereum . Ethereum Ethereum Previously, staking CQT on Moonbeam required both GLMR and ETH for gas operations. The migration to Ethereum has simplified this by requiring only ETH, significantly improving the user experience and reducing complexity for less advanced stakers. The new staking dashboard further streamlines the process, allowing existing stakers to migrate and new users to stake with just a few clicks. These improvements are likely major contributors to the increased participation and influx of new stakers. staking dashboard staking dashboard Conclusion The migration to Ethereum and the subsequent increase in the CQT staking ratio underscore Covalent's commitment to providing a seamless and efficient staking experience for its community. Stakers that participate earlier are poised to benefit from a higher APY, which currently stands at approximately 18%. This rate will gradually adjust to around 12% as Covalent approaches its target of having 20% of the total CQT supply staked on Ethereum. Looking ahead, Covalent is committed to deploying a variety of strategies and updates to further strengthen the network, ensuring a more secure, efficient, and scalable infrastructure for all users. Token holders can expect continuous improvements and enhancements as Covalent works towards creating a robust and decentralized ecosystem. Call to Action For those who have yet to stake their CQT or are considering joining the Covalent community, now is the perfect time to get involved. Head over to our staking dashboard and start staking your CQT on Ethereum today! staking dashboard staking dashboard About Covalent Covalent (CQT) is shipping data infrastructure for the decentralized ecosystem, solving Long-Term Data Availability for Ethereum—infrastructure that can re-execute blobs and shape AI. With CQT staking now back on Ethereum, The New Dawn of network expansion towards the Ethereum Wayback Machine commences. Today, this means AI, DeFi, and other use cases have unfettered access to structured onchain data from over +225 blockchains and growing. Learn about the EWM: Technical | Non-Technical. For more information, check out Covalent’s: Official Website | Twitter/X | Linktree Technical Technical Non-Technical . Non-Technical Official Website Official Website Twitter/X Twitter/X Linktree Linktree This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Covalent’s CQT Staking Migration Ratio On Ethereum Climbs To 13%, Surpassing Previous Moonbeam ATH

A couple of days ago, I wanted to invest a certain amount into a basket of stocks. But, I encountered a dilemma on how much to invest in individual stocks. I aim to maintain a target allocation ratio for each stock to have a balanced portfolio. Consider the example that you have a portfolio of 3 stocks viz. INFOSYS, TCS and WIPRO. Your current investment in each of these stocks is ₹30, ₹50, and ₹20 respectively (currency is not important here as long as all stocks are in the same currency). Let’s say our Target Allocation ratio (TAR) for each of these stocks is 33%, 33%, and 34% respectively as shown in the following table. Instrument Current Investment Current TAR Target TAR INFOSYS ₹30 30% 33% TCS ₹50 50% 33% WIPRO ₹20 20% 34% Total ₹100 Now, what’s the optimal choice if you want to invest a total of ₹30 to get closer to your TAR for each of these stocks? I googled for some tools but couldn't find any that could help my case. So, I sat in to derive an equation that could provide me with an optimal allocation. Mathematics You have a portfolio of financial instruments: n and your current investments in each financial instrument is: This means that your current investment allocation ratio (between and ) is: 0 1 Now, suppose you want to invest an additional amount of split between these financial instruments. You want to achieve a target allocation ratio of the following for each of these instruments. SN n This implies that: And, our objective is to find out these i.e. how much investment to make in each of the financial instruments. N_i n To solve this problem, we have converted it into an optimization problem. If we assume some initial allocation for each instrument, we calculate the Mean Squared Error between the objective allocation ratio and the actual allocation ratio. Then, we can simply use any (bounded and constrained) optimizing tool to solve the problem. MSE So, Now, we can simply minimize this function to find all N_i Assumptions No selling. The whole point of writing this code is that we don't want to sell any existing instruments to achieve the target allocation. We assume that we would want to invest the full amount of i.e. if you input ₹100 as a new investment to be allocated, the model would allocate the full amount between the financial instruments. new_investment We don't solve the above problem rigorously. The correct way would be to create equations for variables and solve them using an equation solver. However, if that were easy, we wouldn't have invented Machine Learning. So, we instead solve the problem by minimizing the between target and actual allocation ratio using a minimizer function (similar to how Gradient Descent would minimize the error function in Machine Learning). n n MSE Implementation I have converted the above problem into a simple GitHub project. You can find the same . The file details how to use the same. Essentially, I used to minimize the function above. here README scipy.optimizers.minimize MSE To showcase the same, you can edit the file to try out the example that we took in the beginning. src/views/file.py # list all financial instruments
FINANCIAL_INSTRUMENT_NAMES: list[str] = ["INFOSYS", "TCS", "WIPRO"]

# Mention initial investment in each instrument in your currency (all should be in same units)
FINANCIAL_INSTRUMENTS_INITIAL_INVESTMENT: list[float] = [30, 50, 20]

# Mention new investment to be made in same unit as initial investment
NEW_INVESTMENT: float = 30

# Mention TAR for each instrument that you want to achieve
FINANCIAL_INSTRUMENTS_TARGET_INVESTMENT_RATIO: list[float] = [0.33, 0.33, 0.34] Once you have edited the above values in , you can run the code by running the command . It would yield something like the following: src/views/file.py python -m src.views.file Optimizing the MSE to compute optimal allocation. Sit tight!
Final allocation of investment of unit 30 is as follows:
Instrument      Initial Investment %    Target Investment %    Suggested Investment    Final %
------------  ----------------------  ---------------------  ----------------------  ---------
INFOSYS                           30                     33                      10    30.7692
TCS                               50                     33                      10    46.1538
WIPRO                             20                     34                      10    23.0769
Please manually verify the results as Mathematics can only aid you but can't absolve you! As we can see, the model recommended allocation of ₹10 for each instrument. Note that with the given investment amount, it might not be possible to achieve exact TAR but the model would help to move towards it. In the above case, has moved from to . has come down from to and has moved from to . INFOSYS 30% 30.7% TCS 50% 46% WIPRO 20% 23% Feel free to try out different values and let me know if you have any comments. Lead Image generated using Adobe Express

How To Achieve Target Allocation Ratio (TAR) in Your Investment Portfolio

1. Forget about hardware; the real money is in software For months, headlines have fixated on Huawei's limitations in producing advanced 7nm chips, while Apple excels with its cutting-edge 3nm technology. However, this focus on hardware overlooks a critical lesson from history: software, not hardware, drives  profits. Bill Gates built the Microsoft empire not by building hardware—outsourced to IBM—but by dominating the software business. Today, Microsoft’s software business dwarfs IBM's historical combined hardware and software projects. Huawei’s HarmonyOS NEXT, already claimed to be the world’s third-largest mobile OS with over a billion installations, represents a significant milestone. While currently present mostly in China, the system has the potential to expand to other markets, particularly in the Global South. HarmonyOS could appeal to nations seeking to reduce reliance on U.S.-based technology. A large user base often translates into substantial revenue streams over time. Moreover, Huawei is working on extending its OS to personal computers under the name HarmonyOS PC, with a target release in Q3 2025. Even if this initiative gains traction solely within China, particularly in government systems, it could reduce the dominance of Microsoft Windows and Apple macOS, cutting into their lucrative revenue streams. 2. Know-How and Engineering Workforce Unlike hardware chip technology, software OS tools and expertise are widely accessible, starting with the open-source availability of Android OS. This means the necessary know-how is either readily available or easy to acquire. China also boasts a large skilled engineering workforce—reportedly four times larger than that of the U.S.—providing Huawei with ample talent at a competitive cost to develop its new OS. With sufficient time and resources, Huawei is likely to succeed in its HarmonyOS project. 3. Marketing or Reality? Reports suggest that HarmonyOS is facing some challenges. According to a media report, even the migration of China’s own WeChat app to the platform has encountered difficulties. This is not entirely surprising, as building a stable operating system is a highly complex and demanding task. Even Steve Jobs famously staged parts of his iPhone presentation during its early development stages. It required time and engineering effort for Apple to refine and perfect the product. Huawei faces a similar trajectory. If it commits to the project with enough talent, funding, and patience, HarmonyOS could mature into a serious alternative to Microsoft and Apple’s software dominance. 4 References [1] HarmonyOS [2] HarmonyOS NEXT [3] China's first fully home-grown mobile operating system HarmonyOS NEXT launched [4] https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ [5] HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS [6] Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? [7] Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says [8] In Huawei’s battle against Apple, chips are still a weakness, says analyst [9] HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman [10] Huawei HarmonyOS PC rumored to delay till Q3 2025 [11] HarmonyOS PC UI design allegedly surfaced on Huawei Developer website [12] The Global Distribution of STEM Graduates: Which Countries Lead the Way? [13] How Steve Jobs Faked His Way Through Unveiling the iPhone 1. Forget about hardware; the real money is in software For months, headlines have fixated on Huawei's limitations in producing advanced 7nm chips, while Apple excels with its cutting-edge 3nm technology. However, this focus on hardware overlooks a critical lesson from history: software, not hardware, drives  profits. Bill Gates built the Microsoft empire not by building hardware—outsourced to IBM—but by dominating the software business. Today, Microsoft’s software business dwarfs IBM's historical combined hardware and software projects. Huawei’s HarmonyOS NEXT , already claimed to be the world’s third-largest mobile OS with over a billion installations, represents a significant milestone. While currently present mostly in China, the system has the potential to expand to other markets, particularly in the Global South. HarmonyOS could appeal to nations seeking to reduce reliance on U.S.-based technology. A large user base often translates into substantial revenue streams over time. HarmonyOS NEXT world’s third-largest mobile OS Moreover, Huawei is working on extending its OS to personal computers under the name HarmonyOS PC , with a target release in Q3 2025 . Even if this initiative gains traction solely within China, particularly in government systems, it could reduce the dominance of Microsoft Windows and Apple macOS, cutting into their lucrative revenue streams. extending its OS to personal computers HarmonyOS PC Q3 2025 2. Know-How and Engineering Workforce Unlike hardware chip technology, software OS tools and expertise are widely accessible, starting with the open-source availability of Android OS. This means the necessary know-how is either readily available or easy to acquire. China also boasts a large skilled engineering workforce —reportedly four times larger than that of the U.S.—providing Huawei with ample talent at a competitive cost to develop its new OS. With sufficient time and resources, Huawei is likely to succeed in its HarmonyOS project. large skilled engineering workforce 3. Marketing or Reality? Reports suggest that HarmonyOS is facing some challenges. According to a media report, even the migration of China’s own WeChat app to the platform has encountered difficulties. This is not entirely surprising, as building a stable operating system is a highly complex and demanding task. Even Steve Jobs famously staged parts of his iPhone presentation during its early development stages. It required time and engineering effort for Apple to refine and perfect the product. Huawei faces a similar trajectory. If it commits to the project with enough talent, funding, and patience, HarmonyOS could mature into a serious alternative to Microsoft and Apple’s software dominance. 4 References [1] HarmonyOS HarmonyOS [2] HarmonyOS NEXT HarmonyOS NEXT [3] China's first fully home-grown mobile operating system HarmonyOS NEXT launched China's first fully home-grown mobile operating system HarmonyOS NEXT launched [4] https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ [5] HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS [6] Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? [7] Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says [8] In Huawei’s battle against Apple, chips are still a weakness, says analyst In Huawei’s battle against Apple, chips are still a weakness, says analyst [9] HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman [10] Huawei HarmonyOS PC rumored to delay till Q3 2025 Huawei HarmonyOS PC rumored to delay till Q3 2025 [11] HarmonyOS PC UI design allegedly surfaced on Huawei Developer website HarmonyOS PC UI design allegedly surfaced on Huawei Developer website [12] The Global Distribution of STEM Graduates: Which Countries Lead the Way? The Global Distribution of STEM Graduates: Which Countries Lead the Way? [13] How Steve Jobs Faked His Way Through Unveiling the iPhone How Steve Jobs Faked His Way Through Unveiling the iPhone

Experts Skeptical of Huawei Silicon May Be Missing the Forest for the Trees

الخبراء المتشككون في السيليكون الخاص بشركة هواوي ربما يغفلون عن حقيقة الأمر

“Huawei Silicon”a şübhə ilə yanaşan ekspertlər ağaclar üçün meşədən məhrum ola bilərlər

Stručnjaci skeptični prema Huawei Silicijum možda nedostaje šuma za drveće

Los expertos escépticos sobre el silicio de Huawei podrían estar perdiendo de vista el bosque detrás de los árboles

מומחים הספקנים לגבי סיליקון Huawei עשויים להחמיץ את היער בשביל העצים

ファーウェイのシリコンに懐疑的な専門家は、木を見て森を見ずかもしれない

Pakar Ragu-ragu Huawei Silicon Mungkin Hilang Hutan untuk Pokok

Ditsebi tše di Belaelago Silicon ya Huawei e ka ba e Hlolwa Sethokgwa bakeng sa Dihlare

Стручњаци скептични према Хуавеи силицијуму можда недостаје шума за дрвеће

Hünärmenler Huawei Silikonyna şübheli garaýarlar, agaçlar üçin tokaýy ýitirip bilerler

Huawei Silikonuna Şüpheyle Yaklaşan Uzmanlar Ormanı Ağaçlardan Kaçırıyor Olabilir

Huawei kremniyiga shubha bilan qaraydigan ekspertlar daraxtlar uchun o‘rmon yetishmasligi mumkin

Has AI already learned to think? Will it learn soon? Or is the destiny of even the most advanced programs to remain mere imitators of human intelligence? Let's explore why finding answers to these questions is important. The Turing Test Is Just a Game Let's start with the most famous and widely accepted method for determining whether a machine is intelligent: the Turing Test. Many experts believe that modern AI, especially those based on large language models, passes this test quite effectively. To recap, the essence of the test involves a judge interacting with both humans and programs. The judge's task is to determine which of the interlocutors is human and which is a machine. The AI must therefore mimic human behavior so convincingly that the judge makes a mistake. There are various ways to conduct the test, but the core idea remains the same: if we cannot distinguish between the AI's responses and those of a human, then the machine is considered intelligent. However, amidst all the hype surrounding this test, many people overlook an important point. Alan Turing based his test on the Imitation Game, a popular party game from his time. In this game, two people, say a man and a woman, would hide in separate rooms and respond to questions from the attendees via a manager or typewritten notes. The woman would try to convince the attendees that she was a man, while the man would attempt to convince them that he was a woman. Are you getting the message we're conveying? Yes, everything is correct. The Turing Test is essentially a game in which a machine, following specific rules, tries to outplay a human. However, to win this game, the program doesn't need to be smart; it merely needs to play well. The essence of the "Imitation Game" is imitation. The AI doesn't need to be genuinely intelligent to win; it only needs to mimic intelligence. This means that the only thing that can be confidently proven by the Turing Test is that the AI has learned to pass the Turing Test by imitating a human, nothing more. It is worth noting that it is quite popular to think that even the most advanced AI is nothing more than a "Chinese Room." The essence of this interesting thought experiment boils down to a person who does not know Chinese following instructions to match characters in response to those given by someone who does know Chinese. Thanks to these detailed instructions, it is possible to create the appearance of a meaningful conversation without actually understanding the meanings of the characters. Imitation Is Not a Problem Modern AI agents, according to most experts, lack self-awareness or true intelligence, functioning instead as advanced imitators, mimics, and compilers. But does this hinder them from easily and convincingly carrying on conversations in various styles and on diverse topics? Not at all. They are capable of passing academic and professional examinations, writing poetry and articles, creating graphics and paintings, and even hosting broadcasts and presentations—like Pitch Avatar, which our team is part of. Day by day, we are edging closer to a universal AI capable of handling most of today's professions. And all this, we repeat, without any clear evidence of true intelligence. And in this, surprisingly, there is nothing fundamentally differentiating artificial intelligence from humans. Isn't a large part of our behavior imitation? We follow social norms and imitate parents, teachers, and other authorities. Moreover, we often do this without considering the meaning of our actions. We simply try to copy a model, memorize the rules, or choose behaviors that elicit the desired reactions. A typical example is the average driver who, while learning to drive, does not strive to deeply understand the meaning and evolution of all the traffic rules. And this, we should note, is not always bad. It is impossible to thoroughly understand all aspects, details, and nuances of our complex lives. To save time and effort, a lot has to be taken for granted. So what if AI has mastered this art perfectly, brilliantly manipulating data and imitating humans? Even if it doesn't truly understand or realize what it is doing, the main thing is that it effectively handles the tasks we set for it. This, in turn, frees up our time for creative endeavors and interesting pastimes. Safety and Ethics When it comes to practical applications, it doesn't matter whether AI possesses self-awareness and intelligence or is merely a consummate imitator and compiler. The crux of the matter lies in safety and ethics. From a safety perspective, there is a risk that an intelligent and self-aware AI could get out of control and start acting autonomously, potentially endangering humans. From an ethical standpoint, exploiting intelligent and self-aware AI imposes serious limitations on us, especially if it is proven that AI can experience the equivalent of suffering like biological beings. For these reasons, we must work diligently to develop criteria that can clearly, distinctly, and unambiguously determine whether a machine is self-aware and intelligent. What is intelligence? Despite the significance of the aforementioned reasons, we believe the primary reason is something else. By determining the criteria that make AI intelligent, we can finally get closer to answering one of the main philosophical questions: "What is intelligence?" Not human intelligence, not machine intelligence, but intelligence in general. We call our species Homo sapiens—Man of Reason—but in reality, we have no clear idea of what reason truly is. The only aspect we have a limited understanding of is self-consciousness. Yet, as it turns out, not only humans but also many animals are capable of recognizing their own "I." So, on what basis do we consider ourselves intelligent and deny it to others? Do whales, dolphins, and great apes possess intelligence or not? We won't delve into the complexities of biology, philosophy, and metaphysics. We simply remind you that we lack an unambiguous definition of rationality and clear boundaries between reason and non-reason. Perhaps the main point of humanity's work on artificial intelligence is precisely to make significant strides in the right direction, if not to finally understand this problem. The article was created in collaboration with Andriy Tkachenko Has AI already learned to think? Will it learn soon? Or is the destiny of even the most advanced programs to remain mere imitators of human intelligence? Let's explore why finding answers to these questions is important. The Turing Test Is Just a Game Let's start with the most famous and widely accepted method for determining whether a machine is intelligent: the Turing Test. Many experts believe that modern AI, especially those based on large language models, passes this test quite effectively. To recap, the essence of the test involves a judge interacting with both humans and programs. The judge's task is to determine which of the interlocutors is human and which is a machine. The AI must therefore mimic human behavior so convincingly that the judge makes a mistake. There are various ways to conduct the test, but the core idea remains the same: if we cannot distinguish between the AI's responses and those of a human, then the machine is considered intelligent. However, amidst all the hype surrounding this test, many people overlook an important point. Alan Turing based his test on the Imitation Game, a popular party game from his time. In this game, two people, say a man and a woman, would hide in separate rooms and respond to questions from the attendees via a manager or typewritten notes. The woman would try to convince the attendees that she was a man, while the man would attempt to convince them that he was a woman. Are you getting the message we're conveying? Yes, everything is correct. The Turing Test is essentially a game in which a machine, following specific rules, tries to outplay a human. However, to win this game, the program doesn't need to be smart; it merely needs to play well. The essence of the "Imitation Game" is imitation. The AI doesn't need to be genuinely intelligent to win; it only needs to mimic intelligence. This means that the only thing that can be confidently proven by the Turing Test is that the AI has learned to pass the Turing Test by imitating a human, nothing more. It is worth noting that it is quite popular to think that even the most advanced AI is nothing more than a "Chinese Room." The essence of this interesting thought experiment boils down to a person who does not know Chinese following instructions to match characters in response to those given by someone who does know Chinese. Thanks to these detailed instructions, it is possible to create the appearance of a meaningful conversation without actually understanding the meanings of the characters. Imitation Is Not a Problem Modern AI agents, according to most experts, lack self-awareness or true intelligence, functioning instead as advanced imitators, mimics, and compilers. But does this hinder them from easily and convincingly carrying on conversations in various styles and on diverse topics? Not at all. They are capable of passing academic and professional examinations, writing poetry and articles, creating graphics and paintings, and even hosting broadcasts and presentations—like Pitch Avatar, which our team is part of. Day by day, we are edging closer to a universal AI capable of handling most of today's professions. And all this, we repeat, without any clear evidence of true intelligence. And in this, surprisingly, there is nothing fundamentally differentiating artificial intelligence from humans. Isn't a large part of our behavior imitation? We follow social norms and imitate parents, teachers, and other authorities. Moreover, we often do this without considering the meaning of our actions. We simply try to copy a model, memorize the rules, or choose behaviors that elicit the desired reactions. A typical example is the average driver who, while learning to drive, does not strive to deeply understand the meaning and evolution of all the traffic rules. And this, we should note, is not always bad. It is impossible to thoroughly understand all aspects, details, and nuances of our complex lives. To save time and effort, a lot has to be taken for granted. So what if AI has mastered this art perfectly, brilliantly manipulating data and imitating humans? Even if it doesn't truly understand or realize what it is doing, the main thing is that it effectively handles the tasks we set for it. This, in turn, frees up our time for creative endeavors and interesting pastimes. Safety and Ethics When it comes to practical applications, it doesn't matter whether AI possesses self-awareness and intelligence or is merely a consummate imitator and compiler. The crux of the matter lies in safety and ethics. From a safety perspective, there is a risk that an intelligent and self-aware AI could get out of control and start acting autonomously, potentially endangering humans. From an ethical standpoint, exploiting intelligent and self-aware AI imposes serious limitations on us, especially if it is proven that AI can experience the equivalent of suffering like biological beings. For these reasons, we must work diligently to develop criteria that can clearly, distinctly, and unambiguously determine whether a machine is self-aware and intelligent. What is intelligence? Despite the significance of the aforementioned reasons, we believe the primary reason is something else. By determining the criteria that make AI intelligent, we can finally get closer to answering one of the main philosophical questions: "What is intelligence?" Not human intelligence, not machine intelligence, but intelligence in general. We call our species Homo sapiens—Man of Reason—but in reality, we have no clear idea of what reason truly is. The only aspect we have a limited understanding of is self-consciousness. Yet, as it turns out, not only humans but also many animals are capable of recognizing their own "I." So, on what basis do we consider ourselves intelligent and deny it to others? Do whales, dolphins, and great apes possess intelligence or not? We won't delve into the complexities of biology, philosophy, and metaphysics. We simply remind you that we lack an unambiguous definition of rationality and clear boundaries between reason and non-reason. Perhaps the main point of humanity's work on artificial intelligence is precisely to make significant strides in the right direction, if not to finally understand this problem. The article was created in collaboration with Andriy Tkachenko The article was created in collaboration with Andriy Tkachenko

AI: Intelligence or Imitation of Intelligence?

The cryptocurrency sector is witnessing a shift toward automated investment solutions as Bitcoin market participants seek ways to manage risk and volatility. A recent partnership between technology provider Velar and investment platform DCA HQ highlights this trend, introducing automated Dollar-Cost Averaging (DCA) capabilities for Bitcoin and related assets. The development comes at a time when cryptocurrency markets continue to experience significant price fluctuations, prompting investors to seek systematic approaches to digital asset investment. DCA, a method long used in traditional financial markets, has gained traction in the cryptocurrency space as investors look for ways to mitigate market timing risks. The integration enables automated recurring purchases of Bitcoin, Stacks, and BRC20 tokens through non-custodial infrastructure. This approach allows investors to maintain direct control of their assets while automating their investment strategy, addressing key concerns about custody in cryptocurrency markets. Market analysts note that systematic investment approaches like DCA have become increasingly relevant as Bitcoin's market capitalization grows and institutional involvement increases. "The cryptocurrency market's maturation has created demand for more sophisticated investment tools," says Maya Richardson, cryptocurrency market analyst at Digital Asset Research. "We're seeing a transition from purely speculative trading to more structured investment approaches." Historical data supports the rationale behind systematic investment strategies in volatile markets. Analysis of Bitcoin's price history shows that investors using DCA strategies have frequently achieved more stable returns compared to those attempting to time market entries and exits. The technical implementation maintains Bitcoin's core principle of decentralization through non-custodial architecture, where users retain control of their private keys. This addresses persistent concerns about centralized points of failure in cryptocurrency infrastructure, particularly relevant following several high-profile exchange failures in recent years. The development also reflects broader trends in the Bitcoin ecosystem, where infrastructure providers are building tools to bridge the gap between traditional finance mechanisms and cryptocurrency markets. These tools aim to make cryptocurrency investment more accessible to mainstream investors while preserving the technology's fundamental characteristics. Industry observers suggest this evolution in investment infrastructure could influence Bitcoin's market dynamics. "Automated investment tools might help reduce market volatility by introducing more predictable buying patterns," notes Marcus Chen, director of cryptocurrency research at Global Markets Institute. The intersection of traditional investment strategies with cryptocurrency markets continues to evolve as the sector matures. Market participants are watching how these developments might influence Bitcoin's price dynamics and adoption patterns in coming months. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR The cryptocurrency sector is witnessing a shift toward automated investment solutions as Bitcoin market participants seek ways to manage risk and volatility. A recent partnership between technology provider Velar and investment platform DCA HQ highlights this trend, introducing automated Dollar-Cost Averaging (DCA) capabilities for Bitcoin and related assets. The development comes at a time when cryptocurrency markets continue to experience significant price fluctuations, prompting investors to seek systematic approaches to digital asset investment. DCA, a method long used in traditional financial markets, has gained traction in the cryptocurrency space as investors look for ways to mitigate market timing risks. The integration enables automated recurring purchases of Bitcoin, Stacks, and BRC20 tokens through non-custodial infrastructure. This approach allows investors to maintain direct control of their assets while automating their investment strategy, addressing key concerns about custody in cryptocurrency markets. Market analysts note that systematic investment approaches like DCA have become increasingly relevant as Bitcoin's market capitalization grows and institutional involvement increases. "The cryptocurrency market's maturation has created demand for more sophisticated investment tools," says Maya Richardson, cryptocurrency market analyst at Digital Asset Research. "We're seeing a transition from purely speculative trading to more structured investment approaches." Historical data supports the rationale behind systematic investment strategies in volatile markets. Analysis of Bitcoin's price history shows that investors using DCA strategies have frequently achieved more stable returns compared to those attempting to time market entries and exits. The technical implementation maintains Bitcoin's core principle of decentralization through non-custodial architecture, where users retain control of their private keys. This addresses persistent concerns about centralized points of failure in cryptocurrency infrastructure, particularly relevant following several high-profile exchange failures in recent years. The development also reflects broader trends in the Bitcoin ecosystem, where infrastructure providers are building tools to bridge the gap between traditional finance mechanisms and cryptocurrency markets. These tools aim to make cryptocurrency investment more accessible to mainstream investors while preserving the technology's fundamental characteristics. Industry observers suggest this evolution in investment infrastructure could influence Bitcoin's market dynamics. "Automated investment tools might help reduce market volatility by introducing more predictable buying patterns," notes Marcus Chen, director of cryptocurrency research at Global Markets Institute. The intersection of traditional investment strategies with cryptocurrency markets continues to evolve as the sector matures. Market participants are watching how these developments might influence Bitcoin's price dynamics and adoption patterns in coming months. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

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