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Failure is Required, So Embrace It: Understanding Fail-Safe and Fail-Fast Strategies In Software

ব্যর্থতা প্রয়োজন, তাই এটিকে আলিঙ্গন করুন: সফ্টওয়্যারে ব্যর্থ-নিরাপদ এবং ব্যর্থ-দ্রুত কৌশলগুলি বোঝা

Fehler sind unvermeidlich, also akzeptieren Sie sie: Verstehen von Fail-Safe- und Fail-Fast-Strategien in der Software

El fracaso es necesario, así que acéptelo: comprensión de las estrategias de software a prueba de fallos y rápido

L'échec est nécessaire, alors acceptez-le : comprendre les stratégies de sécurité et d'échec rapide dans les logiciels

विफलता आवश्यक है, इसलिए इसे स्वीकार करें: सॉफ्टवेयर में विफलता-सुरक्षित और विफलता-त्वरित रणनीतियों को समझना

失敗は必要だから受け入れよう: ソフトウェアにおけるフェイルセーフとフェイルファスト戦略を理解する

실패는 필수이므로 이를 수용하세요: 소프트웨어의 오류 방지 및 오류 방지 전략 이해

A falha é necessária, então aceite-a: entendendo estratégias à prova de falhas e rápidas em software

Провал обязателен, поэтому примите его: понимание отказоустойчивых и отказоустойчивых стратегий в программном обеспечении

Başarısızlık Gereklidir, O halde Onu Kucaklayın: Yazılımda Arızaya Karşı Korumalı ve Arızayı Hızlandırma Stratejilerini Anlamak

Thất bại là bắt buộc, vì vậy hãy chấp nhận nó: Tìm hiểu các chiến lược an toàn và thất bại nhanh trong phần mềm

失败是必然的，所以要接受它：理解软件中的故障安全和快速失败策略

Developers never asked for software security to be a part of their jobs, but it’s become abundantly clear that they really don’t have a choice. This isn’t to say that developers are the only party responsible for securing the software supply chain, of course. But particularly, as artificial intelligence (AI) and machine learning (ML) play an increasingly vital role in software development, considering security at the beginning of the software lifecycle has never been more important. As the scale of AI continues to expand and large language models (LLMs) become more common, developers face the responsibility of integrating AI and ML models into both software updates and new software solutions. While the potential for innovation in AI/ML is immense, it also brings along greater concerns, especially considering that many developers lack the capacity to manage their development securely. Understanding the Impact of AI and ML Every organization wants to leverage the power of AI and ML to benefit their businesses, but they need to do so with a full understanding of the risks they may face. For example, security lapses can inadvertently introduce malicious code into AI/ML models, creating vulnerabilities that attackers may exploit. This threat could entice developers to adopt compromised open-source software (OSS) model variants, which would ultimately expose corporate networks and cause significant harm to an organization. Further, developers are increasingly turning to generative AI to produce code, often without full assurance regarding its security. This poses an additional layer of risk, creating a need for thorough code vetting from the outset to proactively mitigate potential threats to the software supply chain. The prevalence of these threats underscores the ongoing challenges faced by startups as malicious attackers look for new avenues to exploit AI/ML models. The deployment of AI and ML models isn’t likely to slow down anytime soon, creating a major reason why developers need to integrate security into their roles and implement the defenses necessary to enhance resilience against evolving security threats. It’s clear that further education is needed — McKinsey found that even many high-performing organizations have yet to master AI adoption best practices, including machine learning operations (MLOps). For a deeper understanding of the impact and implications of AI, startups might first consider building AI- or ML-driven software or solutions to be used by internal teams, as opposed to a customer-facing application. This teaches development teams the best practices of developing for AI/ML and instills confidence in the organization’s ability to deliver. Building for internal use cases in the solutions first helps teams (and businesses) become more efficient and productive. The Ever-Evolving Duties of Developers Modern software development is a dynamic landscape, with potential threats lingering around every corner. This makes considering security from the outset of the development lifecycle a crucial [albeit relatively new] practice for startups. Unfortunately, many organizations consider security at the binary level a perk rather than a "must-have." Attackers realize this and look for vulnerabilities they can exploit to weaponize ML models and other software components. Many developers, particularly in startup environments, may lack the training needed to incorporate security measures during the initial stages of development. To mitigate this, teams may attempt to fast-track development by using AI-generated code, but this can lead to other serious issues. For example, the AI might be trained on open-source repositories that lack thorough vetting for vulnerabilities and complete end-to-end security controls. While this tactic can save time and resources, it could also unwittingly expose the organization to risks that will prove costly down the road. Once implemented in AI/ML models, these vulnerabilities may go undetected and become more impactful. The era of widespread AI adoption has transformed the traditional developer role; junior software engineers often employed by startups may not have sufficient training or experience to navigate the evolving security landscape. As startups build out their teams, they must help their developers evolve into security professionals to support an organization-wide DevSecOps mentality. By integrating secure solutions from the start, developers can achieve peak workflow efficiency while instilling confidence in the organization's overall security posture. Attracting talent is always a challenge for startups, but the current state of the software development landscape could actually serve as a recruiting tool. By helping prospects understand the importance of baking security into software from the beginning of the development process — along with the fact that this is an industry trend that’s not going away soon — businesses create an attractive culture where developers add to their skill sets and eventually help further their careers. Shifting Security Left as a Cultural Mindset The security landscape for binaries and ML models demands continuous evolution to stay ahead of emerging threats, and it's a mindset that should filter down throughout a company's culture. As AI implementation becomes more widespread, startups can't afford to defer necessary security protocols to later stages of the software development lifecycle. By then, the risks may become insurmountable. It has become clear in recent years that startup leaders should embrace the "shift left" mentality for software development, with a proactive emphasis on security from a project's inception. This ensures that every facet of the software development process prioritizes security, enhancing the overarching security posture of the organization. When specifically applied to AI/ML, the shift-left mindset verifies the security of code generated in external AI/ML systems and ensures that the models are devoid of malicious code and comply with licensing requirements. For startups (or any business, really), it's best not to think of shifting security left as a set of operational guidelines and procedures. It’s much more effective when it serves as a philosophy embedded into the organization’s culture. It can't be overstated: implementing security measures from the beginning of the software development lifecycle is imperative for startups to consistently thwart attacks. This ultimately saves businesses the time and resources it takes to address costly and potentially harmful attacks and allows startups to focus on doing what they do best: innovate. Developers never asked for software security to be a part of their jobs, but it’s become abundantly clear that they really don’t have a choice. This isn’t to say that developers are the only party responsible for securing the software supply chain, of course. But particularly, as artificial intelligence (AI) and machine learning (ML) play an increasingly vital role in software development, considering security at the beginning of the software lifecycle has never been more important. only As the scale of AI continues to expand and large language models (LLMs) become more common, developers face the responsibility of integrating AI and ML models into both software updates and new software solutions. While the potential for innovation in AI/ML is immense, it also brings along greater concerns, especially considering that many developers lack the capacity to manage their development securely. Understanding the Impact of AI and ML Understanding the Impact of AI and ML Every organization wants to leverage the power of AI and ML to benefit their businesses, but they need to do so with a full understanding of the risks they may face. For example, security lapses can inadvertently introduce malicious code into AI/ML models, creating vulnerabilities that attackers may exploit. This threat could entice developers to adopt compromised open-source software (OSS) model variants, which would ultimately expose corporate networks and cause significant harm to an organization. Further, developers are increasingly turning to generative AI to produce code, often without full assurance regarding its security. This poses an additional layer of risk, creating a need for thorough code vetting from the outset to proactively mitigate potential threats to the software supply chain. The prevalence of these threats underscores the ongoing challenges faced by startups as malicious attackers look for new avenues to exploit AI/ML models. The deployment of AI and ML models isn’t likely to slow down anytime soon, creating a major reason why developers need to integrate security into their roles and implement the defenses necessary to enhance resilience against evolving security threats. It’s clear that further education is needed — McKinsey found that even many high-performing organizations have yet to master AI adoption best practices , including machine learning operations (MLOps). AI adoption best practices AI adoption best practices For a deeper understanding of the impact and implications of AI, startups might first consider building AI- or ML-driven software or solutions to be used by internal teams, as opposed to a customer-facing application. This teaches development teams the best practices of developing for AI/ML and instills confidence in the organization’s ability to deliver. Building for internal use cases in the solutions first helps teams (and businesses) become more efficient and productive. The Ever-Evolving Duties of Developers The Ever-Evolving Duties of Developers Modern software development is a dynamic landscape, with potential threats lingering around every corner. This makes considering security from the outset of the development lifecycle a crucial [albeit relatively new] practice for startups. Unfortunately, many organizations consider security at the binary level a perk rather than a "must-have." Attackers realize this and look for vulnerabilities they can exploit to weaponize ML models and other software components. Many developers, particularly in startup environments, may lack the training needed to incorporate security measures during the initial stages of development. To mitigate this, teams may attempt to fast-track development by using AI-generated code, but this can lead to other serious issues. For example, the AI might be trained on open-source repositories that lack thorough vetting for vulnerabilities and complete end-to-end security controls. While this tactic can save time and resources, it could also unwittingly expose the organization to risks that will prove costly down the road. Once implemented in AI/ML models, these vulnerabilities may go undetected and become more impactful. The era of widespread AI adoption has transformed the traditional developer role; junior software engineers often employed by startups may not have sufficient training or experience to navigate the evolving security landscape. As startups build out their teams, they must help their developers evolve into security professionals to support an organization-wide DevSecOps mentality. By integrating secure solutions from the start, developers can achieve peak workflow efficiency while instilling confidence in the organization's overall security posture. Attracting talent is always a challenge for startups, but the current state of the software development landscape could actually serve as a recruiting tool. By helping prospects understand the importance of baking security into software from the beginning of the development process — along with the fact that this is an industry trend that’s not going away soon — businesses create an attractive culture where developers add to their skill sets and eventually help further their careers. Shifting Security Left as a Cultural Mindset Shifting Security Left as a Cultural Mindset The security landscape for binaries and ML models demands continuous evolution to stay ahead of emerging threats, and it's a mindset that should filter down throughout a company's culture. As AI implementation becomes more widespread, startups can't afford to defer necessary security protocols to later stages of the software development lifecycle. By then, the risks may become insurmountable. It has become clear in recent years that startup leaders should embrace the "shift left" mentality for software development, with a proactive emphasis on security from a project's inception. This ensures that every facet of the software development process prioritizes security, enhancing the overarching security posture of the organization. When specifically applied to AI/ML, the shift-left mindset verifies the security of code generated in external AI/ML systems and ensures that the models are devoid of malicious code and comply with licensing requirements. For startups (or any business, really), it's best not to think of shifting security left as a set of operational guidelines and procedures. It’s much more effective when it serves as a philosophy embedded into the organization’s culture. It can't be overstated: implementing security measures from the beginning of the software development lifecycle is imperative for startups to consistently thwart attacks. This ultimately saves businesses the time and resources it takes to address costly and potentially harmful attacks and allows startups to focus on doing what they do best: innovate.

It’s Time for Developers to Embrace Security as a Core Job Function

*** The History of Science Funding The scientist has always been a risky employee in terms of ROI.  They are trying to uncover truths about the universe, and oftentimes, the universe gives them the bird. Because experimenting means risking a lot of money and time on a possibly worthless idea, a scientist is a forever intern, until they make a breakthrough, then they become senior associates. That said, the scientist has been trying to make his own money. With the Alchemists, this was literal. While they invented Chemistry and catalyzed the birth of worldwide gold rushes, turning lead into gold was, alas, a wrong idea believed for centuries. The search consumed the lifetime of one person after another, with only dreams of wealth to get them through. But their tenacity was a testament to their resilience and patience. Patience meant, for the time being, remaining in the employ of someone who could afford the risk of backing science in the past. And that meant a King because they had gold/money collected from taxes. Wizards and fortune tellers didn’t make much sense, and they were comedians come to give the King a jolly time before he went to war. Though, while throwing guts on a wall may be stupid, it was mysterious and if they were right, doubly impressive. And here was this (broke) scientist, insisting that they knew a REAL strategy to help the King win. What was their credibility? Circa 200BC, the scientist is Archimedes and the King is the political head of Syracuse. Being a leader in those times largely meant you needed to win wars and as history had taught Kings the utter uselessness of charlatans, the scientist was given a chance to make his case. Archimedes made his case alright. It that he sunk the ships of the Romans with ingenious cranes and catapults that threw heavy stones. has been recounted Since his time, patronage from Kings, Princes, Bankers and Presidents to scientists has been immense. The scientist is respected, and all great scientists, even Leonardo da Vinci who was mostly fawned over for his impressive artwork, took off some time to design weapons. For weapons help the King win and hence, keep the scientist with a job. Scientists soon became ever more pivotal in war efforts. Especially Physicists and Chemists, who have designed countless forms of military hardware to date. However, the emergence of scientific methods in Economics, Sociology, Psychology, Biology, all riding on the power of statistical analysis and logical inference, quickly made the scientist important in every field of human endeavour. The problem however remained. To get a loan, the scientist usually had to, and still has to, rely on government funding because the government remains the biggest fish able to take on sufficient risk in the financial markets. Hate it all you want, but without credit from the government, be it debt or real money, a lot of scientific work would have stalled. Best example - SpaceX. However, governments and their ability to debase money and finance things on debt, made the world wars last longer to destroy the world much harder. It also led to economic depressions and after the 2008 financial crisis, Satoshi Nakamoto, a genius computer scientist, decided to create Bitcoin – a monetary tool outside the purview of government currency debasement. Birth of a financial model divorced from the King Bitcoin has been controversial from its inception. Of course, Kings and governments hate it. It is cutting into their business. A big part of the private sector also loathes its nature of priding in technological exclusivity, so that six years later, a hard fork was implemented to create Ethereum, a cryptocurrency built to provide extra blockchain services such as smart contracts. Many other hard forks quickly followed from the faithless. However, Bitcoin is still mainstream and has recently become interesting to Big Finance. The Bitcoin ETF proves as much. After 15 years of laughing at a thing that keeps building bigger and better, even the harshest critic will shut up and start listening more intimately. Because they now know they are missing something. Energy scientists are also starting to pay attention, with some claiming that Bitcoin mining may be our . are doing well selling cheaper electricity packages that have been subsidized by Bitcoin mining on the side. fast track to an energy transition Energy engineers But these are late to the party. Computer scientists have been earning well in Bitcoin, Ethereum, Doge for decades. And if any scientist has been comfortably living outside the need to get big budget funding from governments, that has been the computer scientist, from way back in the 20th century. While the field of Computer Science is much younger than Physics, Astronomy, Biology, many computer scientists quickly learned to earn big from private enterprise and individualized work, without begging the Kings and their Big Bankers for a loan. Futuristic Science Funding itself through Bitcoin Bitcoin is money for the people, and science has evolved to also be something done as a public good, on an open source plan, by dedicated people who put in their own time and money to make something good happen. As I mentioned, Computer Scientists now exist on the internet who are eagerly improving the Bitcoin ecosystem using the power of collaboration and their earnest efforts. These mostly come from the land of software development and after getting orange-pilled, decide to stay at Bitcoiner-land and build the Bitcoin ecosystem. However, there has not been much of an on-ramp for other types of scientists and this is the opportunity existing, wide open, for anybody to take. Currently, many a scientist in the Bitcoin world is a computer scientist of the type who builds zero-knowledge proofs, rollups, and more layers of the Bitcoin blockchain. But many more scientific people are needed. To re-iterate, I feel more electrical and power engineering scientists should be at the forefront of how Bitcoin mining equipment can provide electricity access to those without, making investments in clean, stranded energy sources much easier to manage. What better job than to be the electric engineer who designs their own job by bringing electricity to your people? Every university student of electrical engineering, whatever the developing country, should learn about this. Because developing countries in Africa, Asia and Latin America are struggling with scaling electricity usage from otherwise cheap energy sources, because of the high premiums that locals just cannot pay. The knowledge of using Bitcoin to subsidize electricity access from stranded energy sources should be spread as far and as widely as possible. Now, as an aspiring quantum information scientist, I feel there is also a place for me in the Bitcoin ecosystem. While the public might know how quantum information science threatens to undo the good that Bitcoin has done by potentially hacking it, I find that building quantum-resistant algorithms is far from scratching the surface of quantum capabilities. For example, quantum technologies could provide for Bitcoin nodes, allowing even weak signals to be successfully transmitted and ensure blocks are mined. better signal sensors Quantum computing could help guide better on how Bitcoin infrastructure could be better set up across nations and continents for optimum usage. solutions in logistics Quantum networks could provide better ways to securely. transmit Bitcoin information Some problems in the Bitcoin ecosystem could be (Bounded-error Quantum Polynomial) problems, which are the purview of quantum information science and these should be discovered and worked on using quantum computers. BQP Most importantly, quantum computers will be orders of magnitude better at mining proof-of-work cryptocurrencies than classical computers. When Bitcoin befriends Quantum Mechanics Moore’s law is coming to an end. IBM has already built transistors with only 5 atoms. To get smaller, it becomes harder to manage thermal noise and other nanoscale problems. To manage thermal noise, even more cooling will be done, the way it is done for qubits. Also, a 1-atom transistor is effectively a qubit. This is the quantum realm, and like it or not, more people will study quantum mechanics in the not-too-distant future, and by extension, quantum computing. The classical computing world can only scale by a multiplicity of the same technology, which will consume even more energy. The efficiency gains of quantum technologies like quantum computers will be needed. While , the science of building qubits is ramping upwards and systems are getting more powerful. There might not be an exponential growth in qubits with their own Moore’s law, but a linear growth in qubits leads to exponential computational power. Which kind of is a Moore’s law! I have said quantum computing is very expensive You remember this graph; If approximately 14 billion dollars in 2023 can bring in (logical qubits are the real qubits as manipulated in mathematical theory), by linear extrapolation, $125 billion by 2030 as seen above will bring in 428 logical qubits. 48 logical qubits But 7 years from now, Bitcoin itself might have ballooned to $200,000 per bitcoin which is a $4.2 trillion market cap. By linear extrapolation, $3 trillion is needed in global investments to take us to 10,000 logical qubits. As we can see, Bitcoin alone can pay for it over a 7-year span. Why 10,000 qubits? My Research Project Over the past three months, I have been studying QCourse551-1 which was offered in a collaboration between QWorld and Classiq Technologies. My research project was Project 11 - Bitcoin Mining using Grover’s Search Algorithm. The motivation for my project came from that shows that 10,000 logical qubits alone can do Bitcoin mining faster than any classical system using the same energy consumption. a research paper This is because 10,000 qubits can encode the full double SHA256 hashing process that goes into mining Bitcoin, but since they use Grover’s search algorithm, they effectively achieve quadratic speed-up by square_root(N) where N is the number of elements to check in an unstructured database. ASICs mining for bitcoins also check an unstructured database of random hashes for the nonce value. The question is, which one is faster? Most ASIC miners today are checking terahashes or For example checks 120TH/s. That is 120 million hashes every second. 1 million hashes every second. this miner Can a quantum computer beat that? Of course. square_root( 120 million) = only 10954 checks per second. A 1,095,400 % efficiency increase. And the more the competition, the more the quantum computer tears ahead in the race. The is rocketing upwards of 600 million million hashes per second. This is outputting enough heat to make climate scientists worried sick. But what if we replaced all those clunky ASICs with state-of-the-art QPUs from the year 2032, how many comparative hashes per second would they clock? square_root( 600 million million) = 24 million checks per second. A . current hash rate 2.4 billion % increase in efficiency If the above is not an attractive proposition, I don’t know what is. Bitcoin should fund quantum computing research. It will take a while, but when it happens, it will be glorious. It will be a more canonical event than the Moon landing. In our research, we were checking a much-simplified system with only 8-bit inputs and outputs, modeled on a Grover’s operator working with under 25 qubits. Given our really low hashing power, the advantages of Grover’s search could not be gleamed first hand, and could only be inferred with a relevant complexity analysis of the runs/depth of the Classical computer (my laptop) vs the depth of our Grover’s circuit executed on the Classiq Aer simulator. While it would have been good to model the energetics and the complexity graph of what this means, unfortunately, we ran out of time. However, we had some deliverables to show for our work. Below is what we had including the depth of each circuit. a) The simplified MD5 classical algorithm We modeled a simplified version of the MD5 (a most primitive cousin of SHA256) hashing 95 printable ASCII characters to find the nonce for hex values with difficulty = 1. Note: Our hex values were only 2-digit because we are working with 8-bit values. So our possible difficulty 1 values are only in 16 possibilities i.e. 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f. 00 is also a difficulty 2 element. Hence the searching/hashing function is called simplified_MD5_8bit_difficulty_1or2. def simplified_MD5_8bit_difficulty_1or2():
    printables_list = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
    for i in printables_list:
        x2 = ord(i)

        x3 = bin(x2)[2:]

        if len(str(x3))==7:
            x4 = x3+'0'
        if len(str(x3))==6:
            x4 = x3+'00'

        x5 = list(x4)

        a0 = 2*int((x5[0]))
        a1 = int(x5[1])

        b0 = 2*int((x5[2]))
        b1 = int(x5[3])

        c0 = 2*int((x5[4]))
        c1 = int(x5[5])

        d0 = 2*int((x5[6]))
        d1 = int(x5[7])

        a = a0 + a1

        b = b0 + b1

        c = c0 + c1

        d = d0 + d1
    
        a = ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b)
        
        print()

        d1 = d//2
        d0 = d%2
        d = d1*(2**7) + d0*(2**6)

        a1 = a//2
        a0 = a%2
        a = a1*(2**5) + a0*(2**4)

        b1 = b//2
        b0 = b%2
        b = b1*(2**3) + b0*(2**2)

        c1 = c//2
        c0 = c%2
        c = c1*(2**1) + c0*(2**0)

        decimal_result = d + a + b + c
        binary_result = bin(decimal_result)[2:]
        print(binary_result)
        hex_result = hex(decimal_result)
        print(hex_result)

        if hex_result == '0x0':
            difficulty_2.append(hex_result)
            collisions_difficulty_2.append(i)
        if hex_result == ('0x1' or '0x2' or '0x3' or '0x4' or '0x5' or '0x6' or '0x7' or '0x8' or '0x9' or '0xa' or '0xb' or '0xc' or '0xd' or '0xe' or '0xf'):
            difficulty_1.append(hex_result)
            collisions_difficulty_1.append(i)

    print('Difficulty_1 list = ',difficulty_1)
    print('Difficulty_2 list = ',difficulty_2)
    print('Colliding inputs for difficulty 1 = ',collisions_difficulty_1)
    print('Colliding inputs for difficulty 2 = ',collisions_difficulty_2)
    
difficulty_2 = []
difficulty_1 = []     
collisions_difficulty_1 = []
collisions_difficulty_2 = []

import numpy as np
simplified_MD5_8bit_difficulty_1or2() As can be seen, to find the difficulty 1 (and by extension the difficulty 2) elements, we have to reiterate through 40 lines of code (ignoring spaces and printing in the for loop). There are 96 printable ASCII characters to hash and check hence 96 * 40 which gives us a circuit depth of 3840. However, this is run through fast by my laptop. Less than a few seconds. b) The simplified MD5 quantum algorithm We modeled a quantum version of our simplified MD5 algorithm to simulate finding the difficulty 1 nonce values no matter what the input ASCII character to the circuit was. This is possible because all we need is a zero at the beginning of our hex value. Since our hash set was small (only 96 ASCII characters), we tuned the system to ignore the input ASCII value so it is redundant, and to aim to capture all possible zeros given the Arithmetic expression; a == ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b) which is also in the classical Python algorithm. Notice that the 4 functions being added together are derived from the . original MD5 algorithm Since we want difficulty 1 values, we add and to the above function and then synthesize it in the Classiq platform. d = 0 a = 0 (Note: This is a shortcut thanks to the tireless work of the Classiq team. The Python code was also built in the Classiq Python SDK but it had a last-minute glitch. Hence we only have working Python SDK code for the Grover’s operator, but not for the Grover’s operator with a difficulty-1 hash/search, which is what you had to see here ) Hence our input Arithmetic expression is; a == ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b) and d == 0 and    a == 0 The resulting circuit has a depth of only 913 The quantum circuit wins! Endnote While the project time ended, our work is still a work in progress, to continue until we build a functional 10,000 qubit quantum bitcoin miner. Voluntary efforts to support us technically are most welcome. Some of our code is still glitchy and we have only scratched the surface of the Classiq platform’s capabilities. An ingenious software tool that takes away the cumbersome tinkering with gates, as would have been needed for anybody building the same project using Qiskit. Special thanks to Classiq and QWorld for the opportunity. And our mentor - Dr. Eyal Cornfeld, for the guidance. ***

Hash Rate Analysis Projections: Why Bitcoin Should Embrace Quantum Information Science

হ্যাশ রেট বিশ্লেষণ অনুমান: কেন বিটকয়েন কোয়ান্টাম তথ্য বিজ্ঞানকে আলিঙ্গন করা উচিত

Prognosen zur Hash-Rate-Analyse: Warum Bitcoin die Quanteninformationswissenschaft nutzen sollte

Proyecciones del análisis de la tasa de hash: por qué Bitcoin debería adoptar la ciencia de la información cuántica

Projections d'analyse du taux de hachage : pourquoi Bitcoin devrait adopter la science de l'information quantique

हैश दर विश्लेषण अनुमान: बिटकॉइन को क्वांटम सूचना विज्ञान को क्यों अपनाना चाहिए

ハッシュレート分析の予測: ビットコインが量子情報科学を採用すべき理由

해시레이트 분석 예측: 비트코인이 양자 정보 과학을 수용해야 하는 이유

Projeções de análise de taxa de hash: por que o Bitcoin deveria adotar a ciência da informação quântica

Прогнозы анализа скорости хэширования: почему Биткойн должен использовать квантовую информатику

Hash Rate Analizi Projeksiyonları: Bitcoin Neden Kuantum Bilgi Bilimini Kucaklamalı?

Dự đoán phân tích tỷ lệ băm: Tại sao Bitcoin nên áp dụng khoa học thông tin lượng tử

哈希率分析预测：为什么比特币应该拥抱量子信息科学

In the busy world of business today, technology is what takes you one step ahead. Artificial intelligence, or AI, is a powerful tool, and it adds automation which increases value to your safeguard as well as grows your business. Sometimes, AI is made to invite images of sterile and mechanical efficiency, but the truth lies elsewhere. It is a man-made tool for humans. The true worth of AI lies in its ability to accelerate human skills and create more time for innovative projects, as well as other strategic endeavors. Consider the boring things that suck out your team’s time; data input, runway analyses, or even customer inquiries. AI can take over these processes, so your employees will be able to spend time on tasks that involve , and an approach based on individuality. That is where the miracle of synergy between AI and human abilities occurs. emotional intelligence critical thinking AI Safeguards for Your Business Today’s digital atmosphere calls for absolute security. As the business changes, its threats also change. AI acts like a hardworking guard who is always vigilant towards inconsistencies identifying patterns and raising alarms of possible danger before it takes place. Imagine having an intelligent helper who can learn after each login, transaction, and interaction inside your business ecosystem. First, can detect anomalies that the naked eye would overlook, thus warning you against potential cybersecurity threats long before they cause serious consequences. AI algorithms A proactive defense such as this can make the difference between a simple occurrence and a total breach. Overcoming Resistance and Building Trust The integration of AI into the business is undoubtedly not harmful, but people may still be concerned about it. All these are the matters to be dealt with transparently, educatively, and committedness towards ethical AI methods. Humanizing AI means not only demonstrating its advantages but also what is wrong with it. Enhancing Customer Experience Through Personalization AI is one of the best-known benefits because it can process large volumes of data quickly. This is a feature that allows businesses to offer personalized services for their clients. AI detects customer behavior, what they like, and how to iron out their responses that can be employed for purposes of analysis as a manner of transmitting informative data regarding the product services and marketing approaches that will go in line with consumer opinions. Customers today crave individualized interactions. Used ethically and transparently, AI allows organizations to do exactly that. Navigating the Competitive Landscape Adaptability is the key to endure and flourish in business life. By using AI, businesses can have that allows these kinds of companies to foresee market trends and consumers’ preferences. Studying large-scale data sets, AI finds patterns and recommendations that will allow businesses to make informed decisions always leading the others. predictive analytics AI also allows for continuous enhancement through feedback loops. By leveraging AI systems, your organization can foster a culture of innovation through ongoing learning and improvement. Working in such a way guarantees that your business does not only coincide with time but becomes its source and dictates it. Trust is built gradually. First of all, it is necessary to interact with your team through the process of AI integration by training them and conducting open communication.  Demonstrate AI as another colleague instead of taking over human intellect and skill. If you develop an inclusive and open culture, it makes it easy to incorporate s without any problems. AI into your business processe The Future of Business: Human and Artificial Intelligence in Balance AI is far from being a dangerous matter for human workers but rather can be considered as the engine of development and new technologies including improved results. With a more humanized outlook on AI, business organizations can create an equilibrium that elevates the strengths offered by both computers and individuals.

Why Businesses Should Embrace AI to Stay Relevant in 2024

Scientific American, Volume XLIII., No. 25, December 18, 1880, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . The Embrace of the Mantis. here The Embrace of the Mantis. Mr. Addison Ellsworth favors us with a transcript of a letter from Mr. Albert D. Rust, of Ennis, Ellis County, Texas, describing a remarkable exhibition of copulative cannibalism on the part of the mantis. The ferocious nature of these strange insects is well known, and is in striking contrast with the popular name, "praying mantis," which they have gained by the pious attitude they take while watching for the flies and other insects which they feed upon. About sunrise, August 28, 1880, Mr. Rust's attention was attracted by a pair of mantis, whether or not, he was not sure, but from the length of the body and the shortness of the wings he was inclined to think them of some other species. The female had her arms tightly clasped around the head of the male, while his left arm was around her neck. Mr. Rust watched intently to see whether the embrace was one of war or for copulation. It proved to be both. As the two abdomens began to approach each other the female made a ferocious attack upon the male, greedily devouring his head, a part of the body, and all the arm that had encircled her neck. A moment after the eating began, Mr. Rust observed a complete union of the sexual organs, and the eating and copulation went on together. On being forcibly separated the female exhibited signs of fear at her headless mate, and it was with difficulty that they were brought together again. On being suddenly tossed upon the back of the female the male seized her with a grasp from which she could not extricate herself, and immediately the sexual union was renewed, to all appearances as perfectly as before. Mantis religiosa The pair were accidentally killed, otherwise, Mr. Rush thinks, the female would have continued her cannibalistic repast until she had devoured the entire body of her companion. This peculiarity of the mantis seems not to have been observed before, though their mutually destructive disposition has been noted by several. Desiring to study the development of these insects, M. Roesel raised a brood of them from a bag of eggs. Though plentifully supplied with flies, the young mantis fought each other constantly, the stronger devouring the weaker, until but one was left. M. Poiret was not more successful. When a pair of mantis were put together in a glass they fought viciously, the fight ending with the decapitation of the male and his being eaten by the female. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2007). Scientific American, Volume XLIII., No. 25, December 18, 1880. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/21081/pg21081-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

The Embrace of the Mantis

My Journey Into the World of DevOps Once upon a time, in a land not too far from your favorite coffee shop 🙂, I found myself embroiled in the tumultuous world 🌍 of DevOps. Picture this: me, a wide-eyed code wizard, and a bewildering maze of servers, deployments, and late-night coding escapades. It was a place where sleep was optional, caffeine was the elixir 🧪 of life, and version control systems were my best frenemies. As I ventured deeper into the mystical realm of DevOps, I began to realize that it was a bit like trying to juggle flaming chainsaws while riding a unicycle on a tightrope. Okay, maybe not that extreme, but close enough. The pressure to deliver faster, the endless bug 🐞reports, and the ever-looming deadlines were enough to make anyone question their life choices. But then, like a beacon of hope, I stumbled upon the wondrous world of Continuous Integration (CI). It was like discovering the holy grail of software development, except it didn't involve knights or medieval quests, just a whole lot of automation and some seriously nifty tools. You see, CI is the secret sauce that brings sanity to the chaos of DevOps. It's like having a personal👩‍💻 assistant who diligently checks your code for errors every time you make a change, and trust me, my code needed all the checking it could get. No more sleepless nights wondering if my latest code commit had unleashed a digital apocalypse. Now, I know what you're thinking. "But Mr. DevOps Guru, how does CI actually work? Is it some kind of sorcery?" Well, not quite, but it's close. CI systems like Jenkins, Travis CI, and CircleCI watch your code repository like a hawk🔍. As soon as you make a change, they swoop in, grab your code, and run a battery of tests faster than you can say "syntax error." The best part? If your code breaks something, CI lets you know immediately. It's like having a personal trainer for your code, but without the judgmental stares when you skip leg day 🏋️‍♂️ (or test day, in this case). Now, here's where the magic happens 🔮. CI doesn't just find problems; it also fixes them. Picture this: you commit a piece of code that causes your app to crash every time someone clicks the "Submit" button. Without CI, you'd be frantically searching for the bug🐞, possibly drowning your sorrows in a tub of ice cream. But with CI, it's like having a team of code fairies who not only find the bug but also provide a fix. It's almost too good to be true. But wait, there's more! CI doesn't discriminate; it plays nice with others. Whether you're coding in Python, Java, or Klingon, CI has your back. It's like the Swiss Army knife of DevOps♾️, adaptable and always ready to lend a hand. So, no matter what your tech stack looks like, CI will fit right in. Now, I know change can be scary, and you might be thinking, "But I've been doing things this way for years, why fix what ain't broke?" Well, my fellow code wranglers, I used to be in that camp too. But once I embraced CI, I realized that it's not about fixing what's broken; it's about making something good even better. It's like upgrading from your trusty old flip phone to a shiny new smartphone—sure, the old one worked, but the new one opens up a world of possibilities you never knew existed. In the end, my journey into the world of DevOps led me to a profound realization: CI isn't just a tool; it's a philosophy. It's about embracing change, empowering your team, and delivering better software faster. It's about turning the chaos of DevOps into a well-orchestrated symphony where code flows smoothly, bugs are squashed with gusto, and sleepless nights become a thing of the past. So, my fellow DevOps adventurers, take a leap of faith, embrace Continuous Integration, and watch your code transform into a masterpiece. And remember, in the ever-evolving world of software development, it's not about stopping worry—it's about worrying less and coding more. Yours Truly – The DevOps Guru If you want to learn more about #DevOps, check out my article on The Best Practices For DevOps Pipelines Here

Confessions of a DevOps Guru: How I Learned to Stop Worrying and Embrace Continuous Integration 

Introduction In the dynamic realm of software development and operations, the term “DevOps” reverberates with significance. While it’s often associated with tools and practices, its true power lies in the transformation of organizational culture. Beyond a mere role or process, DevOps embodies a paradigm shift that redefines how teams collaborate, innovate, and deliver value to users. This article delves into the compelling journey of treating DevOps as a cultural evolution, catalyzing unprecedented collaboration, and fostering a holistic approach to software delivery. DevOps is not just a set of tools or a methodology; it’s a cultural shift that fosters collaboration, communication, and a shared sense of ownership across teams.” – Gene Kim, Co-author of “The Phoenix Project” and DevOps thought leader. The Genesis of DevOps Culture At its core, the DevOps culture is an embodiment of shared values, open communication, and a collective sense of ownership. Instead of relegating development and operations to separate domains, DevOps thrives on breaking down silos, aligning objectives, and forging partnerships. The genesis of a DevOps cultural shift is a testament to the organization’s commitment to change, innovation, and continual improvement. Rising Beyond Conventional Roles The magic of treating DevOps as a cultural transformation lies in its ability to transcend conventional job roles. Gone are the days of isolating developers from operations, often leading to misunderstandings and inefficiencies. Embracing DevOps as a cultural shift elevates every team member to a realm of shared responsibility. From developers and testers to operations engineers and business stakeholders, all converge as active contributors to the entire software lifecycle. Why Do Most Companies Fail in DevOps Adoption? While the potential benefits of a DevOps cultural shift are undeniable, many organizations struggle to realize its full potential. Several common pitfalls contribute to this failure: : DevOps is not just about tools and processes; it’s a cultural transformation. When leadership fails to fully embrace and advocate for this shift, it can result in half-hearted attempts that yield minimal results. 1. Lack of Cultural Buy-In 2. : Employees accustomed to traditional roles and processes may resist adopting new practices. Overcoming this resistance requires effective communication and education about the benefits of DevOps. Resistance to Change 3. : Breaking down silos between development and operations is a cornerstone of DevOps. Organizations that fail to foster collaboration and shared ownership struggle to realize the full potential of this cultural shift. Siloed Mindset 4. : Many organizations fall into the trap of believing that DevOps is about adopting specific tools. While tools are important, they should complement a broader cultural transformation rather than being the sole focus. Focus on Tools Alone 5. : Cultural changes take time to develop and become ingrained. Organizations that prioritize short-term gains over long-term cultural shifts may miss out on the full benefits of DevOps. Short-Term Mindset Case Study: Spotify’s DevOps Success Spotify, the music streaming giant, serves millions of users with its platform. To support the scale and speed required for its operations, Spotify embraced DevOps practices. Here are some noteworthy facts from their journey: : Spotify introduced a “Squad” structure, where small cross-functional teams are responsible for specific features or components. This promotes autonomy, innovation, and rapid decision-making. Squad Structure 2. : Squads are organized into larger “Tribes,” encouraging collaboration among related squads. “Guilds” facilitate knowledge sharing across Tribes, fostering a culture of continuous learning. Tribes and Guilds 3. : Spotify adopted continuous integration and continuous deployment (CI/CD) practices to streamline software delivery. This resulted in faster feature releases and quicker bug fixes, enhancing the user experience. CI/CD at Scale 4. : Spotify encourages experimentation and data-driven decision-making. Squads are empowered to make changes and measure their impact, contributing to the platform’s evolution. Experimentation Culture 5. : Spotify employs Infrastructure as Code principles, allowing teams to manage and deploy infrastructure using version-controlled code. This approach enhances consistency and reduces manual configuration efforts. Infrastructure as Code (IaC) 6. : Extensive automated testing ensures code quality and prevents regressions. This accelerates the development process and maintains a high level of reliability. Automated Testing 7. : Spotify promotes a blameless culture where failures are viewed as opportunities for learning rather than assigning blame. This encourages transparency and continuous improvement. Blameless Culture The result of Spotify’s DevOps journey? A platform that offers a seamless music streaming experience to millions of users worldwide, with frequent updates, high reliability, and a culture of innovation. How Organizations Can Truly Adopt DevOps Embracing DevOps as a transformative cultural shift requires careful planning, commitment, and a comprehensive approach. Here are key steps organizations can take to successfully adopt DevOps and foster a culture of collaboration and innovation: : Start from the top. Leadership buy-in and support are essential for driving cultural change. When leaders champion the DevOps transformation, it sends a clear message to the entire organization that this shift is a priority. Leadership Commitment 2. : DevOps is not just about tools; it’s about understanding the philosophy behind it. Provide training and workshops for employees at all levels to build a shared understanding of DevOps principles, methodologies, and benefits. Educate and Train 3. : Break down silos by forming cross-functional teams that include developers, operations personnel, testers, and business stakeholders. This approach encourages collaboration, knowledge sharing, and shared ownership. Cross-Functional Teams 4. : Implement continuous integration and continuous deployment (CI/CD) practices to automate and streamline the delivery pipeline. This helps reduce manual errors, increase deployment frequency, and ensure consistent releases. Continuous Integration and Deployment 5. : DevOps relies on data-driven decisions. Implement metrics to track the effectiveness of the DevOps transformation, such as deployment frequency, lead time, and mean time to recovery. Regularly analyze the data to identify areas for improvement. Measure and Improve 6. : Create a safe environment for experimentation. Encourage teams to try new approaches, technologies, and methodologies. Failure is an opportunity to learn and innovate. Encourage Experimentation 7. : Implement automation tools for provisioning, configuration management, testing, and monitoring. Automation reduces manual tasks, enhances consistency, and accelerates the delivery process. Automation 8. Invest in tools that facilitate collaboration, communication, and knowledge sharing among teams. Tools like chat platforms, collaboration boards, and documentation repositories foster a culture of openness. Collaborative Tools: 9. : Recognize and celebrate achievements along the DevOps journey. Acknowledge teams that successfully implement new practices, deliver features faster, or improve overall efficiency. Celebrate Successes 10. : Establish regular feedback loops with customers, end-users, and internal stakeholders. Incorporate their input to continuously refine and enhance products. Feedback Loops 11. : Begin the DevOps adoption journey with a pilot project or a specific team. Once successful practices are established, scale them gradually across the organization. Scale Gradually 12. : DevOps is an evolving field. Encourage teams to stay updated on industry trends, attend conferences, and participate in communities to learn from peers. Continuous Learning 13. : DevOps is not a one-size-fits-all approach. Every organization’s journey will be unique. Be prepared to adapt and iterate based on the organization’s evolving needs and goals. Adapt and Iterate Conclusion The transformation from a role-centric approach to a DevOps cultural shift is akin to opening a door to uncharted possibilities. Beyond the toolset and methodology, it’s a declaration of an organization’s commitment to collaboration, innovation, and resilience. When DevOps is nurtured as a culture, teams coalesce into a force capable of not only delivering software efficiently but also embracing change and evolving in stride with the industry. In this culture-driven DevOps journey, a harmonious symphony of minds transforms the software delivery process into an orchestration of excellence. By learning from common pitfalls, organizations can embark on their own DevOps cultural transformation journey, reaping the rewards of enhanced collaboration, innovation, and value.

Embrace DevOps as a Cultural Shift Rather Than Just a Role

Visual Studio Code (VS Code) has rapidly become the go-to code editor for developers due to its powerful features, extensibility, and community support. JavaScript developers, in particular, can enhance their productivity and streamline their workflow by harnessing a variety of high-quality extensions tailored to their needs. In this article, we'll explore the best VS Code JavaScript extensions that every developer should consider incorporating into their toolkit. ESLint ESLint is an essential extension for JavaScript developers looking to maintain code quality and adhere to best practices. With its customizable rules, ESLint can identify potential issues, enforce coding standards, and ensure consistent code formatting. The extension provides real-time feedback, highlighting errors and warnings directly in the editor, thus helping developers to catch and resolve problems early in the development process. Prettier Consistent code formatting is crucial for maintainability and collaboration. Prettier is a widely used code formatter that automatically enforces a consistent style across your JavaScript codebase. By integrating Prettier with ESLint, developers can ensure both code quality and uniformity, significantly streamlining the development process. GitLens GitLens is a powerful extension that enhances the built-in Git capabilities of VS Code. JavaScript developers can leverage GitLens to gain insights into code authorship, commit history, and changes made over time. The extension allows developers to view blame annotations, navigate through previous versions of files, and understand how specific lines of code have evolved over different commits. Bracket Pair Colorizer Managing nested code structures can be challenging, especially in JavaScript where indentation and bracket pairs play a crucial role. The Bracket Pair Colorizer extension visually enhances code readability by colorizing matching brackets, making it easier to identify code blocks. This simple yet effective tool significantly improves code navigation and editing efficiency. IntelliSense for JavaScript IntelliSense for JavaScript, also known as auto-completion, is a must-have extension for speeding up coding in VS Code. The extension provides intelligent code suggestions based on your JavaScript code, imported modules, and external libraries, making it effortless to find the right function, method, or variable without leaving the editor. JavaScript (ES6) Code Snippets Writing boilerplate code for common JavaScript patterns can be tedious and time-consuming. JavaScript (ES6) Code Snippets extension offers a collection of handy shortcuts for frequently used code snippets. From creating arrow functions to handling promises, this extension provides a plethora of snippets that help reduce repetitive typing and boost productivity. Quokka.js Quokka.js is a powerful live scratchpad extension that allows developers to experiment with code and instantly see the results in real-time. It provides an interactive environment right inside VS Code, enabling JavaScript developers to test expressions, functions, and evaluate variables without the need to run the entire application. Better Comments While collaborating on projects or revisiting old code, understanding comments and their relevance can be challenging. Better Comments is an extension that enhances code commenting by colorizing and categorizing comments based on specific tags. This makes it easier to differentiate between general notes, to-dos, and important reminders within the codebase. npm Intellisense npm Intellisense is a time-saving extension that simplifies the process of importing modules from npm packages. As you type, the extension automatically suggests package names, making it effortless to locate and include the desired modules in your JavaScript projects. Debugger for Chrome Debugging is an integral part of the development process, and Debugger for Chrome extension facilitates smooth debugging of JavaScript code within the VS Code editor. Developers can set breakpoints, inspect variables, and step through code to identify and rectify issues, all while leveraging the Chrome Developer Tools interface. If you like this article and would like to stay in touch with me, then consider subscribing to my Hackernoon newsletter, hit that subscribe button on my profile page.

The Top VS Code JavaScript Extensions Every Developer Should Embrace

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