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AT&T

Relationships at work are important since we are social animals - early experiences with human connection shape how we interact with others as adults, including at work. In the age of AI agents, human connection is what sets us apart, emphasizing empathy, creativity, and nuanced understanding of each other. Strong relationships are key in collaboration, driving innovation, and enabling well-rounded discussions that AI cannot replicate (yet?). Moreover, your relationship patterns can influence your career decisions, work performance, and more so it’s useful to understand them. 1 Attachment Theory Origin and Relationships at Work Attachment theory, initially developed by John Bowlby in the mid-20th century, is a powerful tool to understand how we connect with others. Originally, the theory focused on understanding infant-mother relationships and how early emotional bonds influence development. Mary Ainsworth expanded on Bowlby’s work with her famous Strange Situation experiments, revealing that children exhibit different attachment styles—secure, avoidant, and anxious—depending on their experiences with caregivers. To better understand the experiment, you can watch the video recording below. Warning: it can be distressing for some people so take a deep breath before watching. https://youtu.be/woQhp28ndik?si=Dvl0aMo4WA7GQXb1&embedable=true How is this related to work? Well, over time, attachment theory has evolved. Researchers like Cindy Hazan and Phillip Shaver applied the theory to adult romantic relationships, demonstrating how the attachment patterns formed in childhood influence how we bond in adult relationships. Whether avoidant, anxious, or secure, these early patterns affect the way we love, trust, and interact with others throughout our lives. But attachment theory isn’t just relevant to personal relationships, it relates to relationships at work too. Today, its application has broadened to include fields such as healthcare, management, and organizational behaviors, thanks to scholars like Mario Mikulincer with Phillip Shaver. It turns out, our attachment tendencies—especially insecurity in attachment—can deeply impact how we behave at work and influence our satisfaction and performance in the workplace. 2 Attachment Insecurity and Its Impact on Relationships at Work Attachment insecurity, characterized by patterns of avoidance or anxiety, can cause several issues in workplace dynamics according to a meta-analysis of the existing research. These attachment tendencies, which stem from early life experiences, influence how we form relationships with colleagues, handle stress, and perceive feedback from supervisors. Here’s a breakdown of the workplace challenges that arise due to attachment insecurity: (You might want to take a deep breath again) 2.1 Excessive Independence Do you prefer working alone and often shy away from collaborating with others? This tendency is common in those with attachment avoidance. If you are avoidantly attached, you may resist relying on others and avoid interpersonal closeness in professional settings. While independence can be valuable, excessive independence can create feelings of isolation and lack of support. How do you feel about asking for help? Over time, these feelings can lead to burnout or dissatisfaction, as working in isolation without support structures may cause individuals to feel disconnected from their teams. Eventually, this environment fosters thoughts of leaving the job altogether. 2.2 Trust Issues and Strained Relationships at Work Attachment avoidance also often leads to trust issues at work. When people struggle to trust or rely on colleagues and supervisors, the workplace can become a challenging and even hostile environment. People with high avoidance may find themselves disengaged, unable to form strong team bonds, or feel uncomfortable working under leadership. This lack of trust can make it difficult to thrive in collaborative environments. As mentioned in the previous point, this can lead to thoughts of quitting as the workplace begins to feel increasingly unwelcoming. 2.3 Job Dissatisfaction For those with attachment anxiety, the need for constant validation can be overwhelming. At work, this translates into a desire for continual praise and reassurance about performance. Without it, there could be a growing feeling of job dissatisfaction. Emotional needs go unmet, leading to feelings of inadequacy and concern over whether they’re performing well enough. This dissatisfaction can build up over time, particularly if managers don’t regularly acknowledge the employee's contributions. Those with attachment anxiety may also feel more stressed and vulnerable to burnout as they continuously seek [positive] feedback that never feels sufficient. 2.4 High-Stress Levels and Burnout Both attachment anxiety and avoidance are linked to heightened stress in the workplace. Anxiously attached individuals often struggle with the fear of rejection and worry about how their efforts are perceived. This need for constant reassurance can lead to burnout, as individuals feel pressured to meet perceived expectations. On the other hand, avoidant individuals may cope with workplace stress by overworking or avoiding difficult conversations, such as performance reviews, to maintain their distance. This avoidance leads to overload, as tasks pile up and go unresolved. In both cases, stress levels rise, contributing to an unsustainable work-life balance (or integration). 3 Understanding Your Triggers From Relationships at Work If you relate to any of these descriptions, take time to reflect on the situations and interactions at work that might trigger your thoughts of leaving the job and similar reactions. Attachment theory teaches us that our early experiences shape how we approach relationships, including those at work. Our thoughts and emotions act as signals, helping us understand what matters most to us. Sometimes, those signals can be influenced by past experience and it can be difficult to understand your patterns realistically. In that case, it could be helpful to discuss your patterns with a psychotherapist. By understanding your specific attachment patterns, you can make more informed decisions about your career and relationships at work. Once you’re aware of these patterns, you can use tools like SWOT analysis to evaluate whether staying in your current role aligns with your values and needs—or whether it’s time to move on to a new opportunity. 💡 Thank you for making it here. If you enjoyed reading this story you might like my weekly posts on Instagram too. Relationships at work are important since we are social animals - early experiences with human connection shape how we interact with others as adults, including at work. In the age of AI agents, human connection is what sets us apart, emphasizing empathy, creativity, and nuanced understanding of each other. Strong relationships are key in collaboration, driving innovation, and enabling well-rounded discussions that AI cannot replicate (yet?). Moreover, your relationship patterns can influence your career decisions, work performance, and more so it’s useful to understand them. 1 Attachment Theory Origin and Relationships at Work Attachment theory , initially developed by John Bowlby in the mid-20th century, is a powerful tool to understand how we connect with others. Originally, the theory focused on understanding infant-mother relationships and how early emotional bonds influence development. theory John Bowlby Mary Ainsworth expanded on Bowlby’s work with her famous Strange Situation experiments, revealing that children exhibit different attachment styles—secure, avoidant, and anxious—depending on their experiences with caregivers. To better understand the experiment, you can watch the video recording below. Mary Ainsworth Strange Situation Warning: it can be distressing for some people so take a deep breath before watching. Warning: https://youtu.be/woQhp28ndik?si=Dvl0aMo4WA7GQXb1&embedable=true https://youtu.be/woQhp28ndik?si=Dvl0aMo4WA7GQXb1&embedable=true How is this related to work? Well, over time, attachment theory has evolved. Researchers like Cindy Hazan and Phillip Shaver applied the theory to adult romantic relationships, demonstrating how the attachment patterns formed in childhood influence how we bond in adult relationships. Whether avoidant, anxious, or secure, these early patterns affect the way we love, trust, and interact with others throughout our lives. Cindy Hazan and Phillip Shaver Cindy Hazan and Phillip Shaver But attachment theory isn’t just relevant to personal relationships, it relates to relationships at work too. Today, its application has broadened to include fields such as healthcare, management, and organizational behaviors, thanks to scholars like Mario Mikulincer with Phillip Shaver. relationships at work Mario Mikulincer It turns out, our attachment tendencies—especially insecurity in attachment—can deeply impact how we behave at work and influence our satisfaction and performance in the workplace. It turns out, our attachment tendencies—especially insecurity in attachment—can deeply impact how we behave at work and influence our satisfaction and performance in the workplace. 2 Attachment Insecurity and Its Impact on Relationships at Work Attachment insecurity , characterized by patterns of avoidance or anxiety , can cause several issues in workplace dynamics according to a meta-analysis of the existing research. These attachment tendencies, which stem from early life experiences, influence how we form relationships with colleagues, handle stress, and perceive feedback from supervisors. Here’s a breakdown of the workplace challenges that arise due to attachment insecurity: Attachment insecurity avoidance anxiety meta-analysis relationships with colleagues, handle stress, and perceive feedback (You might want to take a deep breath again) 2.1 Excessive Independence 2.1 Excessive Independence Do you prefer working alone and often shy away from collaborating with others? This tendency is common in those with attachment avoidance. If you are avoidantly attached, you may resist relying on others and avoid interpersonal closeness in professional settings. While independence can be valuable, excessive independence can create feelings of isolation and lack of support. How do you feel about asking for help? Over time, these feelings can lead to burnout or dissatisfaction, as working in isolation without support structures may cause individuals to feel disconnected from their teams. Eventually, this environment fosters thoughts of leaving the job altogether. 2.2 Trust Issues and Strained Relationships at Work Attachment avoidance also often leads to trust issues at work. When people struggle to trust or rely on colleagues and supervisors, the workplace can become a challenging and even hostile environment. People with high avoidance may find themselves disengaged, unable to form strong team bonds, or feel uncomfortable working under leadership. This lack of trust can make it difficult to thrive in collaborative environments. As mentioned in the previous point, this can lead to thoughts of quitting as the workplace begins to feel increasingly unwelcoming. 2.3 Job Dissatisfaction For those with attachment anxiety, the need for constant validation can be overwhelming. At work, this translates into a desire for continual praise and reassurance about performance. Without it, there could be a growing feeling of job dissatisfaction. Emotional needs go unmet, leading to feelings of inadequacy and concern over whether they’re performing well enough. This dissatisfaction can build up over time, particularly if managers don’t regularly acknowledge the employee's contributions. Those with attachment anxiety may also feel more stressed and vulnerable to burnout as they continuously seek [positive] feedback that never feels sufficient. 2.4 High-Stress Levels and Burnout Both attachment anxiety and avoidance are linked to heightened stress in the workplace . Anxiously attached individuals often struggle with the fear of rejection and worry about how their efforts are perceived. This need for constant reassurance can lead to burnout, as individuals feel pressured to meet perceived expectations. heightened stress in the workplace On the other hand, avoidant individuals may cope with workplace stress by overworking or avoiding difficult conversations , such as performance reviews, to maintain their distance. This avoidance leads to overload, as tasks pile up and go unresolved. In both cases, stress levels rise, contributing to an unsustainable work-life balance (or integration). overworking or avoiding difficult conversations 3 Understanding Your Triggers From Relationships at Work If you relate to any of these descriptions, take time to reflect on the situations and interactions at work that might trigger your thoughts of leaving the job and similar reactions. Attachment theory teaches us that our early experiences shape how we approach relationships, including those at work. Our thoughts and emotions act as signals , helping us understand what matters most to us. act as signals Sometimes, those signals can be influenced by past experience and it can be difficult to understand your patterns realistically. In that case, it could be helpful to discuss your patterns with a psychotherapist. By understanding your specific attachment patterns, you can make more informed decisions about your career and relationships at work. Once you’re aware of these patterns, you can use tools like SWOT analysis to evaluate whether staying in your current role aligns with your values and needs—or whether it’s time to move on to a new opportunity. 💡 SWOT analysis Thank you for making it here. If you enjoyed reading this story you might like my weekly posts on Instagram too. Thank you for making it here. If you enjoyed reading this story you might like my weekly posts on Instagram too. weekly posts on Instagram Instagram

Why Are Relationships at Work Important? Meet the Attachment Theory

JavaScript is currently one of the most popular programming languages, according to many platforms. However, does popularity equate to being the most advanced or beloved language? It lacks certain constructs that are considered integral parts of other languages, such as an extensive standard library, immutability, and macros. But there is one detail that, in my opinion, doesn't receive enough attention - generators. In this article, I want to explain possible use cases for iterators and generators and how they improve the verbosity of one's code. I hope that after reading this article, the following piece of code will make all sense: while (true) {
    const data = yield getNextChunk();
    const processed = processData(data);
    try {
        yield sendProcessedData(processed);
        showOkResult();
    } catch (err) {
        showError();
    }
} This is the first part of a series: Iterators and Generators. Iterators So, an iterator is an interface that provides sequential access to data. As you can see, the definition doesn't mention anything about data structures or memory. Indeed, a sequence of null values can be represented as an iterator without occupying any memory space. Let's have a few examples: Array is probably the first thing that comes to mind when you think about iterators. It's a data structure that stores a sequence of values in memory. It's also an iterator because it provides sequential access to its elements. const arr = [1, 2, 3];
for (const item of arr) {
    console.log(item);
} The same goes for strings. They are stored in memory as a sequence of characters and provide sequential access to them. const str = 'abc';
for (const char of str) {
    console.log(char);
} Let's look at the following function example: const fn = () => Math.random(); This function can be considered an iterator because it provides sequential access to random numbers. Okay, why do we need iterators at all if arrays, one of the language's basic data structures, allow us to work with data both sequentially and in arbitrary order? Let's imagine that we need an iterator that implements a sequence of natural numbers, Fibonacci numbers, or any other infinite sequence. It would be difficult to store an infinite sequence in an array. We would need a mechanism for gradually populating the array with data and removing old data to prevent filling up the entire memory of the process. This unnecessary complexity adds extra implementation and maintenance overhead, whereas a solution without an array can be achieved in just a few lines of code: const getNaturalRow = () => {
    let current = 0;
    return () => ++current;
}; Iterators can also be used to represent data retrieved from an external channel, such as a WebSocket. In JavaScript, any object that has a next() method, which returns a structure with value (the current iterator value) and done (a flag indicating the end of the sequence), is considered an iterator. This convention is described in the ECMAScript language standard. Such an object implements the Iterator interface. Let's rewrite the previous example in this format: const getNaturalRow = () => {
    let current = 0;
    return {
        next: () => ({ value: ++current, done: false }),
    };
}; In JavaScript, there is also the Iterable interface. It represents an object that has an @@iterator method (accessible via Symbol.iterator constant) that returns an iterator. Objects that implement this interface can be iterated using the for..of loop. Let's rewrite our example once again, this time as an Iterable implementation: const naturalRowIterator = {
    [Symbol.iterator]: () => ({
        _current: 0,
        next() { return {
            value: ++this._current,
            done: this._current > 3,
       }},
   }),
}

for (num of naturalRowIterator) {
    console.log(num);
}
// output: 1, 2, 3 As you can see, we had to make flag "done" to change at some point, otherwise the loop would be infinite. Generators The next stage in the evolution of iterators was the introduction of generators. They provide syntactic sugar that allows values of an iterator to be returned as if they were the result of a function. A generator is a function declared with an asterisk function* and returns an iterator. The iterator itself is not explicitly returned; instead, the function yields values of the iterator using the yield keyword. When the function completes its execution, the iterator is considered done (subsequent next method calls will return { done: true, value: undefined }. function* naturalRowGenerator() {
    let current = 1;
    while (current <= 3) {
        yield current;
        current++;
    }
}

for (num of naturalRowGenerator()) {
    console.log(num);
}
// output: 1, 2, 3 Even in this simple example, the main nuance of generators is apparent: the code inside a generator function does not execute synchronously. The execution of a generator code occurs incrementally as a result of next method calls on the corresponding iterator. Let's examine how the generator code executes using the previous example. We will use a special cursor to mark where the execution of the generator is paused. At the moment of calling naturalRowGenerator, an iterator is created. function* naturalRowGenerator() {
    ▷let current = 1;
    while (current <= 3) {
        yield current;
        current++;
    }
} Next, when we call the next method three times, or in our case, iterate through the loop three times, the cursor is positioned after the yield statement. function* naturalRowGenerator() {
    let current = 1;
    while (current <= 3) {
        yield current; ▷
        current++;
    }
} For all subsequent next calls, as well as after exiting the loop, the generator completes its execution. The result of subsequent next calls will be { value: undefined, done: true }. Passing parameters to iterators Let's imagine that we need to add the functionality to reset the current counter and start counting from the beginning in our iterator of natural numbers. naturalRowIterator.next() // 1
naturalRowIterator.next() // 2
naturalRowIterator.next(true) // 1
naturalRowIterator.next() // 2 It's clear, how to handle such a parameter in a custom iterator, but what about generators? It turns out that generators support parameter passing! function* naturalRowGenerator() {
    let current = 1;
    while (true) {
        const reset = yield current;
        if (reset) {
          current = 1;
        } else {
          current++;
        }
    }
} The passed parameter becomes available as the result of the yield operator. Let's try to clarify this using the cursor approach. At the moment of creating the iterator, nothing has changed. Now, let's stop after the first next method call: function* naturalRowGenerator() {
    let current = 1;
    while (true) {
        const reset = ▷yield current;
        if (reset) {
          current = 1;
        } else {
          current++;
        }
    }
} The cursor is positioned after returning from the yield operator. With the next next call, the value passed into the function will set the value of the reset variable. But what happens to the value passed in the very first next call? It goes nowhere! If you need to pass an initial value to the generator, you can do so through the generator's arguments. Here's an example: function* naturalRowGenerator(start = 1) {
    let current = start;
    while (true) {
        const reset = yield current;
        if (reset) {
          current = start;
        } else {
          current++;
        }
    }
}

const iterator = naturalRowGenerator(10);
iterator.next() // 10
iterator.next() // 11
iterator.next(true) // 10 Conclusion We have explored the concept of iterators and their implementation in JavaScript. Additionally, we have learned about generators, a syntactic construct for conveniently implementing iterators. Although I provided examples in this article with numeric sequences, iterators in JavaScript can solve a wide range of tasks. They can represent any data sequence and even many finite-state machines. In the next article, I would like to discuss how generators can be used to build asynchronous processes (coroutines, goroutines, CSP, etc.). JavaScript is currently one of the most popular programming languages, according to many platforms. However, does popularity equate to being the most advanced or beloved language? It lacks certain constructs that are considered integral parts of other languages, such as an extensive standard library, immutability, and macros. But there is one detail that, in my opinion, doesn't receive enough attention - generators. In this article, I want to explain possible use cases for iterators and generators and how they improve the verbosity of one's code. I hope that after reading this article, the following piece of code will make all sense: while (true) {
    const data = yield getNextChunk();
    const processed = processData(data);
    try {
        yield sendProcessedData(processed);
        showOkResult();
    } catch (err) {
        showError();
    }
} while (true) {
    const data = yield getNextChunk();
    const processed = processData(data);
    try {
        yield sendProcessedData(processed);
        showOkResult();
    } catch (err) {
        showError();
    }
} This is the first part of a series: Iterators and Generators. Iterators So, an iterator is an interface that provides sequential access to data. As you can see, the definition doesn't mention anything about data structures or memory. Indeed, a sequence of null values can be represented as an iterator without occupying any memory space. Let's have a few examples: Array is probably the first thing that comes to mind when you think about iterators. It's a data structure that stores a sequence of values in memory. It's also an iterator because it provides sequential access to its elements. const arr = [1, 2, 3];
for (const item of arr) {
    console.log(item);
} const arr = [1, 2, 3];
for (const item of arr) {
    console.log(item);
} The same goes for strings. They are stored in memory as a sequence of characters and provide sequential access to them. const str = 'abc';
for (const char of str) {
    console.log(char);
} const str = 'abc';
for (const char of str) {
    console.log(char);
} Let's look at the following function example: const fn = () => Math.random(); const fn = () => Math.random(); This function can be considered an iterator because it provides sequential access to random numbers. Okay, why do we need iterators at all if arrays, one of the language's basic data structures, allow us to work with data both sequentially and in arbitrary order? Let's imagine that we need an iterator that implements a sequence of natural numbers, Fibonacci numbers, or any other infinite sequence. It would be difficult to store an infinite sequence in an array. We would need a mechanism for gradually populating the array with data and removing old data to prevent filling up the entire memory of the process. This unnecessary complexity adds extra implementation and maintenance overhead, whereas a solution without an array can be achieved in just a few lines of code: const getNaturalRow = () => {
    let current = 0;
    return () => ++current;
}; const getNaturalRow = () => {
    let current = 0;
    return () => ++current;
}; Iterators can also be used to represent data retrieved from an external channel, such as a WebSocket. In JavaScript, any object that has a next() method, which returns a structure with value (the current iterator value) and done (a flag indicating the end of the sequence), is considered an iterator. This convention is described in the ECMAScript language standard . Such an object implements the Iterator interface. Let's rewrite the previous example in this format: ECMAScript language standard const getNaturalRow = () => {
    let current = 0;
    return {
        next: () => ({ value: ++current, done: false }),
    };
}; const getNaturalRow = () => {
    let current = 0;
    return {
        next: () => ({ value: ++current, done: false }),
    };
}; In JavaScript, there is also the Iterable interface. It represents an object that has an @@iterator method (accessible via Symbol.iterator constant) that returns an iterator. Objects that implement this interface can be iterated using the for..of loop. Let's rewrite our example once again, this time as an Iterable implementation: Iterable @@iterator Symbol.iterator for..of const naturalRowIterator = {
    [Symbol.iterator]: () => ({
        _current: 0,
        next() { return {
            value: ++this._current,
            done: this._current > 3,
       }},
   }),
}

for (num of naturalRowIterator) {
    console.log(num);
}
// output: 1, 2, 3 const naturalRowIterator = {
    [Symbol.iterator]: () => ({
        _current: 0,
        next() { return {
            value: ++this._current,
            done: this._current > 3,
       }},
   }),
}

for (num of naturalRowIterator) {
    console.log(num);
}
// output: 1, 2, 3 As you can see, we had to make flag "done" to change at some point, otherwise the loop would be infinite. Generators The next stage in the evolution of iterators was the introduction of generators. They provide syntactic sugar that allows values of an iterator to be returned as if they were the result of a function. A generator is a function declared with an asterisk function* and returns an iterator. The iterator itself is not explicitly returned; instead, the function yields values of the iterator using the yield keyword. When the function completes its execution, the iterator is considered done (subsequent next method calls will return { done: true, value: undefined } . function* yield next { done: true, value: undefined } function* naturalRowGenerator() {
    let current = 1;
    while (current <= 3) {
        yield current;
        current++;
    }
}

for (num of naturalRowGenerator()) {
    console.log(num);
}
// output: 1, 2, 3 function* naturalRowGenerator() {
    let current = 1;
    while (current <= 3) {
        yield current;
        current++;
    }
}

for (num of naturalRowGenerator()) {
    console.log(num);
}
// output: 1, 2, 3 Even in this simple example, the main nuance of generators is apparent: the code inside a generator function does not execute synchronously . The execution of a generator code occurs incrementally as a result of next method calls on the corresponding iterator. Let's examine how the generator code executes using the previous example. We will use a special cursor to mark where the execution of the generator is paused. does not execute synchronously next At the moment of calling naturalRowGenerator, an iterator is created. function* naturalRowGenerator() {
    ▷let current = 1;
    while (current <= 3) {
        yield current;
        current++;
    }
} function* naturalRowGenerator() {
    ▷let current = 1;
    while (current <= 3) {
        yield current;
        current++;
    }
} Next, when we call the next method three times, or in our case, iterate through the loop three times, the cursor is positioned after the yield statement. next function* naturalRowGenerator() {
    let current = 1;
    while (current <= 3) {
        yield current; ▷
        current++;
    }
} function* naturalRowGenerator() {
    let current = 1;
    while (current <= 3) {
        yield current; ▷
        current++;
    }
} For all subsequent next calls, as well as after exiting the loop, the generator completes its execution. The result of subsequent next calls will be { value: undefined, done: true } . next next { value: undefined, done: true } Passing parameters to iterators Let's imagine that we need to add the functionality to reset the current counter and start counting from the beginning in our iterator of natural numbers. naturalRowIterator.next() // 1
naturalRowIterator.next() // 2
naturalRowIterator.next(true) // 1
naturalRowIterator.next() // 2 naturalRowIterator.next() // 1
naturalRowIterator.next() // 2
naturalRowIterator.next(true) // 1
naturalRowIterator.next() // 2 It's clear, how to handle such a parameter in a custom iterator, but what about generators? It turns out that generators support parameter passing! function* naturalRowGenerator() {
    let current = 1;
    while (true) {
        const reset = yield current;
        if (reset) {
          current = 1;
        } else {
          current++;
        }
    }
} function* naturalRowGenerator() {
    let current = 1;
    while (true) {
        const reset = yield current;
        if (reset) {
          current = 1;
        } else {
          current++;
        }
    }
} The passed parameter becomes available as the result of the yield operator. Let's try to clarify this using the cursor approach. At the moment of creating the iterator, nothing has changed. Now, let's stop after the first next method call: next function* naturalRowGenerator() {
    let current = 1;
    while (true) {
        const reset = ▷yield current;
        if (reset) {
          current = 1;
        } else {
          current++;
        }
    }
} function* naturalRowGenerator() {
    let current = 1;
    while (true) {
        const reset = ▷yield current;
        if (reset) {
          current = 1;
        } else {
          current++;
        }
    }
} The cursor is positioned after returning from the yield operator. With the next next call, the value passed into the function will set the value of the reset variable. But what happens to the value passed in the very first next call? It goes nowhere! If you need to pass an initial value to the generator, you can do so through the generator's arguments. Here's an example: next reset next function* naturalRowGenerator(start = 1) {
    let current = start;
    while (true) {
        const reset = yield current;
        if (reset) {
          current = start;
        } else {
          current++;
        }
    }
}

const iterator = naturalRowGenerator(10);
iterator.next() // 10
iterator.next() // 11
iterator.next(true) // 10 function* naturalRowGenerator(start = 1) {
    let current = start;
    while (true) {
        const reset = yield current;
        if (reset) {
          current = start;
        } else {
          current++;
        }
    }
}

const iterator = naturalRowGenerator(10);
iterator.next() // 10
iterator.next() // 11
iterator.next(true) // 10 Conclusion We have explored the concept of iterators and their implementation in JavaScript. Additionally, we have learned about generators, a syntactic construct for conveniently implementing iterators. Although I provided examples in this article with numeric sequences, iterators in JavaScript can solve a wide range of tasks. They can represent any data sequence and even many finite-state machines. In the next article, I would like to discuss how generators can be used to build asynchronous processes (coroutines, goroutines, CSP, etc.).

The One Aspect of JavaScript That Doesn't Get Enough Attention

The Top 5 Memecoins of 2024 Are Ones You Probably Weren't Paying Much Attention To

Els 5 millors Memecoins del 2024 són aquells als quals probablement no estaves prestant molta atenció

Las 5 mejores memecoins de 2024 son aquellas a las que probablemente no les prestaste mucha atención

2024ko 5 Memecoin nagusiak ziurrenik arreta handirik jartzen ez zenituenak dira

Os 5 mellores Memecoins de 2024 son aos que probablemente non lles prestaches moita atención

2024年のトップ5ミームコインは、おそらくあまり注目されていなかったもの

Memecoins កំពូលទាំង 5 នៃឆ្នាំ 2024 គឺជាវត្ថុដែលអ្នកប្រហែលជាមិនយកចិត្តទុកដាក់ច្រើនចំពោះ

5 populiariausios 2024 m. memecoinos yra tos, į kurias tikriausiai nekreipėte daug dėmesio

Ny Memecoins 5 ambony indrindra amin'ny 2024 dia ireo izay mety tsy nojerenao loatra

2024 को शीर्ष 5 मेमेकोइनहरू ती हुन् जुन तपाईंले सायद धेरै ध्यान दिनुभएको थिएन

Iwo Epamusoro mashanu Memecoins a2024 Ndiwo Aungangodaro Wanga Usiri Kunyanya Kuteerera Kwauri.

5ta Memecoin ee ugu sareysa sanadka 2024 waa kuwa ay u badan tahay inaadan ahmiyad badan siinin

2024 yilning eng yaxshi 5 ta memecoinlari siz unchalik e'tibor bermagan bo'lsangiz kerak

PALO ALTO, Calif., USA, December 30th, 2024/CyberNewsWire/--SquareX, an industry-first Browser Detection and Response (BDR) solution, leads the way in browser security. About a week ago, SquareX reported large-scale attacks targeting Chrome Extension developers aimed at taking over the Chrome Extension from the Chrome Store. On December 25th, 2024, a malicious version of Cyberhaven’s browser extension was published on the Chrome Store that allowed the attacker to hijack authenticated sessions and exfiltrate confidential information. The malicious extension was available for download for more than 30 hours before being removed by Cyberhaven. The data loss prevention company declined to comment on the extent of the impact when approached by the press, but the extension had over 400,000 users on the Chrome Store at the time of the attack. Unfortunately, the attack took place as SquareX's researchers had identified a similar attack with a video demonstrating the entire attack pathway just a week before the Cyberhaven breach. The attack begins with a phishing email impersonating Chrome Store containing a supposed violation of the platform’s “Developer Agreement”, urging the receiver to accept the policies to prevent their extension from being removed from Chrome Store. Upon clicking on the policy button, the user gets prompted to connect their Google account to a “Privacy Policy Extension”, which grants the attacker access to edit, update and publish extensions on the developer’s account. Fig 1. Phishing email targeting extension developers Fig 2. Fake Privacy Policy Extension requesting access to “edit, update or publish” the developer’s extension Extensions have become an increasingly popular way for attackers to gain initial access. This is because most organizations have limited purview on what browser extensions their employees are using. Even the most rigorous security teams typically do not monitor subsequent updates once an extension is whitelisted. SquareX has conducted extensive research and demonstrated at DEFCON 32, how MV3-compliant extensions can be used to steal video stream feeds, add a silent GitHub collaborator, and steal session cookies, among others. Attackers can create a seemingly harmless extension and later convert it into a malicious one post-installation or, as demonstrated in the attack above, deceive the developers behind a trusted extension to gain access to one that already has hundreds of thousands of users. In Cyberhaven’s case, attackers were able to steal company credentials across multiple websites and web apps through the malicious version of the extension. Given that developer emails are publicly listed on Chrome Store, it is easy for attackers to target thousands of extension developers at once. These emails are typically used for bug reporting. Thus, even support emails listed for extensions from larger companies are usually routed to developers who may not have the level of security awareness required to find suspicion in such an attack. As per SquareX’s attack disclosure and the Cyberhaven breach that occurred within the span of less than two weeks, the company has strong reason to believe that many other browser extension providers are being attacked in the same way. SquareX urges companies and individuals alike to conduct a careful inspection before installing or updating any browser extensions. Fig 3. Contact details of extension developers are publicly available on Chrome Store SquareX team understands that it can be non-trivial to evaluate and monitor every single browser extension in the workforce amidst all the competing security priorities, especially when it comes to zero-day attacks. As demonstrated in the video, the fake privacy policy app involved in Cyberhaven’s breach was not even detected by any popular threat feeds. SquareX’s Browser Detection and Response (BDR) solution takes this complexity off security teams by: Blocking OAuth interactions to unauthorized websites to prevent employees from accidentally giving attackers unauthorized access to your Chrome Store account
Blocking and/or flagging any suspicious extension updates containing new, risky permissions
Blocking and/or flagging any suspicious extensions with a surge of negative reviews
Blocking and/or flagging installations of sideloaded extensions
Streamline all requests for extension installations outside the authorized list for quick approval based on company policy
Full visibility on all extensions installed and used by employees across the organization SquareX’s founder Vivek Ramachandran warns: “Identity attacks targeting browser extensions similar to this OAuth attack will only become more prevalent as employees rely on more browser-based tools to be productive at work. Similar variants of these attacks have been used in the past to steal cloud data from apps like Google Drive and One Drive and we will only see attackers get more creative in exploiting browser extensions. Companies need to remain vigilant and minimize their supply chain risk without hampering employee productivity by equipping them with the right browser native tools.” About SquareX: SquareX helps organizations detect, mitigate, and threat-hunt client-side web attacks happening against their users in real-time. SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can provide contractors and remote workers with secure access to internal applications, and enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here PALO ALTO, Calif., USA, December 30th, 2024/CyberNewsWire/--SquareX, an industry-first Browser Detection and Response (BDR) solution, leads the way in browser security. About a week ago, SquareX reported large-scale attacks targeting Chrome Extension developers aimed at taking over the Chrome Extension from the Chrome Store. reported reported On December 25th, 2024, a malicious version of Cyberhaven’s browser extension was published on the Chrome Store that allowed the attacker to hijack authenticated sessions and exfiltrate confidential information. The malicious extension was available for download for more than 30 hours before being removed by Cyberhaven. The data loss prevention company declined to comment on the extent of the impact when approached by the press, but the extension had over 400,000 users on the Chrome Store at the time of the attack. malicious extension malicious extension Chrome Store Chrome Store Unfortunately, the attack took place as SquareX's researchers had identified a similar attack with a video demonstrating the entire attack pathway just a week before the Cyberhaven breach. identified identified video video The attack begins with a phishing email impersonating Chrome Store containing a supposed violation of the platform’s “Developer Agreement”, urging the receiver to accept the policies to prevent their extension from being removed from Chrome Store. Upon clicking on the policy button, the user gets prompted to connect their Google account to a “Privacy Policy Extension”, which grants the attacker access to edit, update and publish extensions on the developer’s account. Fig 1. Phishing email targeting extension developers Fig 2. Fake Privacy Policy Extension requesting access to “edit, update or publish” the developer’s extension Extensions have become an increasingly popular way for attackers to gain initial access. This is because most organizations have limited purview on what browser extensions their employees are using. Even the most rigorous security teams typically do not monitor subsequent updates once an extension is whitelisted. SquareX has conducted extensive research and demonstrated at DEFCON 32, how MV3-compliant extensions can be used to steal video stream feeds, add a silent GitHub collaborator, and steal session cookies, among others. DEFCON 32, DEFCON 32, Attackers can create a seemingly harmless extension and later convert it into a malicious one post-installation or, as demonstrated in the attack above, deceive the developers behind a trusted extension to gain access to one that already has hundreds of thousands of users. In Cyberhaven’s case, attackers were able to steal company credentials across multiple websites and web apps through the malicious version of the extension. Given that developer emails are publicly listed on Chrome Store, it is easy for attackers to target thousands of extension developers at once. These emails are typically used for bug reporting. Thus, even support emails listed for extensions from larger companies are usually routed to developers who may not have the level of security awareness required to find suspicion in such an attack. As per SquareX’s attack disclosure and the Cyberhaven breach that occurred within the span of less than two weeks, the company has strong reason to believe that many other browser extension providers are being attacked in the same way. SquareX urges companies and individuals alike to conduct a careful inspection before installing or updating any browser extensions. Fig 3. Contact details of extension developers are publicly available on Chrome Store SquareX team understands that it can be non-trivial to evaluate and monitor every single browser extension in the workforce amidst all the competing security priorities, especially when it comes to zero-day attacks. As demonstrated in the video , the fake privacy policy app involved in Cyberhaven’s breach was not even detected by any popular threat feeds. video video SquareX’s Browser Detection and Response (BDR) solution takes this complexity off security teams by: SquareX’s Browser Detection and Response (BDR) solution SquareX’s Browser Detection and Response (BDR) solution Blocking OAuth interactions to unauthorized websites to prevent employees from accidentally giving attackers unauthorized access to your Chrome Store account Blocking and/or flagging any suspicious extension updates containing new, risky permissions Blocking and/or flagging any suspicious extensions with a surge of negative reviews Blocking and/or flagging installations of sideloaded extensions Streamline all requests for extension installations outside the authorized list for quick approval based on company policy Full visibility on all extensions installed and used by employees across the organization Blocking OAuth interactions to unauthorized websites to prevent employees from accidentally giving attackers unauthorized access to your Chrome Store account Blocking and/or flagging any suspicious extension updates containing new, risky permissions Blocking and/or flagging any suspicious extensions with a surge of negative reviews Blocking and/or flagging installations of sideloaded extensions Streamline all requests for extension installations outside the authorized list for quick approval based on company policy Full visibility on all extensions installed and used by employees across the organization SquareX’s founder Vivek Ramachandran warns: “Identity attacks targeting browser extensions similar to this OAuth attack will only become more prevalent as employees rely on more browser-based tools to be productive at work. Similar variants of these attacks have been used in the past to steal cloud data from apps like Google Drive and One Drive and we will only see attackers get more creative in exploiting browser extensions. Companies need to remain vigilant and minimize their supply chain risk without hampering employee productivity by equipping them with the right browser native tools.” SquareX’s founder Vivek Ramachandran warns: “Identity attacks targeting browser extensions similar to this OAuth attack will only become more prevalent as employees rely on more browser-based tools to be productive at work. Similar variants of these attacks have been used in the past to steal cloud data from apps like Google Drive and One Drive and we will only see attackers get more creative in exploiting browser extensions. Companies need to remain vigilant and minimize their supply chain risk without hampering employee productivity by equipping them with the right browser native tools.” Vivek Ramachandran Vivek Ramachandran About SquareX: SquareX helps organizations detect, mitigate, and threat-hunt client-side web attacks happening against their users in real-time. SquareX SquareX SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can provide contractors and remote workers with secure access to internal applications, and enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

SquareX Researchers Expose OAuth Attack On Chrome Extensions Days Before Major Breach

Даследчыкі SquareX выкрылі атаку OAuth на пашырэнні Chrome за некалькі дзён да сур'ёзнага ўзлому

Investigadores de SquareX descubren un ataque OAuth en extensiones de Chrome días antes de una importante vulneración

Istraživači SquareX-a razotkrivaju OAuth napad na Chromeova proširenja danima prije većeg proboja

חוקרי SquareX חושפים את התקפת OAuth על הרחבות Chrome ימים לפני הפרה גדולה

SquareX の研究者が、大規模な侵害の数日前に Chrome 拡張機能への OAuth 攻撃を暴露

SquareX зерттеушілері Chrome кеңейтімдеріндегі OAuth шабуылын үлкен бұзылулардан бірнеше күн бұрын ашты

SquareX 연구원, 주요 침해 발생 며칠 전 크롬 확장 프로그램에 대한 OAuth 공격 폭로

SquareX изилдөөчүлөрү Chrome кеңейтүүлөрүнө OAuth чабуулун ири бузуулардан бир нече күн мурун ашкерелешти

ນັກຄົ້ນຄວ້າ SquareX ເປີດເຜີຍການໂຈມຕີ OAuth ກ່ຽວກັບ Chrome Extensions ມື້ກ່ອນການລະເມີດທີ່ສໍາຄັນ

Badacze SquareX ujawniają atak OAuth na rozszerzenia Chrome na kilka dni przed poważnym naruszeniem

SquareX-forskare avslöjar OAuth-attack på Chrome-tillägg dagar innan större intrång

Abaphandi be-SquareX Baveza uhlaselo lwe-OAuth kwiiNtsuku zoLwandiso lwe-Chrome ngaphambi kokwaphulwa okukhulu

Developer activity is one of the most fundamental metrics for evaluating the future of blockchain or digital asset projects. More developer activity means more potential for growth, more access points, and more apps and services. Slowing or dwindling developer interest might indicate that a project is losing steam or that adoption is waning. Interestingly, as valuable a metric as developer activity is, it is also the most under-reported, and it needs more visibility compared to other kinds of digital asset data. One of the main reasons is that financial metrics, like market cap or TVL, are usually the brightest lights in the room when looking at onchain activity or growth. In many cases, financial metrics are valuable and popular because they are easy to understand and obtain. The issue is that financial metrics can also be fuzzy or easy to game or manipulate. Countless examples of inflated token prices or other economic data don't match reality or utility. Developer metrics might be less glamorous than rocketing prices. However, understanding which chains developers are working on is a good way for market watchers to know where things are headed. That's why the annual Developer Report from Electric Capital is so valuable. The report aggregates data from across the onchain space and worldwide and combines it to help give market moves and narrative trends more context. To see how this year's trends and analysis compare to last year's report, check out this post: Unpacking the Electric Capital Developer Report 2023 Seven insights from the 2024 Developer Report Before diving into the takeaways from this year's report, it's important to note that the data that makes the basis for the report was collected based on code commits made in GitHub, a popular code repository for open-source projects. In other words, this report's existence is a great example of why building with transparent systems is such an important component of Open Money. With that out of the way, here are the trends: SOURCE Crypto developer activity continues to grow Crypto bull markets attract a new crop of developers. And while there is always a dropoff in developer activity during bear market cycles, the number of experienced developers (with 2+ years of experience) continues to grow. The number of experienced developers grew by 27% in 2024. This growth matters because 70% of code commits come from this group of more experienced developers. SOURCE Developer activity is becoming more distributed around the world North America and Europe are losing developers. Now, 1 in 3 developers live in Asia. India is the source of the most new developers, pulling past the United States in 2023. Africa and South America are also gaining developers, with Africa outpacing South America for the past few years. SOURCE Developer activity: Ethereum and Solana are the two most popular developer platforms -- Bitcoin developers working on scaling What's interesting about this data is that while Ethereum continues to be the most popular platform for established developers, Solana is more popular for new developers. It's also interesting that it's not clear platform-wise what comes after second place for developer activity. Base, Polygon, Polkadot, and ICP are all in contention for the third-most-popular chain. SOURCE Blockchain developers are increasingly working on more than one chain, with 1 in 3 working on more than one chain. Overall, the level of Bitcoin developer activity has remained consistent with last year in terms of volume. What's noteworthy is that 42% of Bitcoin developers are working on scaling solutions. SOURCE Onchain activity: Minting NFTs Are NFTs back? In 2024, the number of wallets with minting activity hit 67 million, with the most minting activity on Solana, Polygon, and Base. Minting transactions grew from 153 million in 2023 to 568 million in 2024. While market watchers tie previous spikes in NFT activities to digital art and collectibles, the 2024 non-fungible token mint rally was more distributed among NFT use cases and included DeFi utilities, rewards programs, identity applications, and gaming. February saw the most significant spike in minting-related activities. SOURCE DeFi Innovations continue to hit the DeFi market, with Ethereum still the clear winner for onchain DeFi providers. For more context, Ethereum DeFi is seven times larger than the next biggest chain, Solana. Market charts reflected EigenLayer's continued growth. Restaking is now the largest sector within DeFi, accounting for $30.6B TVL, representing 1,818.47% year-to-date growth. Solana is a leader in the decentralized exchange (DEX) use case. In 2024, 81% of all onchain DEX volume happened on Solana. SOURCE Stablecoins A big trend for 2024 was the rise in the use and utility of stablecoins as a valuable digital asset. Stablecoins broke all kinds of records in 2024. There are now $196 billion in stablecoins in circulation, and $81 billion in stablecoin volume is transacted every day. Ethereum is still the dominant stablecoin platform, with 59% of all stablecoin transactions happening within the Ethereum ecosystem. A trend within the larger stablecoin adoption story is using stablecoins to back real-world assets, such as US Treasuries, which do not account for $90 billion on stablecoins. What does developer activity say about what to expect in 2025? One of the most striking aspects of the 2024 Developer Report was how the major developer trends seemed to be in sync with the major narrative trends of the year. For example: The surging popularity of stablecoins
The adoption of Base as an easy-to-use platform for builders. (Here's a database of Base projects).
Solana is a growing platform for NFT projects and a popular memecoin launcher. Overall, developer activity is growing at a steady pace (despite regular market fluctuations). The continual distribution of developer talent around the world is another example. Here's where you can find the complete report: Developer Report Developer activity is one of the most fundamental metrics for evaluating the future of blockchain or digital asset projects. More developer activity means more potential for growth, more access points, and more apps and services. Slowing or dwindling developer interest might indicate that a project is losing steam or that adoption is waning. Interestingly, as valuable a metric as developer activity is, it is also the most under-reported, and it needs more visibility compared to other kinds of digital asset data. One of the main reasons is that financial metrics, like market cap or TVL , are usually the brightest lights in the room when looking at onchain activity or growth. In many cases, financial metrics are valuable and popular because they are easy to understand and obtain. market cap TVL The issue is that financial metrics can also be fuzzy or easy to game or manipulate. Countless examples of inflated token prices or other economic data don't match reality or utility. Developer metrics might be less glamorous than rocketing prices. However, understanding which chains developers are working on is a good way for market watchers to know where things are headed. That's why the annual Developer Report from Electric Capital is so valuable. The report aggregates data from across the onchain space and worldwide and combines it to help give market moves and narrative trends more context. To see how this year's trends and analysis compare to last year's report, check out this post: Unpacking the Electric Capital Developer Report 2023 Unpacking the Electric Capital Developer Report 2023 Seven insights from the 2024 Developer Report Before diving into the takeaways from this year's report, it's important to note that the data that makes the basis for the report was collected based on code commits made in GitHub, a popular code repository for open-source projects. In other words, this report's existence is a great example of why building with transparent systems is such an important component of Open Money . Open Money With that out of the way, here are the trends: SOURCE SOURCE Crypto developer activity continues to grow Crypto developer activity continues to grow Crypto bull markets attract a new crop of developers. And while there is always a dropoff in developer activity during bear market cycles, the number of experienced developers (with 2+ years of experience) continues to grow. The number of experienced developers grew by 27% in 2024. This growth matters because 70% of code commits come from this group of more experienced developers. SOURCE SOURCE Developer activity is becoming more distributed around the world Developer activity is becoming more distributed around the world North America and Europe are losing developers. Now, 1 in 3 developers live in Asia. India is the source of the most new developers, pulling past the United States in 2023. Africa and South America are also gaining developers, with Africa outpacing South America for the past few years. SOURCE SOURCE Developer activity: Ethereum and Solana are the two most popular developer platforms -- Bitcoin developers working on scaling Developer activity: Ethereum and Solana are the two most popular developer platforms -- Bitcoin developers working on scaling What's interesting about this data is that while Ethereum continues to be the most popular platform for established developers, Solana is more popular for new developers. It's also interesting that it's not clear platform-wise what comes after second place for developer activity. Base, Polygon, Polkadot, and ICP are all in contention for the third-most-popular chain. SOURCE SOURCE Blockchain developers are increasingly working on more than one chain, with 1 in 3 working on more than one chain. Overall, the level of Bitcoin developer activity has remained consistent with last year in terms of volume. What's noteworthy is that 42% of Bitcoin developers are working on scaling solutions. SOURCE SOURCE Onchain activity: Minting NFTs Onchain activity: Minting NFTs Are NFTs back? In 2024, the number of wallets with minting activity hit 67 million, with the most minting activity on Solana, Polygon, and Base. Minting transactions grew from 153 million in 2023 to 568 million in 2024. While market watchers tie previous spikes in NFT activities to digital art and collectibles, the 2024 non-fungible token mint rally was more distributed among NFT use cases and included DeFi utilities, rewards programs, identity applications, and gaming. February saw the most significant spike in minting-related activities. SOURCE SOURCE DeFi DeFi Innovations continue to hit the DeFi market, with Ethereum still the clear winner for onchain DeFi providers. For more context, Ethereum DeFi is seven times larger than the next biggest chain, Solana. Market charts reflected EigenLayer's continued growth. Restaking is now the largest sector within DeFi, accounting for $30.6B TVL, representing 1,818.47% year-to-date growth. Solana is a leader in the decentralized exchange (DEX) use case. In 2024, 81% of all onchain DEX volume happened on Solana. SOURCE SOURCE Stablecoins Stablecoins A big trend for 2024 was the rise in the use and utility of stablecoins as a valuable digital asse t. stablecoins as a valuable digital asse Stablecoins broke all kinds of records in 2024. There are now $196 billion in stablecoins in circulation, and $81 billion in stablecoin volume is transacted every day. Ethereum is still the dominant stablecoin platform, with 59% of all stablecoin transactions happening within the Ethereum ecosystem. A trend within the larger stablecoin adoption story is using stablecoins to back real-world assets , such as US Treasuries, which do not account for $90 billion on stablecoins. real-world assets What does developer activity say about what to expect in 2025? One of the most striking aspects of the 2024 Developer Report was how the major developer trends seemed to be in sync with the major narrative trends of the year. For example: The surging popularity of stablecoins The adoption of Base as an easy-to-use platform for builders. (Here's a database of Base projects). Solana is a growing platform for NFT projects and a popular memecoin launcher. The surging popularity of stablecoins The adoption of Base as an easy-to-use platform for builders. ( Here's a database of Base projects ). Here's a database of Base projects Solana is a growing platform for NFT projects and a popular memecoin launcher. memecoin Overall, developer activity is growing at a steady pace (despite regular market fluctuations). The continual distribution of developer talent around the world is another example. Here's where you can find the complete report: Developer Report Developer Report

Crypto Bull Run Attracts New Developers in 2024, Report From Electric Capital Finds 

Το Crypto Bull Run προσελκύει νέους προγραμματιστές το 2024, αναφορά από το Electric Capital Finds 

El mercado alcista de las criptomonedas atraerá a nuevos desarrolladores en 2024, según un informe de Electric Capital

La corsa al rialzo delle criptovalute attrae nuovi sviluppatori nel 2024, secondo il rapporto di Electric Capital

暗号通貨の強気相場は2024年に新たな開発者を引き付ける、エレクトリックキャピタルのレポートが明らかに

Crypto Bull Run იზიდავს ახალ დეველოპერებს 2024 წელს, ანგარიში Electric Capital Finds-დან 

Crypto Bull Run ebendi ba développeurs ya sika na 2024, Rapport ya Electric Capital emoni 

क्रिप्टो बुल रनले 2024 मा नयाँ विकासकर्ताहरूलाई आकर्षित गर्दछ, इलेक्ट्रिक क्यापिटल फेला पारेको रिपोर्ट 

Crypto Bull Run priťahuje nových vývojárov v roku 2024, správa od spoločnosti Electric Capital 

Crypto Bull Run Inokwezva Vagadziri Vatsva muna 2024, Chirevo Kubva Kumagetsi Capital Inowana 

Crypto Bull Run ayaa soo jiidata horumariyeyaal cusub sanadka 2024, Warbixin laga helay Raasamaal Koronto 

Crypto Bull Run таҳиягарони навро дар соли 2024 ҷалб мекунад, гузориш аз Сармояи электрикӣ пайдо мекунад 

Electric Capital Raporuna Göre Kripto Boğa Koşusu 2024'te Yeni Geliştiricileri Cezbediyor

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 3.2. Problem Formulation This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 3.2. Problem Formulation This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Attempt to Manipulate Lighting on Digital Image Using AI

Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.5 Chatbot 7 Ablation Studies 8 Discussion 9 Related Work 10 Conclusion, Acknowledgement and References Abstract High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM’s source code is publicly available at https://github.com/vllm-project/vllm. 1 Introduction The emergence of large language models (LLMs) like GPT [5, 37] and PaLM [9] have enabled new applications such as programming assistants [6, 18] and universal chatbots [19, 35] that are starting to profoundly impact our work and daily routines. Many cloud companies [34, 44] are racing to provide these applications as hosted services. However, running these applications is very expensive, requiring a large number of hardware accelerators such as GPUs. According to recent estimates, processing an LLM request can be 10× more expensive than a traditional keyword query [43]. Given these high costs, increasing the throughput—and hence reducing the cost per request—of LLM serving systems is becoming more important. At the core of LLMs lies an autoregressive Transformer model [53]. This model generates words (tokens), one at a time, based on the input (prompt) and the previous sequence of the output’s tokens it has generated so far. For each request, this expensive process is repeated until the model outputs a termination token. This sequential generation process makes the workload memory-bound, underutilizing the computation power of GPUs and limiting the serving throughput. Improving the throughput is possible by batching multiple requests together. However, to process many requests in a batch, the memory space for each request should be efficiently managed. For example, Fig. 1 (left) illustrates the memory distribution for a 13B-parameter LLM on an NVIDIA A100 GPU with 40GB RAM. Approximately 65% of the memory is allocated for the model weights, which remain static during serving. Close to 30% of the memory is used to store the dynamic states of the requests. For Transformers, these states consist of the key and value tensors associated with the attention mechanism, commonly referred to as KV cache [41], which represent the context from earlier tokens to generate new output tokens in sequence. The remaining small percentage of memory is used for other data, including activations – the ephemeral tensors created when evaluating the LLM. Since the model weights are constant and the activations only occupy a small fraction of the GPU memory, the way the KV cache is managed is critical in determining the maximum batch size. When managed inefficiently, the KV cache memory can significantly limit the batch size and consequently the throughput of the LLM, as illustrated in Fig. 1 (right). In this paper, we observe that existing LLM serving systems [31, 60] fall short of managing the KV cache memory efficiently. This is mainly because they store the KV cache of a request in contiguous memory space, as most deep learning frameworks [33, 39] require tensors to be stored in contiguous memory. However, unlike the tensors in the traditional deep learning workloads, the KV cache has unique characteristics: it dynamically grows and shrinks over time as the model generates new tokens, and its lifetime and length are not known a priori. These characteristics make the existing systems’ approach significantly inefficient in two ways: First, the existing systems [31, 60] suffer from internal and external memory fragmentation. To store the KV cache of a request in contiguous space, they pre-allocate a contiguous chunk of memory with the request’s maximum length (e.g., 2048 tokens). This can result in severe internal fragmentation, since the request’s actual length can be much shorter than its maximum length (e.g., Fig. 11). Moreover, even if the actual length is known a priori, the pre-allocation is still inefficient: As the entire chunk is reserved during the request’s lifetime, other shorter requests cannot utilize any part of the chunk that is currently unused. Besides, external memory fragmentation can also be significant, since the preallocated size can be different for each request. Indeed, our profiling results in Fig. 2 show that only 20.4% - 38.2% of the KV cache memory is used to store the actual token states in the existing systems. Second, the existing systems cannot exploit the opportunities for memory sharing. LLM services often use advanced decoding algorithms, such as parallel sampling and beam search, that generate multiple outputs per request. In these scenarios, the request consists of multiple sequences that can partially share their KV cache. However, memory sharing is not possible in the existing systems because the KV cache of the sequences is stored in separate contiguous spaces. To address the above limitations, we propose PagedAttention, an attention algorithm inspired by the operating system’s (OS) solution to memory fragmentation and sharing: virtual memory with paging. PagedAttention divides the request’s KV cache into blocks, each of which can contain the attention keys and values of a fixed number of tokens. In PagedAttention, the blocks for the KV cache are not necessarily stored in contiguous space. Therefore, we can manage the KV cache in a more flexible way as in OS’s virtual memory: one can think of blocks as pages, tokens as bytes, and requests as processes. This design alleviates internal fragmentation by using relatively small blocks and allocating them on demand. Moreover, it eliminates external fragmentation as all blocks have the same size. Finally, it enables memory sharing at the granularity of a block, across the different sequences associated with the same request or even across the different requests. In this work, we build vLLM, a high-throughput distributed LLM serving engine on top of PagedAttention that achieves near-zero waste in KV cache memory. vLLM uses block-level memory management and preemptive request scheduling that are co-designed with PagedAttention. vLLM supports popular LLMs such as GPT [5], OPT [62], and LLaMA [52] with varying sizes, including the ones exceeding the memory capacity of a single GPU. Our evaluations on various models and workloads show that vLLM improves the LLM serving throughput by 2-4× compared to the state-of-the-art systems [31, 60], without affecting the model accuracy at all. The improvements are more pronounced with longer sequences, larger models, and more complex decoding algorithms (§4.3). In summary, we make the following contributions: • We identify the challenges in memory allocation in serving LLMs and quantify their impact on serving performance. • We propose PagedAttention, an attention algorithm that operates on KV cache stored in non-contiguous paged memory, which is inspired by the virtual memory and paging in OS. • We design and implement vLLM, a distributed LLM serving engine built on top of PagedAttention. • We evaluate vLLM on various scenarios and demonstrate that it substantially outperforms the previous state-of-theart solutions such as FasterTransformer [31] and Orca [60]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Woosuk Kwon, UC Berkeley with Equal contribution;
(2) Zhuohan Li, UC Berkeley with Equal contribution;
(3) Siyuan Zhuang, UC Berkeley;
(4) Ying Sheng, UC Berkeley and Stanford University;
(5) Lianmin Zheng, UC Berkeley;
(6) Cody Hao Yu, Independent Researcher;
(7) Cody Hao Yu, Independent Researcher;
(8) Joseph E. Gonzalez, UC Berkeley;
(9) Hao Zhang, UC San Diego;
(10) Ion Stoica, UC Berkeley. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.5 Scheduling and Preemption 4.6 Distributed Execution 4.6 Distributed Execution 5 Implementation 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.4 Shared prefix 6.5 Chatbot 6.5 Chatbot 7 Ablation Studies 7 Ablation Studies 8 Discussion 8 Discussion 9 Related Work 9 Related Work 10 Conclusion, Acknowledgement and References 10 Conclusion, Acknowledgement and References Abstract High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM’s source code is publicly available at https://github.com/vllm-project/vllm. 1 Introduction The emergence of large language models (LLMs) like GPT [5, 37] and PaLM [9] have enabled new applications such as programming assistants [6, 18] and universal chatbots [19, 35] that are starting to profoundly impact our work and daily routines. Many cloud companies [34, 44] are racing to provide these applications as hosted services. However, running these applications is very expensive, requiring a large number of hardware accelerators such as GPUs. According to recent estimates, processing an LLM request can be 10× more expensive than a traditional keyword query [43]. Given these high costs, increasing the throughput—and hence reducing the cost per request—of LLM serving systems is becoming more important. At the core of LLMs lies an autoregressive Transformer model [53]. This model generates words (tokens), one at a time, based on the input (prompt) and the previous sequence of the output’s tokens it has generated so far. For each request, this expensive process is repeated until the model outputs a termination token. This sequential generation process makes the workload memory-bound, underutilizing the computation power of GPUs and limiting the serving throughput. Improving the throughput is possible by batching multiple requests together. However, to process many requests in a batch, the memory space for each request should be efficiently managed. For example, Fig. 1 (left) illustrates the memory distribution for a 13B-parameter LLM on an NVIDIA A100 GPU with 40GB RAM. Approximately 65% of the memory is allocated for the model weights, which remain static during serving. Close to 30% of the memory is used to store the dynamic states of the requests. For Transformers, these states consist of the key and value tensors associated with the attention mechanism, commonly referred to as KV cache [41], which represent the context from earlier tokens to generate new output tokens in sequence. The remaining small percentage of memory is used for other data, including activations – the ephemeral tensors created when evaluating the LLM. Since the model weights are constant and the activations only occupy a small fraction of the GPU memory, the way the KV cache is managed is critical in determining the maximum batch size. When managed inefficiently, the KV cache memory can significantly limit the batch size and consequently the throughput of the LLM, as illustrated in Fig. 1 (right). In this paper, we observe that existing LLM serving systems [31, 60] fall short of managing the KV cache memory efficiently. This is mainly because they store the KV cache of a request in contiguous memory space, as most deep learning frameworks [33, 39] require tensors to be stored in contiguous memory. However, unlike the tensors in the traditional deep learning workloads, the KV cache has unique characteristics: it dynamically grows and shrinks over time as the model generates new tokens, and its lifetime and length are not known a priori. These characteristics make the existing systems’ approach significantly inefficient in two ways: First, the existing systems [31, 60] suffer from internal and external memory fragmentation. To store the KV cache of a request in contiguous space, they pre-allocate a contiguous chunk of memory with the request’s maximum length (e.g., 2048 tokens). This can result in severe internal fragmentation, since the request’s actual length can be much shorter than its maximum length (e.g., Fig. 11). Moreover, even if the actual length is known a priori, the pre-allocation is still inefficient: As the entire chunk is reserved during the request’s lifetime, other shorter requests cannot utilize any part of the chunk that is currently unused. Besides, external memory fragmentation can also be significant, since the preallocated size can be different for each request. Indeed, our profiling results in Fig. 2 show that only 20.4% - 38.2% of the KV cache memory is used to store the actual token states in the existing systems. Second, the existing systems cannot exploit the opportunities for memory sharing. LLM services often use advanced decoding algorithms, such as parallel sampling and beam search, that generate multiple outputs per request. In these scenarios, the request consists of multiple sequences that can partially share their KV cache. However, memory sharing is not possible in the existing systems because the KV cache of the sequences is stored in separate contiguous spaces. To address the above limitations, we propose PagedAttention, an attention algorithm inspired by the operating system’s (OS) solution to memory fragmentation and sharing: virtual memory with paging. PagedAttention divides the request’s KV cache into blocks, each of which can contain the attention keys and values of a fixed number of tokens. In PagedAttention, the blocks for the KV cache are not necessarily stored in contiguous space. Therefore, we can manage the KV cache in a more flexible way as in OS’s virtual memory: one can think of blocks as pages, tokens as bytes, and requests as processes. This design alleviates internal fragmentation by using relatively small blocks and allocating them on demand. Moreover, it eliminates external fragmentation as all blocks have the same size. Finally, it enables memory sharing at the granularity of a block, across the different sequences associated with the same request or even across the different requests. In this work, we build vLLM, a high-throughput distributed LLM serving engine on top of PagedAttention that achieves near-zero waste in KV cache memory. vLLM uses block-level memory management and preemptive request scheduling that are co-designed with PagedAttention. vLLM supports popular LLMs such as GPT [5], OPT [62], and LLaMA [52] with varying sizes, including the ones exceeding the memory capacity of a single GPU. Our evaluations on various models and workloads show that vLLM improves the LLM serving throughput by 2-4× compared to the state-of-the-art systems [31, 60], without affecting the model accuracy at all. The improvements are more pronounced with longer sequences, larger models, and more complex decoding algorithms (§4.3). In summary, we make the following contributions: • We identify the challenges in memory allocation in serving LLMs and quantify their impact on serving performance. • We propose PagedAttention, an attention algorithm that operates on KV cache stored in non-contiguous paged memory, which is inspired by the virtual memory and paging in OS. • We design and implement vLLM, a distributed LLM serving engine built on top of PagedAttention. • We evaluate vLLM on various scenarios and demonstrate that it substantially outperforms the previous state-of-theart solutions such as FasterTransformer [31] and Orca [60]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley. Authors: Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley.

PagedAttention: An Attention Algorithm Inspired By the Classical Virtual Memory in Operating Systems

Ethical Considerations in QAnon Authorship Attribution Research

Authors:
(1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab;
(2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Authorship attribution Results Discussion Corpus constitution Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Dealing with quotations and copy/paste Quotes of authors outside of the corpus have been excluded as much as possible by close reading: in particular, quotes from Q, Wikipedia, the Stanford Encyclopedia of Philosophy, Abraham Lincoln, the Intelligence Resource Program (irp-fas), Steve Scully’s biography etc. All these quotes have been removed. Direct quotations (with or without quotation marks) and copy/paste between the writings of the different candidates can also occur. A good deal of them quote Donald, Eric or Melania T. – Q does it too. There is also a certain number of quotations from Q by the others (such as Paul F. for instance). This could lead to small biases in the constitution of idiolectal profiles. To avoid this, we then proceeded to systematically detect citation between the candidates themselves. Direct pairwise comparison being computationally too costly for a corpus of this size, we used a Locality-Sensitive Hashing (LSH) algorithm. To that end, we used the open source TextReuse package (Mullen, 2020). The corpus was tokenised into sentences, and broken word bi-grams (with skip of 1, that is, allowing for any one word to be inserted between the two words of the bigrams) were counted. For all pairs of sentences, a Jaccard similarity score was computed. Be A and B two samples considered as sets of bi-grams, the Jaccard similarity is computed as: All pairs of sentences with a Jaccard similarity score superior or equal to 0.5 (i.e., at least half of their bi-grams in common) were examined by a human expert, and quotations removed. Even for J = 1, we were sometimes confronted to false positives. Dan S. and Melania T. both use once the sentence “we are all in this together”, without directly citing each other. We thus left this passage in both their texts. Rarely used, the sentence “the American people are not stupid” nevertheless appears in different texts. It was kept in the texts studied, as other simple sentences (“thank you for your service” etc.) Other situations were trickier to address. For instance, Dan S. uses once the sentence: “the best is yet to come”. It is used five times by Q, himself quoting former President Donald Trump. This sentence could be used by anyone without directly quoting Q or Donald Trump. Yet, as its use by Dan S. starts with “As the President says. . . ”, we considered it a direct quotation and proceeded to deletion from Dan S.’s text. Yet, we did not delete it from Q’s own writing, as it is never used as an explicit quotation: the sentence could be used in another context, the person(s) writing the Qdrops with this sentence could try to impersonate Donald Trump, etc. In any of these cases, it would be legitimate to leave the information. Same thing goes for expression such as “the world is watching” or “make America great again”, used by Donald Trump. but also by Q and some of the potential candidates here. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Authors: Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Abstract and Introduction Why work on QAnon? Specificities and social impact Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Who is Q? The theories put to test Authorship attribution Authorship attribution Results Results Discussion Discussion Corpus constitution Corpus constitution Quotes of authors outside of the corpus have been Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Ethical statement, Acknowledgements, and References Dealing with quotations and copy/paste Quotes of authors outside of the corpus have been excluded as much as possible by close reading: in particular, quotes from Q, Wikipedia, the Stanford Encyclopedia of Philosophy, Abraham Lincoln, the Intelligence Resource Program (irp-fas), Steve Scully’s biography etc. All these quotes have been removed. Direct quotations (with or without quotation marks) and copy/paste between the writings of the different candidates can also occur. A good deal of them quote Donald, Eric or Melania T. – Q does it too. There is also a certain number of quotations from Q by the others (such as Paul F. for instance). This could lead to small biases in the constitution of idiolectal profiles. To avoid this, we then proceeded to systematically detect citation between the candidates themselves. Direct pairwise comparison being computationally too costly for a corpus of this size, we used a Locality-Sensitive Hashing (LSH) algorithm. To that end, we used the open source TextReuse package (Mullen, 2020). The corpus was tokenised into sentences, and broken word bi-grams (with skip of 1, that is, allowing for any one word to be inserted between the two words of the bigrams) were counted. For all pairs of sentences, a Jaccard similarity score was computed. Be A and B two samples considered as sets of bi-grams, the Jaccard similarity is computed as: All pairs of sentences with a Jaccard similarity score superior or equal to 0.5 (i.e., at least half of their bi-grams in common) were examined by a human expert, and quotations removed. Even for J = 1, we were sometimes confronted to false positives. Dan S. and Melania T. both use once the sentence “we are all in this together”, without directly citing each other. We thus left this passage in both their texts. Rarely used, the sentence “the American people are not stupid” nevertheless appears in different texts. It was kept in the texts studied, as other simple sentences (“thank you for your service” etc.) Other situations were trickier to address. For instance, Dan S. uses once the sentence: “the best is yet to come”. It is used five times by Q, himself quoting former President Donald Trump. This sentence could be used by anyone without directly quoting Q or Donald Trump. Yet, as its use by Dan S. starts with “As the President says. . . ”, we considered it a direct quotation and proceeded to deletion from Dan S.’s text. Yet, we did not delete it from Q’s own writing, as it is never used as an explicit quotation: the sentence could be used in another context, the person(s) writing the Qdrops with this sentence could try to impersonate Donald Trump, etc. In any of these cases, it would be legitimate to leave the information. Same thing goes for expression such as “the world is watching” or “make America great again”, used by Donald Trump. but also by Q and some of the potential candidates here. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Addressing Quotations and Copy-Paste in QAnon Authorship Attribution

The Decentralized Finance (DeFi) sector envisioned lofty goals of universal enfranchisement of users and accessibility. While the goals continue to remain in focus, there are emerging cyber threats that pose serious problems to the integrity of the DeFi systems. Distributed Denial of Service (DDoS) assault is a serious cyber infiltration that can cause problems even for the most powerful, distributed protocols out there. In this age, DDoS attacks have long moved past being minor nuisances and have the potential to steal millions of dollars, ruin carefully managed reputations, and dent the efforts to move to a decentralized, trustless alternative. The DDoS attack is not only limited to the Defi industry alone. For instance, the largest DDoS attack on record is the 2024 Cloudflare attack. It lasted for just 80 seconds reaching about 5.6 terabits per second (Tbps). Other notable examples are the 2016 Mirai Dyn attack, the 2018 GitHub attack, and the 2020 AWS attack, etc. However, this article will shed light on the threat posed by DDoS attacks and their devastating potential on the DeFi economy. Expert industry insight into problem identification and solution-oriented approach will be provided by a top cybersecurity specialist. DeFi: A Prime Target for Cybercriminals The DeFi economy boasts a major incentive to the finance sector. It is a trustless, permissionless, and innovative space with the potential for bigger profits compared to the conventional finance scene. However, DeFi protocols handle a lot of money, mostly in crypto, so they appeal to hackers of all backgrounds. These online hackers study smart contracts in detail and use sophisticated automated systems to take advantage of vulnerabilities. A successful hacking attempt often results in users being unable to track and access their funds, rendering the platform useless. As a result, investors jump ship from the platform, encompassing it completely in financial ruin. Due to the negative effects of such attacks, companies often try to cover up the scale of the intrusion and keep numbers secret. However, the open-source nature of DeFI protocols means that they cannot keep everything under wraps and the secrecy often causes problems to compound further. Notably, the market capitalization and liquidity of hacked platforms often experience a major decline after losing potential millions in DDoS attacks. The psychological impact of a major hack on users cannot be understated as well – It undermines user confidence and could eventually cause a domino effect, resulting in the platform declaring bankruptcy. Maksym Ishchenko, Founder and CEO of Azerux, is a well-known cybersecurity professional specializing in these DDoS attacks and mitigation efforts to counter them. According to Maksym: DeFi's decentralized structure poses a special security risk. Because there isn't a single point of control as in centralized systems, security is very complicated yet undeniably crucial. A successful assault might have disastrous financial consequences, resulting in large losses and irreversible harm to one's image. Ischenko’s comments echo earlier concerns that the attacks themselves are becoming more and more complex and no longer limited to volume-based assaults which involve overloading a platform’s server with a large number of pointless requests, choking it entirely. Attackers now employ an organized approach using high-level tools that have a complete grasp of blockchain technology and its underlying infrastructure. Hence, efforts to limit DDoS attacks are becoming more and more difficult with time in the DeFi ecosystem. Advanced DDoS Attacks and the Threats to DeFi The exponential rise in attacks on the decentralized fintech platforms is largely attributed to DeFi’s explosive growth over the years. It has been a major target for hackers due to abundance of funds and relatively nascent technology that has weak links present in between. "Many projects rush to market with insufficient security testing," Maksym states. According to him, the Solana network is one of the prime examples of decentralized networks coming under DDoS attacks. Malicious automated transactions overloaded the network during the Grape Protocol's IDO on Raydium, resulting in a devastating 17-hour outage that impacted the whole Solana ecosystem. Solana has choked consistently over the few years of its existence but other experts believe that DDoS is not the only one to be blamed for the network’s frequent shortcomings. Another notable incident occurred on January 18, 2024, when the listing of Manta Network's token coincided with a DDoS assault of more than 135 million bogus RPC queries. “While the demand for Manta tokens remained high, the attack showed how susceptible even recently announced, well-known projects are to sophisticated DDoS operations. The network's bandwidth was overloaded by the sheer number of bogus RPC queries, which essentially stopped valid transactions." Ischenko notes that it is crucial to have robust DDoS mitigation techniques, especially at a time when new projects are being deployed on a blockchain. DeFi protocols need to take a hint here. How to Build Robust DDoS Protection for Your DeFi Platform A stellar DDoS defense system is necessary for a DeFi protocol in the long run. It is no longer a technical jargon that needs to be dealt with later on but a basic security mechanism that needs to be implemented from the ground up. The Azerux CEO believes that robust protection for your DeFi platform goes beyond DDoS risk mitigation. He proposes a multi-layered approach, championed by Azerux, consisting of: Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware.


Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users.


Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Adaptive defense mechanisms and proactive threat intelligence: Smart defenses that learn from previous DDoS attacks and continuously refine strategies can make networks more resilient over time. Engaging in proactive threat intelligence ensures that mitigation strategies evolve to counter emerging attack vectors effectively. Why DDoS Protection is Critical for DeFi The DeFI ecosystem is an emerging, dynamic field that has the potential to hit trillions of dollars worth of liquidity in the near future. It already holds tens of billions of dollars in place despite the dangers associated with it and it is expected to only grow with time. Cyber incursions like DDoS attacks are a serious issue and may cause entire platforms to collapse in a matter of days if not hours. The path to a successful DeFi future is to help create a secure, proactive environment with central attention to cybersecurity. The Decentralized Finance (DeFi) sector envisioned lofty goals of universal enfranchisement of users and accessibility. While the goals continue to remain in focus, there are emerging cyber threats that pose serious problems to the integrity of the DeFi systems. Distributed Denial of Service (DDoS) assault is a serious cyber infiltration that can cause problems even for the most powerful, distributed protocols out there. The Decentralized Finance (DeFi) sector envisioned lofty goals of universal enfranchisement of users and accessibility. While the goals continue to remain in focus, there are emerging cyber threats that pose serious problems to the integrity of the DeFi systems. Distributed Denial of Service (DDoS) assault is a serious cyber infiltration that can cause problems even for the most powerful, distributed protocols out there. In this age, DDoS attacks have long moved past being minor nuisances and have the potential to steal millions of dollars, ruin carefully managed reputations, and dent the efforts to move to a decentralized, trustless alternative. DDoS The DDoS attack is not only limited to the Defi industry alone. For instance, the largest DDoS attack on record is the 2024 Cloudflare attack . It lasted for just 80 seconds reaching about 5.6 terabits per second (Tbps). Other notable examples are the 2016 Mirai Dyn attack, the 2018 GitHub attack, and the 2020 AWS attack, etc. 2024 Cloudflare attack However, this article will shed light on the threat posed by DDoS attacks and their devastating potential on the DeFi economy. Expert industry insight into problem identification and solution-oriented approach will be provided by a top cybersecurity specialist. DeFi: A Prime Target for Cybercriminals DeFi: A Prime Target for Cybercriminals The DeFi economy boasts a major incentive to the finance sector. It is a trustless, permissionless, and innovative space with the potential for bigger profits compared to the conventional finance scene. However, DeFi protocols handle a lot of money, mostly in crypto, so they appeal to hackers of all backgrounds. These online hackers study smart contracts in detail and use sophisticated automated systems to take advantage of vulnerabilities. A successful hacking attempt often results in users being unable to track and access their funds, rendering the platform useless. As a result, investors jump ship from the platform, encompassing it completely in financial ruin. Due to the negative effects of such attacks, companies often try to cover up the scale of the intrusion and keep numbers secret. However, the open-source nature of DeFI protocols means that they cannot keep everything under wraps and the secrecy often causes problems to compound further. Notably, the market capitalization and liquidity of hacked platforms often experience a major decline after losing potential millions in DDoS attacks. The psychological impact of a major hack on users cannot be understated as well – It undermines user confidence and could eventually cause a domino effect, resulting in the platform declaring bankruptcy. Maksym Ishchenko , Founder and CEO of Azerux, is a well-known cybersecurity professional specializing in these DDoS attacks and mitigation efforts to counter them. Maksym Ishchenko According to Maksym: DeFi's decentralized structure poses a special security risk. Because there isn't a single point of control as in centralized systems, security is very complicated yet undeniably crucial. A successful assault might have disastrous financial consequences, resulting in large losses and irreversible harm to one's image. DeFi's decentralized structure poses a special security risk. Because there isn't a single point of control as in centralized systems, security is very complicated yet undeniably crucial. A successful assault might have disastrous financial consequences, resulting in large losses and irreversible harm to one's image. Ischenko’s comments echo earlier concerns that the attacks themselves are becoming more and more complex and no longer limited to volume-based assaults which involve overloading a platform’s server with a large number of pointless requests, choking it entirely. Attackers now employ an organized approach using high-level tools that have a complete grasp of blockchain technology and its underlying infrastructure. Hence, efforts to limit DDoS attacks are becoming more and more difficult with time in the DeFi ecosystem. Advanced DDoS Attacks and the Threats to DeFi Advanced DDoS Attacks and the Threats to DeFi The exponential rise in attacks on the decentralized fintech platforms is largely attributed to DeFi’s explosive growth over the years. It has been a major target for hackers due to abundance of funds and relatively nascent technology that has weak links present in between. "Many projects rush to market with insufficient security testing," Maksym states. According to him, the Solana network is one of the prime examples of decentralized networks coming under DDoS attacks. Malicious automated transactions overloaded the network during the Grape Protocol's IDO on Raydium, resulting in a devastating 17-hour outage that impacted the whole Solana ecosystem. Solana has choked consistently over the few years of its existence but other experts believe that DDoS is not the only one to be blamed for the network’s frequent shortcomings. 17-hour outage Another notable incident occurred on January 18, 2024, when the listing of Manta Network's token coincided with a DDoS assault of more than 135 million bogus RPC queries. “While the demand for Manta tokens remained high, the attack showed how susceptible even recently announced, well-known projects are to sophisticated DDoS operations. The network's bandwidth was overloaded by the sheer number of bogus RPC queries, which essentially stopped valid transactions." Ischenko notes that it is crucial to have robust DDoS mitigation techniques, especially at a time when new projects are being deployed on a blockchain. DeFi protocols need to take a hint here. How to Build Robust DDoS Protection for Your DeFi Platform How to Build Robust DDoS Protection for Your DeFi Platform A stellar DDoS defense system is necessary for a DeFi protocol in the long run. It is no longer a technical jargon that needs to be dealt with later on but a basic security mechanism that needs to be implemented from the ground up. The Azerux CEO believes that robust protection for your DeFi platform goes beyond DDoS risk mitigation. He proposes a multi-layered approach, championed by Azerux, consisting of: Azerux Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware. Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users. Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware. Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware. Network-level mitigation: Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users. Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users. Application-level protection: Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Real-time monitoring and automated response: Adaptive defense mechanisms and proactive threat intelligence: Smart defenses that learn from previous DDoS attacks and continuously refine strategies can make networks more resilient over time. Engaging in proactive threat intelligence ensures that mitigation strategies evolve to counter emerging attack vectors effectively. Adaptive defense mechanisms and proactive threat intelligence: Smart defenses that learn from previous DDoS attacks and continuously refine strategies can make networks more resilient over time. Engaging in proactive threat intelligence ensures that mitigation strategies evolve to counter emerging attack vectors effectively. Adaptive defense mechanisms and proactive threat intelligence: Why DDoS Protection is Critical for DeFi Why DDoS Protection is Critical for DeFi The DeFI ecosystem is an emerging, dynamic field that has the potential to hit trillions of dollars worth of liquidity in the near future. It already holds tens of billions of dollars in place despite the dangers associated with it and it is expected to only grow with time. Cyber incursions like DDoS attacks are a serious issue and may cause entire platforms to collapse in a matter of days if not hours. The path to a successful DeFi future is to help create a secure, proactive environment with central attention to cybersecurity.

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