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IN THE CITY WAYS

How to biohack your intelligence — with everything from sex to modafinil to MDMA

Cómo biohackear tu inteligencia: con todo, desde sexo hasta modafinilo y MDMA

Your Guide For Overcoming Procrastination and Getting Down to Work

SINGAPORE, July 24, 2024 — Aggregated rollup infra builder zkLink, today announced the recipients for Phase 1 of its Ecosystem Developer Grants program, a 20 million $ZKL grants program aimed at stimulating the growth of its aggregated DeFi ecosystem. The program, which received over 100 applications, had approved 16 projects for financial and marketing support to build on zkLink Nova, the industry's first aggregated Layer 3 rollup network. Running from March 11 to July 20, 2024, Phase 1 had earmarked 10 million ZKL tokens for its recipients. The grant recipients in Phase 1 include Oku Trade, RedStone, Holdstation, Solv Protocol, Snapshot, NovaSwap, POKT Network, Layerbank, Izumi Finance, AGX, Sturdy, Steer Protocol, Allspark, Protofire, SphereX and Chainnodes. Vince Yang, CEO at zkLink, expressed his enthusiasm. "We are thrilled to announce the Phase 1 recipients of our Ecosystem Developer Grants Program. These projects exemplify the outstanding dedication to cultivate a vibrant and seamless aggregated DeFi ecosystem. Each recipient has demonstrated a commitment to advancing aggregated solutions like zkLink Nova, driving innovative change and inspiring others through their work”. On zkLink Nova, dApps can access a ‘unified Ethereum experience’ of aggregated assets from eight of Ethereum’s broader Layer 2 rollup ecosystems, such as Arbitrum, Optimism, Base, ZKsync, Linea, Scroll, Manta, and Mantle. Secured by zero-knowledge proofs and multi-chain state synchronization, zkLink Nova offers developers the opportunity to explore new interoperable DeFi primitives while benefiting from the lower gas costs of its aggregated Layer 3 zkEVM network. “The grants program reflects our vision to seamlessly bridge Web3 and Web2 together through industry collaboration. We are excited to see how the grant recipients will utilize this funding to further their projects and contribute to the community,” Yang added. “Congratulations to all the winners, and thank you for your contributions. We look forward to seeing the continued progress to unifying Layer 2s into one interoperable platform.” zkLink Ecosystem Developer Grants Program Phase 2 Phase 2 of the Ecosystem Developer Grants Program will commence on 24 July 2024, and conclude on 30 September 2024. The phase will build on the successes and lessons learned from Phase 1, ensuring that grant applicants will receive guidance, flexible funding mechanisms, and reduced decision-making times. The goal of Phase 2 is to support applications that are able to positively impact the on-chain activity and user experience(UX) on zkLink Nova. Protocols from diverse verticals, including but not limited to DeFi, meme projects, gaming, and AI are encouraged to apply. Special considerations will be given to open-sourced solutions and projects with a proven track record in either zero-knowledge (ZK) or Optimism (OP) rollup networks. Phase 2 will also continue to support innovative protocols and encourage the deployment of native dApps on zkLink Nova. For more information about the zkLink Ecosystem Grants Program or to apply, visit website. About zkLink zkLink develops leading zero-knowledge blockchain solutions for the Ethereum ecosystem. Its flagship zkLink Nova is the industry’s first aggregated Layer 3 zkEVM Rollup network built on top of Ethereum and Ethereum Layer 2 rollups (L2s). It is an EVM-compatible, open platform that allows for scattered assets across Ethereum Layer 2s to be aggregated for interoperable trade and transactions. It is secured by zero-knowledge proof technology, charges extremely low gas costs, offers fast finality, and inherits its security from Ethereum. zkLink is funded by notable backers including Coinbase Ventures, Solana Ventures, SIG DTI, Arrington Capital, and others. Media Contact: neil@zklink.org This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SINGAPORE, July 24, 2024 — Aggregated rollup infra builder zkLink, today announced the recipients for Phase 1 of its Ecosystem Developer Grants program, a 20 million $ZKL grants program aimed at stimulating the growth of its aggregated DeFi ecosystem. The program, which received over 100 applications, had approved 16 projects for financial and marketing support to build on zkLink Nova, the industry's first aggregated Layer 3 rollup network. — Running from March 11 to July 20, 2024, Phase 1 had earmarked 10 million ZKL tokens for its recipients. The grant recipients in Phase 1 include Oku Trade, RedStone, Holdstation, Solv Protocol, Snapshot, NovaSwap, POKT Network, Layerbank, Izumi Finance, AGX, Sturdy, Steer Protocol, Allspark, Protofire, SphereX and Chainnodes. Vince Yang, CEO at zkLink, expressed his enthusiasm. "We are thrilled to announce the Phase 1 recipients of our Ecosystem Developer Grants Program. These projects exemplify the outstanding dedication to cultivate a vibrant and seamless aggregated DeFi ecosystem. Each recipient has demonstrated a commitment to advancing aggregated solutions like zkLink Nova, driving innovative change and inspiring others through their work”. Vince Yang, CEO at zkLink, expressed his enthusiasm. "We are thrilled to announce the Phase 1 recipients of our Ecosystem Developer Grants Program. These projects exemplify the outstanding dedication to cultivate a vibrant and seamless aggregated DeFi ecosystem. Each recipient has demonstrated a commitment to advancing aggregated solutions like zkLink Nova, driving innovative change and inspiring others through their work”. On zkLink Nova, dApps can access a ‘unified Ethereum experience’ of aggregated assets from eight of Ethereum’s broader Layer 2 rollup ecosystems, such as Arbitrum, Optimism, Base, ZKsync, Linea, Scroll, Manta, and Mantle. Secured by zero-knowledge proofs and multi-chain state synchronization, zkLink Nova offers developers the opportunity to explore new interoperable DeFi primitives while benefiting from the lower gas costs of its aggregated Layer 3 zkEVM network. “The grants program reflects our vision to seamlessly bridge Web3 and Web2 together through industry collaboration. We are excited to see how the grant recipients will utilize this funding to further their projects and contribute to the community,” Yang added. “The grants program reflects our vision to seamlessly bridge Web3 and Web2 together through industry collaboration. We are excited to see how the grant recipients will utilize this funding to further their projects and contribute to the community,” Yang added. “Congratulations to all the winners, and thank you for your contributions. We look forward to seeing the continued progress to unifying Layer 2s into one interoperable platform.” “Congratulations to all the winners, and thank you for your contributions. We look forward to seeing the continued progress to unifying Layer 2s into one interoperable platform.” zkLink Ecosystem Developer Grants Program Phase 2 Phase 2 of the Ecosystem Developer Grants Program will commence on 24 July 2024, and conclude on 30 September 2024. The phase will build on the successes and lessons learned from Phase 1, ensuring that grant applicants will receive guidance, flexible funding mechanisms, and reduced decision-making times. The goal of Phase 2 is to support applications that are able to positively impact the on-chain activity and user experience(UX) on zkLink Nova. Protocols from diverse verticals, including but not limited to DeFi, meme projects, gaming, and AI are encouraged to apply. Special considerations will be given to open-sourced solutions and projects with a proven track record in either zero-knowledge (ZK) or Optimism (OP) rollup networks. Phase 2 will also continue to support innovative protocols and encourage the deployment of native dApps on zkLink Nova. For more information about the zkLink Ecosystem Grants Program or to apply, visit website . website website About zkLink zkLink develops leading zero-knowledge blockchain solutions for the Ethereum ecosystem. Its flagship zkLink Nova is the industry’s first aggregated Layer 3 zkEVM Rollup network built on top of Ethereum and Ethereum Layer 2 rollups (L2s). It is an EVM-compatible, open platform that allows for scattered assets across Ethereum Layer 2s to be aggregated for interoperable trade and transactions. It is secured by zero-knowledge proof technology, charges extremely low gas costs, offers fast finality, and inherits its security from Ethereum. zkLink is funded by notable backers including Coinbase Ventures, Solana Ventures, SIG DTI, Arrington Capital, and others. Media Contact: neil@zklink.org neil@zklink.org neil@zklink.org This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

zkLink Approves 16 Projects In Developer Grants Program Paving Way For Aggregated DeFi Future

Object-oriented programming (OOP) is a programming model that organizes software around objects(data) and object manipulation. OOP’s use of objects helps to break complex problems into smaller manageable parts, thus making code more straightforward to comprehend and manage, providing developers with a sense of ease.  Over the next few minutes, we will go over the following core concepts as they relate to OOP: Class


Object


Encapsulation


Inheritance


Polymorphism


Association 📓 All upcoming examples will use python code Class a class is a template used to create objects. Classes define the attributes of objects and the methods that the class instances will share. class Parent:
    def __init__(self, name, hair_color):
        self.name = name
        self.hair_color= hair_color

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") The above snippet creates a class named Parent, which contains attributes, name and hair_color and a method, description(). Every instance of the class will have the same attributes and methods. Object Regarding OOP, an object is an instance of a class. It has attributes and can perform actions defined by its class. The following example creates instances of the Parent class; once they have been created, we can access its publicly available attributes. For example, father.hair_color returns a value of Red. father= Parent("Dylan", "Red")
mother= Parent("Grace", "Brown")

father.hair_color # Red
mother.hair_color # Brown Encapsulation Encapsulation bundles attributes and methods within a class and can restrict access to some of the object’s components. For example, the code below defines a Parent class. The class has three attributes: name, hair_color, and __age. The name and hair_color attributes are publicly accessible; however, calling the __age attribute will result in an AttributeError. The double underscore causes an attribute to become private. To get the value of the __age attribute, the get_age() method must be called, as it will return the age attribute given to the Parent. class Parent:
    def __init__(self, name, hair_color, age):
        self.name = name
        self.hair_color= hair_color
        self.__age= age

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") 

    def get_age(self):
        return self.__age

father= Parent("Dylan", "Red", 45)

father.name # Dylan
father.__age# will result in an AttributeError
father.get_age() # 45
father.description() # I'm Dylan, my hair color is Red! Inheritance 👀 ignore super().description() in the following example. We’ll go over this once we get to Polymorphism Inheritance occurs when a class inherits the attributes and methods of another class. In the following example, the Child class inherits the same attributes as the Parent class while creating its own unrelated attributes. class Child(Parent):
    def __init__( self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        super().description()
        print(f"Name: {self.name}")
        print(f"Hair Color: {self.get_hair_color()")
        print(f"Age: {self.get_age()}")
        print(f"Major: {self.major}")

child = Child("Dwight", "Auburn", 20, "Computer Science")
child.description()
#Output
# I'm Dwight, my hair color is Auburn!
# Name: Dwight
# Hair Color: Auburn
# Age: 20
# Major: "Computer Science Polymorphism Polymorphism  allows you to override or modify class methods in subclasses. As seen in the previous snippet on Inheritance, the Child class adapts and modifies the Parent method description(). By including the super().description() within the description() method, you can adapt the inclusion of the Parent output into the Child’s description() method output. In the next snippet, the Child class inherits the attributes of the Parent class but overrides the description() method, resulting in a different output. class Child(Parent):
    def __init__(self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        print(f"Hello my name is {self.name}, I'm {self.get_age()} and I major in {self.major}!")

parent = Parent("Charles", "Grey", 45)
child = Child("Dwight", "Auburn", 20, "Computer Science")
parent.description()
#Output
# I'm Charles, my hair color is Grey!

child.description()
# Hello my name is Dwight, I'm 20 and I major in Computer Science! Association Association in programming is versatile, as it can be one-to-one, one-to-many, or many-to-many. It refers to the relationship between two classes where one class uses another. In the following example, we will use a one-to-many relation between classes. The Parent class can have many children, and we can add multiple Child instances via the add_child() method. class Parent:
    def __init__(self, name):
        self.name = name
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def list_children(self):
        return [child.name for child in self.children]

    def number_of_children(self):
        if not self.children:
            return "No children"
        return len(self.children)

class Child:
    def __init__(self, name, age):
        self.name = name
        self.age = age

parent = Parent("James")
child1 = Child("James Jr.", 12)
child2 = Child("Cameron", 16)

parent.add_child(child1)
parent.add_child(child2)

# List Children
for child in parent.list_children():
    print(child)

# James Jr.
# Cameron

parent.number_of_children()

# 2 Conclusion Object-oriented programming (OOP) offers a robust framework for developing modular, reusable, and maintainable code. Its foundational concepts, such as classes, objects, encapsulation, inheritance, polymorphism, and association, enable developers to create scalable and efficient software solutions. This efficiency makes OOP an indispensable paradigm in modern programming. Embracing OOP enhances code organization and facilitates easier debugging and future-proofing of applications, making developers more productive and effective. Object-oriented programming (OOP) is a programming model that organizes software around objects(data) and object manipulation. OOP’s use of objects helps to break complex problems into smaller manageable parts, thus making code more straightforward to comprehend and manage, providing developers with a sense of ease.  Over the next few minutes, we will go over the following core concepts as they relate to OOP: Class Object Encapsulation Inheritance Polymorphism Association Class Class Object Object Encapsulation Encapsulation Inheritance Inheritance Polymorphism Polymorphism Association Association 📓 All upcoming examples will use python code 📓 All upcoming examples will use python code Class a class is a template used to create objects. Classes define the attributes of objects and the methods that the class instances will share. class Parent:
    def __init__(self, name, hair_color):
        self.name = name
        self.hair_color= hair_color

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") class Parent:
    def __init__(self, name, hair_color):
        self.name = name
        self.hair_color= hair_color

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") The above snippet creates a class named Parent , which contains attributes, name and hair_color and a method, description() . Every instance of the class will have the same attributes and methods. Parent name hair_color description() Object Regarding OOP, an object is an instance of a class. It has attributes and can perform actions defined by its class. The following example creates instances of the Parent class; once they have been created, we can access its publicly available attributes. For example, father.hair_color returns a value of Red . Parent father.hair_color Red father= Parent("Dylan", "Red")
mother= Parent("Grace", "Brown")

father.hair_color # Red
mother.hair_color # Brown father= Parent("Dylan", "Red")
mother= Parent("Grace", "Brown")

father.hair_color # Red
mother.hair_color # Brown Encapsulation Encapsulation bundles attributes and methods within a class and can restrict access to some of the object’s components. For example, the code below defines a Parent class. The class has three attributes: name , hair_color , and __age . The name and hair_color attributes are publicly accessible; however, calling the __age attribute will result in an AttributeError. The double underscore causes an attribute to become private. To get the value of the __age attribute, the get_age() method must be called, as it will return the age attribute given to the Parent . Parent name hair_color __age name hair_color __age __age get_age() Parent class Parent:
    def __init__(self, name, hair_color, age):
        self.name = name
        self.hair_color= hair_color
        self.__age= age

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") 

    def get_age(self):
        return self.__age

father= Parent("Dylan", "Red", 45)

father.name # Dylan
father.__age# will result in an AttributeError
father.get_age() # 45
father.description() # I'm Dylan, my hair color is Red! class Parent:
    def __init__(self, name, hair_color, age):
        self.name = name
        self.hair_color= hair_color
        self.__age= age

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") 

    def get_age(self):
        return self.__age

father= Parent("Dylan", "Red", 45)

father.name # Dylan
father.__age# will result in an AttributeError
father.get_age() # 45
father.description() # I'm Dylan, my hair color is Red! Inheritance 👀 ignore super().description() in the following example. We’ll go over this once we get to Polymorphism 👀 ignore super().description() in the following example. We’ll go over this once we get to Polymorphism super().description() Inheritance occurs when a class inherits the attributes and methods of another class. In the following example, the Child class inherits the same attributes as the Parent class while creating its own unrelated attributes. Child Parent class Child(Parent):
    def __init__( self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        super().description()
        print(f"Name: {self.name}")
        print(f"Hair Color: {self.get_hair_color()")
        print(f"Age: {self.get_age()}")
        print(f"Major: {self.major}")

child = Child("Dwight", "Auburn", 20, "Computer Science")
child.description()
#Output
# I'm Dwight, my hair color is Auburn!
# Name: Dwight
# Hair Color: Auburn
# Age: 20
# Major: "Computer Science class Child(Parent):
    def __init__( self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        super().description()
        print(f"Name: {self.name}")
        print(f"Hair Color: {self.get_hair_color()")
        print(f"Age: {self.get_age()}")
        print(f"Major: {self.major}")

child = Child("Dwight", "Auburn", 20, "Computer Science")
child.description()
#Output
# I'm Dwight, my hair color is Auburn!
# Name: Dwight
# Hair Color: Auburn
# Age: 20
# Major: "Computer Science Polymorphism Polymorphism  allows you to override or modify class methods in subclasses. As seen in the previous snippet on Inheritance, the Child class adapts and modifies the Parent method description() . By including the super().description() within the description() method, you can adapt the inclusion of the Parent output into the Child ’s description() method output. In the next snippet, the Child class inherits the attributes of the Parent class but overrides the description() method, resulting in a different output. Child Parent description() super().description() description() Parent Child description() Child Parent description() class Child(Parent):
    def __init__(self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        print(f"Hello my name is {self.name}, I'm {self.get_age()} and I major in {self.major}!")

parent = Parent("Charles", "Grey", 45)
child = Child("Dwight", "Auburn", 20, "Computer Science")
parent.description()
#Output
# I'm Charles, my hair color is Grey!

child.description()
# Hello my name is Dwight, I'm 20 and I major in Computer Science! class Child(Parent):
    def __init__(self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        print(f"Hello my name is {self.name}, I'm {self.get_age()} and I major in {self.major}!")

parent = Parent("Charles", "Grey", 45)
child = Child("Dwight", "Auburn", 20, "Computer Science")
parent.description()
#Output
# I'm Charles, my hair color is Grey!

child.description()
# Hello my name is Dwight, I'm 20 and I major in Computer Science! Association Association in programming is versatile, as it can be one-to-one, one-to-many, or many-to-many. It refers to the relationship between two classes where one class uses another. In the following example, we will use a one-to-many relation between classes. The Parent class can have many children, and we can add multiple Child instances via the add_child() method. Parent Child add_child() class Parent:
    def __init__(self, name):
        self.name = name
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def list_children(self):
        return [child.name for child in self.children]

    def number_of_children(self):
        if not self.children:
            return "No children"
        return len(self.children)

class Child:
    def __init__(self, name, age):
        self.name = name
        self.age = age

parent = Parent("James")
child1 = Child("James Jr.", 12)
child2 = Child("Cameron", 16)

parent.add_child(child1)
parent.add_child(child2)

# List Children
for child in parent.list_children():
    print(child)

# James Jr.
# Cameron

parent.number_of_children()

# 2 class Parent:
    def __init__(self, name):
        self.name = name
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def list_children(self):
        return [child.name for child in self.children]

    def number_of_children(self):
        if not self.children:
            return "No children"
        return len(self.children)

class Child:
    def __init__(self, name, age):
        self.name = name
        self.age = age

parent = Parent("James")
child1 = Child("James Jr.", 12)
child2 = Child("Cameron", 16)

parent.add_child(child1)
parent.add_child(child2)

# List Children
for child in parent.list_children():
    print(child)

# James Jr.
# Cameron

parent.number_of_children()

# 2 Conclusion Object-oriented programming (OOP) offers a robust framework for developing modular, reusable, and maintainable code. Its foundational concepts, such as classes, objects, encapsulation, inheritance, polymorphism, and association, enable developers to create scalable and efficient software solutions. This efficiency makes OOP an indispensable paradigm in modern programming. Embracing OOP enhances code organization and facilitates easier debugging and future-proofing of applications, making developers more productive and effective.

Object-oriented Programming by Example

I used to really love programming. I didn't really like it during my early college years, but I started to really like it later on when I started to get a good grasp on coding and was able to build my ideas. I love the feeling when I'm getting in the zone and start hyper-focusing on coding. Late in college, I started to make money by doing the projects of my classmates and by freelancing until I got hired to work full-time by a company with a great salary. And then I started to be interested in building side projects. Over the years, I have built many side projects where like 95% of them have failed. I say the other 5% were kinda successful because they gained users and 1 side project gained a paying customer. It was a cycle of coming up with an idea, building it, launching and failing then starting over again. This stopped completely when I got burned out from work (and probably because of the frustration that most of my side projects failed and didn't get any traction). After years of job hopping, I started working for a startup for years and made great money that allowed me to buy a car and a house and have a comfortable life. Working on this startup was very tiring though because I was working for very long hours per day and work consumes the majority of my time daily. Overworking for a few years got me so burnt out that I started hating my job and started to lose my passion and interest in coding and completely stopped building side projects. The burnout lasted for 6 years. For some reason, this year, I feel like I'm starting to recover from burnout and got interested in coding and building side projects again. It's probably because I significantly reduced the hours that I work per day (a few years where I probably only work for ~4 hours on weekdays) and started to consistently exercise and improve my diet for almost a year. I recently started to build side projects again; to get the ball rolling, I just started to build simple websites or web apps that I can build very quickly like a speech to polished notes web app and a calm jobs website that scrapes job posts and calculates a work-life balance score to help job seekers find jobs with better work-life balance. My personal experience of burnout inspired me to build it. After these initial side projects, I plan to start building more complicated ones. Has anyone else gone through something similar? How did you rediscover your love for programming? I used to really love programming. I didn't really like it during my early college years, but I started to really like it later on when I started to get a good grasp on coding and was able to build my ideas. I love the feeling when I'm getting in the zone and start hyper-focusing on coding. Late in college, I started to make money by doing the projects of my classmates and by freelancing until I got hired to work full-time by a company with a great salary. And then I started to be interested in building side projects. Over the years, I have built many side projects where like 95% of them have failed. I say the other 5% were kinda successful because they gained users and 1 side project gained a paying customer. It was a cycle of coming up with an idea, building it, launching and failing then starting over again. This stopped completely when I got burned out from work (and probably because of the frustration that most of my side projects failed and didn't get any traction). After years of job hopping, I started working for a startup for years and made great money that allowed me to buy a car and a house and have a comfortable life. Working on this startup was very tiring though because I was working for very long hours per day and work consumes the majority of my time daily. Overworking for a few years got me so burnt out that I started hating my job and started to lose my passion and interest in coding and completely stopped building side projects. The burnout lasted for 6 years. For some reason, this year, I feel like I'm starting to recover from burnout and got interested in coding and building side projects again. It's probably because I significantly reduced the hours that I work per day (a few years where I probably only work for ~4 hours on weekdays) and started to consistently exercise and improve my diet for almost a year. I recently started to build side projects again; to get the ball rolling, I just started to build simple websites or web apps that I can build very quickly like a speech to polished notes web app and a calm jobs website that scrapes job posts and calculates a work-life balance score to help job seekers find jobs with better work-life balance. calm jobs My personal experience of burnout inspired me to build it. After these initial side projects, I plan to start building more complicated ones. Has anyone else gone through something similar? How did you rediscover your love for programming?

6 Years of Burnout: My Journey Back to Loving Programming

By Amanda Kavanagh While tech sectors like healthcare, climate tech and cybersecurity are urgently focused on retraining in AI, it’s not quite as important for other programmers. That’s according to Christian Rebernik, co-chief executive and co-founder of Tomorrow University of Applied Sciences, who reckons you don’t need to throw the baby out of the bath water, and ditch your expertise for AI. In an interview with the Financial Times, he makes this recommendation: “You can secure yourself, if you are a top-tier expert who can help teach AI in your field . . . [you can] earn from training models on data set created by you.” 3 high-paying roles to apply for today DevOps Engineer, Accenture Federal Services, Chantilly ($76,000 - $144,000)
Principal DevOps Engineer, Autodesk, TX ($141,400 - $228,800)
Staff/Sr. Staff DevOps Engineer, SciTec, Boulder ($93,000 - $137,000) The key is deep knowledge. As subject matter expertise really matters when it comes to creating the best prompts, once you’ve an understanding of prompt engineering, and experiment with AI tools in your day-to-day life, that may be enough for now. This frees programmers up to focus on the languages making waves in the coding community; here we’ll look at three of the newest and well-paid languages of the moment. Zig Created by Andrew Kelley in 2015, Zig is pitched as a general purpose language and as a modern alternative to C, but it’s still relatively obscure. In a Stack Overflow survey of 89,184 software developers across 185 countries, just 0.83% reported proficiency in Zig. However, the same survey also found that Zig developers command the highest median salaries on average. Median earnings of $103,000 may be pocket change to some in highly-paid AI roles, and it won’t put you in the top 10% of earners in the U.S. – you’ll need to be on $167,639 for that – but it’s the only programming language that hits six figures in the Stack Overflow survey. The next closest is Erlang ($99,492), F# ($99,492), Ruby ($98,522), and in fourth place, the former 2022 leader, Clojure ($96,381). Comparatively, the bottom four are: Visual Basic (.Net) with $65,000 median pay, MATLAB with $61,735, PHP at $58,899, and Dart, which commands the least at $55,862. Because Zig is compatible with C codebases, it may be readily incorporated with current projects, making it a solid option for systems development, embedded systems, game design, and scripting. Non-profit Zig boasts a vibrant contributor community where developers are invited to utilize it for personal open-source projects, allowing real-world problems to surface and be resolved in real-time. Its rarity makes it something of a risk, as you may have to plug and play, figuring out mistakes as you go. Depending on the pace in your current role, this may or may not be possible. But clearly, there can be salary gains if you can find a company that utilizes and values Zig. Finch Finch is the latest programming language from MIT, which was announced earlier this year. It aims to resolve the challenges associated with computing over-structured arrays. Finch does this by providing a programming model that unifies control flow and diverse data structures into a single representation that allows for co-optimization. Specifically, it automates the control flow to the data, freeing up performance engineers to experiment with many algorithms. MIT says that Finch is, to the best of its knowledge, the first programming language to support affine indexing or scatter/gather of sparse or structured operands, as well as if-conditions, early breaks, and multiple left hand sides over structured data. Early-adopters are praising the time-saving automatic specialization and smooth control flow, particularly in fields like image processing and scientific computing, where structured data is essential. Being a complete newbie, Finch doesn’t have a vast library of tools and frameworks to refer to, meaning it’s not really suitable for large-scale projects just yet. But if you’re a keen early-adopter, Finch is one to watch, and as the community expands, it may become a very valuable tool in the near future. Swift Apple’s Swift has been knocking around for a decade, but its next iteration – Swift 6 – is slated for release later this year. This is the first major update since Swift 5, five long years ago. This release expands upon the async/await, existentials, and macros capabilities that were first included in the Swift 5.x series. Apple director of languages and runtimes, Ted Kremenek says, “Swift’s safety, speed, and approachability, combined with built-in C and C++ interoperability, mean Swift is the best choice to succeed C++.” Meanwhile, forums and blogs are already advising programmers on how to migrate projects across, which is expected to be tricky for legacy codebases. 3 great tech jobs hiring now DevOps Engineer - Sports Technology, Electronic Arts, Orlando
Sr. DevOps Engineer - Store Frontend, hims & hers, San Francisco ($155,000 - $175,000)
Senior Engineer Fullstack (Java), GTT LLC, Chevy Chase The good news is there is clear demand for Swift programmers. According to jobs site Indeed, iOS developers and senior mobile developers are sought after in major companies all over the world, including Accenture, DataAnnotation, and Netflix. It’s widely-accepted that for a long, productive, and well-paid career in programming, developers must always move with the times. Ideally, your existing role will allow time for experimentation and upskilling, enabling you to explore these languages and integrate them into your projects. But if upskilling isn’t a priority in your workplace, it could be time to look for something new. Ready to find your next role in tech? Whether you want to pivot to AI or put your coding expertise to good use, visit the Hackernoon Job Board today By Amanda Kavanagh While tech sectors like healthcare, climate tech and cybersecurity are urgently focused on retraining in AI, it’s not quite as important for other programmers. That’s according to Christian Rebernik, co-chief executive and co-founder of Tomorrow University of Applied Sciences, who reckons you don’t need to throw the baby out of the bath water, and ditch your expertise for AI. In an interview with the Financial Times , he makes this recommendation: “You can secure yourself, if you are a top-tier expert who can help teach AI in your field . . . [you can] earn from training models on data set created by you.” Financial Times Financial Times 3 high-paying roles to apply for today 3 high-paying roles to apply for today DevOps Engineer, Accenture Federal Services, Chantilly ($76,000 - $144,000) Principal DevOps Engineer, Autodesk, TX ($141,400 - $228,800) Staff/Sr. Staff DevOps Engineer, SciTec, Boulder ($93,000 - $137,000) DevOps Engineer, Accenture Federal Services, Chantilly ($76,000 - $144,000) DevOps Engineer, Accenture Federal Services, Chantilly ($76,000 - $144,000) DevOps Engineer, Accenture Federal Services, Chantilly ($76,000 - $144,000) Principal DevOps Engineer, Autodesk, TX ($141,400 - $228,800) Principal DevOps Engineer, Autodesk, TX ($141,400 - $228,800) Principal DevOps Engineer, Autodesk, TX ($141,400 - $228,800) Staff/Sr. Staff DevOps Engineer, SciTec, Boulder ($93,000 - $137,000) Staff/Sr. Staff DevOps Engineer, SciTec, Boulder ($93,000 - $137,000) Staff/Sr. Staff DevOps Engineer, SciTec, Boulder ($93,000 - $137,000) The key is deep knowledge. As subject matter expertise really matters when it comes to creating the best prompts, once you’ve an understanding of prompt engineering, and experiment with AI tools in your day-to-day life, that may be enough for now. This frees programmers up to focus on the languages making waves in the coding community; here we’ll look at three of the newest and well-paid languages of the moment. Zig Zig Created by Andrew Kelley in 2015, Zig is pitched as a general purpose language and as a modern alternative to C, but it’s still relatively obscure. In a Stack Overflow survey of 89,184 software developers across 185 countries, just 0.83% reported proficiency in Zig. Stack Overflow survey Stack Overflow survey However, the same survey also found that Zig developers command the highest median salaries on average. Median earnings of $103,000 may be pocket change to some in highly-paid AI roles , and it won’t put you in the top 10% of earners in the U.S. – you’ll need to be on $167,639 for that – but it’s the only programming language that hits six figures in the Stack Overflow survey. highly-paid AI roles highly-paid AI roles The next closest is Erlang ($99,492), F# ($99,492), Ruby ($98,522), and in fourth place, the former 2022 leader, Clojure ($96,381). Comparatively, the bottom four are: Visual Basic (.Net) with $65,000 median pay, MATLAB with $61,735, PHP at $58,899, and Dart, which commands the least at $55,862. Because Zig is compatible with C codebases, it may be readily incorporated with current projects, making it a solid option for systems development, embedded systems, game design, and scripting. Non-profit Zig boasts a vibrant contributor community where developers are invited to utilize it for personal open-source projects, allowing real-world problems to surface and be resolved in real-time. vibrant contributor community vibrant contributor community Its rarity makes it something of a risk, as you may have to plug and play, figuring out mistakes as you go. Depending on the pace in your current role, this may or may not be possible. But clearly, there can be salary gains if you can find a company that utilizes and values Zig. company company Finch Finch Finch is the latest programming language from MIT, which was announced earlier this year. It aims to resolve the challenges associated with computing over-structured arrays. Finch Finch Finch does this by providing a programming model that unifies control flow and diverse data structures into a single representation that allows for co-optimization. Specifically, it automates the control flow to the data, freeing up performance engineers to experiment with many algorithms. MIT says that Finch is, to the best of its knowledge, the first programming language to support affine indexing or scatter/gather of sparse or structured operands, as well as if-conditions, early breaks, and multiple left hand sides over structured data. Early-adopters are praising the time-saving automatic specialization and smooth control flow, particularly in fields like image processing and scientific computing, where structured data is essential. Being a complete newbie, Finch doesn’t have a vast library of tools and frameworks to refer to, meaning it’s not really suitable for large-scale projects just yet. But if you’re a keen early-adopter, Finch is one to watch, and as the community expands, it may become a very valuable tool in the near future. Swift Apple’s Swift has been knocking around for a decade, but its next iteration – Swift 6 – is slated for release later this year. This is the first major update since Swift 5, five long years ago. This release expands upon the async/await, existentials, and macros capabilities that were first included in the Swift 5.x series. Apple director of languages and runtimes, Ted Kremenek says, “Swift’s safety, speed, and approachability, combined with built-in C and C++ interoperability, mean Swift is the best choice to succeed C++.” Meanwhile, forums and blogs are already advising programmers on how to migrate projects across, which is expected to be tricky for legacy codebases. 3 great tech jobs hiring now DevOps Engineer - Sports Technology, Electronic Arts, Orlando Sr. DevOps Engineer - Store Frontend, hims & hers, San Francisco ($155,000 - $175,000) Senior Engineer Fullstack (Java), GTT LLC, Chevy Chase DevOps Engineer - Sports Technology, Electronic Arts, Orlando DevOps Engineer - Sports Technology, Electronic Arts, Orlando DevOps Engineer - Sports Technology, Electronic Arts, Orlando Sr. DevOps Engineer - Store Frontend, hims & hers, San Francisco ($155,000 - $175,000) Sr. DevOps Engineer - Store Frontend, hims & hers, San Francisco ($155,000 - $175,000) Sr. DevOps Engineer - Store Frontend, hims & hers, San Francisco ($155,000 - $175,000) Senior Engineer Fullstack (Java), GTT LLC, Chevy Chase Senior Engineer Fullstack (Java), GTT LLC, Chevy Chase Senior Engineer Fullstack (Java), GTT LLC, Chevy Chase The good news is there is clear demand for Swift programmers. According to jobs site Indeed, iOS developers and senior mobile developers are sought after in major companies all over the world, including Accenture, DataAnnotation, and Netflix. It’s widely-accepted that for a long, productive, and well-paid career in programming, developers must always move with the times. Ideally, your existing role will allow time for experimentation and upskilling, enabling you to explore these languages and integrate them into your projects. But if upskilling isn’t a priority in your workplace, it could be time to look for something new. Ready to find your next role in tech? Whether you want to pivot to AI or put your coding expertise to good use, visit the Hackernoon Job Board today Ready to find your next role in tech? Whether you want to pivot to AI or put your coding expertise to good use, visit the Hackernoon Job Board today Ready to find your next role in tech? Whether you want to pivot to AI or put your coding expertise to good use, visit the Hackernoon Job Board today Ready to find your next role in tech? Whether you want to pivot to AI or put your coding expertise to good use, visit the Hackernoon Job Board today Ready to find your next role in tech? Whether you want to pivot to AI or put your coding expertise to good use, visit the Hackernoon Job Board today

3 High Paying Programming Languages Jobs On Developers’ Watchlists

ডেভেলপারদের ওয়াচলিস্টে 3টি উচ্চ অর্থপ্রদানকারী প্রোগ্রামিং ভাষার চাকরি

3 gut bezahlte Jobs im Bereich Programmiersprachen auf den Beobachtungslisten der Entwickler

3 trabajos de lenguajes de programación bien remunerados en las listas de vigilancia de desarrolladores

3 emplois bien rémunérés dans les langages de programmation sur les listes de surveillance des développeurs

डेवलपर्स की वॉचलिस्ट पर 3 उच्च भुगतान वाली प्रोग्रामिंग भाषा नौकरियां

開発者の注目リストに載っている高収入のプログラミング言語の仕事 3 つ

개발자 관심 목록에 있는 고임금 프로그래밍 언어 직업 3가지

3 empregos em linguagens de programação com altos salários nas listas de observação dos desenvolvedores

3 высокооплачиваемые вакансии в области языков программирования в списках разработчиков

Geliştiricilerin İzleme Listelerindeki Yüksek Ücretli 3 Programlama Dili İşi

3 công việc về ngôn ngữ lập trình được trả lương cao trong danh sách theo dõi của các nhà phát triển

开发人员关注的 3 个高薪编程语言职位

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 25 of 26. PRAYER FOR RELIEF WHEREFORE, the Commission respectfully requests that the Court enter a Final Judgment: I. Permanently enjoining Defendant and its agents, servants, employees and attorneys and all persons in active concert or participation with any of them from violating, directly or indirectly, Securities Act Sections 5(a) and (c) [15 U.S.C. §§ 77e(a), 77e(c)] and Exchange Act Section 15(a) [15 U.S.C. § 78o(a)]. II. Ordering Defendant to pay civil monetary penalties under Securities Act Section 20(d) [15 U.S.C. § 77t(d)] and Exchange Act Section 21(d)(3) [15 U.S.C. § 78u(d)(3)]; and III. Granting any other and further relief this Court may deem appropriate or necessary for the benefit of investors pursuant to Section 21(d)(5) of the Exchange Act [15 U.S.C. § 78u(d)(5)]. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 25 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 25 of 26. HackerNoon’s Legal PDF Series here PRAYER FOR RELIEF WHEREFORE, the Commission respectfully requests that the Court enter a Final Judgment: I. Permanently enjoining Defendant and its agents, servants, employees and attorneys and all persons in active concert or participation with any of them from violating, directly or indirectly, Securities Act Sections 5(a) and (c) [15 U.S.C. §§ 77e(a), 77e(c)] and Exchange Act Section 15(a) [15 U.S.C. § 78o(a)]. II. Ordering Defendant to pay civil monetary penalties under Securities Act Section 20(d) [15 U.S.C. § 77t(d)] and Exchange Act Section 21(d)(3) [15 U.S.C. § 78u(d)(3)]; and III. Granting any other and further relief this Court may deem appropriate or necessary for the benefit of investors pursuant to Section 21(d)(5) of the Exchange Act [15 U.S.C. § 78u(d)(5)]. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

SEC Requests Court to Halt Consensys and Impose Fines Over Staking Program Violations

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 19 of 26. B. Liquid Staking Pool Providers: Lido and Rocket Pool 238. Lido and Rocket Pool have each created and maintained respective staking programs, designed to capture the staking rewards described above. 239. Lido and Rocket Pool each call their program a “liquid staking” program because— as described below—investors are issued a tradable token in exchange for depositing funds into the program. This token represents the investor’s interest in the program. The token issued in exchange is referred to as a “liquid staking token” or “LST” and the LST can be traded on the secondary market. 240. In Lido, the LST is called stETH; in Rocket Pool, the LST is called rETH. 241. The amount of LST an investor receives is proportional to the amount of ETH they deposit. 242. These staking programs allow investors to both obtain the rewards of staking and also purportedly retain the ability to redeem the value of their investment at any time. i. The Lido Staking Program Is An Investment Contract. 243. Lido launched its liquid staking platform in December 2020. 244. To participate, investors deposit ETH with Lido. 245. In return, Lido issues the investor another crypto asset, “stETH,” representing the investor’s pro rata interest in Lido’s staking program, including the investor’s original deposit of ETH plus any accumulated returns. 246. Lido then uses investors’ deposited ETH in the Ethereum consensus mechanism to earn staking rewards—the financial returns for investors. 247. Specifically, Lido pools the ETH deposited by investors into a Lido smart contract, which initiates the creation of a validator by depositing a 32 ETH bundle to the Ethereum validator deposit contract. 248. As of June 25, 2024, over 28% of all staked ETH on Ethereum is staked in Lido’s staking program. 249. Lido takes, as a fee, 10% of the staking rewards earned. 250. The remaining rewards are accrued, pro rata, by stETH token holders. 251. Lido markets its staking program as an investment opportunity. 252. Lido also leads investors to reasonably expect that investors’ profits will come from Lido’s efforts. 253. According to Lido’s website, from December 2020 to February 2024, Lido’s staking program returned an annualized percentage gain of 3% to 9%. 254. In a blog post dated December 28, 2020, Lido stated: “Lido allows users to stake any amount of ETH – without the need to maintain complex infrastructure.” 255. From at least June 2021 to June 2024, in a “Help” article on its website, Lido stated that staking “requires expert knowledge and complex and costly infrastructure” and that through Lido “users can eliminate these inconveniences.” 256. Lido claimed in an October 2020 document posted to its website that it is “more profitable” than other staking pool providers because of its fee model and the quality of its node operators. 257. From at least February 2023 to June 2024, another “Help” article on Lido’s website claims to allocate investors’ staked ETH across multiple, high-quality validator node operators (that is, participants in the Ethereum network consensus mechanism), minimizing the risks associated with staking ETH. 258. In the same Help article, Lido states that it elects “professional and reputable node operators” and that the penalty and slashing risks are reduced “given the quality of the Lido validator set and its proven track record.” 259. In short, Lido purportedly offers investors a “simplified participation in staking”— deploying its resources and expertise to achieve staking rewards that individual investors typically would not be able to achieve on their own. 260. The holder of any stETH—whether issued directly from Lido or purchased in the secondary market—has the right to deliver the stETH to Lido to get back the pro-rata staked ETH plus accrued rewards. 261. Lido treats all investors’ deposited ETH as fungible. It does not purport to segregate investor funds. 262. In light of the above, Lido offered and sold its staking program as an investment contract. ii. The Rocket Pool Staking Program Is An Investment Contract 263. Rocket Pool launched its platform in October 2021. 264. To participate, investors deposit ETH with Rocket Pool. 265. In return, Rocket Pool issues another crypto asset, “rETH,” representing the investor’s pro rata interest in Rocket Pool’s staking program, including the investor’s original deposit of ETH plus any accumulated returns. 266. Rocket Pool then uses investors’ deposited ETH in the Ethereum consensus mechanism to earn staking rewards—the financial returns for investors. 267. Specifically, Rocket Pool pools the ETH deposited by investors into a Rocket Pool smart contract, which initiates the creation of a validator by depositing a 32-ETH bundle to the Ethereum validator deposit contract. 268. Rocket Pool takes a 0.05% fee of the staking rewards it earns. 269. The remaining staking rewards accrue, pro-rata, to investors. 270. Rocket Pool also markets its staking program as an investment opportunity. 271. According to Rocket Pool’s website, rETH “accrues value over time.” 272. Rocket Pool also leads investors to reasonably expect that investors’ profits will come from the efforts of Rocket Pool. 273. As of June 2024, Rocket Pool advertised on its website an annual percentage return of approximately 3.11%. 274. In an FAQ available on its website, Rocket Pool notes that its staking service makes staking available to investors who might not otherwise have the technical expertise necessary to interact with smart contracts or keep a node running 24/7. 275. Indeed, according to Rocket Pool’s FAQ, its program “removes several high barriers to entry that exist with Proof of Stake on Ethereum.” 276. In a January 22, 2021, Medium Post, Rocket Pool stated that its program was “an easy and permissionless way to engage in staking without needing to run any staking infrastructure or even have 32 ETH.” 277. The Rocket Pool FAQ states that it will “allow anyone to earn rewards on deposits as small as 0.01 ETH.” 278. On its website, Rocket Pool also attempts to differentiate itself from other staking pool providers, claiming that “Rocket Pool is the only staking platform with a perfect score on ethereum.org.” 279. According to Rocket Pool’s website, its protocol is highly secure and its “smart contracts have been extensively audited, multiple times, by some of the best auditors in the Ethereum ecosystem.” 280. Rocket Pool also touts other reasons for investors to stake their ETH with Rocket Pool. 281. In the January 2021 Medium post, Rocket Pool notes that the value of rETH is “protected against node slashing and downtime by several built in insurance mechanisms.” 282. Specifically, Rocket Pool requires individuals that run Rocket Pool validator nodes to put up collateral to protect against losses that may result from penalties and slashing. 283. Rocket Pool’s website states that “every rETH token is exactly the same, you will automatically receive the benefits of staking just by holding the token!” (Emphasis in original.) 284. In light of the above, Rocket Pool offered and sold its staking program as an investment contract. 285. Neither Lido nor Rocket Pool have ever filed registration statements with the SEC for the offer and sale of their respective staking program investment contracts. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 19 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 19 of 26. HackerNoon’s Legal PDF Series here B. Liquid Staking Pool Providers: Lido and Rocket Pool 238. Lido and Rocket Pool have each created and maintained respective staking programs, designed to capture the staking rewards described above. 239. Lido and Rocket Pool each call their program a “liquid staking” program because— as described below—investors are issued a tradable token in exchange for depositing funds into the program. This token represents the investor’s interest in the program. The token issued in exchange is referred to as a “liquid staking token” or “LST” and the LST can be traded on the secondary market. 240. In Lido, the LST is called stETH; in Rocket Pool, the LST is called rETH. 241. The amount of LST an investor receives is proportional to the amount of ETH they deposit. 242. These staking programs allow investors to both obtain the rewards of staking and also purportedly retain the ability to redeem the value of their investment at any time. i. The Lido Staking Program Is An Investment Contract. i. The Lido Staking Program Is An Investment Contract. 243. Lido launched its liquid staking platform in December 2020. 244. To participate, investors deposit ETH with Lido. 245. In return, Lido issues the investor another crypto asset, “stETH,” representing the investor’s pro rata interest in Lido’s staking program, including the investor’s original deposit of ETH plus any accumulated returns. 246. Lido then uses investors’ deposited ETH in the Ethereum consensus mechanism to earn staking rewards—the financial returns for investors. 247. Specifically, Lido pools the ETH deposited by investors into a Lido smart contract, which initiates the creation of a validator by depositing a 32 ETH bundle to the Ethereum validator deposit contract. 248. As of June 25, 2024, over 28% of all staked ETH on Ethereum is staked in Lido’s staking program. 249. Lido takes, as a fee, 10% of the staking rewards earned. 250. The remaining rewards are accrued, pro rata, by stETH token holders. 251. Lido markets its staking program as an investment opportunity. 252. Lido also leads investors to reasonably expect that investors’ profits will come from Lido’s efforts. 253. According to Lido’s website, from December 2020 to February 2024, Lido’s staking program returned an annualized percentage gain of 3% to 9%. 254. In a blog post dated December 28, 2020, Lido stated: “Lido allows users to stake any amount of ETH – without the need to maintain complex infrastructure.” 255. From at least June 2021 to June 2024, in a “Help” article on its website, Lido stated that staking “requires expert knowledge and complex and costly infrastructure” and that through Lido “users can eliminate these inconveniences.” 256. Lido claimed in an October 2020 document posted to its website that it is “more profitable” than other staking pool providers because of its fee model and the quality of its node operators. 257. From at least February 2023 to June 2024, another “Help” article on Lido’s website claims to allocate investors’ staked ETH across multiple, high-quality validator node operators (that is, participants in the Ethereum network consensus mechanism), minimizing the risks associated with staking ETH. 258. In the same Help article, Lido states that it elects “professional and reputable node operators” and that the penalty and slashing risks are reduced “given the quality of the Lido validator set and its proven track record.” 259. In short, Lido purportedly offers investors a “simplified participation in staking”— deploying its resources and expertise to achieve staking rewards that individual investors typically would not be able to achieve on their own. 260. The holder of any stETH—whether issued directly from Lido or purchased in the secondary market—has the right to deliver the stETH to Lido to get back the pro-rata staked ETH plus accrued rewards. 261. Lido treats all investors’ deposited ETH as fungible. It does not purport to segregate investor funds. 262. In light of the above, Lido offered and sold its staking program as an investment contract. ii. The Rocket Pool Staking Program Is An Investment Contract ii. The Rocket Pool Staking Program Is An Investment Contract 263. Rocket Pool launched its platform in October 2021. 264. To participate, investors deposit ETH with Rocket Pool. 265. In return, Rocket Pool issues another crypto asset, “rETH,” representing the investor’s pro rata interest in Rocket Pool’s staking program, including the investor’s original deposit of ETH plus any accumulated returns. 266. Rocket Pool then uses investors’ deposited ETH in the Ethereum consensus mechanism to earn staking rewards—the financial returns for investors. 267. Specifically, Rocket Pool pools the ETH deposited by investors into a Rocket Pool smart contract, which initiates the creation of a validator by depositing a 32-ETH bundle to the Ethereum validator deposit contract. 268. Rocket Pool takes a 0.05% fee of the staking rewards it earns. 269. The remaining staking rewards accrue, pro-rata, to investors. 270. Rocket Pool also markets its staking program as an investment opportunity. 271. According to Rocket Pool’s website, rETH “accrues value over time.” 272. Rocket Pool also leads investors to reasonably expect that investors’ profits will come from the efforts of Rocket Pool. 273. As of June 2024, Rocket Pool advertised on its website an annual percentage return of approximately 3.11%. 274. In an FAQ available on its website, Rocket Pool notes that its staking service makes staking available to investors who might not otherwise have the technical expertise necessary to interact with smart contracts or keep a node running 24/7. 275. Indeed, according to Rocket Pool’s FAQ, its program “removes several high barriers to entry that exist with Proof of Stake on Ethereum.” 276. In a January 22, 2021, Medium Post, Rocket Pool stated that its program was “an easy and permissionless way to engage in staking without needing to run any staking infrastructure or even have 32 ETH.” 277. The Rocket Pool FAQ states that it will “allow anyone to earn rewards on deposits as small as 0.01 ETH.” 278. On its website, Rocket Pool also attempts to differentiate itself from other staking pool providers, claiming that “Rocket Pool is the only staking platform with a perfect score on ethereum.org.” 279. According to Rocket Pool’s website, its protocol is highly secure and its “smart contracts have been extensively audited, multiple times, by some of the best auditors in the Ethereum ecosystem.” 280. Rocket Pool also touts other reasons for investors to stake their ETH with Rocket Pool. 281. In the January 2021 Medium post, Rocket Pool notes that the value of rETH is “protected against node slashing and downtime by several built in insurance mechanisms.” 282. Specifically, Rocket Pool requires individuals that run Rocket Pool validator nodes to put up collateral to protect against losses that may result from penalties and slashing. 283. Rocket Pool’s website states that “every rETH token is exactly the same, you will automatically receive the benefits of staking just by holding the token!” (Emphasis in original.) automatically receive the benefits of staking just by holding the token!” 284. In light of the above, Rocket Pool offered and sold its staking program as an investment contract. 285. Neither Lido nor Rocket Pool have ever filed registration statements with the SEC for the offer and sale of their respective staking program investment contracts. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

Crypto Giants Lido and Rocket Pool Face SEC Scrutiny Over Staking Programs

The EOS Network launched a new staking rewards program on July 8, allocating 250 million EOS tokens to incentivize network participation. This move comes as part of a broader effort to revamp the network's tokenomics and potentially increase its competitiveness in the blockchain space. Under the new program, 85,600 EOS tokens will be distributed daily to participants who stake their tokens. The network claims this could initially yield an annual percentage yield (APY) of over 60% for early adopters, though this rate is likely to fluctuate as more users join the program. A key change in the new staking model is the extension of the lock-up period from four to 21 days. This longer commitment period may impact liquidity for participants but could contribute to increased network stability. The program also introduces new incentives for EOS Block Producers, the entities responsible for validating transactions and maintaining the network. These validators will now receive network-generated fees in addition to their standard block rewards, a move that ties their compensation more directly to network usage. Industry analysts have mixed views on the potential impact of this program. Some see it as a necessary step to revitalize interest in the EOS ecosystem, which has faced challenges in recent years. "This staking program could attract new participants to EOS, potentially improving network security and stability," said blockchain economist Dr. Jane Smith. "However, the success of such initiatives often depends on broader market conditions and the network's ability to attract and retain developers." Critics, however, argue that high staking rewards are not a sustainable long-term strategy. While attractive yields may boost short-term participation, the real test will be whether EOS can generate enough genuine economic activity to support these rewards over time. The launch of this staking program comes at a time when many blockchain networks are refining their tokenomic models to balance network security, user incentives, and sustainable growth. As the crypto market continues to evolve, the effectiveness of EOS's new approach will likely be closely watched by industry observers. Final Thoughts The EOS Network Foundation, which oversees the development of the EOS blockchain, has stated that this program is part of a larger strategy to enhance the network's value proposition. However, they have not provided specific targets for staking participation or other metrics to measure the program's success. As the program unfolds, its impact on EOS token value, network participation, and overall ecosystem health will become clearer. For now, it represents a significant shift in the network's approach to incentivizing stakeholders and securing its blockchain infrastructure. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. The EOS Network launched a new staking rewards program on July 8, allocating 250 million EOS tokens to incentivize network participation. This move comes as part of a broader effort to revamp the network's tokenomics and potentially increase its competitiveness in the blockchain space. EOS Network Under the new program, 85,600 EOS tokens will be distributed daily to participants who stake their tokens. The network claims this could initially yield an annual percentage yield (APY) of over 60% for early adopters, though this rate is likely to fluctuate as more users join the program. A key change in the new staking model is the extension of the lock-up period from four to 21 days. This longer commitment period may impact liquidity for participants but could contribute to increased network stability. The program also introduces new incentives for EOS Block Producers, the entities responsible for validating transactions and maintaining the network. These validators will now receive network-generated fees in addition to their standard block rewards, a move that ties their compensation more directly to network usage. Industry analysts have mixed views on the potential impact of this program. Some see it as a necessary step to revitalize interest in the EOS ecosystem, which has faced challenges in recent years. "This staking program could attract new participants to EOS, potentially improving network security and stability," said blockchain economist Dr. Jane Smith. "However, the success of such initiatives often depends on broader market conditions and the network's ability to attract and retain developers." Critics, however, argue that high staking rewards are not a sustainable long-term strategy. While attractive yields may boost short-term participation, the real test will be whether EOS can generate enough genuine economic activity to support these rewards over time. The launch of this staking program comes at a time when many blockchain networks are refining their tokenomic models to balance network security, user incentives, and sustainable growth. As the crypto market continues to evolve, the effectiveness of EOS's new approach will likely be closely watched by industry observers. Final Thoughts The EOS Network Foundation, which oversees the development of the EOS blockchain, has stated that this program is part of a larger strategy to enhance the network's value proposition. However, they have not provided specific targets for staking participation or other metrics to measure the program's success. As the program unfolds, its impact on EOS token value, network participation, and overall ecosystem health will become clearer. For now, it represents a significant shift in the network's approach to incentivizing stakeholders and securing its blockchain infrastructure. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

EOS Network Unveils 250M Token Staking Program

EOS নেটওয়ার্ক 250M টোকেন স্টেকিং প্রোগ্রাম উন্মোচন করেছে

EOS Network stellt 250M Token Staking-Programm vor

EOS Network presenta un programa de participación de tokens de 250 millones

Le réseau EOS dévoile un programme de jalonnement de 250 millions de jetons

EOS नेटवर्क ने 250M टोकन स्टेकिंग प्रोग्राम का अनावरण किया

EOSネットワークが2億5000万トークンのステーキングプログラムを発表

EOS 네트워크, 2억 5천만 토큰 스테이킹 프로그램 공개

EOS Network lança programa de piquetagem de tokens de 250 milhões

Сеть EOS представляет программу ставок на 250 миллионов токенов

EOS Ağı 250 Milyon Token Staking Programını Açıkladı

Mạng EOS ra mắt chương trình đặt cược mã thông báo 250 triệu

EOS 网络推出 2.5 亿代币质押计划

We live in a time where there is a social network, Discord server, subreddits, WhatsApp group and other online one-offs for almost every topic and affinity group. The catch is that often times social media platforms often struggle to find meaningful purpose beyond connectivity. Yummypets, the largest online community of pets and owners, network, found a way to transform online engagement into tangible support for animal welfare through its Voice program. Launched in 2017, the Voice program is Yummypets' proprietary click-to-donate initiative. The name "Voice" was chosen with a profound purpose - to give a voice to those who don't have one: animals. Matthieu Glayrouse, Co-Founder of Yummypets, explains, "Voice was created because we wanted to do something bigger than the Yummypets community. We wanted to engage the community and make a meaningful contribution to the well-being of animals." The program has evolved significantly since its inception. Initially present only in France and focused on helping animal welfare organizations spay and neuter stray cats, Voice underwent a major transformation in 2020. Its mission shifted to feeding rescue animals from animal welfare organizations, broadening its impact and reach. The expansion of Voice has been rapid and impressive: November 2021: Launched in the United States
October 2023: Expanded to Canada
December 2023: Launched in the United Kingdom This international growth reflects not only the program's success but also the universal appeal of its mission. The mechanics of Voice are elegantly simple yet profoundly effective. Each month, four animal welfare organizations are featured on the platform. Users can click once daily for each organization, with each click contributing towards a goal of 165 pounds of kibble. To incentivize engagement, Yummypets members who are logged in see their clicks doubled. "The goal of the program is to raise clicks for kibble to feed rescue animals," Glayrouse explains. "To receive 165 lb of kibble, the participating organizations need to reach 100% before the end of the month." Users can participate through the Yummypets website under the Voice tab or via the Voice tab on the Yummypets mobile app, making it accessible and convenient for the community to contribute. The Voice program's achievements to date are impressive: Over 11 million clicks generated
385 campaigns completed
28 tons of kibble distributed
326 different organizations supported These numbers translate into countless meals for animals in need, supporting everything from local shelters to large-scale rescue operations. The program has helped organizations across participating countries, with some using the donated kibble to support their foster families. "Voice has helped so many organizations feed the animals in their care,"Glayrouse notes. "We've seen participation from organizations on both the East and West coasts in the United States alone." At its core, Voice is a testament to the power of technology as a force for good. While specific technologies powering the program weren't detailed, it's clear that the platform leverages advanced digital technologies to ensure seamless user engagement and efficient resource distribution. Data analytics play a crucial role in optimizing the program's effectiveness. By tracking metrics such as the number of clicks, campaigns completed, and kibble distributed, Yummypets can continually refine its strategies and maximize impact. In an era of increasing concerns about data privacy, Yummypets takes user security seriously. "To ensure security and privacy, we do as we have been doing since 2012 with Yummypets," Glayrouse, emphasizes. "Security constraints and personal data protection are at the heart of our activity since day one." The company adheres to international regulations such as GDPR, ensuring that users can participate in the Voice program with confidence, knowing their personal information is protected. Yummypets' success goes beyond its technological innovations. The platform has mastered the art of community building, fostering genuine connections among its 3.5 million users through interactive features like monthly photo contests and engaging content. "To create a sense of connection within our community, we prioritize interaction and empowerment," Glayrouse explains. "We want our community to know that they matter—that their voices matter." This philosophy is evident in every aspect of the Voice program, which unites users around a common cause, creating a sense of shared purpose that transcends typical social media interactions. The Voice program's success is closely tied to its integration with social media and digital engagement strategies. Yummypets leverages its social media presence to create a sense of community around the Voice program. "When it comes to fostering a sense of community among our social media fans, we make it a point to post content that encourages interaction," the spokesperson shares. "We ask questions and keep up with the trends to keep people interested. Most importantly, we create a sense of community through Voice's social media accounts." This approach has proven effective in mobilizing the Yummypets community towards a common goal, demonstrating the power of social media when harnessed for social good. Yummypets' ambitions for Voice are as bold as the program itself. "By 2026, we aim to increase the impact of Voice and distribute 50 tons of kibble to our partner organizations," the spokesperson reveals. This goal represents a significant increase from the current distribution of 15 tons per year Plans are also underway to expand the program to new countries, further globalizing their efforts. "We intend to help other countries in the future," the spokesperson adds, hinting at the program's potential for even broader impact. The process for selecting animal welfare organizations to participate in the Voice program is straightforward and inclusive. Animal welfare organizations can sign up to participate in the program. The link to sign up is available on the Voice webpage. The organizations are chosen on a first come, first served basis. This approach ensures that a diverse range of organizations, both large and small, have the opportunity to benefit from the program. The journey of Yummypets and the Voice program offers valuable insights for other organizations looking to leverage technology for social good. Perhaps the most crucial lesson is the importance of authentic community building. "One of the most important lessons we have learned is that no one can buy a community," Glayrouse emphasizes. "Over the years, we've seen firsthand that attempting to do so is not only impossible, but also a huge waste of time, energy, and money." This philosophy underscores the importance of genuine engagement and the power of shared purpose in building a thriving online community. The Future of Digital Philanthropy As we navigate an increasingly digital world, initiatives like Yummypets' Voice program point the way towards a future where technology and compassion go hand in hand. By harnessing the power of social networks for social good, Yummypets has created a model that could revolutionize not just animal welfare, but digital philanthropy as a whole. The Voice program stands as a powerful reminder of what we can achieve when we come together, one click at a time, to make a difference in the lives of those who need it most. As technology continues to evolve, the potential for programs like Voice to create meaningful, large-scale impact in animal welfare and beyond is truly exciting. Yummypets' Voice program offers a glimpse of how technology can unite us in common cause, turning our daily digital interactions into a force for good. It's a vision of social networking that goes beyond connection to create real-world change, one click at a time. We live in a time where there is a social network, Discord server, subreddits, WhatsApp group and other online one-offs for almost every topic and affinity group. The catch is that often times social media platforms often struggle to find meaningful purpose beyond connectivity. Yummypets , the largest online community of pets and owners, network, found a way to transform online engagement into tangible support for animal welfare through its Voice program. Yummypets Launched in 2017, the Voice program is Yummypets' proprietary click-to-donate initiative. The name "Voice" was chosen with a profound purpose - to give a voice to those who don't have one: animals. Matthieu Glayrouse, Co-Founder of Yummypets, explains, "Voice was created because we wanted to do something bigger than the Yummypets community. We wanted to engage the community and make a meaningful contribution to the well-being of animals." The program has evolved significantly since its inception. Initially present only in France and focused on helping animal welfare organizations spay and neuter stray cats, Voice underwent a major transformation in 2020. Its mission shifted to feeding rescue animals from animal welfare organizations, broadening its impact and reach. The expansion of Voice has been rapid and impressive: November 2021: Launched in the United States October 2023: Expanded to Canada December 2023: Launched in the United Kingdom November 2021: Launched in the United States October 2023: Expanded to Canada December 2023: Launched in the United Kingdom This international growth reflects not only the program's success but also the universal appeal of its mission. The mechanics of Voice are elegantly simple yet profoundly effective. Each month, four animal welfare organizations are featured on the platform. Users can click once daily for each organization, with each click contributing towards a goal of 165 pounds of kibble. To incentivize engagement, Yummypets members who are logged in see their clicks doubled. "The goal of the program is to raise clicks for kibble to feed rescue animals," Glayrouse explains. "To receive 165 lb of kibble, the participating organizations need to reach 100% before the end of the month." "The goal of the program is to raise clicks for kibble to feed rescue animals," Glayrouse explains. "To receive 165 lb of kibble, the participating organizations need to reach 100% before the end of the month." Users can participate through the Yummypets website under the Voice tab or via the Voice tab on the Yummypets mobile app, making it accessible and convenient for the community to contribute. The Voice program's achievements to date are impressive: Over 11 million clicks generated 385 campaigns completed 28 tons of kibble distributed 326 different organizations supported Over 11 million clicks generated 385 campaigns completed 28 tons of kibble distributed 326 different organizations supported These numbers translate into countless meals for animals in need, supporting everything from local shelters to large-scale rescue operations. The program has helped organizations across participating countries, with some using the donated kibble to support their foster families. "Voice has helped so many organizations feed the animals in their care,"Glayrouse notes. "We've seen participation from organizations on both the East and West coasts in the United States alone." At its core, Voice is a testament to the power of technology as a force for good. While specific technologies powering the program weren't detailed, it's clear that the platform leverages advanced digital technologies to ensure seamless user engagement and efficient resource distribution. Data analytics play a crucial role in optimizing the program's effectiveness. By tracking metrics such as the number of clicks, campaigns completed, and kibble distributed, Yummypets can continually refine its strategies and maximize impact. In an era of increasing concerns about data privacy, Yummypets takes user security seriously. "To ensure security and privacy, we do as we have been doing since 2012 with Yummypets," Glayrouse, emphasizes. "Security constraints and personal data protection are at the heart of our activity since day one." The company adheres to international regulations such as GDPR, ensuring that users can participate in the Voice program with confidence, knowing their personal information is protected. Yummypets' success goes beyond its technological innovations. The platform has mastered the art of community building, fostering genuine connections among its 3.5 million users through interactive features like monthly photo contests and engaging content. "To create a sense of connection within our community, we prioritize interaction and empowerment," Glayrouse explains. "We want our community to know that they matter—that their voices matter." "To create a sense of connection within our community, we prioritize interaction and empowerment," Glayrouse explains. "We want our community to know that they matter—that their voices matter." This philosophy is evident in every aspect of the Voice program, which unites users around a common cause, creating a sense of shared purpose that transcends typical social media interactions. The Voice program's success is closely tied to its integration with social media and digital engagement strategies. Yummypets leverages its social media presence to create a sense of community around the Voice program. "When it comes to fostering a sense of community among our social media fans, we make it a point to post content that encourages interaction," the spokesperson shares. "We ask questions and keep up with the trends to keep people interested. Most importantly, we create a sense of community through Voice's social media accounts." This approach has proven effective in mobilizing the Yummypets community towards a common goal, demonstrating the power of social media when harnessed for social good. Yummypets' ambitions for Voice are as bold as the program itself. "By 2026, we aim to increase the impact of Voice and distribute 50 tons of kibble to our partner organizations," the spokesperson reveals. This goal represents a significant increase from the current distribution of 15 tons per year Plans are also underway to expand the program to new countries, further globalizing their efforts. "We intend to help other countries in the future," the spokesperson adds, hinting at the program's potential for even broader impact. The process for selecting animal welfare organizations to participate in the Voice program is straightforward and inclusive. Animal welfare organizations can sign up to participate in the program. The link to sign up is available on the Voice webpage. The organizations are chosen on a first come, first served basis. This approach ensures that a diverse range of organizations, both large and small, have the opportunity to benefit from the program. The journey of Yummypets and the Voice program offers valuable insights for other organizations looking to leverage technology for social good. Perhaps the most crucial lesson is the importance of authentic community building. "One of the most important lessons we have learned is that no one can buy a community," Glayrouse emphasizes. "Over the years, we've seen firsthand that attempting to do so is not only impossible, but also a huge waste of time, energy, and money." "One of the most important lessons we have learned is that no one can buy a community," Glayrouse emphasizes. "Over the years, we've seen firsthand that attempting to do so is not only impossible, but also a huge waste of time, energy, and money." This philosophy underscores the importance of genuine engagement and the power of shared purpose in building a thriving online community. The Future of Digital Philanthropy The Future of Digital Philanthropy As we navigate an increasingly digital world, initiatives like Yummypets' Voice program point the way towards a future where technology and compassion go hand in hand. By harnessing the power of social networks for social good, Yummypets has created a model that could revolutionize not just animal welfare, but digital philanthropy as a whole. The Voice program stands as a powerful reminder of what we can achieve when we come together, one click at a time, to make a difference in the lives of those who need it most. As technology continues to evolve, the potential for programs like Voice to create meaningful, large-scale impact in animal welfare and beyond is truly exciting. Yummypets' Voice program offers a glimpse of how technology can unite us in common cause, turning our daily digital interactions into a force for good. It's a vision of social networking that goes beyond connection to create real-world change, one click at a time.

How Yummypets’ Voice Program is Leveraging Tech for Animal Welfare

Authors:
(1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com);
(2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com);
(3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com);
(4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). Table of Links Abstract and Introduction Literature Survey Methodology Performance Analysis Conclusion and References V. CONCLUSION This paper examines the enormous potential for programming work automation offered by natural language processing. We propose a two-stage methodology to convert English language user stories into pseudocode. The advantage of pseudocode is that it allows easy conversion into any programming language of the developer’s choice. The two stages of this approach are text to code conversion and code to pseudocode conversion. Each of these stages is treated as a language translation task. We use the CodeT5 model for this task, getting a BLEU score of 0.4 for Stage 1 and 0.74 for Stage 2. Our proposed system simplifies the software development process in organizations. In the future, we plan to curate a larger dataset for English text to Python code conversion. We can obtain higher accuracy and better generalisation to new examples with a larger and more varied dataset. This would necessitate considerable data gathering, but it might open the door to more efficient and useful text to code conversion methods. Additionally, a pertinent text to pseudocode dataset might be produced to help simplify the conversion architecture. REFERENCES [1] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) [2] GraphCodeBERT: Pre-training Code Representations with Data Flow ICLR 2021 · Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou [3] Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Cha (2021) [4] Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data Sajad Norouzi, Keyi Tang, Yanshuai Cao (2021) [5] Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, KaiWei Chang (2021) [6] Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer Guang Yang, Yanlin Zhou, Xiang Chen, Chi Yu (2021) [7] Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura (2021) [8] Code2Text: Dual Attention Syntax Annotation Networks for StructureAware Code Translation Authors : Yun Xiong, Shaofeng Xu, Keyao Rong, Xinyue Liu, Xiangnan Kong, Shanshan Li, Philip Yu, Yangyong Zhu [9] Home Database Systems for Advanced Applications Conference paper Code2Text: Dual Attention Syntax Annotation Networks for StructureAware Code Translation Yun Xiong, Shaofeng Xu, Keyao Rong, Xinyue Liu, Xiangnan Kong, Shanshan Li, Philip Yu & Yangyong Zhu [10] DeepPseudo: Deep Pseudo-code Generation via Transformer and Code Feature Extraction Guang Yang, Xiang Chen, +1 author Chi Yu (2021) [11] Dense Passage Retrieval for Open-Domain Question Answering Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell ˘ Wu, Sergey Edunov, Danqi Chen, Wen-tau (2020) [12] Shah, V.: The implication of agile traditional method as a practice in pharmaceutical industry (2017) [13] Cohn, M.: User Stories Applied: For Agile Software Development. Addison- Wesley Professional (2004) [14] Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Mapping language to code in programmatic context. arXiv preprint arXiv:1808.09588 (2018) [15] Saeki, M., Horai, H., Enomoto, H.: Software development process from natural language specification. In: Proceedings of the 11th International Conference on Software Engineering, pp. 6473 (1989) [16] Gilson, F., Galster, M., Georis, F.: Extracting quality attributes from user stories for early architecture decision making. In: 2019 IEEE International Conference on Software Architecture Companion (ICSAC), pp. 129136 (2019). IEEE [17] Robeer, M., Lucassen, G., Van DerWerf, J.M.E., Dalpiaz, F., Brinkkemper, S.: Automated extraction of conceptual models from user stories via nlp. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 196–205 (2016). IEEE [18] Husain, H.,Wu, H.-H., Gazit, T., Allamanis, M., Brockschmidt, M.: Code- SearchNet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 (2019) [19] Fudaba, H., Oda, Y., Akabe, K., Neubig, G., Hata, H., Sakti, S., Toda, T., Nakamura, S.: Pseudogen: A tool to automatically generate pseudo-code from source code. In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 824{829 (2015). IEEE [20] Alhefdhi, A., Dam, H.K., Hata, H., Ghose, A.: Generating pseudocode from source code using deep learning. In: 2018 25th Australasian Software Engineering Conference (ASWEC), pp. 21{25 (2018). IEEE [21] Rai, S., Gupta, A.: Generation of pseudo code from the python source code using rule-based machine translation. arXiv preprint arXiv:1906.06117 (2019) [22] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104{3112 (2014) [23] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) [24] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019) [25] Dataset for text to code generation [26] Dataset for code to pseudo-code generation [27] BLEU: a Method for Automatic Evaluation of Machine Translation Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zh This paper is available on arxiv under CC 4.0 license. Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). Authors: Authors: (1) Gaurav Kolhatkar, SCTR’s Pune Institute of Computer Technology, Pune, India (gauravk403@gmail.com); SCTR’s Pune Institute of Computer Technology, (2) Akshit Madan, SCTR’s Pune Institute of Computer Technology, Pune, India (akmadan17@gmail.com); SCTR’s Pune Institute of Computer Technology, (3) Nidhi Kowtal, SCTR’s Pune Institute of Computer Technology, Pune, India (kowtalnidhi@gmail.com); SCTR’s Pune Institute of Computer Technology, (4) Satyajit Roy, SCTR’s Pune Institute of Computer Technology, Pune, India (satyajit12.roy@gmail.com). SCTR’s Pune Institute of Computer Technology, Table of Links Abstract and Introduction Abstract and Introduction Literature Survey Literature Survey Methodology Methodology Performance Analysis Performance Analysis Conclusion and References Conclusion and References V. CONCLUSION This paper examines the enormous potential for programming work automation offered by natural language processing. We propose a two-stage methodology to convert English language user stories into pseudocode. The advantage of pseudocode is that it allows easy conversion into any programming language of the developer’s choice. The two stages of this approach are text to code conversion and code to pseudocode conversion. Each of these stages is treated as a language translation task. We use the CodeT5 model for this task, getting a BLEU score of 0.4 for Stage 1 and 0.74 for Stage 2. Our proposed system simplifies the software development process in organizations. In the future, we plan to curate a larger dataset for English text to Python code conversion. We can obtain higher accuracy and better generalisation to new examples with a larger and more varied dataset. This would necessitate considerable data gathering, but it might open the door to more efficient and useful text to code conversion methods. Additionally, a pertinent text to pseudocode dataset might be produced to help simplify the conversion architecture. REFERENCES [1] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) [2] GraphCodeBERT: Pre-training Code Representations with Data Flow ICLR 2021 · Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou [3] Unified Pre-training for Program Understanding and Generation Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Cha (2021) [4] Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data Sajad Norouzi, Keyi Tang, Yanshuai Cao (2021) [5] Retrieval Augmented Code Generation and Summarization Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, KaiWei Chang (2021) [6] Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer Guang Yang, Yanlin Zhou, Xiang Chen, Chi Yu (2021) [7] Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura (2021) [8] Code2Text: Dual Attention Syntax Annotation Networks for StructureAware Code Translation Authors : Yun Xiong, Shaofeng Xu, Keyao Rong, Xinyue Liu, Xiangnan Kong, Shanshan Li, Philip Yu, Yangyong Zhu [9] Home Database Systems for Advanced Applications Conference paper Code2Text: Dual Attention Syntax Annotation Networks for StructureAware Code Translation Yun Xiong, Shaofeng Xu, Keyao Rong, Xinyue Liu, Xiangnan Kong, Shanshan Li, Philip Yu & Yangyong Zhu [10] DeepPseudo: Deep Pseudo-code Generation via Transformer and Code Feature Extraction Guang Yang, Xiang Chen, +1 author Chi Yu (2021) [11] Dense Passage Retrieval for Open-Domain Question Answering Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell ˘ Wu, Sergey Edunov, Danqi Chen, Wen-tau (2020) [12] Shah, V.: The implication of agile traditional method as a practice in pharmaceutical industry (2017) [13] Cohn, M.: User Stories Applied: For Agile Software Development. Addison- Wesley Professional (2004) [14] Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Mapping language to code in programmatic context. arXiv preprint arXiv:1808.09588 (2018) [15] Saeki, M., Horai, H., Enomoto, H.: Software development process from natural language specification. In: Proceedings of the 11th International Conference on Software Engineering, pp. 6473 (1989) [16] Gilson, F., Galster, M., Georis, F.: Extracting quality attributes from user stories for early architecture decision making. In: 2019 IEEE International Conference on Software Architecture Companion (ICSAC), pp. 129136 (2019). IEEE [17] Robeer, M., Lucassen, G., Van DerWerf, J.M.E., Dalpiaz, F., Brinkkemper, S.: Automated extraction of conceptual models from user stories via nlp. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 196–205 (2016). IEEE [18] Husain, H.,Wu, H.-H., Gazit, T., Allamanis, M., Brockschmidt, M.: Code- SearchNet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436 (2019) [19] Fudaba, H., Oda, Y., Akabe, K., Neubig, G., Hata, H., Sakti, S., Toda, T., Nakamura, S.: Pseudogen: A tool to automatically generate pseudo-code from source code. In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 824{829 (2015). IEEE [20] Alhefdhi, A., Dam, H.K., Hata, H., Ghose, A.: Generating pseudocode from source code using deep learning. In: 2018 25th Australasian Software Engineering Conference (ASWEC), pp. 21{25 (2018). IEEE [21] Rai, S., Gupta, A.: Generation of pseudo code from the python source code using rule-based machine translation. arXiv preprint arXiv:1906.06117 (2019) [22] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104{3112 (2014) [23] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) [24] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019) [25] Dataset for text to code generation [26] Dataset for code to pseudo-code generation [27] BLEU: a Method for Automatic Evaluation of Machine Translation Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zh This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Future of Programming: Enhancing Agile Development with Automated Pseudocode

\BENGALURU, India, July 3rd, 2024/Chainwire/--E Money Network, a modular RWA blockchain, has launched the biggest RWA grant program in the crypto space, totaling $2 million, to enable RWA projects to build on their blockchain and accelerate the development of the RWA economy. E Money Network’s Architecture Unlocks a World of Possibilities E Money Network is an L1 blockchain that acts as a foundational network for building RWA projects. Apart from a developer-friendly SDK for customized features, it also has standard ready-made modules such as KYC verified onboarding, banking, staking, governance and more that can be incorporated directly into applications. The E Money Network supports interoperability with Inter-Blockchain Communication (IBC), allowing the development of applications that can communicate with other IBC-compatible blockchains. E Money Network has built-in compliance modules including Know Your Customer (KYC), Know Your Ownership (KYB) and Know Your Transaction (KYT) to enable adherence to the upcoming Markets in Crypto Assets (MiCA) regulations. These features ensure that every entity and transaction in an application built on the E Money Network is also MiCA compliant by default. Objectives of the Grant Program As a part of its #BUIDLonEMN initiative, E Money Network invites developer teams with disruptive RWA-centric ideas to build on the E Money Network and bring innovative solutions to life. Applicants can submit Requests for Proposals (RFPs) detailing their ideas for RWA applications . Its $2 million grant pool will be distributed as initial capital among eligible projects. E Money Network's CEO Raj Bagadi quoted "RWA ecosystem is in its nascent stages, and building the RWA ecosystem with numerable RWA-specific applications will help in shaping E Money Network’s trajectory and spearheading new advancements for the wider RWA ecosystem. Hence, we are launching the biggest grant program to enable the next generation of entrepreneurs seeking to rebuild the financial markets with RWA applications." Evaluation Criteria for Winning Grants A group of technology experts, marketing professionals and experienced community members will form the selection committee for reviewing grant applications. Applications to the grant program will be evaluated on the basis of the following criteria - The project’s objective and scope
The anticipated impact of the project
The project’s milestones and KPIs
Expected budget
Team details including roles, responsibilities and prior experience of each team member
Product roadmap Each proposal must include at least three milestones with measurable KPIs such as user growth, TVL increase, transaction volume, etc. Depending on the afore-mentioned factors, projects can become eligible to win grants ranging from $5000 to $50000 based on requirements and scale of the product. Additionally, E Money Network will also provide winners of the grant program access to mentorship from industry experts, legal and compliance support, marketing and growth support, networking opportunities with other RWA projects and E Money Network’s core developers. How to Apply Interested developers and builders can join the #BUIDLonEMN initiative by filling out the grant application form here and joining E Money Network’s grant channel on Discord. After the submission of the application, the selection committee will evaluate the project. Successful applications to the grant program will be notified along with further instructions. About E Money Network E Money Network is the regulated Modular RWA blockchain. It serves as an L1 blockchain designed for seamless interoperability between DeFi 2.0 and RWA tokenisation, effectively establishing a network that aims to bridge the liquidity divide between Web 2.0 and Web 3.0. For more information users can visit: X | LinkedIn | Blogs | Whitepaper . For media queries, users can contact: marketing@emoney.network Contact Head of Marketing & Branding Shivangini Agarwal E Money Network marketing@emoney.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. \BENGALURU, India, July 3rd, 2024/Chainwire/--E Money Network, a modular RWA blockchain, has launched the biggest RWA grant program in the crypto space, totaling $2 million, to enable RWA projects to build on their blockchain and accelerate the development of the RWA economy. E Money Network’s Architecture Unlocks a World of Possibilities E Money Network is an L1 blockchain that acts as a foundational network for building RWA projects. Apart from a developer-friendly SDK for customized features, it also has standard ready-made modules such as KYC verified onboarding, banking, staking, governance and more that can be incorporated directly into applications. The E Money Network supports interoperability with Inter-Blockchain Communication (IBC), allowing the development of applications that can communicate with other IBC-compatible blockchains. E Money Network E Money Network E Money Network has built-in compliance modules including Know Your Customer (KYC), Know Your Ownership (KYB) and Know Your Transaction (KYT) to enable adherence to the upcoming Markets in Crypto Assets (MiCA) regulations. These features ensure that every entity and transaction in an application built on the E Money Network is also MiCA compliant by default. Objectives of the Grant Program As a part of its #BUIDLonEMN initiative, E Money Network invites developer teams with disruptive RWA-centric ideas to build on the E Money Network and bring innovative solutions to life. Applicants can submit Requests for Proposals (RFPs) detailing their ideas for RWA applications . Its $2 million grant pool will be distributed as initial capital among eligible projects. E Money Network's CEO Raj Bagadi quoted "RWA ecosystem is in its nascent stages, and building the RWA ecosystem with numerable RWA-specific applications will help in shaping E Money Network’s trajectory and spearheading new advancements for the wider RWA ecosystem. Hence, we are launching the biggest grant program to enable the next generation of entrepreneurs seeking to rebuild the financial markets with RWA applications." E Money Network's CEO Raj Bagadi quoted "RWA ecosystem is in its nascent stages, and building the RWA ecosystem with numerable RWA-specific applications will help in shaping E Money Network’s trajectory and spearheading new advancements for the wider RWA ecosystem. Hence, we are launching the biggest grant program to enable the next generation of entrepreneurs seeking to rebuild the financial markets with RWA applications." Raj Bagadi Raj Bagadi Evaluation Criteria for Winning Grants A group of technology experts, marketing professionals and experienced community members will form the selection committee for reviewing grant applications. Applications to the grant program will be evaluated on the basis of the following criteria - The project’s objective and scope The anticipated impact of the project The project’s milestones and KPIs Expected budget Team details including roles, responsibilities and prior experience of each team member Product roadmap The project’s objective and scope The anticipated impact of the project The project’s milestones and KPIs Expected budget Team details including roles, responsibilities and prior experience of each team member Product roadmap Each proposal must include at least three milestones with measurable KPIs such as user growth, TVL increase, transaction volume, etc. Depending on the afore-mentioned factors, projects can become eligible to win grants ranging from $5000 to $50000 based on requirements and scale of the product. Additionally, E Money Network will also provide winners of the grant program access to mentorship from industry experts, legal and compliance support, marketing and growth support, networking opportunities with other RWA projects and E Money Network’s core developers. How to Apply Interested developers and builders can join the #BUIDLonEMN initiative by filling out the grant application form here and joining E Money Network’s grant channel on Discord . After the submission of the application, the selection committee will evaluate the project. Successful applications to the grant program will be notified along with further instructions. here here Discord Discord About E Money Network E Money Network is the regulated Modular RWA blockchain. It serves as an L1 blockchain designed for seamless interoperability between DeFi 2.0 and RWA tokenisation, effectively establishing a network that aims to bridge the liquidity divide between Web 2.0 and Web 3.0. For more information users can visit: X | LinkedIn | Blogs | Whitepaper . For media queries, users can contact: marketing@emoney.network X X LinkedIn LinkedIn Blogs Blogs Whitepaper Whitepaper marketing@emoney.network marketing@emoney.network Contact Head of Marketing & Branding Shivangini Agarwal E Money Network marketing@emoney.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

E Money Network Launches $2 MILLION RWA Grant Program To Spearhead RWA Ecosystem

Sophon, an entertainment-focused blockchain network built on zkSync's technology, has announced the launch of its liquidity farming program. The initiative aims to reward users who contribute to the Total Value Locked (TVL) of the Sophon ecosystem by allocating 10% of the total $SOPH token supply. Users can participate in the liquidity farming program by depositing eligible assets into designated farming pools on the Ethereum mainnet and later on the Sophon mainnet once it launches. Participants will earn "Sophon Points (SP)" for each block in which their assets are deposited, with the accrual rate varying based on the pool and the user's share of the total deposits. One notable aspect of Sophon's farming pools is the optional booster program. By permanently locking a portion or all of their deposited assets, users can multiply their potential rewards by up to 5 times. However, it is crucial to understand that assets used to activate boosters will be permanently allocated to provide liquidity on the Sophon mainnet and cannot be withdrawn in the future. The primary assets accepted for deposit include BEAM and BEAM/ETH LP, with more pools expected to be added in the coming weeks. Users can monitor their accumulated rewards through Sophon's Farming Portal, and participation is subject to agreeing to the Token Airdrop Terms and Conditions and applicable Disclaimers. Final Thoughts Sophon's liquidity farming program is a strategic move to incentivize user participation and increase the TVL of the ecosystem. By dedicating a significant portion of the $SOPH token supply to rewards, Sophon aims to attract a large number of users and create a thriving community around its entertainment-focused blockchain. The booster program is an interesting feature that encourages long-term commitment from participants. However, users should carefully consider the implications of permanently locking their assets, as this liquidity cannot be withdrawn once allocated. As Sophon leverages zkSync's ZK Stack technology, it is well-positioned to offer high throughput applications in the entertainment and gaming industries. The ecosystem's partnerships with zkSync, Beam, Zentry, and Aethir further strengthen its potential to become a leading provider in the blockchain space. Overall, Sophon's liquidity farming program is an exciting development that could drive significant growth and adoption for the ecosystem. As the project progresses, it will be interesting to see how the community responds and how Sophon evolves to meet the needs of its users in the ever-changing world of blockchain technology. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Sophon, an entertainment-focused blockchain network built on zkSync's technology, has announced the launch of its liquidity farming program. The initiative aims to reward users who contribute to the Total Value Locked (TVL) of the Sophon ecosystem by allocating 10% of the total $SOPH token supply. Users can participate in the liquidity farming program by depositing eligible assets into designated farming pools on the Ethereum mainnet and later on the Sophon mainnet once it launches. Participants will earn "Sophon Points (SP)" for each block in which their assets are deposited, with the accrual rate varying based on the pool and the user's share of the total deposits. One notable aspect of Sophon's farming pools is the optional booster program. By permanently locking a portion or all of their deposited assets, users can multiply their potential rewards by up to 5 times. However, it is crucial to understand that assets used to activate boosters will be permanently allocated to provide liquidity on the Sophon mainnet and cannot be withdrawn in the future. The primary assets accepted for deposit include BEAM and BEAM/ETH LP, with more pools expected to be added in the coming weeks. Users can monitor their accumulated rewards through Sophon's Farming Portal, and participation is subject to agreeing to the Token Airdrop Terms and Conditions and applicable Disclaimers. Final Thoughts Final Thoughts Sophon's liquidity farming program is a strategic move to incentivize user participation and increase the TVL of the ecosystem. By dedicating a significant portion of the $SOPH token supply to rewards, Sophon aims to attract a large number of users and create a thriving community around its entertainment-focused blockchain. The booster program is an interesting feature that encourages long-term commitment from participants. However, users should carefully consider the implications of permanently locking their assets, as this liquidity cannot be withdrawn once allocated. As Sophon leverages zkSync's ZK Stack technology, it is well-positioned to offer high throughput applications in the entertainment and gaming industries. The ecosystem's partnerships with zkSync, Beam, Zentry, and Aethir further strengthen its potential to become a leading provider in the blockchain space. Overall, Sophon's liquidity farming program is an exciting development that could drive significant growth and adoption for the ecosystem. As the project progresses, it will be interesting to see how the community responds and how Sophon evolves to meet the needs of its users in the ever-changing world of blockchain technology. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

How Sophon's Booster Program Multiplies Liquidity Farming Rewards

Author:
(1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu);
(2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Table of Links Abstract and Introduction Related Works Preliminaries Optimal Prebunking Problem Deterministic Baseline Policies Temporally Equidistant Prebunking Numerical Results Conclusion and References III. PRELIMINARIES A. Discrete-Time SI Model on Graphs A discrete-time susceptible-infected (SI) model [16] is a widely-utilized framework for understanding epidemic spread through a network. It models how a contagion spreads in a network over consecutive iterations until it cannot infect any more nodes in the network. B. Modelling Misinformation Propagation A social networking platform is an online social media platform that allows its users to communicate, share opinions, and form social networks. To date, popular examples of such services include Facebook, LinkedIn, Instagram, and Twitter. These platforms provide a user interface for users to communicate with each other by either broadcasting messages, commenting on other’s messages, or sending direct messages to other users. In addition, these platforms can display advertisements, or notifications to the users, depending on the purpose or the design of the specific social networking platform. We let the term content refer to any message or information that the users can interact with on a social networking platform. Contents can either be static or contagious. Static contents is those that the social networking platform provides to the users. These contents do not propagate between users. Examples of such static contents are advertisements, notifications, and alerts that the social networking platform shows to its users. Contagious content, on the other hand, spreads between users. In a social networking platform, contagious content can be user posts, messages, comments, or replies. We define a contagion as a collection of contagious contents conveying the same claim. Contagions represent the rumors that spread from user to user on a social networking platform. For example, all contagious content claiming that “5G causes COVID-19” constitutes a single contagion. A misinformation denotes a contagion we do not want to spread in the network. Often, misinformations contain information that is incorrect, or harmful to the individual. We denote the set of all misinformations that exist on the social networking platform by M = {M0, M1, . . . }, where we assume M to be a countable set, either finite or infinite. Referring to misinformation as a countable noun as we have done in this paper is clearly a diversion from existing literature. However, as we are using the term to refer to a specific contagion, and since there are many different contagions that can be classified as misinformation, we will use the plural noun misinformations when referring to more than one misinformation. We model the social networking platform as a weighted directed graph G = (V, E, p) where the set of vertices V represents users, the set of edges E represents possible connections between users, and pij are normalized edge weights, representing the probability of misinformation propagation over the associated edge ij ∈ E. For all ij ̸∈ E we define pij = 0. We define the local network around a central user c as the subgraph Gc = (Vc, Ec, p) of G denoting the m-neighborhood of the c. That is, Gc is the subgraph of G containing all users that have a shortest path length of at most m to c. Here, m is a spatial horizon parameter we can choose as any integer between 1 to diam(G), with higher values providing a more accurate model, with a larger number or users. We also assume without generality that Vc = {1, 2, . . . , N}, meaning we have N users in the local network and they are indexed from 1 to N. This paper is available on arxiv under CC 4.0 license. Author: (1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu); (2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Author: Author: (1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu); (2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Table of Links Abstract and Introduction Abstract and Introduction Related Works Related Works Preliminaries Preliminaries Optimal Prebunking Problem Optimal Prebunking Problem Deterministic Baseline Policies Deterministic Baseline Policies Temporally Equidistant Prebunking Temporally Equidistant Prebunking Numerical Results Numerical Results Conclusion and References Conclusion and References III. PRELIMINARIES A. Discrete-Time SI Model on Graphs A. Discrete-Time SI Model on Graphs A discrete-time susceptible-infected (SI) model [16] is a widely-utilized framework for understanding epidemic spread through a network. It models how a contagion spreads in a network over consecutive iterations until it cannot infect any more nodes in the network. B. Modelling Misinformation Propagation B. Modelling Misinformation Propagation A social networking platform is an online social media platform that allows its users to communicate, share opinions, and form social networks. To date, popular examples of such services include Facebook, LinkedIn, Instagram, and Twitter. These platforms provide a user interface for users to communicate with each other by either broadcasting messages, commenting on other’s messages, or sending direct messages to other users. In addition, these platforms can display advertisements, or notifications to the users, depending on the purpose or the design of the specific social networking platform. We let the term content refer to any message or information that the users can interact with on a social networking platform. Contents can either be static or contagious. Static contents is those that the social networking platform provides to the users. These contents do not propagate between users. Examples of such static contents are advertisements, notifications, and alerts that the social networking platform shows to its users. Contagious content, on the other hand, spreads between users. In a social networking platform, contagious content can be user posts, messages, comments, or replies. We define a contagion as a collection of contagious contents conveying the same claim. Contagions represent the rumors that spread from user to user on a social networking platform. For example, all contagious content claiming that “5G causes COVID-19” constitutes a single contagion. A misinformation denotes a contagion we do not want to spread in the network. Often, misinformations contain information that is incorrect, or harmful to the individual. We denote the set of all misinformations that exist on the social networking platform by M = {M0, M1, . . . }, where we assume M to be a countable set, either finite or infinite. Referring to misinformation as a countable noun as we have done in this paper is clearly a diversion from existing literature. However, as we are using the term to refer to a specific contagion, and since there are many different contagions that can be classified as misinformation, we will use the plural noun misinformations when referring to more than one misinformation. We model the social networking platform as a weighted directed graph G = (V, E, p) where the set of vertices V represents users, the set of edges E represents possible connections between users, and pij are normalized edge weights, representing the probability of misinformation propagation over the associated edge ij ∈ E. For all ij ̸∈ E we define pij = 0. We define the local network around a central user c as the subgraph Gc = (Vc, Ec, p) of G denoting the m-neighborhood of the c. That is, Gc is the subgraph of G containing all users that have a shortest path length of at most m to c. Here, m is a spatial horizon parameter we can choose as any integer between 1 to diam(G), with higher values providing a more accurate model, with a larger number or users. We also assume without generality that Vc = {1, 2, . . . , N}, meaning we have N users in the local network and they are indexed from 1 to N. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Modeling Misinformation Propagation on Social Media

Authors:
(1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu);
(2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Table of Links Abstract and Introduction Related Works Preliminaries Optimal Prebunking Problem Deterministic Baseline Policies Temporally Equidistant Prebunking Numerical Results Conclusion and References Abstract—The growing reliance on social media for news consumption necessitates effective countermeasures to mitigate the rapid spread of misinformation. Prebunking, a proactive method that arms users with accurate information before they come across false content, has garnered support from journalism and psychology experts. We formalize the problem of optimal prebunking as optimizing the timing of delivering accurate information, ensuring users encounter it before receiving misinformation while minimizing the disruption to user experience. Utilizing a susceptible-infected epidemiological process to model the propagation of misinformation, we frame optimal prebunking as a policy synthesis problem with safety constraints. We then propose a policy that approximates the optimal solution to a relaxed problem. The experiments show that this policy cuts the user experience cost of repeated information delivery in half, compared to delivering accurate information immediately after identifying a misinformation propagation. I. INTRODUCTION Social media have become an integral part of modern communication, with more than 70% of adults in the U.S. using at least one social media service [1]. At least 60% of these users use social networking platforms, a type of social media platform in which users build social networks and communicate with other users who share similar interests, opinions, or backgrounds. These platforms allow their users to easily share information and their opinions. However, the ease of information sharing on social networking platforms has also led to the rise of misinformation and fake news. Misinformation can spread faster and more broadly than accurate information, causing significant harm to public discourse. The consequences of misinformation can range from confusion to substantial damage to public health [2], or political manipulation [3]. The widespread adoption of social networking platforms and the growing reliance on social networking services as sources of news consumption exacerbates this issue, often resulting in significant portions of the population believing in unsubstantiated or provably incorrect claims, making it difficult to correct these false beliefs later through debunking. Prebunking has emerged as a promising solution for mitigating the spread of misinformation. Unlike debunking, which aims to correct false information after its spread, prebunking exposes people to factual information before they encounter false claims. The idea is to inoculate the public against misinformation, akin to a vaccine [4]. Studies indicate that this preemptive approach has the potential to reduce the spread of misinformation [5], without relying on media censorship. The effectiveness of prebunking depends on the timing of delivery of the factual information. It is clear that for prebunking to work as intended the user has to see the factual information before seeing the related misinformation. Furthermore, the effectiveness of prebunking depends on the time gap between the delivery of the factual information and the arrival of the misinformation. Research in characterizing this relation has been sparse, leaving a significant gap in understanding the optimal timing for successful prebunking interventions [6]. Another consideration is the effect of prebunking on the userexperience. Too frequent delivery of factual information can push users to other social networking platforms that do not utilize prebunking, rendering prebunking ineffective. We formalize optimal prebunking as a mathematical optimization problem over the timing of factual information delivery. The primary objective is to ensure that users encounter the factual information before being exposed to corresponding misinformation while minimizing any disruptions to the userexperience. We model the effect of factual information on user-experience by adopting a leaky bucket cost, that is an exponentially decaying cumulative count over the number of prebunking related factual informations the user receives. Minimizing this cost discourages repeated and frequent delivery of factual information, minimizing the impact of prebunking on user-experience. We use the susceptible-infected (SI) epidemiological model to characterize misinformation propagation and frame optimal prebunking as a policy synthesis problem with constraints to ensure the user receives the factual information before the corresponding misinformation. Building on this framework, we propose a locally optimal policy that attempts to minimize the leaky bucket cost, while ensuring that the user receives factual information prior to misinformation that is propagating over the social network. Contributions • We present the problem of optimally delivering pregenerated factual information for the purpose of prebunking as a policy synthesis problem. • We analyze two baseline methods that provide guaranteed delivery of factual information before its corresponding misinformation. • We provide a third method that approximates the optimal solution to a relaxed problem and provides the same guarantees as the previously mentioned methods with less cost. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu); (2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Authors: Authors: (1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu); (2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Table of Links Abstract and Introduction Abstract and Introduction Related Works Related Works Preliminaries Preliminaries Optimal Prebunking Problem Optimal Prebunking Problem Deterministic Baseline Policies Deterministic Baseline Policies Temporally Equidistant Prebunking Temporally Equidistant Prebunking Numerical Results Numerical Results Conclusion and References Conclusion and References Abstract—The growing reliance on social media for news consumption necessitates effective countermeasures to mitigate the rapid spread of misinformation. Prebunking, a proactive method that arms users with accurate information before they come across false content, has garnered support from journalism and psychology experts. We formalize the problem of optimal prebunking as optimizing the timing of delivering accurate information, ensuring users encounter it before receiving misinformation while minimizing the disruption to user experience. Utilizing a susceptible-infected epidemiological process to model the propagation of misinformation, we frame optimal prebunking as a policy synthesis problem with safety constraints. We then propose a policy that approximates the optimal solution to a relaxed problem. The experiments show that this policy cuts the user experience cost of repeated information delivery in half, compared to delivering accurate information immediately after identifying a misinformation propagation. Abstract —The growing reliance on social media for news consumption necessitates effective countermeasures to mitigate the rapid spread of misinformation. Prebunking, a proactive method that arms users with accurate information before they come across false content, has garnered support from journalism and psychology experts. We formalize the problem of optimal prebunking as optimizing the timing of delivering accurate information, ensuring users encounter it before receiving misinformation while minimizing the disruption to user experience. Utilizing a susceptible-infected epidemiological process to model the propagation of misinformation, we frame optimal prebunking as a policy synthesis problem with safety constraints. We then propose a policy that approximates the optimal solution to a relaxed problem. The experiments show that this policy cuts the user experience cost of repeated information delivery in half, compared to delivering accurate information immediately after identifying a misinformation propagation. Abstract I. INTRODUCTION Social media have become an integral part of modern communication, with more than 70% of adults in the U.S. using at least one social media service [1]. At least 60% of these users use social networking platforms, a type of social media platform in which users build social networks and communicate with other users who share similar interests, opinions, or backgrounds. These platforms allow their users to easily share information and their opinions. However, the ease of information sharing on social networking platforms has also led to the rise of misinformation and fake news. Misinformation can spread faster and more broadly than accurate information, causing significant harm to public discourse. The consequences of misinformation can range from confusion to substantial damage to public health [2], or political manipulation [3]. The widespread adoption of social networking platforms and the growing reliance on social networking services as sources of news consumption exacerbates this issue, often resulting in significant portions of the population believing in unsubstantiated or provably incorrect claims, making it difficult to correct these false beliefs later through debunking. Prebunking has emerged as a promising solution for mitigating the spread of misinformation. Unlike debunking, which aims to correct false information after its spread, prebunking exposes people to factual information before they encounter false claims. The idea is to inoculate the public against misinformation, akin to a vaccine [4]. Studies indicate that this preemptive approach has the potential to reduce the spread of misinformation [5], without relying on media censorship. The effectiveness of prebunking depends on the timing of delivery of the factual information. It is clear that for prebunking to work as intended the user has to see the factual information before seeing the related misinformation. Furthermore, the effectiveness of prebunking depends on the time gap between the delivery of the factual information and the arrival of the misinformation. Research in characterizing this relation has been sparse, leaving a significant gap in understanding the optimal timing for successful prebunking interventions [6]. Another consideration is the effect of prebunking on the userexperience. Too frequent delivery of factual information can push users to other social networking platforms that do not utilize prebunking, rendering prebunking ineffective. We formalize optimal prebunking as a mathematical optimization problem over the timing of factual information delivery. The primary objective is to ensure that users encounter the factual information before being exposed to corresponding misinformation while minimizing any disruptions to the userexperience. We model the effect of factual information on user-experience by adopting a leaky bucket cost, that is an exponentially decaying cumulative count over the number of prebunking related factual informations the user receives. Minimizing this cost discourages repeated and frequent delivery of factual information, minimizing the impact of prebunking on user-experience. We use the susceptible-infected (SI) epidemiological model to characterize misinformation propagation and frame optimal prebunking as a policy synthesis problem with constraints to ensure the user receives the factual information before the corresponding misinformation. Building on this framework, we propose a locally optimal policy that attempts to minimize the leaky bucket cost, while ensuring that the user receives factual information prior to misinformation that is propagating over the social network. Contributions • We present the problem of optimally delivering pregenerated factual information for the purpose of prebunking as a policy synthesis problem. • We analyze two baseline methods that provide guaranteed delivery of factual information before its corresponding misinformation. • We provide a third method that approximates the optimal solution to a relaxed problem and provides the same guarantees as the previously mentioned methods with less cost. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Prebunking Design as a Defense Mechanism Against Misinformation Propagation on Social Networks

Covalent, the leading modular data infrastructure layer in Web3, is proud to announce a $2.5 million commitment in grants dedicated to advancing the Consensys ecosystem through the Consensys Scale Program. This substantial initiative underscores Covalent's mission to empower developers working with Linea and MetaMask, driving the creation of groundbreaking onchain applications. “Consensys Scale supports leading dapps building on Linea and MetaMask with a range of bespoke support and benefits. Ecosystem partners like Covalent are part of this package and make important contributions to our builders”, says Alex Greinacher, Consensys Scale Program Lead. In parallel, Consensys will make these grants directly available to high performing dApps that choose to deploy on Linea, its new L2 zkEVM. “Supporting top Linea dapps with impactful partner benefits is a core part of our mission so builders are supported to grow their builders,” says Suzie Godlewski, Lead Partner Marketing for Linea. This initiative not only highlights Covalent’s commitment to supporting the Web3 community but also demonstrates the vital role of structured data infrastructure in fostering innovation and growth within the blockchain space. With reaching over 300 million active wallets utilizing its extensive and comprehensive data services, Covalent’s grants aim to unlock new potentials for Consensys developers and builders. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Powering Innovation with Largest Reservoir of Structured Web3 Data In the near future, MetaMask will also become much more developer-focused, and Consensys will integrate Covalent into instances where builders also need access to its data product suite. Covalent’s grant initiative will offer up to $25,000 USD in credits per project, enabling developers to access Covalent's robust data tools. These grants can be utilized to retrieve data from Linea, Ethereum, and over 225 other blockchain networks, enabling a wide range of applications. “We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps,” said Ganesh Swami, CEO of Covalent. The grants will target several key areas within Consensys notably: Building on Linea: Supporting the world’s most innovative Web3 projects to deploy and grow on Linea, the Consensys zK-EVM
Access via MetaMask: Integration of the world’s most trusted Web3 wallet in great projects Together, Covalent and Consensys are working with builders to enhance onchain infrastructure and dapps to help onboard the next billion users into Web3. Grant Application and Developer Resources Projects in the Consensys ecosystem can apply for the Covalent grants. In addition, the following resources provide more details on the grants and other benefits available to Consensys builders by checking out the Consensys Scale website. About Covalent Covalent is the leading modular data infrastructure layer that’s dedicated to solving major challenges in blockchain and AI. Covalent’s largest reservoir of structured, verifiable data augments decentralized training and inference, reducing the likelihood of manipulated or biased AI models. Additionally, Covalent’s Ethereum Wayback Machine ensures decentralized access to Ethereum’s transaction data, keeping it secure. Trusted by over 3,000 leading organizations, Covalent powers AI, DeFi, GameFi, and other use cases with unfettered access to onchain data from over 225 blockchains and counting. Learn about the EWM: Technical | Non-Technical For more information, check out Covalent’s: Official Website | Twitter/X | Linktree About Consensys Consensys is the leading blockchain and Web3 software company, pioneering technological developments within the Web3 ecosystem since 2014. Through its product suite, including MetaMask, Infura, Linea, Diligence, and Phosphor, Consensys has become a trusted collaborator for users, creators, and developers. Consensys inspires and champions the builder instinct in everyone by making Web3 universally easy to use and develop on. To explore their products and solutions, visit Consensys. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Covalent, the leading modular data infrastructure layer in Web3, is proud to announce a $2.5 million commitment in grants dedicated to advancing the Consensys ecosystem through the Consensys Scale Program. This substantial initiative underscores Covalent's mission to empower developers working with Linea and MetaMask, driving the creation of groundbreaking onchain applications. “Consensys Scale supports leading dapps building on Linea and MetaMask with a range of bespoke support and benefits. Ecosystem partners like Covalent are part of this package and make important contributions to our builders”, says Alex Greinacher, Consensys Scale Program Lead. “Consensys Scale supports leading dapps building on Linea and MetaMask with a range of bespoke support and benefits. Ecosystem partners like Covalent are part of this package and make important contributions to our builders”, says Alex Greinacher, Consensys Scale Program Lead. In parallel, Consensys will make these grants directly available to high performing dApps that choose to deploy on Linea, its new L2 zkEVM. “Supporting top Linea dapps with impactful partner benefits is a core part of our mission so builders are supported to grow their builders,” says Suzie Godlewski, Lead Partner Marketing for Linea. “Supporting top Linea dapps with impactful partner benefits is a core part of our mission so builders are supported to grow their builders,” says Suzie Godlewski, Lead Partner Marketing for Linea. This initiative not only highlights Covalent’s commitment to supporting the Web3 community but also demonstrates the vital role of structured data infrastructure in fostering innovation and growth within the blockchain space. With reaching over 300 million active wallets utilizing its extensive and comprehensive data services, Covalent’s grants aim to unlock new potentials for Consensys developers and builders. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Covalent recently added Arthur Hayes as strategic advisor to expand the network's direction in the areas of Artificial Intelligence and Long-Term Data Availability. Powering Innovation with Largest Reservoir of Structured Web3 Data In the near future, MetaMask will also become much more developer-focused, and Consensys will integrate Covalent into instances where builders also need access to its data product suite. Covalent’s grant initiative will offer up to $25,000 USD in credits per project, enabling developers to access Covalent's robust data tools. These grants can be utilized to retrieve data from Linea, Ethereum, and over 225 other blockchain networks, enabling a wide range of applications. “We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps,” said Ganesh Swami, CEO of Covalent. “ We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps ,” said Ganesh Swami , CEO of Covalent. We're thrilled to be supporting Consensys Scale and builders on Linea and MetaMask. This collaboration enhances our reach and empowers developers with robust and scalable access to structured data infrastructure to innovate and optimize their dapps Ganesh Swami Ganesh Swami The grants will target several key areas within Consensys notably: Building on Linea: Supporting the world’s most innovative Web3 projects to deploy and grow on Linea, the Consensys zK-EVM Access via MetaMask: Integration of the world’s most trusted Web3 wallet in great projects Building on Linea: Supporting the world’s most innovative Web3 projects to deploy and grow on Linea, the Consensys zK-EVM Access via MetaMask: Integration of the world’s most trusted Web3 wallet in great projects Together, Covalent and Consensys are working with builders to enhance onchain infrastructure and dapps to help onboard the next billion users into Web3. Grant Application and Developer Resources Projects in the Consensys ecosystem can apply for the Covalent grants . In addition, the following resources provide more details on the grants and other benefits available to Consensys builders by checking out the Consensys Scale website . Covalent grants Covalent grants Consensys Scale website Consensys Scale website About Covalent Covalent is the leading modular data infrastructure layer that’s dedicated to solving major challenges in blockchain and AI. Covalent’s largest reservoir of structured, verifiable data augments decentralized training and inference, reducing the likelihood of manipulated or biased AI models. Additionally, Covalent’s Ethereum Wayback Machine ensures decentralized access to Ethereum’s transaction data, keeping it secure. Trusted by over 3,000 leading organizations, Covalent powers AI, DeFi, GameFi, and other use cases with unfettered access to onchain data from over 225 blockchains and counting. Learn about the EWM: Technical | Non-Technical Technical Technical Non-Technical Non-Technical For more information, check out Covalent’s: Official Website | Twitter/X | Linktree Official Website Official Website Twitter/X Twitter/X Linktree Linktree About Consensys Consensys is the leading blockchain and Web3 software company, pioneering technological developments within the Web3 ecosystem since 2014. Through its product suite, including MetaMask, Infura, Linea, Diligence, and Phosphor, Consensys has become a trusted collaborator for users, creators, and developers. Consensys inspires and champions the builder instinct in everyone by making Web3 universally easy to use and develop on. To explore their products and solutions, visit Consensys . Consensys Consensys This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Covalent Announces $2.5M in Grants to the Consensys Builders Scale Program

This article will introduce the concepts and topics common to all programming languages that beginners and experts must know! Do you want to learn a programming language for the first time? Do you want to improve as a Programmer? Well, then you’re in the right place to start. Learn any programming language without difficulty by learning the concepts and topics common to all programming languages. Let me start by answering the following questions: Why learn Programming?
What is Programming?
How to Learn a Programming Language? Why Learn Programming❔ Programming develops creative thinking Programmers solve a problem by breaking it down into workable pieces to understand it better. When you start learning to program, you develop the habit of working your way out in a very structured format. You analyze the problem and start thinking logically, and this gives rise to more creative solutions you’ve ever given. Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn.

–Stephen Hawking* Everybody in this country should learn how to program a computer… because it teaches you how to think.-Steve Jobs Programming Provides Life-Changing Experiences Programming always provides you with a new challenge to take risks every time and teaches you to take risks in your personal life too. The world is filled up with websites, apps, and software and when you build these yourself, you’ll feel more confident. When a programmer solves a problem that no one has ever solved before, it becomes a life-changing experience for them. What Is Programming🤔? A program is a set of instructions to perform a task on a computer. Programming is the process of designing and building an executable computer program to accomplish a specific task. Well, according to me, programming is like raising a baby. We provide knowledge (data) to help a baby understand what’s happening around them. We teach a baby to be disciplined (and much more) by making rules. Similarly, a computer is like a baby. We set rules and provide data to the computer through executable programs with the help of a Programming Language. (Photo by Clément H on Unsplash) That’s it👍. If you can understand this basic concept of programming, you’re good to go. Pick up a programming language, and start learning. Read the following section to get an idea of where to start. My recommendation is to choose Python Programming Language as a start because it’s beginner-friendly. How to Learn a Programming Language? Anyone can start to learn a programming language, but to learn it effectively, you need to learn the Building Blocks of a Programming Language.👇 Syntax In the English language, Syntax is the rules for how a sentence is constructed. In a programming language, Syntax is the rules for how each instruction is written. Semantics In the English language, Semantics is the actual meaning of statements. In a programming language, Semantics is the effect the instructions have (logic). Data Types A Data Type is a classification of data we want to store in memory. Data Types can vary from one language to another. But the type of data we would like to store is common across all languages. The type of data we'd like to store are Numbers(Integers, Floating-point numbers), Characters(Strings), Booleans(True/False), Arrays, etc... Terms When it comes to a programming language, there are these terms (buzzwords) you may come across in your learning journey. Some commonly used terms are Variables, Expressions, Statements, Functions, Classes, and many more. You don’t need to worry if you feel any difficulties; use this Dictionary👇 as a resource. Resource: Data type
The Tech Terms Computer Dictionary Coming up are the most important concepts for a programmer to improve their skills. And these concepts help you excel in the technical interviews for programming-based roles! Data Structures A data structure is how we can store, access, organize, and manage the data we have created on a computer. In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.— Wikipedia 👉A good programmer must know when and where they can utilize a data structure in a program. And this comes with practice, so use this resource and start practicing using your favorite programming language👇. Algorithms An algorithm is a step-by-step procedure to solve any computational problem. And when it comes to Computer Science, there are a bunch of algorithms that are must-to-know ones. In mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.— Wikipedia Well, there are many types of algorithms, but the most fundamental types of algorithms are: Recursive algorithms
Dynamic programming algorithm
Backtracking algorithm
Divide and conquer algorithm
Greedy algorithm
Brute Force algorithm
Randomized algorithm Use the following resources to practice and learn more about Data Structures and Algorithms. Solve Data Structures Code Challenges
Data Structures- GeeksforGeeks
Solve Data Structures Code Challenges
Algorithms- GeeksforGeeks Best Practices The last one is the best practices and coding style. This is an important point for a programmer to work in a team setup because the programs and codebases you write must be programmer-friendly. And even this comes with practice, so take time and refer to these resources below, whenever you’re ready.👍 The Trend in Programming Languages Most Popular Programming, Scripting, and Markup Languages Most Loved, Dreaded, and Wanted Languages Keep Learning👨‍💻👩‍💻 Now, I guess this is where I say goodbye👋. But it’s time for you to start learning📖 with your newfound Knowledge🧠 and Power💪. 🙂Good job that you made this far, and thank you so much for reading my Blog. And stay tuned by following me for updates.😇 (Lead Photo by La-Rel Easter on Unsplash) This article will introduce the concepts and topics common to all programming languages that beginners and experts must know! Do you want to learn a programming language for the first time? Do you want to learn a programming language for the first time? Do you want to improve as a Programmer? Do you want to improve as a Programmer? Well, then you’re in the right place to start. Learn any programming language without difficulty by learning the concepts and topics common to all programming languages. Let me start by answering the following questions: Why learn Programming? What is Programming? How to Learn a Programming Language? Why learn Programming? What is Programming? How to Learn a Programming Language? Why Learn Programming❔ Programming develops creative thinking Programming develops creative thinking Programmers solve a problem by breaking it down into workable pieces to understand it better. When you start learning to program, you develop the habit of working your way out in a very structured format. You analyze the problem and start thinking logically, and this gives rise to more creative solutions you’ve ever given. Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn. –Stephen Hawking* Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn. Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn. –Stephen Hawking* – Stephen Hawking * Stephen Hawking Everybody in this country should learn how to program a computer… because it teaches you how to think.-Steve Jobs Everybody in this country should learn how to program a computer… because it teaches you how to think.-Steve Jobs Everybody in this country should learn how to program a computer… because it teaches you how to think. - Steve Jobs Steve Jobs Programming Provides Life-Changing Experiences Programming Provides Life-Changing Experiences Programming always provides you with a new challenge to take risks every time and teaches you to take risks in your personal life too. The world is filled up with websites, apps, and software and when you build these yourself, you’ll feel more confident. When a programmer solves a problem that no one has ever solved before, it becomes a life-changing experience for them. What Is Programming🤔? A program is a set of instructions to perform a task on a computer. program Programming is the process of designing and building an executable computer program to accomplish a specific task. Programming Well, according to me, programming is like raising a baby. We provide knowledge (data) to help a baby understand what’s happening around them. We teach a baby to be disciplined (and much more) by making rules. Similarly, a computer is like a baby. We set rules and provide data to the computer through executable programs with the help of a Programming Language. Programming Language. (Photo by Clément H on Unsplash ) Clément H Unsplash That’s it👍. If you can understand this basic concept of programming, you’re good to go. Pick up a programming language, and start learning. Read the following section to get an idea of where to start. My recommendation is to choose Python Programming Language as a start because it’s beginner-friendly. How to Learn a Programming Language? Anyone can start to learn a programming language, but to learn it effectively, you need to learn the Building Blocks of a Programming Language.👇 Building Blocks Syntax Syntax In the English language, Syntax is the rules for how a sentence is constructed. In a programming language, Syntax is the rules for how each instruction is written. Semantics Semantics In the English language, Semantics is the actual meaning of statements. In a programming language, Semantics is the effect the instructions have (logic). Data Types Data Types A Data Type is a classification of data we want to store in memory. Data Types can vary from one language to another. But the type of data we would like to store is common across all languages. The type of data we'd like to store are Numbers(Integers, Floating-point numbers), Characters(Strings), Booleans(True/False), Arrays, etc... Terms Terms When it comes to a programming language, there are these terms (buzzwords) you may come across in your learning journey. Some commonly used terms are Variables, Expressions, Statements, Functions, Classes, and many more. You don’t need to worry if you feel any difficulties; use this Dictionary👇 as a resource. Resource: Resource: Data type The Tech Terms Computer Dictionary Data type Data type The Tech Terms Computer Dictionary The Tech Terms Computer Dictionary Coming up are the most important concepts for a programmer to improve their skills. And these concepts help you excel in the technical interviews for programming-based roles! Coming up Data Structures Data Structures A data structure is how we can store, access, organize, and manage the data we have created on a computer. In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.— Wikipedia In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. — Wikipedia 👉A good programmer must know when and where they can utilize a data structure in a program. And this comes with practice, so use this resource and start practicing using your favorite programming language👇. Algorithms Algorithms An algorithm is a step-by-step procedure to solve any computational problem. And when it comes to Computer Science, there are a bunch of algorithms that are must-to-know ones. In mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.— Wikipedia In mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation. — Wikipedia Well, there are many types of algorithms, but the most fundamental types of algorithms are: Recursive algorithms Dynamic programming algorithm Backtracking algorithm Divide and conquer algorithm Greedy algorithm Brute Force algorithm Randomized algorithm Recursive algorithms Dynamic programming algorithm Backtracking algorithm Divide and conquer algorithm Greedy algorithm Brute Force algorithm Randomized algorithm Use the following resources to practice and learn more about Data Structures and Algorithms. Solve Data Structures Code Challenges Data Structures- GeeksforGeeks Solve Data Structures Code Challenges Algorithms- GeeksforGeeks Solve Data Structures Code Challenges Solve Data Structures Code Challenges Data Structures- GeeksforGeeks Data Structures- GeeksforGeeks Solve Data Structures Code Challenges Solve Data Structures Code Challenges Algorithms- GeeksforGeeks Algorithms- GeeksforGeeks Best Practices Best Practices The last one is the best practices and coding style. This is an important point for a programmer to work in a team setup because the programs and codebases you write must be programmer-friendly. And even this comes with practice, so take time and refer to these resources below, whenever you’re ready.👍 The Trend in Programming Languages Most Popular Programming, Scripting, and Markup Languages Most Popular Programming, Scripting, and Markup Languages Most Loved, Dreaded, and Wanted Languages Most Loved, Dreaded, and Wanted Languages Keep Learning👨‍💻👩‍💻 Now, I guess this is where I say goodbye👋. But it’s time for you to start learning 📖 with your newfound Knowledge🧠 and Power💪. learning 🙂Good job that you made this far, and thank you so much for reading my Blog. And stay tuned by following me for updates.😇 thank you (Lead Photo by La-Rel Easter on Unsplash ) La-Rel Easter Unsplash

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