Crypto Enthusiasts Targeted in Multi-Vector Malware Attack Disguised As Python-Based Trading Tool: Report

October 30, 2024 The Daily Hodl

History of Python programming language

July 24, 2023 Coin Telegraph

Delve into the remarkable evolution of Python, and learn how it grew to become a prominent and beloved programming language in the tech world.

Python is an interpreted, object-oriented and high-level programming language created by Guido van Rossum. The language’s design philosophy emphasizes code readability and simplicity, making it a popular choice for beginners and experienced developers.

Python’s journey began in the late 1980s and early 1990s, and it has since evolved into one of the most widely used and beloved programming languages across various domains.

Birth of Python

Van Rossum, a Dutch programmer, started working on Python in December 1989 during his Christmas holidays while working at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. His goal was to create a language that would be easy to read, write and maintain.

In my high school senior year I was voted "least likely to invent his own programming language."
— Guido van Rossum (@gvanrossum) April 24, 2023

Early development

Early in the 1990s, Python continued to be developed, and on February 20, 1991, it had its first public release, version 0.9.0 — the first time the programming community saw Python. The language design used elements from several other programming languages, including ABC, Modula-3, C, and others, to produce a flexible and expressive syntax.

Python 2 vs. Python 3

Python 2.0, which included several new features and enhancements, was released in 2000. However, as the language developed, some design choices in Python 2 made it more difficult to maintain and advance the language.

In response, Python 3 was created and launched on Dec. 3, 2008. With the introduction of backward-incompatible improvements to enhance consistency and eliminate unnecessary functionality, Python 3 represented a substantial advancement.

However, Python 3 is not fully backward compatible with Python 2 — which has reached its end of life and is no longer actively maintained — requiring some code adjustments when migrating between versions.

fwiw, I just noticed that filter() does work differently in Python 2 vs 3.

Python 2:
filter(lambda x: x % 2 == 0, range(10))
[0, 2, 4, 6, 8]

Python 3:
>>> a = filter(lambda x: x % 2 == 0, range(10))
>>> a
<filter object at 0x7f53364a7130>
>>> list(a)
[0, 2, 4, 6, 8]
— Anatoly Karp (@akarp) March 3, 2021

The most recent stable release of Python 3.10.4, the fourth maintenance release of Python 3.10, was officially published on March 24, 2023.

Increased popularity

Python’s popularity has grown over the years due to its readability, adaptability and simplicity of usage. Its clear and simple syntax was well-liked by developers because it simplified writing and understanding code.

The availability of a sizable standard library and several third-party packages via the Python Package Index accelerated Python’s acceptance. These applications include web development, data analysis, artificial intelligence, scripting, automation and more.

Related: How to learn Python with ChatGPT

Python in the modern era

Python had become one of the most popular programming languages by the 2010s, and its use has only grown since then. Python’s thriving community and environment were boosted by the adoption of the language by several IT businesses, startups, and organizations for their projects.

What is Python called Python?

Van Rossum named Python in honor of the British comic troupe Monty Python’s Flying Circus, who he was a fan of.

Despite its humorous beginnings, Python has developed into a serious and important programming language that powers important projects and systems all around the world. However, the amusing association with Monty Python has remained a charming feature of Python’s past.

What should Python not be used for?

Python is a powerful and versatile programming language that can be used for a variety of purposes. However, there are several circumstances where it might not be the best option. Some examples are real-time systems, resource-intensive software, mobile app development, high-frequency trading, system-level programming, memory-intensive software, mobile or console gaming, and certain cryptographic algorithms.

Nonetheless, it is crucial to remember that using the correct libraries and tools can frequently help overcome Python’s restrictions. The choice of programming language ultimately depends on the project’s needs, the team’s experience and the development goals. Python is still a well-liked and helpful language that can be used alone or in combination with other languages to accomplish many tasks.

5 programming languages to learn for AI development

April 12, 2023 Coin Telegraph

AI development

Python, Lisp, Java, C++ and R are popular programming languages for AI development.

Programming languages are important because they are the tools that developers use to create software, applications, and websites. Different programming languages have their own syntax, structure, and functionality, making them suited for specific tasks and projects. Learning and understanding programming languages is essential for developers to write efficient and effective code, as well as to collaborate with other developers on projects.

Here are five programming languages to learn for AI development.

Python

Python is a popular choice for artificial intelligence (AI) development due to its simplicity, readability and versatility. It has a vast collection of libraries and frameworks for machine learning, natural language processing and data analysis, including TensorFlow, Keras, PyTorch, Scikit-learn and NLTK.

With the help of these tools, one can create and train neural networks, work with massive data sets, interpret natural language and much more. Also, Python is a well-liked language for AI research and education, and there are numerous online tutorials and courses available for people who want to get started with AI development thanks to its user-friendliness and community support.

Lisp

Lisp is a programming language that was created in the late 1950s, making it one of the oldest programming languages still in use today. Lisp is known for its unique syntax and its powerful support for functional programming.

Since it was used to create some of the earliest AI systems, Lisp has traditionally had a significant impact on the area of AI. Lisp is a good choice for AI research and development because it supports symbolic computation and can handle code as data.

Despite the fact that Lisp is not used as frequently as some of the other languages discussed previously in the development of AI, it nevertheless maintains a devoted following among AI experts. The expressiveness and complexity-handling capabilities of Lisp are valued by many AI researchers and developers. Common Lisp Artificial Intelligence (CLAI) and Portable Standard Lisp (PSL) are two well-known AI frameworks and libraries that are implemented in Lisp.

CLAI and PSL are both Lisp-based artificial intelligence frameworks, with CLAI focusing on expert systems and PSL providing a portable implementation of the Common Lisp programming language.

Lisp is the Metallica of the programming world
— Stephen Bolton (@skbolton) April 4, 2023

Java

Java is a general-purpose programming language that is often used in the development of large-scale enterprise AI applications. Because of Java’s reputation for security, dependability and scalability, it is frequently used to create sophisticated AI systems that must manage vast volumes of data.

Deeplearning4j, Weka and Java-ML are just a few of the libraries and frameworks for AI development available in Java. With the help of these tools, you may create and train neural networks, process data, and work with machine learning algorithms.

Moreover, Java is a well-liked alternative for creating AI apps that operate across several devices or in distributed contexts because of its platform freedom and support for distributed computing. Due to Java’s acceptance in enterprise development, a sizable Java developer community and a wealth of materials are accessible to those wishing to begin AI development in Java.

C++

While developing AI, C++ is a high-performance programming language that is frequently utilized, especially when creating algorithms and models that must be quick and effective. Because of its well-known low-level hardware control, C++ is frequently used to create AI systems that need precise control over memory and processor resources.

TensorFlow, Caffe and MXNet are just a few of the libraries and frameworks for AI development available in C++. With the help of these tools, you may create and train neural networks, process data, and work with machine learning algorithms.

C++ is also popular in the gaming industry, where it is used to build real-time game engines and graphics libraries. This experience has translated into the development of AI applications that require real-time processing, such as autonomous vehicles or robotics.

Although C++ can be more difficult to learn than some other languages, its power and speed make it a popular choice for building high-performance AI systems.

Do you know?

The name of C++ signifies the evolutionary nature of the changes from C. “++” is the C increment operator#cplusplus #coding #CodeNewbie #learntocode #programming #100DaysOfCode #codinglife
— C++ Programming (@CProgramming1) August 12, 2022

R

R is a programming language and software environment for statistical computing and graphics. R is widely used in the field of AI development, particularly for statistical modeling and data analysis. R is a popular choice for developing and examining machine learning models because of its strong support for statistical analysis and visualization.

Caret, mlr and h2o are just a few of the libraries and frameworks available in R for developing AI. Building and training neural networks, using machine learning methods, and processing data are all made possible by these technologies.

In the academic world, where research and data analysis are common, R is also well-liked. Researchers who want to carry out sophisticated data analyses or create prediction models frequently use it because of its user-friendly interface and strong statistical analytical capabilities.

Which programming language is used in DApp development?

Blockchain technology has emerged as a disruptive force across a wide range of industries, from finance to healthcare to supply chain management. As a result, there is growing demand for developers with expertise in blockchain programming languages.

Solidity is one of the most popular programming languages for creating smart contracts on the Ethereum blockchain, while JavaScript is frequently used to create decentralized applications (DApps). Python is a flexible language that is used for a variety of blockchain-related tasks, from designing analytics platforms to creating smart contracts, whereas Go and C++ are popular alternatives for creating high-performance blockchain systems.

It is conceivable that new programming languages may develop in response to the needs of developers working in this fascinating and quickly expanding subject as the blockchain environment continues to change.

5 high-paying careers in data science

February 8, 2023 Coin Telegraph

Big Data

Data science careers tend to have high salaries — often over six figures — as the demand for skilled professionals in this field continues to grow.

Data science plays a critical role in supporting decision-making processes by providing insights and recommendations based on data analysis. In order to create new products, services and procedures, businesses can use data science to gain a deeper understanding of consumer behavior, market trends and corporate performance.

By giving businesses a competitive edge in the market through better decision-making, increased consumer involvement and more efficient corporate processes, it enables companies to achieve a competitive advantage. The demand for data science experts is rising quickly, opening up new possibilities for development on both a personal and professional level.

Here are five high-paying careers in data science.

Data scientist

A data scientist is a specialist who draws conclusions and knowledge from both structured and unstructured data using scientific methods, processes, algorithms and systems. They create models and algorithms to categorize data, make predictions and find hidden patterns. Additionally, they clearly and effectively communicate their findings and outcomes to all relevant parties.

Data scientists have solid backgrounds in statistics, mathematics and computer science, as well as a practical understanding of the Python and R programming languages and expertise in dealing with sizable data sets. The position calls for a blend of technical and analytical abilities, as well as the capacity to explain complicated results to non-technical audiences.

A data scientist in the United States can expect to earn $121,169 per year, according to Glassdoor. Additionally, advantages like stock options, bonuses and profit-sharing are frequently included in remuneration packages for data scientists. However, a data scientist’s pay might vary significantly depending on a number of variables, including geography, industry, years of experience and educational background.

Machine learning engineer

A machine learning engineer is responsible for designing, building and deploying scalable machine learning models for real-world applications. They create and use algorithms to decipher complex data, interpret it and make predictions. In order to incorporate these models into a finished product, they also work with software engineers.

Typically, a machine learning engineer has a solid foundation in programming, computer science and mathematics. In the U.S., the average income for a machine learning engineer is $136,150, while top earners in big cities or those with substantial expertise may make considerably more.

Big data engineer

The architecture of a company’s big data infrastructure is created, built and maintained by big data engineers. They use a variety of big data technologies, including Hadoop, Spark and NoSQL databases, to design, build and manage the storage, processing and analysis of huge and complex data sets.

They also work along with data scientists, data analysts and software engineers to develop and implement big data solutions that satisfy an organization’s business needs. In the U.S., a data engineer can expect to make an average annual salary of $114,501.

Business intelligence manager

An organization’s decision-making processes are supported by data-driven solutions, which are developed and implemented under the direction of a business intelligence (BI) manager. They coordinate the implementation of BI tools and systems, create and prioritize business intelligence initiatives, and work in close collaboration with data analysts, data scientists and IT teams.

The data used in these solutions must be of a high standard, and BI managers must convey the findings and insights to senior leaders and stakeholders in order to inform business strategy. They are essential in creating and maintaining data governance and security rules that safeguard confidential corporate data. The salary range for a business intelligence manager in the U.S. normally ranges from $122,740 to $157,551. And the average compensation is $140,988 per annum.

Data analyst manager

A data analyst manager is responsible for leading a team of data analysts and overseeing the collection, analysis and interpretation of large and complex data sets. They develop and implement data analysis strategies, using various tools and technologies, to support decision-making processes and inform business strategy.

To make sure that data analysis initiatives are in line with company goals and objectives, data analyst managers closely collaborate with data scientists, business intelligence teams and senior management. They also play a crucial part in guaranteeing the accuracy and quality of the data used in analytic initiatives, as well as in conveying findings and suggestions to stakeholders. They could also be in charge of overseeing the allocation of resources and managing the budget for projects involving data analysis. In the U.S., a data analyst makes an average base salary of $66,859.

How to improve your coding skills using ChatGPT

February 1, 2023 Coin Telegraph

chatGPT

ChatGPT can generate code snippets and solutions to coding problems quickly and efficiently. Here’s how.

As a language model, ChatGPT is primarily used for natural language processing tasks such as text generation and language understanding. While it can be used to generate code samples, it’s not designed to help improve coding skills. However, here are a few ways ChatGPT can be used to help improve coding skills.

Practice explaining coding concepts

Use ChatGPT to explain coding concepts and algorithms to help solidify one’s understanding of them. This can also help users identify areas where they may need to study further.

For instance, when using ChatGPT to practice explaining coding concepts, one can input a prompt that describes a specific coding concept or algorithm, such as “Explain how a hash table works” or “How does the quicksort algorithm work?”

ChatGPT will then generate a response that explains the concept in a clear and concise manner, using natural language. This can help users understand the concept better by hearing it explained in different ways and also help them identify areas where they may need to do further study.

ChatGPT can explain a JavaScript code in plain English. It “understood” the code was computing the pixel differences between a previous and next frame. Really good to start blog posts from code snippets! This function is used in @screenrunapp to detect mouse positions in a video pic.twitter.com/a44r7z5Qoy
— Laurent Denoue (@ldenoue) January 28, 2023

One can also use this approach to practice explaining coding concepts to others, which can be an important skill for technical communication and teaching. By reviewing the output generated by ChatGPT, users can identify areas where they might need to improve their explanations and practice different ways to present the information.

Generate code snippets

ChatGPT can be used to generate code snippets based on certain inputs. This can be useful as a starting point for one’s coding projects or to help understand how a certain function or algorithm works.

To use ChatGPT for this purpose, users can input a prompt that describes the code snippet they want to generate, such as “generate a Python function to reverse a string” or “generate JavaScript code for a simple calculator.”

ChatGPT will then generate a code snippet based on the input prompt, and the output will be coherent and functional code that one can use as a reference or starting point for their project. However, keep in mind that the code generated by ChatGPT may require some modifications and debugging to fit one’s specific use case or project requirements. Additionally, users should always review and test the code before using it in a production environment.

Research and learning

ChatGPT can be used for coding research and learning by inputting prompts that ask for information on a specific technology or programming language. For example, one can input a prompt like “What are the key features of Python 3.0?” or “What are the best practices for writing efficient JavaScript code?”

ChatGPT will then generate a response that summarizes the key concepts and information users need to know about the topic, which they can use as a starting point for their research and learning. Additionally, they can use the generated output as a reference, while they are learning the new technology or language.

Nonetheless, while ChatGPT can provide a good starting point, it’s not a substitute for hands-on practice and in-depth learning. It’s essential to supplement the information provided by ChatGPT with additional resources and practice.

Practice coding challenges

By entering prompts that outline a challenge or problem that users desire to tackle, ChatGPT can be used to practice coding problems. For example, one can input a prompt like “Write a function that finds the second largest element in an array” or “Create a script that takes a string and returns the number of vowels in it.” ChatGPT will then generate a response that includes a code snippet that solves the problem or challenge.

One can then use the generated code as a reference and try to implement the solution on their own, comparing their code with the generated one. This can help users practice their coding skills and improve their understanding of specific concepts or algorithms. Additionally, users can modify the generated code to fit their specific needs or to add more complexity to the problem.

" 5 Days Challenge Achieved! I learned #Angular and built a simple #ecommerce app with it. Big shoutout to #ChatGPT and @maxedapps for providing concise explanations and guidance!
Check out my demo here: https://t.co/BaQ1kEOyst #coding #webdevelopment #success "
— Mayowa.js (@dami0la) January 30, 2023

It is critical to note that while ChatGPT can generate functional code, it’s not a substitute for hands-on practice and learning. Reviewing the generated code and trying to implement the solution on their own will help users solidify their understanding of the concepts and algorithms used. Additionally, users should always test and debug the code before using it in a production environment.

Collaborate with other developers

ChatGPT can be used to collaborate with other developers by inputting prompts that describe a specific coding problem or challenge and then sharing the generated response with other developers for review and feedback. For example, one can input a prompt like “I am having trouble with this function; can you help me optimize it?” along with the code snippet and share it with other developers. They can then use the generated response to provide feedback and suggestions on how to improve the code.

ChatGPT can also be used to generate detailed explanations of the code, which can be helpful when working on a team or trying to understand the code written by others. Additionally, ChatGPT can be used to generate comments and documentation for the code, which can make it easier for other developers to understand and maintain the codebase.

Dogecoin’s parents are fighting: Musk and Jackson Palmer exchange barbs

June 1, 2022 Coin Telegraph

Coin Telegraph

The world’s richest man and the co-founder of Dogecoin are sparring over whether the latter actually has a Python script that could put a huge dent in Twitter bot activity.

Billionaire Elon Musk and Dogecoin (DOGE) co-founder Jackson Palmer are locked in battle on social media over Palmer’s claim that he could remove Twitter bots with a simple Python script.

Australian Palmer said in an interview that his script was capable of automatically tweeting replies to scam tweets as a way of indicating that users should beware of the danger. He told news outlet Crikey on May 30 that Musk had reached out to get the script, but claimed the billionaire's technical knowledge was so deficient that he didn’t know how to run it.

“Elon reached out to me to get hold of that script and it became apparent very quickly that he didn’t understand coding as well as he made out.”

Adding insult to injury, Palmer recounted a year ago calling the SpaceX founder, a “grifter” who “sells a vision in hopes that he can one day deliver what he’s promising, but he doesn’t know that.”

Musk took the comments badly, and fired back at Palmer on May 31 on Twitter. He suggested Palmer’s code could not deliver on its promise of addressing the Twitter bot problem, adding “My kids wrote better code when they were 12.”

“You falsely claimed ur lame snippet of Python gets rid of bots. Ok buddy, then share it with the world …”

He challenged Palmer to make the script public, which would open it to greater scrutiny. Palmer has not yet done so.

On May 17, Musk tweeted that his deal to buy Twitter could not “move forward” unless Twitter CEO Parag Agrawal shows proof that less than 5% of the platform’s users are bots.

Palmer’s beef with Musk was all-too apparent during the interview with Crikey where he claimed Musk intended to destroy Twitter rather than actually acquire it. He said Musk may actually just want to “drive it into the ground at a much lower price, and I think that’s what he’s doing.”

The DOGE co-founder left the project way back in 2015 and he harbors a deep resentment for the entire crypto industry, calling it an inequitable “cartel of wealthy figures” last year. Musk meanwhile is one of the memecoin's biggest proponents and has been nicknamed the CEO of Dogecoin.

The argument between Musk and Palmer comes just two days after Musk announced SpaceX would accept DOGE as payment for merchandise from the space exploration company on May 29.

Investors of the major altcoin DOGE have not reacted to the spat between the two tech moguls as it is down just 1.9% over the past 24 hours, trading at $0.086 according to CoinGecko.

Python

Birth of Python

Early development

Python 2 vs. Python 3

Increased popularity

Python in the modern era

What is Python called Python?

What should Python not be used for?

Python

Lisp

Java

C++

R

Which programming language is used in DApp development?

Data scientist

Machine learning engineer

Big data engineer

Business intelligence manager

Data analyst manager

Practice explaining coding concepts

Generate code snippets

Research and learning

Practice coding challenges

Collaborate with other developers

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