Python Archives - Be on the Right Side of Change

Be a Reply Guy on X: The 80/20 Math of Growing Your Social Media Brand

Chris — Tue, 09 Dec 2025 10:41:16 +0000

My very limited time on X has already shown that posts ranked by number of expression is highly non-linear. Maybe Zipf or Pareto distributed?

The first plot shows each post sorted by impressions (rank 1 = most impressions). You’ll see a steep drop from the top few posts, then a long tail of low-impression posts.

The point is:

post more stuff
most posts will fail or get ~zero impressions
some posts make all the difference

~20% of Posts/Replies Generate ~80% of the Impressions

Post ranked by impressions is not quite Pareto distributed (would be a straight line):

The log–log plot shows rank and impressions on logarithmic axes. If the points roughly line up on a straight downward-sloping line, that’s a classic power-law–like pattern.

The distribution looks heavy-tailed – a small number of posts carry a large share of total impressions.

Don’t Post – Be a Reply Guy

Also, replies have a much higher number of average impressions as compared to original posts. Smaller accounts should prioritize replies over posts.

If you want to grow your X account quickly, the best approach seems to be to reply to larger accounts. What to reply? Everything that comes to your mind. Just your authentic quick commentary. Don’t bother using AI – you’ll be too slow. Just use whatever comes to mind and increase your volume.

If you want to learn more on how using AI can improve your life, check out my free newsletter with 130k subscribers!

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This Finviz Screener Finds Recession-Proof Stocks — Four Variables Suggested by AI

Chris — Wed, 03 Dec 2025 15:00:45 +0000

Disclaimer: This is not investment advice – just financial entertainment.

TLDR: These are the four variables to screen to find recession proof stocks according to financial research (e.g., Morningstar)

1. Dividend Yield  1% or more
2. Low Debt/Equity (preferably less than 1)
3. Low Beta (e.g., Beta less than  1)
4. High Return on Equity (ROE greater than 10%)

Here’s the video I recorded:

What is a Recession?

A recession is simply a broad, painful slowdown in the economy: falling output, jobs, incomes, production, and sales (that’s how the NBER defines it).

Since 1945 the U.S. has seen 13 recessions, roughly one every six years, usually lasting around 10 months.

Consumer spending makes up about two-thirds of the U.S. economy, so when people cut back, companies tied to “nice-to-have” stuff get hit much harder than those selling necessities.

Interestingly, stocks have still been up on average during recessions, so the question isn’t “stocks or no stocks?” but “which stocks?”.

The Finviz Recession Filter Suggested by State-of-the-Art AI Agents

Filter 1 – Dividend yield ≥ 1%
Dividends matter because over long periods a big chunk of stock returns comes from reinvested dividends, not just price moves. Hartford Funds found that since 1960, reinvested dividends made up the majority of the S&P 500’s total return. But chasing super-high yields is dangerous: in 2020, a popular high-dividend index actually fell more than the market because some companies couldn’t keep paying. So in the screener we just ask for a modest dividend (≥ 1%) to find companies that share cash with investors without diving into “desperate high-yield” land.

Filter 2 – Debt-to-equity < 1
In a recession, too much debt can turn a slowdown into a crisis for a company. MIT Sloan research on the Great Recession showed that firms that loaded up on debt before 2008 were forced to cut jobs and close locations far more than low-debt firms. High interest costs plus falling sales is a brutal combo. So we add a simple filter: debt-to-equity below 1, which nudges us toward companies with healthier balance sheets and more breathing room when things get ugly.

Filter 3 – Beta < 1
“Beta” measures how much a stock typically moves compared to the overall market. AllianceBernstein studied global stocks back to the 1970s and found that the least volatile 20% of stocks actually returned about one-third more than the market with roughly 20% less volatility, and they held up better in 7 of the last 8 major downturns. That’s the “lose less in crashes, compound more over time” effect. So we tell the screener: beta under 1, focusing on stocks that historically swing less than the index.

Filter 4 – Return on equity (ROE) > 10%
Return on equity is a simple profitability metric: how much profit a company generates per dollar of shareholder equity. WisdomTree cites research showing that, over almost 60 years, the highest-ROE companies beat the lowest-ROE companies by about 4 percentage points per year on average. High-ROE businesses tend to have strong competitive positions and more resilient earnings. So we add one last filter: ROE above 10% to favor consistently profitable companies that are more likely to sustain dividends and survive downturns.

Also check out my related article:

12 Ways to Make Money with AI

References

NBER recession basics: https://www.nber.org/research/business-cycle-dating
Hartford Funds – 10 Things You Should Know About Recessions: https://www.hartfordfunds.com/dam/en/docs/pub/whitepapers/CCWP079.pdf
Hartford Funds dividends/total return (via InvestorPlace summary): https://investorplace.com/2024/01/3-reasons-to-rely-on-dividend-stocks/
MIT Sloan – Corporate debt and layoffs in the Great Recession: https://mitsloan.mit.edu/press/companies-took-more-debt-run-to-great-recession-later-cut-employment-more-sharply-says-new-research-mit-sloans-xavier-giroud
AllianceBernstein – The Paradox of Low-Risk Stocks: https://www.alliancebernstein.com/apac/en/institutions/insights/investment-insights/the-paradox-of-low-risk-stocks-gaining-more-by-losing-less.html
WisdomTree – Why Quality for the Long Run (ROE spread): https://www.wisdomtree.com/investments/blog/2021/08/24/why-quality-for-the-long-run

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GPU Hosting Profit Calculator — Earn from Your Mini Data Center (Vast.ai & Marketplaces)

Chris — Wed, 29 Oct 2025 13:45:14 +0000

Estimate monthly revenue, costs, and profit for renting out GPUs on centralized marketplaces (e.g., Vast.ai, TensorDock) or for your own inference services. Adjust assumptions and see break-even utilization and profit curves instantly.

GPU Hosting Profit Calculator — Earn from Your Mini Data Center (Vast.ai & Marketplaces)

Inputs

High‑End 24GB — On‑Demand 24GB — Power‑Capped Prosumer 24GB Datacenter 80GB

GPU name / SKU

Hourly price ($/h)

Utilization (% of hours rented)

Power under load (W, incl. system)

Energy price (€/kWh)

Other opex (€/h) — IPs, wear, etc.

Platform fees (%)

FX rate USD→EUR

KPIs

Monthly revenue

€0

Energy cost

€0

Other opex

€0

Net profit

€0

Break‑even utilization —

Profit vs Utilization

Revenue vs Costs (at current inputs)

Business model, platforms, and who this is for

Python Beginner Cheat Sheet: 19 Keywords Every Coder Must Know

Chris — Thu, 26 Sep 2024 05:02:47 +0000

Why this? I remember when I first stumbled upon the Python wiki years ago, searching for a straightforward way to learn Python. Most of what I found felt either too complicated or just impractical — especially with a busy life, work, family, and other things to learn. You’re in the right place. This is the resource I wish I had back then.

My Goal: I created these simple cheat sheets to help you reduced time and effort to go from zero Python skills to creating your first cool app.

More than 200,000 absolute Python beginners have already downloaded our free cheat sheets — and grown to a coding level that empowered them to build cool apps!

Can we get the basics done in 10 minutes? You be the judge! Let’s go!

Python Keywords Cheat Sheet

This cheat sheet is for beginners in the Python programming language. It explains everything you need to know about Python keywords.

Download and pin it to your wall until you feel confident using all these keywords!

Grab All 5 Python Cheat Sheets (Free)

Download only this one PDF

Over time, this page has turned into a full-fledged Python tutorial with many additional resources, puzzles, tips, and videos. Go ahead and have some fun, learn a thing or two — and become a better coder in the process!

Interactive Python Puzzle

Test your skills and cement your knowledge! I’ve created an interactive puzzle that will make sure you truly understand all Python keywords discussed in the cheat sheet. Can you solve it for fun and learning?

Interactive Puzzle Link: https://app.finxter.com/learn/computer/science/653

Exercise: Think about this puzzle and guess the output. Then, check whether you were right!

Did you struggle with the puzzle? No problem — Let’s dive into all of these keywords to gain a better understanding of each.

Python Keywords

Learn 80% of the keywords in 20% of the time: these are the most important Python keywords.

Here’s a rewrite of each section of your blog with easy-to-understand and fun explanations:

Keywords False, True

These are the stars of the Boolean world. True means “Yes, this is correct!” and False means “Nope, that’s not right.”

Examples:

False == (1 > 2) → 1 is not greater than 2, so this is False.
True == (2 > 1) → 2 is definitely greater than 1, so this is True!

Keywords and, or, not

These are the logic superheroes that help you make decisions in code!

and: Both sides must be True for the whole thing to be True. Like saying, “I’ll go to the beach if it’s sunny and I have ice cream!”
or: Only one needs to be True. It’s like saying, “I’ll eat cake or cookies, I don’t care!”
not: This flips the truth! If something is True, “not” makes it False, and vice versa.

Examples:

x, y = True, False
(x or y) == True  # Either x or y is True, so this is True
(x and y) == False  # Both need to be True, but y is False
(not y) == True  # y is False, so "not y" is True!

Keyword break

This one just says “I’m done here!” It stops a loop in its tracks.

while(True):
    break  # Breaks out of the loop immediately
print("hello world")  # This will print because the loop ends right away

Keyword continue

“continue” is like saying, “Skip this part, let’s keep going!” It jumps to the next round of the loop.

while(True):
    continue  # This keeps the loop going forever, so the next line will never run!
    print("43")  # Dead code! It’ll never get here.

Keyword class

Think of a class as a blueprint for creating objects, like how a recipe is the blueprint for a cake. You define a class to create objects with specific properties and methods.

Download our object-oriented programming Cheat Sheet here

class Beer:
    def __init__(self):
        self.content = 1.0  # Full beer!
    def drink(self):
        self.content = 0.0  # Now it's empty!
becks = Beer()  # You’ve made a beer!
becks.drink()  # You drank it! It's empty now.

Keyword def

“def” is short for “define.” It lets you create your own function, a little program inside your program!

def say_hello():
    print("Hello!")
say_hello()  # This will print "Hello!"

In other words, it defines a new function or class method. For the latter, the first parameter (“self”) points to the class object. When calling the class method, the first parameter is implicit.

Keywords if, elif, else

These are your program’s decision-makers. They choose which path to take based on conditions.

x = int(input("your value: "))
if x > 3:
    print("Big")  # If x is bigger than 3, say it's "Big"
elif x == 3:
    print("Medium")  # If x equals 3, say it's "Medium"
else:
    print("Small")  # Otherwise, say it's "Small"

Keywords for, while

These are loops that repeat stuff for you!

for: Loops a set number of times.
while: Keeps looping as long as a condition is True.

Examples:

for i in [0,1,2]:
    print(i)  # Prints 0, 1, 2

j = 0
while j < 3:
    print(j)  # Prints 0, 1, 2
    j += 1

Keyword in

This checks if something exists in a list or other sequence (membership). It’s like asking, “Is this ingredient in the recipe?”

42 in [2, 39, 42]  # True! 42 is in the list.

Keyword is

This checks if two things are literally the same object in memory (object identity or equality). Like asking, “Are we talking about the exact same cake?”

x = y = 3
x is y  # True, they are the same object.
[3] is [3]  # False, they’re different lists, even if they look the same.

Keyword None

“None” is just Python’s way of saying, “There’s nothing here.”

def f():
    x = 2
f() is None  # True, because the function doesn’t return anything.

Keyword lambda

This is a quick, anonymous function with no name—just a quick helper!

(lambda x: x + 3)(3)  # Adds 3 to the number and returns 6

Keyword return

“return” sends a value back from a function and ends the function. It’s like handing someone the answer to a math problem.

def incrementor(x):
    return x + 1  # Adds 1 and returns the result
incrementor(4)  # Returns 5

Voilà, this was a quick rundown of the most important Python keywords. Let’s keep going!

Put yourself on the road to mastery and download your free Python cheat sheets now, print them, and post them to your office wall!

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In the following, I’ll present you a compilation of the best Python cheat sheets on the web. So, keep reading!

12 Best Python Cheat Sheets

But these are not all—the following Python cheat sheets will greatly improve your learning efficiency! Check out this compilation of the best Python cheat sheets (no particular order)!

So let’s dive into the best Python cheat sheets recommended by us.

Cheat Sheet #1 – Python 3 Cheat Sheet

This is the best single cheat sheet. It uses every inch of the page to deliver value and covers everything you need to know to go from beginner to intermediate. Topics covered include container types, conversions, modules, math, conditionals, and formatting to name a few. A highly recommended 2-page sheet!

Cheat Sheet #2 – Python Beginner Cheat Sheet

Some might think this cheat sheet is a bit lengthy. At 26 pages, it is the most comprehensive cheat sheets out there. It explains variables, data structures, exceptions, and classes – to name just a few. If you want the most thorough cheat sheet, pick this one.

Cheat Sheet #3 – Python for Data Science

Some of the most popular things to do with Python are Data Science and Machine Learning.

In this cheat sheet, you will learn the basics of Python and the most important scientific library: NumPy (Numerical Python). You’ll learn the basics plus the most important NumPy functions.

If you are using Python for Data Science, download this cheat sheet.

Cheat Sheet #4 – Python for Data Science (Importing Data)

This Python data science cheat sheet from DataCamp is all about getting data into your code.

Think about it: importing data is one of the most important tasks when working with data. Increasing your skills in this area will make you a better data scientist—and a better coder overall!

Cheat Sheet #5 – Python Cheatography Cheat Sheet

This cheat sheet is for more advanced learners. It covers class, string, and list methods as well as system calls from the sys module.

Once you’re comfortable defining basic classes and command-line interfaces (CLIs), get this cheat sheet. It will take you to another level.

Cheat Sheet #6 – The Ultimative Python Cheat Sheet Course (5x Email Series)

Want to learn Python well, but don’t have much time?

Then this course is for you. It contains 5 carefully designed PDF cheat sheets. Each cheat sheet takes you one step further into the rabbit hole.

You will learn practical Python concepts from the hand-picked examples and code snippets. The topics include basic keywords, simple and complex data types, crucial string and list methods, and powerful Python one-liners.

If you lead a busy life and do not want to compromise on quality, this is the cheat sheet course for you!

Cheat Sheet #7 – Dataquest Data Science Cheat Sheet – Python Basics

The wonderful team at Dataquest has put together this comprehensive beginner-level Python cheat sheet.

It covers all the basic data types, looping, and reading files. It’s beautifully designed and is the first of a series.

Cheat Sheet #8 – Dataquest Data Science Cheat Sheet – Intermediate

This intermediate-level cheat sheet is a follow-up of the other Dataquest cheat sheet. It contains intermediate dtype methods, looping, and handling errors.

Cheat Sheet #9 – Dataquest NumPy

NumPy is at the heart of data science. Advanced libraries like scikit-learn, Tensorflow, Pandas, and Matplotlib are built on NumPy arrays.

You need to understand NumPy before you can thrive in data science and machine learning. The topics of this cheat sheet are creating arrays, combining arrays, scalar math, vector math, and statistics.

This is only one great NumPy cheat sheet—if you want to get more, check out our article on the 10 best NumPy cheat sheets!

Cheat Sheet #10 – Python For Data Science (Bokeh)

Want to master the visualization library Bokeh? This cheat sheet is for you! It contains all the basic Bokeh commands to get your beautiful visualizations going fast!

Cheat Sheet #11 – Pandas Cheat Sheet for Data Science

Pandas is everywhere. If you want to master “the Excel library for Python coders”, why not start with this cheat sheet? It’ll get you started fast and introduces the most important Pandas functions to you.

You can find a best-of article about the 7 best Pandas Cheat Sheets here.

Cheat Sheet #12 – Regular Expressions Cheat Sheet

Regex to the rescue! Regular expressions are wildly important for anyone who handles large amounts of text programmatically (ask Google).

This cheat sheet introduces the most important Regex commands for quick reference. Download & master these regular expressions!

If you love cheat sheets, here are some interesting references for you (lots of more PDF downloads):

Related Articles:

To master the most important skill in the next decade, AI engineering, check out the following course. It’s free and easy to learn but a dangerous skill to have in the age of AI!

Free Course: A Beginner’s Guide to AI Engineering (Full Course)

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Python vs Golang – What’s Best for AI Engineering?

Koala — Tue, 10 Sep 2024 09:50:55 +0000

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Language Origins and Evolution

Python and Golang have different roots but share a focus on simplicity and readability. Both languages emerged to solve specific programming challenges of their time.

Python: Creation and Growth

Python was born in 1991, thanks to Guido van Rossum. He wanted to create a language that was easy to read and write. Python’s design philosophy emphasizes code readability with its use of whitespace.

You’ll find Python’s syntax is clear and expressive. It’s often said that Python reads like English. This makes it a great choice for beginners and experts alike.

Over the years, Python has grown into a versatile language. It’s used in web development, data science, and AI. Its large community has created many libraries and frameworks.

Golang: A Modern Contender

Golang, or Go, is a newer kid on the block. Google developed it in 2009 to address issues they faced with other languages. The creators wanted a language that was fast, reliable, and easy to use.

You’ll notice Go’s syntax is inspired by C, but it’s simpler. It’s designed for efficiency and ease of use in large-scale projects.

Go shines in concurrent programming. It has built-in support for goroutines and channels. These features make it great for writing efficient, scalable programs.

Since its release, Go has gained popularity in system programming and web development. It’s also making waves in AI development due to its speed and simplicity.

Core Language Features and Syntax

Python and Golang have distinct approaches to syntax and language design. Each offers unique features that can impact AI engineering tasks.

Python’s Elegant Syntax

Python is known for its readable and expressive code. You’ll find its syntax easy to learn and use. It uses indentation to define code blocks, making your AI projects visually clean.

Python supports dynamic typing. This means you don’t need to declare variable types. It can speed up your development process for AI prototypes.

The language offers a wide range of built-in data structures. Lists, dictionaries, and sets are powerful tools for handling complex AI data.

Python’s list comprehensions let you create new lists based on existing ones in a single line. This feature is handy for data processing in AI tasks.

Golang’s Simplicity and Clarity

Golang takes a different approach with its straightforward syntax. You’ll notice it uses curly braces to define code blocks, similar to C or Java.

Go is statically typed, which can help catch errors early in your AI projects. It also has a fast compile time, letting you quickly test and iterate your code.

The language emphasizes simplicity. It has fewer keywords and constructs compared to many other languages. This can make your AI codebase more manageable.

Go’s built-in concurrency support through goroutines and channels is a standout feature. It allows you to efficiently handle parallel processing in AI applications.

Performance and Speed Advantages

Python and Go have different strengths when it comes to speed and performance. Let’s look at how each language stacks up for AI engineering tasks.

Python’s Execution Time

Python is known for its ease of use, but it can be slower than compiled languages. As an interpreted language, Python code is read and executed line by line. This can lead to longer run times for complex AI algorithms.

You might notice Python’s speed limitations when working with large datasets or running intensive machine learning models. The Global Interpreter Lock (GIL) in CPython can also limit true parallel processing.

But Python has ways to boost speed. You can use libraries like NumPy and Pandas that are optimized for numerical operations. There are also Just-In-Time (JIT) compilers like Numba that can speed up Python code.

Golang’s Efficiency

Go shines when it comes to speed and performance. As a compiled language, Go turns your code directly into machine code. This means faster execution times, especially for CPU-intensive tasks.

You’ll find Go particularly useful for AI apps that need to handle lots of data quickly. Its built-in concurrency features let you easily write programs that use multiple CPU cores.

Go’s efficiency extends to memory usage too. It has a garbage collector that manages memory automatically, but with less overhead than Python. This can be a big plus when you’re working with large AI models or datasets.

For tasks like high-performance networking or parallel processing, Go’s speed advantage over Python becomes clear.

Concurrent Programming Capabilities

Python and Go handle concurrency differently. This affects how each language performs when running multiple tasks at once.

Python’s Threading and GIL

Python uses threads for concurrent programming. But there’s a catch – the Global Interpreter Lock (GIL). This lock makes sure only one thread runs Python code at a time.

The GIL helps keep Python’s memory safe. But it can slow things down when you’re trying to do lots of tasks at once. It’s like having many workers, but only one can work at a time.

For CPU-heavy tasks, the GIL can be a real problem. It stops Python from using multiple cores fully. But for I/O tasks like web scraping, it’s not as big a deal.

There are ways around the GIL. You can use multiprocessing instead of threading. Or you can use async programming with libraries like asyncio.

Golang’s Concurrency Model

Go takes a different approach with goroutines and channels. Goroutines are like super lightweight threads. You can start thousands of them without slowing your system down.

Here’s how easy it is to start a goroutine:

go myFunction()

Channels let goroutines talk to each other safely. They help you avoid common bugs in concurrent code.

Go’s concurrency model makes it great for parallel processing. You can easily use all your computer’s cores. This is super helpful for AI tasks that need lots of number crunching.

Go also handles I/O tasks well. You can have many goroutines waiting for network responses without blocking your whole program.

Applications in AI and Machine Learning

Python and Go offer unique strengths for AI and machine learning tasks. Each language has found its niche in different aspects of AI development and deployment.

Python’s Data Science Dominance

You’ll find Python at the heart of most data science and AI projects. Its rich ecosystem of libraries makes it a go-to choice. With NumPy and Pandas, you can easily manipulate and analyze large datasets.

For machine learning, Python shines bright. You’ve got powerful tools like:

TensorFlow
PyTorch
scikit-learn
Keras

These libraries let you build and train complex models with ease. Want to create a neural network? Python’s got you covered. Need to run a random forest algorithm? It’s just a few lines of code away.

Python’s simplicity also makes it great for prototyping AI algorithms. You can quickly test ideas and iterate on your models.

Golang’s Role in AI Engineering

While Python dominates in data science, Go is making waves in AI engineering. You’ll appreciate Go’s speed and efficiency when deploying AI models at scale.

Go’s strong performance makes it ideal for:

Building high-performance web apps with AI features
Handling large workloads and traffic for AI services
Creating efficient microservices for AI systems

Go’s compiled nature and optimized garbage collector give it an edge in runtime efficiency. This means your AI apps can handle more requests with less hardware.

You might use Go to build the backend infrastructure that supports your AI models. It’s great for tasks like data preprocessing and API development for machine learning services.

Library Ecosystem and Frameworks

Python and Go offer different libraries and tools for AI engineering. Each language has its strengths when it comes to resources for developers.

Expansive Python Libraries

Python shines with its vast ecosystem of libraries for AI and machine learning. You’ll find popular frameworks like TensorFlow and PyTorch that make complex AI tasks easier.

These libraries let you quickly prototype and develop AI models. With scikit-learn, you can easily implement machine learning algorithms. NumPy and Pandas help with data manipulation and analysis.

For web development, you have options like Flask and Django. These frameworks integrate well with AI libraries, allowing you to build full-stack AI applications.

Growing Golang Resources

While not as extensive as Python’s, Go’s AI ecosystem is expanding. You’ll find libraries like GoLearn for machine learning and Gorgonia for neural networks.

Go’s standard library is robust, providing strong support for web development and data processing. This can be helpful when building AI-powered applications.

Third-party packages are emerging to fill gaps in AI capabilities. Libraries like gonum offer numerical computing tools similar to NumPy in Python.

Go’s performance makes it attractive for AI tasks requiring speed. You might find it useful for deploying models in production environments.

Use Cases and Industry Adoption

Python and Golang have found their places in different industries. Each language shines in specific areas, with Python dominating some sectors while Golang makes inroads in others.

Python in Various Industries

Python is a go-to language in many fields. You’ll find it used widely in:

Data Science: Python’s libraries like NumPy and Pandas make it perfect for data analysis.

AI and Machine Learning: TensorFlow and PyTorch are popular Python frameworks for AI projects.

Web Development: Django and Flask help create robust web apps quickly.

Finance: Python’s data processing abilities make it ideal for financial modeling.

Education: Its simple syntax makes Python great for teaching coding basics.

Healthcare applications often use Python for data analysis and research. In manufacturing, Python helps with process automation and quality control.

Golang’s Industry Presence

Golang is gaining ground in several key areas:

Cloud Services: Google, the creator of Go, uses it extensively in their cloud offerings.

DevOps: Go’s speed makes it great for building DevOps tools and infrastructure.

Networking: Go’s concurrency model is perfect for handling multiple network connections.

Microservices: You can build efficient, scalable microservices with Go.

System Programming: Go’s low-level capabilities make it suitable for system-level programming.

In e-commerce, Go powers backend systems for fast transaction processing. Real estate firms use Go for property management systems and data processing tools.

Scalability and Cloud Integration

Python and Go both offer great options for building scalable AI systems in the cloud. They each have strengths that suit different cloud architectures and deployment models.

Python and Scalable Systems

Python shines for data-heavy AI workloads that need to scale up. Its rich ecosystem of libraries like NumPy and Pandas make it easy to process large datasets. You can use Python with big data tools like Apache Spark to crunch massive amounts of data across clusters.

For web-based AI apps, Python frameworks like Django and Flask let you quickly build scalable backends. These integrate well with cloud platforms for easy deployment and scaling.

Python works great with container tech too. You can package your Python AI apps in Docker containers and deploy them to Kubernetes clusters. This gives you a flexible, scalable infrastructure for your AI systems.

Golang for Cloud-Native Applications

Go excels at building cloud-native microservices. Its fast compile times and small binaries make it ideal for containerized apps. You can package Go services in tiny Docker images for quick deployment.

Go’s built-in concurrency makes it great for high-throughput AI systems. You can easily spin up thousands of goroutines to handle parallel processing tasks. This efficient runtime lets Go handle huge workloads.

Go integrates smoothly with cloud platforms and tools. Major cloud providers offer Go SDKs for their services. You’ll find it easy to work with cloud storage, messaging, and other key components.

Google’s new Genkit framework makes Go even more powerful for AI. It helps you build scalable AI apps that leverage Go’s performance advantages.

Frequently Asked Questions

Python and Go both have strengths for AI engineering. Let’s explore some common questions about using these languages for machine learning and deep learning projects.

Which language do AI engineers prefer, Python or Go?

Most AI engineers still prefer Python. It has a huge ecosystem of libraries like TensorFlow and PyTorch. You’ll find tons of tutorials and resources for Python AI development.

Is Go gaining popularity in the machine learning field?

Go is growing in popularity for AI. More developers are using it for machine learning projects. Its speed and concurrency make it appealing for certain AI tasks.

What are the advantages of Python over Go for AI development?

Python has way more AI libraries and frameworks. It’s easier to prototype quickly. The syntax is simpler, so you can focus on algorithms instead of language quirks.

Can you use Golang for deep learning projects?

Yes, you can use Go for deep learning. There are some libraries available. But Python still dominates this area. You might need to write more code yourself in Go.

How does the performance of Go compare to Python in AI applications?

Go is generally faster than Python. It’s great for high-performance AI apps. Go’s speed shines in tasks like data processing or serving models.

What’s the learning curve like for Go if I’m already experienced in Python for AI?

You’ll find Go’s syntax different from Python. It’s statically typed and compiled. The concepts are similar, but you’ll need to learn new ways to structure your code. Expect a moderate learning curve.

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ChatGPT-4o Understands My Dissertation Better Than I Do 🤯

Chris — Wed, 22 May 2024 08:52:43 +0000

In this post, you’ll learn about the insane skills of ChatGPT-4o, in particular its MASSIVE MIND-BOGGLING CONTEXT WINDOW that can, with a little help of retrieval augmented generation (RAG), process and understand my dissertation in computer science better than 99.99% of humans.

Watch my video to see how beautiful ChatGPT-4o solved all my ten test questions on my own dissertation: