Santiago Profile picture
Feb 11 17 tweets 6 min read
11 ways ChatGPT saves me hours of work every day, and why you'll never outcompete those who use AI effectively.

A list for those who write code:

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1. Explaining code

Take some code you want to understand and ask ChatGPT to explain it.

I've found explanations are very detailed. This is much quicker than trying to figure out convoluted code.

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2. Improve existing code

Ask ChatGPT to improve existing code by describing what you want to accomplish.

It will give you instructions about how to do it, including the modified code.

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3. Rewriting code using the correct style

This is great when refactoring code written by non-native Python developers who used a different naming convention.

Notice how ChatGPT not only gives you the updated code; it also explains the reason for the changes.

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4. Rewriting code using idiomatic constructs

Very helpful when reviewing and refactoring code written by non-native Python developers.

ChatGPT knows the "Pythonic" way, and it will give you suggestions to improve your code and make it much more readable.

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5. Simplifying code

This is one of my favorite tricks: Ask ChatGPT to simplify complex code.

The result will be a much more compact version of the original code.

Notice the explanation and how it tells us this is simpler but not the most efficient.

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6. Writing test cases

This has become one of my favorite ChatGPT abilities: Ask it to help you test a function, and it will write test cases for you.

This example focuses on the quick_sort function from the previous example.

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7. Exploring alternatives

ChatGPT told me its Quick Sort implementation wasn't the most efficient, so I asked for an alternative implementation.

This is great when you want to explore different ways to accomplish the same thing.

I've learned a ton from this!

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8. Translating code

Anytime you want to port some code from one language to another, ask ChatGPT to help you.

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9. Writing documentation

This is another one of my favorite tricks.

Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job.

It even includes usage examples as part of the documentation!

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10. Tracking down bugs

If you are having trouble finding a bug in your code, ask ChatGPT for help.

It took ChatGPT seconds to find the bug in the attached example. I don't know about you, but it would have taken me much longer than that.

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11. Scaffolding

Probably the way I use it the most is to kick off the structure of anything new I want to write. GitHub Copilot does a great job at this as well.

An example where this is very useful is when interacting with a RESTful API.

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Something to keep in mind:

I have 2+ decades of programming experience. I like to think I know what I'm doing.

I don't trust people's code (especially mine,) and I surely don't trust ChatGPT's output.

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This is not about letting ChatGPT do my work. This is about using it to 10x my output.

ChatGPT is flawed. I find it makes mistakes when dealing with code, but that's why I'm here: to supervise it.

Together we form a more perfect Union. (Sorry, couldn't help it)

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Developers who shit on this are missing the point.

The story is not about ChatGPT taking programmers' jobs. It's not about a missing import here or a subtle mistake there.

The story is how, overnight, AI gives programmers a 100x boost.

Ignore this at your own peril.

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Next time, I'll show you how to build an end-to-end deep learning model using ChatGPT.

I'll only ask questions.

It'll blow your mind.

Follow me @svpino to ensure you don't miss it.

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This Friday, I'll share a story of one of the most surprising paradoxes in Game Theory.

Subscribe below if you don't want to miss it!

underfitted.svpino.com

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More from @svpino

Feb 8
I've been studying machine learning for half a decade, and here is the most popular function we use every day:

f(x) = max(0, x)

Yet many people don't understand a fundamental characteristic of this function.

Here is what you need to know:
The name of this function is "Rectified Linear Unit" or ReLU.

Most people think ReLU is both continuous and differentiable.

They are wrong.

I know because I've asked this question several times in @0xbnomial and interviews.

Many make the same mistake.
Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.
Read 14 tweets
Feb 7
You can 2x your productivity overnight using AI.

But you won't get far unless you understand Prompt Engineering and how to use it effectively.

Here are 8 techniques that will put you ahead of everyone else:
Every example on this thread uses the @CohereAI's API.

If you are a developer and want to integrate AI into your application, you can sign up for free right here: bit.ly/40gUImY

It takes 5 seconds, and you'll be up and running immediately!

Let's get started!
The way you ask matters.

A good prompt is crucial in getting good results from a model.

That's why we talk about "engineering" your prompts. The better you get at it, the more these models help you.

Here are the principles and techniques you need to master:
Read 13 tweets
Feb 3
If you think ChatGPT is impressive, wait until you see what's coming in the next 3 to 5 years.

But to build that future, we need more people learning Machine Learning.

Most people think that starting is hard. They are wrong.

Here is how you can start:
These recommendations will get you through the first month.

Six different steps:

1. Where do you write the code?
2. Learning to deal with data
3. A picture is worth 1000 data points
4. Learning Decision Trees
5. Putting everything together
6. Building your first project
For this guide to be helpful, you need to know Python.

If you are comfortable writing Python, keep going. If you aren't, I'd suggest you start there.
Read 10 tweets
Jan 31
How many jobs will be lost to Artificial Intelligence?

If you don't think it will happen, here are 10 examples to refresh your memory (and a few interesting statistics):
Today, we have a machine that resets bowling pins.

We used to pay people to do that manually. Image
Elevators didn't always work like they do today.

They were complex, and to stop on a specific floor, you needed somebody to time it and stop it manually. Image
Read 14 tweets
Jan 25
Apple just released their MacBook Pro laptops with their M2 chip.

Their GPU is extremely fast, yet many people have no idea how to use it to run their machine learning models.

Here is how you allow TensorFlow to use your GPU:
Step 1

Install Homebrew from this link: brew.sh

You need to copy the command they give you and run it.
Step 2

Download Miniforge3 from this link: github.com/conda-forge/mi…
Read 11 tweets
Jan 24
I've shared 100+ data science courses in the last 2 years.

But most throw a lot of content at you and don't spend enough time thinking about first principles.

I finally found a course that starts with the core ideas and builds from there! Here is what it looks like:
I'm going through the Data Science Foundations path in Brilliant, and I've never seen a course like this!

Leave all your preconceived notions about courses at the door.

This platform knows how to break fundamentals into core building blocks.

And it does so interactively!
Brilliant turns the idea of a course upside down:

Most courses are one-way streets: they are a race to see how quickly you can cram a lot of information into your brain.

Brilliant does something different: bite-sized lessons where you learn by interacting with the course.
Read 7 tweets

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