BC GPT CHEAT LL
Saved by @Smatpix

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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More from Santiago

Santiago
Santiago
@svpino
The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

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4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

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Santiago
Santiago
@svpino
A surprising situation.

A machine learning model predicting 3 classes ends up with these results:

• Class A: 90% accuracy
• Class B: 80% accuracy
• Class C: 70% accuracy

Class C seems to be the worst-performing.

But you know already that I'll say it isn't. ↓

Here is the correct answer: We don't know yet which class is the worst.

This is important to understand, so keep reading.

Many people make this same mistake every day and try to fix things that ain't broken.

We are missing something, so let's go and get it.

How about if we ask a friend to look at the dataset manually?

Let's say these are the results of our friend doing the job:

• Class A: 95% accuracy
• Class B: 100% accuracy
• Class C: 71% accuracy

Do you see what just happened?

The model is actually really accurate when predicting Class C! 70% versus 71% when done by a human.

Class B, however, is a disaster: 80% versus 100% when done manually.

Class B seems to be the worse performing class!

The work that our friend did is called a "baseline."

Without having it, it's hard to determine how good are the results of a model.

I've seen tons of money down a drain just because teams didn't create a baseline beforehand.

But now, that won't happen to you 😉.
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Santiago
Santiago
@svpino
You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through


You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

👇


Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

👇

Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

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Santiago
Santiago
@svpino
Many machine learning courses that target developers want you to start with algebra, calculus, probabilities, ML theory, and only then—if you haven't quit already—you may see some code.

I want you to know there's another way.

↓

2. For me, there's no substitute to seeing things working, trying them out myself, hitting a wall, fixing them, seeing the results.

A hands-on approach engages me in a way pages of theory never will.

And I know many of you reading this are wired just like me.

3. I feel that driving a car is a good analogy.

While understanding some basics are necessary to start driving, you don't need to read the entire manual before jumping behind the wheel.

As long as you practice in empty parking lots and backroads, you'll be fine.

4. As you make your way to public roads, you can start incorporating more of the theory that will help you stay safe.

At this point, that theory won't be lost on you: your hours behind the wheel will help you make the necessary connections.

Things will start clicking quick.

5. I've talked to people struggling with derivatives that have no idea why or when they'll become helpful.

I've seen others memorizing what eigenvectors are, or manually transposing matrices because "that's what it takes."

Honestly, for the most part, it's not.
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Santiago
Santiago
@svpino
Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇

Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]

What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]

What kind of problems have you worked on in the past?

Can you list the specific use cases related to each one of these?

[4 / 10]

What is the most exciting project you have ever worked on?

What was your role and responsibilities on that project?

Why do you think your work on that project was important?

[5 / 10]
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