Is that really true? What do you think?
I believe it's not AI that's changing the world — it’s the people who program it.
That’s who’s truly behind the change. Programming AI, of course.
Do you want to be one of those people changing the world?
When I started teaching myself to code (way back in the early 2000s), a lot of things were different. Chat rooms were still a thing. Now it’s all about Tinder, Snapchat, and TikTok.
Or so I’ve heard — I’m not on any of those.
I’m more of a Facebook–X–Instagram kind of guy. Grandpa tech, with my 42 years.
Back then, PHP and MySQL ruled the scene. Now it’s Python, NodeJS, and NoSQL databases :(
At that time, people were saying a new era was beginning — “the computer era.”
Eventually, that became the “Internet era.”
Because the Internet is what gave computers their true purpose.
Makes sense, right?
Now we’ve been three years deep into what everyone’s calling the “AI era.”
And this time, I think the name is going to stick.
But there’s one key difference between the “Computer Era,” the “AI Era,” and the people who changed the world.
Back then, to learn how to code, you either had to be rich enough to go to MIT or spend hours digging through Yahoo with a dial-up connection (1–2 kb/s), usually ending up stuck or giving up.
Today, information is everywhere — even on TikTok. And with 100 Mbps speeds.
There’s so much info, in fact, it’s overwhelming.
You can get lost in it. Or paralyzed.
Back then, only a few people managed to change the world through programming.
Now? A lot more could — if programming excites you.
Take the OpenAI API, for example. You can build actual, functional AI applications that solve real problems in the time it takes you to finish reading this page.
Don’t believe it? Here are three examples:
1. A natural language assistant
Answers questions like a pro — with just a few lines of code.
import openai
openai.api_key = "YOUR_API_KEY"
def answer(question):
response = openai.ChatCompletion.create(
model="gpt-4o",
messages=[{"role": "user", "content": question}]
)
return response.choices[0].message.content
print(answer("What is machine learning?"))
2. An audio-to-text transcriber with Whisper
Upload audio, get clean text. Great for interviews, lectures, podcasts.
import openai
audio = open("audio.mp3", "rb")
transcription = openai.Audio.transcribe("whisper-1", audio)
print(transcription["text"])
3. A creative social media post generator
Turn basic ideas into attention-grabbing posts — instantly.
import openai
idea = "Promote an online AI course for beginners"
response = openai.ChatCompletion.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "Turn ideas into engaging social media posts."},
{"role": "user", "content": idea}
]
)
print(response.choices[0].message.content)
That easy. That fast. That useful.
Now imagine what you could do if you learned all this properly.
Would anything be off-limits?
How much could you be earning?
Do these code snippets make you want to build something useful with AI?
If you already know how to do this — or if you're building much more advanced stuff — then honestly, this book isn’t for you.
Maybe Volume 2. Or Volume 3.
But if you want to learn, and you answered “Yes” to the question above —
(In case you forgot, it was “Do you want to be one of those people changing the world?”)
Then you’re in the right place. Don’t go anywhere.
To save you dozens of Google searches and countless hours of YouTube (and a few wasted on TikTok), we’ve distilled everything you need to know to “change the world with AI programming” into three volumes — so you don’t have to spend a fortune, just piggyback on the billions $$$$$$$$$$$$$$$$$$ OpenAI already invested.
This is the first one.
OpenAI API Bible – Volume 1: Foundations of GPT and the OpenAI Ecosystem
What will you find in this book?
I’ll tell you in a second.
But first — you can buy it right here (cheaper), or grab it on Amazon.
This book was written for people who already know some programming.
But that doesn’t mean it’s not for you — it just means you need to know a bit of Python. The basics.
The book takes care of the rest: learning to use the OpenAI API to build real AI apps.
(If you don’t know the basics of Python yet, no problem. Start here or here)
Then back to here. Remember it!
Into the technical stuff? Here you go:
This first volume covers everything you need to build smart, useful AI-powered tools:
✅ How to set up your environment
✅ How to use GPT, Whisper, DALL·E, and Embeddings
✅ How to structure API calls
✅ How to build multi-turn conversations with memory
✅ How to implement function calling and tool chaining
✅ How to secure your keys and avoid billing surprises
✅ How to think like a product builder, not just a coder
Each chapter includes hands-on exercises. As it should.
Because hey — the idea is to write code, right?
Who is this book for?
- Developers who want to build smarter products
- Creators who want to automate and scale
- Entrepreneurs who want to launch fast
- Students looking for real-world AI skills
- Anyone tired of watching tutorials and never applying them
- Or maybe... just for you
What’s next?
This is Volume 1 of a 3-part series:
- Volume 1: Foundations — models, prompts, memory, chatbots
- Volume 2: Audio, vision, and multimodal apps
- Volume 3: Deployment, vector databases, LangChain, mobile, and full-stack AI
Start with this one.
Read it. Code with it.
Use it to build things that matter. Things that change the world.
Or just for fun — you’ll change the world later.
Below you'll find the full chapter list. Take a look.
We’ve also added answers to some common FAQs.
You don’t have to read them. But you could.
And if you have a question — reach out.
If not... read the book.
See you soon.
I mean it.
M.A. Gonzalez
P.S.
If you’ve read this far, you already know what to do.
Don’t overthink it.
Read the book, write a few lines of code...
And start building things that — who knows — might one day show up on TechCrunch.
Or just earn you some nice extra cash.
Either way, totally worth it.
