You’re Not Behind (Yet): How to Learn AI in 29 Minutes

AI is becoming more powerful and more

deeply woven into everything we do. Some

people try to ignore it, but it's not

going away. If you're watching this, you

already knew that. You're not asking if

you should learn AI. You're asking how.

Whether you want to work smarter, spark

new ideas, automate parts of your

business, or just buy back your time,

this video will give you the full road

map. You'll learn the key concepts, the

right tools, and a clear step-by-step

action plan. It's simpler than you

think, and by the end, you'll be ahead

of 99% of people trying to figure this

out. But I also know the AI landscape

can feel overwhelming. So, before we

dive in, let's break down the biggest

barriers that keep most people stuck.

I'm not technical. That's totally fine.

Most modern AI tools are built for

non-technical users. If you're even a

little tech curious and willing to learn

and experiment, which you probably are

if you clicked this video, that's all

you need. And just to be clear, there

will be zero coding involved here.

It's changing too fast. Every week

there's a new model, a new update, a

shiny new benchmark. One day it's

ChatGpt in the lead, then it's Cloud,

then Gemini. But the truth is, most of

that is just noise. If you stuck with

one solid model instead of chasing every

new release, you'd be way better off.

They all catch up to each other within a

month anyway. What actually matters is

the fundamentals, the core skills, and

those don't change. I'll walk through

all of them soon.

There are too many tools. Yep, there are

thousands, but you don't need most of

them. In fact, you can do 90% of what

you need with just three to five solid

tools. The rest are either repetitive or

super niche. I'll help you narrow down

that list later in this video, too. I

can't keep up with all the AI news.

Honestly, don't. Unless you're creating

AI content like I do, there's no reason

to follow every headline or test every

new tool. You're better off focusing on

the bigger picture, the underlying

trends, and stay aware of the updates

that actually matter. The easiest way to

do that is by subscribing to a couple

good newsletters, people whose job it is

to sift through everything, test what's

worth testing, and summarize the

highlights. There are plenty out there,

including ones tailored to your

industry. We run one at Futureedia. I'm

obviously biased, but I think it's the

best. That's not the point of this

video though.

There's no one-sizefits-all here, but

most people fall into one of three

paths. Path one is the everyday

explorer. You're not trying to build

anything complex. You just want to make

life easier. Summarize documents, write

clearer emails, prep presentations,

organize your learning. You're here for

more time, less stress. like maybe a

teacher using Chat GPT to draft lesson

plans and tailor them to different grade

levels or a student using Notebook LM to

organize notes and prep for exams. Path

two is the power user. You want to do

more faster. Whether that's content

creation, brainstorming, or solving

problems. Maybe you're a creator using

Perplexity for research, chatbt to write

scripts, MidJourney for thumbnails,

runway for B-roll, Sunno for music,

Dcript for editing, and n to automate

your posting workflow. Stacking tools

can become extremely powerful.

Path three is the builder. You want to

go deeper. Automate tasks, build custom

tools, or scale parts of your business.

Tools like NADN, Manis, and Cursor. They

let you connect apps, automate complex

tasks, and build powerful systems all

without writing code. Maybe you create

an agent to handle support tickets or

automate your lead genen or build an

internal tool that saves your team hours

every week. And just to be clear, in

this video, I'm focusing on no code

builders. Everything I'm talking about

here is totally accessible. And the cool

part is moving from one path to the next

is easier than you think. You might

start as an explorer and end up building

real tools a few weeks later, hopefully

with the help of this video. Let's break

down a few core concepts before we jump

into the tools. Artificial intelligence

is the broad umbrella software designed

to simulate human intelligence like

learning, reasoning, or problem solving.

Within that you have machine learning

which is how AI systems actually learn

by finding patterns in data and

improving over time without being

explicitly programmed. Then there's deep

learning, a sub field of machine

learning that uses neural networks. And

these days when most people talk about

AI they're usually referring to

generative AI tools that can create new

content text images videos music

and more. That's what we'll be focusing

on in this video. I just mentioned the

others to give a bit of context. And

there will be some new terms that show

up and I'll explain them in context.

Now, let's talk about tools. One of the

most important parts of this video, but

also the one that can feel the most

overwhelming. There are literally

thousands of AI tools out there, but

I'll break this down into five main

categories. LLMs, research, image,

video, and audio. Then there's one more

category I'll cover that probably 80% of

the AI tools you'll come across will

fall into. These are specialized

wrappers that use a foundation model and

build a nice UI and additional features

on top. There's more to it that I'll

cover in that section, but understanding

this makes the entire AI tool landscape

feel less overwhelming. You don't need

to spend hours researching every tool.

Instead, start by identifying the

problem you want to solve, the task

that's eating up your time or energy,

and look for the best tool to help with

that. In a huge number of cases, the

solution will be a large language model

or LLM.

The LLM is the most important tool in

most people's AI toolkit. There are a

ton of options and honestly, it doesn't

matter that much which one you use.

Maybe you go with Chat GBT because

you're used to it, Gemini because you

use Google products, or Claude because

you like their philosophy, or Grock

because you're an Elon fan, or Meta

because you're into open source. They

all have slightly different strengths

and vibes, but the core functionality is

very similar, and the underlying

concepts, especially prompt engineering,

are the same across the board. For this

video, I'll be using Chat GPT in most of

the examples since it's the most widely

used, but everything I show here applies

no matter which model you choose. These

tools are all powered by what's called a

large language model or LLM, a type of

neural network trained on massive

amounts of text data to understand,

generate, and manipulate human language.

They're incredibly versatile and

powerful. People use them for everything

from content creation and research to

coding, translation, customer support,

and more. This is where most people

start and for good reason. Almost

everyone can find high impact use cases

for an LLM in their work or day-to-day

life. Many of these models including

Chat GPT, Claude, Gemini, and Grock are

also multimodal, meaning they can work

with more than just text. They can

analyze images, describe visuals, and in

some cases process video or audio.

Gemini, for example, is currently one of

the best at understanding video input.

But here are a few terms you'll see

around LLMs that are helpful to

understand. So, a prompt is the

instruction or input you give the model.

A token is a small chunk of text,

usually just a few characters or part of

a word. LLMs process input and output in

tokens, not words. Understanding tokens

is useful when you're dealing with

length limits or pricing, since most

models charge by the number of tokens

used. Hallucination is when the model

makes something up, usually with

confidence. This happens frequently, so

never assume the answer is 100%

accurate. Always double check important

outputs. Rag or retrieval augmented

generation. This is a setup where the

model retrieves real data or documents

to ground its answer instead of relying

only on its training like searching the

internet and using that information.

Neural networks are the underlying

architecture powering LLMs. They're

inspired by how the human brain

processes information and are designed

to recognize patterns and relationships

in data. You don't need to memorize

these. They'll make more sense as we

keep going and you see them in context.

Here are a few simple use cases using

ChachiBT. Paste in a URL and get a

summary of an article. Upload a rough

script and ask it to tighten the writing

while keeping your voice. Drop in a

massive PDF and get a digestible

breakdown. Solve complex math problems.

Brainstorm ideas, automate writing,

simplify tasks, the list goes on and on.

If you have a problem you want to solve,

start here. If you want a full deep dive

into everything ChatGBT can do, I've

made a separate video on that. Another

fast way to level up with chatbt is with

this free chatbt resource bundle

provided by HubSpot. There's a total of

five PDFs that go in-depth on how you

can utilize chatbt in your career to get

ahead, solve problems or save time. My

favorite is called supercharge your

workday with chatbt. It covers specific

examples of how chatt can be used in

various industries sales and marketing,

project management, enhanced decision-m

and problem solving, time management and

organization. It walks through step by

step with different tips and even has a

section titled 100 ways to try chatbt

today with 100 sample prompts you can

use and modify no matter what career you

have. There's sure to be a bunch in

there that apply. And that's just one of

the resources in the bundle. Use the

link in the description to go download

that. Thank you to HubSpot for

sponsoring this video and providing free

resources to the people that watch this

channel. This next category is

technically built on top of LLMs, but

it's so useful and distinct in how it

helps you think that it deserves its own

category. At the core, these tools

combine language models with real-time

information and or your personal data

sources to help you search, summarize,

and synthesize fast. Perplexity is one

of the biggest players here. It's an AI

powered search engine that uses rag,

retrieval augmented generation, to give

you answers grounded in real sources.

Tools like ChateBT and Claude can search

the internet, but Perplexity is built

from the ground up to specialize in

research and is so good at it, it's

worth checking out. Another standout

tool is Notebook LM. This might be the

most powerful second brain I've used so

far. You upload your own materials,

notes, PDFs, articles, YouTube videos,

and it helps you query, summarize, and

connect them in genuinely useful ways.

It's like having an AI research

assistant that knows your personal

knowledge base inside and out. It can

find and locate sources directly within

any of your documents and show you where

it got it from. But whether you're a

student, strategist, researcher, or just

trying to think more clearly, these

types of tools can seriously upgrade how

you process and apply information. The

image category has exploded, and the

quality of what these tools can create

is honestly incredible. Now, we're

talking hyperrealistic scenes, branded

graphics, stylized illustrations, and

even clean editable text, all from a

single prompt. Most image models today

are based on something called diffusion.

They start with a field of random noise

and gradually remove that noise to

reveal a final image that matches your

prompt. Different tools have different

strengths. The midjourney is still my

favorite for realism and aesthetic

quality. Chat GPT's image generator is

amazing for interactive creation. You

can generate an image, ask it to change

small details, remove the background, or

add new elements, all using natural

language. Ideogram is especially strong

when it comes to graphic design and text

within images like posters, logos, or UI

mock-ups. And to be clear, all of these

tools can do a bit of everything pretty

good. But depending on your goal, one

may serve you better than the others,

and there are far more than what I

listed. Video is one of the fastest

moving areas in AI, and new updates are

constantly reshaping what's possible.

Just recently, V3 from Google dropped a

huge update that's gone super viral that

you've probably seen. It can generate

full scenes with synchronized video,

dialogue, sound effects, and like

emotions all from a single prompt.

>> We can talk.

>> No more silence.

>> Yes, we can talk.

[Music]

>> That used to take a whole production

pipeline. Now it happens in minutes, and

Hyo 2 has pushed things even further

with insane physics. You can create

scenes with complex motions that felt

impossible just months ago. The list of

other amazing video tools is continually

growing. There are two main ways to

generate AI video. There's text to

video. You just write a prompt and it

generates the full scene. Then there's

image to video. You provide a start

frame, an end frame, or both, and the

model animates from that. This gives you

more control and lets you control the

aesthetic while guiding the action

through prompting. There are additional

tools that let you animate characters

using real motion. Runways Act 2 lets

you upload a video of yourself or

someone else and drive a character or

scene with that motion. Mo is really

good with restyling footage into any

style you can imagine. Topaz can

creatively upscale videos, enhancing the

quality while reimagining the details.

There's a ton of fun stuff to play with

here, and it's evolving fast. Many

people are using it to go viral on

social media, but also to create full

music videos or even advertisements for

major companies. There are a few main

areas in AI audio. Text to speech has

come a long way, and 11 Labs is still

the leader here. You can generate

hyperrealistic voiceovers, clone your

own voice, or create custom voices with

different accents and tones. Write a

script, pick a voice, and generate a

polished narration in seconds.

>> These voices can sound very natural and

conversational. It's amazing.

>> Music generation is a category that's

kind of mind-blowing. There's a few key

players here, mostly suno and yo, that

let you create fulllength

multi-instrument songs with singing just

from a text prompt.

[Music]

champagne and cyanide.

>> Or you can also guide the generations by

uploading a reference track.

[Music]

>> Then there's voice input like what you

can do in chat GPT. You can talk to it

in real time and it responds with a

natural conversational voice. It's

surprisingly fluid, like having a back

and forth conversation with a super

helpful assistant. Isn't that right?

>> Exactly. It's pretty cool how natural it

can feel, right? It's almost like

chatting with a friend who just happens

to know a ton of stuff. It definitely

makes things super convenient,

especially when you're on the go or

multitasking.

And then pushing things even further,

tools like Google AI Studio can listen

to your voice and watch your screen at

the same time, giving you real-time

guidance or instructions as you work.

I've used this before as an assistant to

help me learn new softwares. Yeah.

What's next? The background is still

there. Okay. Now, go to the effect

controls panel at the top left of the

screen. There you should see the options

for the ultra key effect. Click the

eyropper icon next to the key color

option and then click on the blue

background in the program monitor.

There's one additional category I want

to cover. Let's call it specialized

rappers for now. You'll see thousands of

tools online that look brand new, but

under the hood, most of them are just

custom interfaces built on top of

foundational models like chatbt, Claude,

or Gemini. They're designed for very

specific use cases, things like writing

emails, fixing resumes, reviewing PDFs,

or generating marketing copy. and they

usually add a clean UI, some guard

rails, and pre-loaded prompt engineering

to make those models easier to use for

that one task. And that's not a bad

thing. These tools can be genuinely

useful. But it's important to understand

what you're actually looking at. Just

ask yourself, is this a new capability

or just a polished wrapper? If it's the

latter, you might be able to recreate it

yourself inside Chacht with a

well-crafted prompt and a few examples.

From there, it's a choice. Do you want

to pay for the convenience and user

experience, or would you rather build it

yourself? that might take more time but

could be more cost-effective and

customizable. That said, some platforms

go far beyond basic rappers. They

combine multiple tools into full

endto-end workflows. For example, a

marketing platform that writes ad copy,

generates branded visuals and videos,

runs Facebook ad campaigns, and then AB

tests the results all automatically. And

those can be game changers for the right

use case. And could you recreate

something like that with LLMs,

automations, and custom agents?

Absolutely. And I'll show you how later

when we get to those sections. That's

where we're changing paths from the

power user to the builder. It involves a

lot more setup, testing, and trial and

error. For many people, paying an extra

$20 or $50 a month is worth avoiding

that hassle. My goal here isn't to tell

you which path to take, just to help you

clearly see what these tools are, why

they exist, and how to decide what's

worth your time and money. Those are the

main categories. And to cut the learning

curve on some of these tools, we do have

an entire learning platform on

Futuredia. There's over 20 full deep

dive courses into all aspects of AI,

including courses on most of the leading

tools like chatbt, notebook, LM,

midjourney, and others. Then many of the

skills and other aspects I'll cover like

prompt engineering or building a chatbot

for your site. There is a whole library

if you want to take the next step there.

Of course, there's other resources

across the internet, but we have tried

to make this the most userfriendly and

comprehensive platform for learning AI.

But moving on, let's zoom out for a

second. The tools will change. The

features will evolve, but these four

core skills will stay useful no matter

what. Prompting is the most essential

skill. Learning how to clearly

communicate with AI will get you better,

more useful responses. You don't need

advanced prompt engineering for most

tasks, but a few simple best practices

can dramatically improve your results.

Just start by being specific. If you use

vague prompt, catch PT has to guess what

you really want and fill in all the

gaps. One of the easiest ways to improve

those prompts is to follow a simple

structure. Aim, context, rules. Aim is

what do you want the AI to do? Write a

product description. Explain this

concept. Brainstorm five ideas. Number

two is context. This is critical. Give

the model relevant background and

information. Who is this for? What's it

about? Like for a Gen Z audience based

on this resume from these bullet points.

Or a powerful form of context is

examples, especially in writing. If you

want a specific tone or format, include

a sample. Then number three is rules.

Add any limits, formatting, or style

preferences. Use bullet points. Keep it

under a 100 words. Use simple language.

Respond with JSON. Make it sound like a

friendly expert. Include a table or

flowchart. Let's do a quick example. So,

here's a vague prompt. Write a blog post

about productivity. After I send that,

you can already see what it had to

guess. Who was the audience? What kind

of tone do you want? How long should it

be? What kind of productivity are we

talking about? That's a vague term. Now,

compare it to this. I'm a business

productivity coach. Write a 500word blog

post for busy entrepreneurs about how to

plan a productive Monday. Make it casual

and include three actionable tips. End

with a motivational quote. This is much

more useful. It doesn't matter if you

follow the aim context rule structure in

the exact order. What matters is that

you cover those elements. Like in this

example, aim is write a 500word blog

post. Context is I am a business

productivity coach for busy

entrepreneurs about planning Mondays.

Rules was 500 words, casual tone, three

tips, end with a quote. And in the case

of a blog post, you'll typically have

previous blog posts that you can upload

to ask for it to write in your style.

You can just add to the end, here's an

example blog post. Write in this style.

Easy. Roll prompting is another powerful

technique. It's like a shortcut that

instantly shifts the tone, perspective,

and depth of the response just by

telling the model who it is. Here's a

quick example. You are a travel vlogger.

Describe the experience of visiting

Tokyo for the first time versus you are

a business travel consultant. Describe

the experience of visiting Tokyo for the

first time. This is a simplified

example, but notice how much of the

context and tone is shaped just by

assigning a role. even before adding the

additional details you normally would.

The first response will tend to focus on

food, culture, street scenes, and

sensory details. The second will

highlight airport efficiency,

transportation, meeting spaces, and

business etiquette. It's the same city,

same question, completely different

output. Now, over time, you'll start

thinking this way naturally. You won't

always follow a strict order like aim,

context, rules. It will all be included,

but mixed in together naturally. The key

is just to think clearly about what you

want, who it's for, and how it should

sound. That mindset will help no matter

what you're trying to create. The more

you practice, the more powerful it

becomes. For a deeper dive, I'd

recommend this resource that has a bunch

of additional tips and techniques you

can use. You don't need to know every AI

tool, just the landscape. Understand the

main categories and what's possible.

That way, when you run into a problem,

you'll recognize that it's solvable, and

you'll know where to start looking.

Workflow thinking is the ability to

break big tasks into smaller steps that

AI can help with. If you try to throw a

huge multi-step request at an LLM all at

once, it usually falls apart. But if you

break it up into clear steps and use the

right tools for each one, you'll get way

better results. Sometimes it might seem

like a task can't be done with AI, but

maybe 80% of it can. That's still a

massive timesaver. Creative remixing is

the skill of combining tools in

unexpected ways. Not always to follow a

plan, but to explore what's possible.

Sometimes you start with a clear goal.

Other times you try something, get an

interesting result, and decide to follow

that direction instead. This happens a

lot with AI, especially the creative

tools. The results aren't always

predictable, but sometimes leaning into

what the AI is good at produces better

results than sticking rigidly to your

original plan. Now, it's time to level

up. Once you understand how individual

tools work and start linking them

together, you can begin automating

tasks. That means building workflows

that complete steps for you without

manual input. Platforms like Zapier and

Make have been around for years to do

this, but Naden has become especially

popular lately. Part of that verality is

because it lets users sell workflow

templates, and that has led to some

grifting. You know, make $1,000 a day on

autopilot if you buy my $50 template,

that kind of thing. So, if you're

watching YouTube videos about it, just

know what to look out for. That said,

the platform itself is incredibly

powerful. And one big reason for its

rise is the introduction of the AI agent

node. That's one of the most intuitive

ways to build agents. So, it's a great

entry point into one of the most hyped

and genuinely useful concepts in AI. And

there's an important distinction here

between automations and agents.

Automations are fixed. They follow a

step-by-step sequence A to B to C. Even

if they get complex with branching

logic, they still follow a predetermined

path. Agents are dynamic. They can

reason, make decisions, and choose which

actions to take based on context. To

function, an agent needs three things. A

brain, usually a large language model,

memory to retain context or past

interactions, and tools, actions it can

take, like sending messages, updating

documents, triggering workflows, or

calling APIs. A great way to practice is

by slowly building an AI personal

assistant. You start simple and add

tools and functionality as you go. So,

maybe you start just with an agent that

reads your calendar and gives you a

quick summary of your day, prioritizing

what matters most. Then you add the

ability to reschedule events or time

block. And after all that, maybe it

starts reading and summarizing your

emails and eventually even sending

replies on your behalf. Then you could

give it access to your SOPs or notion

docs for added context and connect

everything through a simple chat

interface. And that could just be in

Telegram or WhatsApp. Over time, you'll

be able to just send a quick message

like something came up, rearrange my

schedule for tomorrow, and it will be

able to execute that. Or it could be

summarize anything urgent for me today

or write me hooks for a video on AI

agents inspired by my hook database in

notion or summarize the comments on my

latest YouTube video. You can build in

all sorts of things that apply to you.

And I recommend starting with something

like this because you'll catch every

error and it's a safe way to experiment,

debug, and iterate before building

agents that run inside your business. I

do have a full video on how to build

this kind of workflow if you want to go

deeper. It is probably the most

straightforward agent guide out there.

And I'll mention you may already be

using agents. Chat GPT's deep research

mode or the similar feature in

perplexity in Gemini. It's a simple but

powerful agent. So you give it a

research task and then it plans the best

way to approach it. It searches multiple

sources all over the internet,

identifies gaps, pivots its strategy,

and then compiles everything into a

clean report. It is incredibly useful.

But learning how to build your own

agents that give you that same kind of

reasoning and execution power tailored

to whatever task you choose is the next

level. Vibe coding is a new approach to

building software and tools that's

emerged from some of the later AI

updates. But here's the basic idea of

how vibe coding usually works. You

describe what you want in plain language

using voice or text. The AI generates

the code or a basic app structure. You

test it, see what works and what

doesn't. You describe your changes. Then

the AI updates the app. And you just

repeat that until it's working the way

you want. You're just going with the

flow of what the AI gives you, vibing,

until you get something functional with

no coding required. Now, this isn't at

the point where you'll get full-scale

productionready software through vibe

coding, unless you're Jack Dorsey, I

guess, but you can get a proof of

concept prototype or an MVP you can

test. I mean, there are cases of people

fully vibe coding apps and publishing

them to the app store. But an amazing

way a lot of people are using this right

now is building personal use tools or

internal apps that streamline their own

productivity. Like for example, you

might build a lightweight CRM just for

your sales workflow or a content

creation app with your voice and hook

templates and storytelling formats built

in. A few tools that support this kind

of workflow. Windsurf lets you build

simple, usable apps with a polished

interface. No code required. It's best

for MVPs or internal tools. Lovable is

designed for solo creators and small

teams. It helps you design and build AI

powered products quickly with a focus on

user experience. Replet lets you build

and test full apps with a clean UI all

in your browser. It's good for rapid

prototyping, especially with some light

technical knowledge. Cursor is the most

powerful. It's a desktop coding

environment powered by AI. This is ideal

if you already know a bit of code and

want hands-on control. You can use it if

you don't know how to code, but it will

look more intimidating when you first

start. But why this all matters is it

makes software creation more accessible

than it's ever been. If you're building

for yourself or just testing an idea,

it's often faster and more enjoyable

than traditional coding. And as the

tools get better, more people will be

able to replace subscription-based SAS

tools with personalized versions just by

prompting for them. Now, I don't have a

deep dive video on this yet. I haven't

gotten to the level of expertise I'd

want before making one, but if you want

to go further, there are already a lot

of good resources out there to explore.

To make this actionable, I've broken it

down into a simple plan. First, identify

the biggest pain points in your life,

work, or business, like what causes the

most stress or procrastination, and what

takes the most time. Next, write out

what a potential solution could look

like, even if it feels rough or

incomplete. Then, research tools could

help solve it, and ask CHACHT to help.

In many cases, it will be a large

language model like ChachPT, but based

on the categories I covered earlier, you

should have a pretty good idea where to

look if it's not. From there, iterate.

You may need to break it up into

subtasks or use a bit of the prompt

engineering we covered. You don't need

to get it perfect right away, but just

make adjustments, iterate until you can

solve that task. Just dedicate whatever

time you can to this. You don't have to

go all in. Even 15 minutes a couple

times a week can lead to serious time

savings later on. Now, in parallel with

this, just try exploring new tools. If

you're already using chatbt, try doing

something new inside of it, like

creating a project, generating an image,

making a mind map, or analyzing a

document or data set. It has way more

built-in capabilities than most people

realize. I've got videos that cover all

of them. I'd also recommend

experimenting with tools like Perplexity

and Notebook LM. They're both incredibly

useful and their free versions give you

a lot to work with. And once you've

explored individual tools, start

combining them. Just build a simple

workflow that connects two or more. Then

take the next step and automate

something. Pick a basic repetitive task

and set up a simple workflow that does

it automatically. Once you get over the

hurdle of building your first

automation, you'll start seeing

opportunities everywhere. So to sum all

that up, start with a painoint, find the

right tool, iterate, combine, then

automate. That's the full road map.

Don't just use AI because it's cool. Use

it to actually solve problems. Start

with one friction point in your life or

work and see how far you can get with

the tools and concepts I covered today.

Most of this will come a lot easier than

you first expect, and you don't need to

keep up with every new release. The

tools will keep changing. The core

skills and principles won't. Even if you

only apply a small part of what we

covered here, you're already ahead of

99% of people. And if you do want to go

deeper, we've built a full course

platform at Futuredia. It has over 500

lessons across over 20 AI courses.

You'll find full learning paths on chat

GPT, prompt engineering, automation,

custom GPTs, video generation, coding

with AI, and more. All included in one

subscription. So whether you're just

getting started, you're building

internal systems, or applying AI in your

business, there's probably a course that

fits exactly where you're at. You can

get a 7-day free trial using the link in

the description. Or if courses aren't

your thing, the newsletter will keep you

in the loop with the most important

updates. But bottom line, you don't need

to master everything today, but the next

step is to just keep going. If you're

ready for that, this video is the one

I'd recommend watching next.