Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Well done!
You have completed AI Tools for No-Code Development!
You have completed AI Tools for No-Code Development!
Preview
Learn about powerful AI Tools for No-Code development in this workshop, enabling you to build robust apps and launch businesses without writing a single line of code. Explore how innovative AI tools are transforming industries, sparking creativity, and opening exciting entrepreneurial opportunities. Whether you're a tech enthusiast, business leader, or just curious, this preview is your first step into a world of endless possibilities.
This is part of the AI for Everyone: Build Power Apps Without Coding track. Check out the track to see the rest of our No-Code content. Stay tuned—new content is continually being added, so be sure to check back regularly for updates!
Glossary
| Term | Definition |
|---|---|
| AI (Artificial Intelligence) | The simulation of human intelligence by machines, enabling them to think, learn, and perform tasks similar to human capabilities. |
| AI Tools | Software applications powered by AI that assist in problem-solving, decision-making, or creative tasks. |
| Agent (in AI) | A specialized AI entity trained to perform specific tasks or act as a helper in a designated area (e.g., coding, marketing). |
| Algorithm | A step-by-step set of instructions that a computer follows to solve problems or complete tasks. |
| API (Application Programming Interface) | A set of rules and protocols that allows different software applications to communicate and exchange data with each other. |
| Automation | The use of technology to perform tasks automatically without human intervention, often triggered by specific events. |
| Back End | The server-side of an application where data processing, business logic, and storage (such as databases) occur. |
| ChatGPT | An AI chatbot by OpenAI that can understand and generate human-like text for conversations, learning, and various tasks. |
| Claude | A text-based AI agent by Anthropic that focuses on providing safe and helpful assistance for work and personal tasks. |
| Cursor | An IDE with built-in AI features that assist with coding, debugging, and understanding code errors. |
| Database | A structured collection of data that is stored and managed for quick access, often used to store user information and other data in applications. |
| Front End | The part of an application or website that users interact with directly; it includes the design, layout, and user interface. |
| Google Gemini | Google’s advanced AI system designed for answering questions, generating text, and integrating with other applications. |
| Hallucinations (in AI) | Instances where an AI generates outputs that may sound plausible but are factually incorrect or made-up. |
| IDE (Integrated Development Environment) | A software application that provides comprehensive facilities to programmers for software development, including code editing, debugging, and testing. |
| Integration | The process of connecting different apps and services so they can work together, such as linking a form builder with a spreadsheet tool. |
| Large Language Model (LLM) | An advanced AI model trained on vast amounts of text data to understand and generate human-like language. |
| Lovable.dev | A user-friendly web app generator that uses AI to build applications based on vague prompts. |
| Machine Learning | A subset of AI where computers are trained on large datasets to improve their performance at tasks without being explicitly programmed. |
| Microsoft Copilot | An AI assistant integrated within Microsoft’s suite of tools (like Word, Excel, and PowerPoint) to aid with tasks such as writing, data analysis, and image generation. |
| Model (in AI) | The “brain” of an AI system, trained on data to perform specific tasks like image recognition or text generation. |
| MVP (Minimum Viable Product) | The simplest version of a product that still works, used to test and validate an idea with minimal resources. |
| No-Code Platform | A tool that allows users to create apps, websites, or workflows without needing to write code, often through drag-and-drop interfaces. |
| Replit | An online platform that allows users to code or generate web apps from prompts without needing extensive coding experience. |
| Tech Stack | The collection of tools, programming languages, frameworks, and services used to build and run an application. |
| Windsurf | An IDE similar to Cursor that offers AI-assisted coding and debugging, guiding users through code changes. |
| Workflow | A series of automated or manual steps that organize tasks to improve productivity and streamline processes. |
Related Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign upRelated Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign up
Hello, everybody.
0:10
I'm Dan and I'm Christian, and we're here
to introduce a new type of program
0:11
to Treehouse all about AI tools
and building with No-Code.
0:16
Now, who am I?
0:21
I am a content producer here at Treehouse
and a UX designer.
0:23
But truth be told, I don't have
much experience in coding myself.
0:26
I designed a lot in my career, but
0:30
I've only produced a few small projects
as far as development goes.
0:33
I've never completed
too many courses in development,
0:37
so I don't have a large
catalog of knowledge to pull from.
0:40
I just to, you know, experiment
and hack things together.
0:44
But with the recent development of these
AI tools such as, you know, Replit,
0:47
Lovable and et cetera,
we are in a time to where we can create
0:52
virtually anything
and without much effort.
0:56
and we want to take you along for the ride
and show you how you too can start
0:59
harnessing these tools for your own good
and start building projects
1:03
you would to build.
1:07
But that's enough yapping from me.
1:08
Christian, would you to introduce yourself
to the Treehouse community?
1:09
Hey everyone, my name is Christian.
1:13
I am excited to kind of start
this series with y'all
1:15
because I have no coding
experience whatsoever.
1:18
I've tried so many different programs
1:22
to learn, but I can never stick with it.
1:25
And I've never built anything
from zero to 100 MVP.
1:28
And I think this is going
to be a great series where Dan and myself,
1:33
we're going to be walking
through no code tools
1:36
and to learn with you
how to use these tools
1:39
and also upskill ourselves
and creating our first MVP.
1:43
OK, so before we begin,
I want to give you a scenario.
1:49
Imagine you're playing a video game
and you have a helpful character
1:53
that assists you in completing levels
by giving hints and providing tools.
1:55
You can think of this game as, you know,
the game of life.
2:00
AI tools are kind
of those helpful characters,
2:03
but in the real world of technology.
2:05
They can help you along your journey
and make your path much easier
2:08
and much smoother.
2:11
But first, let's take a step back
and explain a little bit
2:12
about what AI actually is.
2:16
So when we begin,
we need to understand what is AI.
2:18
and AI by definition is an acronym
for artificial intelligence.
2:22
It's a way of making computers
think and learn similar to how humans
2:27
learn how to read and write.
2:30
After that when we kind of learn about
2:32
artificial intelligence
we go to deeper level of machine learning.
2:35
This is where the
AI is training itself on huge data
2:38
sets about what to do, how to predict,
and what to do next
2:43
which kind of goes
into the world of algorithms.
2:48
You might have heard this
2:51
in your math class, dread that moment
when you hear the word algorithm.
2:51
But for algorithm, think of it as
these are step-by-step instructions
2:55
for a computer
to go through their instructions
2:59
to produce the results
that we want based on what you're asking.
3:02
So with that, let's talk about some
AI tools.
3:06
And before we do,
what are AI tools exactly?
3:09
AI tools are a type of technology
where computers are programmed
3:12
to think and learn humans, helping them
solve problems or make decisions.
3:15
I mentioned a few of these tools earlier,
but let's take a deeper
3:20
dive into some of these tools.
3:23
First,
we have ChatGPT, which is by OpenAI.
3:25
It's an AI chatbot
that understands and generates human- text
3:27
to assist with conversations,
learning, and tasks.
3:31
So you ask ChatGPT a question
or you give it some data
3:34
and it will respond with an answer
and it can organize data, generate images.
3:38
It can do a few general
input and output tasks.
3:43
Next, we have Claude,
which is another text based agent.
3:47
It's an assistant
by Anthropic designed to provide safe
3:51
and helpful text based support for work
and personal tasks.
3:54
Claude is backed by Amazon
and a few other companies,
3:57
and they've been given
a lot of funding and research.
4:02
And their biggest priority is safety,
whereas chat GPT,
4:05
they focus solely on, you know,
bringing the most powerful model.
4:09
Claude likes to balance it
with power and safety.
4:14
They don't want to provide the public
with anything that could be detrimental
4:17
to the public and kind of cause
more harm on the Internet.
4:21
Next we
4:26
have Google Gemini and this is Google
advanced AI system for answering questions
4:26
creating text and integrating
with other apps Lastly we have Microsoft
4:31
Copilot This is Microsoft's
version of their built-in assistant.
4:35
You can access Microsoft Copilot
by going to copilot.microsoft.com,
4:39
as well as within the Microsoft
suite of tools, such as Microsoft Word,
4:44
Microsoft Excel, and Microsoft PowerPoint.
4:48
They've integrated it in the tools
to help you complete
4:51
tasks
such as writing tasks, data analysis, etc.
4:53
Copilot, just ChatGPT, can help you
by answering your questions,
4:57
providing any text-related queries,
and as well as generating images.
5:00
This is because Copilot
is running off the ChatGPT and DALL-E
5:05
3 models under the hood.
5:08
Next up, we have the term model.
5:10
You could think of a model
as the brain of an AI.
5:12
It's been trained on data
5:15
to perform specific tasks
recognizing images or predicting outcomes.
5:16
Now, what
you'll come to find with these chat tools
5:21
is they change the models often,
in this screenshot, for example.
5:24
This is chat GPT
and we have the option to pick
5:29
from GPT4o as well as o1, o1-mini.
5:32
These are all different types of models.
5:36
They're better at different
tasks themselves.
5:38
Some are faster than others.
5:41
Some do deeper levels of thinking.
5:42
So these are just things to keep in mind.
5:44
They all work great, but specific models
might be better for your specific tasks.
5:47
And we'll definitely touch on that
when building live later on.
5:52
Now, one of the biggest advancements is
5:56
no code solutions such as Replit.
6:00
And you might heard Dan
say Replit, Lovable and a few others.
6:03
And what Replit is, it's
an online platform.
6:07
You can also download it for your Mac
or PC where you give it a prompt
6:10
and it will develop a web app
based on that prompt.
6:14
For example, build me a to do list
6:19
or build me a fitness tracker.
6:22
It will do its best to kind of write
and develop what you mean by that.
6:25
Next, we have cursor, which is an IDE.
6:29
Think of it as your platform
or tool for the actual coding
6:32
that's taking place, where you can
alter code, where you can debug.
6:37
These are all words
that we're saying that might scare you.
6:40
But with Cursor, it has an AI built in
6:44
where you can ask a question such as, ,
what does it mean when I have this error?
6:48
Or why does this error occur?
6:52
And it will tell you what it means,
exactly where in the file it is.
6:53
and you can go and read and start slowly
6:58
ingraining yourself
in what it means to be a developer.
7:02
Next, we have another kind of web app
generator called Lovable.dev.
7:05
This is probably one of the most
7:11
user-friendly type of web development
AI tools out there where you can give it
7:14
a very vague prompt
and it will generate to its best ability
7:19
exactly what you want
and probably a very high success rate.
7:24
Next,
we have another IDE similar to Cursor,
7:29
which is called Windsurf by Codeium.
7:32
It's very similar to Cursor.
7:34
Each of them have their own pros and cons.
7:36
Windsurf does their own kind of version
of debugging where you ask Cascade
7:38
to kind of help guide you through
and write different types of code for you.
7:44
You'll prompt it
as you would do in ChatGPT or Claude
7:50
and it will kind of walk you through
what changes you can do.
7:53
They kind of introduce
this idea of a flow state
7:58
where it will ask you,
do you want to make this change?
8:01
Yes or no.
And it will move forward from there.
8:04
So all of these tools, you've
kind of heard me say the word agent.
8:07
These are specific
AI collections of data sets
8:12
that are trained to produce
or act a certain individual.
8:17
And we call these individuals “Agents”.
8:22
And agents are
what Dan said in the beginning,
8:25
a specialized helper for your AI journey.
8:28
These can be agents for coding, agents
8:31
for marketing, agents for a self-help
therapy therapist.
8:34
So it's all up to you
on what you want your agents to do.
8:39
and you can train your specific ChatGPT
or Claude to be acting a certain way,
8:43
do their own customization, and
we'll dive into that later in our series.
8:50
And all of these tools are No-Code
platforms.
8:56
Basically a No-Code platform is
just any tool that helps you create apps
8:59
internal tools
websites workflows without needing
9:04
to know how to write code
They primarily use drag and drop features,
9:07
but some others use different
UIs or user interfaces.
9:11
But the overall goal is to help
you build your app
9:15
without having to know too much coding
knowledge yourself.
9:19
And all of these tools
at the end of the day are here to help
9:22
you generate your MVP,
which is a minimum viable product.
9:26
And that's just the simplest version
of your project that still works.
9:31
And this example
is the basic model of the Lego set.
9:34
So you think of all these no code tools
9:37
as Legos, different building blocks
that you can connect with each other
9:39
to build larger and bigger structures
that do more and more tasks.
9:43
However, you don't have to use
all the Legos.
9:48
You can use some of the Legos.
You can use one set of Legos.
9:51
It's completely fine.
9:54
So you can use Lovable
or you can use Lovable in collaboration
9:55
with Cursor and different outlets that.
10:00
And again, we'll touch on
using these agents alone and together
10:03
to help build MVPs.
10:07
That is definitely one of the key
concepts through this series is us
10:09
generating MVPs.
10:13
We're going to do different types,
10:14
but this is what we really want to help
bring home to everybody
10:15
is that starting a small version
of your product, your ideal
10:19
or even your business is no longer taboo
or an uphill battle.
10:23
It's something that you can do
with relatively ease with these tools.
10:28
Amazing.
10:33
And we're
going to cover some additional key terms
10:33
just so that when you hear these words,
you're not going to be lost
10:36
on what they mean.
10:39
Or if you do have some notion
of what they mean, we're aligning
10:40
and calibrating everyone on what we,
when we say these words, what they mean.
10:45
So the first word that we're going to talk
about is a tech stack.
10:50
You might hear this a lot
when you hear founders
10:53
and co-founders and technical folks
say , our tech stack is this.
10:55
Think of your tech stack as the collection
of tools that you're going to be using.
10:59
Just what Dan mentioned about Legos.
11:03
Think of it as you are preparing
11:05
the different types of Legos
that you're going to use to build with.
11:08
And that's your tech stack.
11:12
Next up, we have front end.
11:13
And you can think of front end
as the part of the Web site
11:15
or application that you see and interact
with.
11:18
Now, we have a front end
and there's a back end, which
11:21
I'm kind of getting ahead of myself here.
11:24
But again, the front end is
what the user will see and interact with,
11:26
whereas the back end is the brain behind
it, handling the data and logic.
11:31
It's the hidden parts
that make everything work.
11:35
So this is, you know, your database,
your business logic.
11:38
All this is behind the visual application
11:41
and working behind the scenes
to make sure that actions and tasks
11:45
that your users are taking on the front
end of your web app
11:50
are actually being completed
and moved along.
11:53
And just as Dan was saying about the back
end being the brain
11:55
and how things are working,
The database is one of the most important
11:58
pieces of a MVP,
which is where it stores everything,
12:02
such as your logins, the password,
anything a user uploads.
12:08
It's stored in a database
that's secured, hopefully secured
12:12
by the person.
12:15
And it basically allows your front end
and back
12:17
end to get information from a database
12:21
that will say, this is Christian's login.
12:26
These are all the materials
that Christian has uploaded up done.
12:28
It connects the front end,
which is how the user logs
12:32
in, to the back end,
which is what the user is doing,
12:35
the database, which is what all the stuff
the user has stored
12:39
or have done on your website.
12:42
And then all a database
is a collection of data.
12:45
This is anything from text, images,
numbers.
12:48
Think of everything
that you collect in your life,
12:52
from credit cards
to business cards to different Lego sets.
12:56
Those are all pieces of data
13:00
that is stored in the database,
which is going to be very important
13:02
for your product
to kind of organize and file
13:06
so that they're producing
the correct piece of data or a certain
13:10
user's database that when they call it, it
brings the correct file.
13:14
And all of these chatbots
that we've mentioned,
13:20
they're all powered by large language
models,
13:22
which are a type of advanced
AI model trained on vast amounts
13:24
of text data
to understand and generate human language
13:27
enabling tasks such as answering questions
writing text translating languages
13:30
or even coding Now,
when you think of a large language model,
13:35
you can think of it being trained
on, , all the data in the world,
13:38
all the written books,
all the articles on the Internet,
13:41
and it needs as much and as much data
as it can gather
13:46
to start formulating responses
and help us in a very helpful way.
13:49
Now, one thing I'd to point out
with these large language models
13:54
is they don't exactly understand the data.
13:57
It's not that they can comprehend
and answer correctly.
14:01
It's that they have so much data
to pull from
14:05
that they're actually guessing the answer
and guessing the next line of text
14:07
when they're generating text.
14:12
And all that is based on all the text
that they processed beforehand,
14:13
all that training data.
14:17
So that's why we get answers
that are correct
14:18
based on the data
that is learned beforehand.
14:22
But that's also why sometimes the data
might not be correct in their answers.
14:24
And these are known as hallucinations.
14:28
And an LLM might hallucinate
14:31
when it's trying to piece together
the next line of information,
14:34
but it's not technically correct
because it doesn't know the answer.
14:38
It's just kind of predicting
it based on what it's learned.
14:41
and all of this is to say that we can now
14:46
build automations
and automations are different
14:49
completed lego pieces
14:54
that will make one
14:56
lego piece trigger another lego piece
these are simple things as
14:59
if you receive an email
the automation will read the email
15:05
categorize it for you
and generate a response for your approval.
15:09
That's one automation.
15:13
Another automation could be as simple
as if you get a calendar invite,
15:15
it will ping you on your phone saying,
hey, you have an invite.
15:19
So these are all different types
of automation that we can get into
15:23
called a workflow.
15:26
So workflows are a fancy way
15:27
of saying here's a series of automations
or step-by-step
15:32
tasks that we utilize No-Code
or other different platforms
15:36
to help automate
and increase your workflow productivity.
15:40
You may have heard this being said
15:45
by a lot of tech people
or by a lot of tech influencers
15:47
saying it has improved my workflow,
my workflow increased my productivity.
15:51
All they're saying is they have automated
a portion of their work
15:55
so that they don't have to think about it
anymore, such as the email intake
16:01
or writing blog posts or generating ideas.
16:05
There are workflows
that people have incorporated
16:10
into their life that makes it easier
so that they can focus a lot
16:13
more of their time on the bigger picture
or more important topics of their work.
16:17
And the next term we're going to
discuss is integration.
16:22
All of these workflows
are powered by integrations,
16:24
and that's just the ability to connect
different apps and services together.
16:27
An example of this would be linking
a form builder to a spreadsheet tool.
16:30
So that way, every time someone fills out
16:34
a form,
you're getting that data in a spreadsheet.
16:37
You're getting the email of the user,
the message, and everything separated
16:40
in different columns of a spreadsheet
just so you can stay well organized.
16:44
And lastly, we have the API, which is the
application programming interface.
16:48
And this is just a fancy technical way
of saying
16:53
this is a way for different software tools
to communicate with each other.
16:57
And even if you don't see it happening
behind the scenes, you're No-Code app.
17:00
It's still happening.
17:03
The way you would connect
different services,
17:04
if we were to build a weather app
and we wanted to pull weather data from
17:08
the weather, weather dot com, as opposed
to manually putting that data in,
17:12
we could pull that data from the weather
channel or weather dot com using the API.
17:16
The API is the connection that sends
the data from one app to another.
17:21
And it's a quick and easy way
to increase your data
17:25
or to flesh out your data in your program,
17:28
as opposed to having to manually
input every bit of that data yourself.
17:32
And with that,
we have concluded our introduction
17:36
to what AI is and some AI tools.
17:40
This is all to prepare you
for our brand new series
17:43
where we'll walk through together
on building a certain app using specific
17:47
AI tools
so that you can begin to understand
17:51
how to think
and start building your first MVP.
17:55
You need to sign up for Treehouse in order to download course files.
Sign upYou need to sign up for Treehouse in order to set up Workspace
Sign up