The easiest online explanation.

Web3 is growing. Crypto companies are growing.

Instagram, Adidas, and Stripe adopted cryptocurrency.

Bitcoin and other cryptocurrencies made web3 famous.

Most don't know where to start. Cryptocurrency, DeFi, etc. are investments.

Since we don't understand web3, I'll help you today.

Let’s go.

1. Web3

It is the third generation of the web, and it is built on the decentralization idea which means no one can control it.

There are static webpages that we can only read on the first generation of the web (i.e. Web 1.0).

Web 2.0 websites are interactive. Twitter, Medium, and YouTube.

Each generation controlled the website owner. Simply put, the owner can block us. However, data breaches and selling user data to other companies are issues.

They can influence the audience's mind since they have control.

Assume Twitter's CEO endorses Donald Trump. Result? Twitter would have promoted Donald Trump with tweets and graphics, enhancing his chances of winning.

We need a decentralized, uncontrollable system.

And then there’s Web3.0 to consider. As Bitcoin and Ethereum values climb, so has its popularity. Web3.0 is uncontrolled web evolution. It's good and bad.

Dapps, DeFi, and DAOs are here. It'll all be explained afterwards.

2. Cryptocurrencies:

No need to elaborate.

Bitcoin, Ethereum, Cardano, and Dogecoin are cryptocurrencies. It's digital money used for payments and other uses.

Programs must interact with cryptocurrencies.

3. Blockchain:

Blockchain facilitates bitcoin transactions, investments, and earnings.

This technology governs Web3. It underpins the web3 environment.

Let us delve much deeper.

Blockchain is simple. However, the name expresses the meaning.

Blockchain is a chain of blocks.

Let's use an image if you don't understand.

The graphic above explains blockchain. Think Blockchain. The block stores related data.

Here's more.

4. Smart contracts

Programmers and developers must write programs. Smart contracts are these blockchain apps.

That’s reasonable.

Decentralized web3.0 requires immutable smart contracts or programs.

5. NFTs

Blockchain art is NFT. Non-Fungible Tokens.

Explaining Non-Fungible Token may help.

Two sorts of tokens:

1. These tokens are fungible, meaning they can be changed. Think of Bitcoin or cash. The token won't change if you sell one Bitcoin and acquire another.

2. Non-Fungible Token: Since these tokens cannot be exchanged, they are exclusive. For instance, music, painting, and so forth.

Right now, Companies and even individuals are currently developing worthless NFTs.

The concept of NFTs is much improved when properly handled.

6. Dapp

Decentralized apps are Dapps. Instagram, Twitter, and Medium apps in the same way that there is a lot of decentralized blockchain app.

Curve, Yearn Finance, OpenSea, Axie Infinity, etc. are dapps.

7. DAOs

DAOs are member-owned and governed.

Consider it a company with a core group of contributors.

8. DeFi

We all utilize centrally regulated financial services. We fund these banks.

If you have $10,000 in your bank account, the bank can invest it and retain the majority of the profits.

We only get a penny back. Some banks offer poor returns. To secure a loan, we must trust the bank, divulge our information, and fill out lots of paperwork.

DeFi was built for such issues.

Decentralized banks are uncontrolled. Staking, liquidity, yield farming, and more can earn you money.

Web3 beginners should start with these resources.

Sketch or Figma?

Designers are pissed.

The beast ate the beauty.

Figma deserves $20B.

Do designers deserve Adobe?

Adobe devours new creative tools and spits them out with a slimy Adobe aftertaste.

• Frame.io — $1.3B

• Magento — $1.7B

• Macromedia — $3.6B

Nothing compares to the risky $20B acquisition.

If they can't be beaten, buy them.

And then make them boring.

Adobe's everywhere.

Like that friend who dabbles in everything creatively, there's not enough time to master one thing.

Figma was Adobe's thigh-mounted battle axe.

• a UX design instrument with a sizable free tier.

• a UX design tool with a simple and quick user interface.

• a tool for fluid collaboration in user experience design.

• a web-based UX design tool that functions well.

• a UX design tool with a singular goal of perfection.

UX design software that replaced Adobe XD.

Adobe XD could do many of Figma's things, but it didn't focus on the details. This is a major issue when working with detail-oriented professionals.

UX designers.

Design enthusiasts first used Figma. More professionals used it. Institutions taught it. Finally, major brands adopted Figma.

Adobe hated that.

Adobe dispatched a team of lawyers to resolve the Figma issue, as big companies do. Figma didn’t bite for months.

Oh no.

Figma resisted.

Figma helped designers leave Adobe. Figma couldn't replace Photoshop, but most designers used it to remove backgrounds.

Online background removal tools improved.

The Figma problem grew into a thorn, a knife, and a battle ax in Adobe's soft inner thigh.

Figma appeared to be going public. Adobe couldn’t allow that. It bought Figma for $20B during the IPO drought.

Adobe has a new issue—investors are upset.

The actual cause of investors' ire toward Adobe

Spoiler: The math just doesn’t add up.

According to Adobe's press release, Figma's annual recurring revenue (ARR) is $400M and growing rapidly.

The $20B valuation requires a 50X revenue multiple, which is unheard of.

Venture capitalists typically use:

• 10% to 29% growth per year: ARR multiplied by 1 to 5

• 30% to 99% growth per year: ARR multiplied by 6 to 10

• 100% to 400% growth per year: ARR multiplied by 10 to 20

Showing an investor a 50x multiple is like telling friends you saw a UFO. They'll think you're crazy.

Adobe's stock fell immediately after the acquisition because it didn't make sense to a number-cruncher.

Designers started a Tweet storm in the digital town hall where VCs and designers often meet.

Adobe acquired Workfront for $1.5 billion at the end of 2020. This purchase made sense for investors.

Many investors missed the fact that Adobe is acquiring Figma not only for its ARR but also for its brilliant collaboration tech.

Adobe could use Figmas web app technology to make more products web-based to compete with Canva.

Figma's high-profile clients could switch to Adobe's enterprise software.

However, questions arise:

• Will Adobe make Figma boring?

• Will Adobe tone down Figma to boost XD?

• Would you ditch Adobe and Figma for Sketch?

Most don't understand the issue until it's too late.

My first startup was what entrepreneurs call the hardest. A two-sided marketplace.

Two-sided marketplaces are the hardest startups because founders must solve the chicken or the egg conundrum.

A two-sided marketplace needs suppliers and buyers. Without suppliers, buyers won't come. Without buyers, suppliers won't come. An empty marketplace and a founder striving to gain momentum result.

My first venture made me a struggling founder seeking to achieve traction for a two-sided marketplace. The company failed, and I vowed never to start another like it.

I didn’t. Unfortunately, my second venture was almost as hard. It failed like the second-hardest startup.

What kind of startup is the second-hardest?

The second-hardest startup, which is almost as hard to develop, is rarely discussed in the startup community. Because of this, I predict more founders fail each year trying to develop the second-toughest startup than the hardest.

Fairly, I have no proof. I see many startups, so I have enough of firsthand experience. From what I've seen, for every entrepreneur developing a two-sided marketplace, I'll meet at least 10 building this other challenging startup.

I'll describe a startup I just met with its two co-founders to explain the second hardest sort of startup and why it's so hard. They created a financial literacy software for parents of high schoolers.

The issue appears plausible. Children struggle with money. Parents must teach financial responsibility. Problems?

It's possible.

Buyers and users are different.

Buyer-user mismatch.

The financial literacy app I described above targets parents. The parent doesn't utilize the app. Child is end-user. That may not seem like much, but it makes customer and user acquisition and onboarding difficult for founders.

The difficulty of a buyer-user imbalance

The company developing a product faces a substantial operational burden when the buyer and end customer are different. Consider classic firms where the buyer is the end user to appreciate that responsibility.

Entrepreneurs selling directly to end users must educate them about the product's benefits and use. Each demands a lot of time, effort, and resources.

Imagine selling a financial literacy app where the buyer and user are different. To make the first sale, the entrepreneur must establish all the items I mentioned above. After selling, the entrepreneur must supply a fresh set of resources to teach, educate, or train end-users.

Thus, a startup with a buyer-user mismatch must market, sell, and train two organizations at once, requiring twice the work with the same resources.

The second hardest startup is hard for reasons other than the chicken-or-the-egg conundrum. It takes a lot of creativity and luck to solve the chicken-or-egg conundrum.

The buyer-user mismatch problem cannot be overcome by innovation or luck. Buyer-user mismatches must be solved by force. Simply said, when a product buyer is different from an end-user, founders have a lot more work. If they can't work extra, their companies fail.

OpenAI's ChatGPT is an amazing AI-powered chatbot. It can write poetry, stories, and marketing campaigns, and it's also adept at coding. ChatGPT's coding is so amazing that it replicated itself. What happened:

Me: Hi

Hello! Can I help? I'll answer any inquiries you have.

Me: Build a python and flask AI chatbot that responds to user messages on the frontend.

CGPT:

from flask import Flask, request, jsonify

import openai
openai.api_key = "YOUR_API_KEY"

@app.route('/chatbot', methods=['POST'])
def chatbot():
    user_message = request.get_json()['message']
    response = chatbot_response(user_message)
    return jsonify({'response': response})


def chatbot_response(user_message):
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=user_message,
        max_tokens=1024,
        n = 1,
        temperature=0.5
    )
    return response['choices'][0]['text']

if __name__ == '__main__':
    app.run()

To integrate a chatbot with a frontend, use Flask to serve an HTML page with a form for user messages and the chatbot's answer. AJAX can submit the form to the /chatbot route, and JavaScript can show the chatbot's response.

I imported code snippets at the top of the file.

Let's add a frontend interface, I said.

CGPT:

<!DOCTYPE html>
<html>
<head>
    <title>AI Chatbot</title>
</head>
<body>
    <h1>AI Chatbot</h1>
    <form id="chatbot-form">
        <label for="message">Message:</label><br>
        <input type="text" id="message" name="message"><br>
        <input type="submit" value="Submit">
    </form> 
    <div id="chatbot-response"></div>

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
    <script>
        $(document).ready(function() {
            $("#chatbot-form").submit(function(e) {
                e.preventDefault();
                var userMessage = $("#message").val();
                $.ajax({
                    type: "POST",
                    url: "/chatbot",
                    data: JSON.stringify({message: userMessage}),
                    contentType: "application/json; charset=utf-8",
                    dataType: "json",
                    success: function(response) {
                        $("#chatbot-response").text(response.response);
                    }
                });
            });
        });
    </script>
</body>
</html>

To serve this HTML page using Flask, build a new route that returns the page as a GET response.

So far, so good.

Looked quite basic so I got chatGPT to add some tailwind to the project.

Me: Tailwind the chatbot.html page, please. Give it a futuristic style with purple and neon

After a few more prompts, Talk GPT aligned the form to the center, changed some colors, and added padding.

CGPT:

<!DOCTYPE html>
<html>
<head>
    <title>AI Chatbot</title>
    <link href="https://unpkg.com/tailwindcss@^1.0/dist/tailwind.min.css" rel="stylesheet">
</head>
<body class="bg-black flex flex-col justify-center items-center">
    <h1 class="sm:text-center text-purple-500">AI Chatbot</h1>
    <form id="chatbot-form" class="border-purple-500 bg-purple-800 rounded">
        <label for="message">Message:</label><br>
        <input type="text" id="message" name="message" class="min-w-30 rounded"><br>
        <input type="submit" value="Submit" class="bg-purple-500 border-red-500 transform hover:scale-110 rounded">
    </form> 
    <div class="flex justify-center">
        <div id="chatbot-response" class="text-white rounded"></div>
    </div>

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
    <script>
        var colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'];
        var colorIndex = 0;
        
        $(document).ready(function() {
            setInterval(function() {
                $("#chatbot-form").css('border-color', colors[colorIndex]);
                colorIndex = (colorIndex + 1) % colors.length;
            }, 1000);

            $("#chatbot-form").submit(function(e) {
                e.preventDefault();
                var userMessage = $("#message").val();
                $.ajax({
                    type: "POST",
                    url: "/chatbot",
                    data: JSON.stringify({message: userMessage}),
                    contentType: "application/json; charset=utf-8",
                    dataType: "json",
                    success: function(response) {
                        $("#chatbot-response").text(response.response);
                    }
                });
            });
        });
    </script>
</body>
</html>

Improved:

A few more prompts like “Add more vertical and horizontal padding to the submit button and vertical margin, and vertically align this file's body.”

The result:

Is it effective? Now, I'll chat with ChatGPT's AI Chatbot. I intervened minimally throughout construction. Chat GPT created numerous files, connected and interacted with its own API, and presented the user request response on the front end by itself.

Me to AI Chatbot: Hey, can you give me 6 name suggestions for a black horse?

Me to AI Chatbot: Write a poem in the style of Jim Morrison. Make sure to mention cacti and black holes

ChatGPTs Chatbot:

In the desert night, I am confronted by cacti, 
Their arms reaching out, As if to grab me and take me away. 
The stars in the sky, Beckon me to come, 
To a world of black holes, Where I can be free. 
I am in a trance, My body and mind, 
In a state of limbo, As I wander the night. 
I hear a voice, Calling me to the unknown, 
I cannot resist, The lure of the black hole. 
The cacti watch, As I take my leave, 
To a place of mystery, Where I can find peace.

Of course, the next question I gave it was to build a chatbot!

Me: Build a python and flask AI chatbot that responds to user messages on the frontend.

ChatbotGPTs:

import os from flask 
import Flask, request, jsonify 
from chatterbot 
import ChatBot 
from chatterbot.trainers 
import ListTrainer 

# Create a new chatbot 
bot = ChatBot( 
  "My Bot", 
  storage_adapter="chatterbot.storage.SQLStorageAdapter", 
  logic_adapters=[ 
    "chatterbot.logic.BestMatch", 
    { "import_path": "chatterbot.logic.BestMatch", 
      "statement_comparison_function": "chatterbot.comparisons.levenshtein_distance", 
       "response_selection_method": "chatterbot.response_selection.get_first_response" } 
    ], 
    database_uri="sqlite:///database.sqlite3" ) 

# Train the chatbot with a few sample conversations 
conversation = [ "Hello", "Hi there!", "How are you doing?", "I'm doing great.", "That is good to hear", "Thank you.", "You're welcome." ] 
trainer = ListTrainer(bot) trainer.train(conversation) 

# Create a Flask app 
app = Flask(__name__) 

# Define a route for the default URL 
@app.route("/") def index(): return "<h1>Chatbot</h1>" 

# Define a route for the /get_response URL 
@app.route("/get_response", methods=["POST"]) 

def get_response(): 
# Get the user's message 
user_message = request.form["user_message"] 
# Get the bot's response 
bot_response = bot.get_response(user_message) 
# Return the bot's response 
return str(bot_response) 
# Run the Flask 
app if __name__ == "__main__": app.run()

No matter how many times I tried, the second generation always used chatterbot instead of the ChatGPT API. Even when I told it to use the ChatGPT API, it didn't.

ChatGTP's ability to reproduce or construct other machine learning algorithms is interesting and possibly terrifying. Nothing prevents ChatGPT from replicating itself ad infinitum throughout the Internet other than a lack of desire. This may be the first time a machine repeats itself, so I've preserved the project as a reference. Adding a requirements.txt file and python env for easier deployment is the only change to the code.

I hope you enjoyed this.