More on Technology
CyberPunkMetalHead
3 years ago
I made ChatGPT copy itself.
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.
Jussi Luukkonen, MBA
3 years ago
Is Apple Secretly Building A Disruptive Tsunami?
A TECHNICAL THOUGHT
The IT giant is seeding the digital Great Renaissance.
Recently, technology has been dull.
We're still fascinated by processing speeds. Wearables are no longer an engineer's dream.
Apple has been quiet and avoided huge announcements. Slowness speaks something. Everything in the spaceship HQ seems to be turning slowly, unlike competitors around buzzwords.
Is this a sign of the impending storm?
Metas stock has fallen while Google milks dumb people. Microsoft steals money from corporations and annexes platforms like Linkedin.
Just surface bubbles?
Is Apple, one of the technology continents, pushing against all others to create a paradigm shift?
The fundamental human right to privacy
Apple's unusual remarks emphasize privacy. They incorporate it into their business models and judgments.
Apple believes privacy is a human right. There are no compromises.
This makes it hard for other participants to gain Apple's ecosystem's efficiencies.
Other players without hardware platforms lose.
Apple delivers new kidneys without rejection, unlike other software vendors. Nothing compromises your privacy.
Corporate citizenship will become more popular.
Apples have full coffers. They've started using that flow to better communities, which is great.
Apple's $2.5B home investment is one example. Google and Facebook are building or proposing to build workforce housing.
Apple's funding helps marginalized populations in more than 25 California counties, not just Apple employees.
Is this a trend, and does Apple keep giving back? Hope so.
I'm not cynical enough to suspect these investments have malicious motives.
The last frontier is the environment.
Climate change is a battle-to-win.
Long-term winners will be companies that protect the environment, turning climate change dystopia into sustainable growth.
Apple has been quietly changing its supply chain to be carbon-neutral by 2030.
“Apple is dedicated to protecting the planet we all share with solutions that are supporting the communities where we work.” Lisa Jackson, Apple’s vice president of environment.
Apple's $4.7 billion Green Bond investment will produce 1.2 gigawatts of green energy for the corporation and US communities. Apple invests $2.2 billion in Europe's green energy. In the Philippines, Thailand, Nigeria, Vietnam, Colombia, Israel, and South Africa, solar installations are helping communities obtain sustainable energy.
Apple is already carbon neutral today for its global corporate operations, and this new commitment means that by 2030, every Apple device sold will have net zero climate impact. -Apple.
Apple invests in green energy and forests to reduce its paper footprint in China and the US. Apple and the Conservation Fund are safeguarding 36,000 acres of US working forest, according to GreenBiz.
Apple's packaging paper is recycled or from sustainably managed forests.
What matters is the scale.
$1 billion is a rounding error for Apple.
These small investments originate from a tree with deep, spreading roots.
Apple's genes are anchored in building the finest products possible to improve consumers' lives.
I felt it when I switched to my iPhone while waiting for a train and had to pack my Macbook. iOS 16 dictation makes writing more enjoyable. Small change boosts productivity. Smooth transition from laptop to small screen and dictation.
Apples' tiny, well-planned steps have great growth potential for all consumers in everything they do.
There is clearly disruption, but it doesn't have to be violent
Digital channels, methods, and technologies have globalized human consciousness. One person's responsibility affects many.
Apple gives us tools to be privately connected. These technologies foster creativity, innovation, fulfillment, and safety.
Apple has invented a mountain of technologies, services, and channels to assist us adapt to the good future or combat evil forces who cynically aim to control us and ruin the environment and communities. Apple has quietly disrupted sectors for decades.
Google, Microsoft, and Meta, among others, should ride this wave. It's a tsunami, but it doesn't have to be devastating if we care, share, and cooperate with political decision-makers and community leaders worldwide.
A fresh Renaissance
Renaissance geniuses Michelangelo and Da Vinci. Different but seeing something no one else could yet see. Both were talented in many areas and could discover art in science and science in art.
These geniuses exemplified a period that changed humanity for the better. They created, used, and applied new, valuable things. It lives on.
Apple is a digital genius orchard. Wozniak and Jobs offered us fertile ground for the digital renaissance. We'll build on their legacy.
We may put our seeds there and see them bloom despite corporate greed and political ignorance.
I think the coming tsunami will illuminate our planet like the Renaissance.
Stephen Moore
3 years ago
A Meta-Reversal: Zuckerberg's $71 Billion Loss
The company's epidemic gains are gone.
Mark Zuckerberg was in line behind Jeff Bezos and Bill Gates less than two years ago. His wealth soared to $142 billion. Facebook's shares reached $382 in September 2021.
What comes next is either the start of something truly innovative or the beginning of an epic rise and fall story.
In order to start over (and avoid Facebook's PR issues), he renamed the firm Meta. Along with the new logo, he announced a turn into unexplored territory, the Metaverse, as the next chapter for the internet after mobile. Or, Zuckerberg believed Facebook's death was near, so he decided to build a bigger, better, cooler ship. Then we saw his vision (read: dystopian nightmare) in a polished demo that showed Zuckerberg in a luxury home and on a spaceship with aliens. Initially, it looked entertaining. A problem was obvious, though. He might claim this was the future and show us using the Metaverse for business, play, and more, but when I took off my headset, I'd realize none of it was genuine.
The stock price is almost as low as January 2019, when Facebook was dealing with the aftermath of the Cambridge Analytica crisis.
Irony surrounded the technology's aim. Zuckerberg says the Metaverse connects people. Despite some potential uses, this is another step away from physical touch with people. Metaverse worlds can cause melancholy, addiction, and mental illness. But forget all the cool stuff you can't afford. (It may be too expensive online, too.)
Metaverse activity slowed for a while. In early February 2022, we got an earnings call update. Not good. Reality Labs lost $10 billion on Oculus and Zuckerberg's Metaverse. Zuckerberg expects losses to rise. Meta's value dropped 20% in 11 minutes after markets closed.
It was a sign of things to come.
The corporation has failed to create interest in Metaverse, and there is evidence the public has lost interest. Meta still relies on Facebook's ad revenue machine, which is also struggling. In July, the company announced a decrease in revenue and missed practically all its forecasts, ending a decade of exceptional growth and relentless revenue. They blamed a dismal advertising demand climate, and Apple's monitoring changes smashed Meta's ad model. Throw in whistleblowers, leaked data revealing the firm knows Instagram negatively affects teens' mental health, the current Capital Hill probe, and the fact TikTok is eating its breakfast, lunch, and dinner, and 2022 might be the corporation's worst year ever.
After a rocky start, tech saw unprecedented growth during the pandemic. It was a tech bubble and then some.
The gains reversed after the dust settled and stock markets adjusted. Meta's year-to-date decline is 60%. Apple Inc is down 14%, Amazon is down 26%, and Alphabet Inc is down 29%. At the time of writing, Facebook's stock price is almost as low as January 2019, when the Cambridge Analytica scandal broke. Zuckerberg owns 350 million Meta shares. This drop costs him $71 billion.
The company's problems are growing, and solutions won't be easy.
Facebook's period of unabated expansion and exorbitant ad revenue is ended, and the company's impact is dwindling as it continues to be the program that only your parents use. Because of the decreased ad spending and stagnant user growth, Zuckerberg will have less time to create his vision for the Metaverse because of the declining stock value and decreasing ad spending.
Instagram is progressively dying in its attempt to resemble TikTok, alienating its user base and further driving users away from Meta-products.
And now that the corporation has shifted its focus to the Metaverse, it is clear that, in its eagerness to improve its image, it fired the launch gun too early. You're fighting a lost battle when you announce an idea and then claim it won't happen for 10-15 years. When the idea is still years away from becoming a reality, the public is already starting to lose interest.
So, as I questioned earlier, is it the beginning of a technological revolution that will take this firm to stratospheric growth and success, or are we witnessing the end of Meta and Zuckerberg himself?
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Sofien Kaabar, CFA
2 years ago
Innovative Trading Methods: The Catapult Indicator
Python Volatility-Based Catapult Indicator
As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.
Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.
The Foundation: Volatility
The Catapult predicts significant changes with the 21-period Relative Volatility Index.
The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.
Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.
Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:
As stated, standard deviation is:
# The function to add a number of columns inside an array
def adder(Data, times):
for i in range(1, times + 1):
new_col = np.zeros((len(Data), 1), dtype = float)
Data = np.append(Data, new_col, axis = 1)
return Data
# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
for i in range(1, times + 1):
Data = np.delete(Data, index, axis = 1)
return Data
# The function to delete a number of rows from the beginning
def jump(Data, jump):
Data = Data[jump:, ]
return Data
# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)
# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)
# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)
# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file
def volatility(Data, lookback, what, where):
for i in range(len(Data)):
try:
Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
except IndexError:
pass
return Data
The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.
The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.
RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.
def ma(Data, lookback, close, where):
Data = adder(Data, 1)
for i in range(len(Data)):
try:
Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
# Cleaning
Data = jump(Data, lookback)
return Data
def ema(Data, alpha, lookback, what, where):
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
# First value is a simple SMA
Data = ma(Data, lookback, what, where)
# Calculating first EMA
Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)
# Calculating the rest of EMA
for i in range(lookback + 2, len(Data)):
try:
Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
except IndexError:
pass
return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
# Adding a few columns
Data = adder(Data, 7)
# Calculating Differences
for i in range(len(Data)):
Data[i, where] = Data[i, close] - Data[i - width, close]
# Calculating the Up and Down absolute values
for i in range(len(Data)):
if Data[i, where] > 0:
Data[i, where + 1] = Data[i, where]
elif Data[i, where] < 0:
Data[i, where + 2] = abs(Data[i, where])
# Calculating the Smoothed Moving Average on Up and Down
absolute values
lookback = (lookback * 2) - 1 # From exponential to smoothed
Data = ema(Data, 2, lookback, where + 1, where + 3)
Data = ema(Data, 2, lookback, where + 2, where + 4)
# Calculating the Relative Strength
Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
# Calculate the Relative Strength Index
Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))
# Cleaning
Data = deleter(Data, where, 6)
Data = jump(Data, lookback)
return Data
def relative_volatility_index(Data, lookback, close, where):
# Calculating Volatility
Data = volatility(Data, lookback, close, where)
# Calculating the RSI on Volatility
Data = rsi(Data, lookback, where, where + 1)
# Cleaning
Data = deleter(Data, where, 1)
return DataThe Arm Section: Speed
The Catapult predicts momentum direction using the 14-period Relative Strength Index.
As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:
A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.
When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.
Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.
The direction-finding filter in the frame
The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.
Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.
This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:
We defined the moving average function above. Create the Catapult indication now.
Indicator of the Catapult
The indicator is a healthy mix of the three indicators:
The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.
If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.
The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.
lookback_rvi = 21
lookback_rsi = 14
lookback_ma = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.
The chart below shows recent EURUSD hourly values.
def signal(Data, rvi_col, signal):
Data = adder(Data, 10)
for i in range(len(Data)):
if Data[i, rvi_col] < 30 and \
Data[i - 1, rvi_col] > 30 and \
Data[i - 2, rvi_col] > 30 and \
Data[i - 3, rvi_col] > 30 and \
Data[i - 4, rvi_col] > 30 and \
Data[i - 5, rvi_col] > 30:
Data[i, signal] = 1
return Data
Signals are straightforward. The indicator can be utilized with other methods.
my_data = signal(my_data, 6, 7)
Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.
Summary
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation. Technical analysis will lose its reputation as subjective and unscientific.
After you find a trading method or approach, follow these steps:
Put emotions aside and adopt an analytical perspective.
Test it in the past in conditions and simulations taken from real life.
Try improving it and performing a forward test if you notice any possibility.
Transaction charges and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be included in your tests.
After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.
NonConformist
3 years ago
Before 6 AM, read these 6 quotations.
These quotes will change your perspective.
I try to reflect on these quotes daily. Reading it in the morning can affect your day, decisions, and priorities. Let's start.
1. Friedrich Nietzsche once said, "He who has a why to live for can bear almost any how."
What's your life goal?
80% of people don't know why they live or what they want to accomplish in life if you ask them randomly.
Even those with answers may not pursue their why. Without a purpose, life can be dull.
Your why can guide you through difficult times.
Create a life goal. Growing may change your goal. Having a purpose in life prevents feeling lost.
2. Seneca said, "He who fears death will never do anything fit for a man in life."
FAILURE STINKS Yes.
This quote is great if you're afraid to try because of failure. What if I'm not made for it? What will they think if I fail?
This wastes most of our lives. Many people prefer not failing over trying something with a better chance of success, according to studies.
Failure stinks in the short term, but it can transform our lives over time.
3. Two men peered through the bars of their cell windows; one saw mud, the other saw stars. — Dale Carnegie
It’s not what you look at that matters; it’s what you see.
The glass-full-or-empty meme is everywhere. It's hard to be positive when facing adversity.
This is a skill. Positive thinking can change our future.
We should stop complaining about our life and how easy success is for others.
Seductive pessimism. Realize this and start from first principles.
4. “Smart people learn from everything and everyone, average people from their experiences, and stupid people already have all the answers.” — Socrates.
Knowing we're ignorant can be helpful.
Every person and situation teaches you something. You can learn from others' experiences so you don't have to. Analyzing your and others' actions and applying what you learn can be beneficial.
Reading (especially non-fiction or biographies) is a good use of time. Walter Issacson wrote Benjamin Franklin's biography. Ben Franklin's early mistakes and successes helped me in some ways.
Knowing everything leads to disaster. Every incident offers lessons.
5. “We must all suffer one of two things: the pain of discipline or the pain of regret or disappointment.“ — James Rohn
My favorite Jim Rohn quote.
Exercise hurts. Healthy eating can be painful. But they're needed to get in shape. Avoiding pain can ruin our lives.
Always choose progress over hopelessness. Myth: overnight success Everyone who has mastered a craft knows that mastery comes from overcoming laziness.
Turn off your inner critic and start working. Try Can't Hurt Me by David Goggins.
6. “A champion is defined not by their wins, but by how they can recover when they fail.“ — Serena Williams
Have you heard of Traf-o-Data?
Gates and Allen founded Traf-O-Data. After some success, it failed. Traf-o-Data's failure led to Microsoft.
Allen said Traf-O-Data's setback was important for Microsoft's first product a few years later. Traf-O-Data was a business failure, but it helped them understand microprocessors, he wrote in 2017.
“The obstacle in the path becomes the path. Never forget, within every obstacle is an opportunity to improve our condition.” — Ryan Holiday.
Bonus Quotes
More helpful quotes:
“Those who cannot change their minds cannot change anything.” — George Bernard Shaw.
“Do something every day that you don’t want to do; this is the golden rule for acquiring the habit of doing your duty without pain.” — Mark Twain.
“Never give up on a dream just because of the time it will take to accomplish it. The time will pass anyway.” — Earl Nightingale.
“A life spent making mistakes is not only more honorable, but more useful than a life spent doing nothing.” — George Bernard Shaw.
“We don’t stop playing because we grow old; we grow old because we stop playing.” — George Bernard Shaw.
Conclusion
Words are powerful. Utilize it. Reading these inspirational quotes will help you.
DC Palter
3 years ago
Is Venture Capital a Good Fit for Your Startup?
5 VC investment criteria
I reviewed 200 startup business concepts last week. Brainache.
The enterprises sold various goods and services. The concepts were achingly similar: give us money, we'll produce a product, then get more to expand. No different from daily plans and pitches.
Most of those 200 plans sounded plausible. But 10% looked venture-worthy. 90% of startups need alternatives to venture finance.
With the success of VC-backed businesses and the growth of venture funds, a common misperception is that investors would fund any decent company idea. Finding investors that believe in the firm and founders is the key to funding.
Incorrect. Venture capital needs investing in certain enterprises. If your startup doesn't match the model, as most early-stage startups don't, you can revise your business plan or locate another source of capital.
Before spending six months pitching angels and VCs, make sure your startup fits these criteria.
Likely to generate $100 million in sales
First, I check the income predictions in a pitch deck. If it doesn't display $100M, don't bother.
The math doesn't work for venture financing in smaller businesses.
Say a fund invests $1 million in a startup valued at $5 million that is later acquired for $20 million. That's a win everyone should celebrate. Most VCs don't care.
Consider a $100M fund. The fund must reach $360M in 7 years with a 20% return. Only 20-30 investments are possible. 90% of the investments will fail, hence the 23 winners must return $100M-$200M apiece. $15M isn't worth the work.
Angel investors and tiny funds use the same ideas as venture funds, but their smaller scale affects the calculations. If a company can support its growth through exit on less than $2M in angel financing, it must have $25M in revenues before large companies will consider acquiring it.
Aiming for Hypergrowth
A startup's size isn't enough. It must expand fast.
Developing a great business takes time. Complex technology must be constructed and tested, a nationwide expansion must be built, or production procedures must go from lab to pilot to factories. These can be enormous, world-changing corporations, but venture investment is difficult.
The normal 10-year venture fund life. Investments are made during first 3–4 years.. 610 years pass between investment and fund dissolution. Funds need their investments to exit within 5 years, 7 at the most, therefore add a safety margin.
Longer exit times reduce ROI. A 2-fold return in a year is excellent. Loss at 2x in 7 years.
Lastly, VCs must prove success to raise their next capital. The 2nd fund is raised from 1st fund portfolio increases. Third fund is raised using 1st fund's cash return. Fund managers must raise new money quickly to keep their jobs.
Branding or technology that is protected
No big firm will buy a startup at a high price if they can produce a competing product for less. Their development teams, consumer base, and sales and marketing channels are large. Who needs you?
Patents, specialist knowledge, or brand name are the only answers. The acquirer buys this, not the thing.
I've heard of several promising startups. It's not a decent investment if there's no exit strategy.
A company that installs EV charging stations in apartments and shopping areas is an example. It's profitable, repeatable, and big. A terrific company. Not a startup.
This building company's operations aren't secret. No technology to protect, no special information competitors can't figure out, no go-to brand name. Despite the immense possibilities, a large construction company would be better off starting their own.
Most venture businesses build products, not services. Services can be profitable but hard to safeguard.
Probable purchase at high multiple
Once a software business proves its value, acquiring it is easy. Pharma and medtech firms have given up on their own research and instead acquire startups after regulatory permission. Many startups, especially in specialized areas, have this weakness.
That doesn't mean any lucrative $25M-plus business won't be acquired. In many businesses, the venture model requires a high exit premium.
A startup invents a new glue. 3M, BASF, Henkel, and others may buy them. Adding more adhesive to their catalogs won't boost commerce. They won't compete to buy the business. They'll only buy a startup at a profitable price. The acquisition price represents a moderate EBITDA multiple.
The company's $100M revenue presumably yields $10m in profits (assuming they’ve reached profitability at all). A $30M-$50M transaction is likely. Not terrible, but not what venture investors want after investing $25M to create a plant and develop the business.
Private equity buys profitable companies for a moderate profit multiple. It's a good exit for entrepreneurs, but not for investors seeking 10x or more what PE firms pay. If a startup offers private equity as an exit, the conversation is over.
Constructed for purchase
The startup wants a high-multiple exit. Unless the company targets $1B in revenue and does an IPO, exit means acquisition.
If they're constructing the business for acquisition or themselves, founders must decide.
If you want an indefinitely-running business, I applaud you. We need more long-term founders. Most successful organizations are founded around consumer demands, not venture capital's urge to grow fast and exit. Not venture funding.
if you don't match the venture model, what to do
VC funds moonshots. The 10% that succeed are extraordinary. Not every firm is a rocketship, and launching the wrong startup into space, even with money, will explode.
But just because your startup won't make $100M in 5 years doesn't mean it's a bad business. Most successful companies don't follow this model. It's not venture capital-friendly.
Although venture capital gets the most attention due to a few spectacular triumphs (and disasters), it's not the only or even most typical option to fund a firm.
Other ways to support your startup:
Personal and family resources, such as credit cards, second mortgages, and lines of credit
bootstrapping off of sales
government funding and honors
Private equity & project financing
collaborating with a big business
Including a business partner
Before pitching angels and VCs, be sure your startup qualifies. If so, include them in your pitch.