AI images are like funhouse mirrors.

Google's AI Blog introduced the puppy-slug in the summer of 2015.

Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.

DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.

Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!

Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.

When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.

DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.

Some of these images look scary because the models don't "know" anything, at least not in the way we do.

These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.

You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.

Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.

Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.

This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.

The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":

As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.

Apocodes helps entrepreneurs create SaaS products without writing code. This post introduces micro-SaaS and outlines its basic strategy.

Strategy

Vision and strategy differ when starting a startup.

• The company's long-term future state is outlined in the vision. It establishes the overarching objectives the organization aims to achieve while also justifying its existence. The company's future is outlined in the vision.

• The strategy consists of a collection of short- to mid-term objectives, the accomplishment of which will move the business closer to its vision. The company gets there through its strategy.

The vision should be stable, but the strategy must be adjusted based on customer input, market conditions, or previous experiments.

Begin modestly and aim high.

Be truthful. It's impossible to automate SaaS product creation from scratch. It's like climbing Everest without running a 5K. Physical rules don't prohibit it, but it would be suicide.

Apocodes 5K equivalent? Two options:

• (A) Create a feature that includes every setting option conceivable. then query potential clients “Would you choose us to build your SaaS solution if we offered 99 additional features of the same caliber?” After that, decide which major feature to implement next.

• (B) Build a few straightforward features with just one or two configuration options. Then query potential clients “Will this suffice to make your product?” What's missing if not? Finally, tweak the final result a bit before starting over.

(A) is an all-or-nothing approach. It's like training your left arm to climb Mount Everest. My right foot is next.

(B) is a better method because it's iterative and provides value to customers throughout.

Focus on a small market sector, meet its needs, and expand gradually. Micro-SaaS is Apocode's first market.

What is micro-SaaS.

Micro-SaaS enterprises have these characteristics:

• A limited range: They address a specific problem with a small number of features.

• A small group of one to five individuals.

• Low external funding: The majority of micro-SaaS companies have Total Addressable Markets (TAM) under $100 million. Investors find them unattractive as a result. As a result, the majority of micro-SaaS companies are self-funded or bootstrapped.

• Low competition: Because they solve problems that larger firms would rather not spend time on, micro-SaaS enterprises have little rivalry.

• Low upkeep: Because of their simplicity, they require little care.

• Huge profitability: Because providing more clients incurs such a small incremental cost, high profit margins are possible.

Micro-SaaS enterprises created with no-code are Apocode's ideal first market niche.

We'll create our own micro-SaaS solutions to better understand their needs. Although not required, we believe this will improve community discussions.

The challenge

In 100 days (September 12–December 20, 2022), we plan to build 10 micro-SaaS enterprises using Apocode.

They will be:

• Self-serve: Customers will be able to use the entire product experience without our manual assistance.

• Real: They'll deal with actual issues. They won't be isolated proofs of concept because we'll keep up with them after the challenge.

• Both free and paid options: including a free plan and a free trial period. Although financial success would be a good result, the challenge's stated objective is not financial success.

This will let us design Apocodes features, showcase them, and talk to customers.

(Edit: The first micro-SaaS was launched!)

Follow along

If you want to follow the story of Apocode or our progress in this challenge, you can subscribe here.

If you are interested in using Apocode, sign up here.

If you want to provide feedback, discuss the idea further or get involved, email me at nicolas.tresegnie@gmail.com

This method scrapes websites with ChatGPT (demo with Amazon and Twitter)

In a recent article, I demonstrated how to scrape websites using ChatGPT prompts like scrape website X using Python.

But that doesn’t always work.

After scraping dozens of websites with ChatGPT, I realized that simple prompts rarely work for web scraping.

Using ChatGPT and basic HTML, we can scrape any website.

First things first, use ChatGPT's advanced version (Playground)

ChatGPT Playground's enhanced version is needed to scrape websites quickly. It generates code faster and has less constraints.

Here’s how it looks.

The classic ChatGPT display just allows you to type a prompt. Playground is more customizable and generates code faster than the basic version.

No more delays or restrictions.

In this tutorial, we'll write prompts underneath Playground.

How to Use ChatGPT to Scrape Any Website

To show you how to utilize ChatGPT to scrape any website, we'll use subslikescript, which lists movies.

Later in this post, I'll show you how to scrape Amazon and Twitter, but let's start easy.

If we try a basic prompt like “scrape the movie titles on this website: https://subslikescript.com/movies” it won’t scrape anything. Here’s when we need some HTML knowledge to build the right prompt for ChatGPT.Let's scrape the above movie titles. First, check the webpage. Right-click any element and choose Inspect.

Then see below.

We identify the element to scrape (those in the red box). The data elements are ul, a, and scripts-list.

Here's the prompt for movie titles.

Scrape this website: https://subslikescript.com/movies with Python and Beautiful Soup. Locate the element with tag “ul” and class “scripts-list”. Scrape all the “a” elements inside. Get the text attribute and print it.

Code generated:

import requests
from bs4 import BeautifulSoup

page = requests.get('https://subslikescript.com/movies/')

soup = BeautifulSoup(page.content, 'html.parser')

scripts_list = soup.find(class_="scripts-list")

all_a_elements = scripts_list.find_all('a')

for element in all_a_elements:
    print(element.get_text())

It extracts movie titles successfully.

Let's scrape Amazon and Twitter.

ChatGPT's Amazon scraping

Consider scraping Amazon for self-help books. First, copy the Amazon link for self-help books.

Here’s the link I got. Location-dependent connection. Use my link to replicate my results.

Now we'll check book titles. Here's our element.

If we want to extract the book titles, we need to use the tag name span, class attribute name and a-size-base-plus a-color-base a-text-normalattribute value.

This time I'll use Selenium. I'll add Selenium-specific commands like wait 5 seconds and generate an XPath.

Scrape this website https://www.amazon.com/s?k=self+help+books&sprefix=self+help+%2Caps%2C158&ref=nb_sb_ss_ts-doa-p_2_10 with Python and Selenium.

Wait 5 seconds and locate all the elements with the following xpath: “span” tag, “class” attribute name, and “a-size-base-plus a-color-base a-text-normal” attribute value. Get the text attribute and print them.

Code generated: (I only had to manually add the path where my chromedriver is located).

from selenium import webdriver
from selenium.webdriver.common.by import By
from time import sleep

#initialize webdriver
driver = webdriver.Chrome('<add path of your chromedriver>')

#navigate to the website
driver.get("https://www.amazon.com/s?k=self+help+books&sprefix=self+help+%2Caps%2C158&ref=nb_sb_ss_ts-doa-p_2_10")

#wait 5 seconds to let the page load
sleep(5)

#locate all the elements with the following xpath
elements = driver.find_elements(By.XPATH, '//span[@class="a-size-base-plus a-color-base a-text-normal"]')

#get the text attribute of each element and print it
for element in elements:
    print(element.text)

#close the webdriver
driver.close()

It pulls Amazon book titles.

Utilizing ChatGPT to scrape Twitter

Say you wish to scrape ChatGPT tweets. Search Twitter for ChatGPT and copy the URL.

Here’s the link I got. We must check every tweet. Here's our element.

To extract a tweet, use the div tag and lang attribute.

Again, Selenium.

Scrape this website: https://twitter.com/search?q=chatgpt&src=typed_query using Python, Selenium and chromedriver.

Maximize the window, wait 15 seconds and locate all the elements that have the following XPath: “div” tag, attribute name “lang”. Print the text inside these elements.

Code generated: (again, I had to add the path where my chromedriver is located)

from selenium import webdriver
import time

driver = webdriver.Chrome("/Users/frankandrade/Downloads/chromedriver")
driver.maximize_window()
driver.get("https://twitter.com/search?q=chatgpt&src=typed_query")
time.sleep(15)

elements = driver.find_elements_by_xpath("//div[@lang]")
for element in elements:
    print(element.text)

driver.quit()

You'll get the first 2 or 3 tweets from a search. To scrape additional tweets, click X times.

Congratulations! You scraped websites without coding by using ChatGPT.

Unexpected Success

Shopify CEO and co-founder Tobias Lutke. Shopify is worth $48 billion.

World-renowned entrepreneur Tobi

Tobi never expected his first online snowboard business to become a multimillion-dollar software corporation.

Tobi founded Shopify to establish a 20-person company.

The publicly traded corporation employs over 10,000 people.

Here's Tobi Lutke's incredible story.

Elon Musk tweeted his admiration for the Shopify creator.

30-October-2019.

Musk praised Shopify founder Tobi Lutke on Twitter.

Happened:

Explore this programmer's journey.

What difficulties did Tobi experience as a young child?

Germany raised Tobi.

Tobi's parents realized he was smart but had trouble learning as a toddler.

Tobi was learning disabled.

Tobi struggled with school tests.

Tobi's learning impairments were undiagnosed.

Tobi struggled to read as a dyslexic.

Tobi also found school boring.

Germany's curriculum didn't inspire Tobi's curiosity.

“The curriculum in Germany was taught like here are all the solutions you might find useful later in life, spending very little time talking about the problem…If I don’t understand the problem I’m trying to solve, it’s very hard for me to learn about a solution to a problem.”

Studying computer programming

After tenth grade, Tobi decided school wasn't for him and joined a German apprenticeship program.

This curriculum taught Tobi software engineering.

He was an apprentice in a small Siemens subsidiary team.

Tobi worked with rebellious Siemens employees.

Team members impressed Tobi.

Tobi joined the team for this reason.

Tobi was pleased to get paid to write programming all day.

His life could not have been better.

Devoted to snowboarding

Tobi loved snowboarding.

He drove 5 hours to ski at his folks' house.

His friends traveled to the US to snowboard when he was older.

However, the cheap dollar conversion rate led them to Canada.

2000.

Tobi originally decided to snowboard instead than ski.

Snowboarding captivated him in Canada.

On the trip to Canada, Tobi encounters his wife.

Tobi meets his wife Fiona McKean on his first Canadian ski trip.

They maintained in touch after the trip.

Fiona moved to Germany after graduating.

Tobi was a startup coder.

Fiona found work in Germany.

Her work included editing, writing, and academics.

“We lived together for 10 months and then she told me that she need to go back for the master's program.”

With Fiona, Tobi immigrated to Canada.

Fiona invites Tobi.

Tobi agreed to move to Canada.

Programming helped Tobi move in with his girlfriend.

Tobi was an excellent programmer, therefore what he did in Germany could be done anywhere.

He worked remotely for his German employer in Canada.

Tobi struggled with remote work.

Due to poor communication.

No slack, so he used email.

Programmers had trouble emailing.

Tobi's startup was developing a browser.

After the dot-com crash, individuals left that startup.

It ended.

Tobi didn't intend to work for any major corporations.

Tobi left his startup.

He believed he had important skills for any huge corporation.

He refused to join a huge corporation.

Because of Siemens.

Tobi learned to write professional code and about himself while working at Siemens in Germany.

Siemens culture was odd.

Employees were distrustful.

Siemens' rigorous dress code implies that the corporation doesn't trust employees' attire.

It wasn't Tobi's place.

“There was so much bad with it that it just felt wrong…20-year-old Tobi would not have a career there.”

Focused only on snowboarding

Tobi lived in Ottawa with his girlfriend.

Canada is frigid in winter.

Ottawa's winters last.

Almost half a year.

Tobi wanted to do something worthwhile now.

So he snowboarded.

Tobi began snowboarding seriously.

He sought every snowboarding knowledge.

He researched the greatest snowboarding gear first.

He created big spreadsheets for snowboard-making technologies.

Tobi grew interested in selling snowboards while researching.

He intended to sell snowboards online.

He had no choice but to start his own company.

A small local company offered Tobi a job.

Interested.

He must sign papers to join the local company.

He needed a work permit when he signed the documents.

Tobi had no work permit.

He was allowed to stay in Canada while applying for permanent residency.

“I wasn’t illegal in the country, but my state didn’t give me a work permit. I talked to a lawyer and he told me it’s going to take a while until I get a permanent residency.”

Tobi's lawyer told him he cannot get a work visa without permanent residence.

His lawyer said something else intriguing.

Tobis lawyer advised him to start a business.

Tobi declined this local company's job offer because of this.

Tobi considered opening an internet store with his technical skills.

He sold snowboards online.

“I was thinking of setting up an online store software because I figured that would exist and use it as a way to sell snowboards…make money while snowboarding and hopefully have a good life.”

What brought Tobi and his co-founder together, and how did he support Tobi?

Tobi lived with his girlfriend's parents.

In Ottawa, Tobi encounters Scott Lake.

Scott was Tobis girlfriend's family friend and worked for Tobi's future employer.

Scott and Tobi snowboarded.

Tobi pitched Scott his snowboard sales software idea.

Scott liked the idea.

They planned a business together.

“I was looking after the technology and Scott was dealing with the business side…It was Scott who ended up developing relationships with vendors and doing all the business set-up.”

Issues they ran into when attempting to launch their business online

Neither could afford a long-term lease.

That prompted their online business idea.

They would open a store.

Tobi anticipated opening an internet store in a week.

Tobi seeks open-source software.

Most existing software was pricey.

Tobi and Scott couldn't afford pricey software.

“In 2004, I was sitting in front of my computer absolutely stunned realising that we hadn’t figured out how to create software for online stores.”

They required software to:

• to upload snowboard images to the website.

• people to look up the types of snowboards that were offered on the website. There must be a search feature in the software.

• Online users transmit payments, and the merchant must receive them.

• notifying vendors of the recently received order.

No online selling software existed at the time.

Online credit card payments were difficult.

How did they advance the software while keeping expenses down?

Tobi and Scott needed money to start selling snowboards.

Tobi and Scott funded their firm with savings.

“We both put money into the company…I think the capital we had was around CAD 20,000(Canadian Dollars).”

Despite investing their savings.

They minimized costs.

They tried to conserve.

No office rental.

They worked in several coffee shops.

Tobi lived rent-free at his girlfriend's parents.

He installed software in coffee cafes.

How were the software issues handled?

Tobi found no online snowboard sales software.

Two choices remained:

1. Change your mind and try something else.

2. Use his programming expertise to produce something that will aid in the expansion of this company.

Tobi knew he was the sole programmer working on such a project from the start.

“I had this realisation that I’m going to be the only programmer who has ever worked on this, so I don’t have to choose something that lots of people know. I can choose just the best tool for the job…There is been this programming language called Ruby which I just absolutely loved ”

Ruby was open-source and only had Japanese documentation.

Latin is the source code.

Tobi used Ruby twice.

He assumed he could pick the tool this time.

Why not build with Ruby?

How did they find their first time operating a business?

Tobi writes applications in Ruby.

He wrote the initial software version in 2.5 months.

Tobi and Scott founded Snowdevil to sell snowboards.

Tobi coded for 16 hours a day.

His lifestyle was unhealthy.

He enjoyed pizza and coke.

“I would never recommend this to anyone, but at the time there was nothing more interesting to me in the world.”

Their initial purchase and encounter with it

Tobi worked in cafes then.

“I was working in a coffee shop at this time and I remember everything about that day…At some time, while I was writing the software, I had to type the email that the software would send to tell me about the order.”

Tobi recalls everything.

He checked the order on his laptop at the coffee shop.

Pennsylvanian ordered snowboard.

Tobi walked home and called Scott. Tobi told Scott their first order.

They loved the order.

How were people made aware about Snowdevil?

2004 was very different.

Tobi and Scott attempted simple website advertising.

Google AdWords was new.

Ad clicks cost 20 cents.

Online snowboard stores were scarce at the time.

Google ads propelled the snowdevil brand.

Snowdevil prospered.

They swiftly recouped their original investment in the snowboard business because to its high profit margin.

Tobi and Scott struggled with inventories.

“Snowboards had really good profit margins…Our biggest problem was keeping inventory and getting it back…We were out of stock all the time.”

Selling snowboards returned their investment and saved them money.

They did not appoint a business manager.

They accomplished everything alone.

Sales dipped in the spring, but something magical happened.

Spring sales plummeted.

They considered stocking different boards.

They naturally wanted to add boards and grow the business.

However, magic occurred.

Tobi coded and improved software while running Snowdevil.

He modified software constantly. He wanted speedier software.

He experimented to make the software more resilient.

Tobi received emails requesting the Snowdevil license.

They intended to create something similar.

“I didn’t stop programming, I was just like Ok now let me try things, let me make it faster and try different approaches…Increasingly I got people sending me emails and asking me If I would like to licence snowdevil to them. People wanted to start something similar.”

Software or skateboards, your choice

Scott and Tobi had to choose a hobby in 2005.

They might sell alternative boards or use software.

The software was a no-brainer from demand.

Daniel Weinand is invited to join Tobi's business.

Tobis German best friend is Daniel.

Tobi and Scott chose to use the software.

Tobi and Scott kept the software service.

Tobi called Daniel to invite him to Canada to collaborate.

Scott and Tobi had quit snowboarding until then.

How was Shopify launched, and whence did the name come from?

The three chose Shopify.

Named from two words.

First:

• Shop

Final part:

• Simplify

Shopify

Shopify's crew has always had one goal:

• creating software that would make it simple and easy for people to launch online storefronts.

Launched Shopify after raising money for the first time.

Shopify began fundraising in 2005.

First, they borrowed from family and friends.

They needed roughly $200k to run the company efficiently.

$200k was a lot then.

When questioned why they require so much money. Tobi told them to trust him with their goals. The team raised seed money from family and friends.

Shopify.com has a landing page. A demo of their goal was on the landing page.

In 2006, Shopify had about 4,000 emails.

Shopify rented an Ottawa office.

“We sent a blast of emails…Some people signed up just to try it out, which was exciting.”

How things developed after Scott left the company

Shopify co-founder Scott Lake left in 2008.

Scott was CEO.

“He(Scott) realized at some point that where the software industry was going, most of the people who were the CEOs were actually the highly technical person on the founding team.”

Scott leaving the company worried Tobi.

Tobis worried about finding a new CEO.

To Tobi:

A great VC will have the network to identify the perfect CEO for your firm.

Tobi started visiting Silicon Valley to meet with venture capitalists to recruit a CEO.

Initially visiting Silicon Valley

Tobi came to Silicon Valley to start a 20-person company.

This company creates eCommerce store software.

Tobi never wanted a big corporation. He desired a fulfilling existence.

“I stayed in a hostel in the Bay Area. I had one roommate who was also a computer programmer. I bought a bicycle on Craiglist. I was there for a week, but ended up staying two and a half weeks.”

Tobi arrived unprepared.

When venture capitalists asked him business questions.

He answered few queries.

Tobi didn't comprehend VC meetings' terminology.

He wrote the terms down and looked them up.

Some were fascinated after he couldn't answer all these queries.

“I ended up getting the kind of term sheets people dream about…All the offers were conditional on moving our company to Silicon Valley.”

Canada received Tobi.

He wanted to consult his team before deciding. Shopify had five employees at the time.

2008.

A global recession greeted Tobi in Canada. The recession hurt the market.

His term sheets were useless.

The economic downturn in the world provided Shopify with a fantastic opportunity.

The global recession caused significant job losses.

Fired employees had several ideas.

They wanted online stores.

Entrepreneurship was desired. They wanted to quit work.

People took risks and tried new things during the global slump.

Shopify subscribers skyrocketed during the recession.

“In 2009, the company reached neutral cash flow for the first time…We were in a position to think about long-term investments, such as infrastructure projects.”

Then, Tobi Lutke became CEO.

How did Tobi perform as the company's CEO?

“I wasn’t good. My team was very patient with me, but I had a lot to learn…It’s a very subtle job.”

2009–2010.

Tobi limited the company's potential.

He deliberately restrained company growth.

Tobi had one costly problem:

• Whether Shopify is a venture or a lifestyle business.

The company's annual revenue approached $1 million.

Tobi battled with the firm and himself despite good revenue.

His wife was supportive, but the responsibility was crushing him.

“It’s a crushing responsibility…People had families and kids…I just couldn’t believe what was going on…My father-in-law gave me money to cover the payroll and it was his life-saving.”

Throughout this trip, everyone supported Tobi.

They believed it.

$7 million in donations received

Tobi couldn't decide if this was a lifestyle or a business.

Shopify struggled with marketing then.

Later, Tobi tried 5 marketing methods.

He told himself that if any marketing method greatly increased their growth, he would call it a venture, otherwise a lifestyle.

The Shopify crew brainstormed and voted on marketing concepts.

Tested.

“Every single idea worked…We did Adwords, published a book on the concept, sponsored a podcast and all the ones we tracked worked.”

To Silicon Valley once more

Shopify marketing concepts worked once.

Tobi returned to Silicon Valley to pitch investors.

He raised $7 million, valuing Shopify at $25 million.

All investors had board seats.

“I find it very helpful…I always had a fantastic relationship with everyone who’s invested in my company…I told them straight that I am not going to pretend I know things, I want you to help me.”

Tobi developed skills via running Shopify.

Shopify had 20 employees.

Leaving his wife's parents' home

Tobi left his wife's parents in 2014.

Tobi had a child.

Shopify has 80,000 customers and 300 staff in 2013.

Public offering in 2015

Shopify investors went public in 2015.

Shopify powers 4.1 million e-Commerce sites.

Shopify stores are 65% US-based.

It is currently valued at $48 billion.

Decentralized perpetual may be the next crypto market boom; with tons of perpetual popping up, let's look at two protocols that offer organic, non-inflationary yields.

Decentralized derivatives exchanges' market share has increased tenfold in a year, but it's still 2% of CEXs'. DEXs have a long way to go before they can compete with centralized exchanges in speed, liquidity, user experience, and composability.

I'll cover gains.trade and GMX protocol in Polygon, Avalanche, and Arbitrum. Both protocols support leveraged perpetual crypto, stock, and Forex trading.

Why these protocols?

Decentralized GMX Gains protocol

Organic yield: path to sustainability

I've never trusted Defi's non-organic yields. Example: XYZ protocol. 20–75% of tokens may be set aside as farming rewards to provide liquidity, according to tokenomics.

Say you provide ETH-USDC liquidity. They advertise a 50% APR reward for this pair, 10% from trading fees and 40% from farming rewards. Only 10% is real, the rest is "Ponzi." The "real" reward is in protocol tokens.

Why keep this token? Governance voting or staking rewards are promoted services.

Most liquidity providers expect compensation for unused tokens. Basic psychological principles then? — Profit.

Nobody wants governance tokens. How many out of 100 care about the protocol's direction and will vote?

Staking increases your token's value. Currently, they're mostly non-liquid. If the protocol is compromised, you can't withdraw funds. Most people are sceptical of staking because of this.

"Free tokens," lack of use cases, and skepticism lead to tokens moving south. No farming reward protocols have lasted.

It may have shown strength in a bull market, but what about a bear market?

What is decentralized perpetual?

A perpetual contract is a type of futures contract that doesn't expire. So one can hold a position forever.

You can buy/sell any leveraged instruments (Long-Short) without expiration.

In centralized exchanges like Binance and coinbase, fees and revenue (liquidation) go to the exchanges, not users.

Users can provide liquidity that traders can use to leverage trade, and the revenue goes to liquidity providers.

Gains.trade and GMX protocol are perpetual trading platforms with a non-inflationary organic yield for liquidity providers.

GMX protocol

GMX is an Arbitrum and Avax protocol that rewards in ETH and Avax. GLP uses a fast oracle to borrow the "true price" from other trading venues, unlike a traditional AMM.

GLP and GMX are protocol tokens. GLP is used for leveraged trading, swapping, etc.

GLP is a basket of tokens, including ETH, BTC, AVAX, stablecoins, and UNI, LINK, and Stablecoins.

GLP composition on arbitrum

GLP composition on Avalanche

GLP token rebalances based on usage, providing liquidity without loss.

Protocol "runs" on Staking GLP. Depending on their chain, the protocol will reward users with ETH or AVAX. Current rewards are 22 percent (15.71 percent in ETH and the rest in escrowed GMX) and 21 percent (15.72 percent in AVAX and the rest in escrowed GMX). escGMX and ETH/AVAX percentages fluctuate.

Where is the yield coming from?

Swap fees, perpetual interest, and liquidations generate yield. 70% of fees go to GLP stakers, 30% to GMX. Organic yields aren't paid in inflationary farm tokens.

Escrowed GMX is vested GMX that unlocks in 365 days. To fully unlock GMX, you must farm the Escrowed GMX token for 365 days. That means less selling pressure for the GMX token.

GMX's status

These are the fees in Arbitrum in the past 11 months by GMX.

GMX works like a casino, which increases fees. Most fees come from Margin trading, which means most traders lose money; this money goes to the casino, or GLP stakers.

Strategies

My personal strategy is to DCA into GLP when markets hit bottom and stake it; GLP will be less volatile with extra staking rewards.

GLP YoY return vs. naked buying

Let's say I invested $10,000 in BTC, AVAX, and ETH in January.

• BTC price: 47665$

• ETH price: 3760$

• AVAX price: $145

Current prices

• BTC $21,000 (Down 56 percent )

• ETH $1233 (Down 67.2 percent )

• AVAX $20.36 (Down 85.95 percent )

Your $10,000 investment is now worth around $3,000.

How about GLP? My initial investment is 50% stables and 50% other assets ( Assuming the coverage ratio for stables is 50 percent at that time)

Without GLP staking yield, your value is $6500.

Let's assume the average APR for GLP staking is 23%, or $1500. So 8000$ total. It's 50% safer than holding naked assets in a bear market.

In a bull market, naked assets are preferable to GLP.

Short farming using GLP

Simple GLP short farming.

You use a stable asset as collateral to borrow AVAX. Sell it and buy GLP. Even if GLP rises, it won't rise as fast as AVAX, so we can get yields.

Let's do the maths

You deposit $10,000 USDT in Aave and borrow Avax. Say you borrow $8,000; you sell it, buy GLP, and risk 20%.

After a year, ETH, AVAX, and BTC rise 20%. GLP is $8800. $800 vanishes. 20% yields $1600. You're profitable. Shorting Avax costs $1600. (Assumptions-ETH, AVAX, BTC move the same, GLP yield is 20%. GLP has a 50:50 stablecoin/others ratio. Aave won't liquidate

In naked Avax shorting, Avax falls 20% in a year. You'll make $1600. If you buy GLP and stake it using the sold Avax and BTC, ETH and Avax go down by 20% - your profit is 20%, but with the yield, your total gain is $2400.

Issues with GMX

GMX's historical funding rates are always net positive, so long always pays short. This makes long-term shorts less appealing.

Oracle price discovery isn't enough. This limitation doesn't affect Bitcoin and ETH, but it affects less liquid assets. Traders can buy and sell less liquid assets at a lower price than their actual cost as long as GMX exists.

As users must provide GLP liquidity, adding more assets to GMX will be difficult. Next iteration will have synthetic assets.

Gains Protocol

Best leveraged trading platform. Smart contract-based decentralized protocol. 46 crypto pairs can be leveraged 5–150x and 10 Forex pairs 5–1000x. $10 DAI @ 150x (min collateral x leverage pos size is $1500 DAI). No funding fees, no KYC, trade DAI from your wallet, keep funds.

DAI single-sided staking and the GNS-DAI pool are important parts of Gains trading. GNS-DAI stakers get 90% of trading fees and 100% swap fees. 10 percent of trading fees go to DAI stakers, which is currently 14 percent!

Trade volume

When a trader opens a trade, the leverage and profit are pulled from the DAI pool. If he loses, the protocol yield goes to the stakers.

If the trader's win rate is high and the DAI pool slowly depletes, the GNS token is minted and sold to refill DAI. Trader losses are used to burn GNS tokens. 25%+ of GNS is burned, making it deflationary.

Due to high leverage and volatility of crypto assets, most traders lose money and the protocol always wins, keeping GNS deflationary.

Gains uses a unique decentralized oracle for price feeds, which is better for leverage trading platforms. Let me explain.

Gains uses chainlink price oracles, not its own price feeds. Chainlink oracles only query centralized exchanges for price feeds every minute, which is unsuitable for high-precision trading.

Gains created a custom oracle that queries the eight chainlink nodes for the current price and, on average, for trade confirmation. This model eliminates every-second inquiries, which waste gas but are more efficient than chainlink's per-minute price.

This price oracle helps Gains open and close trades instantly, eliminate scam wicks, etc.

Other benefits include:

• Stop-loss guarantee (open positions updated)

• No scam wicks

• Spot-pricing

• Highest possible leverage

• Fixed-spreads. During high volatility, a broker can increase the spread, which can hit your stop loss without the price moving.

• Trade directly from your wallet and keep your funds.

• >90% loss before liquidation (Some platforms liquidate as little as -50 percent)

• KYC-free

• Directly trade from wallet; keep funds safe

Further improvements

GNS-DAI liquidity providers fear the impermanent loss, so the protocol is migrating to its own liquidity and single staking GNS vaults. This allows users to stake GNS without permanent loss and obtain 90% DAI trading fees by staking. This starts in August.

Their upcoming improvements can be found here.

Gains constantly add new features and change pairs. It's an interesting protocol.

Conclusion

Next bull run, watch decentralized perpetual protocols. Effective tokenomics and non-inflationary yields may attract traders and liquidity providers. But still, there is a long way for them to develop, and I don't see them tackling the centralized exchanges any time soon until they fix their inherent problems and improve fast enough.

Read the full post here.

The same way Amazon killed bookstores. You just can’t see it yet.

Shultz globalized coffee. Before Starbucks, coffee sucked.

All accounts say 1970s coffee was awful.

Starbucks had three stores selling ground Indonesian coffee in the 1980s.

What a show!

A year after joining the company at 29, Shultz traveled to Italy for R&D.

He noticed the coffee shops' sense of theater and community and realized Starbucks was in the wrong business.

Integrating coffee and destination created a sense of community in the store.

Brilliant!

He told Starbucks' founders about his experience.

They disapproved.

For two years.

Shultz left and opened an Italian coffee shop chain like any good entrepreneur.

Starbucks ran into financial trouble, so the founders offered to sell to Shultz.

Shultz bought Starbucks in 1987 for $3.8 million, including six stores and a payment plan.

Starbucks is worth $100.79Billion, per Google Finance.

26,500 times Shultz's initial investment

Starbucks is releasing its own NFT Platform under Shultz and his early Vision.

This year, Starbucks Odyssey launches. The new digital experience combines a Loyalty Rewards program with NFT.

The side chain Polygon-based platform doesn't require a Crypto Wallet. Customers can earn and buy digital assets to unlock incentives and experiences.

They've removed all friction, making it more immersive and convenient than a coffee shop.

Brilliant!

NFTs are the access coupon to their digital community, but they don't highlight the technology.

They prioritize consumer experience by adding non-technical users to Web3. Their collectables are called journey stamps, not NFTs.

No mention of bundled gas fees.

Brady Brewer, Starbucks' CMO, said;

“It happens to be built on blockchain and web3 technologies, but the customer — to be honest — may very well not even know that what they’re doing is interacting with blockchain technology. It’s just the enabler,”

Rewards members will log into a web app using their loyalty program credentials to access Starbucks Odyssey. They won't know about blockchain transactions.

Join the waitlist here

Starbucks has just dealt its rivals a devastating blow.

It generates more than ten times the revenue of its closest competitor Costa Coffee.

The coffee giant is booming.

Starbucks is ahead of its competitors. No wonder.

They have an innovative, adaptable leadership team.

Starbucks' DNA challenges the narrative, especially when others reject their ideas.

I’m off for a cappuccino.