More on Society & Culture
1 month ago
Why personal ambition and poor leadership caused Google layoffs
Google announced 6% layoffs recently (or 12,000 people). This aligns it with most tech companies. A publicly contrite CEO explained that they had overhired during the COVID-19 pandemic boom and had to address it, but they were sorry and took full responsibility. I thought this was "bullshit" too. Meta, Amazon, Microsoft, and others must feel similarly. I spent 10 years at Google, and these things don't reflect well on the company's leaders.
All publicly listed companies have a fiduciary duty to act in the best interests of their shareholders. Dodge vs. Ford Motor Company established this (1919). Henry Ford wanted to reduce shareholder payments to offer cheaper cars and better wages. Ford stated.
My ambition is to employ still more men, to spread the benefits of this industrial system to the greatest possible number, to help them build up their lives and their homes. To do this we are putting the greatest share of our profits back in the business.
The Dodge brothers, who owned 10% of Ford, opposed this and sued Ford for the payments to start their own company. They won, preventing Ford from raising prices or salaries. If you have a vocal group of shareholders with the resources to sue you, you must prove you are acting in their best interests. Companies prioritize shareholders. Giving activist investors a stick to threaten you almost enshrines short-term profit over long-term thinking.
This underpins Google's current issues. Institutional investors who can sue Google see it as a wasteful company they can exploit. That doesn't mean you have to maximize profits (thanks to those who pointed out my ignorance of US corporate law in the comments and on HN), but it allows pressure. I feel for those navigating this. This is about unrestrained capitalism.
When Google went public, Larry Page and Sergey Brin knew the risks and worked hard to keep control. In their Founders' Letter to investors, they tried to set expectations for the company's operations.
Our long-term focus as a private company has paid off. Public companies do the same. We believe outside pressures lead companies to sacrifice long-term opportunities to meet quarterly market expectations.
The company has transformed since that letter. The company has nearly 200,000 full-time employees and a trillion-dollar market cap. Large investors have bought company stock because it has been a good long-term bet. Why are they restless now?
Other big tech companies emerged and fought for top talent. This has caused rising compensation packages. Google has also grown rapidly (roughly 22,000 people hired to the end of 2022). At $300,000 median compensation, those 22,000 people added $6.6 billion in salary overheads in 2022. Exorbitant. If the company still makes $16 billion every quarter, maybe not. Investors wonder if this value has returned.
Investors are right. Google uses people wastefully. However, by bluntly reducing headcount, they're not addressing the root causes and hurting themselves. No studies show that downsizing this way boosts productivity. There is plenty of evidence that they'll lose out because people will be risk-averse and distrust their leadership.
The company's approach also stinks. Finding out that you no longer have a job because you can’t log in anymore (sometimes in cases where someone is on call for protecting your production systems) is no way to fire anyone. Being with a narcissistic sociopath is like being abused. First, you receive praise and fancy perks for making the cut. You're fired by text and ghosted. You're told to appreciate the generous severance package. This firing will devastate managers and teams. This type of firing will take years to recover self-esteem. Senior management contributed to this. They chose the expedient answer, possibly by convincing themselves they were managing risk and taking the Macbeth approach of “If it were done when ’tis done, then ’twere well It were done quickly”.
Recap. Google's leadership did a stupid thing—mass firing—in a stupid way. How do we get rid of enough people to make investors happier? and "have 6% less people." Empathetic leaders should not emulate Elon Musk. There is no humane way to fire 12,000 people, but there are better ways. Why is Google so wasteful?
Ambition answers this. There aren't enough VP positions for a group of highly motivated, ambitious, and (increasingly) ruthless people. I’ve loitered around the edges of this world and a large part of my value was to insulate my teams from ever having to experience it. It’s like Game of Thrones played out through email and calendar and over video call.
Your company must look a certain way to be promoted to director or higher. You need the right people at the right levels under you. Long-term, growing your people will naturally happen if you're working on important things. This takes time, and you're never more than 6–18 months from a reorg that could start you over. Ambitious people also tend to be impatient. So, what do you do?
Hiring and vanity projects. To shape your company, you hire at the right levels. You value vanity metrics like active users over product utility. Your promo candidates get through by subverting the promotion process. In your quest for growth, you avoid performance managing people out. You avoid confronting toxic peers because you need their support for promotion. Your cargo cult gets you there.
Its ease makes Google wasteful. Since they don't face market forces, the employees don't see it as a business. Why would you do when the ads business is so profitable? Complacency causes senior leaders to prioritize their own interests. Empires collapse. Personal ambition often trumped doing the right thing for users, the business, or employees. Leadership's ambition over business is the root cause. Vanity metrics, mass hiring, and vague promises have promoted people to VP. Google goes above and beyond to protect senior leaders.
The decision-makers and beneficiaries are not the layoffees. Stock price increase beneficiaries. The people who will post on LinkedIn how it is about misjudging the market and how they’re so sorry and take full responsibility. While accumulating wealth, the dark room dwellers decide who stays and who goes. The billionaire investors. Google should start by addressing its bloated senior management, but — as they say — turkeys don't vote for Christmas. It should examine its wastefulness and make tough choices to fix it. A 6% cut is a blunt tool that admits you're not running your business properly. why aren’t the people running the business the ones shortly to be entering the job market?
This won't fix Google's wastefulness. The executives may never regain trust after their approach. Suppressed creativity. Business won't improve. Google will have lost its founding vision and us all. Large investors know they can force Google's CEO to yield. The rich will get richer and rationalize leaving 12,000 people behind. Cycles repeat.
It doesn’t have to be this way. In 2013, Nintendo's CEO said he wouldn't fire anyone for shareholders. Switch debuted in 2017. Nintendo's stock has increased by nearly five times, or 19% a year (including the drop most of the stock market experienced last year). Google wasted 12,000 talented people. To please rich people.
1 month ago
The reasons why our civilization is deteriorating
The Industrial Revolution's Curse: Why One Age's Power Prevents the Next Ones
A surprising fact. Recently, Big Oil's 1970s climate change projections were disturbingly accurate. Of course, we now know that it worked tirelessly to deny climate change, polluting our societies to this day. That's a small example of the Industrial Revolution's curse.
Let me rephrase this nuanced and possibly weird thought. The chart above? Disruptive science is declining. The kind that produces major discoveries, new paradigms, and shattering prejudices.
Not alone. Our civilisation reached a turning point suddenly. Progress stopped and reversed for the first time in centuries.
The Industrial Revolution's Big Bang started it all. At least some humans had riches for the first time, if not all, and with that wealth came many things. Longer, healthier lives since now health may be publicly and privately invested in. For the first time in history, wealthy civilizations could invest their gains in pure research, a good that would have sounded frivolous to cultures struggling to squeeze out the next crop, which required every shoulder to the till.
So. Don't confuse me with the Industrial Revolution's curse. Industry progressed. Contrary. I'm claiming that the Big Bang of Progress is slowing, plateauing, and ultimately reversing. All social indicators show that. From progress itself to disruptive, breakthrough research, everything is slowing down.
It's troubling. Because progress slows and plateaus, pre-modern social problems like fascism, extremism, and fundamentalism return. People crave nostalgic utopias when they lose faith in modernity. That strongman may shield me from this hazardous life. If I accept my place in a blood-and-soil hierarchy, I have a stable, secure position and someone to punch and detest. It's no coincidence that as our civilization hits a plateau of progress, there is a tsunami pulling the world backwards, with people viscerally, openly longing for everything from theocracy to fascism to fundamentalism, an authoritarian strongman to soothe their fears and tell them what to do, whether in Britain, heartland America, India, China, and beyond.
However, one aspect remains unknown. Technology. Let me clarify.
How do most people picture tech? Say that without thinking. Most people think of social media or AI. Well, small correlation engines called artificial neurons are a far cry from biological intelligence, which functions in far more obscure and intricate ways, down to the subatomic level. But let's try it.
Today, tech means AI. But. Do you foresee it?
Consider why civilisation is plateauing and regressing. Because we can no longer provide the most basic necessities at the same rate. On our track, clean air, water, food, energy, medicine, and healthcare will become inaccessible to huge numbers within a decade or three. Not enough. There isn't, therefore prices for food, medicine, and energy keep rising, with occasional relief.
Why our civilizations are encountering what economists like me term a budget constraint—a hard wall of what we can supply—should be evident. Global warming and extinction. Megafires, megadroughts, megafloods, and failed crops. On a civilizational scale, good luck supplying the fundamentals that way. Industrial food production cannot feed a planet warming past two degrees. Crop failures, droughts, floods. Another example: glaciers melt, rivers dry up, and the planet's fresh water supply contracts like a heart attack.
Now. Let's talk tech again. Mostly AI, maybe phone apps. The unsettling reality is that current technology cannot save humanity. Not much.
AI can do things that have become cliches to titillate the masses. It may talk to you and act like a person. It can generate art, which means reproduce it, but nonetheless, AI art! Despite doubts, it promises to self-drive cars. Unimportant.
We need different technology now. AI won't grow crops in ash-covered fields, cleanse water, halt glaciers from melting, or stop the clear-cutting of the planet's few remaining forests. It's not useless, but on a civilizational scale, it's much less beneficial than its proponents claim. By the time it matures, AI can help deliver therapy, keep old people company, and even drive cars more efficiently. None of it can save our culture.
Expand that scenario. AI's most likely use? Replacing call-center workers. Support. It may help doctors diagnose, surgeons orient, or engineers create more fuel-efficient motors. This is civilizationally marginal.
Non-disruptive. Do you see the connection with the paper that indicated disruptive science is declining? AI exemplifies that. It's called disruptive, yet it's a textbook incremental technology. Oh, cool, I can communicate with a bot instead of a poor human in an underdeveloped country and have the same or more trouble being understood. This bot is making more people unemployed. I can now view a million AI artworks.
AI illustrates our civilization's trap. Its innovative technologies will change our lives. But as you can see, its incremental, delivering small benefits at most, and certainly not enough to balance, let alone solve, the broader problem of steadily dropping living standards as our society meets a wall of being able to feed itself with fundamentals.
Contrast AI with disruptive innovations we need. What do we need to avoid a post-Roman Dark Age and preserve our civilization in the coming decades? We must be able to post-industrially produce all our basic needs. We need post-industrial solutions for clean water, electricity, cement, glass, steel, manufacture for garments and shoes, starting with the fossil fuel-intensive plastic, cotton, and nylon they're made of, and even food.
Consider. We have no post-industrial food system. What happens when crop failures—already dangerously accelerating—reach a critical point? Our civilization is vulnerable. Think of ancient civilizations that couldn't survive the drying up of their water sources, the failure of their primary fields, which they assumed the gods would preserve forever, or an earthquake or sickness that killed most of their animals. Bang. Lost. They failed. They splintered, fragmented, and abandoned vast capitols and cities, and suddenly, in history's sight, poof, they were gone.
We're getting close. Decline equals civilizational peril.
We believe dumb notions about AI becoming disruptive when it's incremental. Most of us don't realize our civilization's risk because we believe these falsehoods. Everyone should know that we cannot create any thing at civilizational scale without fossil fuels. Most of us don't know it, thus we don't realize that the breakthrough technologies and systems we need don't manipulate information anymore. Instead, biotechnologies, largely but not genes, generate food without fossil fuels.
We need another Industrial Revolution. AI, apps, bots, and whatnot won't matter unless you think you can eat and drink them while the world dies and fascists, lunatics, and zealots take democracy's strongholds. That's dramatic, but only because it's already happening. Maybe AI can entertain you in that bunker while society collapses with smart jokes or a million Mondrian-like artworks. If civilization is to survive, it cannot create the new Industrial Revolution.
The revolution has begun, but only in small ways. Post-industrial fundamental systems leaders are developing worldwide. The Netherlands is leading post-industrial agriculture. That's amazing because it's a tiny country performing well. Correct? Discover how large-scale agriculture can function, not just you and me, aged hippies, cultivating lettuce in our backyards.
Iceland is leading bioplastics, which, if done well, will be a major advance. Of sure, microplastics are drowning the oceans. What should we do since we can't live without it? We need algae-based bioplastics for green plastic.
That's still young. Any of the above may not function on a civilizational scale. Bioplastics use algae, which can cause problems if overused. None of the aforementioned indicate the next Industrial Revolution is here. Contrary. Slowly.
We have three decades until everything fails. Before life ends. Curtain down. No more fields, rivers, or weather. Freshwater and life stocks have plummeted. Again, we've peaked and declined in our ability to live at today's relatively rich standards. Game over—no more. On a dying planet, producing the fundamentals for a civilisation that left it too late to construct post-industrial systems becomes next to impossible, with output dropping faster and quicker each year, quarter, and day.
Too slow. That's because it's not really happening. Most people think AI when I say tech. I get a politicized response if I say Green New Deal or Clean Industrial Revolution. Half the individuals I talk to have been politicized into believing that climate change isn't real and that any breakthrough technical progress isn't required, desirable, possible, or genuine. They'll suffer.
The Industrial Revolution curse. Every revolution creates new authorities, which ossify and refuse to relinquish their privileges. For fifty years, Big Oil has denied climate change, even though their scientists predicted it. We also have a software industry and its venture capital power centers that are happy for the average person to think tech means chatbots, not being able to produce basics for a civilization without destroying the planet, and billionaires who buy comms platforms for the same eye-watering amount of money it would take to save life on Earth.
The entire world's vested interests are against the next industrial revolution, which is understandable since they were established from fossil money. From finance to energy to corporate profits to entertainment, power in our world is the result of the last industrial revolution, which means it has no motivation or purpose to give up fossil money, as we are witnessing more brutally out in the open.
Thus, the Industrial Revolution's curse—fossil power—rules our globe. Big Agriculture, Big Pharma, Wall St., Silicon Valley, and many others—including politics, which they buy and sell—are basically fossil power, and they have no interest in generating or letting the next industrial revolution happen. That's why tiny enterprises like those creating bioplastics in Iceland or nations savvy enough to shun fossil power, like the Netherlands, which has a precarious relationship with nature, do it. However, fossil power dominates politics, economics, food, clothes, energy, and medicine, and it has no motivation to change.
Allow disruptive innovations again. As they occur, its position becomes increasingly vulnerable. If you were fossil power, would you allow another industrial revolution to destroy its privilege and wealth?
You might, since power and money haven't corrupted you. However, fossil power prevents us from building, creating, and growing what we need to survive as a society. I mean the entire economic, financial, and political power structure from the last industrial revolution, not simply Big Oil. My friends, fossil power's chokehold over our society is likely to continue suffocating the advances that could have spared our civilization from a decline that's now here and spiraling closer to oblivion.
8 months ago
Why the Creator Economy needs a Web3 upgrade
Looking back into the past can help you understand what's happening today and why.
"Creator economy" conjures up images of originality, sincerity, and passion. Where do Michelangelos and da Vincis push advancement with their gifts without battling for bread and proving themselves posthumously?
Creativity has been as long as humanity, but it's just recently become a new economic paradigm. We even talk about Web3 now.
Let's examine the creative economy's history to better comprehend it. What brought us here? Looking back can help you understand what's happening now.
No yawning, I promise 😉.
Creator Economy's history
Long, uneven transition to creator economy. Let's examine the economic and societal changes that led us there.
1. Agriculture to industry
Mid-18th-century Industrial Revolution led to shift from agriculture to manufacturing. The industrial economy lasted until World War II.
The industrial economy's principal goal was to provide more affordable, accessible commodities.
Unlike today, products were scarce and inaccessible.
To fulfill its goals, industrialization triggered enormous economic changes, moving power from agrarians to manufacturers. Industrialization brought hard work, rivalry, and new ideas connected to production and automation. Creative thinkers focused on that then.
It doesn't mean music, poetry, or painting had no place back then. They weren't top priority. Artists were independent. The creative field wasn't considered a different economic subdivision.
2. The consumer economy
Manufacturers produced more things than consumers desired after World War II. Stuff was no longer scarce.
The economy must make customers want to buy what the market offers.
The consumer economic paradigm supplanted the industrial one. Customers (or consumers) replaced producers as the new economic center.
Salesmen, marketing, and journalists also played key roles (TV, radio, newspapers, etc.). Mass media greatly boosted demand for goods, defined trends, and changed views regarding nearly everything.
Mass media also gave rise to pop culture, which focuses on mass-market creative products. Design, printing, publishing, multi-media, audio-visual, cinematographic productions, etc. supported pop culture.
The consumer paradigm generated creative occupations and activities, unlike the industrial economy. Creativity was limited by the need for wide appeal.
Most creators were corporate employees.
Creating a following and making a living from it were difficult.
Paul Saffo said that only journalists and TV workers were known. Creators who wished to be known relied on producers, publishers, and other gatekeepers. To win their favor was crucial. Luck was the best tactic.
3. The creative economy
Consumer economy was digitized in the 1990s. IT solutions transformed several economic segments. This new digital economy demanded innovative, digital creativity.
Later, states declared innovation a "valuable asset that creates money and jobs." They also introduced the "creative industries" and the "creative economy" (not creator!) and tasked themselves with supporting them. Australia and the UK were early adopters.
Individual skill, innovation, and intellectual property fueled the creative economy. Its span covered design, writing, audio, video material, etc. The creative economy required IT-powered activity.
The new challenge was to introduce innovations to most economic segments and meet demand for digital products and services.
Despite what the title "creative economy" may imply, it was primarily oriented at meeting consumer needs. It didn't provide inventors any new options to become entrepreneurs. Instead of encouraging innovators to flourish on their own, the creative economy emphasized "employment-based creativity."
4. The creator economy
Next, huge IT platforms like Google, Facebook, YouTube, and others competed with traditional mainstream media.
During the 2008 global financial crisis, these mediums surpassed traditional media. People relied on them for information, knowledge, and networking. That was a digital media revolution. The creator economy started there.
The new economic paradigm aimed to engage and convert clients. The creator economy allowed customers to engage, interact, and provide value, unlike the consumer economy. It gave them instruments to promote themselves as "products" and make money.
Writers, singers, painters, and other creators have a great way to reach fans. Instead of appeasing old-fashioned gatekeepers (producers, casting managers, publishers, etc.), they can use the platforms to express their talent and gain admirers. Barriers fell.
It's not only for pros. Everyone with a laptop and internet can now create.
2022 creator economy:
Since there is no academic description for the current creator economy, we can freestyle.
The current (or Web2) creator economy is fueled by interactive digital platforms, marketplaces, and tools that allow users to access, produce, and monetize content.
No entry hurdles or casting in the creative economy. Sign up and follow platforms' rules. Trick: A platform's algorithm aggregates your data and tracks you. This is the payment for participation.
The platforms offer content creation, design, and ad distribution options. This is platforms' main revenue source.
The creator economy opens many avenues for creators to monetize their work. Artists can now earn money through advertising, tipping, brand sponsorship, affiliate links, streaming, and other digital marketing activities.
Even if your content isn't digital, you can utilize platforms to promote it, interact and convert your audience, and more. No limits. However, some of your income always goes to a platform (well, a huge one).
The creator economy aims to empower online entrepreneurship by offering digital marketing tools and reducing impediments.
Barriers remain. They are just different. Next articles will examine these.
Why update the creator economy for Web3?
I could address this question by listing the present creator economy's difficulties that led us to contemplate a Web3 upgrade.
I don't think these difficulties are the main cause. The mentality shift made us see these challenges and understand there was a better reality without them.
Crypto drove this thinking shift. It promoted disintermediation, independence from third-party service providers, 100% data ownership, and self-sovereignty. Crypto has changed the way we view everyday things.
Crypto's disruptive mission has migrated to other economic segments. It's now called Web3. Web3's creator economy is unique.
Here's the essence of the Web3 economy:
Eliminating middlemen between creators and fans.
100% of creators' data, brand, and effort.
Business and money-making transparency.
Authentic originality above ad-driven content.
In the next several articles, I'll explain. We'll also discuss the creator economy and Web3's remedies.
The creator economy is the organic developmental stage we've reached after all these social and economic transformations.
The Web3 paradigm of the creator economy intends to allow creators to construct their own independent "open economy" and directly monetize it without a third party.
If this approach succeeds, we may enter a new era of wealth creation where producers aren't only the products. New economies will emerge.
This article is a summary. To read the full post, click here.
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7 months ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
Run a business.
Promote a product
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
1 month ago
7 Mac Apps That Are Exorbitantly Priced But Totally Worth It
Wish you more bang for your buck
By ‘Cost a Bomb’ I didn’t mean to exaggerate. It’s an idiom that means ‘To be very expensive’. In fact, no app on the planet costs a bomb lol.
So, to the point.
(Freemium. For Pro, $24.99 | Available on Setapp)
You probably have trouble keeping track of dozens of bills and subscriptions each month.
Add payment due dates and receive reminders,
Save payment documentation,
Analyze your spending by season, year, and month.
Observe expenditure trends and create new budgets.
Best of all, Chronicle features an integrated browser for fast payment and logging.
iOS and macOS sync.
($39 for lifetime)
Background Music, a free macOS program, was featured in #6 of this post last month.
It controls per-app volume, stereo balance, and audio over its max level.
Background Music is fully supported. Additionally,
Connect various speakers to various apps (Wow! ),
change the audio sample rate for each app,
To facilitate access, add a floating SoundSource window.
Use its blocks in Shortcuts app,
On the menu bar, include meters for output/input devices and running programs.
($39 for lifetime | Available on Setapp)
This software is heaven for UI designers.
It aids you.
quickly calculate screen distances (in pixels) ,
Drag an area around an object to determine its borders,
Measure the distances between the additional guides,
screenshots should be pixel-perfect.
Adapt your tolerance for items with poor contrast and shadows.
Use your Touch Bar to perform important tasks, if you have one.
($3.99 a month / $29.99 a year | Available on Setapp)
Mate Translate resembles a roided-up version of BarTranslate, which I wrote about in #1 of this piece last month.
If you translate often, utilize Mate Translate on macOS and Safari.
I'm really vocal about it.
It stays on the menu bar, and is accessible with a click or ⌥+shift+T hotkey.
It lets you
Translate in 103 different languages,
To translate text, double-click or right-click on it.
Totally translate websites. Additionally, Netflix subtitles,
Listen to their pronunciation to see how close it is to human.
iPhone and Mac sync Mate-ing history.
($16 for lifetime | Available on Setapp)
Swish is awesome!
Swipe, squeeze, tap, and hold movements organize chaotic desktop windows. Swish operates with mouse and trackpad.
• Pinch Once: Close an app
• Pinch Twice: Quit an app
• Swipe down once: Minimise an app
• Pinch Out: Enter fullscreen mode
• Tap, Hold, & Swipe: Arrange apps in grids
and many more...
After getting acquainted to the movements, your multitasking will improve.
($24.99 for lifetime | Available on Setapp)
It turns webapps into macOS apps. The end.
Unite's functionality is a million times better.
Provide extensive customization (incl. its icon, light and dark modes)
make menu bar applications,
Get badges for web notifications and automatically refresh websites,
Replace any dock icon in the window with it (Wow!) by selecting that portion of the window.
Use PiP (Picture-in-Picture) on video sites that support it.
Throughout macOS, use floating windows
and many more…
I feel $24.99 one-off for this tool is a great deal, considering all these features. What do you think?
(Basic: $29 one-off. Pro: $8/month | Available on Setapp)
CleanShot X can achieve things the macOS screenshot tool cannot. Complete screenshot toolkit.
CleanShot X, like Pixel Snap 2 (#3), is fantastic.
Scroll to capture a long page,
With webcam on,
• With mic and system audio,
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8 months ago
Say goodbye to Ponzi yields - A new era of decentralized perpetual
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 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.
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.
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
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.
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!
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
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)
Directly trade from wallet; keep funds safe
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.
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.