Key Takeaways:

Another hack. This time was different. This is a doozy.

Why? Nomad got exploited for $190m. It was crypto's 5th-biggest hack. Ouch.

It wasn't hackers, but random folks. What happened:

A Nomad smart contract flaw was discovered. They couldn't drain the funds at once, so they tried numerous transactions. Rookie!

People noticed and copied the attack.

They just needed to discover a working transaction, substitute the other person's address with theirs, and run it.

In a two-and-a-half-hour attack, $190M was siphoned from Nomad Bridge.

Nomad is a novel approach to blockchain interoperability that leverages an optimistic mechanism to increase the security of cross-chain communication.  — nomad.xyz

This hack was permissionless, therefore anyone could participate.

After the fatal blow, people fought over the scraps.

Cross-chain bridges remain a DeFi weakness and exploit target. When they collapse, it's typically total.

$190M...gobbled.

Unbacked assets are hurting Nomad-dependent chains. Moonbeam, EVMOS, and Milkomeda's TVLs dropped.

This incident is every-man-for-himself, although numerous whitehats exploited the issue... 

But what triggered the feeding frenzy?

How did so many pick the bones?

After a normal upgrade in June, the bridge's Replica contract was initialized with a severe security issue. The  0x00 address was a trusted root, therefore all messages were valid by default.

After a botched first attempt (costing $350k in gas), the original attacker's exploit tx called process() without first 'proving' its validity.

The process() function executes all cross-chain messages and checks the merkle root of all messages (line 185).

The upgrade caused transactions with a'messages' value of 0 (invalid, according to old logic) to be read by default as 0x00, a trusted root, passing validation as 'proven'

Any process() calls were valid. In reality, a more sophisticated exploiter may have designed a contract to drain the whole bridge.

Copycat attackers simply copied/pasted the same process() function call using Etherscan, substituting their address.

The incident was a wild combination of crowdhacking, whitehat activities, and MEV-bot (Maximal Extractable Value) mayhem.

For example, 🍉🍉🍉. eth stole $4M from the bridge, but claims to be whitehat.

Others stood out for the wrong reasons. Repeat criminal Rari Capital (Artibrum) exploited over $3M in stablecoins, which moved to Tornado Cash.

The top three exploiters (with 95M between them) are:

$47M: 0x56D8B635A7C88Fd1104D23d632AF40c1C3Aac4e3

$40M: 0xBF293D5138a2a1BA407B43672643434C43827179

$8M: 0xB5C55f76f90Cc528B2609109Ca14d8d84593590E

Here's a list of all the exploiters:

The project conducted a Quantstamp audit in June; QSP-19 foreshadowed a similar problem.

The auditor's comments that "We feel the Nomad team misinterpreted the issue" speak to a troubling attitude towards security that the project's "Long-Term Security" plan appears to confirm:

Concerns were raised about the team's response time to a live, public exploit; the team's official acknowledgement came three hours later.

"Removing the Replica contract as owner" stopped the exploit, but it was too late to preserve the cash.

Closed blockchain systems are only as strong as their weakest link.

The Harmony network is in turmoil after its bridge was attacked and lost $100M in late June.

What's next for Nomad's ecosystems?

Moonbeam's TVL is now $135M, EVMOS's is $3M, and Milkomeda's is $20M.

Loss of confidence may do more damage than $190M.

Cross-chain infrastructure is difficult to secure in a new, experimental sector. Bridge attacks can pollute an entire ecosystem or more.

Nomadic liquidity has no permanent home, so consumers will always migrate in pursuit of the "next big thing" and get stung when attentiveness wanes.

DeFi still has easy prey...

Sources: rekt.news & The Milk Road.

Sam Bankman Fried (SDBF) is a legendary con man. But the NYT might not tell you that...

Since SBF's empire was revealed to be a lie, mainstream news organizations and commentators have failed to give readers a straightforward assessment. The New York Times and Wall Street Journal have uncovered many key facts about the scandal, but they have also soft-peddled Bankman-Fried's intent and culpability.

It's clear that the FTX crypto exchange and Alameda Research committed fraud to steal money from users and investors. That’s why a recent New York Times interview was widely derided for seeming to frame FTX’s collapse as the result of mismanagement rather than malfeasance. A Wall Street Journal article lamented FTX's loss of charitable donations, bolstering Bankman's philanthropic pose. Matthew Yglesias, court chronicler of the neoliberal status quo, seemed to whitewash his own entanglements by crediting SBF's money with helping Democrats in 2020 – sidestepping the likelihood that the money was embezzled.

Many outlets have called what happened to FTX a "bank run" or a "run on deposits," but Bankman-Fried insists the company was overleveraged and disorganized. Both attempts to frame the fallout obscure the core issue: customer funds misused.

Because banks lend customer funds to generate returns, they can experience "bank runs." If everyone withdraws at once, they can experience a short-term cash crunch but there won't be a long-term problem.

Crypto exchanges like FTX aren't banks. They don't do bank-style lending, so a withdrawal surge shouldn't strain liquidity. FTX promised customers it wouldn't lend or use their crypto.

Alameda's balance sheet blurs SBF's crypto empire.

The funds were sent to Alameda Research, where they were apparently gambled away. This is massive theft. According to a bankruptcy document, up to 1 million customers could be affected.

In less than a month, reporting and the bankruptcy process have uncovered a laundry list of decisions and practices that would constitute financial fraud if FTX had been a U.S.-regulated entity, even without crypto-specific rules. These ploys may be litigated in U.S. courts if they enabled the theft of American property.

The list is very, very long.

The many crimes of Sam Bankman-Fried and FTX

At the heart of SBF's fraud are the deep and (literally) intimate ties between FTX and Alameda Research, a hedge fund he co-founded. An exchange makes money from transaction fees on user assets, but Alameda trades and invests its own funds.

Bankman-Fried called FTX and Alameda "wholly separate" and resigned as Alameda's CEO in 2019. The two operations were closely linked. Bankman-Fried and Alameda CEO Caroline Ellison were romantically linked.

These circumstances enabled SBF's sin.  Within days of FTX's first signs of weakness, it was clear the exchange was funneling customer assets to Alameda for trading, lending, and investing. Reuters reported on Nov. 12 that FTX sent $10 billion to Alameda. As much as $2 billion was believed to have disappeared after being sent to Alameda. Now the losses look worse.

It's unclear why those funds were sent to Alameda or when Bankman-Fried betrayed his depositors. On-chain analysis shows most FTX to Alameda transfers occurred in late 2021, and bankruptcy filings show both lost $3.7 billion in 2021.

SBF's companies lost millions before the 2022 crypto bear market. They may have stolen funds before Terra and Three Arrows Capital, which killed many leveraged crypto players.

FTT loans and prints

CoinDesk's report on Alameda's FTT holdings ignited FTX and Alameda Research. FTX created this instrument, but only a small portion was traded publicly; FTX and Alameda held the rest. These holdings were illiquid, meaning they couldn't be sold at market price. Bankman-Fried valued its stock at the fictitious price.

FTT tokens were reportedly used as collateral for loans, including FTX loans to Alameda. Close ties between FTX and Alameda made the FTT token harder or more expensive to use as collateral, reducing the risk to customer funds.

This use of an internal asset as collateral for loans between clandestinely related entities is similar to Enron's 1990s accounting fraud. These executives served 12 years in prison.

Alameda's margin liquidation exemption

Alameda Research had a "secret exemption" from FTX's liquidation and margin trading rules, according to legal filings by FTX's new CEO.

FTX, like other crypto platforms and some equity or commodity services, offered "margin" or loans for trades. These loans are usually collateralized, meaning borrowers put up other funds or assets. If a margin trade loses enough money, the exchange will sell the user's collateral to pay off the initial loan.

Keeping asset markets solvent requires liquidating bad margin positions. Exempting Alameda would give it huge advantages while exposing other FTX users to hidden risks. Alameda could have kept losing positions open while closing out competitors. Alameda could lose more on FTX than it could pay back, leaving a hole in customer funds.

The exemption is criminal in multiple ways. FTX was fraudulently marketed overall. Instead of a level playing field, there were many customers.

Above them all, with shotgun poised, was Alameda Research.

Alameda front-running FTX listings

Argus says there's circumstantial evidence that Alameda Research had insider knowledge of FTX's token listing plans. Alameda was able to buy large amounts of tokens before the listing and sell them after the price bump.

If true, these claims would be the most brazenly illegal of Alameda and FTX's alleged shenanigans. Even if the tokens aren't formally classified as securities, insider trading laws may apply.

In a similar case this year, an OpenSea employee was charged with wire fraud for allegedly insider trading. This employee faces 20 years in prison for front-running monkey JPEGs.

Huge loans to executives

Alameda Research reportedly lent FTX executives $4.1 billion, including massive personal loans. Bankman-Fried received $1 billion in personal loans and $2.3 billion for an entity he controlled, Paper Bird. Nishad Singh, director of engineering, was given $543 million, and FTX Digital Markets co-CEO Ryan Salame received $55 million.

FTX has more smoking guns than a Texas shooting range, but this one is the smoking bazooka – a sign of criminal intent. It's unclear how most of the personal loans were used, but liquidators will have to recoup the money.

The loans to Paper Bird were even more worrisome because they created another related third party to shuffle assets. Forbes speculates that some Paper Bird funds went to buy Binance's FTX stake, and Paper Bird committed hundreds of millions to outside investments.

FTX Inner Circle: Who's Who

That included many FTX-backed VC funds. Time will tell if this financial incest was criminal fraud. It fits Bankman-pattern Fried's of using secret flows, leverage, and funny money to inflate asset prices.

FTT or loan 'bailouts'

Also. As the crypto bear market continued in 2022, Bankman-Fried proposed bailouts for bankrupt crypto lenders BlockFi and Voyager Digital. CoinDesk was among those deceived, welcoming SBF as a J.P. Morgan-style sector backstop.

In a now-infamous interview with CNBC's "Squawk Box," Bankman-Fried referred to these decisions as bets that may or may not pay off.

But maybe not. Bloomberg's Matt Levine speculated that FTX backed BlockFi with FTT money. This Monopoly bailout may have been intended to hide FTX and Alameda liabilities that would have been exposed if BlockFi went bankrupt sooner. This ploy has no name, but it echoes other corporate frauds.

Secret bank purchase

Alameda Research invested $11.5 million in the tiny Farmington State Bank, doubling its net worth. As a non-U.S. entity and an investment firm, Alameda should have cleared regulatory hurdles before acquiring a U.S. bank.

In the context of FTX, the bank's stake becomes "ominous." Alameda and FTX could have done more shenanigans with bank control. Compare this to the Bank for Credit and Commerce International's failed attempts to buy U.S. banks. BCCI was even nefarious than FTX and wanted to buy U.S. banks to expand its money-laundering empire.

The mainstream's mistakes

These are complex and nuanced forms of fraud that echo traditional finance models. This obscurity helped Bankman-Fried masquerade as an honest player and likely kept coverage soft after the collapse.

Bankman-Fried had a scruffy, nerdy image, like Mark Zuckerberg and Adam Neumann. In interviews, he spoke nonsense about an industry full of jargon and complicated tech. Strategic donations and insincere ideological statements helped him gain political and social influence.

SBF' s'Effective' Altruism Blew Up FTX

Bankman-Fried has continued to muddy the waters with disingenuous letters, statements, interviews, and tweets since his con collapsed. He's tried to portray himself as a well-intentioned but naive kid who made some mistakes. This is a softer, more pernicious version of what Trump learned from mob lawyer Roy Cohn. Bankman-Fried doesn't "deny, deny, deny" but "confuse, evade, distort."

It's mostly worked. Kevin O'Leary, who plays an investor on "Shark Tank," repeats Bankman-SBF's counterfactuals.  O'Leary called Bankman-Fried a "savant" and "probably one of the most accomplished crypto traders in the world" in a Nov. 27 interview with Business Insider, despite recent data indicating immense trading losses even when times were good.

O'Leary's status as an FTX investor and former paid spokesperson explains his continued affection for Bankman-Fried despite contradictory evidence. He's not the only one promoting Bankman-Fried. The disgraced son of two Stanford law professors will defend himself at Wednesday's DealBook Summit.

SBF's fraud and theft rival those of Bernie Madoff and Jho Low. Whether intentionally or through malign ineptitude, the fraud echoes Worldcom and Enron.

The Perverse Impacts of Anti-Money-Laundering

The principals in all of those scandals wound up either sentenced to prison or on the run from the law. Sam Bankman-Fried clearly deserves to share their fate.

Read the full article here.

DeCap is DeFi's killer app.

“Software is eating the world.” Marc Andreesen, venture capitalist

DeFi. Imagine a blockchain-based alternative financial system that offers the same products and services as traditional finance, but with more variety, faster, more secure, lower cost, and simpler access.

Decentralised finance (DeFi) is a marketplace without gatekeepers or central authority managing the flow of money, where customers engage directly with smart contracts running on a blockchain.

DeFi grew exponentially in 2020/21, with Total Value Locked (an inadequate estimate for market size) topping at $100 billion. After that, it crashed.

The accumulation of funds by individuals with high discretionary income during the epidemic, the novelty of crypto trading, and the high yields given (5% APY for stablecoins on established platforms to 100%+ for risky assets) are among the primary elements explaining this exponential increase.

No longer your older brothers DeFi

Since transactions are anonymous, borrowers had to overcollateralize DeFi 1.0. To borrow $100 in stablecoins, you must deposit $150 in ETH. DeFi 1.0's business strategy raises two problems.

• Why does DeFi offer interest rates that are higher than those of the conventional financial system?;

• Why would somebody put down more cash than they intended to borrow?

Maxed out on their own resources, investors took loans to acquire more crypto; the demand for those loans raised DeFi yields, which kept crypto prices increasing; as crypto prices rose, investors made a return on their positions, allowing them to deposit more money and borrow more crypto.

This is a bull market game. DeFi 1.0's overcollateralization speculation is dead. Cryptocrash sank it.

The “speculation by overcollateralisation” world of DeFi 1.0 is dead

At a JP Morgan digital assets conference, institutional investors were more interested in DeFi than crypto or fintech. To me, that shows DeFi 2.0's institutional future.

DeFi 2.0 protocols must handle KYC/AML, tax compliance, market abuse, and cybersecurity problems to be institutional-ready.

Stablecoins gaining market share under benign regulation and more CBDCs coming online in the next couple of years could help DeFi 2.0 separate from crypto volatility.

DeFi 2.0 will have a better footing to finally decouple from crypto volatility

Then we can transition from speculation through overcollateralization to DeFi's genuine comparative advantages: cheaper transaction costs, near-instant settlement, more efficient price discovery, faster time-to-market for financial innovation, and a superior audit trail.

Akin to Amazon for financial goods

Amazon decimated brick-and-mortar shops by offering millions of things online, warehouses by keeping just-in-time inventory, and back-offices by automating invoicing and payments. Software devoured retail. DeFi will eat banking with software.

DeFi is the Amazon for financial items that will replace fintech. Even the most advanced internet brokers offer only 100 currency pairings and limited bonds, equities, and ETFs.

Old banks settlement systems and inefficient, hard-to-upgrade outdated software harm them. For advanced gamers, it's like driving an F1 vehicle on dirt.

It is like driving a F1 car on a dirt road, for the most sophisticated players

Central bankers throughout the world know how expensive and difficult it is to handle cross-border payments using the US dollar as the reserve currency, which is vulnerable to the economic cycle and geopolitical tensions.

Decentralization is the only method to deliver 24h global financial markets. DeFi 2.0 lets you buy and sell startup shares like Google or Tesla. VC funds will trade like mutual funds. Or create a bundle coverage for your car, house, and NFTs. Defi 2.0 consumes banking and creates Global Wall Street.

Defi 2.0 is how software eats banking and delivers the global Wall Street

Decentralized Capitalism is Emerging

90% of markets are digital. 10% is hardest to digitalize. That's money creation, ID, and asset tokenization.

90% of financial markets are already digital. The only problem is that the 10% left is the hardest to digitalize

Debt helped Athens construct a powerful navy that secured trade routes. Bonds financed the Renaissance's wars and supply chains. Equity fueled industrial growth. FX drove globalization's payments system. DeFi's plans:

If the 20th century was a conflict between governments and markets over economic drivers, the 21st century will be between centralized and decentralized corporate structures.

Offices vs. telecommuting. China vs. onshoring/friendshoring. Oil & gas vs. diverse energy matrix. National vs. multilateral policymaking. DAOs vs. corporations Fiat vs. crypto. TradFi vs.

An age where the network effects of the sharing economy will overtake the gains of scale of the monopolistic competition economy

This is the dawn of Decentralized Capitalism (or DeCap), an age where the network effects of the sharing economy will reach a tipping point and surpass the scale gains of the monopolistic competition economy, further eliminating inefficiencies and creating a more robust economy through better data and automation. DeFi 2.0 enables this.

DeFi needs to pay the piper now.

DeCap won't be Web3.0's Shangri-La, though. That's too much for an ailing Atlas. When push comes to shove, DeFi folks want to survive and fight another day for the revolution. If feasible, make a tidy profit.

Decentralization wasn't meant to circumvent regulation. It circumvents censorship. On-ramp, off-ramp measures (control DeFi's entry and exit points, not what happens in between) sound like a good compromise for DeFi 2.0.

The sooner authorities realize that DeFi regulation is made ex-ante by writing code and constructing smart contracts with rules, the faster DeFi 2.0 will become the more efficient and safe financial marketplace.

More crucially, we must boost system liquidity. DeFi's financial stability risks are downplayed. DeFi must improve its liquidity management if it's to become mainstream, just as banks rely on capital constraints.

This reveals the complex and, frankly, inadequate governance arrangements for DeFi protocols. They redistribute control from tokenholders to developers, which is bad governance regardless of the economic model.

But crypto can only ride the existing banking system for so long before forming its own economy. DeFi will upgrade web2.0's financial rails till then.

Kim Kardashian created Skky Partners. Consumer products, internet & e-commerce, consumer media, hospitality, and luxury are company targets.

Some call this another Kardashian publicity gimmick.

This maneuver is brilliance upon closer inspection. Why?

1) Kim has amassed a sizable social media fan base:

Over 320 million Instagram and 70 million Twitter users follow Kim Kardashian.

Kim Kardashian's Instagram account ranks 8th. Three Kardashians in top 10 is ridiculous.

This gives her access to consumer data. She knows what people are discussing. Investment firms need this data.

Quality, not quantity, of her followers matters. Studies suggest that her following are more engaged than Selena Gomez and Beyonce's.

Kim's followers are worth roughly $500 million to her brand, according to a research. They trust her and buy what she recommends.

2) She has a special aptitude for identifying trends.

Kim Kardashian can sense trends.

She's always ahead of fashion and beauty trends. She's always trying new things, too. She doesn't mind making mistakes when trying anything new. Her desire to experiment makes her a good business prospector.

Kim has also created a lifestyle brand that followers love. Kim is an entrepreneur, mom, and role model, not just a reality TV star or model. She's established a brand around her appearance, so people want to buy her things.

Her fragrance collection has sold over $100 million since its 2009 introduction, and her Sears apparel line did over $200 million in its first year.

SKIMS is her latest $3.2bn brand. She can establish multibillion-dollar firms with her enormous distribution platform.

Early founders would kill for Kim Kardashian's network.

Making great products is hard, but distribution is more difficult. — David Sacks, All-in-Podcast

3) She can delegate the financial choices to Jay Sammons, one of the greatest in the industry.

Jay Sammons is well-suited to develop Kim Kardashian's new private equity fund.

Sammons has 16 years of consumer investing experience at Carlyle. This will help Kardashian invest in consumer-facing enterprises.

Sammons has invested in Supreme and Beats Electronics, both of which have grown significantly. Sammons' track record and competence make him the obvious choice.

Kim Kardashian and Jay Sammons have joined forces to create a new business endeavor. The agreement will increase Kardashian's commercial empire. Sammons can leverage one of the world's most famous celebrities.

“Together we hope to leverage our complementary expertise to build the next generation consumer and media private equity firm” — Kim Kardashian

Kim Kardashian is a successful businesswoman. She developed an empire by leveraging social media to connect with fans. By developing a global lifestyle brand, she has sold things and experiences that have made her one of the world's richest celebrities.

She's a shrewd entrepreneur who knows how to maximize on herself and her image.

Imagine how much interest Kim K will bring to private equity and venture capital.

I'm curious about the company's growth.

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.

Five lessons:

• 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:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• 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.

You'll learn:

• 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:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. 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.

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.

Final thoughts

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.

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