Reddit's NFT market is alive and well.

NFT owners outnumber OpenSea on Reddit.

Reddit NFTs flip in OpenSea in days:

Fast-selling.

NFT sales will make Reddit's current communities more engaged.

I don't think NFTs will affect existing groups, but they will build hype for people to acquire them.

The first season of Collectibles is unique, but many missed the first season.

Second-season NFTs are less likely to be sold for a higher price than first-season ones.

If you use Reddit, it's fun to own NFTs.

New standards help NFTs reach their full potential.

NFTs lack interoperability and functionality. They have great potential but are mostly speculative. To maximize NFTs, we need flexible smart contracts.

Current requirements are too restrictive.

Most NFTs are based on ERC-721, which makes exchanging them easy. CryptoKitties, a popular online game, used the 2017 standard to demonstrate NFTs' potential.

This simple standard includes a base URI and incremental IDs for tokens. Add the tokenID to the base URI to get the token's metadata.

This let creators collect NFTs. Many NFT projects store metadata on IPFS, a distributed storage network, but others use Google Drive. NFT buyers often don't realize that if the creators delete or move the files, their NFT is just a pointer.

This isn't the standard's biggest issue. There's no way to validate NFT projects.

Creators are one of the most important aspects of art, but nothing is stored on-chain.

ERC-721 contracts only have a name and symbol.

Most of the data on OpenSea's collection pages isn't from the NFT's smart contract. It was added through a platform input field, so it's in the marketplace's database. Other websites may have different NFT information.

In five years, your NFT will be just a name, symbol, and ID.

Your NFT doesn't mention its creators. Although the smart contract has a public key, it doesn't reveal who created it.

The NFT's creators and their reputation are crucial to its value. Think digital fashion and big brands working with well-known designers when more professionals use NFTs. Don't you want them in your NFT?

Would paintings be as valuable if their artists were unknown? Would you believe it's real?

Buying directly from an on-chain artist would reduce scams. Current standards don't allow this data.

Most creator profiles live on centralized marketplaces and could disappear. Current platforms have outpaced underlying standards. The industry's standards are lagging.

For NFTs to grow beyond pointers to a monkey picture file, we may need to use new Web3-based standards.

Introducing NFTs 2.0

Fabian Vogelsteller, creator of ERC-20, developed new web3 standards. He proposed LSP7 Digital Asset and LSP8 Identifiable Digital Asset, also called NFT 2.0.

NFT and token metadata inputs are extendable. Changes to on-chain metadata inputs allow NFTs to evolve. Instead of public keys, the contract can have Universal Profile addresses attached. These profiles show creators' faces and reputations. NFTs can notify asset receivers, automating smart contracts.

LSP7 and LSP8 use ERC725Y. Using a generic data key-value store gives contracts much-needed features:

• The asset can be customized and made to stand out more by allowing for unlimited data attachment.

• Recognizing changes to the metadata

• using a hash reference for metadata rather than a URL reference

This base will allow more metadata customization and upgradeability. These guidelines are:

• Genuine and Verifiable Now, the creation of an NFT by a specific Universal Profile can be confirmed by smart contracts.

• Dynamic NFTs can update Flexible & Updatable Metadata, allowing certain things to evolve over time.

• Protected metadata Now, secure metadata that is readable by smart contracts can be added indefinitely.

• Better NFTS prevent the locking of NFTs by only being sent to Universal Profiles or a smart contract that can interact with them.

Summary

NFTS standards lack standardization and powering features, limiting the industry.

ERC-721 is the most popular NFT standard, but it only represents incremental tokenIDs without metadata or asset representation. No standard sender-receiver interaction or security measures ensure safe asset transfers.

NFT 2.0 refers to the new LSP7-DigitalAsset and LSP8-IdentifiableDigitalAsset standards.

They have new standards for flexible metadata, secure transfers, asset representation, and interactive transfer.

With NFTs 2.0 and Universal Profiles, creators could build on-chain reputations.

NFTs 2.0 could bring the industry's needed innovation if it wants to move beyond trading profile pictures for speculation.

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.

From oil to attention.

Oil has been the most important commodity for a century. It's sparked wars. Pearl Harbor was a preemptive strike to guarantee Japanese access to Indonesian oil, and it made desert tribes rich. Oil's heyday is over. From oil to attention.

We talked about an information economy. In an age of abundant information, what's scarce? Attention. Scale of the world's largest enterprises, wealth of its richest people, and power of governments all stem from attention extraction, monetization, and custody.

Attention-grabbing isn't new. Humans have competed for attention and turned content into wealth since Aeschylus' Oresteia. The internal combustion engine, industrial revolutions in mechanization and plastics, and the emergence of a mobile Western lifestyle boosted oil. Digitization has put wells in pockets, on automobile dashboards, and on kitchen counters, drilling for attention.

The most valuable firms are attention-seeking enterprises, not oil companies. Big Tech dominates the top 4. Tech and media firms are the sheikhs and wildcatters who capture our attention. Blood will flow as the oil economy rises.

Attention to Detail

More than IT and media companies compete for attention. Podcasting is a high-growth, low-barrier-to-entry chance for newbies to gain attention and (for around 1%) make money. Conferences are good for capturing in-person attention. Salesforce paid $30 billion for Slack's dominance of workplace attention, while Spotify is transforming music listening attention into a media platform.

Conferences, newsletters, and even music streaming are artisan projects. Even 130,000-person Comic Con barely registers on the attention economy's Richter scale. Big players have hundreds of millions of monthly users.

Supermajors

Even titans can be disrupted in the attention economy. TikTok is fracking king Chesapeake Energy, a rule-breaking insurgent with revolutionary extraction technologies. Attention must be extracted, processed, and monetized. Innovators disrupt the attention economy value chain.

Attention pre-digital Entrepreneurs commercialized intriguing or amusing stuff like a newspaper or TV show through subscriptions and ads. Digital storage and distribution's limitless capacity drove the initial wave of innovation. Netflix became dominant by releasing old sitcoms and movies. More ad-free content gained attention. By 2016, Netflix was greater than cable TV. Linear scale, few network effects.

Social media introduced two breakthroughs. First, users produced and paid for content. Netflix's economics are dwarfed by TikTok and YouTube, where customers create the content drill rigs that the platforms monetize.

Next, social media businesses expanded content possibilities. Twitter, Facebook, and Reddit offer traditional content, but they transform user comments into more valuable (addictive) emotional content. By emotional resonance, I mean they satisfy a craving for acceptance or anger us. Attention and emotion are mined from comments/replies, piss-fights, and fast-brigaded craziness. Exxon has turned exhaust into heroin. Should we be so linked without a commensurate presence? You wouldn't say this in person. Anonymity allows fraudulent accounts and undesirable actors, which platforms accept to profit from more pollution.

FrackTok

A new entrepreneur emerged as ad-driven social media anger contaminated the water table. TikTok is remaking the attention economy. Short-form video platform relies on user-generated content, although delivery is narrower and less social.

Netflix grew on endless options. Choice requires cognitive effort. TikTok is the least demanding platform since TV. App video plays when opened. Every video can be skipped with a swipe. An algorithm watches how long you watch, what you finish, and whether you like or follow to create a unique streaming network. You can follow creators and respond, but the app is passive. TikTok's attention economy recombination makes it apex predator. The app has more users than Facebook and Instagram combined. Among teens, it's overtaking the passive king, TV.

Externalities

Now we understand fossil fuel externalities. A carbon-based economy has harmed the world. Fracking brought large riches and rebalanced the oil economy, but at a cost: flammable water, earthquakes, and chemical leaks.

TikTok has various concerns associated with algorithmically generated content and platforms. A Wall Street Journal analysis discovered new accounts listed as belonging to 13- to 15-year-olds would swerve into rabbitholes of sex- and drug-related films in mere days. TikTok has a unique externality: Chinese Communist Party ties. Our last two presidents realized the relationship's perils. Concerned about platform's propaganda potential.

No evidence suggests the CCP manipulated information to harm American interests. A headjack implanted on America's youth, who spend more time on TikTok than any other network, connects them to a neural network that may be modified by the CCP. If the product and ownership can't be separated, the app should be banned. Putting restrictions near media increases problems. We should have a reciprocal approach with China regarding media firms. Ban TikTok

It was a conference theme. I anticipated Axel Springer CEO Mathias Döpfner to say, "We're watching them." (That's CEO protocol.) TikTok should be outlawed in every democracy as an espionage tool. Rumored regulations could lead to a ban, and FCC Commissioner Brendan Carr pushes for app store prohibitions. Why not restrict Chinese propaganda? Some disagree: Several renowned tech writers argued my TikTok diatribe last week distracted us from privacy and data reform. The situation isn't zero-sum. I've warned about Facebook and other tech platforms for years. Chewing gum while walking is possible.

The Future

Is TikTok the attention-economy titans' final evolution? The attention economy acts like it. No original content. CNN+ was unplugged, Netflix is losing members and has lost 70% of its market cap, and households are canceling cable and streaming subscriptions in historic numbers. Snap Originals closed in August after YouTube Originals in January.

Everyone is outTik-ing the Tok. Netflix debuted Fast Laughs, Instagram Reels, YouTube Shorts, Snap Spotlight, Roku The Buzz, Pinterest Watch, and Twitter is developing a TikTok-like product. I think they should call it Vine. Just a thought.

Meta's internal documents show that users spend less time on Instagram Reels than TikTok. Reels engagement is dropping, possibly because a third of the videos were generated elsewhere (usually TikTok, complete with watermark). Meta has tried to downrank these videos, but they persist. Users reject product modifications. Kim Kardashian and Kylie Jenner posted a meme urging Meta to Make Instagram Instagram Again, resulting in 312,000 signatures. Mark won't hear the petition. Meta is the fastest follower in social (see Oculus and legless hellscape fever nightmares). Meta's stock is at a five-year low, giving those who opposed my demands to break it up a compelling argument.

Blue Pill

TikTok's short-term dominance in attention extraction won't be stopped by anyone who doesn't hear Hail to the Chief every time they come in. Will TikTok still be a supermajor in five years? If not, YouTube will likely rule and protect Kings Landing.

56% of Americans regularly watch YouTube. Compared to Facebook and TikTok, 95% of teens use Instagram. YouTube users upload more than 500 hours of video per minute, a number that's likely higher today. Last year, the platform garnered $29 billion in advertising income, equivalent to Netflix's total.

Business and biology both value diversity. Oil can be found in the desert, under the sea, or in the Arctic. Each area requires a specific ability. Refiners turn crude into gas, lubricants, and aspirin. YouTube's variety is unmatched. One-second videos to 12-hour movies. Others are studio-produced. (My Bill Maher appearance was edited for YouTube.)

You can dispute in the comment section or just stream videos. YouTube is used for home improvement, makeup advice, music videos, product reviews, etc. You can load endless videos on a topic or creator, subscribe to your favorites, or let the suggestion algo take over. YouTube relies on user content, but it doesn't wait passively. Strategic partners advise 12,000 creators. According to a senior director, if a YouTube star doesn’t post once week, their manager is “likely to know why.”

YouTube's kevlar is its middle, especially for creators. Like TikTok, users can start with low-production vlogs and selfie videos. As your following expands, so does the scope of your production, bringing longer videos, broadcast-quality camera teams and performers, and increasing prices. MrBeast, a YouTuber, is an example. MrBeast made gaming videos and YouTube drama comments.

Donaldson's YouTube subscriber base rose. MrBeast invests earnings to develop impressive productions. His most popular video was a $3.5 million Squid Game reenactment (the cost of an episode of Mad Men). 300 million people watched. TikTok's attention-grabbing tech is too limiting for this type of material. Now, Donaldson is focusing on offline energy with a burger restaurant and cloud kitchen enterprise.

Steps to Take

Rapid wealth growth has externalities. There is no free lunch. OK, maybe caffeine. The externalities are opaque, and the parties best suited to handle them early are incentivized to construct weapons of mass distraction to postpone and obfuscate while achieving economic security for themselves and their families. The longer an externality runs unchecked, the more damage it causes and the more it costs to fix. Vanessa Pappas, TikTok's COO, didn't shine before congressional hearings. Her comms team over-consulted her and said ByteDance had no headquarters because it's scattered. Being full of garbage simply promotes further anger against the company and the awkward bond it's built between the CCP and a rising generation of American citizens.

This shouldn't distract us from the (still existent) harm American platforms pose to our privacy, teenagers' mental health, and civic dialogue. Leaders of American media outlets don't suffer from immorality but amorality, indifference, and dissonance. Money rain blurs eyesight.

Autocratic governments that undermine America's standing and way of life are immoral. The CCP has and will continue to use all its assets to harm U.S. interests domestically and abroad. TikTok should be spun to Western investors or treated the way China treats American platforms: kicked out.

So rich,