More on Web3 & Crypto

Max Parasol
3 years ago
Are DAOs the future or just a passing fad?
How do you DAO? Can DAOs scale?
DAO: Decentralized Autonomous. Organization.
“The whole phrase is a misnomer. They're not decentralized, autonomous, or organizations,” says Monsterplay blockchain consultant David Freuden.
As part of the DAO initiative, Freuden coauthored a 51-page report in May 2020. “We need DAOs,” he says. “‘Shareholder first' is a 1980s/90s concept. Profits became the focus, not products.”
His predictions for DAOs have come true nearly two years later. DAOs had over 1.6 million participants by the end of 2021, up from 13,000 at the start of the year. Wyoming, in the US, will recognize DAOs and the Marshall Islands in 2021. Australia may follow that example in 2022.
But what is a DAO?
Members buy (or are rewarded with) governance tokens to vote on how the DAO operates and spends its money. “DeFi spawned DAOs as an investment vehicle. So a DAO is tokenomics,” says Freuden.
DAOs are usually built around a promise or a social cause, but they still want to make money. “If you can't explain why, the DAO will fail,” he says. “A co-op without tokenomics is not a DAO.”
Operating system DAOs, protocol DAOs, investment DAOs, grant DAOs, service DAOs, social DAOs, collector DAOs, and media DAOs are now available.
Freuden liked the idea of people rallying around a good cause. Speculators and builders make up the crypto world, so it needs a DAO for them.
,Speculators and builders, or both, have mismatched expectations, causing endless, but sometimes creative friction.
Organisms that boost output
Launching a DAO with an original product such as a cryptocurrency, an IT protocol or a VC-like investment fund like FlamingoDAO is common. DAOs enable distributed open-source contributions without borders. The goal is vital. Sometimes, after a product is launched, DAOs emerge, leaving the company to eventually transition to a DAO, as Uniswap did.
Doing things together is a DAO. So it's a way to reward a distributed workforce. DAOs are essentially productivity coordination organisms.
“Those who work for the DAO make permissionless contributions and benefit from fragmented employment,” argues Freuden. DAOs are, first and foremost, a new form of cooperation.
DAO? Distributed not decentralized
In decentralized autonomous organizations, words have multiple meanings. DAOs can emphasize one aspect over another. Autonomy is a trade-off for decentralization.
DAOstack CEO Matan Field says a DAO is a distributed governance system. Power is shared. However, there are two ways to understand a DAO's decentralized nature. This clarifies the various DAO definitions.
A decentralized infrastructure allows a DAO to be decentralized. It could be created on a public permissionless blockchain to prevent a takeover.
As opposed to a company run by executives or shareholders, a DAO is distributed. Its leadership does not wield power
Option two is clearly distributed.
But not all of this is “automated.”
Think quorum, not robot.
DAOs can be autonomous in the sense that smart contracts are self-enforcing and self-executing. So every blockchain transaction is a simplified smart contract.
Dao landscape
The DAO landscape is evolving.
Consider how Ethereum's smart contracts work. They are more like self-executing computer code, which Vitalik Buterin calls “persistent scripts”.
However, a DAO is self-enforcing once its members agree on its rules. As such, a DAO is “automated upon approval by the governance committee.” This distinguishes them from traditional organizations whose rules must be interpreted and applied.
Why a DAO? They move fast
A DAO can quickly adapt to local conditions as a governance mechanism. It's a collaborative decision-making tool.
Like UkraineDAO, created in response to Putin's invasion of Ukraine by Ukrainian expat Alona Shevchenko, Nadya Tolokonnikova, Trippy Labs, and PleasrDAO. The DAO sought to support Ukrainian charities by selling Ukrainian flag NFTs. With a single mission, a DAO can quickly raise funds for a country accepting crypto where banks are distrusted.
This could be a watershed moment for DAOs.
ConstitutionDAO was another clever use case for DAOs for Freuden. In a failed but “beautiful experiment in a single-purpose DAO,” ConstitutionDAO tried to buy a copy of the US Constitution from a Sotheby's auction. In November 2021, ConstitutionDAO raised $47 million from 19,000 people, but a hedge fund manager outbid them.
Contributions were returned or lost if transactional gas fees were too high. The ConstitutionDAO, as a “beautiful experiment,” proved exceptionally fast at organizing and crowdsourcing funds for a specific purpose.
We may soon be applauding UkraineDAO's geopolitical success in support of the DAO concept.
Some of the best use cases for DAOs today, according to Adam Miller, founder of DAOplatform.io and MIDAO Directory Services, involve DAO structures.
That is, a “flat community is vital.” Prototyping by the crowd is a good example. To succeed, members must be enthusiastic about DAOs as an alternative to starting a company. Because DAOs require some hierarchy, he agrees that "distributed is a better acronym."
Miller sees DAOs as a “new way of organizing people and resources.” He started DAOplatform.io, a DAO tooling advisery that is currently transitioning to a DAO due to the “woeful tech options for running a DAO,” which he says mainly comprises of just “multisig admin keys and a voting system.” So today he's advising on DAO tech stacks.
Miller identifies three key elements.
Tokenization is a common method and tool. Second, governance mechanisms connected to the DAO's treasury. Lastly, community.”
How a DAO works...
They can be more than glorified Discord groups if they have a clear mission. This mission is a mix of financial speculation and utopianism. The spectrum is vast.
The founder of Dash left the cryptocurrency project in 2017. It's the story of a prophet without an heir. So creating a global tokenized evangelical missionary community via a DAO made sense.
Evan Duffield, a “libertarian/anarchist” visionary, forked Bitcoin in January 2014 to make it instant and essentially free. He went away for a while, and DASH became a DAO.
200,000 US retailers, including Walmart and Barnes & Noble, now accept Dash as payment. This payment system works like a gift card.
Arden Goldstein, Dash's head of crypto, DAO, and blockchain marketing, claims Dash is the “first successful DAO.” It was founded in 2016 and disbanded after a hack, an Ethereum hard fork and much controversy. But what are the success metrics?
Crypto success is measured differently, says Goldstein. To achieve common goals, people must participate or be motivated in a healthy DAO. People are motivated to complete tasks in a successful DAO. And, crucially, when tasks get completed.
“Yes or no, 1 or 0, voting is not a new idea. The challenge is getting people to continue to participate and keep building a community.” A DAO motivates volunteers: Nothing keeps people from building. The DAO “philosophy is old news. You need skin in the game to play.”
MasterNodes must stake 1000 Dash. Those members are rewarded with DASH for marketing (and other tasks). It uses an outsourced team to onboard new users globally.
Joining a DAO is part of the fun of meeting crazy or “very active” people on Discord. No one gets fired (usually). If your work is noticed, you may be offered a full-time job.
DAO community members worldwide are rewarded for brand building. Dash is also a great product for developing countries with high inflation and undemocratic governments. The countries with the most Dash DAO members are Russia, Brazil, Venezuela, India, China, France, Italy, and the Philippines.
Grassroots activism makes this DAO work. A DAO is local. Venezuelans can't access Dash.org, so DAO members help them use a VPN. DAO members are investors, fervent evangelicals, and local product experts.
Every month, proposals and grant applications are voted on via the Dash platform. However, the DAO may decide not to fund you. For example, the DAO once hired a PR firm, but the community complained about the lack of press coverage. This raises a great question: How are real-world contractual obligations met by a DAO?
Does the DASH DAO work?
“I see the DAO defund projects I thought were valuable,” Goldstein says. Despite working full-time, I must submit a funding proposal. “Much faster than other companies I've worked on,” he says.
Dash DAO is a headless beast. Ryan Taylor is the CEO of the company overseeing the DASH Core Group project.
The issue is that “we don't know who has the most tokens [...] because we don't know who our customers are.” As a result, “the loudest voices usually don't have the most MasterNodes and aren't the most invested.”
Goldstein, the only female in the DAO, says she worked hard. “I was proud of the DAO when I made the logo pink for a day and got great support from the men.” This has yet to entice a major influx of female DAO members.
Many obstacles stand in the way of utopian dreams.
Governance problems remain
And what about major token holders behaving badly?
In early February, a heated crypto Twitter debate raged on about inclusion, diversity, and cancel culture in relation to decentralized projects. In this case, the question was how a DAO addresses alleged inappropriate behavior.
In a corporation, misconduct can result in termination. In a DAO, founders usually hold a large number of tokens and the keys to the blockchain (multisignature) or otherwise.
Brantly Millegan, the director of operations of Ethereum Name Service (ENS), made disparaging remarks about the LGBTQ community and other controversial topics. The screenshotted comments were made in 2016 and brought to the ENS board's attention in early 2022.
His contract with ENS has expired. But what of his large DAO governance token holdings?
Members of the DAO proposed a motion to remove Millegan from the DAO. His “delegated” votes net 370,000. He was and is the DAO's largest delegate.
What if he had refused to accept the DAO's decision?
Freuden says the answer is not so simple.
“Can a DAO kick someone out who built the project?”
The original mission “should be dissolved” if it no longer exists. “Does a DAO fail and return the money? They must r eturn the money with interest if the marriage fails.”
Before an IPO, VCs might try to remove a problematic CEO.
While DAOs use treasury as a governance mechanism, it is usually controlled (at least initially) by the original project creators. Or, in the case of Uniswap, the venture capital firm a16z has so much voting power that it has delegated it to student-run blockchain organizations.
So, can DAOs really work at scale? How to evolve voting paradigms beyond token holdings?
The whale token holder issue has some solutions. Multiple tokens, such as a utility token on top of a governance token, and quadratic voting for whales, are now common. Other safeguards include multisignature blockchain keys and decision time locks that allow for any automated decision to be made. The structure of each DAO will depend on the assets at stake.
In reality, voter turnout is often a bigger issue.
Is DAO governance scalable?
Many DAOs have low participation. Due to a lack of understanding of technology, apathy, or busy lives. “The bigger the DAO, the fewer voters who vote,” says Freuden.
Freuden's report cites British anthropologist Dunbar's Law, who argued that people can only maintain about 150 relationships.
"As the DAO grows in size, the individual loses influence because they perceive their voting power as being diminished or insignificant. The Ringelmann Effect and Dunbar's Rule show that as a group grows in size, members become lazier, disenfranchised, and detached.
Freuden says a DAO requires “understanding human relationships.” He believes DAOs work best as investment funds rooted in Cryptoland and small in scale. In just three weeks, SyndicateDAO enabled the creation of 450 new investment group DAOs.
Due to SEC regulations, FlamingoDAO, a famous NFT curation investment DAO, could only have 100 investors. The “LAO” is a member-directed venture capital fund and a US LLC. To comply with US securities law, they only allow 100 members with a 120ETH minimum staking contribution.
But how did FlamingoDAO make investment decisions? How often did all 70 members vote? Art and NFTs are highly speculative.
So, investment DAOs are thought to work well in a small petri dish environment. This is due to a crypto-native club's pooled capital (maximum 7% per member) and crowdsourced knowledge.
While scalability is a concern, each DAO will operate differently depending on the goal, technology stage, and personalities. Meetups and hackathons are common ways for techies to collaborate on a cause or test an idea. But somebody still organizes the hack.
Holographic consensus voting
But clever people are working on creative solutions to every problem.
Miller of DAOplatform.io cites DXdao as a successful DAO. Decentralized product and service creator DXdao runs the DAO entirely on-chain. “You earn voting rights by contributing to the community.”
DXdao, a DAOstack fork, uses holographic consensus, a voting algorithm invented by DAOstack founder Matan Field. The system lets a random or semi-random subset make group-wide decisions.
By acting as a gatekeeper for voters, DXdao's Luke Keenan explains that “a small predictions market economy emerges around the likely outcome of a proposal as tokens are staked on it.” Also, proposals that have been financially boosted have fewer requirements to be successful, increasing system efficiency.” DXdao “makes decisions by removing voting power as an economic incentive.”
Field explains that holographic consensus “does not require a quorum to render a vote valid.”
“Rather, it provides a parallel process. It is a game played (for profit) by ‘predictors' who make predictions about whether or not a vote will be approved by the voters. The voting process is valid even when the voting quorum is low if enough stake is placed on the outcome of the vote.
“In other words, a quorum is not a scalable DAO governance strategy,” Field says.
You don't need big votes on everything. If only 5% vote, fine. To move significant value or make significant changes, you need a longer voting period (say 30 days) and a higher quorum,” says Miller.
Clearly, DAOs are maturing. The emphasis is on tools like Orca and processes that delegate power to smaller sub-DAOs, committees, and working groups.
Miller also claims that “studies in psychology show that rewarding people too much for volunteering disincentivizes them.” So, rather than giving out tokens for every activity, you may want to offer symbolic rewards like POAPs or contributor levels.
“Free lunches are less rewarding. Random rewards can boost motivation.”
Culture and motivation
DAOs (and Web3 in general) can give early adopters a sense of ownership. In theory, they encourage early participation and bootstrapping before network effects.
"A double-edged sword," says Goldstein. In the developing world, they may not be fully scalable.
“There must always be a leader,” she says. “People won't volunteer if they don't want to.”
DAO members sometimes feel entitled. “They are not the boss, but they think they should be able to see my calendar or get a daily report,” Goldstein gripes. Say, “I own three MasterNodes and need to know X, Y, and Z.”
In most decentralized projects, strong community leaders are crucial to influencing culture.
Freuden says “the DAO's community builder is the cryptoland influencer.” They must “disseminate the DAO's culture, cause, and rally the troops” in English, not tech.
They must keep members happy.
So the community builder is vital. Building a community around a coin that promises riches is simple, but keeping DAO members motivated is difficult.
It's a human job. But tools like SourceCred or coordinate that measure contributions and allocate tokens are heavily marketed. Large growth funds/community funds/grant programs are common among DAOs.
The Future?
Onboarding, committed volunteers, and an iconic community builder may be all DAOs need.
It takes a DAO just one day to bring together a passionate (and sometimes obsessive) community. For organizations with a common goal, managing stakeholder expectations is critical.
A DAO's core values are community and cause, not scalable governance. “DAOs will work at scale like gaming communities, but we will have sub-DAOs everywhere like committees,” says Freuden.
So-called holographic consensuses “can handle, in principle, increasing rates of proposals by turning this tension between scale and resilience into an economical cost,” Field writes. Scalability is not guaranteed.
The DAO's key innovation is the fragmented workplace. “Voting is a subset of engagement,” says Freuden. DAO should allow for permissionless participation and engagement. DAOs allow for remote work.”
In 20 years, DAOs may be the AI-powered self-organizing concept. That seems far away now. But a new breed of productivity coordination organisms is maturing.

Vitalik
4 years ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
Alex Bentley
3 years ago
Why Bill Gates thinks Bitcoin, crypto, and NFTs are foolish
Microsoft co-founder Bill Gates assesses digital assets while the bull is caged.

Bill Gates is well-respected.
Reasonably. He co-founded and led Microsoft during its 1980s and 1990s revolution.
After leaving Microsoft, Bill Gates pursued other interests. He and his wife founded one of the world's largest philanthropic organizations, Bill & Melinda Gates Foundation. He also supports immunizations, population control, and other global health programs.
When Gates criticized Bitcoin, cryptocurrencies, and NFTs, it made news.
Bill Gates said at the 58th Munich Security Conference...
“You have an asset class that’s 100% based on some sort of greater fool theory that somebody’s going to pay more for it than I do.”
Gates means digital assets. Like many bitcoin critics, he says digital coins and tokens are speculative.
And he's not alone. Financial experts have dubbed Bitcoin and other digital assets a "bubble" for a decade.
Gates also made fun of Bored Ape Yacht Club and NFTs, saying, "Obviously pricey digital photographs of monkeys will help the world."
Why does Bill Gates dislike digital assets?
According to Gates' latest comments, Bitcoin, cryptos, and NFTs aren't good ways to hold value.
Bill Gates is a better investor than Elon Musk.
“I’m used to asset classes, like a farm where they have output, or like a company where they make products,” Gates said.
The Guardian claimed in April 2021 that Bill and Melinda Gates owned the most U.S. farms. Over 242,000 acres of farmland.
The Gates couple has enough farmland to cover Hong Kong.

Bill Gates is a classic investor. He wants companies with an excellent track record, strong fundamentals, and good management. Or tangible assets like land and property.
Gates prefers the "old economy" over the "new economy"
Gates' criticism of Bitcoin and cryptocurrency ventures isn't surprising. These digital assets lack all of Gates's investing criteria.
Volatile digital assets include Bitcoin. Their costs might change dramatically in a day. Volatility scares risk-averse investors like Gates.
Gates has a stake in the old financial system. As Microsoft's co-founder, Gates helped develop a dominant tech company.
Because of his business, he's one of the world's richest men.
Bill Gates is invested in protecting the current paradigm.
He won't invest in anything that could destroy the global economy.
When Gates criticizes Bitcoin, cryptocurrencies, and NFTs, he's suggesting they're a hoax. These soapbox speeches are one way he protects his interests.
Digital assets aren't a bad investment, though. Many think they're the future.
Changpeng Zhao and Brian Armstrong are two digital asset billionaires. Two crypto exchange CEOs. Binance/Coinbase.
Digital asset revolution won't end soon.
If you disagree with Bill Gates and plan to invest in Bitcoin, cryptocurrencies, or NFTs, do your own research and understand the risks.
But don’t take Bill Gates’ word for it.
He’s just an old rich guy with a lot of farmland.
He has a lot to lose if Bitcoin and other digital assets gain global popularity.
This post is a summary. Read the full article here.
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Jano le Roux
3 years ago
Apple Quietly Introduces A Revolutionary Savings Account That Kills Banks
Would you abandon your bank for Apple?
Banks are struggling.
not as a result of inflation
not due to the economic downturn.
not due to the conflict in Ukraine.
But because they’re underestimating Apple.
Slowly but surely, Apple is looking more like a bank.
An easy new savings account like Apple
Apple has a new savings account.
Apple says Apple Card users may set up and manage savings straight in Wallet.
No more charges
Colorfully high yields
With no minimum balance
No minimal down payments
Most consumer-facing banks will have to match Apple's offer or suffer disruption.
Users may set it up from their iPhones without traveling to a bank or filling out paperwork.
It’s built into the iPhone in your pocket.
So now more waiting for slow approval processes.
Once the savings account is set up, Apple will automatically transfer all future Daily Cash into it. Users may also add these cash to an Apple Cash card in their Apple Wallet app and adjust where Daily Cash is paid at any time.
Apple Pay and Apple Wallet VP Jennifer Bailey:
Savings enables Apple Card users to grow their Daily Cash rewards over time, while also saving for the future.
Bailey says Savings adds value to Apple Card's Daily Cash benefit and offers another easy-to-use tool to help people lead healthier financial lives.
Transfer money from a linked bank account or Apple Cash to a Savings account. Users can withdraw monies to a connected bank account or Apple Cash card without costs.
Once set up, Apple Card customers can track their earnings via Wallet's Savings dashboard. This dashboard shows their account balance and interest.
This product targets younger people as the easiest way to start a savings account on the iPhone.
Why would a Gen Z account holder travel to the bank if their iPhone could be their bank?
Using this concept, Apple will transform the way we think about banking by 2030.
Two other nightmares keep bankers awake at night
Apple revealed two new features in early 2022 that banks and payment gateways hated.
Tap to Pay with Apple
Late Apple Pay
They startled the industry.
Tap To Pay converts iPhones into mobile POS card readers. Apple Pay Later is pushing the BNPL business in a consumer-friendly direction, hopefully ending dodgy lending practices.
Tap to Pay with Apple
iPhone POS
Millions of US merchants, from tiny shops to huge establishments, will be able to accept Apple Pay, contactless credit and debit cards, and other digital wallets with a tap.
No hardware or payment terminal is needed.
Revolutionary!
Stripe has previously launched this feature.
Tap to Pay on iPhone will provide companies with a secure, private, and quick option to take contactless payments and unleash new checkout experiences, said Bailey.
Apple's solution is ingenious. Brilliant!
Bailey says that payment platforms, app developers, and payment networks are making it easier than ever for businesses of all sizes to accept contactless payments and thrive.
I admire that Apple is offering this up to third-party services instead of closing off other functionalities.
Slow POS terminals, farewell.
Late Apple Pay
Pay Apple later.
Apple Pay Later enables US consumers split Apple Pay purchases into four equal payments over six weeks with no interest or fees.
The Apple ecosystem integration makes this BNPL scheme unique. Nonstick. No dumb forms.
Frictionless.
Just double-tap the button.
Apple Pay Later was designed with users' financial well-being in mind. Apple makes it easy to use, track, and pay back Apple Pay Later from Wallet.
Apple Pay Later can be signed up in Wallet or when using Apple Pay. Apple Pay Later can be used online or in an app that takes Apple Pay and leverages the Mastercard network.
Apple Pay Order Tracking helps consumers access detailed receipts and order tracking in Wallet for Apple Pay purchases at participating stores.
Bad BNPL suppliers, goodbye.
Most bankers will be caught in Apple's eye playing mini golf in high-rise offices.
The big problem:
Banks still think about features and big numbers just like other smartphone makers did not too long ago.
Apple thinks about effortlessness, seamlessness, and frictionlessness that just work through integrated hardware and software.
Let me know what you think Apple’s next power moves in the banking industry could be.

Stephen Moore
3 years ago
Trading Volume on OpenSea Drops by 99% as the NFT Boom Comes to an End
Wasn't that a get-rich-quick scheme?
OpenSea processed $2.7 billion in NFT transactions in May 2021.
Fueled by a crypto bull run, rumors of unfathomable riches, and FOMO, Bored Apes, Crypto Punks, and other JPEG-format trash projects flew off the virtual shelves, snatched up by retail investors and celebrities alike.
Over a year later, those shelves are overflowing and warehouses are backlogged. Since March, I've been writing less. In May and June, the bubble was close to bursting.
Apparently, the boom has finally peaked.
This bubble has punctured, and deflation has begun. On Aug. 28, OpenSea processed $9.34 million.
From that euphoric high of $2.7 billion, $9.34 million represents a spectacular decline of 99%.
OpenSea contradicts the data. A trading platform spokeswoman stated the comparison is unfair because it compares the site's highest and lowest trading days. They're the perfect two data points to assess the drop. OpenSea chooses to use ETH volume measures, which ignore crypto's shifting price. Since January 2022, monthly ETH volume has dropped 140%, according to Dune.
Unconvincing counterargument.
Further OpenSea indicators point to declining NFT demand:
Since January 2022, daily user visits have decreased by 50%.
Daily transactions have decreased by 50% since the beginning of the year in the same manner.
Off-platform, the floor price of Bored Apes has dropped from 145 ETH to 77 ETH. (At $4,800, a reduction from $700,000 to $370,000). Google search data shows waning popular interest.
It is a trend that will soon vanish, just like laser eyes.
NFTs haven't moved since the new year. Eminem and Snoop Dogg can utilize their apes in music videos or as 3D visuals to perform at the VMAs, but the reality is that NFTs have lost their public appeal and the market is trying to regain its footing.
They've lost popularity because?
Breaking records. The technology still lacks genuine use cases a year and a half after being popular.
They're pricey prestige symbols that have made a few people rich through cunning timing or less-than-savory scams or rug pulling. Over $10.5 billion has been taken through frauds, most of which are NFT enterprises promising to be the next Bored Apes, according to Web3 is going wonderfully. As the market falls, many ordinary investors realize they purchased into a self-fulfilling ecosystem that's halted. Many NFTs are sold between owner-held accounts to boost their price, data suggests. Most projects rely on social media excitement to debut with a high price before the first owners sell and chuckle to the bank. When they don't, the initiative fails, leaving investors high and dry.
NFTs are fading like laser eyes. Most people pushing the technology don't believe in it or the future it may bring. No, they just need a Kool-Aid-drunk buyer.
Everybody wins. When your JPEGs are worth 99% less than when you bought them, you've lost.
When demand reaches zero, many will lose.

Zuzanna Sieja
3 years 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.
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:
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.