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Howey Test and Cryptocurrencies: 'Every ICO Is a Security'
What Is the Howey Test?
To determine whether a transaction qualifies as a "investment contract" and thus qualifies as a security, the Howey Test refers to the U.S. Supreme Court cass: the Securities Act of 1933 and the Securities Exchange Act of 1934. According to the Howey Test, an investment contract exists when "money is invested in a common enterprise with a reasonable expectation of profits from others' efforts."
The test applies to any contract, scheme, or transaction. The Howey Test helps investors and project backers understand blockchain and digital currency projects. ICOs and certain cryptocurrencies may be found to be "investment contracts" under the test.
Understanding the Howey Test
The Howey Test comes from the 1946 Supreme Court case SEC v. W.J. Howey Co. The Howey Company sold citrus groves to Florida buyers who leased them back to Howey. The company would maintain the groves and sell the fruit for the owners. Both parties benefited. Most buyers had no farming experience and were not required to farm the land.
The SEC intervened because Howey failed to register the transactions. The court ruled that the leaseback agreements were investment contracts.
This established four criteria for determining an investment contract. Investing contract:
- An investment of money
- n a common enterprise
- With the expectation of profit
- To be derived from the efforts of others
In the case of Howey, the buyers saw the transactions as valuable because others provided the labor and expertise. An income stream was obtained by only investing capital. As a result of the Howey Test, the transaction had to be registered with the SEC.
Howey Test and Cryptocurrencies
Bitcoin is notoriously difficult to categorize. Decentralized, they evade regulation in many ways. Regardless, the SEC is looking into digital assets and determining when their sale qualifies as an investment contract.
The SEC claims that selling digital assets meets the "investment of money" test because fiat money or other digital assets are being exchanged. Like the "common enterprise" test.
Whether a digital asset qualifies as an investment contract depends on whether there is a "expectation of profit from others' efforts."
For example, buyers of digital assets may be relying on others' efforts if they expect the project's backers to build and maintain the digital network, rather than a dispersed community of unaffiliated users. Also, if the project's backers create scarcity by burning tokens, the test is met. Another way the "efforts of others" test is met is if the project's backers continue to act in a managerial role.
These are just a few examples given by the SEC. If a project's success is dependent on ongoing support from backers, the buyer of the digital asset is likely relying on "others' efforts."
Special Considerations
If the SEC determines a cryptocurrency token is a security, many issues arise. It means the SEC can decide whether a token can be sold to US investors and forces the project to register.
In 2017, the SEC ruled that selling DAO tokens for Ether violated federal securities laws. Instead of enforcing securities laws, the SEC issued a warning to the cryptocurrency industry.
Due to the Howey Test, most ICOs today are likely inaccessible to US investors. After a year of ICOs, then-SEC Chair Jay Clayton declared them all securities.
SEC Chairman Gensler Agrees With Predecessor: 'Every ICO Is a Security'
Howey Test FAQs
How Do You Determine If Something Is a Security?
The Howey Test determines whether certain transactions are "investment contracts." Securities are transactions that qualify as "investment contracts" under the Securities Act of 1933 and the Securities Exchange Act of 1934.
The Howey Test looks for a "investment of money in a common enterprise with a reasonable expectation of profits from others' efforts." If so, the Securities Act of 1933 and the Securities Exchange Act of 1934 require disclosure and registration.
Why Is Bitcoin Not a Security?
Former SEC Chair Jay Clayton clarified in June 2018 that bitcoin is not a security: "Cryptocurrencies: Replace the dollar, euro, and yen with bitcoin. That type of currency is not a security," said Clayton.
Bitcoin, which has never sought public funding to develop its technology, fails the SEC's Howey Test. However, according to Clayton, ICO tokens are securities.
A Security Defined by the SEC
In the public and private markets, securities are fungible and tradeable financial instruments. The SEC regulates public securities sales.
The Supreme Court defined a security offering in SEC v. W.J. Howey Co. In its judgment, the court defines a security using four criteria:
- An investment contract's existence
- The formation of a common enterprise
- The issuer's profit promise
- Third-party promotion of the offering
Read original post.
More on Web3 & Crypto
mbvissers.eth
3 years ago
Why does every smart contract seem to implement ERC165?
ERC165 (or EIP-165) is a standard utilized by various open-source smart contracts like Open Zeppelin or Aavegotchi.
What's it? You must implement? Why do we need it? I'll describe the standard and answer any queries.
What is ERC165
ERC165 detects and publishes smart contract interfaces. Meaning? It standardizes how interfaces are recognized, how to detect if they implement ERC165, and how a contract publishes the interfaces it implements. How does it work?
Why use ERC165? Sometimes it's useful to know which interfaces a contract implements, and which version.
Identifying interfaces
An interface function's selector. This verifies an ABI function. XORing all function selectors defines an interface in this standard. The following code demonstrates.
// SPDX-License-Identifier: UNLICENCED
pragma solidity >=0.8.0 <0.9.0;
interface Solidity101 {
function hello() external pure;
function world(int) external pure;
}
contract Selector {
function calculateSelector() public pure returns (bytes4) {
Solidity101 i;
return i.hello.selector ^ i.world.selector;
// Returns 0xc6be8b58
}
function getHelloSelector() public pure returns (bytes4) {
Solidity101 i;
return i.hello.selector;
// Returns 0x19ff1d21
}
function getWorldSelector() public pure returns (bytes4) {
Solidity101 i;
return i.world.selector;
// Returns 0xdf419679
}
}This code isn't necessary to understand function selectors and how an interface's selector can be determined from the functions it implements.
Run that sample in Remix to see how interface function modifications affect contract function output.
Contracts publish their implemented interfaces.
We can identify interfaces. Now we must disclose the interfaces we're implementing. First, import IERC165 like so.
pragma solidity ^0.4.20;
interface ERC165 {
/// @notice Query if a contract implements an interface
/// @param interfaceID The interface identifier, as specified in ERC-165
/// @dev Interface identification is specified in ERC-165.
/// @return `true` if the contract implements `interfaceID` and
/// `interfaceID` is not 0xffffffff, `false` otherwise
function supportsInterface(bytes4 interfaceID) external view returns (bool);
}We still need to build this interface in our smart contract. ERC721 from OpenZeppelin is a good example.
// SPDX-License-Identifier: MIT
// OpenZeppelin Contracts (last updated v4.5.0) (token/ERC721/ERC721.sol)
pragma solidity ^0.8.0;
import "./IERC721.sol";
import "./extensions/IERC721Metadata.sol";
import "../../utils/introspection/ERC165.sol";
// ...
contract ERC721 is Context, ERC165, IERC721, IERC721Metadata {
// ...
function supportsInterface(bytes4 interfaceId) public view virtual override(ERC165, IERC165) returns (bool) {
return
interfaceId == type(IERC721).interfaceId ||
interfaceId == type(IERC721Metadata).interfaceId ||
super.supportsInterface(interfaceId);
}
// ...
}I deleted unnecessary code. The smart contract imports ERC165, IERC721 and IERC721Metadata. The is keyword at smart contract declaration implements all three.
Kind (interface).
Note that type(interface).interfaceId returns the same as the interface selector.
We override supportsInterface in the smart contract to return a boolean that checks if interfaceId is the same as one of the implemented contracts.
Super.supportsInterface() calls ERC165 code. Checks if interfaceId is IERC165.
function supportsInterface(bytes4 interfaceId) public view virtual override returns (bool) {
return interfaceId == type(IERC165).interfaceId;
}So, if we run supportsInterface with an interfaceId, our contract function returns true if it's implemented and false otherwise. True for IERC721, IERC721Metadata, andIERC165.
Conclusion
I hope this post has helped you understand and use ERC165 and why it's employed.
Have a great day, thanks for reading!
Max Parasol
3 years ago
What the hell is Web3 anyway?
"Web 3.0" is a trendy buzzword with a vague definition. Everyone agrees it has to do with a blockchain-based internet evolution, but what is it?
Yet, the meaning and prospects for Web3 have become hot topics in crypto communities. Big corporations use the term to gain a foothold in the space while avoiding the negative connotations of “crypto.”
But it can't be evaluated without a definition.
Among those criticizing Web3's vagueness is Cobie:
“Despite the dominie's deluge of undistinguished think pieces, nobody really agrees on what Web3 is. Web3 is a scam, the future, tokenizing the world, VC exit liquidity, or just another name for crypto, depending on your tribe.
“Even the crypto community is split on whether Bitcoin is Web3,” he adds.
The phrase was coined by an early crypto thinker, and the community has had years to figure out what it means. Many ideologies and commercial realities have driven reverse engineering.
Web3 is becoming clearer as a concept. It contains ideas. It was probably coined by Ethereum co-founder Gavin Wood in 2014. His definition of Web3 included “trustless transactions” as part of its tech stack. Wood founded the Web3 Foundation and the Polkadot network, a Web3 alternative future.
The 2013 Ethereum white paper had previously allowed devotees to imagine a DAO, for example.
Web3 now has concepts like decentralized autonomous organizations, sovereign digital identity, censorship-free data storage, and data divided by multiple servers. They intertwine discussions about the “Web3” movement and its viability.
These ideas are linked by Cobie's initial Web3 definition. A key component of Web3 should be “ownership of value” for one's own content and data.
Noting that “late-stage capitalism greedcorps that make you buy a fractionalized micropayment NFT on Cardano to operate your electric toothbrush” may build the new web, he notes that “crypto founders are too rich to care anymore.”
Very Important
Many critics of Web3 claim it isn't practical or achievable. Web3 critics like Moxie Marlinspike (creator of sslstrip and Signal/TextSecure) can never see people running their own servers. Early in January, he argued that protocols are more difficult to create than platforms.
While this is true, some projects, like the file storage protocol IPFS, allow users to choose which jurisdictions their data is shared between.
But full decentralization is a difficult problem. Suhaza, replying to Moxie, said:
”People don't want to run servers... Companies are now offering API access to an Ethereum node as a service... Almost all DApps interact with the blockchain using Infura or Alchemy. In fact, when a DApp uses a wallet like MetaMask to interact with the blockchain, MetaMask is just calling Infura!
So, here are the questions: Web3: Is it a go? Is it truly decentralized?
Web3 history is shaped by Web2 failure.
This is the story of how the Internet was turned upside down...
Then came the vision. Everyone can create content for free. Decentralized open-source believers like Tim Berners-Lee popularized it.
Real-world data trade-offs for content creation and pricing.
A giant Wikipedia page married to a giant Craig's List. No ads, no logins, and a private web carve-up. For free usage, you give up your privacy and data to the algorithmic targeted advertising of Web 2.
Our data is centralized and savaged by giant corporations. Data localization rules and geopolitical walls like China's Great Firewall further fragment the internet.
The decentralized Web3 reflects Berners-original Lee's vision: "No permission is required from a central authority to post anything... there is no central controlling node and thus no single point of failure." Now he runs Solid, a Web3 data storage startup.
So Web3 starts with decentralized servers and data privacy.
Web3 begins with decentralized storage.
Data decentralization is a key feature of the Web3 tech stack. Web2 has closed databases. Large corporations like Facebook, Google, and others go to great lengths to collect, control, and monetize data. We want to change it.
Amazon, Google, Microsoft, Alibaba, and Huawei, according to Gartner, currently control 80% of the global cloud infrastructure market. Web3 wants to change that.
Decentralization enlarges power structures by giving participants a stake in the network. Users own data on open encrypted networks in Web3. This area has many projects.
Apps like Filecoin and IPFS have led the way. Data is replicated across multiple nodes in Web3 storage providers like Filecoin.
But the new tech stack and ideology raise many questions.
Giving users control over their data
According to Ryan Kris, COO of Verida, his “Web3 vision” is “empowering people to control their own data.”
Verida targets SDKs that address issues in the Web3 stack: identity, messaging, personal storage, and data interoperability.
A big app suite? “Yes, but it's a frontier technology,” he says. They are currently building a credentialing system for decentralized health in Bermuda.
By empowering individuals, how will Web3 create a fairer internet? Kris, who has worked in telecoms, finance, cyber security, and blockchain consulting for decades, admits it is difficult:
“The viability of Web3 raises some good business questions,” he adds. “How can users regain control over centralized personal data? How are startups motivated to build products and tools that support this transition? How are existing Web2 companies encouraged to pivot to a Web3 business model to compete with market leaders?
Kris adds that new technologies have regulatory and practical issues:
"On storage, IPFS is great for redundantly sharing public data, but not designed for securing private personal data. It is not controlled by the users. When data storage in a specific country is not guaranteed, regulatory issues arise."
Each project has varying degrees of decentralization. The diehards say DApps that use centralized storage are no longer “Web3” companies. But fully decentralized technology is hard to build.
Web2.5?
Some argue that we're actually building Web2.5 businesses, which are crypto-native but not fully decentralized. This is vital. For example, the NFT may be on a blockchain, but it is linked to centralized data repositories like OpenSea. A server failure could result in data loss.
However, according to Apollo Capital crypto analyst David Angliss, OpenSea is “not exactly community-led”. Also in 2021, much to the chagrin of crypto enthusiasts, OpenSea tried and failed to list on the Nasdaq.
This is where Web2.5 is defined.
“Web3 isn't a crypto segment. “Anything that uses a blockchain for censorship resistance is Web3,” Angliss tells us.
“Web3 gives users control over their data and identity. This is not possible in Web2.”
“Web2 is like feudalism, with walled-off ecosystems ruled by a few. For example, an honest user owned the Instagram account “Meta,” which Facebook rebranded and then had to make up a reason to suspend. Not anymore with Web3. If I buy ‘Ethereum.ens,' Ethereum cannot take it away from me.”
Angliss uses OpenSea as a Web2.5 business example. Too decentralized, i.e. censorship resistant, can be unprofitable for a large company like OpenSea. For example, OpenSea “enables NFT trading”. But it also stopped the sale of stolen Bored Apes.”
Web3 (or Web2.5, depending on the context) has been described as a new way to privatize internet.
“Being in the crypto ecosystem doesn't make it Web3,” Angliss says. The biggest risk is centralized closed ecosystems rather than a growing Web3.
LooksRare and OpenDAO are two community-led platforms that are more decentralized than OpenSea. LooksRare has even been “vampire attacking” OpenSea, indicating a Web3 competitor to the Web2.5 NFT king could find favor.
The addition of a token gives these new NFT platforms more options for building customer loyalty. For example, OpenSea charges a fee that goes nowhere. Stakeholders of LOOKS tokens earn 100% of the trading fees charged by LooksRare on every basic sale.
Maybe Web3's time has come.
So whose data is it?
Continuing criticisms of Web3 platforms' decentralization may indicate we're too early. Users want to own and store their in-game assets and NFTs on decentralized platforms like the Metaverse and play-to-earn games. Start-ups like Arweave, Sia, and Aleph.im propose an alternative.
To be truly decentralized, Web3 requires new off-chain models that sidestep cloud computing and Web2.5.
“Arweave and Sia emerged as formidable competitors this year,” says the Messari Report. They seek to reduce the risk of an NFT being lost due to a data breach on a centralized server.
Aleph.im, another Web3 cloud competitor, seeks to replace cloud computing with a service network. It is a decentralized computing network that supports multiple blockchains by retrieving and encrypting data.
“The Aleph.im network provides a truly decentralized alternative where it is most needed: storage and computing,” says Johnathan Schemoul, founder of Aleph.im. For reasons of consensus and security, blockchains are not designed for large storage or high-performance computing.
As a result, large data sets are frequently stored off-chain, increasing the risk for centralized databases like OpenSea
Aleph.im enables users to own digital assets using both blockchains and off-chain decentralized cloud technologies.
"We need to go beyond layer 0 and 1 to build a robust decentralized web. The Aleph.im ecosystem is proving that Web3 can be decentralized, and we intend to keep going.”
Aleph.im raised $10 million in mid-January 2022, and Ubisoft uses its network for NFT storage. This is the first time a big-budget gaming studio has given users this much control.
It also suggests Web3 could work as a B2B model, even if consumers aren't concerned about “decentralization.” Starting with gaming is common.
Can Tokenomics help Web3 adoption?
Web3 consumer adoption is another story. The average user may not be interested in all this decentralization talk. Still, how much do people value privacy over convenience? Can tokenomics solve the privacy vs. convenience dilemma?
Holon Global Investments' Jonathan Hooker tells us that human internet behavior will change. “Do you own Bitcoin?” he asks in his Web3 explanation. How does it feel to own and control your own sovereign wealth? Then:
“What if you could own and control your data like Bitcoin?”
“The business model must find what that person values,” he says. Putting their own health records on centralized systems they don't control?
“How vital are those medical records to that person at a critical time anywhere in the world? Filecoin and IPFS can help.”
Web3 adoption depends on NFT storage competition. A free off-chain storage of NFT metadata and assets was launched by Filecoin in April 2021.
Denationalization and blockchain technology have significant implications for data ownership and compensation for lending, staking, and using data.
Tokenomics can change human behavior, but many people simply sign into Web2 apps using a Facebook API without hesitation. Our data is already owned by Google, Baidu, Tencent, and Facebook (and its parent company Meta). Is it too late to recover?
Maybe. “Data is like fruit, it starts out fresh but ages,” he says. "Big Tech's data on us will expire."
Web3 founder Kris agrees with Hooker that “value for data is the issue, not privacy.” People accept losing their data privacy, so tokenize it. People readily give up data, so why not pay for it?
"Personalized data offering is valuable in personalization. “I will sell my social media data but not my health data.”
Purists and mass consumer adoption struggle with key management.
Others question data tokenomics' optimism. While acknowledging its potential, Box founder Aaron Levie questioned the viability of Web3 models in a Tweet thread:
“Why? Because data almost always works in an app. A product and APIs that moved quickly to build value and trust over time.”
Levie contends that tokenomics may complicate matters. In addition to community governance and tokenomics, Web3 ideals likely add a new negotiation vector.
“These are hard problems about human coordination, not software or blockchains,”. Using a Facebook API is simple. The business model and user interface are crucial.
For example, the crypto faithful have a common misconception about logging into Web3. It goes like this: Web 1 had usernames and passwords. Web 2 uses Google, Facebook, or Twitter APIs, while Web 3 uses your wallet. Pay with Ethereum on MetaMask, for example.
But Levie is correct. Blockchain key management is stressed in this meme. Even seasoned crypto enthusiasts have heart attacks, let alone newbies.
Web3 requires a better user experience, according to Kris, the company's founder. “How does a user recover keys?”
And at this point, no solution is likely to be completely decentralized. So Web3 key management can be improved. ”The moment someone loses control of their keys, Web3 ceases to exist.”
That leaves a major issue for Web3 purists. Put this one in the too-hard basket.
Is 2022 the Year of Web3?
Web3 must first solve a number of issues before it can be mainstreamed. It must be better and cheaper than Web2.5, or have other significant advantages.
Web3 aims for scalability without sacrificing decentralization protocols. But decentralization is difficult and centralized services are more convenient.
Ethereum co-founder Vitalik Buterin himself stated recently"
This is why (centralized) Binance to Binance transactions trump Ethereum payments in some places because they don't have to be verified 12 times."
“I do think a lot of people care about decentralization, but they're not going to take decentralization if decentralization costs $8 per transaction,” he continued.
“Blockchains need to be affordable for people to use them in mainstream applications... Not for 2014 whales, but for today's users."
For now, scalability, tokenomics, mainstream adoption, and decentralization believers seem to be holding Web3 hostage.
Much like crypto's past.
But stay tuned.
OnChain Wizard
3 years ago
How to make a >800 million dollars in crypto attacking the once 3rd largest stablecoin, Soros style
Everyone is talking about the $UST attack right now, including Janet Yellen. But no one is talking about how much money the attacker made (or how brilliant it was). Lets dig in.
Our story starts in late March, when the Luna Foundation Guard (or LFG) starts buying BTC to help back $UST. LFG started accumulating BTC on 3/22, and by March 26th had a $1bn+ BTC position. This is leg #1 that made this trade (or attack) brilliant.
The second leg comes in the form of the 4pool Frax announcement for $UST on April 1st. This added the second leg needed to help execute the strategy in a capital efficient way (liquidity will be lower and then the attack is on).
We don't know when the attacker borrowed 100k BTC to start the position, other than that it was sold into Kwon's buying (still speculation). LFG bought 15k BTC between March 27th and April 11th, so lets just take the average price between these dates ($42k).
So you have a ~$4.2bn short position built. Over the same time, the attacker builds a $1bn OTC position in $UST. The stage is now set to create a run on the bank and get paid on your BTC short. In anticipation of the 4pool, LFG initially removes $150mm from 3pool liquidity.
The liquidity was pulled on 5/8 and then the attacker uses $350mm of UST to drain curve liquidity (and LFG pulls another $100mm of liquidity).
But this only starts the de-pegging (down to 0.972 at the lows). LFG begins selling $BTC to defend the peg, causing downward pressure on BTC while the run on $UST was just getting started.
With the Curve liquidity drained, the attacker used the remainder of their $1b OTC $UST position ($650mm or so) to start offloading on Binance. As withdrawals from Anchor turned from concern into panic, this caused a real de-peg as people fled for the exits
So LFG is selling $BTC to restore the peg while the attacker is selling $UST on Binance. Eventually the chain gets congested and the CEXs suspend withdrawals of $UST, fueling the bank run panic. $UST de-pegs to 60c at the bottom, while $BTC bleeds out.
The crypto community panics as they wonder how much $BTC will be sold to keep the peg. There are liquidations across the board and LUNA pukes because of its redemption mechanism (the attacker very well could have shorted LUNA as well). BTC fell 25% from $42k on 4/11 to $31.3k
So how much did our attacker make? There aren't details on where they covered obviously, but if they are able to cover (or buy back) the entire position at ~$32k, that means they made $952mm on the short.
On the $350mm of $UST curve dumps I don't think they took much of a loss, lets assume 3% or just $11m. And lets assume that all the Binance dumps were done at 80c, thats another $125mm cost of doing business. For a grand total profit of $815mm (bf borrow cost).
BTC was the perfect playground for the trade, as the liquidity was there to pull it off. While having LFG involved in BTC, and foreseeing they would sell to keep the peg (and prevent LUNA from dying) was the kicker.
Lastly, the liquidity being low on 3pool in advance of 4pool allowed the attacker to drain it with only $350mm, causing the broader panic in both BTC and $UST. Any shorts on LUNA would've added a lot of P&L here as well, with it falling -65% since 5/7.
And for the reply guys, yes I know a lot of this involves some speculation & assumptions. But a lot of money was made here either way, and I thought it would be cool to dive into how they did it.
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Jayden Levitt
3 years ago
Billionaire who was disgraced lost his wealth more quickly than anyone in history
If you're not genuine, you'll be revealed.
Sam Bankman-Fried (SBF) was called the Cryptocurrency Warren Buffet.
No wonder.
SBF's trading expertise, Blockchain knowledge, and ability to construct FTX attracted mainstream investors.
He had a fantastic worldview, donating much of his riches to charity.
As the onion layers peel back, it's clear he wasn't the altruistic media figure he portrayed.
SBF's mistakes were disastrous.
Customer deposits were traded and borrowed by him.
With ten other employees, he shared a $40 million mansion where they all had polyamorous relationships.
Tone-deaf and wasteful marketing expenditures, such as the $200 million spent to change the name of the Miami Heat stadium to the FTX Arena
Democrats received a $40 million campaign gift.
And now there seems to be no regret.
FTX was a 32-billion-dollar cryptocurrency exchange.
It went bankrupt practically overnight.
SBF, FTX's creator, exploited client funds to leverage trade.
FTX had $1 billion in customer withdrawal reserves against $9 billion in liabilities in sister business Alameda Research.
Bloomberg Billionaire Index says it's the largest and fastest net worth loss in history.
It gets worse.
SBF's net worth is $900 Million, however he must still finalize FTX's bankruptcy.
SBF's arrest in the Bahamas and SEC inquiry followed news that his cryptocurrency exchange had crashed, losing billions in customer deposits.
A journalist contacted him on Twitter D.M., and their exchange is telling.
His ideas are revealed.
Kelsey Piper says they didn't expect him to answer because people under investigation don't comment.
Bankman-Fried wanted to communicate, and the interaction shows he has little remorse.
SBF talks honestly about FTX gaming customers' money and insults his competition.
Reporter Kelsey Piper was outraged by what he said and felt the mistakes SBF says plague him didn't evident in the messages.
Before FTX's crash, SBF was a poster child for Cryptocurrency regulation and avoided criticizing U.S. regulators.
He tells Piper that his lobbying is just excellent PR.
It shows his genuine views and supports cynics' opinions that his attempts to win over U.S. authorities were good for his image rather than Crypto.
SBF’s responses are in Grey, and Pipers are in Blue.
It's unclear if SBF cut corners for his gain. In their Twitter exchange, Piper revisits an interview question about ethics.
SBF says, "All the foolish sh*t I said"
SBF claims FTX has never invested customer monies.
Piper challenged him on Twitter.
While he insisted FTX didn't use customer deposits, he said sibling business Alameda borrowed too much from FTX's balance sheet.
He did, basically.
When consumers tried to withdraw money, FTX was short.
SBF thought Alameda had enough money to cover FTX customers' withdrawals, but life sneaks up on you.
SBF believes most exchanges have done something similar to FTX, but they haven't had a bank run (a bunch of people all wanting to get their deposits out at the same time).
SBF believes he shouldn't have consented to the bankruptcy and kept attempting to raise more money because withdrawals would be open in a month with clients whole.
If additional money came in, he needed $8 billion to bridge the creditors' deficit, and there aren't many corporations with $8 billion to spare.
Once clients feel protected, they will continue to leave their assets on the exchange, according to one idea.
Kevin OLeary, a world-renowned hedge fund manager, says not all investors will walk through the open gate once the company is safe, therefore the $8 Billion wasn't needed immediately.
SBF claims the bankruptcy was his biggest error because he could have accumulated more capital.
Final Reflections
Sam Bankman-Fried, 30, became the world's youngest billionaire in four years.
Never listen to what people say about investing; watch what they do.
SBF is a trader who gets wrecked occasionally.
Ten first-time entrepreneurs ran FTX, screwing each other with no risk management.
It prevents opposing or challenging perspectives and echo chamber highs.
Twitter D.M. conversation with a journalist is the final nail.
He lacks an experienced crew.
This event will surely speed up much-needed regulation.
It's also prompted cryptocurrency exchanges to offer proof of reserves to calm customers.
Jay Peters
3 years ago
Apple AR/VR heaset
Apple is said to have opted for a standalone AR/VR headset over a more powerful tethered model.
It has had a tumultuous history.
Apple's alleged mixed reality headset appears to be the worst-kept secret in tech, and a fresh story from The Information is jam-packed with details regarding the device's rocky development.
Apple's decision to use a separate headgear is one of the most notable aspects of the story. Apple had yet to determine whether to pursue a more powerful VR headset that would be linked with a base station or a standalone headset. According to The Information, Apple officials chose the standalone product over the version with the base station, which had a processor that later arrived as the M1 Ultra. In 2020, Bloomberg published similar information.
That decision appears to have had a long-term impact on the headset's development. "The device's many processors had already been in development for several years by the time the choice was taken, making it impossible to go back to the drawing board and construct, say, a single chip to handle all the headset's responsibilities," The Information stated. "Other difficulties, such as putting 14 cameras on the headset, have given hardware and algorithm engineers stress."
Jony Ive remained to consult on the project's design even after his official departure from Apple, according to the story. Ive "prefers" a wearable battery, such as that offered by Magic Leap. Other prototypes, according to The Information, placed the battery in the headset's headband, and it's unknown which will be used in the final design.
The headset was purportedly shown to Apple's board of directors last week, indicating that a public unveiling is imminent. However, it is possible that it will not be introduced until later this year, and it may not hit shop shelves until 2023, so we may have to wait a bit to try it.
For further down the line, Apple is working on a pair of AR spectacles that appear like Ray-Ban wayfarer sunglasses, but according to The Information, they're "still several years away from release." (I'm interested to see how they compare to Meta and Ray-Bans' true wayfarer-style glasses.)
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.