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Amelie Carver

Amelie Carver

3 years ago

Web3 Needs More Writers to Educate Us About It

More on Web3 & Crypto

Ajay Shrestha

Ajay Shrestha

2 years ago

Bitcoin's technical innovation: addressing the issue of the Byzantine generals

The 2008 Bitcoin white paper solves the classic computer science consensus problem.

Figure 1: Illustration of the Byzantine Generals problem by Lord Belbury, CC BY-SA 4.0 / Source

Issue Statement

The Byzantine Generals Problem (BGP) is called after an allegory in which several generals must collaborate and attack a city at the same time to win (figure 1-left). Any general who retreats at the last minute loses the fight (figure 1-right). Thus, precise messengers and no rogue generals are essential. This is difficult without a trusted central authority.

In their 1982 publication, Leslie Lamport, Robert Shostak, and Marshall Please termed this topic the Byzantine Generals Problem to simplify distributed computer systems.

Consensus in a distributed computer network is the issue. Reaching a consensus on which systems work (and stay in the network) and which don't makes maintaining a network tough (i.e., needs to be removed from network). Challenges include unreliable communication routes between systems and mis-reporting systems.

Solving BGP can let us construct machine learning solutions without single points of failure or trusted central entities. One server hosts model parameters while numerous workers train the model. This study describes fault-tolerant Distributed Byzantine Machine Learning.

Bitcoin invented a mechanism for a distributed network of nodes to agree on which transactions should go into the distributed ledger (blockchain) without a trusted central body. It solved BGP implementation. Satoshi Nakamoto, the pseudonymous bitcoin creator, solved the challenge by cleverly combining cryptography and consensus mechanisms.

Disclaimer

This is not financial advice. It discusses a unique computer science solution.

Bitcoin

Bitcoin's white paper begins:

“A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution.” Source: https://www.ussc.gov/sites/default/files/pdf/training/annual-national-training-seminar/2018/Emerging_Tech_Bitcoin_Crypto.pdf

Bitcoin's main parts:

  1. The open-source and versioned bitcoin software that governs how nodes, miners, and the bitcoin token operate.

  2. The native kind of token, known as a bitcoin token, may be created by mining (up to 21 million can be created), and it can be transferred between wallet addresses in the bitcoin network.

  3. Distributed Ledger, which contains exact copies of the database (or "blockchain") containing each transaction since the first one in January 2009.

  4. distributed network of nodes (computers) running the distributed ledger replica together with the bitcoin software. They broadcast the transactions to other peer nodes after validating and accepting them.

  5. Proof of work (PoW) is a cryptographic requirement that must be met in order for a miner to be granted permission to add a new block of transactions to the blockchain of the cryptocurrency bitcoin. It takes the form of a valid hash digest. In order to produce new blocks on average every 10 minutes, Bitcoin features a built-in difficulty adjustment function that modifies the valid hash requirement (length of nonce). PoW requires a lot of energy since it must continually generate new hashes at random until it satisfies the criteria.

  6. The competing parties known as miners carry out continuous computing processing to address recurrent cryptography issues. Transaction fees and some freshly minted (mined) bitcoin are the rewards they receive. The amount of hashes produced each second—or hash rate—is a measure of mining capacity.

Cryptography, decentralization, and the proof-of-work consensus method are Bitcoin's most unique features.

Bitcoin uses encryption

Bitcoin employs this established cryptography.

  1. Hashing

  2. digital signatures based on asymmetric encryption

Hashing (SHA-256) (SHA-256)

Figure 2: SHA-256 Hash operation on Block Header’s Hash + nonce

Hashing converts unique plaintext data into a digest. Creating the plaintext from the digest is impossible. Bitcoin miners generate new hashes using SHA-256 to win block rewards.

A new hash is created from the current block header and a variable value called nonce. To achieve the required hash, mining involves altering the nonce and re-hashing.

The block header contains the previous block hash and a Merkle root, which contains hashes of all transactions in the block. Thus, a chain of blocks with increasing hashes links back to the first block. Hashing protects new transactions and makes the bitcoin blockchain immutable. After a transaction block is mined, it becomes hard to fabricate even a little entry.

Asymmetric Cryptography Digital Signatures

Figure 3: Transaction signing and verifying process with asymmetric encryption and hashing operations

Asymmetric cryptography (public-key encryption) requires each side to have a secret and public key. Public keys (wallet addresses) can be shared with the transaction party, but private keys should not. A message (e.g., bitcoin payment record) can only be signed by the owner (sender) with the private key, but any node or anybody with access to the public key (visible in the blockchain) can verify it. Alex will submit a digitally signed transaction with a desired amount of bitcoin addressed to Bob's wallet to a node to send bitcoin to Bob. Alex alone has the secret keys to authorize that amount. Alex's blockchain public key allows anyone to verify the transaction.

Solution

Now, apply bitcoin to BGP. BGP generals resemble bitcoin nodes. The generals' consensus is like bitcoin nodes' blockchain block selection. Bitcoin software on all nodes can:

Check transactions (i.e., validate digital signatures)

2. Accept and propagate just the first miner to receive the valid hash and verify it accomplished the task. The only way to guess the proper hash is to brute force it by repeatedly producing one with the fixed/current block header and a fresh nonce value.

Thus, PoW and a dispersed network of nodes that accept blocks from miners that solve the unfalsifiable cryptographic challenge solve consensus.

Suppose:

  1. Unreliable nodes

  2. Unreliable miners

Bitcoin accepts the longest chain if rogue nodes cause divergence in accepted blocks. Thus, rogue nodes must outnumber honest nodes in accepting/forming the longer chain for invalid transactions to reach the blockchain. As of November 2022, 7000 coordinated rogue nodes are needed to takeover the bitcoin network.

Dishonest miners could also try to insert blocks with falsified transactions (double spend, reverse, censor, etc.) into the chain. This requires over 50% (51% attack) of miners (total computational power) to outguess the hash and attack the network. Mining hash rate exceeds 200 million (source). Rewards and transaction fees encourage miners to cooperate rather than attack. Quantum computers may become a threat.

Visit my Quantum Computing post.

Quantum computers—what are they? Quantum computers will have a big influence. towardsdatascience.com

Nodes have more power than miners since they can validate transactions and reject fake blocks. Thus, the network is secure if honest nodes are the majority.

Summary

Table 1 compares three Byzantine Generals Problem implementations.

Table 1: Comparison of Byzantine Generals Problem implementations

Bitcoin white paper and implementation solved the consensus challenge of distributed systems without central governance. It solved the illusive Byzantine Generals Problem.

Resources

Resources

  1. https://en.wikipedia.org/wiki/Byzantine_fault

  2. Source-code for Bitcoin Core Software — https://github.com/bitcoin/bitcoin

  3. Bitcoin white paper — https://bitcoin.org/bitcoin.pdf

  4. https://en.wikipedia.org/wiki/Bitcoin

  5. https://www.microsoft.com/en-us/research/publication/byzantine-generals-problem/

  6. https://www.microsoft.com/en-us/research/uploads/prod/2016/12/The-Byzantine-Generals-Problem.pdf

  7. https://en.wikipedia.org/wiki/Hash_function

  8. https://en.wikipedia.org/wiki/Merkle_tree

  9. https://en.wikipedia.org/wiki/SHA-2

  10. https://en.wikipedia.org/wiki/Public-key_cryptography

  11. https://en.wikipedia.org/wiki/Digital_signature

  12. https://en.wikipedia.org/wiki/Proof_of_work

  13. https://en.wikipedia.org/wiki/Quantum_cryptography

  14. https://dci.mit.edu/bitcoin-security-initiative

  15. https://dci.mit.edu/51-attacks

  16. Genuinely Distributed Byzantine Machine LearningEl-Mahdi El-Mhamdi et al., 2020. ACM, New York, NY, https://doi.org/10.1145/3382734.3405695

Max Parasol

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.

Max Parasol

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.

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Dmitrii Eliuseev

Dmitrii Eliuseev

2 years ago

Creating Images on Your Local PC Using Stable Diffusion AI

Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Image generated by Stable Diffusion 2.1

Let’s get started.

What It Does

Stable Diffusion uses numerous components:

  • A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).

  • An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).

  • A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).

This figure shows all data flow:

Model architecture, Source © https://arxiv.org/pdf/2112.10752.pdf

The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.

Install

Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults conda

Install the source and prepare the environment:

git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgrade

Download the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.

Running the optimized version

Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:

python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).

Running Stable Diffusion without GPU

If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:

  • Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().

  • Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().

  • Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.

  • Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().

Run the script again.

Testing

Test the model. Text-to-image is the first choice. Test the command line example again:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:

The SD V1.4 first example, Image by the author

Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:

The SD V1.4 second example, Image by the author

Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):

An image sketch, Image by the author

I can create an image from this drawing:

python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8

It was far better than my initial drawing:

The SD V1.4 third example, Image by the author

I hope readers understand and experiment.

Stable Diffusion UI

Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:

  • Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).

  • Start the script.

Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:

Stable Diffusion UI © Image by author

V2.1 of Stable Diffusion

I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:

  • alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.

  • a new depth model that may be used to the output of image-to-image generation.

  • a revolutionary upscaling technique that can quadruple the resolution of an image.

  • Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.

The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:

conda deactivate  
conda env remove -n ldm  # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldm

Hugging Face offers a new weights ckpt file.

The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:

A Stable Diffusion 2.1 example

It looks different from v1, but it functions and has a higher resolution.

The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):

python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckpt

This code allows the web browser UI to select the image to upscale:

The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:

Stable Diffusion 4X upscaler running on CPU © Image by author

Stable Diffusion Limitations

When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:

V1:

V2.1:

The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.

I can also ask the model to draw a gorgeous woman:

V1:

V2.1:

The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.

If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:

V1:

V2.1:

Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:

V1:

V2.1: improved but not perfect.

V1 produces a fun cartoon flying mouse if I want something more abstract:

I tried multiple times with V2.1 but only received this:

The image is OK, but the first version is closer to the request.

Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:

V1:

V2.1:

Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:

“Modern art painting” © Google’s Image search result

I typed "abstract oil painting of people dancing" and got this:

V1:

V2.1:

It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.

The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:

This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.

I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).

Conclusion

The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).

Is Generative AI a game-changer? My humble experience tells me:

  • I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.

  • Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.

  • It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).

  • When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.

Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

Liz Martin

Liz Martin

3 years ago

A Search Engine From Apple?

Apple's search engine has long been rumored. Recent Google developments may confirm the rumor. Is Apple about to become Google's biggest rival?

Here's a video:

People noted Apple's changes in 2020. AppleBot, a web crawler that downloads and caches Internet content, was more active than in the last five years.

Apple hired search engine developers, including ex-Googlers, such as John Giannandrea, Google's former search chief.

Apple also changed the way iPhones search. With iOS 14, Apple's search results arrived before Google's.

These facts fueled rumors that Apple was developing a search engine.

Apple and Google Have a Contract

Many skeptics said Apple couldn't compete with Google. This didn't affect the company's competitiveness.

Apple is the only business with the resources and scale to be a Google rival, with 1.8 billion active devices and a $2 trillion market cap.

Still, people doubted that due to a license deal. Google pays Apple $8 to $12 billion annually to be the default iPhone and iPad search engine.

Apple can't build an independent search product under this arrangement.

Why would Apple enter search if it's being paid to stay out?

Ironically, this partnership has many people believing Apple is getting into search.

A New Default Search Engine May Be Needed

Google was sued for antitrust in 2020. It is accused of anticompetitive and exclusionary behavior. Justice wants to end Google's monopoly.

Authorities could restrict Apple and Google's licensing deal due to its likely effect on market competitiveness. Hence Apple needs a new default search engine.

Apple Already Has a Search Engine

The company already has a search engine, Spotlight.

Since 2004, Spotlight has aired. It was developed to help users find photos, documents, apps, music, and system preferences.

Apple's search engine could do more than organize files, texts, and apps.

Spotlight Search was updated in 2014 with iOS 8. Web, App Store, and iTunes searches became available. You could find nearby places, movie showtimes, and news.

This search engine has subsequently been updated and improved. Spotlight added rich search results last year.

If you search for a TV show, movie, or song, photos and carousels will appear at the top of the page.

This resembles Google's rich search results.

When Will the Apple Search Engine Be Available?

When will Apple's search launch? Robert Scoble says it's near.

Scoble tweeted a number of hints before this year's Worldwide Developer Conference.

Scoble bases his prediction on insider information and deductive reasoning. January 2023 is expected.

Will you use Apple's search engine?

Pat Vieljeux

Pat Vieljeux

3 years ago

In 5 minutes, you can tell if a startup will succeed.

Or the “lie to me” method.

I can predict a startup's success in minutes.

Just interview its founder.

Ask "why?"

I question "why" till I sense him.

I need to feel the person I have in front of me. I need to know if he or she can deliver. Startups aren't easy. Without abilities, a brilliant idea will fail.

Good entrepreneurs have these qualities: He's a leader, determined, and resilient.

For me, they can be split in two categories.

The first entrepreneur aspires to live meaningfully. The second wants to get rich. The second is communicative. He wants to wow the crowd. He's motivated by the thought of one day sailing a boat past palm trees and sunny beaches.

What drives the first entrepreneur is evident in his speech, face, and voice. He will not speak about his product. He's (nearly) uninterested. He's not selling anything. He's not a salesman. He wants to succeed. The product is his fuel.

He'll explain his decision. He'll share his motivations. His desire. And he'll use meaningful words.

Paul Ekman has shown that face expressions aren't cultural. His study influenced the American TV series "lie to me" about body language and speech.

Passionate entrepreneurs are obvious. It's palpable. Faking passion is tough. Someone who wants your favor and money will expose his actual motives through his expressions and language.

The good liar will be able to fool you for a while, but not for long if you pay attention to his body language and how he expresses himself.

And also, if you look at his business plan.

His business plan reveals his goals. Read between the lines.

Entrepreneur 1 will focus on his "why", whereas Entrepreneur 2 will focus on the "how".

Entrepreneur 1 will develop a vision-driven culture.

The second, on the other hand, will focus on his EBITDA.

Why is the culture so critical? Because it will allow entrepreneur 1 to develop a solid team that can tackle his problems and trials. His team's "why" will keep them together in tough times.

"Give me a terrific start-up team with a mediocre idea over a weak one any day." Because a great team knows when to pivot and trusts each other. Weak teams fail.” — Bernhard Schroeder

Closings thoughts

Every VC must ask Why. Entrepreneur's motivations. This "why" will create the team's culture. This culture will help the team adjust to any setback.