More on NFTs & Art

Yuga Labs
3 years ago
Yuga Labs (BAYC and MAYC) buys CryptoPunks and Meebits and gives them commercial rights
Yuga has acquired the CryptoPunks and Meebits NFT IP from Larva Labs. These include 423 CryptoPunks and 1711 Meebits.
We set out to create in the NFT space because we admired CryptoPunks and the founders' visionary work. A lot of their work influenced how we built BAYC and NFTs. We're proud to lead CryptoPunks and Meebits into the future as part of our broader ecosystem.
"Yuga Labs invented the modern profile picture project and are the best in the world at operating these projects. They are ideal CrytoPunk and Meebit stewards. We are confident that in their hands, these projects will thrive in the emerging decentralized web.”
–The founders of Larva Labs, CryptoPunks, and Meebits
This deal grew out of discussions between our partner Guy Oseary and the Larva Labs founders. One call led to another, and now we're here. This does not mean Matt and John will join Yuga. They'll keep running Larva Labs and creating awesome projects that help shape the future of web3.
Next steps
Here's what we plan to do with CryptoPunks and Meebits now that we own the IP. Owners of CryptoPunks and Meebits will soon receive commercial rights equal to those of BAYC and MAYC holders. Our legal teams are working on new terms and conditions for both collections, which we hope to share with the community soon. We expect a wide range of third-party developers and community creators to incorporate CryptoPunks and Meebits into their web3 projects. We'll build the brand alongside them.
We don't intend to cram these NFT collections into the BAYC club model. We see BAYC as the hub of the Yuga universe, and CryptoPunks as a historical collection. We will work to improve the CryptoPunks and Meebits collections as good stewards. We're not in a hurry. We'll consult the community before deciding what to do next.
For us, NFTs are about culture. We're deeply invested in the BAYC community, and it's inspiring to see them grow, collaborate, and innovate. We're excited to see what CryptoPunks and Meebits do with IP rights. Our goal has always been to create a community-owned brand that goes beyond NFTs, and now we can include CryptoPunks and Meebits.

Sea Launch
3 years ago
A guide to NFT pre-sales and whitelists
Before we dig through NFT whitelists and pre-sales, if you know absolutely nothing about NFTs, check our NFT Glossary.
What are pre-sales and whitelists on NFTs?
An NFT pre-sale, as the name implies, allows community members or early supporters of an NFT project to mint before the public, usually via a whitelist or mint pass.
Coin collectors can use mint passes to claim NFTs during the public sale. Because the mint pass is executed by “burning” an NFT into a specific crypto wallet, the collector is not concerned about gas price spikes.
A whitelist is used to approve a crypto wallet address for an NFT pre-sale. In a similar way to an early access list, it guarantees a certain number of crypto wallets can mint one (or more) NFT.
New NFT projects can do a pre-sale without a whitelist, but whitelists are good practice to avoid gas wars and a fair shot at minting an NFT before launching in competitive NFT marketplaces like Opensea, Magic Eden, or CNFT.
Should NFT projects do pre-sales or whitelists? 👇
The reasons to do pre-sales or a whitelist for NFT creators:
Time the market and gain traction.
Pre-sale or whitelists can help NFT projects gauge interest early on.
Whitelist spots filling up quickly is usually a sign of a successful launch, though it does not guarantee NFT longevity (more on that later). Also, full whitelists create FOMO and momentum for the public sale among non-whitelisted NFT collectors.
If whitelist signups are low or slow, projects may need to work on their vision, community, or product. Or the market is in a bear cycle. In either case, it aids NFT projects in market timing.
Reward the early NFT Community members.
Pre-sale and whitelists can help NFT creators reward early supporters.
First, by splitting the minting process into two phases, early adopters get a chance to mint one or more NFTs from their collection at a discounted or even free price.
Did you know that BAYC started at 0.08 eth each? A serum that allowed you to mint a Mutant Ape has become as valuable as the original BAYC.
(2) Whitelists encourage early supporters to help build a project's community in exchange for a slot or status. If you invite 10 people to the NFT Discord community, you get a better ranking or even a whitelist spot.
Pre-sale and whitelisting have become popular ways for new projects to grow their communities and secure future buyers.
Prevent gas wars.
Most new NFTs are created on the Ethereum blockchain, which has the highest transaction fees (also known as gas) (Solana, Cardano, Polygon, Binance Smart Chain, etc).
An NFT public sale is a gas war when a large number of NFT collectors (or bots) try to mint an NFT at the same time.
Competing collectors are willing to pay higher gas fees to prioritize their transaction and out-price others when upcoming NFT projects are hyped and very popular.
Pre-sales and whitelisting prevent gas wars by breaking the minting process into smaller batches of members or season launches.
The reasons to do pre-sales or a whitelists for NFT collectors:
How do I get on an NFT whitelist?
- Popular NFT collections act as a launchpad for other new or hyped NFT collections.
Example: Interfaces NFTs gives out 100 whitelist spots to Deadfellaz NFTs holders. Both NFT projects win. Interfaces benefit from Deadfellaz's success and brand equity.
In this case, to get whitelisted NFT collectors need to hold that specific NFT that is acting like a launchpad.
- A NFT studio or collection that launches a new NFT project and rewards previous NFT holders with whitelist spots or pre-sale access.
The whitelist requires previous NFT holders or community members.
NFT Alpha Groups are closed, small, tight-knit Discord servers where members share whitelist spots or giveaways from upcoming NFTs.
The benefit of being in an alpha group is getting information about new NFTs first and getting in on pre-sale/whitelist before everyone else.
There are some entry barriers to alpha groups, but if you're active in the NFT community, you'll eventually bump into, be invited to, or form one.
- A whitelist spot is awarded to members of an NFT community who are the most active and engaged.
This participation reward is the most democratic. To get a chance, collectors must work hard and play to their strengths.
Whitelisting participation examples:
- Raffle, games and contest: NFT Community raffles, games, and contests. To get a whitelist spot, invite 10 people to X NFT Discord community.
- Fan art: To reward those who add value and grow the community by whitelisting the best fan art and/or artists is only natural.
- Giveaways: Lucky number crypto wallet giveaways promoted by an NFT community. To grow their communities and for lucky collectors, NFT projects often offer free NFT.
- Activate your voice in the NFT Discord Community. Use voice channels to get NFT teams' attention and possibly get whitelisted.
The advantage of whitelists or NFT pre-sales.
Chainalysis's NFT stats quote is the best answer:
“Whitelisting isn’t just some nominal reward — it translates to dramatically better investing results. OpenSea data shows that users who make the whitelist and later sell their newly-minted NFT gain a profit 75.7% of the time, versus just 20.8% for users who do so without being whitelisted. Not only that, but the data suggests it’s nearly impossible to achieve outsized returns on minting purchases without being whitelisted.” Full report here.
Sure, it's not all about cash. However, any NFT collector should feel secure in their investment by owning a piece of a valuable and thriving NFT project. These stats help collectors understand that getting in early on an NFT project (via whitelist or pre-sale) will yield a better and larger return.
The downsides of pre-sales & whitelists for NFT creators.
Pre-sales and whitelist can cause issues for NFT creators and collectors.
NFT flippers
NFT collectors who only want to profit from early minting (pre-sale) or low mint cost (via whitelist). To sell the NFT in a secondary market like Opensea or Solanart, flippers go after the discounted price.
For example, a 1000 Solana NFT collection allows 100 people to mint 1 Solana NFT at 0.25 SOL. The public sale price for the remaining 900 NFTs is 1 SOL. If an NFT collector sells their discounted NFT for 0.5 SOL, the secondary market floor price is below the public mint.
This may deter potential NFT collectors. Furthermore, without a cap in the pre-sale minting phase, flippers can get as many NFTs as possible to sell for a profit, dumping them in secondary markets and driving down the floor price.
Hijacking NFT sites, communities, and pre-sales phase
People try to scam the NFT team and their community by creating oddly similar but fake websites, whitelist links, or NFT's Discord channel.
Established and new NFT projects must be vigilant to always make sure their communities know which are the official links, how a whitelist or pre-sale rules and how the team will contact (or not) community members.
Another way to avoid the scams around the pre-sale phase, NFT projects opt to create a separate mint contract for the whitelisted crypto wallets and then another for the public sale phase.
Scam NFT projects
We've seen a lot of mid-mint or post-launch rug pulls, indicating that some bad NFT projects are trying to scam NFT communities and marketplaces for quick profit. What happened to Magic Eden's launchpad recently will help you understand the scam.
We discussed the benefits and drawbacks of NFT pre-sales and whitelists for both projects and collectors.
Finally, some practical tools and tips for finding new NFTs 👇
Tools & resources to find new NFT on pre-sale or to get on a whitelist:
In order to never miss an update, important pre-sale dates, or a giveaway, create a Tweetdeck or Tweeten Twitter dashboard with hyped NFT project pages, hashtags ( #NFTGiveaways , #NFTCommunity), or big NFT influencers.
Search for upcoming NFT launches that have been vetted by the marketplace and try to get whitelisted before the public launch.
Save-timing discovery platforms like sealaunch.xyz for NFT pre-sales and upcoming launches. How can we help 100x NFT collectors get projects? A project's official social media links, description, pre-sale or public sale dates, price and supply. We're also working with Dune on NFT data analysis to help NFT collectors make better decisions.
Don't invest what you can't afford to lose because a) the project may fail or become rugged. Find NFTs projects that you want to be a part of and support.
Read original post here

CyberPunkMetalHead
2 years ago
Why Bitcoin NFTs Are Incomprehensible yet Likely Here to Stay
I'm trying to understand why Bitcoin NFTs aren't ready.
Ordinals, a new Bitcoin protocol, has been controversial. NFTs can be added to Bitcoin transactions using the protocol. They are not tokens or fungible. Bitcoin NFTs are transaction metadata. Yes. They're not owned.
In January, the Ordinals protocol allowed data like photos to be directly encoded onto sats, the smallest units of Bitcoin worth 0.00000001 BTC, on the Bitcoin blockchain. Ordinals does not need a sidechain or token like other techniques. The Ordinals protocol has encoded JPEG photos, digital art, new profile picture (PFP) projects, and even 1993 DOOM onto the Bitcoin network.
Ordinals inscriptions are permanent digital artifacts preserved on the Bitcoin blockchain. It differs from Ethereum, Solana, and Stacks NFT technologies that allow smart contract creators to change information. Ordinals store the whole image or content on the blockchain, not just a link to an external server, unlike centralized databases, which can change the linked image, description, category, or contract identifier.
So far, more than 50,000 ordinals have been produced on the Bitcoin blockchain, and some of them have already been sold for astronomical amounts. The Ethereum-based CryptoPunks NFT collection spawned Ordinal Punk. Inscription 620 sold for 9.5 BTC, or $218,000, the most.
Segwit and Taproot, two important Bitcoin blockchain updates, enabled this. These protocols store transaction metadata, unlike Ethereum, where the NFT is the token. Bitcoin's NFT is a sat's transaction details.
What effects do ordinary values and NFTs have on the Bitcoin blockchain?
Ordinals will likely have long-term effects on the Bitcoin Ecosystem since they store, transact, and compute more data.
Charges Ordinals introduce scalability challenges. The Bitcoin network has limited transaction throughput and increased fees during peak demand. NFTs could make network transactions harder and more expensive. Ordinals currently occupy over 50% of block space, according to Glassnode.
One of the protocols that supported Ordinals Taproot has also seen a huge uptick:
Taproot use increases block size and transaction costs.
This could cause network congestion but also support more L2s with Ordinals-specific use cases. Dune info here.
Storage Needs The Bitcoin blockchain would need to store more data to store NFT data directly. Since ordinals were introduced, blocksize has tripled from 0.7mb to over 2.2mb, which could increase storage costs and make it harder for nodes to join the network.
Use Case Diversity On the other hand, NFTs on the Bitcoin blockchain could broaden Bitcoin's use cases beyond storage and payment. This could expand Bitcoin's user base. This is two-sided. Bitcoin was designed to be trustless, decentralized, peer-to-peer money.
Chain to permanently store NFTs as ordinals will change everything.
Popularity rise This new use case will boost Bitcoin appeal, according to some. This argument fails since Bitcoin is the most popular cryptocurrency. Popularity doesn't require a new use case. Cryptocurrency adoption boosts Bitcoin. It need not compete with Ethereum or provide extra benefits to crypto investors. If there was a need for another chain that supports NFTs (there isn't), why would anyone choose the slowest and most expensive network? It appears contradictory and unproductive.
Nonetheless, holding an NFT on the Bitcoin blockchain is more secure than any other blockchain, but this has little utility.
Bitcoin NFTs are undoubtedly controversial. NFTs are strange and perhaps harmful to Bitcoin's mission. If Bitcoin NFTs are here to stay, I hope a sidechain or rollup solution will take over and leave the base chain alone.
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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.

Nikhil Vemu
3 years ago
7 Mac Tips You Never Knew You Needed
Unleash the power of the Option key ⌥
#1 Open a link in the Private tab first.
Previously, if I needed to open a Safari link in a private window, I would:
copied the URL with the right click command,
choose File > New Private Window to open a private window, and
clicked return after pasting the URL.
I've found a more straightforward way.
Right-clicking a link shows this, right?
Hold option (⌥) for:
Click Open Link in New Private Window while holding.
Finished!
#2. Instead of searching for specific characters, try this
You may use unicode for business or school. Most people Google them when they need them.
That is lengthy!
You can type some special characters just by pressing ⌥ and a key.
For instance
• ⌥+2 -> ™ (Trademark)
• ⌥+0 -> ° (Degree)
• ⌥+G -> © (Copyright)
• ⌥+= -> ≠ (Not equal to)
• ⌥+< -> ≤ (Less than or equal to)
• ⌥+> -> ≥ (Greater then or equal to)
• ⌥+/ -> ÷ (Different symbol for division)#3 Activate Do Not Disturb silently.
Do Not Disturb when sharing my screen is awkward for me (because people may think Im trying to hide some secret notifications).
Here's another method.
Hold ⌥ and click on Time (at the extreme right on the menu-bar).
Now, DND is activated (secretly!). To turn it off, do it again.
Note: This works only for DND focus.#4. Resize a window starting from its center
Although this is rarely useful, it is still a hidden trick.
When you resize a window, the opposite edge or corner is used as the pivot, right?
However, if you want to resize it with its center as the pivot, hold while doing so.
#5. Yes, Cut-Paste is available on Macs as well (though it is slightly different).
I call it copy-move rather than cut-paste. This is how it works.
Carry it out.
Choose a file (by clicking on it), then copy it (⌘+C).
Go to a new location on your Mac. Do you use ⌘+V to paste it? However, to move it, press ⌘+⌥+V.
This removes the file from its original location and copies it here. And it works exactly like cut-and-paste on Windows.
#6. Instantly expand all folders
Set your Mac's folders to List view.
Assume you have one folder with multiple subfolders, each of which contains multiple files. And you wanted to look at every single file that was over there.
How would you do?
You're used to clicking the ⌄ glyph near the folder and each subfolder to expand them all, right? Instead, hold down ⌥ while clicking ⌄ on the parent folder.
This is what happens next.
Everything expands.
View/Copy a file's path as an added bonus
If you want to see the path of a file in Finder, select it and hold ⌥, and you'll see it at the bottom for a moment.
To copy its path, right-click on the folder and hold down ⌥ to see this
Click on Copy <"folder name"> as Pathname to do it.
#7 "Save As"
I was irritated by the lack of "Save As" in Pages when I first got a Mac (after 15 years of being a Windows guy).
It was necessary for me to save the file as a new file, in a different location, with a different name, or both.
Unfortunately, I couldn't do it on a Mac.
However, I recently discovered that it appears when you hold ⌥ when in the File menu.
Yay!

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.
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:
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 condaInstall 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 --upgradeDownload 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 1Almost. 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 1Stable 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 1The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:
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:
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):
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 0.8It was far better than my initial drawing:
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:
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 ldmHugging 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:
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.ckptThis 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 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:
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.
