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“During the pandemic, I got super into collecting Kobe Bryant cards and Pokémon cards,” professional skateboarder Mike Mo Capaldi told nft now, recounting how his upcoming NFT project came to be.
At 33-years-old, Capaldi was at the height of his skateboarding career when Instagram came onto the scene. Skateboarders, perhaps more than athletes in any other sport, had to get with the times or they’d be left behind. “Our careers really were the bridge between the old model of skateboarding with magazines and video parts, and posting footage every day on Instagram,” Capaldi added.
Given that the skateboarding industry isn’t on a fixed time schedule, professional skaters risk fading off into obscurity if they don’t post clips of themselves frequently enough on Instagram and other channels. However, it may have been this necessity to digitize his career that prompted Capaldi to think outside of the box on his latest NFT venture.Fully Flared
Before the social media age, professional skateboarders would release a video part of themselves every few years in order to showcase the tricks they’d been working on. In the meantime, their fans would have to go to local skateboarding stores and purchase magazines to see pictures of their favorite professional – otherwise, it was radio silence until a video part came out.
In Capaldi’s case, his rapid launch to skateboarding stardom came on the heels of his part in the 2007 video called Fully Flared, which was co-directed by Spike Jonze. Jonze’s involvement in the skateboarding film resulted in a higher level of production quality than had previously been seen in the industry, and Capaldi had the opening part at just 18 years old.
Fully Flared not only put Capaldi’s name on the map, but it put him at the forefront of the industry as one of the leading skateboarders of his generation.
Now, he’s leading the digital frontier of skateboarding into the blockchain.Capturing historic moments in skateboarding
Capaldi’s new venture entitled ‘ABD Collectibles’ is a series of 150 randomized NFTs, which contain one of 33 clips of Capaldi skateboarding in the iconic Fully Flared video. The professional skateboarder already released the first 50 of those 150 NFTs, which sold out within 60 seconds.
Purchasers of the NFTs may find the golden ticket of ABD Collectibles by opening a ‘Legendary’ status NFT, which contains the historic clip from Fully Flared of Capaldi doing a switch kickflip down a set of stairs right before the staircase is blown up with explosives.
Mike Mo Capaldi with an explosive switch flip. Photo: Atiba Jefferson
Those that open up the legendary NFT will also be sent a physical card, signed by Capaldi, which contains a piece of the shirt he wore in the infamous clip.
The acronym ABD is skateboarding nomenclature for the professional faux pas of doing a trick that’s ‘Already Been Done’ by another professional within the skateboarding industry.
“We called it ABD Collectibles because to me, it’s already been done by the NBA, and by Pokémon. You get a Michael Jordan card or a Kobe Bryant rookie card, and you’re stoked. You don’t necessarily do anything with it, you’re just happy to own this card. However, nobody has done this within skateboarding.”
“There isn’t really anything that’s related to skateboarding history. There’s no prominent brand that hones in on old moments and makes collectibles out of them as we see with the MLB or the NBA. That doesn’t exist,” Capaldi explained.
The physical version of Capaldi’s collectible NFT series.
For those that haven’t paid close attention to Capaldi’s career, his foray into the digital space may seem out of left-field. To others, it’s clear that Capaldi has always been interested in taking the road less traveled. For the past eleven years, Capaldi and his brother Vince have been running a successful eyewear company called Glassy, which started due to a similar observation to the one which prompted Capaldi to start ABD Collectibles.
“Just as I’ve recently realized that there’s no skateboarding collectibles company, my brother and I realized in 2011 that there was no eyewear brand in skateboarding,” Capaldi explained. “There was Oakley, and Nike also made some glasses, but there was nothing that was specifically made by and for skateboarders.”
From that moment Glassy Eyewear was born, and Capaldi and his brother leveraged their connections in the skateboarding industry to get the company off the ground and into the mainstream.
Now, Capaldi is taking that same entrepreneurial spirit to ABD collectibles.
“I remember putting a Zion Williamson card up for sale online, and five minutes later I sold it. I got that card in a $14 pack and sold it in five minutes for $300. That’s the power of collectibles. And then I just thought to myself, it’d be cool to bring this format to skating. Then the NFT craze started happening, and this opportunity to merge all of my interests came up. I’m really into like, just nerdy tech stuff. And to me, this is a big, nerdy tech project.”Collectibles with utility: The future of ABD
Capaldi also floated the idea of giving NFT holders access to private skateboarding events.
“Maybe we buy 100 tickets to a Street League skateboarding contest, and we have our own ABD section. There are drinks and food and if you have one of the legendary NFTs, then you get access to that zone. These are just ideas and it’s very early days, but I think giving the NFTs a community-based utility is the right way to do it. Like having a secret password to a private club.”
For now, however, Capaldi is intent on simply turning historic skateboarding moments into collectibles. Though it is a balancing act.
You're reading Skateboarding Nfts And The Explosive Rise Of ‘Abd Collectibles’
When Mark Pickett was a captain in the Marines, he knew he couldn’t be there to make every decision for his soldiers.
“You can’t rehearse every scenario, and there will be times when you can’t communicate,” he explained. “You want to groom your Marines to be able to rely on themselves and their unit.”
It’s not so different in the business world in this era of big data.
Now senior director for online analytics and business intelligence at Sears, Pickett has been an early champion of the so-called citizen data scientist movement, by which employees in multiple parts of an organization are empowered with the analytics tools and skills to get the answers they need from their data.
“The business understands the business more deeply than we ever could,” he said. “We’re trying to coach these people up and provide them with the data they need to craft their own reporting and do their own analyses.”
In Sears’ case, the motivation is particularly strong. Though a retail business overall, the company is in many ways a conglomeration of numerous vertical businesses, each focusing on different product types.
“We have a very multicategory sort of business, from lawn and garden to appliances to clothing and jewelry to mattresses,” Pickett said. “My team is built to support all of them, but we’ll never understand their businesses the way they do.”
By curating the right tools — in Sears’ case, Platfora’s big-data analytics platform for Hadoop — Pickett’s group aims to enable businesspeople to answer 80 percent of their data questions themselves. More than 300 trained citizen data scientists at the company are now using those tools to generate thousands of data-analysis reports each week without any assistance.
“The only reason we’d touch one is if someone had questions, or needed data added,” Pickett said.
A new generation of tools
Sears may have a particularly pressing need by virtue of the diverse nature of its business, but companies of all kinds are feeling the acute shortage of trained data scientists today. Even for those lucky enough to snag such a professional, “janitorial” tasks such as data preparation are still taking up an inordinate proportion of those workers’ time.
Empowering businesspeople to do much of the analysis themselves frees up highly trained data scientists to focus on the things that require their expertise — or so the thinking goes.
Part of what’s making it possible is the growing set of powerful self-service tools available on the market today, putting capabilities like artificial intelligence within reach for virtually anyone.
“Companies have more and more data,” said Lukas Biewald, CEO and founder at data-focused crowdsourcing site CrowdFlower.
Gartner predicts that the market for self-service data-preparation tools will reach $1 billion by 2023.
“Large enterprises are moving to data lakes, so all the data is in one place,” said Jason Zintak, Platfora’s president and CEO.
Next, companies need to help their employees make the most of it. Platfora bills its Hadoop-focused platform as a way to let anyone within a company run analyses across the entire organization’s data, including transactions, customer interactions and machine data.
‘They can build their own reports’
In many ways, the citizen data scientist represents an evolution of the traditional business analyst role.
“When I think about the traditional business analyst, they’d have a good understanding of the business but were not necessarily conversant with regard to the data,” Sears’ Pickett said.
Such professionals have often been focused on gleaning insights from Excel or other reporting tools without necessarily working knee-deep in the data, in other words.
“What I’m observing is that people who have a strong understanding of the business now have some capability in terms of the data,” he explained. “They can build their own reports, they know what attributes go together and they know what questions to ask not just from a business perspective but from a data perspective.”
Not everyone is sold on the citizen data scientist concept, however.
‘A recipe for disaster’
“I don’t like the ‘citizen data scientist’ term,” said Gregory Piatetsky-Shapiro, president of KDnuggets, an analytics and data-science consultancy.
For one thing, “the term implies that people without much training can do the work of a data scientist,” Piatetsky-Shapiro said.
It’s all too easy to discount the importance of education, in other words, even as big data is in many ways making it more important than ever before. With statistics at its core, data science often relies on an understanding of the assumptions underlying various statistical techniques, for example — factors that aren’t always apparent to those who haven’t formally learned about them.
“Would you trust your teeth to a ‘citizen dentist’ or fly in a plane piloted by ‘citizen pilot’?” Piatetsky-Shapiro asked. “Having untrained citizen data scientists analyze the data may be easy, but if they will be making decisions without proper training in data analysis and without an understanding of the business, it is a recipe for disaster.”
Platfora’s Zintak says built-in corporate governance structures can address that issue by controlling security and access levels, for example. At Sears, two weeks of training for the company’s 300+ citizen data scientists have helped as well.
‘Data is viral – everybody wants it’
Sears finalized its migration from a DB2 relational database management system to a Hadoop data lake in 2023. It had already adopted Platfora for a small group of specialists, but it wasn’t long before the need for broader availability became clear.
“Data is viral — everybody wants it,” Pickett said. “It quickly became apparent that we had to solve for the volume of data requested by people by enabling them to become self-sufficient.”
Focusing on the 300 or so people who handled many of the reporting needs for their teams, Sears’ own in-house experts conducted the training to bring those users up to speed. Topics covered included nomenclature and data-set manipulation, for example.
Today, those employees request data, not reports, he said: “That’s when we knew this was starting to take shape.”
Now freed up from the bulk of the company’s ad hoc reporting needs, Pickett’s team can focus on higher-level tasks such as data curation, model building and governance.
‘Start small and just do it’
Overall, Pickett touts decentralized decision-making as one of the chief benefits of the citizen data scientist model.
“It’s not just about reducing reliance on us,” he said. “It’s empowering people to become more capable with their own data, and that’s enabling them to think about their business in new ways.”
If Pickett had to do it all over again, he’d make the transition to the citizen data scientist model sooner, he said.
Leonardo.ai is an artificial intelligence platform that specializes in generating stunning and photorealistic images. Whether you’re an artist or just someone who wants to create amazing art with ease, this platform has something for everyone. The best part is that you don’t need to have any experience with AI or complex software to use it!
Leonardo AI is a cutting-edge tool that harnesses the power of artificial intelligence to create stunning game assets, including items, environments, helmets, buildings, and concept art. With its intuitive and artist-friendly interface, users can ideate and train their own AI models, resulting in unique production-ready assets that are perfect for video games.
Revolutionizing the way creative professionals, businesses, and individuals create high-quality visual content, Leonardo AI offers an exclusive early-access program that enables users to sign up and explore its features. Unlike other AI image generators, Leonardo AI focuses solely on generating assets for video games, making it an essential tool for game designers and developers.
Before you can start using chúng tôi you’ll need to sign up for a free account. The sign-up process can be a bit confusing, but we found a little workaround for immediate access. Here’s how it works:
Go to Leonardo.ai
To the top right, hit “Launch App”
First, sign up for early access
You will be taken to another screen
Hit back and go back to this screen, and then hit “Yes I’m whitelisted”
You will be taken to the Login page, where you can simply log in with Google or Microsoft, or you can hit sign up
Once you’ve completed, the first time you will be asked to select your interests
Once you’ve signed up, you’ll be taken to the home page, where you’ll find a list of featured models and a community feed showcasing recent creations.
Also read: How to Sign up and Use Leonardo AI
One of the most notable features of chúng tôi is the availability of pre-trained models that users can choose from. These models include photorealistic and artistic styles, vintage photography, magical creatures, and paper art. Our testing shows that these are fine-tuned models that produce specific and excellent outputs.
Another outstanding feature is the ability for users to create their own custom data sets and models by uploading photos. It’s relatively simple and fast to do this, which allows users to train chúng tôi in a specific style and get the results they want.
With Leonardo AI, you can design real-life ideas into spectacular art. Its AI-powered algorithms can generate staggering game resources, such as items, characters, caps, structures, and concept art. The result is a seamless integration of technology and creativity that allows users to bring their visions to life.
With Leonardo AI’s exclusive early-access program, users can sign up to explore the platform before its official launch. This program enables users to gain early access to new features, offer feedback, and be a part of the platform’s development process.
The user interface of chúng tôi is web-based and easy to navigate. The options available to users include the number of images they want to generate, the image dimensions, the guidance scale, and the tiling. On the right side of the screen, users have the ability to input their prompts, select the model and style they want to use, and include negative prompts if desired.
The interface has a clean and organized design, making navigation simple. The functions and features are presented clearly and do not obstruct the user. It is reminiscent of the layout of Midjourney’s account page.
Training a model in Leonardo AI is a crucial step in creating your desired outputs. There are several factors to consider to ensure a successful training run.
The characteristics of your dataset, specifically the balance between variation and consistency, will also play a role in the training process.
It is important to have a common theme or pattern between your images for the model to learn from. The elements that are consistent between images are what the model will learn and show up in the outputs.
Leonardo.ai features an AI canvas, a powerful, simple and easy-to-navigate editor that allows you to create and edit AI art images in new and innovative ways. If you have been previously intimidated by other Stable Diffusion models, this could be the solution for you.
You can either upload an image from your computer, a previous generation, or from the community. Once you have selected an image, you can copy the prompt and edit it in the canvas. The AI canvas provides a block-based interface that makes editing your prompts a breeze. You can move the blocks around, delete them, and add new ones with ease.
One of the most exciting features of the AI canvas is the ability to control the output of the generated images. You can modify the number of samples, the temperature, and the time taken to generate the output. Additionally, you can choose the type of output you want, such as still images or animations.
Once you have generated an image or animation that you are happy with, you can share it with the chúng tôi community. This is a great way to get feedback and see what other users are creating. You can also download the image or animation to your computer and use it as you wish.
Leonardo.ai is an exciting and innovative platform that allows users to generate AI art with ease. Its user interface is easy to navigate, and its features are well-designed and easy to use. The ability to create custom models and datasets is particularly impressive, as it allows users to create outputs that are unique and personalized. Overall, if you are interested in creating AI art, chúng tôi is definitely worth checking out.
Ethereum’s grip on DEX dominance is slipping, signaling a new era in decentralized trading.
However, Ethereum’s innovative Layer 2 solutions are recapturing lost traffic and solidifying its position as a dominant platform.
Ethereum emerged as a second-generation blockchain, revolutionizing the digital landscape by introducing smart contract functionality.
It ingeniously filled a void left by the Bitcoin network, which lacked this essential feature. Among its notable achievements, Ethereum solidified its position as the epicenter of Decentralized Exchanges (DEX).
However, Ethereum’s stronghold on the DEX throne is gradually slipping away, giving rise to a new era in decentralized trading.Is Ethereum lagging in DEX dominance?
Ethereum has long reigned supreme as the go-to network for Decentralized Applications (Dapps) and Decentralized Exchanges (DEX), with most smart contract platforms operating on its blockchain.
However, recent data from Messari suggested that Ethereum’s grip on DEX dominance was waning. This shift can be attributed to two factors.
Firstly, the decreasing dominance in DEX volumes could be attributed to the emergence of alternative Layer-1 (L1) DeFi ecosystems. Also, the strong bull market throughout 2023.
However, when market downturns hit in 2023, many large entities were wiped out. It also caused trading volumes to shift back to the mainnet.
Furthermore, this trend culminated in March 2023, during the USDC depeg. During this time its DEX volume dominance reached an impressive 80% – a level not seen since the beginning of 2023.
Secondly, users who migrate from the Ethereum mainnet to L2 DEXs are less likely to revert to their previous course. L2s inherit their security properties and base assets (ETH) from Ethereum.Ethereum L2s
In order to improve scalability and increase transaction throughput, ETH Layer 2 solutions have emerged as a potential solution. They exist to address the limitations of existing blockchain networks. These solutions are built on top of layer 1 networks to enhance performance.
One popular example of a Layer 2 solution on Ethereum is Polygon, which utilizes a side-chain approach. Another type of Layer 2 solution is rollups, which can be either Zero Knowledge (ZK) based, such as zkSync, or Optimistic Rollup, like Optimism.
These solutions allow for a higher volume of transactions to be processed while maintaining security and integrity.Total Value Locked of mainnet and L2s
According to data from L2 Beat, Ethereum rollups have been experiencing a notable upward trend in Total Value Locked (TVL). As of this writing, the TVL had surpassed the $9 billion mark, with Arbitrum and Optimism taking the lead in TVL. These leading Layer 2 (L2) solutions are categorized as Optimistic Rollups.
Furthermore, data from DefiLlama revealed that the TVL of Ethereum stood at an impressive $28.73 billion, at the time of writing. This represented over half of the total TVL in the market, which amounted to $49.09 billion.
How much are 1,10,100 ETHs worth today?
Although Ethereum’s DEX dominance may be diminishing, its Layer 2 (L2) solutions successfully recaptured the traffic it was losing.
While attention may have shifted away from the mainnet, it remains a dominant platform thanks to the adoption of side chains and rollups.
The platform’s innovative approach to scaling through side chains and rollups has allowed it to maintain prominence.
ML applications in the healthcare industry are evolving quickly and changing how doctors identify, treat, and prevent diseases. The potential uses of machine learning in the healthcare industry are numerous and varied, ranging from predicting disease outbreaks to finding intricate medical patterns and assisting researchers in developing targeted medicines.
ML algorithms have the potential to offer medical professionals previously unheard-of insights into patient health by analyzing massive datasets and seeing patterns that may not be obvious to the human eye. Machine learning has the potential to significantly transform healthcare, benefiting patient outcomes and the whole healthcare experience in this quickly developing industry. So, let us look at some of the machine learning applications in healthcare.Drug Creation and Production
Clinical applications for machine learning have great promise, especially in the early stages of the drug discovery process. This covers the development of alternate therapy strategies for complex disorders using next-generation sequencing and precision medicine. Currently, methods for unsupervised learning are utilized to find patterns in data without making predictions.Management of Health Records
Maintaining correct and current health data can be time-consuming and labor-intensive in the healthcare business. Although data input processes have been simplified by technology, many activities still take a lot of time and effort. A promising method for optimizing healthcare procedures and conserving time and money is machine learning.Disease Recognition
Machine learning is increasingly being used in healthcare to identify and diagnose difficult-to-detect diseases and maladies, such as cancer and genetic disorders. The ability to combine cognitive computing with genome-based tumor sequencing to allow IBM Watson Genomics best demonstrates quick and precise diagnostics.Clinical Trial Optimisation
Machine learning can significantly enhance clinical trials and research effectiveness and efficiency. Applying ML-based predictive analytics to find trial participants can help researchers access a wide range of data sources, such as previous doctor visits, social media activity, etc. Clinical trials are notoriously time- and money-consuming. Additionally, machine learning may choose the best sample size for testing, use electronic health records to reduce data-based errors, and enable real-time monitoring and data access for trial participants. These cutting-edge machine-learning algorithms can speed up drug discovery and enhance patient outcomes.Personalized Medicine
Predictive analytics is used in personalized medicine, a promising healthcare method, to match patient health data with customized treatment alternatives. Using machine learning, Personalized medicine can improve disease evaluation and increase treatment efficacy. Doctors may only choose diagnoses based on symptom history and genetic data.Predictive Modeling for Epidemics of Diseases
The monitoring and forecasting of epidemics on a global scale are becoming more dependent on machine learning and AI-based technology. Scientists may utilize artificial neural networks to gather information and forecast epidemics of everything from malaria to severe chronic infectious diseases because of the availability of large volumes of data from satellites, social media, and websites.Collection of Data Techniques
In the medical industry, crowdsourcing is a fast-spreading practice that gives researchers and practitioners access to a plethora of health data that individuals have voluntarily uploaded. This live health data will significantly impact the future of medicine. For instance, Apple’s ResearchKit uses interactive apps and facial recognition powered by machine learning to treat Asperger’s and Parkinson’s disease.
In 2023, new rules and regulations are entering the NFT space at an unprecedented pace. The collapse of FTX kicked Web3 regulation into overdrive, and prominent NFT projects are being investigated for fraud. Yet, perhaps the best evidence of this turning of the tide is the recent resolution of Hermès v. Rothschild trademark lawsuit.
Last year, Hermès International sued artist Mason Rothschild for trademark infringement following the release of MetaBirkins — a collection of 100 NFT Birkin bags covered in faux fur in a range of colors and designs. On February 8, 2023, Hermès won the lawsuit. A jury found that Rothschild’s collection of NFT purses bore such a striking resemblance to Hermès Birkin bags that it was “likely to cause consumer confusion and mistake in the minds of the public.” Hermès ultimately won the lawsuit after only six days of proceedings in a Manhattan courtroom.
While many expected a ruling stating that the sale of the NFTs violated Hermès’ rights to the “Birkin” trademark, the finding that Rothschild’s NFTs aren’t protected speech under the First Amendment understandably stirred up a bit of conversation throughout Web3. The situation — and what it means for the future of Web3 — is best distilled through the reactions of the lawyers and attorneys with an understanding of the case.What lawyers and attorneys have to say
In a statement sent to nft now, Jonathan Harris, a lawyer for Rothschild, implied that the lawsuit would be a blow to independent artists everywhere and a boon for big brands. Specifically, he stated that the decision marked a “good day for luxury brands” and a “bad day for artists.” Another of Rothschild’s attorneys, Rhett Millsaps, issued a similar statement to nft now. “Great day for big brands. Terrible day for artists and the First Amendment,” he said.
Speaking to the Financial Times, Gaëtan Cordier, partner at Eversheds Sutherland in Paris, said it was an “important decision” and a reminder that a lack of regulation does not mean people are free to do as they please with no ramifications. Ultimately, she argued that it sends a “message to NFT developers, reminding them that in the absence of specific regulations, intellectual property standards that apply in the physical world as well as on the internet remain applicable to NFTs.”
Meanwhile, Megan Noh, an art lawyer unaffiliated with the case, went on the record arguing that the closing of the case will likely open the floodgates and lead to a host of new brands entering Web3. “Some brand owners have likely been waiting for better guideposts before jumping into Web3 and enforcing their marks in that space,” she said to the New York Times. Noh went on to add that this verdict would finally provide brands with some needed guidance, “specifically in the context of digital artworks and collectibles, about the line between works of artistic expression and commercial goods.”
In a previous article by nft now, Andrew Rossow, an attorney who focuses on fintech and intellectual property law, noted that the case will ultimately determine how future Web3 cases are decided. “Hermès’ lawsuit against Rothschild will undoubtedly set the stage for how intellectual property is applied to the world of digital assets and NFTs. As more luxury brands enter into the metaverse and launch their respective NFT projects, courts will be required to weigh in on the confines and parameters of what it means to introduce originality while balancing artistic expression and the right to create,” he wrote.
However, statements made by David Leichtman, Managing Partner at Leichtman Law, indicate that the case may not have as wide of an impact as many believe. Speaking on CoinDesk TV, Leichtman noted that the case wasn’t really about what qualifies as art or even Rothschild’s use of the Birkin brand in his work. Rather, he noted that the case was specifically about whether Rothschild intended to mislead consumers into thinking that MetaBirkin NFTs were associated with Hermès. “The question is, were [consumers] really going to be confused by the MetaBirkins, whether or not the relevant consuming audience for Hermès products would be confused by the defendant’s works,” he said.
Rebecca Tushnet, a Harvard Law School professor who helped prepare Rothschild’s defense, seemingly reinforced Leichtman’s understanding of the case being more about intent than freedom of speech and the First Amendment. In a statement, she noted that “you can’t hold someone liable for infringement unless their work is artistically irrelevant or explicitly misleading.”The takeaways
Who is right? It’s difficult to say at this point. But one thing, at least, is certain. This case will set the tone for future proceedings on how intellectual property law is applied in Web3. And in light of the derivative and copycat NFT collections that are frequently launched in response to notable brands (like Porsche) entering the space, Web3 creators should think carefully before launching — or buying — new NFTs.
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