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Speaking to the media ahead of opening night, Lakers star LeBron James gave some flowers to his longtime teammate and co-star, Anthony Davis. In response to a question, the 4x MVP called Davis one

LeBron James on Anthony Davis: 'He's one of the greatest bigs to play for the Lakers'

It is about Tom Landry and Tex Schramm and Roger Staubach and Drew Pearson and Tony Dorsett, of course. It’s also about CBS on Sunday afternoons and the Dallas Cowboys Cheerleaders and “The Hole in the Roof” at Texas Stadium.

How Did Cowboys Earn ‘America’s Team’ Nickname? FISH PODCAST

This is our Friday show, and we’re talking about the week’s biggest startup and tech news. Mary Ann and Alex ran as a pair this week, parsing through a veritable mountain of news. Deals of the Week: Allara raised $10 million for women's hormonal health,

We're sending a cake to the next fintech startup that goes public

Simplecast offers a powerful solution with advanced features and seamless podcast management for podcasters seeking a comprehensive podcast hosting provider. Simplecast provides podcasters with an intuitive platform to upload, manage, and distribute ...

Discover the Best Podcast Hosting Platforms 2024

After months of uncertainty, yesterday the legislation required to implement the Pacific Engagement Visa (PEV) passed through the Australian Senate, with the Greens and other crossbenchers providing the numbers the government needed in the face of ...

Pacific Engagement Visa legislation finally through

RealClearPolitics president and co-founder Tom Bevan and Washington bureau chief Carl Cannon discuss the crisis in leadership among House Republicans and President Joe Biden's trip to Israel on ...

Biden Goes to Israel and Leadership Crisis in the House

Social protection systems currently play a limited role in supporting people with disabilities across much of the globe. While some countries achieve broad social protection coverage for persons with disabilities through a combination of income security ...

The path towards inclusive social protection for people with disabilities in the Pacific

“James Harden still wants to be traded and specifically to the Los Angeles Clippers,” Woj said. “The Clippers would like very much to acquire James Harden, put him with Paul George, Kawhi ...

Woj: James Harden still wants to be traded and specifically to the Clippers

The question of how to make development aid more effective is a challenging one. A major issue is that most development aid is an external intervention. In current debates about international development aid, terms such as “shifting the power” and ...

Taking the back seat: community philanthropy in PNG

Meet the Winners of Startups in North and Central America

Create a Podcast Player with React and AG Grid

Podcast listening is hard to track, but that doesn’t mean advertisers aren’t trying By Adrianne Jeffries In these muddled times, at least one thing is clear: Nearly everyone’s tracking you, always. But there is one area that has been largely off-limits to digital data collectors for 20 years: podcast listening. Traditionally, the podcast ecosystem has been tracking-resistant, in part because podcasters release their shows through RSS, free technology dating back to 1999. Podcast players, also known as , like Apple Podcasts and Castro then aggregate those episodes in easy-to-use apps. Listener behavior is strewn across a plethora of apps, many of which don’t share data with anyone, including the podcast creators. podcatchers That’s all changing. Advertisers are on podcasts in 2020, and companies are devising ways to provide them with data that will persuade them to spend more. The most common tactics include using IP addresses to identify users, adding tracking URLs to ads, and abandoning RSS in favor of proprietary platforms that already track their users. projected to spend more than $800 million The change has provoked considerable debate, , within the podcasting industry. sometimes combative In September 2019, Basecamp its podcast hosting service, Art19, after discovering that the company was building the capability for personalized ads. In December 2019, Libsyn, one of the major podcast hosting services, banned Podsights, one of the major podcast data companies, , “[W]e feel it is necessary to protect listener privacy.” In February, podcasting giant PRX hosted a on “emerging threats” to privacy in order to encourage more productive discussion. Then in August, one of the major podcast apps, Overcast, a feature that lets users see which podcasts have enabled tracking. publicly dumped saying conference added “I see the storm clouds gathering,” said Michelle De Mooy, a privacy consultant who spoke at PRX’s conference. “There’s a danger that this is a moment that can change away from privacy.” Podcast listening habits, like browsing history, can potentially reveal a lot about someone’s interests. There are podcasts about mental illness, substance abuse, sexuality, debt, and other sensitive topics. So what do your podcasts know about you? Traditionally, Podcasts Couldn’t Track Much The main metric of success for most of podcasting history has been the one thing the RSS infrastructure allows: how many times the file was downloaded. It’s like the early days of the web, when websites proudly displayed a “hit counter” that tracked the raw number of visits. But the act of downloading a podcast still passes some data, and whoever hosts the file can log a download’s date and time, the podcatcher used, the IP address (which also reveals rough location), and typically, the type of device. I see the storm clouds gathering. - Michelle De Mooy, privacy consultant Advertisers, meanwhile, have tended to target listeners based on a podcast’s subject matter. If you’ve ever heard a host give out a promo code for listeners while reading an ad, it’s because that’s basically how advertisers would track which podcasts were actually driving customers. Ads were not personalized because there was no data to personalize them with, and no way to match an ad to a specific user. This could rapidly change if the podcasting industry were to ever shift away from RSS. And there are some signs of that happening. Spotify has been signing exclusive deals with megastars like Joe Rogan and Kim Kardashian West. Bloomberg that Apple is hiring an executive to lead the charge on original and exclusive podcasts, and Amazon has its first platform-exclusive podcast. reported announced Needless to say, these exclusives will likely not be available via RSS. “They are not podcasts. They’re audio shows,” said Andrew Kuklewicz, the chief technology officer of PRX. However, when the venture-capital-funded podcast network Luminary to be copying audio and rehosting it instead of using RSS, it triggered an exodus of podcasters. (Luminary clarified that it was not copying audio and was still using RSS, just rerouting traffic first.) appeared Meanwhile, as podcasts have grown and become more professional, the commercial industry around them has found ways to work around RSS’s limitations. How Are You Being Tracked Now? In 2014, the podcast “Serial” debuted, introducing the medium to mainstream listeners. (As of September 2018, “Serial” had across its first two seasons.) more than 340 million downloads That same year, Acast, a podcasting company based in Sweden, announced “dynamic ad insertion.” Previously, ads were “baked in” to a recording, but now podcasters could mark their files with ad breaks, enabling a hosting company to switch in a different ad based on the time or location the podcast was downloaded. It also paved the way for ads to be sold programmatically, based on a bidding system that automatically matches buyers and sellers without the need for salespeople. Much of the advertising on the web, including Google Ads, is bought and sold this way. Programmatic buying requires consumer data in order for advertisers to rapidly experiment with different ways to target users and optimize ads for the best results. “That’s probably when I certainly saw things start to change,” said Kuklewicz, the CTO of PRX. “Once you can actually dynamically inject ads, then that data that I’d be able to get from requests becomes actionable. And once that’s possible, then we start to look a lot like the rest of ad tech right now.” Our goal here is to make tools that will let this medium grow in general and bring more people into the space and create better content. - Andy Pellett, co-founder of Podsights Suddenly, companies on the hosting and ad tech side started to try to squeeze as much data as possible out of the limited interaction they had with listeners, using a couple of different methods: They could add an extra web address in the metadata of an ad, forcing a tiny download that allows the advertiser to register the listener’s IP address, the podcatcher they used, and usually the type of device. They could also use “prefixes” or redirects that take the user on a brief detour to another webpage so a third party can register them, similar to the way a URL shortening service like Bit.ly works. At this point, we’re still just talking about basic data. But advertisers want more granular data: Did someone listen to my ad on a podcast, and then visit my website or buy something? And what else can I learn about podcast listeners, so I can decide whom to target with my ads? Some companies, like Podsights, now collect basic data, such as IP address and device type, and combine it with information from third-party data collection companies. Podsights co-founder Andy Pellett said the company uses this data only to establish “attribution”—that the same person who listened to an ad also visited a brand’s website, for example—and does not share any data that could be used to identify listeners. “Our goal here is to make tools that will let this medium grow in general and bring more people into the space and create better content,” he said. Other companies at least advertise the ability to do more granular targeting, though it’s not clear how many podcasts actually use such services. Megaphone says that its Megaphone Targeted Marketplace, which allows automated buying and selling of ads, allows advertisers to target users by “ .” Megaphone did not respond to a request for comment. demographic and purchase intent Your Podcast Player Could Know More About You Podcatchers themselves have access to far more accurate data about users, such as the full list of podcasts a listener subscribes to and how far they listen in any given episode. Some of the smaller podcatchers, however, have emerged as bulwarks against anything potentially invasive. When Russell Ivanovic, head of product for the podcatcher Pocket Casts, heard that some analytics companies had figured out a way to place cookies on users’ devices when they downloaded a podcast, he immediately disabled the capability in Pocket Casts. “They might be doing innocent things with it, but we felt like that was like a bridge too far,” Ivanovic said. “That’s not something users are expecting.” They might be doing innocent things with it, but we felt like that was like a bridge too far. - Russell Ivanovic, Pocket Casts Companies like Spotify, Apple, Amazon, and Google, which all offer apps and devices that play podcasts, are in the best position to collect user data. These companies already have “first party” data, collected through their other services, that they can combine with someone’s podcast listening habits, as well as additional insights from the device you’ve installed them on. Spotify knows if your phone is , for example. in your hand or in your pocket Another way to collect more data would be if different layers of the ecosystem agreed to work together. National Public Radio attempted this with a project called , or RAD, essentially instructions for how podcasters and advertisers could finally measure if someone who downloaded a podcast actually listened to it, and for how long. NPR publishes lots of podcasts and also has its own podcast player, the NPR One app, giving it access to more data about how people listen. This data was used to reassure advertisers that their ads were actually getting heard, and also to tweak show formats. The creators of the show “Pop Culture Happy Hour,” for example, noticed that their listeners were dropping off in the middle of an episode. “The team was able to pivot and release episodes more frequently, but much shorter and more focused,” said Stacey Goers, senior product manager for podcasts at NPR. “And it really gives users what they wanted.” Remote Audio Data The ad server NPR uses, AdsWizz, also allows ads and stories to be swapped in and out of episodes. This enables NPR to experiment with its fund-raising campaigns as well as the content of the show. The first half of “Consider This,” for instance, is the same for everyone, but the second half comes from a station in the listener’s local area. The catch with RAD is that podcatchers also have to participate, and so far, they haven’t taken it up. “RAD has not seen a large amount of adoption,” Goers said. Part of that is because app developers would rather spend their limited resources on features that would attract new users, she said, and part of it is because of the lack of centralization. “Podcasting still doesn’t necessarily have a big governing body that’s putting its foot down and asking for it,” she said. The Changes Are Controversial—and It’s Not Clear How Widespread They Are “Ultimately if podcasts are going to grow as a media type, as they emerge, the advertisers are going to require the more granular level of tracking,” said Eric Picard, a consultant and former vice president of advertising product management at Pandora. But not everyone agrees that podcasting’s health relies on gathering more data. “That’s bullshit,” said Rob Walch, vice president of podcaster relations at Libsyn, a major hosting service that also sells ads for podcasters. “The podcasters that make the most money are the ones that are doing host-read ads that don’t do any of this. Joe Rogan makes more money podcasting with advertising than any of these people.” At least for now, podcasting is still a fairly private activity, in that it doesn’t produce much data on its own. “That’s one of the things, to be honest, that’s stunted podcast growth initially,” said Brad Smith, CEO of Simplecast, a hosting and analytics company. “Advertisers want to go where the deepest, richest data lives. And by default, you’re not going to get data out of podcasting.” Nothing has fundamentally changed, he said. “We gather the exact same information about a listening session today that we did five years ago.” Correction An earlier version of this story incorrectly identified the technology NPR uses for dynamic ad and content insertion. It is AdsWizz, not RAD. . Originally published as " When Zombie Data Costs You a Home " with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license

How Much Exactly are Podcasts Able to Track us

Podcasting for Beginners: A Complete Guide For 2020 and Beyond [Step-by-Step Guide]

Let's say you're planning to throw an awesome party. Sounds fun, right? But how will you make sure it is successful? The indicator of success can be the profit you generate by attracting a large number of attendees to the party. Additionally, you would want to ensure that attendees will return for future parties. Who do you need to work with to coordinate all of this? At a minimum, you may want to involve: Venue owner DJ Security Bartender Promo manager Designer ...and this may not be an exhaustive list. All of these individuals will have a vested interest in the successful completion of the party. However, they may not necessarily have identical needs and expectations. For instance, the venue owner may desire to generate income by renting out the space and anticipate the venue to be cleaned without any noticeable harm after the party. Meanwhile, the DJ, for instance, anticipates an audience that will value his artistic expression, and an increase in his followers on , , etc. Spotify Instagram , meaning they are parties with a vested interest in the project. → All these individuals will be your stakeholders How to categorize your stakeholders? By classifying stakeholders into different subgroups, you will be able to develop strategies around: Communications (when and how to inform) Decision-making (who and when to involve) Project work (how to cultivate ownership and engagement in the project). Typically, stakeholders are categorized based on their , , and occasionally their . interest influence behavior/attitude Let's consider, for instance, a bartender who isn't particularly concerned about the success of the party. For him, the priority is to attract a large number of attendees and receive a commission from the bar sales. Therefore, the bartender has a low interest in the overall goal and a small level of influence, as his presence or absence does not determine whether the party will take place or not. When it comes to behavior, the bartender can sometimes act as a blocker. For instance, they might refuse to work until they have an XL shaker, stating that it is impossible to work without such equipment. While they may be correct, a bartender with a more positive work attitude would make an effort to assist in finding a solution to the problem. Based on the bartender profile and using a stakeholder categorization matrix, we can create a plan outlining how we will work with them, for example: Due to low influence, we will not include the bartender in making decisions regarding the party, such as the location, DJ, etc. Due to low interest, we are unlikely to inform the bartender about all the decisions related to the party, possibly only those that concern the bar. Knowing that there may be issues with the bartender, we will try to avoid them in advance to prevent disagreements from arising a day before the event. When working with people involved in organizing the party, you will be engaged in . stakeholder management Stakeholder management is a process that helps understand the needs and expectations of interested parties and build relationships with them. This will ultimately assist in hosting a successful party. How stakeholder management is assessed during the interview process? At interviews, this skill is assessed through the following questions: Describe a situation in which you had to work with a difficult stakeholder. How did you handle it? Share a situation in which you had to convince a stakeholder to collaborate on your project (provide feedback/resources, etc.). How do you communicate with stakeholders? Have you ever faced a situation in which your management denied you resources or the opportunity to work on a new project or change the strategy? How did you persuade them? How do you manage stakeholders' expectations?

Stakeholder Management 101 – The Simplest Explanation Ever

Cryptography has been around long before modern computers and has been a great way to protect messages. When it comes to providing information while keeping it concealed from those who wish to use it for ill intentions, you need and codes to encrypt messages to keep them confidential. Back in the days of Rome and Julius Caesar, code was invented - a form of basic encryption and one of the earliest encryption algorithms in humanity’s recorded history - that would allow his messages of military significance to stay safe from prying eyes. This code was named after Julius himself and it was called the cipher. cryptography Caesar According to the Roman historian Suetonius, the cipher was used to shift three letters to create a new code to protect messages. For example, if you want to encrypt the word/letter A, you would shift three letters down the alphabet and translate that ‘A’ into the letter ‘D’ & you decrypt the letter by going three letters back into an A. Suetonius describes the encryption in one of his writings: "If he had anything confidential to say, he wrote it in cipher, that is, by so changing the order of the letters of the alphabet, that not a word could be made out. If anyone wishes to decipher these, and get at their meaning, he must substitute the fourth letter of the alphabet, namely D, for A, and so with the others." - Suetonius, Life of Julius Caesar 56 One of the great things about the Caesar cipher is how easy this form of encryption is to recreate with modern code. Even with basic , you can easily turn some text into a basic encrypted cipher using the following function: Python def encrypt(text,s):
    result = ""
 
    
    for i in range(len(text)):
        char = text[i]
 
        # Encrypt the uppercase characters or lowercase letters as determined by an if statement
        # before adding to the result variable
        if (char.isupper()):
            result += chr((ord(char) + s-65) % 26 + 65)
 
        
        else:
            result += chr((ord(char) + s - 97) % 26 + 97)
 
    return result Unfortunately, the Caesar cipher probably would not work very well in a modern context since it is pretty easily broken since there are only so many different combinations of letter shifting you can use and, eventually, the code can be broken with brute force attacks. This is something you can also easily program in Python: def crack(message):

for i in range(len(LETTERS)):
   translated = ''
   for s in message:
      if s in LETTERS:
         num = LETTERS.find(symbol)
         num = num - i
         if num < 0:
            num = num + len(LETTERS)
         translated = translated + LETTERS[num]
      else:
         translated = translated + s

print('Potential Hacked Messages #%s: %s' % (i, translated)) Even Caesar himself worked to combine the Caesar cipher with more complicated systems in order to make more complicated encryptions. That all being said, the Caesar cipher is a pretty important part of the history of cryptography and is one of the earliest recorded encryption algorithms used in a practical context.

The Caesar Cipher: One of the Earliest, and Simplest, Encryptions

As a frontend developer, I have been working on building collaborative apps, in which the realtime performance of  multiplayer features is especially important. It needs to synchronize the state of users in less than 100ms between two users. In this article, I would like to demonstrate how " " helped me to implement these multiplayer features within 2 hours. Furthermore, presencejs is totally open source, and also does well in . Throughout my experience using this library, I have been impressed by its power and capabilities. presencejs security, low-latency, and geo-distributed deployment Therefore, I have decided to write this blog to introduce this excellent forward-looking JavaScript library to you. is a JavaScript library that enables the creation of real-time multiplayer/ collaboration web applications that supporting WebTransport. Presencejs It provides many ready-made components like Cursor-Chat, Live-Cursor, Grouphug etc. Certainly, we can create more functional custom components using the basic functions and API of presencejs. Through the ready-made components, I created a nextjs real-time web application demo in minutes. Presencejs: https://github.com/yomorun/presencejs Demo: https://cursorchat-grouphug-demo.vercel.app/ How did I create this demo? For this demo, I used the Presencejs library to create a real-time multi-user collaborative web application.  I chose a simple but effective design for the demo. I opted for a clean and modern look with a subtle color scheme that wouldn't distract from the content. Presencejs provides several pre-built components such as Cursor Chat, Realtime Cursor, and Grouphug. I used these components along with some custom features to build this demo. Here's a brief overview of how I created it: First, I installed and imported the Presencejs library into my code. npm install @yomo/cursor-chat-react
npm install @yomo/group-hug-react import CursorChat from '@yomo/cursor-chat-react';
import GroupHug from '@yomo/group-hug-react'; Next, I used the components provided by Presencejs to build my application. For this demo, I used  Cursor-Chat, and Grouphug components. // other code

import CursorChat from '@yomo/cursor-chat-react';
import GroupHug from '@yomo/group-hug-react';


export default function Home(){
// other code

return(

// other code

<CursorChat />
<Grouphug />

// other code

)
} Then, I added some custom features . I got a public_key from and created a file .env in the main path. I entered the code below: AllegroCloud.io PUBLIC_KEY=**** And I saved this file, then ran the demo locally. Finally, I deployed my application online using Next.js so that others can access it. Overall, creating a real-time web application using Presencejs is straightforward. Presencejs provides many pre-built components that allow developers to quickly build their own applications. Additionally, Presencejs supports custom development, which allows advanced developers to create more complex features. I hope this demo inspires more developers to try using Presencejs to create their own real-time web applications. The special benefits of presencejs From my experience with it, presencejs has been particularly helpful to me in the following ways: The support to WebTransport makes presencejs show better performance than other collaboration js lib. Compatible with websocket Ready-made components and customized development. Geo-distributed architecture lets me build amazing and fast response web applications. Both serverless and self-hosting. Benefits in detail: Support for WebTransport:
Presencejs supports WebTransport, which is a newer real-time application protocol based on the QUIC protocol. It establishes reliable, secure, low-latency connections without requiring handshakes and negotiations like websocket. It also supports advanced features such as multiplexing and connection migration. This provides faster connection establishment speed, data transfer speed, and latency. Compatible with websocket:
Presencejs also supports websocket. When the user's browser doesn't support WebTransport, presencejs will replace the communication protocol with websocket. Ready-made components and customized development:
Presencejs provides ready-made components like grouphug, livecursor, and cursorchat, which allow real-time web applications. For advanced developers, the complete toolkit can be used to create custom components. Geo-distributed architecture backend:
Presencejs is a serverless architecture framework using the YoMo framework for real-time data processing. This provides faster response speed, stable performance, and secure service. Both serverless and self-hosting:
Presencejs supports self-hosting, which provides greater control over data and applications. Final Notes and Thoughts In conclusion, presencejs offers a variety of benefits that make it an excellent choice for developers looking to build real-time web applications. Its support for WebTransport and websocket protocols, ready-made components, customizable development, geo-distributed architecture, and serverless or self-hosting options provide developers with a versatile toolkit to create amazing applications. By adopting presencejs, developers can ensure their applications have faster response times, greater stability, and improved security. Furthermore, as an open-source product, presencejs will continue to improve and evolve in the coming years. Overall, presencejs is a forward-thinking product that can meet the real-time needs of the web today and in the future. As a developer experience personnel of AllegroCloud and a frontend developer, I am fully aware of the shortcomings of Presence. However, as an open-source product, Presence possesses a more robust vitality and a more transparent technological reserve. In the coming years, Presence will continue to be updated and iterated, providing even higher-quality services.

PresenceJS: The Simplest Way to Power Realtime Experience in My Collaborative Apps

This is by (source: ). Twitter Thread Compound248 12-01-2022 Dear Media, Do you want a simple description of HOW did what he did? @SBF_FTX Why does nobody seem capable of pinning Sam down? His SECRET is, when asked pointed questions, he switches to market-plumbing speak, hiding behind lingo. Let me help you understand SBF's moves. 🧵 👇 Anytime you hear or read "Alameda," replace with, "Sam's personal account." When Alameda borrows FTX customer money, that means "Sam borrows customer money": Alameda is 100% owned by Sam & a few friends. Alameda had no clients. "Alameda's capital" is Sam's, Caroline's, etc. 👇 Sam says he poorly "risk-managed" the FTX and Alameda relationship. Recall: Alameda=Sam. What transpired? • Alameda (Sam) Had Liquidity Issues • Alameda (Sam) Organized a Loan from FTX • To Paper the Loan, Alameda Posted Collateral to FTX • The Collateral Was Pretend Money 3) Alameda (Sam) Had Issues: Nobody has nailed down what happened: was Alameda *always* a fraud or did it suffer losses at some point and then get desperate (perhaps the Terra Luna implosion in the spring)? This is a question begging for an answer. I don't know, but... https://tenor.com/bhNAA.gif?embedable=true 4)...Sam claims he was no longer closely involved in Alameda (again, Alameda = Sam's personal capital). Sam claims Caroline & Team ran it independently (other than its venture investments). This claim gives Sam cover to state he doesn't know the details about Alameda's issues. 5) Alameda (Sam) Organized a Loan from FTX: At some point - time uncertain - Alameda (Sam) needed access to liquidity. It almost certainly had margin loans from 3rd parties being called (3rd parties like Gemini or Binance...maybe FTX?). To repay these, Alameda needed liquidity. 6) To Paper the Loan, Alameda Posted Collateral to FTX: FTX client assets were the liquidity that Alameda (Sam) needed to repay its other loans. To pretend this was OK, FTX needed to hold collateral from Alameda as security against the loan (in case Alameda (Sam) didn't repay). 7) When you borrow cash on margin, you generally have to post MORE collateral than you borrow. Ex: you might borrow against your Schwab account. Schwab holds your account's assets as protection against you defaulting on the loan. This lets you turn investments into liquidity. 8) Elon's margin loan is a prominent example: $TSLA Elon can borrow $1 for every $5 of TSLA stock he posts (giving him cash to do anything, like buy , without selling TSLA). $TWTR After borrowing from the facility, if TSLA stock falls below a certain threshold (say $1 to $3)... 9)...Elon needs to either add more collateral to the margin facility (post more ) or pay down the loan. $TSLA If he does neither, the lender can liquidate the it holds as collateral and repay the loan from the proceeds. $TSLA Now back to Alameda (Sam)... 10) Together, FTX & Alameda (Sam & Sam) developed a few shitcoins...err "tokens": the most relevant are and . $FTT $SRM Both FTX & Alameda received huge grants of and (I'm just going to use going forward). $FTT $SRM $FTT F&A (Sam) controlled the VAST majority of the issuance... 11)...and only a tiny portion of / traded. Alameda was the largest trader. If you own 95% of something and then manipulate the price of the other 5%, you can make your 95% look REALLY valuable - just keep the price of that 5% up and look how valuable your 95% becomes! $FTT $SRM https://tenor.com/va2h.gif?embedable=true Guess what Alameda (Sam) used as collateral for those margin loans we were talking about? and . $FTT $SRM And it doesn't appear that FTX required a big haircut on those. Remember that Elon can borrow $1 for every $5 of stock he posts? That's an 80% haircut. But... $TSLA 13)...it doesn't appear FTX required a substantial haircut on the that Alameda (Sam) posted. Ultimately, using these shitcoins...I mean tokens, as collateral, Alameda (Sam) "borrowed" $9-$10 billion from FTX. That money was, almost by definition, FTX customer money. $FTT 14) You might recall that Sam famously described tokens like these to and as "magic money in a box." It was recognized immediately as Sam describing a Ponzi. But most of us didn't realize *Sam* was issuing these magic tokens. @TheStalwart @tracyalloway http://youtube.com/watch?v=KZYqL79GDXU&t=1292s Sam may argue this is just how banks (and brokerages) work: they take deposits, they lend them out based on credit judgments to earn a spread, and - as long as depositors don't ask for deposits back en masse - all is perfectly fine. But let me tell you what banks don't do: 16) Banks do not: • Lend 70% of their deposits to one borrower • Have that borrower be the founder of the bank • Accept made-up "tokens" as collateral - the actual worst collateral ever conceived • Have almost all that collateral be just two (made up) assets 17) • Apply de minimis haircuts to collateral • Accept derivatives of the bank itself as collateral! is not random - it relates to FTX $FTT As said, Goldman Sachs should not take equity as collateral - it is circular risk. And definitely not pretend-GS equity. @matt_levine $GS 18) This Collateral Was Pretend Money Alameda (Sam) & FTX (also Sam) invented intrinsically worthless tokens, keeping almost all of it for themselves. They traded a tiny percentage, controlling the trading price. This manipulated the implied value of all of Sam's tokens... $FTT 19)...then used the resultant implied token value as collateral to extract ("borrow") customer funds from FTX. The bogus collateral was one giant identical (non-diversified) risk, funding related-party transactions. And FTX lied about doing it via misleading Terms of Service. 20) When Sam says, "he poorly risk-managed" it means, "I, FTX's CEO Sam, should not have allowed Alameda Sam to use pretend Sam-money as collateral to take real-$$ from Sam's customers." It's like in Dumb & Dumber when Lloyd spends all the found money, but replaced it with IOUs. https://tenor.com/buMGg.gif?embedable=true This insanity could have lasted longer, except Sam made one horrible mistake. At FTX's founding, Binance/ was a seed investor. As FTX evolved into a full-fledged Binance competitor, FTX reacquired Binance's seed stake using a mix of payments. One of those was... @cz_binance https://tenor.com/bONrD.gif?embedable=true 22)... ! $FTT Amazingly, the only big part of 's float that Sam did not control was controlled by his arch-f'ing-rival: CZ. $FTT When it became public that Alameda was relying heavily on as collateral, CZ understood his wasn't worth the magic paper it wasn't written on. $FTT $FTT 23) If CZ didn't know before, he knew then: all of Sam's prior "bailouts" were attempts to prevent Alameda *itself* from being liquidated - it was built on magic token money. And CZ was the biggest independent owner of the magic money. CZ knew there were zero natural buyers. $FTT 24) CZ held the keys to killing his loudest, most annoying competitor & he used them. Good on CZ - hopefully Binance itself is sound. We don't know what's next or where the $10B of customer $$ went. Perhaps Alameda lost it fair & square, perhaps it's stolen. Time MAY tell. -END The key question is, “who at FTX approved $10B of loans to Alameda (Sam)?” Sam says he didn’t understand the exposure. He was CEO. Who approved it? My “worlds colliding” dream is for to reply to my viral true crime thread. 👆 @elonmusk @SBF_FTX Like this to make my dream come true.😅 ...or Retweet. Feature image generated via prompt of ‘The Simplest Guide of What Happened With SBF That You Will Ever Come Across’ HackerNoon Stable Diffusion

The Simplest Guide of What Happened With SBF That You Will Ever Come Across

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References III. METHODOLOGY A. System Architecture The CUP framework is based on a two-tower architecture for representation learning (one ”tower” for users, the other for items, following the prevalent architecture in neural information retrieval with query and document/passage encodings). The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not. Importantly, unlike prior works, we allow the top-most layer of BERT itself to be fine-tuned as part of the end-to-end training process. The per-item text usually comprises book titles, tags like categories or genre labels, which can be coarse (e.g., “thriller”) or fine-grained (e.g., “Scandinavian crime noir”), and a short description of the book contents. The per-user text can comprise the titles and tags of her training-set books and the entirety of her review texts, which vary widely in length and informativeness, hence the need for smart text selection. In the following, we present the CUP training procedure in Subsection III-B, the test/deployment-time inference in Subsection III-C, the judicious selection of input text snippets in Subsection III-D, and the selection of negative training samples in Subsection III-E. B. Training Another optional mechanism is to process multiple chunks of bounded-length text, which could be of interest for long user reviews. In this case, the per-user representations are learned for each chunk, and there is an additional max-pooling step between the FFN and the dot product, to combine the per-chunk vectors. We use this option for comparison in experiments. As it incurs m times higher computational and energy costs, with m being the number of chunks, we do not favor it. A user-item pair is classified with score calculated by with <, > denoting dot product and σ for the sigmoid function. We use the Adam optimizer to minimize the binary cross entropy loss between predicted labels and the ground truth with sampled negatives. During training we update the topmost layer of BERT, which allows end-to-end training of all components. This is an important difference to earlier works with similar architectures, which use frozen BERT to encode user and item representations. C. Inference Prediction for Ranking. At test time, a prediction is made for user-item pairs. We encode the item description by running it through the trained network, and we compare it to the already learned user vector, which is based on the user’s training-time reviews (none for the given test item). The scores for different test items, computed by the final dot product, yield the ranking of the candidate items. This is a very efficient computation, following established practice in neural information retrieval [37]. Search-based Recommendation. In a deployed system (as opposed to lab experiments with test samples), a typical usage mode would be search-based re-ranking: a user provides context with a tag-based query or an example of a specific liked item, which can be thought of as query-by-example. The user’s expectation is to see a ranked list of recommended items that are similar to her positive sample (as opposed to recommendations from all kinds of categories). The system achieves this by first computing approximate matches to the query item (i.e., similarity-search neighbors), and then re-ranking a shortlist of say top-100 candidates. The CUP framework supports this mode, by using a light-weight BM25 retrieval model, querying all unlabeled items with the category and textual description of the positive point at hand, and keeping the top-100 highest scoring matches. D. Coping with Long and Noisy Texts For constructing user profiles from text, the simplest idea would be to concatenate all available reviews into a long token sequence. Two problems arise, though. User reviews are a noisy mix of descriptive elements (e.g., “the unusual murder weapon”), sentiment expressions (e.g., “it was fun to read”) and personal but irrelevant statements (e.g., “I read only on weekends”). Only the first aspect is helpful for content-based profiling (as the sentiment is already captured by the fact that the user likes the book). Second, the entirety of user-provided text can be too long to be fully digested by the Transformer. Even when it would fit into the token budget, the computational and energy cost is quadratic in the number of input tokens. Therefore, we tightly limit the tokens for each user’s text profile to 128, and devise a suite of light-weight techniques for judiciously selecting the most informative pieces. Variants of our methodology are to create concise profiles by generative language models upfront (e.g., using T5 or ChatGPT), or to digest multiple chunks of text with downstream aggregation by max-pooling or averaging. We include these techniques in our experimental comparisons, but emphasize that both of these alternatives have much higher computational cost and climate footprint. Our techniques for selecting the most informative parts of user reviews to construct concise profiles, are as follows: • Weighted Phrases: selected unigrams, bigrams or trigrams, ordered by descending tf-idf weights, where tf is the frequency of the phrase in all of the given user’s reviews, and idf is pre-computed on Google books n-grams to capture informativeness. • Weighted Sentences: selected sentences, ordered by descending idf weights, where a sentence’s total weight is the sum of the per-word idf weights normalized by sentence length. • Similar Sentences: selected sentences, ordered by descending similarity scores computed via Sentence-BERT [38] for comparing the user-review sentences against the sentences of the item description. To ensure that the selected set is not dominated by a single item, the sentences are picked from different items in a round-robin manner. • ChatGPT-generated Profiles: feeding all reviews of a user, in large chunks, into ChatGPT and instructing it to characterize the user’s book interests with a few short keyphrases. The output size is limited per chunk with a total budget of 128 tokens, to enforce concise profiles. • T5-generated Keywords: using a T5 model fine-tuned for keyword generation, to cast each user’s review text into a set of keywords, concatenated to create the profile. We give examples of selected profile constructions for a Goodreads and an Amazon user in Table VII. An online demo of the CUP framework is accessible on our website[2]. E. Coping with Unlabeled Data A challenge for training in the data-poor regime is how to handle the extreme skew between positive samples and unlabeled data points for sparse users. The crux in many recommender applications is that there are extremely few, if any, explicitly negative samples, such as books rated with low marks. This holds also for the datasets in this work. Therefore, we introduce and experiment with two different techniques to construct negative training samples from unlabeled data: • Uniform random samples. Under the closed world assumption (CWA), aka Selected Completely At Random, negative training points are sample uniformly from all unlabeled data. This is the baseline technique, widely used in prior works. • Weighted pos-neg samples. Prior works on PU learning [39], with positive and unlabeled data and without explicitly negative samples, is largely based on treating unlabeled points as pairs of samples, one positive and one negative with fractional weights. The weights can be based on (learned estimates of) class priors, but the extreme skew in our data renders these techniques ineffective. Instead we leverage the fact that relatedness measures between item pairs can be derived from tagging features or interaction data. We compute item-item relatedness via basic matrix factorization of the user-item matrix for the entire dataset. The relatedness of two items is set to the scalar product between their latent vectors, re-scaled for normalization between 0 and 1. Then, each originally unlabeled sample is cloned, with one instance positive with weight proportional to its average relatedness to the user’s explicitly positive points. The negative clone’s weight is set to the complement. [2] https://sirup.mpi-inf.mpg.de/ This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References III. METHODOLOGY A. System Architecture A. System Architecture The CUP framework is based on a two-tower architecture for representation learning (one ”tower” for users, the other for items, following the prevalent architecture in neural information retrieval with query and document/passage encodings). The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not. Importantly, unlike prior works, we allow the top-most layer of BERT itself to be fine-tuned as part of the end-to-end training process. The per-item text usually comprises book titles, tags like categories or genre labels, which can be coarse (e.g., “thriller”) or fine-grained (e.g., “Scandinavian crime noir”), and a short description of the book contents. The per-user text can comprise the titles and tags of her training-set books and the entirety of her review texts, which vary widely in length and informativeness, hence the need for smart text selection. In the following, we present the CUP training procedure in Subsection III-B, the test/deployment-time inference in Subsection III-C, the judicious selection of input text snippets in Subsection III-D, and the selection of negative training samples in Subsection III-E. B. Training B. Training Another optional mechanism is to process multiple chunks of bounded-length text, which could be of interest for long user reviews. In this case, the per-user representations are learned for each chunk, and there is an additional max-pooling step between the FFN and the dot product, to combine the per-chunk vectors. We use this option for comparison in experiments. As it incurs m times higher computational and energy costs, with m being the number of chunks, we do not favor it. A user-item pair is classified with score calculated by with <, > denoting dot product and σ for the sigmoid function. We use the Adam optimizer to minimize the binary cross entropy loss between predicted labels and the ground truth with sampled negatives. During training we update the topmost layer of BERT, which allows end-to-end training of all components. This is an important difference to earlier works with similar architectures, which use frozen BERT to encode user and item representations. C. Inference C. Inference Prediction for Ranking. At test time, a prediction is made for user-item pairs. We encode the item description by running it through the trained network, and we compare it to the already learned user vector, which is based on the user’s training-time reviews (none for the given test item). The scores for different test items, computed by the final dot product, yield the ranking of the candidate items. This is a very efficient computation, following established practice in neural information retrieval [37]. Prediction for Ranking. Search-based Recommendation. In a deployed system (as opposed to lab experiments with test samples), a typical usage mode would be search-based re-ranking: a user provides context with a tag-based query or an example of a specific liked item, which can be thought of as query-by-example. The user’s expectation is to see a ranked list of recommended items that are similar to her positive sample (as opposed to recommendations from all kinds of categories). The system achieves this by first computing approximate matches to the query item (i.e., similarity-search neighbors), and then re-ranking a shortlist of say top-100 candidates. The CUP framework supports this mode, by using a light-weight BM25 retrieval model, querying all unlabeled items with the category and textual description of the positive point at hand, and keeping the top-100 highest scoring matches. D. Coping with Long and Noisy Texts D. Coping with Long and Noisy Texts For constructing user profiles from text, the simplest idea would be to concatenate all available reviews into a long token sequence. Two problems arise, though. User reviews are a noisy mix of descriptive elements (e.g., “the unusual murder weapon”), sentiment expressions (e.g., “it was fun to read”) and personal but irrelevant statements (e.g., “I read only on weekends”). Only the first aspect is helpful for content-based profiling (as the sentiment is already captured by the fact that the user likes the book). Second, the entirety of user-provided text can be too long to be fully digested by the Transformer. Even when it would fit into the token budget, the computational and energy cost is quadratic in the number of input tokens. Therefore, we tightly limit the tokens for each user’s text profile to 128, and devise a suite of light-weight techniques for judiciously selecting the most informative pieces. Variants of our methodology are to create concise profiles by generative language models upfront (e.g., using T5 or ChatGPT), or to digest multiple chunks of text with downstream aggregation by max-pooling or averaging. We include these techniques in our experimental comparisons, but emphasize that both of these alternatives have much higher computational cost and climate footprint. Our techniques for selecting the most informative parts of user reviews to construct concise profiles, are as follows: • Weighted Phrases: selected unigrams, bigrams or trigrams, ordered by descending tf-idf weights, where tf is the frequency of the phrase in all of the given user’s reviews, and idf is pre-computed on Google books n-grams to capture informativeness. • Weighted Sentences: selected sentences, ordered by descending idf weights, where a sentence’s total weight is the sum of the per-word idf weights normalized by sentence length. • Similar Sentences: selected sentences, ordered by descending similarity scores computed via Sentence-BERT [38] for comparing the user-review sentences against the sentences of the item description. To ensure that the selected set is not dominated by a single item, the sentences are picked from different items in a round-robin manner. • ChatGPT-generated Profiles: feeding all reviews of a user, in large chunks, into ChatGPT and instructing it to characterize the user’s book interests with a few short keyphrases. The output size is limited per chunk with a total budget of 128 tokens, to enforce concise profiles. • T5-generated Keywords: using a T5 model fine-tuned for keyword generation, to cast each user’s review text into a set of keywords, concatenated to create the profile. We give examples of selected profile constructions for a Goodreads and an Amazon user in Table VII. An online demo of the CUP framework is accessible on our website[2]. E. Coping with Unlabeled Data E. Coping with Unlabeled Data A challenge for training in the data-poor regime is how to handle the extreme skew between positive samples and unlabeled data points for sparse users. The crux in many recommender applications is that there are extremely few, if any, explicitly negative samples, such as books rated with low marks. This holds also for the datasets in this work. Therefore, we introduce and experiment with two different techniques to construct negative training samples from unlabeled data: • Uniform random samples. Under the closed world assumption (CWA), aka Selected Completely At Random, negative training points are sample uniformly from all unlabeled data. This is the baseline technique, widely used in prior works. Uniform random samples. • Weighted pos-neg samples. Prior works on PU learning [39], with positive and unlabeled data and without explicitly negative samples, is largely based on treating unlabeled points as pairs of samples, one positive and one negative with fractional weights. The weights can be based on (learned estimates of) class priors, but the extreme skew in our data renders these techniques ineffective. Instead we leverage the fact that relatedness measures between item pairs can be derived from tagging features or interaction data. We compute item-item relatedness via basic matrix factorization of the user-item matrix for the entire dataset. The relatedness of two items is set to the scalar product between their latent vectors, re-scaled for normalization between 0 and 1. Then, each originally unlabeled sample is cloned, with one instance positive with weight proportional to its average relatedness to the user’s explicitly positive points. The negative clone’s weight is set to the complement. [2] https://sirup.mpi-inf.mpg.de/

Recommendations by Concise User Profiles from Review Text: Methodology

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Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal, it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment. Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? Electronic waste — better known as e-waste — has been a massive problem for years. However, most people have never even heard of the term. Unfortunately, whether they realize it or not, it impacts them directly. How does e-waste hacking put you at risk? The Overlooked Problem With E-Waste The Overlooked Problem With E-Waste Most people don’t think twice about what happens to their electronics when they throw them away or trade them in. In almost every case, their old devices become e-waste. Common examples include phones, computers, printers, monitors, smart devices and wearables. Almost all electronics contain materials like nickel, flame retardants, and cadmium that either are or can become hazardous if disposed of improperly. For example, lithium-ion batteries can produce noxious smoke if damaged and toxic liquid if exposed to water. In landfills, these devices poison the air, waterways, and soil by leeching heavy metals and poisonous chemicals. Since e-waste is a relatively new problem, it remains largely unaddressed. Today, it accounts for roughly 70% of the toxic waste in U.S. landfills. Only a fraction is ever formally recycled, leaving the rest to pollute local environments for thousands of years until they naturally break down. 70% of the toxic waste 70% of the toxic waste While there’s no breakdown of where the country’s e-waste comes from, the consumer electronics sector is likely responsible for most of it. People have been buying more than ever in recent years — computer purchases surged by 34% during the COVID-19 pandemic — suggesting they’ve been throwing away more than ever. computer purchases surged by 34% computer purchases surged by 34% The main focus of e-waste is its environmental risks, for good reason. However, that’s not its only problem. Many people don’t realize they’re essentially handing all their device’s data directly to a cybercriminal when they dispose of it improperly. E-waste hacking is becoming increasingly profitable for cybercriminals as people toss more electronics every year. Unfortunately, buying something new is often cheaper than having something repaired. Once a device leaves the owner’s hands and lands in the trash, it’s forfeit — along with every shred of personally identifiable information (PII) and sensitive data it ever stored. What E-Waste Hacking Looks Like While e-waste hacking sounds futuristic, it’s concerningly simplistic. Once an electronic device is thrown away or given to a third party for disposal, it often ends up in a landfill. At some point along the journey, people pick through the waste for hard drives to recover data. They either misuse it themselves or sell it to a third party for a profit. There’s 100 times more gold in one ton of smartphones than in one ton of gold ore. Thus, urban mining — extracting usable material like copper, glass, aluminum or gold from discarded electronics — is becoming popular in places like Ghana, China and India. 100 times more gold 100 times more gold How does e-waste hacking work? Usually, cybercriminals can pry out a device’s physical drives and recover the data from them, as many people forget to wipe or destroy those. While they might have to use specialized tools to bypass security measures, they often only have to plug it in to retrieve sensitive information. Back in 2009, a group of journalism students from the University of British Columbia bought a $40 hard drive at an open-air market in Ghana for an international reporting course. Once they plugged it in, a multi-million dollar U.S. defense contract between the Pentagon, the Department of Homeland Security and a military contractor was sitting there, waiting to be read. bought a $40 hard drive bought a $40 hard drive What Are the Security Risks of E-Waste? Once cybercriminals get ahold of an improperly disposed-of device’s storage system, they can see the previous owner’s PII. This could include names, social security numbers, addresses and contact information. They also gain access to whatever files remain intact. Regarding e-waste generated by consumer electronics, individuals become susceptible to identity theft, phishing scams, cyberattacks and financial fraud. If cybercriminals choose to sell their information on the dark web, they remain at risk for the rest of their lives. While individuals are usually the focus of cybersecurity concerns related to e-waste, businesses are also at risk. If even a single employee improperly disposes of their old work laptop, the company’s data breach risk skyrockets. E-waste hacking can expose proprietary information, give cybercriminals a way to infiltrate the brand’s network legitimately or lead to an uptick in cyberattacks. Considering the average data breach cost over $4.45 million in 2023, the consequences could be catastrophic. cost over $4.45 million cost over $4.45 million How Do Electronics End up in Hackers' Hands? The logistics of electronics going from a garbage can into a hacker’s hands is more straightforward than most people would assume. One of the simplest means is through theft, where the person retrieves the thrown-away device from the trash and takes it home. While most cybercriminals don’t make dumpster diving a pastime, many know where to go — or who to call — to get their hands on recently thrown-out electronics. Considering roughly 800 laptops worth of e-waste are generated every second in the U.S., the simplicity of such a plan shouldn’t come as a surprise. roughly 800 laptops worth of e-waste roughly 800 laptops worth of e-waste Another illicit means cybercriminals use to get their hands on PII is through a third party like a recycling center, buy-back platform or trade-in program. In this scenario, the intermediary sells used consumer electronics to them directly. Whether due to negligence or an effort to make a quick profit, sensitive data doesn’t get wiped before the sale. One of the most concerning ways e-waste ends up in hackers’ hands is through cargo ships. According to a 2016 study — one of the few on the matter — the U.S. exports up to 40% of its e-waste for dismantling or recycling. Apparently, there have been multiple cases where countries have had to reject cargo loads of e-waste shipments. up to 40% of its e-waste up to 40% of its e-waste While some say this claim is untrue, the U.S. has set a precedent by exporting plastic waste for decades. Despite international law making it illegal , it has increased its exports to places like Malaysia and Indonesia. Moreover, while most shipments are categorized as recycling, they end up in landfills, meaning e-waste export isn’t an unreasonable concept. international law making it illegal international law making it illegal What to Do to Avoid E-Waste Hacking While most people’s first thought is to avoid e-waste hacking by holding on to their technology, unused electronics are attractive targets to thieves. Besides, lost devices pose a host of other cybersecurity risks. The only way to guarantee security is through proper disposal channels. 1. Shred Data Storage Systems People must get used to “shredding” their hard disk drives and solid state drives (SSDs) like they do their sensitive documents. Once they wipe their device, they should render their storage systems unusable by physically destroying them and their chips. 2. Use Darik's Boot and Nuke Darik's Boot and Nuke (DBAN) is a free, open-source software people use to wipe physical drives. While it’s effective, it should only be used by individuals and not businesses, as it doesn’t meet any formal destruction requirements like NIST or HIPAA. Notably, it does not work on all devices and will not work for SSDs due to compatibility issues. 3. Leverage Data Encryption People should proactively encrypt every shred of data on their devices even if they use DBAN since data residue — recoverable digital traces of data — remains on electronics and is recoverable by hackers even after the manual erasure process. As a bonus, this measure can protect them from experiencing the effects of various cyberattacks. 4. Use Reputable Third Parties Simply destroying and throwing away electronics to prevent e-waste hacking is irresponsible because it is incredibly damaging to the environment. Instead, people should find reputable, transparent third parties to ensure their devices don’t end up being sold off or landfilled. The World Cannot Ignore the Issue of E-Waste Even without the security risks e-waste hacking poses, improperly disposed-of electronics are a problem. They poison the air, waterways and soil for thousands of years as they sit in landfills. Individuals and enterprises must do their part to prevent this from happening for the good of data protection and the environment.

What Is E-Waste Hacking?

ই-ওয়েস্ট হ্যাকিং কি?

Was ist Elektroschrott-Hacking?

¿Qué es el pirateo de residuos electrónicos?

Qu’est-ce que le piratage des déchets électroniques ?

ई-कचरा हैकिंग क्या है?

電子廃棄物ハッキングとは何ですか?

전자 폐기물 해킹이란 무엇입니까?

O que é hackeamento de lixo eletrônico?

Что такое взлом электронных отходов?

E-Atık Hacking Nedir?

Hack rác thải điện tử là gì?

什么是电子垃圾黑客？

Language models (LMs) represent a highly versatile category of technology, processing text inputs to generate textual outputs. Contemporary research has illuminated that this adaptable text-to-text framework can be harnessed to address a myriad of tasks without necessitating task-specific adjustments (i.e., absent fine-tuning or architectural changes) through the use of prompting methods to enable precise zero and few-shot inference. In essence, LMs are initially trained on extensive, unlabelled text corpora (guided by a language modeling objective), after which the LMs are directed via textual prompts to resolve specific problems, thereby allowing the pre-trained model to be efficiently repurposed for varied applications. Despite the immense promise of LMs as task-neutral foundation models, initial endeavours to apply pre-trained LMs to downstream tasks (e.g., GPT and GPT-2 [4, 5]) encountered significant challenges. This overview will explore how recent advancements have improved upon these preliminary efforts, leading to LMs that deliver significantly enhanced task-neutral performance. A critical discovery in this research trajectory is the enhanced capability of LMs as they are scaled up. (from [1] and [2]) More detailed examination reveals that large LMs (LLMs) are (i) more sample-efficient compared to their smaller counterparts and (ii) exhibit greater proficiency in task-neutral transfer to downstream applications. Notably, the performance of these LLMs demonstrates predictable patterns relative to various parameters (e.g., model scale and training data volume). This empirical evidence has culminated in the development of GPT-3, a colossal LLM with 175 billion parameters, which not only surpasses its predecessors in task-neutral performance but also exceeds contemporary, supervised deep learning approaches in certain domains. Key concepts necessary for understanding LMs have been discussed in my previous posts, including the language modeling objective, the architecture of decoder-only transformer models, and their integration to forge robust foundation models. For a more thorough understanding, refer to the provided link. Background I will offer a succinct overview of these concepts here, while also delineating a few additional notions pertinent to comprehending LLMs like GPT-3. Modern LMs utilize standard pre-training methods to tackle a broad spectrum of tasks without subsequent modifications (i.e., no architectural adjustments, fine-tuning, etc.). Employing a voluminous corpus of unlabelled text, we pre-train our LM with a language modeling objective that (i) extracts a text sample from our corpus and (ii) attempts to predict the succeeding word. This represents a form of self-supervised learning, where the correct next word can always be verified by examining the data in our corpus. Language modeling at a glance Modern LMs employ decoder-only transformer architectures, which apply a series of layers that include masked self-attention and forward transformations to the input. Masked self-attention, as opposed to bidirectional self-attention, precludes the model from 'peeking ahead' in a sequence to predict the subsequent word. Architecture Beyond these decoder-only layers, the LM architecture encompasses embedding layers that store vectors for all potential tokens within a predefined vocabulary size. Through these embedding layers, raw text is transformed into a matrix that the model can process, achieved by: Tokenizing raw text into discrete tokens (i.e., words or sub-words), Retrieving the corresponding embedding vector for each token, Concatenating these token embeddings to form a matrix or sequence of token vectors, Integrating position (and other) embeddings to each token vector. By pre-training LMs over expansive text corpora, we obtain models capable of accurately predicting the next token within a given token sequence. However, to utilize these models for language understanding tasks such as sentence classification and language translation, we do not modify the LM. Instead, we leverage the model’s inherent generic text-to-text input-output structure by supplying textual "prompts," such as: Adaptation “Translate this sentence to English: <sentence> =>”
“Summarize the following document: <document> =>”. With these problem-solving prompts, a proficient LM should yield a textual sequence that addresses the query effectively. For scenarios requiring a selection from a predetermined set of responses (e.g., multiple choice or classification), the LM can be used to assess the likelihood of generating each potential response and select the most probable outcome. The essence of modern LLMs lies in utilizing language model pre-training as a mechanism to develop generic foundation models capable of resolving diverse challenges without the need for adaptation or fine-tuning. While previous LMs like GPT and GPT-2 [4, 5] demonstrated inferior performance relative to tailored or supervised language understanding methods, this learning paradigm holds considerable promise and—as evidenced by GPT-3—can yield impressive results when the underlying LM is substantially enlarged. Main takeaway Power Laws In this overview, we will frequently refer to the concept of power laws. To illustrate, consider a statement from a research paper: "The LM’s test loss demonstrates a power law dependency on the number of model parameters." This assertion establishes a correlation between two variables—the loss and the number of model parameters—wherein adjustments to one variable induce proportionate, scale-independent alterations in the other. For clarity, a power law is mathematically expressed as follows. In this equation, x and y represent the variables under examination, while a and p influence the power law's shape and dynamics. When this power law (with a = 1, p = 0.5, and 0 < x, y < 1) is graphed, converting both axes to a logarithmic scale reveals a distinct linear trend, characteristic of power laws. Power laws describe how one variable scales as a power of another. Our discussions will include an inverse version of a power law, represented as: This equation employs a negative exponent for p, producing a graph where one variable decreases as the other increases. In our analysis of LMs, we observe that the LM loss decreases in accordance with a power law across various dimensions, such as model or dataset size. Further elaboration on this will follow in subsequent sections. Additional Useful Concepts In addition to the foundational principles of language modeling, there are several ancillary concepts that will be beneficial to understand moving forward. The primary focus of the papers discussed in this overview is on scaling models like GPT and GPT-2 [4, 5]. As models grow in complexity, training them becomes increasingly challenging due to heightened computational and memory demands. Distributed training methods, which utilize additional hardware resources (e.g., more servers/GPUs), facilitate the scalability of large training operations. Distributed Training There are various strategies for distributing the training workload of neural networks. One such strategy is data parallel training, which involves: Taking a large mini-batch, Dividing it into smaller sub-batches, Performing computations for each sub-batch in parallel across different GPUs, Consolidating the results from each GPU to update the central model. This method enhances training efficiency by parallelizing computations across multiple GPUs. Alternatively, model-parallel training divides the model itself across several GPUs. For instance, each layer—or even sub-components of each layer—might be allocated to different GPUs. This distribution allows the forward pass of the training process to occur across multiple devices, enabling the training of larger models and potentially improving efficiency through sophisticated pipelining and parallelization. For the purposes of this overview, it is important to recognize that leveraging multiple GPUs can make training of large language models (LLMs) more manageable and efficient. Data and model parallel training represent popular techniques in distributed training. There are numerous other strategies and considerations in this field that significantly enhance practical outcomes in deep learning. Utilizing large batches in data parallel training is generally advantageous for computational efficiency. However, excessively large batches may impair model performance and escalate computational costs, requiring more hardware. This phenomenon of diminishing returns becomes evident beyond a certain batch size; see below. Critical Batch Size (from [3]) Research has empirically determined the 'critical batch size', a metric that estimates the optimal batch size across various applications. Beyond this critical size, performance gains and computational efficiency start to diminish. As we will explore, some research adopts this critical batch size as a standard for efficient resource utilization during training. Language models generate text sequences in response to prompts through an autoregressive process, predicting each subsequent word based on the previous context. The simplest method continuously selects the most likely next word, but this approach may not yield the optimal overall sequence. Beam Search Beam search offers an alternative: instead of choosing the single most probable next word, it selects the top-k most probable options at each step, maintaining a list of potential sequences. At the conclusion of the generation process, the most likely overall sequence from this list is selected. This method provides a more effective approximation of the best sequence by considering multiple potential continuations at each step, thereby enhancing the quality of the generated text. Key Insights and Strategic Implications The foundational goal of GPT models was to develop universal language models capable of addressing a broad spectrum of tasks without necessitating task-specific tuning. This ambition is grounded in the premise that a profound comprehension of language modeling—specifically, the prediction of subsequent words in sequences—can be extrapolated to a variety of applications. Initially, models such as GPT and GPT-2 did not meet these expectations, delivering subpar performance in task-agnostic settings compared to supervised models. However, this overview has demonstrated that amplifying the scale of these language models (LMs) is an effective strategy for achieving high-performance, task-agnostic language understanding capabilities. This approach culminated in the conceptualization and deployment of GPT-3, an enormously scaled language model (approximately 100 times larger than GPT-2) that significantly outstrips the task-agnostic performance of its predecessors. The principle of scaling—enhancing model performance through the augmentation of model size, data volume, and computational resources—has been pivotal. Empirical data suggest that to optimize the efficacy of language model training, it is critical to substantially expand the model's dimensions while moderately increasing the dataset and batch size used during pre-training. This results in models that not only exhibit greater sample efficiency but also improve performance in accordance with power laws related to model size, data volume, and computational investment. Scaling Laws The advent of GPT-3, a model boasting 175 billion parameters, exemplifies the practical validation of these scaling theories. When such a colossal model is pre-trained across extensive text corpora, it demonstrates marked enhancements in task-agnostic, few-shot learning capabilities. Although GPT-3 may still lag behind specialized, supervised methods in certain benchmarks, subsequent studies have convincingly shown that as LLMs increase in size, their capacity for in-context learning also intensifies. Despite GPT-3’s architectural similarities to GPT-2, engineering a model of this magnitude underscores the transformative potential and scalability of foundational language models. Prospects of Scaling In conclusion, the journey from the initial GPT models to GPT-3 highlights a significant shift in the understanding and application of language models. The scalability of these systems not only advances their performance but also broadens their applicability across a diverse range of language understanding tasks, setting a new benchmark for future developments in the field of artificial intelligence. Bibliography [1] Kaplan, Jared, et al. “Scaling laws for neural language models.” arXiv preprint arXiv:2001.08361 (2020). [2] Brown, Tom, et al. “Language models are few-shot learners.” Advances in neural information processing systems 33 (2020): 1877–1901. [3] McCandlish, Sam, et al. “An empirical model of large-batch training.” arXiv preprint arXiv:1812.06162 (2018). [4] Radford, Alec, et al. “Improving language understanding by generative pre-training.” (2018). [5] Radford, Alec, et al. “Language Models are Unsupervised Multitask Learners.” [6] Child, Rewon, et al. “Generating long sequences with sparse transformers.” arXiv preprint arXiv:1904.10509 (2019). [7] Raffel, Colin, et al. “Exploring the limits of transfer learning with a unified text-to-text transformer.” J. Mach. Learn. Res. 21.140 (2020): 1–67. [8] Liu, Yinhan, et al. “Roberta: A robustly optimized bert pretraining approach.” arXiv preprint arXiv:1907.11692 (2019).

Understanding Scaling Law through Data Science Lenses

Mastering Scheduling and Tax Execution in Spring Boot

Non-Fungible Tokens (NFTs) took the digital world by storm in recent years, providing a novel foundation for extreme innovation in digital art, collectibles, and other assets using blockchain technology. However, after an initial frenzy of excitement and exorbitant sales, the NFT market experienced a significant slowdown. Now, signs are pointing to a potential resurgence. But what factors are necessary for NFTs to make a successful comeback? In this article, we'll delve into the key elements the NFT market needs to reignite its momentum. For NFTs to regain traction, they need to offer more than just digital bragging rights. Enhancing the utility of NFTs by enabling interoperability across different platforms and applications could unlock new use cases and value propositions. For example, NFTs could be integrated into gaming ecosystems, virtual worlds, or even financial services, allowing users to leverage their digital assets in diverse ways. Nevertheless, several industry participants have looked clearly into the market and are now working to ensure that developers in the NFT sub-genre have the resources that they need to really optimize their profitability and efficiency. Olly Wilson, CEO and co-founder of the leading NFT data aggregator, SimpleHash, contributed to our research to highlight some of the key issues that need to be tackled to start seeing scale. 1. What role do you envision SimpleHash playing in the NFT space? We abstract away all of the complexity of dealing with NFT data for developers. Instead of worrying about standards, support for different chains, integrating with multiple marketplaces, and spending time and money on NFT storage and compute, we handle it for them. Instead of worrying about infrastructure, crypto teams working with NFTs can focus on building the best end-user experience possible. 2. How do you see the data gap evolving for NFTs? Our original thesis was that NFT activity was rapidly going multi-chain (i.e., not just on Ethereum), and that’s absolutely borne out over the past two years. As an example, Solana and Bitcoin Ordinals NFTs now see more volume than Ethereum. The number of EVM L2s continues to proliferate, as do other L1s - and each of these ecosystems have a constantly changing set of standards to support. NFT marketplaces continue to pop up.  Plus, as NFT media becomes more complex, the storage, compute, and real-time processing becomes even more of a challenge. Taking all of this together, this creates a significant “data gap” where most teams are better off offloading this set of problems to a trusted provider. 3. Do you believe that there are more use cases that can be seen with NFTs? Yes - some of the use cases I’m most excited by include loyalty and reward programs that are actually better than their off-chain equivalents. There’s still massive untapped potential for NFTs within games too. Because of where we fit in the stack and are generalized across all chains, we can serve any of these use cases - whether their biggest needs are metadata, history, or market prices. In terms of how SimpleHash fits in – we’ll continue to abstract away the complexity of working with this data, regardless of use case. Developers and creators building the next big on-chain game shouldn’t have to care about indexing arbitrary tokens on the chains they want to support, nor should they have to cache media, metadata, or other essential data on the NFTs themselves. By working with us, they can focus on what matters - building an awesome end-user experience. The same is true for any other conceivable use case - we’ll be there in the future as a reliable partner who can say “no matter what kind of app you’re building, if you’re using NFTs or tokens, let us do the heavy lifting on setting up your infra, while you do what you do best” – ideally they don’t even have to think about us! 4. What bespoke resources does your platform plan to provide to improve the standing of NFTs among NFT collectors? SimpleHash has a mission to help all stakeholders within the NFT and broader digital asset ecosystem. We make it super easy for developers to build sophisticated applications that help inform anything a collector might care about. For example, we have a bespoke set of endpoints to provide wallet valuation, along with those that show the top and trending collections. Getting even more granular, we offer a full set of market data streams - including all NFT sales, listings, bids and floor prices across the main marketplaces and chains. Using all these, app developers can help provide detailed insights to collectors on which of their collections are doing well, which might be up and coming, along with the details on specific holders and trading patterns. We firmly believe that more information, and easier access to tooling is crucial for future development of the space. This extends to chain ecosystems too – much of the excitement within the NFT community has come from lower cost chains, like Base (where we have a special subsidized plan program), Zora, and Solana – so supporting these chains from the get-go has been crucial for us. 5. What do you believe is the future of the NFT space in general? Digital assets (of which NFTs are just one form) will underpin a huge part of the future internet, and we intend on helping power it all. As mentioned, the most exciting future we see for NFTs include loyalty and reward programs, core primitives in games, proving provenance of content in an AI content generated world, and yes, collectibles / PFPs too. We also expect the space to democratize somewhat too, given the activity trends on lower-cost chains (Base and Solana most notably). The days of paying hundreds of dollars just to get access to a mint are over - and we’re excited to see what creators and developers do when it becomes much easier (and cheaper) to build on these networks. Conclusion As the NFT market looks to make a comeback, it’s important to focus on reliable and solid projects with proper use cases. And even though the skepticism remains, there are several projects that are looking rather interesting across the board. Non-Fungible Tokens (NFTs) took the digital world by storm in recent years, providing a novel foundation for extreme innovation in digital art, collectibles, and other assets using blockchain technology. However, after an initial frenzy of excitement and exorbitant sales, the NFT market experienced a significant slowdown. Now, signs are pointing to a potential resurgence. But what factors are necessary for NFTs to make a successful comeback? In this article, we'll delve into the key elements the NFT market needs to reignite its momentum. For NFTs to regain traction, they need to offer more than just digital bragging rights. Enhancing the utility of NFTs by enabling interoperability across different platforms and applications could unlock new use cases and value propositions. For example, NFTs could be integrated into gaming ecosystems, virtual worlds, or even financial services, allowing users to leverage their digital assets in diverse ways. Nevertheless, several industry participants have looked clearly into the market and are now working to ensure that developers in the NFT sub-genre have the resources that they need to really optimize their profitability and efficiency. Olly Wilson, CEO and co-founder of the leading NFT data aggregator, SimpleHash, contributed to our research to highlight some of the key issues that need to be tackled to start seeing scale. 1. What role do you envision SimpleHash playing in the NFT space? We abstract away all of the complexity of dealing with NFT data for developers. Instead of worrying about standards, support for different chains, integrating with multiple marketplaces, and spending time and money on NFT storage and compute, we handle it for them. Instead of worrying about infrastructure, crypto teams working with NFTs can focus on building the best end-user experience possible. 2. How do you see the data gap evolving for NFTs? Our original thesis was that NFT activity was rapidly going multi-chain (i.e., not just on Ethereum), and that’s absolutely borne out over the past two years. As an example, Solana and Bitcoin Ordinals NFTs now see more volume than Ethereum. The number of EVM L2s continues to proliferate, as do other L1s - and each of these ecosystems have a constantly changing set of standards to support. NFT marketplaces continue to pop up.  Plus, as NFT media becomes more complex, the storage, compute, and real-time processing becomes even more of a challenge. Taking all of this together, this creates a significant “data gap” where most teams are better off offloading this set of problems to a trusted provider. 3. Do you believe that there are more use cases that can be seen with NFTs? Yes - some of the use cases I’m most excited by include loyalty and reward programs that are actually better than their off-chain equivalents. There’s still massive untapped potential for NFTs within games too. Because of where we fit in the stack and are generalized across all chains, we can serve any of these use cases - whether their biggest needs are metadata, history, or market prices. In terms of how SimpleHash fits in – we’ll continue to abstract away the complexity of working with this data, regardless of use case. Developers and creators building the next big on-chain game shouldn’t have to care about indexing arbitrary tokens on the chains they want to support, nor should they have to cache media, metadata, or other essential data on the NFTs themselves. By working with us, they can focus on what matters - building an awesome end-user experience. The same is true for any other conceivable use case - we’ll be there in the future as a reliable partner who can say “no matter what kind of app you’re building, if you’re using NFTs or tokens, let us do the heavy lifting on setting up your infra, while you do what you do best” – ideally they don’t even have to think about us! 4. What bespoke resources does your platform plan to provide to improve the standing of NFTs among NFT collectors? SimpleHash has a mission to help all stakeholders within the NFT and broader digital asset ecosystem. We make it super easy for developers to build sophisticated applications that help inform anything a collector might care about. For example, we have a bespoke set of endpoints to provide wallet valuation, along with those that show the top and trending collections. Getting even more granular, we offer a full set of market data streams - including all NFT sales, listings, bids and floor prices across the main marketplaces and chains. Using all these, app developers can help provide detailed insights to collectors on which of their collections are doing well, which might be up and coming, along with the details on specific holders and trading patterns. We firmly believe that more information, and easier access to tooling is crucial for future development of the space. This extends to chain ecosystems too – much of the excitement within the NFT community has come from lower cost chains, like Base (where we have a special subsidized plan program), Zora, and Solana – so supporting these chains from the get-go has been crucial for us. 5. What do you believe is the future of the NFT space in general? Digital assets (of which NFTs are just one form) will underpin a huge part of the future internet, and we intend on helping power it all. As mentioned, the most exciting future we see for NFTs include loyalty and reward programs, core primitives in games, proving provenance of content in an AI content generated world, and yes, collectibles / PFPs too. We also expect the space to democratize somewhat too, given the activity trends on lower-cost chains (Base and Solana most notably). The days of paying hundreds of dollars just to get access to a mint are over - and we’re excited to see what creators and developers do when it becomes much easier (and cheaper) to build on these networks. Conclusion Conclusion As the NFT market looks to make a comeback, it’s important to focus on reliable and solid projects with proper use cases. And even though the skepticism remains, there are several projects that are looking rather interesting across the board.

NFTs Are Looking To Make A Comeback. What Does The Market Need?

Left Curve

বাম বক্ররেখা

Linkskurve

Curva izquierda

Courbe gauche

बायां वक्र

左カーブ

왼쪽 곡선

Curva Esquerda

Левая кривая

Sol Eğri

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左弯道

Software developers rarely design the most aesthetically pleasing software. These principles can help you and other developers change this narrative. Keep it simple. Simplicity is a principle to follow in design. Overcomplicating your design won’t make it better in most cases. Beautiful designs can be created from the simplest ideas. Sometimes, good ideas become useless due to over-complication. People like beautiful things that aren’t complicated. It does not matter how good your software is if the users cannot find their way around it. A simple and beautiful design is more beneficial than a complicated design. Keep things simple and functional. Your clientele will thank you for this. Avoid Reinventing the Wheel Reinventing the wheel is re-creating something already created, tested, and proven effective. It harms overall development and often leads to unnecessary complexity. It's better to use existing and proven solutions. An underrated tactic is gaining inspiration from another creation and making it your own with your twist. We have used this principle to enhance the aesthetics of Goleko. We took inspiration from MacOS, customizing our buttons and features using it as a guide. We plan to take some inspiration from Windows and incorporate it into the design, creating a fresh and unique outlook that is Goleko. We’ll make designs similar to Windows and MacOS, allowing users to experience a theme familiar to their machine. It is okay to steal designs and add your flare to them. You can steal such designs from other websites or get them from themes. Our designs are simple and functional, conforming to design best practices. We don’t do too much. We improve where necessary to improve user experience (UX), but we do not try to change what has been proven to be efficient. Avoid Tunnel Vision. It’s not rare for designers to get engulfed in their designs. Tunnel vision makes it hard for designers to see the holes or weaknesses in their design. The result is a design that’s pleasing for them but not for their target clientele. Focusing on completion instead of other effects is detrimental to your design. Take time to determine what works well for your design and what your users want. Design something that incorporates the two. This will help you avoid tunnel vision, allowing you to create something that works well for users. Consistency Over Fanciness Creating a fancy product is a good thing, and it’s highly recommended. This does not mean you ignore its consistency. Clients prefer a product that remains consistent in delivering what they need. Design a product that works and delivers as advertised. You can incorporate consistent paddings and margins into this as well. It helps to make a site appear clean and easily readable. This also follows the law of continuity. This law states that we tend to see continuous patterns and lines. These create a sense of flow and hierarchy in designs by using continuous lines or color gradients, creating a sense of movement. These guide the user's eye toward important information. Such simple solutions promote consistency for your products, making them viable and usable by your clients. Proportions Excellent designs have elements of different sizes that fit nicely together to create coherent proportions. Here is an example: Websites can display different content types, such as articles, videos, and images. Highlight the most crucial content and guide the user's eye toward it. A good example specific for me is making the article section livelier than the other sections. I do this when articles are the most pivotal content on a website. Implement different proportions to create emphasis. You can use smaller proportions for less critical content, such as images or videos. This creates a hierarchy of importance and guides the user's eye to the most crucial elements of the website. Color Adherence The best designers understand how to combine different colors that complement each other. This mere statement sounds simple, but is a science. Flatuicolors has numerous color palettes showcasing different colors that accentuate and complement each other. A successful product is both functional and visually pleasing. Picking the best color scheme goes a long way in making your product beautiful and enjoyable to use by everyone. Colors brighten everything, and pairing them with their ideal partners is the first step to creating a masterpiece. Conclusion Software developers focus on designing a product that works well. Designing the aesthetics isn’t a concern for them. This makes it easy for them to create software that’s not easy on the eye or welcoming. These principles can help bridge this gap between development and design. It shouldn’t be rare for a developer to design a pleasing product. This piece can help start this trend. FAQs Are software developers creative?Contrary to popular belief, software developers are creative beasts. Writing good code requires logic as you need to get things done properly, but you also need the creativity to make a good solution. Software developers combine these two skills with excellent drive and efficiency to create software that solves issues and improves the lives of people. Can you learn to build beautiful software?Yes, you can. You practice your coding, learn from your mistakes, and improve your application - this is the same for design. It takes time to develop these skills and to perfect them. Be patient with yourself and work on your skills. It helps to note your lessons and improve upon your mistakes. Doing this will help you build beautiful and elegant software. Is software development an artistic field?Yes, it is. It incorporates the style of a savant with the imagination of a visionary to create software that works as expected. It's like interacting with a wine connoisseur. They take in every part of the process, analyze every detail, and put it together to create the best software for people to use. They put effort into the user interface, the user experience, and functionality. The outcome is elegant software that works well and gives the users what they want. Is building software a hard thing to do?Yes, it is. It takes a lot of time and effort to build elegant software. Software developers hone their skills and practice their coding to create elegant software. They learn from their mistakes and implement their skills to create better software to help people in society. They learn to use different coding tools and methods to create elegant software. They give everything to make the world a better place one code at a time. Are software engineers creative?Software engineers are creative. They work well and remain on topic. They have valuable insight they use to enhance their ideas to improve code and applications. They use their creativity to implement visual and structural changes to the programs. They make software that benefits the users and helps the community at large. Is creativity essential to software development?Yes, it is. The software developers are like other professionals. They take in feedback, analyze it, and creatively formulate solutions. Their decision-making and dedication make them excellent workers with cool insights. They generally follow a routine but have complex processes to solve team and community problems. They are creative and effective in their work. Are there services that need software developers?Yes, there are. Software developers are needed in web development, app development, and specialized coding. They are needed in marketing, programming, and various projects. A software developer's skills are diverse and needed in almost every industry. For these and more thoughts, guides and insights visit my blog at martinbaun.com Software developers rarely design the most aesthetically pleasing software. These principles can help you and other developers change this narrative. Keep it simple. Simplicity is a principle to follow in design. Overcomplicating your design won’t make it better in most cases. Beautiful designs can be created from the simplest ideas. Sometimes, good ideas become useless due to over-complication. People like beautiful things that aren’t complicated. It does not matter how good your software is if the users cannot find their way around it. A simple and beautiful design is more beneficial than a complicated design. Keep things simple and functional. Your clientele will thank you for this. Avoid Reinventing the Wheel Reinventing the wheel is re-creating something already created, tested, and proven effective. It harms overall development and often leads to unnecessary complexity. It's better to use existing and proven solutions. An underrated tactic is gaining inspiration from another creation and making it your own with your twist. We have used this principle to enhance the aesthetics of Goleko. We took inspiration from MacOS, customizing our buttons and features using it as a guide. We plan to take some inspiration from Windows and incorporate it into the design, creating a fresh and unique outlook that is Goleko. We’ll make designs similar to Windows and MacOS, allowing users to experience a theme familiar to their machine. It is okay to steal designs and add your flare to them. You can steal such designs from other websites or get them from themes. Our designs are simple and functional, conforming to design best practices. We don’t do too much. We improve where necessary to improve user experience (UX), but we do not try to change what has been proven to be efficient. Avoid Tunnel Vision. It’s not rare for designers to get engulfed in their designs. Tunnel vision makes it hard for designers to see the holes or weaknesses in their design. The result is a design that’s pleasing for them but not for their target clientele. Focusing on completion instead of other effects is detrimental to your design. Take time to determine what works well for your design and what your users want. Design something that incorporates the two. This will help you avoid tunnel vision, allowing you to create something that works well for users. Consistency Over Fanciness Creating a fancy product is a good thing, and it’s highly recommended. This does not mean you ignore its consistency. Clients prefer a product that remains consistent in delivering what they need. Design a product that works and delivers as advertised. You can incorporate consistent paddings and margins into this as well. It helps to make a site appear clean and easily readable. This also follows the law of continuity. This law states that we tend to see continuous patterns and lines. These create a sense of flow and hierarchy in designs by using continuous lines or color gradients, creating a sense of movement. These guide the user's eye toward important information. Such simple solutions promote consistency for your products, making them viable and usable by your clients. Proportions Excellent designs have elements of different sizes that fit nicely together to create coherent proportions. Here is an example: Websites can display different content types, such as articles, videos, and images. Highlight the most crucial content and guide the user's eye toward it. A good example specific for me is making the article section livelier than the other sections. I do this when articles are the most pivotal content on a website. Implement different proportions to create emphasis. You can use smaller proportions for less critical content, such as images or videos. This creates a hierarchy of importance and guides the user's eye to the most crucial elements of the website. Color Adherence The best designers understand how to combine different colors that complement each other. This mere statement sounds simple, but is a science. Flatuicolors has numerous color palettes showcasing different colors that accentuate and complement each other. A successful product is both functional and visually pleasing. Picking the best color scheme goes a long way in making your product beautiful and enjoyable to use by everyone. Colors brighten everything, and pairing them with their ideal partners is the first step to creating a masterpiece. Conclusion Software developers focus on designing a product that works well. Designing the aesthetics isn’t a concern for them. This makes it easy for them to create software that’s not easy on the eye or welcoming. These principles can help bridge this gap between development and design. It shouldn’t be rare for a developer to design a pleasing product. This piece can help start this trend. FAQs Are software developers creative? Contrary to popular belief, software developers are creative beasts. Writing good code requires logic as you need to get things done properly, but you also need the creativity to make a good solution. Software developers combine these two skills with excellent drive and efficiency to create software that solves issues and improves the lives of people. Can you learn to build beautiful software? Yes, you can. You practice your coding, learn from your mistakes, and improve your application - this is the same for design. It takes time to develop these skills and to perfect them. Be patient with yourself and work on your skills. It helps to note your lessons and improve upon your mistakes. Doing this will help you build beautiful and elegant software. Is software development an artistic field? Yes, it is. It incorporates the style of a savant with the imagination of a visionary to create software that works as expected. It's like interacting with a wine connoisseur. They take in every part of the process, analyze every detail, and put it together to create the best software for people to use. They put effort into the user interface, the user experience, and functionality. The outcome is elegant software that works well and gives the users what they want. Is building software a hard thing to do? Yes, it is. It takes a lot of time and effort to build elegant software. Software developers hone their skills and practice their coding to create elegant software. They learn from their mistakes and implement their skills to create better software to help people in society. They learn to use different coding tools and methods to create elegant software. They give everything to make the world a better place one code at a time. Are software engineers creative? Software engineers are creative. They work well and remain on topic. They have valuable insight they use to enhance their ideas to improve code and applications. They use their creativity to implement visual and structural changes to the programs. They make software that benefits the users and helps the community at large. Is creativity essential to software development? Yes, it is. The software developers are like other professionals. They take in feedback, analyze it, and creatively formulate solutions. Their decision-making and dedication make them excellent workers with cool insights. They generally follow a routine but have complex processes to solve team and community problems. They are creative and effective in their work. Are there services that need software developers? Yes, there are. Software developers are needed in web development, app development, and specialized coding. They are needed in marketing, programming, and various projects. A software developer's skills are diverse and needed in almost every industry. For these and more thoughts, guides and insights visit my blog at martinbaun.com martinbaun.com

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