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Introduction Generic Programming in Go has always been awkward compared to other compiled languages. The most popular ways to implement it in Go are using interfaces, typecasting, and code-generation. But every method has its significant limitations. For instance, using interfaces requires implementing an interface for each data type. Typecasting leads to potential runtime errors. And in the case of code generation, we have to write generators, which takes a lot of time. The article's primary goal is to understand how Generics work in Go and compare their performance with previous Generic Programming methods in Go. I will implement a popular function, " ," that iterates over an array of data and transforms each element using a callback function. Map The article code examples are stored in the . Github repository Go Generic Functions Generic functions were added in Go in the 1.18 release. Generic functions refer to a mechanism for compile-time polymorphism, specifically parametric polymorphism. These are functions defined with Type Parameters, intended to be resolved with compile-time type information. The compiler uses these types to instantiate suitable versions, resolving any function overloading appropriately. Generic functions in Go allow: define type parameters for function and types. define interface types as sets of types, including types that don’t have methods. define type inference, which permits omitting type arguments in many cases when calling a function In practice, it gives the following features: code writing becomes comfortable for developers since there is no need to implement or generate code for every new data type a compile-time type safety a runtime type safety Go Generic Function — Map Example Let’s see how Generic Functions work in Go. And for the real-world example, we will use the function. The function takes a slice and a callback that modifies every item and returns a new slice. Map The function implementation for integer values looks like this: Map // Map modifies every item of list and returns a new modified slice.
func Map[T any](list []T, modify func(item T) T) []T {
	if list == nil {
		return nil
	}

	if modify == nil {
		return list
	}

	mapped := make([]T, len(list))
	for i, item := range list {
		mapped[i] = modify(item)
	}

	return mapped
} There are two checks on values for a list and a callback function for safety. After that, there is a new slice with the same length as a list. Then, the function iterates through a list, modifies each item, and writes the item in a new slice . An example of how the function works: nil mapped package main

func main() {
    // prints 2, 4, 6
    fmt.Println(Map([]int{1,2,3}, func(item int) int {
		return item * 2
	}))
} However, if there is a need to use the function with another type, it needs to implement a new function. This architecture isn't scalable and hard to maintain. But using new Generic Functions, we can create a single function that will work with all types that we need: Map // Map modifies every item of list and returns a new modified slice.
func Map[V any](list []V, modify func(item V) V) []V {
	if list == nil {
		return nil
	}

	if modify == nil {
		return list
	}

	mapped := make([]V, len(list))
	for i, item := range list {
		mapped[i] = modify(item)
	}

	return mapped
} Type is a new alias for . any interface{} The function implementation looks similar to the integer mapping implementation. The only difference is the function signature. Now there is a type parameter definition which means that the function can handle any type, but it should be the same type in a callback function . Let’s see how the function works for different types: [V any] modify func(item V) V) []V Map package main

import (
	"fmt"
	"strings"
)

type person struct {
	name string
	age  int
}

func main() {
	// prints [2 4 6]
	fmt.Println(Map([]int{1, 2, 3}, func(item int) int {
		return item * 2
	}))

	// prints [HELLO WORLD]
	fmt.Println(Map([]string{"hello", "world"}, func(item string) string {
		return strings.ToUpper(item)
	}))

	// prints [{Linda 19} {John 23}]
	fmt.Println(Map([]person{{name: "linda", age: 18}, {name: "john", age: 22}}, func(p person) person {
		p.name = strings.Title(p.name)
		p.age += 1

		return p
	}))
} Go Generic Functions Internals This section will research how Generic Functions behave during compilation and program runtime. Runtime type safety For the research, I will use the following Go program: package main

import (
	"fmt"
	"runtime"
	"strings"
)

type person struct {
	name string
	age  int
}

func main() {
	ints := []int{1, 2, 3}
	doubledInts := Map(ints, func(item int) int {
		return item * 2
	})

	// prints [2 4 6]
	fmt.Println(doubledInts)

	words := []string{"hello", "world"}
	capitalizedWords := Map(words, func(item string) string {
		return strings.ToUpper(item)
	})

	// prints [HELLO WORLD]
	fmt.Println(capitalizedWords)

	people := []person{{name: "linda", age: 18}, {name: "john", age: 22}}
	modifiedPeople := Map(people, func(p person) person {
		p.name = strings.Title(p.name)
		p.age += 1

		return p
	})

	// prints [{Linda 19} {John 23}]
	fmt.Println(modifiedPeople)

	runtime.Breakpoint()
}

// Map modifies every item of list and returns a new modified slice.
func Map[T any](list []T, modify func(item T) T) []T {
	if list == nil {
		return nil
	}

	if modify == nil {
		return list
	}

	mapped := make([]T, len(list))
	for i, item := range list {
		mapped[i] = modify(item)
	}

	runtime.Breakpoint()

	return mapped
} There are two runtime breakpoints for debug purposes. For convenience, I use JetBrains Goland IDEA to debug the code. After running the program in debug mode we can see that the first call of the function contains the with . Map list []int However, the following two function calls have different types in runtime: Therefore, a specific, strictly defined type is passed from a caller function in runtime inside the function. Map Also, in the function all types are defined as well: main We can conclude, that Generics in Go retain their type information at runtime, and in fact, Go does not know about the generic "template" at runtime - only how it was instantiated. do To make sure that type is retained during compilation, we can try to assign to : capitalizedWords []string doubledInts []int ints := []int{1, 2, 3}
	doubledInts := Map(ints, func(item int) int {
		return item * 2
	})

	words := []string{"hello", "world"}
	capitalizedWords := Map(words, func(item string) string {
		return strings.ToUpper(item)
	})

	doubledInts = capitalizedWords Here happens a compilation error: ./main.go:24:16: cannot use capitalizedWords (variable of type []string) as type []int in assignment Therefore, Go does enforce type safety for generic types at runtime. Runtime instantiation Let’s see what happens if we try to inspect a Generic Function in runtime using reflection: package main

import (
	"fmt"
	"reflect"
)

func main() {
	fmt.Println(reflect.TypeOf(Map))
}

// Map modifies every item of list and returns a new modified slice.
func Map[T any](list []T, modify func(item T) T) []T {
	if list == nil {
		return nil
	}

	if modify == nil {
		return list
	}

	mapped := make([]T, len(list))
	for i, item := range list {
		mapped[i] = modify(item)
	}

	return mapped
} Trying to compile the program returns an error: ./main.go:9:29: cannot use generic function Map without instantiation Therefore, Generics are not helpful in Go until instantiated. There is no way to refer to generic "templates" using reflection, which means it is not possible to instantiate new types using Generics at runtime. It is almost as if generic types do not exist in compiled Golang binaries. Generic Functions Performance We researched how Generic Functions work during compilation and runtime. The last important question is: how fast do they perform? There are three different implementations of the function: Map // Map modifies every item of list and returns a new modified slice.
func Map[T any](list []T, modify func(item T) T) []T {
   if list == nil {
      return nil
   }

   if modify == nil {
      return list
   }

   mapped := make([]T, len(list))
   for i, item := range list {
      mapped[i] = modify(item)
   }

   return mapped
}

// MapTyped modifies every item of list and returns a new modified slice. It works only with Integer values.
func MapTyped(list []int, modify func(item int) int) []int {
   if list == nil {
      return nil
   }

   if modify == nil {
      return list
   }

   mapped := make([]int, len(list))
   for i, item := range list {
      mapped[i] = modify(item)
   }

   return mapped
}

// MapAny modifies every item of list and returns a new modified slice. It works with Any type, so you should cast types by yourself.
func MapAny(list []any, modify func(item any) any) []any {
   if list == nil {
      return nil
   }

   if modify == nil {
      return list
   }

   mapped := make([]any, len(list))
   for i, item := range list {
      mapped[i] = modify(item)
   }

   return mapped
} For benchmark, we use a list of integers and a callback function that doubles each integer value: []int{1,2,3} func BenchmarkGenericMap(b *testing.B) {
	for i := 0; i < b.N; i++ {
		Map([]int{1, 2, 3}, func(item int) int {
			return item * 2
		})
	}
}

func BenchmarkTypedMap(b *testing.B) {
	for i := 0; i < b.N; i++ {
		MapTyped([]int{1, 2, 3}, func(item int) int {
			return item * 2
		})
	}
}

func BenchmarkAnyMap(b *testing.B) {
	for i := 0; i < b.N; i++ {
		MapAny([]any{1, 2, 3}, func(item any) any {
			return item.(int) * 2
		})
	}
} After calling the command, we have the benchmark results that are described in the table below: go test -bench=. -benchmem -v ./... Map function type Operations count ns/op bytes/op allocs/op Generic 42033705 28.90 24 1 Typed 41317022 29.16 24 1 Any (using type casting) 17563975 68.61 48 1 The benchmark takeaways: the Generic Function has the same performance as a specifically-typed function implementation on average, the Generic Function outperformed the Any (using type casting) implementation: operations are ~2.4 times faster consumed half the memory the performance improvements were the result of removing the need to use a type casting Conclusion We researched what the Generic Functions are and how they work in Go. Also, we compared the Generic Functions' performance with previous Generic Programming methods. Finally, there are the main conclusions: Generics allow to write a function only once and use it for different data types without any extra code. Therefore, it’s easier to scale and maintain. Go Generic Functions are instantiated during compilation, and they aren’t presented in program runtime. That prevents unexpected behavior in a program and guarantees a type-safety. Go Generic Functions have the same performance as a specifically-typed function implementation, but it’s preferable since there is no need to write a new function for each data type. References Generic function - Wikipedia ( ) https://en.wikipedia.org/wiki/Generic_function An introduction to Generics - The Go Programming Language ( ) https://go.dev/blog/intro-generics Go generics the hard way ( ) https://github.com/akutz/go-generics-the-hard-way

Generic Programming in Go

Crypto Crash? Not Really... 

In this article I’ll introduce an implementation for Singly and Doubly Linked Lists, which you can reuse or modify for your own purposes. All the or as an npm package. code is available in github Coding data structures in Solidity is weird and beautiful. A few months ago I implemented a in Solidity for a client. More recently I decided that I might take on implementing a in Solidity as a pet project (aren’t nerds fun!) and Linked Lists appeared again. Linked List Fast Limit Order Book As I recently wrote in an article about : when to use different data structures Linked Lists are your data structure of choice when you need to preserve the insertion order, and also when you want to insert in arbitrary positions. I love coding and no one seemed to have done this one*, so I took to it happily. basic building blocks *Disclaimer: While writing this article, and after having coded the contracts, I found this from . Like him, I also considered using an array. Compared to using a mapping, it simplifies the creation of new items but also makes deletion more difficult. earlier implementation chriseth Implementation For this article I’m going to ignore that and code the lists in a single contract. Doing that will allow me to focus on the basics such as data usage. An , but the trade offs deserve an article of its own. Solidity is an Object Oriented Programming Language OOP implementation is possible Implementing a Linked List in a single contract Solidity was not an obvious thing to do. This code would be very convenient, but not doable in Solidity since you can’t have recursive . structs contract ImpossibleLinkedList {
   struct Item {
      Item next;
      address data;
   }
Item public head; The only dynamic contract variable that exists in Solidity are mappings. Even arrays are mappings under the hood. Given that constraint the best implementation for a Linked List in Solidity that I could come up is based on this: contract LinkedList {
  struct Item {
    uint256 id;
    uint256 next;
    address data;
}
mapping ( Item) public items;
uint256 public head;
uint256 public idCounter; => uint256 The Linked List is made of Item. The Item has a unique id, a member for the id of another Item, and an address which is the data payload. Then all items created are stored in a mapping indexed by Item id. You can retrieve any Item at cost O(1) if you know its id, just by looking it up in the mapping. If you are looking at an Item in the List and want to proceed to the next one you have to retrieve item.next which is an Item id, and then look up that next Item in the mapping. If this gets you confused, don’t feel bad. I also got very confused. My first question was “is there any point to a Linked List if you can just arbitrarily retrieve any Item from a list?”. The question is that yes, there is a point, but with a very limited scope. Linked Lists are your data structure of choice when you need to preserve the insertion order, and also when you want to insert in arbitrary positions. The fact that you can iterate over the list is useful to a point, if you have to do that in a transaction the list cannot grow indefinitely. You can use this data structure when a contract needs to frequently use several items in an ordered list that you can assume will be of limited size. For example if you need a contract to always know the 100 largest holders of a token to give them some perks. Unlike in previous articles, this time I’m not going to paste the whole code here. Instead I will direct you to the . There is an implementation for a Singly Linked List and another one for a Doubly Linked List, with each one being about 200 lines of code, which I’ve crafted carefully for maximum clarity. full implementation In this case I think it is more important to discuss the trade offs between a Singly Linked List and Doubly Linked List, in particular given that the Ethereum blockchain is quite limited in the algorithms that you can . execute safely Usage When I started the implementation of linked lists, I thought that doing a Doubly Linked List would be more complex than a Singly Linked List. Interestingly enough, it is slightly easier to implement the former. Adding in each item a link for the previous one allows you to remove this inefficient method: { (_id == head) ;
  Item memory prevItem = items[head]; (prevItem.next != _id) {
    prevItem = items[prevItem.next];
  } prevItem.id;
} /**
 * @dev Given an Item, denoted by `_id`, returns the id of the Item
 * that points to it, or 0 if `_id` refers to the Head.
 */ ( ) ( ) function findPrevId uint256 _id public view returns uint256 if return 0 while return That statement is evil. while You would use this method anytime that you know of an item and you want to insert another before it. Quite a common use case. A gas comparison between LinkedList.sol and DoubleLinkedList.sol sheds some more light on the issue. For these tests I used a list with 100 items. and operations with LinkedList are O(1) and cost about 100K gas. Data retrieval is not depicted but given that we use a mapping the cost will be O(1). addHead insertAfter The issue is when we need to loop over the list. Every item we loop through seems to cost about 1K gas as seen in findTailIdWithGas (which is a mock function that encloses findTailId in a transaction, wasting some gas). Maybe we can make without adding items at the tail or inserting before a known item, but the remove function is more of an issue. In a LinkedList you have to loop through the list from the head to remove items. In smart contracts a method with a cost of O(N) needs to be approached very carefully, or better even, avoided. A Doubly Linked List is easier to implement and more practical, even if a bit more expensive to use. In this specific case, and with a gas block limit of about ten million, it means that you can’t remove items that are more than 10,000 positions away from the head. That can be quite dangerous. For , on the other hand, all the methods are O(1). addHead and insertAfter are more costly than in because we need to update an extra pointer. If you need to insert at the end of the list, find neighbouring items in both directions, or remove items, you’ll benefit from O(1) cost. I haven’t included gas costs for looping the list but they should be identical to LinkedList. DoubleLinkedList LinkedList And as I said before, it’s interesting that for the same functionality, costs less to deploy than LinkedList. Not that important but interesting. DoubleLinkedList As with anything, your mileage will vary. Maybe you can do with a Singly Linked List, maybe you need a Doubly Linked List. Maybe you should use an array. At least now you know them all. Other Implementations The implementations discussed above are not the only ones, I just thought they would be easy to understand. There are other interesting implementations that might fit your use case better: You want to save some gas, then remove the id field from the Item struct. You don’t actually need it, funny enough. You don’t like the structs, them replace each struct variable with a mapping as a state variable, it will work exactly the same. You are happy with just appending items at the tail, and maybe those items expire after a set period of time: You can use . It would work great for a traditional fast limit order book. RenounceableQueue.sol If all the items in your list are unique, then you can use . I like the compactness and elegance of it. OrderedSet.sol If all the items in your list are unique, but you don’t care about the order, then you are after a canonical , get it from . Set OpenZeppelin Conclusion are the first complex data structure that is considered in smart contracts. Given the constraints in smart contracts that force us to code as simply as possible it is necessary to know the trade offs between different linked list implementations. Linked Lists In this article I’ve shown both Singly Linked Lists and Doubly Linked Lists, pointing to . An analysis of gas costs has also been provided, along with guidance for safe use. code ready to be reused I feel quite privileged to have the opportunity to code basic data structures. Sometimes coding smart contracts feels like a trip many years back when programming meant being very close to the hardware and was very close to mathematics. I like that. If you are considering using this code in a project, want to contribute, or have ideas to explore, please ! Talking to those that read me is always a pleasure :) drop me a line

A Linked List Implementation for Ethereum [Deep Dive]

“Proof-of-Nuclear-Bombs”: a most reliable design for stablecoin utility (Part I)

Note - this article was originally published on the in November 2017. Given the recent changes in Twitter, I thought it was a good time to look back at the work I and others did to double the original character count. Twitter Design blog · Looking After Number One-forty Solving a number of design challenges One defining aspect of Twitter, starting way back when we were still Twttr, is 140-character Tweets. For some, the character constraint has been a fun challenge. It motivates people to be briefer and more creative with their words. We’ve even heard people say that it’s helped them improve their writing. For many people, though, the limitation can be an incredibly frustrating part of using Twitter. It forces them to hack their sentences, severely limits their ability to express their thoughts, and often leads people to abandon their Tweets. We love the brevity of Twitter, but we also want people to be able to easily share their thoughts. When we looked into the data on Tweet length, we discovered an interesting pattern: depending on language, people have a very different experience with the 140-character limit. For example, in information dense languages, like Japanese or Korean, 140 characters isn’t very limiting. People in Japan are able to express a lot in 140 characters and don’t have any complaints about the limit. In languages like English, Spanish, French, and especially German, the character limit is a consistent complaint we’ve seen in our research over the years. A great example of this disparity can be seen below: The same basic idea expressed in three languages, English, Spanish, and Japanese. With this in mind, we designed a system that defines two types of written languages, dense and non-dense, and expands the character limit for non-dense languages. By grouping languages this way, we can give people writing in non-dense languages like English and Spanish the same space to express themselves as people writing in information-dense languages like Japanese. This will make sharing thoughts and ideas on Twitter a lot less frustrating for many more people, while maintaining brevity on Twitter overall. This is where the design challenge comes in: How can we make a UI that communicates these different character constraints that is still easily understood globally? Simply replacing the number doesn’t work because we can’t be certain which language you’re going to be Tweeting in. We could guess which language you’ll use, based on your location or system language, but that falls apart quickly, as many people live in foreign countries or travel regularly. Additionally, many people Tweet in multiple languages, sometimes within a single Tweet. Because we count dense alphabets differently than non-dense, mixed language Tweets can result in some intricate math that we want to be able to abstract away. The challenge here was to create a design that adapts to different character limits without relying on a number, works with the many ways people compose Tweets, and is intuitive enough that people don’t have to spend time thinking about it. During the initial design brainstorms, it became quickly apparent that there are lots of questions we needed research to help with. We knew we needed to understand all the different circumstances around how people compose Tweets, but we also had to answer some other key unknowns: Do people look at the number while composing a Tweet now? When does the number become important? Is just a warning sufficient enough? What happens when they go over the limit? When they’re getting close to the end, how soon do they worry about it? How important is UI progress indicator for people to understand how much space they have left as they compose? Based on these questions, I went through quite a few design explorations. This included 27 different, animated prototypes ( is my jam, in case you were interested), more than a couple design brainstorms, internal crit sessions, and a lot of revisions. At the end of all these explorations, we conducted in-person testing across two countries (Japan and the US) with a handful of functional prototypes. Principle In-person testing was very valuable in helping us identify what was working and what wasn’t. A great example of this is how one of my favorite designs totally failed. For this prototype in particular, I had decided to remove the counter completely until they got close to the limit (20 characters left). At that point they would see a small tracking number following their cursor. Despite what I initially thought, it actually ended up being quite confusing to people in our testing session. One of the research participants in Tokyo said, “I like the one with both circle and the number at the end. With the circle, I can intuitively know how much left to go. Also, without the number counting down from the beginning, I don’t have to feel pressured as I type in the Tweet.” This reasoning for liking the a combination of abstract + granular design was shared by our Japanese research participants. While I thought removing the UI until it was relevant would help alleviate the stress of the character limit, it ended up having the opposite effect; people were unable to properly plan their Tweet since they had no feel for how far along they actually were. Through our research, we were able to define some clear design constraints, including answering a lot of the unknowns. In the end, the research refocused our design needs: Be an abstract counter to support multiple counting methods Show progress throughout Have a granular counter appear as people got close to the end Use color or another visual indicator as a warning Lean less on subtlety or people won’t notice it Support all our composers Ultimately, we landed on a design that solves those needs and still felt “Twittery.” We wanted something lightweight and clear with some small elements of delight. Of course, just feeling something isn’t enough, so we’ve been tracking whether it’s working or not. Not only do we want people to understand the experience when composing Tweets, but we need to understand the impact of this change overall on brand perceptions — 140 was so core to who we were for more than a decade. In addition to researching people’s expression experience on the platform, we also worked to understand the impact of the character limit change on the overall perception. We wanted to make sure we maintained our identity as a brief way to consume information. Our brand research found that people in the experiment continued to think of Twitter as a concise way to consume information. We also discovered a measurable drop in these same people citing the character limit as a reason for dissatisfaction. One advantage of working on a digital product like Twitter is that our jobs are never done. Another advantage is that people can tell us what they think of our changes simply . We’ll be watching how people use their newfound 280 characters, and seeing if there are areas we can improve. I’m looking forward to seeing and hearing how everyone is using this update. with a Tweet There were lots of people involved in this project, but I want to give a huge thanks to Kiyotoshi Yamauchi and Dandan Zhang for helping with the research on this project. Photo help from Aastha Bhargava and . Also, for more details on some of the more technical aspects of this project check out our great engineering Josh Silverman blog . posts

Looking Back at Doubling the Twitter Character Count - Looking After Number One-forty

TrustToken, the team responsible for launching TrueFi, the leading unsecured lending protocol and maker of popular stablecoins, including TrueUSD, announces the acquisition of EthWorks, a leading Web3 development company with a deep portfolio of successful blockchain projects. The acquisition doubles TrustToken’s team size quadruples the company’s technical unit and brings senior leadership to take TrustToken’s mission to the next level. Recognized as a significant Web3 engineering firm, has been a partner for over a year and contributed to developing many of the most widely adopted Layer 1 blockchains and DeFi protocols. EthWorks has worked with the Ethereum Foundation, Bitcoin.org, Maker DAO, Polkadot, Dharma, and many more. The EthWorks team will fully integrate into TrustToken’s staff and join the technical, design, and operational roadmap across all products and services, with a particular focus on scaling the TrueFi protocol. EthWorks TrustToken Expanding the Team In onboarding the EthWorks team, the TrustToken team is growing to over 100 members and significantly expands the company’s engineering, design, and cybersecurity capabilities. TrustToken plans to compress its development schedule, enhance user experience, and increase protocol security across various products and services. TrustToken will also maintain and expand EthWorks’ open-source projects, including Waffle and useDApp, used by Ethereum developers globally. EthWorks’ engineering and design leadership join TrustToken in senior positions. The senior leadership team will see Marek Kirejczyk, Founder and CEO of EthWorks, become Chief Technical Officer (CTO) of TrustToken. EthWorks’ Head of Design, Natalia Kirejczyk, takes on TrustToken’s VP of Design role, while EthWorks’ CTO Krzysztof Jelski will become TrustToken’s VP of Engineering. With this acquisition, Poland will become TrustToken’s primary engineering hub. What’s Next? It’s been quite a year for TrustToken. Recently, it added crypto credit scores to power reputation-based lending on the TrueFi platform. The decentralized protocol adds USDC and USDT support, creating new ways to lend and borrow, with significant starting incentives. In May, the launch of version 3 added to its existing method of assessing borrower creditworthiness with a sophisticated due diligence process, replacing the usual DeFi requirement for posted collateral. TrueFi now requires incoming borrowers to report details about company background, repayment history, operating and trading history, assets under management, and credit metrics like leverage and risk exposure. The platform has traditionally provided loans that are sizeable in nature, so coming up with a method that protects all parties is crucial to its success. “The credit and lending models are both the most difficult and most important pieces of TrueFi,” Michael Gasiorek, Head of Marketing at TrustToken, told me. “If designed well, on-chain unsecured lending has the potential to take a bite of an $11 trillion TradFi industry, but if designed poorly, borrowers and lenders alike are at risk. We started with a few assumptions: TrueFi would largely lend to institutions because they’re low risk and can take out large loans; it would collect deposits from anyone in any quantity, with an emphasis on exit liquidity on one’s deposits; and token holders of the native currency would serve many roles, from new borrower selection, to loan approvals, to assuring the loans are repaid (or suffer a 10% liquidation for approving a bad loan). So far, we’ve originated over $110 million in unsecured loans with no defaults - and our models keep on improving.” And in August, it raised $12.5 million to scale TrueFi, led by Blocktower with participation from Andreessen Horowitz (a16z) and Alameda Research through the purchase of TRU, TrueFi’s native token, under lockup. With the addition of new partners, the acquisition of EthWorks, a fresh infusion of capital, growing adoption, and an expanding team, TrustToken claims that TrueFi is on track to clear $1 billion in originations before 2022. This author will be watching with interest.

TrustToken acquires EthWorks, Doubling its Team to Scale TrueFi

For some time now I’ve been making an unassailable and unchallenged argument that Bitcoin and Bitcoin services are no different to the monies and services found in video games. Now, thanks to my favourite gamer, we have one of the best examples yet of “Bitcoin Doublethink”, second only to the very cute which uses a “Blockchain” for its database. Pigsby I’m talking about Warframe. Warframe Market is no different to any Bitcoin exchange. It has no legal relationship with the game maker, and simply organises and connects the users of the game into a viable market. Warframe is a website where people can post bids and asks for items in the popular game , which has almost registered players. Warframe Market is updated in real time, with new orders coming in at the top, pushing previous orders down. Warframe Market Warframe 50 million The order book for , with a different GUI arrangement but identical function to a Bitcoin exchange. Warframe Market This is identical to the function of Bitcoin exchanges, and is global in reach. The Warframe Market service is not authorised by Warframe; it is an entirely separate entity, just as Bitcoin exchanges are not authorised by Bitcoin, and are totally independent and separate from each other and Bitcoin, which is an opt in, peer to peer messaging platform owned by no one. Clicking on either “Want to Sell” or “Want to Buy” opens a link: users paste these links into Warframe, and the link sends a direct message to the poster of the message so the two users of the game can meet and make the trade “in person” inside the game-space. This is identical to Local Bitcoins, where people post orders and then arrange to meet in “the real world” ( ): meat-space /w ***USRER_NAME *** Hi! I want to buy: Arcane Grace for 110 platinum. (warframe.market) The knee-jerk objection that will be immediately be raised by the “Bitcoin is Money and must be regulated” advocates is that you can’t exchange fiat for Warframe’s “Platinum” money. Sadly for them, this is not true. Warframe sells its “Platinum” currency on its website that you must buy with fiat. Bear in mind that the prices for items in Warframe are set by the users, so you can send This means you are able to pay for with Platinum, by meat space arrangement. an arbitrary amount of Platinum to another user. anything Once again, the Pavlovian objection to this is to assert that “Warframe doesn’t use Blockchain”, but this objection is easily dismissed because the function of Warframe’s money, whether provided by a clone of Bitcoin or a Berkley DB, PostgreSQL, or MySQL database is . irrelevant What the user experiences is a money simulation ; how that simulation is performed is immaterial to the nature of the primary function of simulated monetary exchange . Claiming a Blockchain changes the nature of a math calculation would be like claiming eggs fried in a frying pan over a gas fire are different to eggs cooked on an electric stove or a camp fire. Bitcoin and Bitcoin clones are nothing more than databases used to keep records. They do not magically create a new form of something; they are novel database arrangements used to keep records, nothing more. The only thing missing from the Warframe Market example is a real-time chart showing the price for each Warframe item across many users who want to buy and sell. By the logic of the “Bitcoin is money” crowd, this would make “Platinum” like money, and would immediately require the urgent attention of the Securities and Exchange Commission, FinCEN and the IRS at a minimum. even more Bear in mind also, that no user of Warframe or the separate entity Warframe Market are required to KYC/AML, keep records or accounts, pay taxes on profits made in Platinum or anything else some jurisdictions demand of Bitcoin users and users of Bitcoin exchanges. No one has a logical answer as to why this is so. Argumentam Pennny Anteam The next predictable argument to be mounted is that the Warframe Market market is “small”, and “not worthy of attention”. The Warframe Market market is in fact, with 11,500 concurrent users trading Warframe items in real time. There is a lot of money trading hands on Warframe; why is this activity not regulated, and why don’t advocates for Bitcoin regulation call for the regulation of Warframe and other MMORPGs? People have been prosecuted for amounts as small as $30,000 in Bitcoin, and operators arranging contacts between users gaoled for life; surely the operators of this website are in violation? very large There is no difference between a score in a game held in a MySQL database and a score held on “a Blockchain”, which is nothing more than another style of database. But it gets much better… Now we have an example of a game that uses to keep score in the form of . This is , as I’m sure you’ve instantly detected. explicitly a Blockchain Pigzbe ; “A pocket money app that helps kids age 6+ become the money maestros of tomorrow” very problematic Has Pigzbe applied for a BitLicense for it to offer it’s product in New York, or is New York geoblocked, it’s token originating in Switzerland? It is based on Blockchain, so will it apply for a BitLicense, and if it has a reason other than geoblocking why not, what is that reason? Is Pigzbe KYC/AMLing the children who are using this “game” or the Mothers and Fathers of those children? In the USA you must be, “18 years or older to open an account in your name only. Minors cannot own an account in their name alone. You must be at least 14 years old to open a checking account. Legal guardian co-owner is required for those between 14 and 18 years”. Pigzbe is available for children from the age of 6 years old and up. Outrageous. Will the SEC/FinCEN investigate and forbid this application entirely until they’ve applied, and will they ban it because, “Children can’t be qualified investors allowed access to financial tools unsupervised”? Will the… Consumer Financial Protection Bureau (CFPB) Federal Deposit Insurance Corporation (FDIC) Federal Financial Institutions Examination Council (FFIEC) Federal Reserve (FRB) Federal Trade Commission (FTC) Internal Revenue Service (IRS) Office of the Comptroller of the Currency (OCC) Securities and Exchange Commission (SEC) or any of the the other agencies claiming Ultra Vires to control databases attempt to capture and supervise Pigzbe? Why not? No one has satisfactory answers to of these questions. any And remember; this Pigzbe app ; it is a self contained ecosystem where the Pigzbe “tokens” only have meaning and utility inside the service, just like Bitcoin only has meaning inside the block chain of its records. These are the plain statements of fact that you can’t ignore if you want to be entirely truthful and logical. is no different to Bitcoin And Pigzbe’s tokens are not available on exchanges…yet…and if they forbid their tokens from being listed, there is nothing to stop an independent Warframe Market style operation from springing up connecting Pigzbe users to each other the same way that Local Bitcoins does outside the Pigzbe app. No argument for control of Pigzbe would be complete without asserting that terrorists could use Pigzbe to keep accounts and move money around undetected. Why are Pigzbe, Rainbow Six Siege, and Warframe not taken seriously as threats to national security whereas Bitcoin is, and Bitcoin is smaller than Warframe and Rainbow Six and other similar simulations? Snipers on Target Identical to Pigzbe and Warframe, Rainbow Six Siege has its own in-game money called “Renown”. Rainbow Six has , all of whom are using the in game money as money. 1.1 billion in bookings and 45 million players Renown Renown is identical to Bitcoin; it is an in-game money that only has a context in “Rainbow Six Siege”. If the EU / USA makes a regulatory exemption for Pigzbe and Rainbow Six Seige and all video games, then they will be forced to explain their “thinking” in court unless Bitcoin services are bundled along with them. If they don’t accept the truth, and try to argue Bitcoin is different, they will lose any properly mounted legal challenge. “Renown” the money in R6S is indistinguishable from any score or in-game money or Bitcoin. There is historical precedent for large, thriving in game monies being totally unregulated; none of this should come as a surprise to anyone with an interest in Bitcoin. FarmVille had 83,760,000 monthly active users and was subjected to KYC/AML to exchange fiat for FarmBucks or FarmCash. Why not? What happened to that money? Why weren’t FinCEN or SEC all over that game as they are on ICOs? No one can explain this adequately. This example is very useful as a tool to pull back the curtain on the people who assert that Bitcoin is a money and is different to a money kept in a game. not a single one fundamentally As , and once again, unarguably, there is no logical reason to treat Bitcoin any differently to any other database where a score is kept, and certainly in the United States of America, where there is a written Constitution and guaranteed rights, Bitcoin regulation is un-Constitutional and inexcusable. I explain thoroughly Hoops When you play an arcade basketball hoop game and you get tickets that are redeemable for prizes, why isn't the arcade owner required to KYC/AML you? The tickets are being used as money redeemable for goods. If, instead of printing tickets, the game sent a token to your Hoops app, that was redeemable for goods, would that suddenly by magic turn the process into a money process? Of course not. This is the insanity that we’re hearing from the people who wrongly assert that “Blockchains” are money. And a Hoops app would be very useful. You could take your Hoops app to any arcade and use HoopCoins to buy soda and popcorn and play games, as well as win rewards directly in the app. Arcades would save money by not having to handle money and all sorts of other advantages, like selling advertisements and inducements to fun lovers. And Hoops users could send Hoops to each other to play games in any other context. All arcade owners could run a copy of the Hoops Client, and share the administrative burden of managing the HoopsCoins. It makes perfect sense on every level. And all of this does not require HoopsCoin to be classified as money to get all the efficiencies and marketing opportunities. Coupez Finally let’s look at Supermarket Coupons. About two billion supermarket coupons were redeemed in 2016, with the total value of coupons issued a whopping $406 billion, saving shoppers $3.6 billion. None of these coupons required the users to identify themselves; they simply presented them either at the checkout or online with a redeemable code, and traded them in for goods at a discount as a form money. The market for redeemable coupons is much larger than Bitcoin. nor keep track of which of their customers are using what token. They simply publish them and reap the benefits. There is no record of what company is using which sort of database to keep track of which coupon has been redeemed, and each coupon is unique, with a randomly generated serial number, just like Bitcoin. None of the issuers of these redeemable tokens are required to register with the SEC or FinCEN The Dcode plugin for Shopify. Totally Unregulated money creation out of the box. Every time you use a discount code on a website, you’re engaging in this same process; someone has issued you a redeemable code that you enter at the bottom of the shopping cart to receive “value”. These tokens are generated by the shopping cart software, can be sent anywhere and redeemed by anyone, and the services owners in fact encourage this to help them sell. None of this activity is regulated in any way. Supermarket Coupons are such a huge market there are specialised tools for removing them from magazine pages… Slice 00100 Ceramic Blade Safety Cutter https://www.amazon.com/Slice-00100-Ceramic-Safety-Cutter/dp/B002OOVC84 The facts of this speak for themselves, and have nothing to do with the utility of Bitcoin or any database. Bitcoin cannot be treated differently to other databases, and should not be subject to a special legal regime. It doesn’t matter how well meaning the advocates for Bitcoin regulation are; what they’re doing is wrong, damaging, borne of ignorance and unethical. If the advocates for Bitcoin regulation were to try their paternalistic, nanny-state, toxic nonsense on the gaming community of billions of users or supermarket owners or Apple, they would experience a push back that would flatten them flatter than a pancake. It’s only because the Bitcoin community is small, highly intellectual and that they’ve managed to get away with the threadbare, defective arguments and unarguable damage they’ve done so far. polite There will be a court challenge on this matter, and so far, the US courts have overwhelmingly favoured this correct argument wherever the rights of American citizens are asserted rgument: using the Constitution as the a Bitcoin is speech stored in a database, nothing more. Video game scores, Pay as You Go top-ups, Supermarket Coupons and in game currency examples going back decades will be a key element in the winning argument to kill the idea that Bitcoin requires special treatment in law. The people asserting that Bitcoin should be subject to regulation but Renown, Supermarket Coupons, Warframe’s “Platinum” and iTunes Gift Cards are wrong, they will be proven wrong and their arguments made unenforceable. Once this happens, then Bitcoin will have a clear run to exceed all of these markets and more, opening the way to many opportunities on a global scale, far bigger than any video game or the entire video game market, for fortunes in the billions to be made, and a total re-ordering of how money works both off and online. Were you stimulated by this article? Feel free to send me Bitcoin!

Bitcoin Doublethink.

Hi Guys , last article we spoke about addition , one of the most important “ ” operations on eliptic curve arithmetic . There’s been great feedback highlihghting that the rule might not be . invented “if a line crosses two points , it will cross a third point” absolute That’s is true , there’s a few exceptions and today I will try to explain one , what would happen if point and share the same position (x and y wise): P Q , well this is called “Point Doubling” also referred as adding a point to itself. So how do you add two points (P and Q ) that share the same location? This is nothing more than another invented operation and as we saw in EC addition it is represented by another mathematical identity , as well as a different “ ” equation. Slope Point Doubling Slope: To calculate the slope when we apply the following equation: P == Q s = (3 * Px ** 2 +a ) / (2 * Py) Slope in point doubling That will give you the value of the slope , remember that the slope plus some other domain parameters are curcial to then caculate addition or in this case point doubling. Point Doubling: Point doubling or “adding a point to itself” follows a very simple equation , and of course it requires the slope and the domain parameters that we’ve seen in previous articles. But basically it goes like this: Point Doubling in action RX = S ** 2 - 2 * PXRY = -1 * (PY + S * ( RX - PX)) And again the same catch , R becomes the negative reflection of R , so it looks something like: Reflection of R Conclusion: So point doubing is the second invented arithmetic operation we’ve talked about when it comes to Elliptic Curves , and some of the same rules apply than with addition: by knowing X you know Y R is the reflection of -R over the X axis Some gisted code so you can have a play: PS: Thanks for all the comments and questions

Elliptic Curve Crypto ,Point Doubling

Translating Business Needs into Technical Solutions

TOKYO, Japan, July 5th, 2024/Chainwire/--$DOP, the token of the Data Ownership Protocol(DOP) will be available for trading in 7 major cryptocurrency exchanges such as Bybit, Kucoin, HTX, Bitfinex, Gate.io, MEXC and WhiteBIT starting July 5th, 2024. The Data Ownership Protocol clarified that on Bybit, the token will be listed as $DOP1 to avoid confusion with an existing fiat coin. On KuCoin, Gate.io, Bitfinex, MEXC, and WhiteBIT, the token will be listed as $DOP. DOP launched its mainnet six weeks ago, following a highly successful testnet phase that saw participation from 2.67 million users. In the last six months, the DOP ecosystem has grown significantly. Over 1 million DOP wallets have been opened, and assets worth more than 10 million USD have been encrypted using the protocol, showcasing the increasing demand for user-centric data ownership solutions. “$DOP is the token of Data Ownership Protocol, a project that seeks to balance transparency and privacy on the blockchain, putting users in control of their data. The listing of our utility token marks a significant milestone and enhances our vision for the future of data ownership. We are thankful to our supporters and community who have believed in our mission from day one”, said Kohji Hirokado, co-founder of DOP and ex-core member at Cardano. Currently, the $DOP token serves two primary functions within the ecosystem: facilitating fee payments and granting community rewards. The usage of the protocol's data ownership features incurs fees payable in DOP tokens, creating a native demand for the token. This system incentivizes DOP token retention while implementing a deflationary mechanism through fee burning. It also rewards network supporters via staking distributions. To date, 210,000,000 DOP tokens have been allocated for staking rewards, with over 1.1 billion DOP tokens already staked. The listing announcement comes on the heels of Bybit's positioning as the world's second-largest cryptocurrency exchange by trading volume, surpassing Coinbase and trailing only Binance. Bybit's ascent has been remarkable, with its market share doubling from 8% to 16% since October 2023. To communicate the plans for the second half of 2024, DOP released a new roadmap following Q1's success. The plan introduces a $5 million developer grant program, expands protocol capabilities, and plans deployment on EVM-compatible chains beyond Ethereum, reducing gas fees and reaching more users. Moreover, the new developer SDK will enable developers to easily integrate and build dApps within the DOP ecosystem. Other objectives aim to enhance user security for managing NFTs by expanding functionality beyond ERC-20 tokens to include encryption and decryption of NFTs. About Data Ownership Protocol (DOP) The Data Ownership Protocol enables users to own their data. In crypto, financial data such as holdings, balances, and transaction history is publicly available on the blockchain. The mission of DOP technology is to let users decide what to share and with whom. DOP aims to empower individuals and businesses with more control over their data through selective transparency, utilizing zero-knowledge cryptography and other advanced technologies. For more information, users can visit Data Ownership Protocol's: Official Website | Twitter | Linkedin Data Ownership Protocol is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact Marketing DOP marketing@dop.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. TOKYO, Japan, July 5th, 2024/Chainwire/-- $DOP, the token of the Data Ownership Protocol(DOP) will be available for trading in 7 major cryptocurrency exchanges such as Bybit, Kucoin, HTX, Bitfinex, Gate.io, MEXC and WhiteBIT starting July 5th, 2024. TOKYO, Japan, July 5th, 2024/Chainwire/-- The Data Ownership Protocol clarified that on Bybit, the token will be listed as $DOP1 to avoid confusion with an existing fiat coin. On KuCoin, Gate.io, Bitfinex, MEXC, and WhiteBIT, the token will be listed as $DOP. DOP launched its mainnet six weeks ago, following a highly successful testnet phase that saw participation from 2.67 million users. In the last six months, the DOP ecosystem has grown significantly. Over 1 million DOP wallets have been opened, and assets worth more than 10 million USD have been encrypted using the protocol, showcasing the increasing demand for user-centric data ownership solutions. “$DOP is the token of Data Ownership Protocol, a project that seeks to balance transparency and privacy on the blockchain, putting users in control of their data. The listing of our utility token marks a significant milestone and enhances our vision for the future of data ownership. We are thankful to our supporters and community who have believed in our mission from day one”, said Kohji Hirokado, co-founder of DOP and ex-core member at Cardano. “$DOP is the token of Data Ownership Protocol, a project that seeks to balance transparency and privacy on the blockchain, putting users in control of their data. The listing of our utility token marks a significant milestone and enhances our vision for the future of data ownership. We are thankful to our supporters and community who have believed in our mission from day one”, said Kohji Hirokado, co-founder of DOP and ex-core member at Cardano. Currently, the $DOP token serves two primary functions within the ecosystem: facilitating fee payments and granting community rewards. The usage of the protocol's data ownership features incurs fees payable in DOP tokens, creating a native demand for the token. This system incentivizes DOP token retention while implementing a deflationary mechanism through fee burning. It also rewards network supporters via staking distributions. To date, 210,000,000 DOP tokens have been allocated for staking rewards, with over 1.1 billion DOP tokens already staked. The listing announcement comes on the heels of Bybit's positioning as the world's second-largest cryptocurrency exchange by trading volume, surpassing Coinbase and trailing only Binance. Bybit's ascent has been remarkable, with its market share doubling from 8% to 16% since October 2023. To communicate the plans for the second half of 2024, DOP released a new roadmap following Q1's success. The plan introduces a $5 million developer grant program, expands protocol capabilities, and plans deployment on EVM-compatible chains beyond Ethereum, reducing gas fees and reaching more users. Moreover, the new developer SDK will enable developers to easily integrate and build dApps within the DOP ecosystem. Other objectives aim to enhance user security for managing NFTs by expanding functionality beyond ERC-20 tokens to include encryption and decryption of NFTs. About Data Ownership Protocol (DOP) The Data Ownership Protocol enables users to own their data. In crypto, financial data such as holdings, balances, and transaction history is publicly available on the blockchain. The mission of DOP technology is to let users decide what to share and with whom. DOP aims to empower individuals and businesses with more control over their data through selective transparency, utilizing zero-knowledge cryptography and other advanced technologies. For more information, users can visit Data Ownership Protocol's: Official Website | Twitter | Linkedin Official Website Official Website Twitter Twitter Linkedin Linkedin Data Ownership Protocol is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact Marketing DOP marketing@dop.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

$DOP Announces Listing On 7 Exchanges Including BYBIT, Kucoin, Gate.io, And Bitfinex

**ROAD TOWN, Tortola, British Virgin Islands, June 25th, 2024/GamingWire/--**Cryptopia, has plenty to celebrate this month thanks to the crypto community’s phenomenal response to its Airdrop. Not only did the campaign surpass the team's expectations with over 50,000 participants completing an impressive 137,000 missions, but it also helped Cryptopia secure exchanges and Tier 1 Launchpads. Cryptopia understands that more people means a diluted prize pool. To show Cryptopia gratitude for its growing community’s overwhelming support, Cryptopia is thrilled to announce a doubling of the Airdrop allocation, pumping it up from a cool 25 million $TOS to a whopping 50 million $TOS. This translates to a prize pool value jump from $87,500 to $175,000 USD. Even with new recruits joining in on the fun, this ensures every Airdrop participant gets their fair share of the bounty. Enhanced Referral Rewards Cryptopia continues to emphasize community growth. Currently, members earn Power Points through direct and indirect referrals. To further incentivize participation, Cryptopia will introduce new milestones to enhance referral rewards. Detailed information on these updates will be shared on Cryptopia’s social media platforms in the coming weeks. Recognition for Beta Testers Acknowledging the efforts of its beta testers, Cryptopia has allocated 4% of the Airdrop pool, approximately $7,000, exclusively for these contributors. Summary of Airdrop Enhancements Increased Rewards: The prize pool now stands at 50 million $TOS, equating to $175,000 USD.


New Referral Milestones: Additional opportunities to earn enhanced referral rewards.


Beta Tester Appreciation: A dedicated 4% of the Airdrop pool reserved for beta testers. Cryptopia invites participation in its Airdrop welcoming new members to its expanding community. For updates on the game and airdrop details, users follow Cryptopia on X, Facebook, Telegram, and the announcements Telegram channel. About Cryptopia Free to earn: Accessible to all players without the need for prior NFT purchases. 100% decentralized: Operates on the blockchain with a node network. Engaging gameplay: Offers flexible play options. Official Links: Website | Wiki | Discord | Facebook | X | YouTube | Instagram | CoinMarketCap | Twitch | Spotify | Telegram Community | Telegram Announcements | For Investors: Early-access NFTs | Cryptopia Roadmap Contact Co-Founder Hans Leek Cryptopia Limited info@cryptopia.com This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **ROAD TOWN, Tortola, British Virgin Islands, June 25th, 2024/GamingWire/--**Cryptopia, has plenty to celebrate this month thanks to the crypto community’s phenomenal response to its Airdrop. Not only did the campaign surpass the team's expectations with over 50,000 participants completing an impressive 137,000 missions, but it also helped Cryptopia secure exchanges and Tier 1 Launchpads. Cryptopia understands that more people means a diluted prize pool. To show Cryptopia gratitude for its growing community’s overwhelming support, Cryptopia is thrilled to announce a doubling of the Airdrop allocation, pumping it up from a cool 25 million $TOS to a whopping 50 million $TOS. This translates to a prize pool value jump from $87,500 to $175,000 USD. Even with new recruits joining in on the fun, this ensures every Airdrop participant gets their fair share of the bounty. Enhanced Referral Rewards Cryptopia continues to emphasize community growth. Currently, members earn Power Points through direct and indirect referrals. To further incentivize participation, Cryptopia will introduce new milestones to enhance referral rewards. Detailed information on these updates will be shared on Cryptopia’s social media platforms in the coming weeks. Recognition for Beta Testers Acknowledging the efforts of its beta testers, Cryptopia has allocated 4% of the Airdrop pool, approximately $7,000, exclusively for these contributors. Summary of Airdrop Enhancements Increased Rewards: The prize pool now stands at 50 million $TOS, equating to $175,000 USD. New Referral Milestones: Additional opportunities to earn enhanced referral rewards. Beta Tester Appreciation: A dedicated 4% of the Airdrop pool reserved for beta testers. Increased Rewards: The prize pool now stands at 50 million $TOS, equating to $175,000 USD. Increased Rewards: The prize pool now stands at 50 million $TOS, equating to $175,000 USD. New Referral Milestones: Additional opportunities to earn enhanced referral rewards. New Referral Milestones: Additional opportunities to earn enhanced referral rewards. Beta Tester Appreciation: A dedicated 4% of the Airdrop pool reserved for beta testers. Beta Tester Appreciation: A dedicated 4% of the Airdrop pool reserved for beta testers. Cryptopia invites participation in its Airdrop welcoming new members to its expanding community. For updates on the game and airdrop details, users follow Cryptopia on X , Facebook , Telegram , and the announcements Telegram channel. Airdrop Airdrop X X Facebook Facebook Telegram Telegram announcements Telegram announcements Telegram About Cryptopia Free to earn: Accessible to all players without the need for prior NFT purchases. 100% decentralized: Operates on the blockchain with a node network. Engaging gameplay: Offers flexible play options. Official Links: Website | Wiki | Discord | Facebook | X | YouTube | Instagram | CoinMarketCap | Twitch | Spotify | Telegram Community | Telegram Announcements | Website Website Wiki Wiki Discord Discord Facebook Facebook X X YouTube YouTube Instagram Instagram CoinMarketCap CoinMarketCap Twitch Twitch Spotify Spotify Telegram Community Telegram Community Telegram Announcements Telegram Announcements For Investors: Early-access NFTs | Cryptopia Roadmap Early-access NFTs Early-access NFTs Cryptopia Roadmap Cryptopia Roadmap Contact Co-Founder Hans Leek Cryptopia Limited info@cryptopia.com This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Cryptopia Announces Increased Airdrop Allocation And Beta Tester Rewards

**SINGAPORE, Singapore, June 19th, 2024/Chainwire/--**SynFutures, the leading DeFi derivatives protocol, today announced its plans to expand to Base, Coinbase’s L2 network. To mark this milestone, SynFutures is launching "Meme Perp Summer," a six-week campaign blitz aimed at expanding its community asset listings and rewarding the meme communities driving organic traction on Base. With a total rewards pool of 100,000 USDC and future airdrop allocations, this campaign underscores Synfutures’ commitment to fostering the growth of emerging token projects. "We are incredibly excited about our expansion to Base. This move not only reinforces our commitment to providing users with unique trading opportunities but also reflects our dedication to supporting and growing vibrant community-driven projects," said Rachel Lin, Co-founder and CEO of SynFutures. This expansion to Base is a key part of SynFutures’ multichain strategy for V3. Having seen immense success since launching V3 on Blast, where the protocol consistently ranked as the number one DeFi derivatives protocol by trading volume, SynFutures is now bringing its onchain orderbook for perps to Base. In just four months on Blast, the protocol has achieved nearly $100 billion in cumulative volume, with over 4 million transactions and 142,000 on-chain users. SynFutures will continue to support Blast while extending its reach, doubling down on its commitment to being the go-to destination for trading trending tokens, including memecoins and altcoins. V3 has already supported perp pairs for trending tokens like PAC, DEGEN, YES, WIF, and ESE on Blast, and is now bringing this strategy to Base. Memecoin Perp Summer Running from June 18 to July 29, 2024, Meme Perp Summer aims to spotlight and reward emerging token projects that drive authentic traction and engagement. Selected projects can earn up to 10,000 USDC from the reward pool, to be used for purchasing the project's token from their treasury or designated DEX and listing on SynFutures. Meme trading pairs will be listed against WETH or USDC, offering additional trading opportunities and liquidity. Timeline: June 18, 2024 - July 29, 2024
Rewards Pool: 100,000 USDC plus future designated airdrop allocations Key Dates: June 18: Campaign launch and token project sign-up begins
June 25: Deadline for token project sign-ups and announcement of eligible projects and reward allocations
June 26: Eligible token trading pairs listing on SynFutures with one-day exclusive promotion on our social media channels
July 29: Tentative end date for the Meme Perp Summer Campaign Eligibility and Participation: Token projects interested in participating must have an ERC20 token on Base, an active and engaged community, and a proven track record of driving traction without the intent of rug-pull. Key requirements include: 5,000 followers on social media
$100K trading volume in the last 24 hours
Regular and active social media engagement and events
Trading pairs listed on Uniswap v2
Willingness to co-market with SynFutures Token projects interested in participating in Meme Perp Summer should send a direct message to SynFutures’ official X account, @SynFuturesDeFi. A SynFutures team member will reply with the registration form. The sign-up process will require project details, community metrics, and a commitment to meet the participation requirements. About SynFutures SynFutures is a decentralized perpetual futures protocol that facilitates open and transparent trading on any assets and listings instantly. The V3 Oyster AMM launched the industry’s first-ever unified AMM and onchain order book model. Backers include Tier 1 Web3 institutional investors Pantera Capital, Polychain Capital, Susquehanna International Group (SIG), Dragonfly Capital, Standard Crypto, and Framework Ventures, and the team has extensive experience at global financial institutions, fintech companies and blockchain technology companies such as Alipay, Bitmain, Credit Suisse, Deutsche Bank, Matrixport, and Nomura Securities. For more information, users can visit SynFutures: Website | Twitter | Warpcast Contact CMO Mark Lee SynFutures mark.lee@synfutures.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SINGAPORE, Singapore, June 19th, 2024/Chainwire/--**SynFutures, the leading DeFi derivatives protocol, today announced its plans to expand to Base, Coinbase’s L2 network. To mark this milestone, SynFutures is launching "Meme Perp Summer," a six-week campaign blitz aimed at expanding its community asset listings and rewarding the meme communities driving organic traction on Base. With a total rewards pool of 100,000 USDC and future airdrop allocations, this campaign underscores Synfutures’ commitment to fostering the growth of emerging token projects. "We are incredibly excited about our expansion to Base. This move not only reinforces our commitment to providing users with unique trading opportunities but also reflects our dedication to supporting and growing vibrant community-driven projects," said Rachel Lin, Co-founder and CEO of SynFutures. "We are incredibly excited about our expansion to Base. This move not only reinforces our commitment to providing users with unique trading opportunities but also reflects our dedication to supporting and growing vibrant community-driven projects," said Rachel Lin, Co-founder and CEO of SynFutures. This expansion to Base is a key part of SynFutures’ multichain strategy for V3. Having seen immense success since launching V3 on Blast, where the protocol consistently ranked as the number one DeFi derivatives protocol by trading volume, SynFutures is now bringing its onchain orderbook for perps to Base. In just four months on Blast, the protocol has achieved nearly $100 billion in cumulative volume, with over 4 million transactions and 142,000 on-chain users. SynFutures will continue to support Blast while extending its reach, doubling down on its commitment to being the go-to destination for trading trending tokens, including memecoins and altcoins. V3 has already supported perp pairs for trending tokens like PAC, DEGEN, YES, WIF, and ESE on Blast, and is now bringing this strategy to Base. Memecoin Perp Summer Running from June 18 to July 29, 2024, Meme Perp Summer aims to spotlight and reward emerging token projects that drive authentic traction and engagement. Selected projects can earn up to 10,000 USDC from the reward pool, to be used for purchasing the project's token from their treasury or designated DEX and listing on SynFutures. Meme trading pairs will be listed against WETH or USDC, offering additional trading opportunities and liquidity. Timeline: June 18, 2024 - July 29, 2024 Rewards Pool: 100,000 USDC plus future designated airdrop allocations Timeline: June 18, 2024 - July 29, 2024 Rewards Pool: 100,000 USDC plus future designated airdrop allocations Key Dates: June 18: Campaign launch and token project sign-up begins June 25: Deadline for token project sign-ups and announcement of eligible projects and reward allocations June 26: Eligible token trading pairs listing on SynFutures with one-day exclusive promotion on our social media channels July 29: Tentative end date for the Meme Perp Summer Campaign June 18: Campaign launch and token project sign-up begins June 25: Deadline for token project sign-ups and announcement of eligible projects and reward allocations June 26: Eligible token trading pairs listing on SynFutures with one-day exclusive promotion on our social media channels July 29: Tentative end date for the Meme Perp Summer Campaign Eligibility and Participation: Token projects interested in participating must have an ERC20 token on Base, an active and engaged community, and a proven track record of driving traction without the intent of rug-pull. Key requirements include: 5,000 followers on social media $100K trading volume in the last 24 hours Regular and active social media engagement and events Trading pairs listed on Uniswap v2 Willingness to co-market with SynFutures 5,000 followers on social media $100K trading volume in the last 24 hours Regular and active social media engagement and events Trading pairs listed on Uniswap v2 Willingness to co-market with SynFutures Token projects interested in participating in Meme Perp Summer should send a direct message to SynFutures’ official X account, @SynFuturesDeFi. A SynFutures team member will reply with the registration form. The sign-up process will require project details, community metrics, and a commitment to meet the participation requirements. About SynFutures SynFutures is a decentralized perpetual futures protocol that facilitates open and transparent trading on any assets and listings instantly. The V3 Oyster AMM launched the industry’s first-ever unified AMM and onchain order book model. SynFutures SynFutures Backers include Tier 1 Web3 institutional investors Pantera Capital, Polychain Capital, Susquehanna International Group (SIG), Dragonfly Capital, Standard Crypto, and Framework Ventures, and the team has extensive experience at global financial institutions, fintech companies and blockchain technology companies such as Alipay, Bitmain, Credit Suisse, Deutsche Bank, Matrixport, and Nomura Securities. For more information, users can visit SynFutures: Website | Twitter | Warpcast Website Website Twitter Twitter Warpcast Warpcast Contact CMO Mark Lee SynFutures mark.lee@synfutures.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

SynFutures To Expand Its Perp Markets To Base with New Memecoin Initiative

Artificial Intelligence has been advancing at an unprecedented rate, causing fear about whether AI will dominate various aspects of our lives in the near future and “take our jobs.” In this article, by leveraging Moore's Law and other technological growth principles, I’m going to make an educated estimation about the state of AI in the coming years. Let’s explore these predictions and explain why AI may not completely take over within the next five years, and what the timeline might look like for AI to reach a truly advanced state. The Current State of AI AI technology has made significant strides in recent years. From natural language processing to computer vision, AI applications are becoming increasingly sophisticated, whether that’s the recent GPT-4o model, Google’s Gemini, or any one of the models in the race to become the most capable AI model. There are lots of people using these models in positive ways to unlock new methodologies in research and education, but there are also lots of people using these technologies for evil such as deepfakes. However, despite these advancements, there are still considerable challenges and limitations that need to be addressed. Current AI systems need massive amounts of data, computational power, and energy to function effectively. Moore's Law and Its Implications for AI Moore's Law, the observation that the number of transistors on a microchip doubles approximately every two years, has been a reliable predictor of technological progress for decades. More generally, Moore’s Law points to technology getting twice as accessible, cheaper, and efficient every two years. According to Moore's Law, we can expect computational power to continue increasing exponentially. However, there are physical and economic limits to this growth that may impact the pace of AI development. Estimating AI's Growth Using Known Laws By extrapolating from Moore's Law and considering other technological growth trends, we can make predictions about AI's capabilities in the coming years. Computational Power If we consider top-of-the-notch GPUs today, let’s say NVIDIA A100, it provides approximately 312 teraflops (TFLOPS) of performance. Moore's Law suggests a doubling of transistors every two years, which implies a potential 8-fold increase in computational power over five years (2^2.5 ≈ 5.66). Future GPU Performance = 312 TFLOPS × 5.66 ≈ 1766 TFLOPS However, the physical limitations of silicon and the economic costs of producing ever-smaller transistors could slow this growth. The increase might realistically be closer to 4 times due to these constraints: Realistic Future GPU Performance=312 TFLOPS×4≈1248 TFLOPS Data Availability and Quality According to projections by IDC (International Data Corporation), the size of the global datasphere will increase from 64.2 zettabytes in 2020 to 175 zettabytes by 2025. It is essential for the training of increasingly complex AI models that data is growing exponentially. But there are still a lot of obstacles to overcome, including concerns about data privacy, data quality, and effectively labeling and processing this data.The availability of high-quality, labeled data is critical, even with an increase in data output. Preprocessing and data labeling are obstacles that will not scale linearly with data increase, according to current trends. Energy Consumption AI training models like GPT-3 require substantial energy. GPT-3 used approximately 1,287 MWh to train. GPT-4, developed approximately 3 years after its predecessor required over 50 gigawatt-hours, so around 50 times the amount it took to train GPT-3. Assuming AI models become more complex and require more data and computational power, energy consumption could grow significantly. If we estimate a similar 50x increase or a 5000% increase in complexity to around 2500 gigawatt-hours for the next model that could be released in the next 5 years, this would require large improvements in energy production and efficiency. Given current energy efficiency improvements of around 10% per year (source: Lawrence Livermore National Laboratory), this only translates to approximately a 60% increase in energy efficiencies over a 5-year period if the energy production technologies don’t improve significantly. The Need for Increased Complexity in AI Models Current AI models struggle with many major issues today. Lack of Up-to-date Information: Current models are trained on static datasets and do not have real-time access to the internet for the most up-to-date information. Hallucinations: AI models sometimes generate information that is plausible-sounding but incorrect or nonsensical. Understanding Context and Nuance: Handling nuanced human language, context, and intent remains a challenge for AI. To address these issues, future AI models will need to incorporate: Real-Time Data Integration: Access to and processing of real-time data from the internet to provide up-to-date responses. Improved Model Architectures: Enhancements in model architectures to reduce hallucinations and improve the reliability of generated content. Contextual Understanding: Advanced natural language processing techniques to better understand and maintain context over long conversations. Increased Model Complexity Solving these problems will significantly increase the complexity of AI models. For example: Real-Time Data Integration: Requires constant data updates and real-time processing capabilities.
Improved Model Architectures: Likely to involve larger and more complex models with more parameters.
Contextual Understanding: Demands more sophisticated training algorithms and potentially longer training times. Let's assume each of these improvements independently doubles the complexity of the AI model. If we start with the current complexity (C), integrating these advancements might lead to: Future Complexity=C×2(real-time data)×2(improved architectures)×2(contextual understanding)=C×2^3 =C×8 Let’s estimate a 5x increase in complexity as a more conservative yet substantial figure. Data Quality and Ethics AI systems depend heavily on high-quality, unbiased data for training and decision-making. While the quantity of data is increasing rapidly, the quality and ethical considerations surrounding this data are equally crucial. Data Generation and Processing With the increase from 64.2 to 175 zettabytes, data generation will likely outpace the current capabilities of processing and labeling. Ensuring data quality and addressing biases require significant resources. Let's consider the human labor and computational resources needed for data labeling. Assuming 1 hour of human labor is needed to label 1 GB of data, labeling 175 zettabytes would require: 175 ZB×1 hour/GB×10^9  GB/ZB =175×10^9  hours This is not feasible with current resources, indicating a substantial gap in data quality and ethical processing. Conclusion: When Will AI Be in a Truly Advanced State In the Next, Say 5 Years? The idea that AI will "take over" in the next five years seems improbable, even if it will surely continue to advance and become more deeply integrated into many industries. There are several obstacles to overcome, including issues with energy usage, data quality, and processing power limitations. AI will not be able to evolve as fast without significant advancements in energy efficiency, data ethics, and technology.It seems more reasonable to anticipate that AI will become truly advanced within the next ten to fifteen years, based on existing patterns and reasonable development estimates. This prolonged period enables the development and successful implementation of essential ethical, technological, and resource-based breakthroughs. Sources and References: Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8), 114-117.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Markoff, J. (2015). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. HarperCollins.
IDC. (2020). Global DataSphere Forecast 2021-2025.
Lawrence Livermore National Laboratory. (2021). Energy Efficiency Trends in Computing. Artificial Intelligence has been advancing at an unprecedented rate, causing fear about whether AI will dominate various aspects of our lives in the near future and “take our jobs.” In this article, by leveraging Moore's Law and other technological growth principles, I’m going to make an educated estimation about the state of AI in the coming years. Let’s explore these predictions and explain why AI may not completely take over within the next five years, and what the timeline might look like for AI to reach a truly advanced state. The Current State of AI AI technology has made significant strides in recent years. From natural language processing to computer vision, AI applications are becoming increasingly sophisticated, whether that’s the recent GPT-4o model, Google’s Gemini, or any one of the models in the race to become the most capable AI model. There are lots of people using these models in positive ways to unlock new methodologies in research and education, but there are also lots of people using these technologies for evil such as deepfakes. However, despite these advancements, there are still considerable challenges and limitations that need to be addressed. Current AI systems need massive amounts of data, computational power, and energy to function effectively. Moore's Law and Its Implications for AI Moore's Law , the observation that the number of transistors on a microchip doubles approximately every two years, has been a reliable predictor of technological progress for decades. More generally, Moore’s Law points to technology getting twice as accessible, cheaper, and efficient every two years. According to Moore's Law, we can expect computational power to continue increasing exponentially. Moore's Law However, there are physical and economic limits to this growth that may impact the pace of AI development. Estimating AI's Growth Using Known Laws By extrapolating from Moore's Law and considering other technological growth trends, we can make predictions about AI's capabilities in the coming years. Computational Power If we consider top-of-the-notch GPUs today, let’s say NVIDIA A100, it provides approximately 312 teraflops (TFLOPS) of performance. Moore's Law suggests a doubling of transistors every two years, which implies a potential 8-fold increase in computational power over five years (2^2.5 ≈ 5.66). Future GPU Performance = 312 TFLOPS × 5.66 ≈ 1766 TFLOPS However, the physical limitations of silicon and the economic costs of producing ever-smaller transistors could slow this growth. The increase might realistically be closer to 4 times due to these constraints: Realistic Future GPU Performance=312 TFLOPS×4≈1248 TFLOPS Data Availability and Quality According to projections by IDC (International Data Corporation), the size of the global datasphere will increase from 64.2 zettabytes in 2020 to 175 zettabytes by 2025. It is essential for the training of increasingly complex AI models that data is growing exponentially. But there are still a lot of obstacles to overcome, including concerns about data privacy, data quality, and effectively labeling and processing this data. The availability of high-quality, labeled data is critical, even with an increase in data output. Preprocessing and data labeling are obstacles that will not scale linearly with data increase, according to current trends. Energy Consumption AI training models like GPT-3 require substantial energy. GPT-3 used approximately 1,287 MWh to train. GPT-4, developed approximately 3 years after its predecessor required over 50 gigawatt-hours , so around 50 times the amount it took to train GPT-3. Assuming AI models become more complex and require more data and computational power, energy consumption could grow significantly. 50 gigawatt-hours If we estimate a similar 50x increase or a 5000% increase in complexity to around 2500 gigawatt-hours for the next model that could be released in the next 5 years, this would require large improvements in energy production and efficiency. 2500 gigawatt-hours Given current energy efficiency improvements of around 10% per year (source: Lawrence Livermore National Laboratory), this only translates to approximately a 60% increase in energy efficiencies over a 5-year period if the energy production technologies don’t improve significantly. The Need for Increased Complexity in AI Models Current AI models struggle with many major issues today. Lack of Up-to-date Information: Current models are trained on static datasets and do not have real-time access to the internet for the most up-to-date information. Lack of Up-to-date Information: Hallucinations: AI models sometimes generate information that is plausible-sounding but incorrect or nonsensical. Hallucinations: Understanding Context and Nuance: Handling nuanced human language, context, and intent remains a challenge for AI. Understanding Context and Nuance: To address these issues, future AI models will need to incorporate: Real-Time Data Integration: Access to and processing of real-time data from the internet to provide up-to-date responses. Real-Time Data Integration: Access to and processing of real-time data from the internet to provide up-to-date responses. Real-Time Data Integration: Improved Model Architectures: Enhancements in model architectures to reduce hallucinations and improve the reliability of generated content. Improved Model Architectures: Enhancements in model architectures to reduce hallucinations and improve the reliability of generated content. Improved Model Architectures: Contextual Understanding: Advanced natural language processing techniques to better understand and maintain context over long conversations. Contextual Understanding: Advanced natural language processing techniques to better understand and maintain context over long conversations. Contextual Understanding: Increased Model Complexity Increased Model Complexity Solving these problems will significantly increase the complexity of AI models. For example: Real-Time Data Integration: Requires constant data updates and real-time processing capabilities. Improved Model Architectures: Likely to involve larger and more complex models with more parameters. Contextual Understanding: Demands more sophisticated training algorithms and potentially longer training times. Real-Time Data Integration: Requires constant data updates and real-time processing capabilities. Real-Time Data Integration: Improved Model Architectures: Likely to involve larger and more complex models with more parameters. Improved Model Architectures: Contextual Understanding: Demands more sophisticated training algorithms and potentially longer training times. Contextual Understanding: Let's assume each of these improvements independently doubles the complexity of the AI model. If we start with the current complexity (C), integrating these advancements might lead to: Future Complexity=C×2(real-time data)×2(improved architectures)×2(contextual understanding)=C×2^3 =C×8 Future Complexity=C×2(real-time data)×2(improved architectures)×2(contextual understanding)=C×2^3 =C×8 Let’s estimate a 5x increase in complexity as a more conservative yet substantial figure. Let’s estimate a 5x increase in complexity as a more conservative yet substantial figure. Data Quality and Ethics AI systems depend heavily on high-quality, unbiased data for training and decision-making. While the quantity of data is increasing rapidly, the quality and ethical considerations surrounding this data are equally crucial. Data Generation and Processing With the increase from 64.2 to 175 zettabytes, data generation will likely outpace the current capabilities of processing and labeling. Ensuring data quality and addressing biases require significant resources. Let's consider the human labor and computational resources needed for data labeling. Assuming 1 hour of human labor is needed to label 1 GB of data, labeling 175 zettabytes would require: 175 ZB×1 hour/GB×10^9  GB/ZB =175×10^9  hours 175 ZB×1 hour/GB×10^9  GB/ZB = 175×10^9  hours 175×10^9  hours This is not feasible with current resources, indicating a substantial gap in data quality and ethical processing. Conclusion: When Will AI Be in a Truly Advanced State In the Next, Say 5 Years? The idea that AI will "take over" in the next five years seems improbable, even if it will surely continue to advance and become more deeply integrated into many industries. There are several obstacles to overcome, including issues with energy usage, data quality, and processing power limitations. AI will not be able to evolve as fast without significant advancements in energy efficiency, data ethics, and technology. It seems more reasonable to anticipate that AI will become truly advanced within the next ten to fifteen years, based on existing patterns and reasonable development estimates. This prolonged period enables the development and successful implementation of essential ethical, technological, and resource-based breakthroughs. Sources and References : Sources and References Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8), 114-117. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Markoff, J. (2015). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. HarperCollins. IDC. (2020). Global DataSphere Forecast 2021-2025. Lawrence Livermore National Laboratory. (2021). Energy Efficiency Trends in Computing. Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics , 38(8), 114-117. Electronics Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning . MIT Press. Deep Learning Markoff, J. (2015). Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. HarperCollins . HarperCollins IDC. (2020). Global DataSphere Forecast 2021-2025. Lawrence Livermore National Laboratory. (2021). Energy Efficiency Trends in Computing.

Predicting the Future of AI With Moore's Law: When Will It Really Take Over?

Nvidia has become successful. So successful, in fact, that it does not care about alienating an entire demographic that was core to its success. This was underscored at the recent Computex event in Taiwan where the CEOs of three most prominent silicon companies (Nvidia's included) took to the stage to discuss new products and their future road map. And if you're a gamer, boy, was it depressing. Nvidia CEO Jensen Huang's two-hour long presentation was only artificial intelligence focused, to the point that it got boring and irked many who were hoping to learn more about the company's gaming-focused hardware. As TechRadar's John Loeffler put it, Huang dismissed the very same GeForce graphic cards that put the company on the map all those decades ago. This was corroborated by Hardware Unboxed's Steve Walton, who described the keynote address' overall vibe as one where Nvidia wanted to distance itself from the "silliness of gaming" and be taken seriously as a data-center type company instead. So why the shift? Nvidia's most recent quarterly results showed that the data center segment, which sells the hardware powering the world's generative AI tools, made roughly nine times more money than the gaming division ($22.6 billion vs. $2.6 billion). In fact, the data center segment contributed 86% towards the company's record revenue ($26.04 billion) this past quarter, compared to a measly 10% by the gaming segment. So Huang's just doubling down on what's working for the business. What's quite bothersome is that because Nvidia is the market leader,  Intel and AMD have no choice but to follow the trends set by Team Green — so of course, both companies also spent a considerable amount of time talking about their plans for profiting off of that sweet, sweet AI pie. But not without whetting the appetites of eager gamers with some product announcements. Despite holding next to no market share in the GPU market, Intel isn't giving up on the hardware that powers video games and AI workloads. After weeks of leaks and speculation, the company finally gave its first glimpse of the architecture that would power its next generation of graphic cards. Codenamed Battlemage, the upcoming graphic cards are designed to be more efficient than the earlier Alchemist variants, which, "if realized, [..] would rectify a major usability shortcoming of Alchemist," GamersNexus wrote in a blog post. For those that did not follow Intel's attempts at creating discrete GPUs, the Alchemist cards were plagued with driver issues that made them a hard sell for reviewers. The only reason the likes of Linus Tech Tips recommended the card was to encourage competition in the space. The Alchemist cards fare much better today than when they launched — thanks to driver-side improvements from Intel — but are still a hard sell. Which brings us to AMD. Team Red is the underdog in both the GPU and CPU markets, losing out to Nvidia on graphic cards, and Intel on central processors (though that seems to be changing). Competing on both fronts is obviously not easy (or cheap!), but the company has seen a massive turn around under the leadership of Lisa Su — going from holding 1.4% of the CPU market share in 2017 to a respectable 23.6% at the end of 2022. The company also holds 12% of the GPU market, which might not sound like a lot, but does offer a bit of competition to Nvidia which dominates the market. So, what is Team Red doing to be more of a contender in the market? For starters, the company announced its next line of processors at Computex. The AMD Ryzen 9000-series chips are supposedly going to be 15% faster than their predecessors, but we'll have to wait for the reviews to roll in to know for sure (15% doesn't really sound like a lot, if I'm being honest). Not a lot on GPUs though; the company's 7-series cards were pretty 'meh', and with Nvidia not actually offering any details on the 50-series cards, AMD probably didn't feel the need to share any details on what to expect next. Apart from these three companies, there was a lot of chatter about Qualcomm, the maker of Snapdragon chips that power mobile devices. Apparently, the company expects its processors to power roughly 50% of Windows PC devices within the next five years. That's a bold claim, but has a lot to do with a recent Microsoft announcement about a new class of Windows computers designed just for AI: Copilot+ PCs. Those PCs will be powered using Qualcomm's Snapdragon X processors, which is supposedly designed to handle AI workflows, and is thus better at it. Anyway, that was your Computex recap. Nvidia ranked #1 on HackerNoon's Tech Company Rankings this week. Sponsored Content Meet the device ushering in a new era of hearing clarity Discover unparalleled speech clarity with this advanced, state-of-the-art hearing aid. With crystal-clear sound delivered to your ears and a sleek, discreet design powered by German technology, you can now enjoy clear speech with minimum background distractions, even in the noisiest environments. Take comfort in our commitment to your hearing and try our 45-day no-risk trial today. Start your no-risk trial today In Other News.. 📰 Bitcoin Mining Stocks Soar Amid Takeover Frenzy — via CoinDesk
Apple needs to focus on making AI useful, not flashy — via TechCrunch
Nvidia’s CEO is given the rockstar treatment in AI-powerhouse Taiwan — via CNN
Microsoft unveils all-digital Xbox consoles, 'Doom' title at Games Showcase — via Reuters
Retailers can't keep scammers away from their favorite payment form: gift cards — via Axios
Amazon, Best Buy, Google may soon sell home smart devices with ‘hacker-safe’ label — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page.*Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here.* Nvidia has become successful. So successful, in fact, that it does not care about alienating an entire demographic that was core to its success. Nvidia Nvidia care care This was underscored at the recent Computex event in Taiwan where the CEOs of three most prominent silicon companies (Nvidia's included) took to the stage to discuss new products and their future road map. And if you're a gamer, boy, was it depressing. Nvidia CEO Jensen Huang's two-hour long presentation was only artificial intelligence focused, to the point that it got boring and irked many who were hoping to learn more about the company's gaming-focused hardware . two-hour long presentation two-hour long presentation only got boring got boring irked many irked many gaming-focused hardware gaming-focused hardware As TechRadar's John Loeffler put it , Huang dismissed the very same GeForce graphic cards that put the company on the map all those decades ago. This was corroborated by Hardware Unboxed's Steve Walton, who described the keynote address' overall vibe as one where Nvidia wanted to distance itself from the "silliness of gaming" and be taken seriously as a data-center type company instead. put it put it described described So why the shift? Nvidia's most recent quarterly results showed that the data center segment, which sells the hardware powering the world's generative AI tools, made roughly nine times more money than the gaming division ($22.6 billion vs. $2.6 billion). quarterly results quarterly results In fact, the data center segment contributed 86% towards the company's record revenue ($26.04 billion) this past quarter, compared to a measly 10% by the gaming segment. So Huang's just doubling down on what's working for the business. What's quite bothersome is that because Nvidia is the market leader, Intel and AMD have no choice but to follow the trends set by Team Green — so of course, both companies also spent a considerable amount of time talking about their plans for profiting off of that sweet, sweet AI pie. But not without whetting the appetites of eager gamers with some product announcements. Intel Intel AMD AMD Despite holding next to no market share in the GPU market, Intel isn't giving up on the hardware that powers video games and AI workloads. After weeks of leaks and speculation , the company finally gave its first glimpse of the architecture that would power its next generation of graphic cards. no market share no market share leaks leaks speculation speculation first glimpse first glimpse Codenamed Battlemage, the upcoming graphic cards are designed to be more efficient than the earlier Alchemist variants, which, "if realized, [..] would rectify a major usability shortcoming of Alchemist," GamersNexus wrote in a blog post. For those that did not follow Intel's attempts at creating discrete GPUs, the Alchemist cards were plagued with driver issues that made them a hard sell for reviewers. The only reason the likes of Linus Tech Tips recommended the card was to encourage competition in the space. recommended recommended The Alchemist cards fare much better today than when they launched — thanks to driver-side improvements from Intel — but are still a hard sell . driver-side improvements driver-side improvements hard sell hard sell Which brings us to AMD. Team Red is the underdog in both the GPU and CPU markets, losing out to Nvidia on graphic cards, and Intel on central processors (though that seems to be changing ). changing changing Competing on both fronts is obviously not easy (or cheap!), but the company has seen a massive turn around under the leadership of Lisa Su — going from holding 1.4% of the CPU market share in 2017 to a respectable 23.6% at the end of 2022. The company also holds 12% of the GPU market, which might not sound like a lot, but does offer a bit of competition to Nvidia which dominates the market. respectable respectable So, what is Team Red doing to be more of a contender in the market? For starters, the company announced its next line of processors at Computex. The AMD Ryzen 9000-series chips are supposedly going to be 15% faster than their predecessors, but we'll have to wait for the reviews to roll in to know for sure (15% doesn't really sound like a lot, if I'm being honest). 15% faster 15% faster Not a lot on GPUs though; the company's 7-series cards were pretty 'meh', and with Nvidia not actually offering any details on the 50-series cards, AMD probably didn't feel the need to share any details on what to expect next. Apart from these three companies, there was a lot of chatter about Qualcomm, the maker of Snapdragon chips that power mobile devices. Apparently, the company expects its processors to power roughly 50% of Windows PC devices within the next five years. lot of chatter lot of chatter roughly 50% roughly 50% That's a bold claim, but has a lot to do with a recent Microsoft announcement about a new class of Windows computers designed just for AI: Copilot+ PCs. Microsoft Microsoft announcement announcement just Those PCs will be powered using Qualcomm's Snapdragon X processors, which is supposedly designed to handle AI workflows, and is thus better at it. Anyway, that was your Computex recap. Nvidia ranked #1 on HackerNoon's Tech Company Rankings this week. Tech Company Rankings Tech Company Rankings Sponsored Content Sponsored Content Sponsored Content Meet the device ushering in a new era of hearing clarity Meet the device ushering in a new era of hearing clarity Discover unparalleled speech clarity with this advanced, state-of-the-art hearing aid. With crystal-clear sound delivered to your ears and a sleek, discreet design powered by German technology, you can now enjoy clear speech with minimum background distractions, even in the noisiest environments. Take comfort in our commitment to your hearing and try our 45-day no-risk trial today. Start your no-risk trial today Start your no-risk trial today In Other News.. 📰 In Other News.. 📰 Bitcoin Mining Stocks Soar Amid Takeover Frenzy — via CoinDesk Apple needs to focus on making AI useful, not flashy — via TechCrunch Nvidia’s CEO is given the rockstar treatment in AI-powerhouse Taiwan — via CNN Microsoft unveils all-digital Xbox consoles, 'Doom' title at Games Showcase — via Reuters Retailers can't keep scammers away from their favorite payment form: gift cards — via Axios Amazon, Best Buy, Google may soon sell home smart devices with ‘hacker-safe’ label — via CNBC Bitcoin Mining Stocks Soar Amid Takeover Frenzy — via CoinDesk CoinDesk CoinDesk Apple needs to focus on making AI useful, not flashy — via TechCrunch TechCrunch TechCrunch Nvidia’s CEO is given the rockstar treatment in AI-powerhouse Taiwan — via CNN CNN CNN Microsoft unveils all-digital Xbox consoles, 'Doom' title at Games Showcase — via Reuters Reuters Reuters Retailers can't keep scammers away from their favorite payment form: gift cards — via Axios Axios Axios Amazon, Best Buy, Google may soon sell home smart devices with ‘hacker-safe’ label — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page.*Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. * *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings here here

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