SuperScanis in the Productivity Software Industry

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In Swift, using for asynchronous code may seem like pure hell because, under the hood, are considered complete if the compilation of their synchronous code is completed. OperationQueue Operations In other words, compiling the example described below will output a broken execution order since, by the time the asynchronous code is executed, the itself will have already been completed. Operation let operationQueue = OperationQueue()
operationQueue.maxConcurrentOperationCount = 1

operationQueue.addOperation {
    DispatchQueue.main.asyncAfter(deadline: .now() + 1) {
        print("First async operation complete")
    }
    print("First sync operation complete")
}

operationQueue.addOperation {
    DispatchQueue.main.asyncAfter(deadline: .now() + 1.5) {
        print("Second async operation complete")
    }
    print("Second sync operation complete")
} This code will print: First sync operation complete
Second sync operation complete
First async operation complete
Second async operation complete However, there is a way to circumvent these restrictions. To understand how to solve the problem, you need to understand how works under the hood. Operation The itself has four flags by which you can track the life cycle of the operation: Operation — indicates whether the can be performed at this time. isReady Operation —indicates whether an is currently in progress. isExecuting Operation —indicates whether the is currently completed. isFinished Operation —indicates whether the was canceled. isCancelled Operation In theory, the enters the state before the itself is executed asynchronously, so we need to develop a technique by which we will be able to manipulate the life cycle of the . Operation isFinished Operation Operation This possibility can be solved by subclassing the and also by redefining the / methods, as well as all the flags on which the operation's life cycle is built. Operation start cancel Here’s the code: public class AsyncOperation: Operation {
    // MARK: Open

    override open var isAsynchronous: Bool {
        true
    }

    override open var isReady: Bool {
        super.isReady && self.state == .ready
    }

    override open var isExecuting: Bool {
        self.state == .executing
    }

    override open var isFinished: Bool {
        self.state == .finished
    }

    override open func start() {
        if isCancelled {
            state = .finished
            return
        }
        main()
        state = .executing
    }

    override open func cancel() {
        super.cancel()
        state = .finished
    }

    // MARK: Public

    public enum State: String {
        case ready
        case executing
        case finished

        // MARK: Fileprivate

        fileprivate var keyPath: String {
            "is" + rawValue.capitalized
        }
    }

    public var state = State.ready {
        willSet {
            willChangeValue(forKey: newValue.keyPath)
            willChangeValue(forKey: state.keyPath)
        }
        didSet {
            didChangeValue(forKey: oldValue.keyPath)
            didChangeValue(forKey: state.keyPath)
        }
    }
} The subclass we received from the is basic and allows us to forcefully complete it manually. Operation To work with completion blocks, you should create another subclass. However, this will not be a subclass of the , but of . Operation AsyncOperation public typealias VoidClosure = () -> Void
public typealias Closure<T> = (T) -> Void

public class CompletionOperation: AsyncOperation {
    // MARK: Lifecycle

    public init(completeBlock: Closure<VoidClosure?>?) {
        self.completeBlock = completeBlock
    }

    // MARK: Public

    override public func main() {
        DispatchQueue.main.async { [weak self] in
            self?.completeBlock? {
                DispatchQueue.main.async {
                    self?.state = .finished
                }
            }
        }
    }

    // MARK: Private

    private let completeBlock: Closure<VoidClosure?>?
} This subclass will allow us to pass a closure to the , after which the will be completed. Operation Operation Let’s try this type of operation in practice: let operationQueue = OperationQueue()
operationQueue.maxConcurrentOperationCount = 1

operationQueue.addOperation(
    CompletionOperation { completion in
        DispatchQueue.main.asyncAfter(deadline: .now() + 1) {
            print("First async operation complete")
            completion?()
        }
        print("First sync operation complete")
    }
)

operationQueue.addOperation(
    CompletionOperation { completion in
        DispatchQueue.main.asyncAfter(deadline: .now() + 1.5) {
            print("Second async operation complete")
            completion?()
        }
        print("Second sync operation complete")
    }
) As a result, we were able to achieve synchronous execution of : Operations First sync operation complete
First async operation complete
Second sync operation complete
Second async operation complete Don’t hesitate to contact me on if you have any questions. Also, you can always . Twitter buy me a coffee Also published here

OperationQueue + Asynchronous Code: Everything You Need to Know

OperationQueue + অ্যাসিঙ্ক্রোনাস কোড: আপনার যা কিছু জানা দরকার

OperationQueue + Asynchroner Code: Alles, was Sie wissen müssen

OperationQueue + Código asincrónico: todo lo que necesita saber

OperationQueue + Code asynchrone : tout ce que vous devez savoir

ऑपरेशनक्यू + एसिंक्रोनस कोड: वह सब कुछ जो आपको जानना आवश्यक है

OperationQueue + 非同期コード: 知っておくべきことすべて

OperationQueue + 비동기 코드: 알아야 할 모든 것

OperationQueue + código assíncrono: tudo o que você precisa saber

OperationQueue + асинхронный код: все, что вам нужно знать

OperationQueue + Asenkron Kod: Bilmeniz Gereken Her Şey

OperationQueue + Mã không đồng bộ: Mọi thứ bạn cần biết

OperationQueue + 异步代码：您需要了解的一切

In the ever-evolving landscape of computing, the quest for optimal performance has led to the exploration of parallelism — the art of breaking down complex tasks into smaller, manageable ones. At the heart of this pursuit lies the fundamental concept of threading, a powerful technique that enables programs to execute multiple tasks concurrently, unleashing the true potential of modern computing systems. Threading involves breaking a program into smaller, independently executable units called threads, running simultaneously to enhance efficiency and speed. This is one of the fundamental concepts of parallel programming. Before delving into the intricacies of threading, it’s essential to grasp the theoretical framework that underpins the world of parallel computing. Flynn’s Taxonomy, named after computer scientist Michael Flynn, provides a comprehensive classification system for parallel processing architectures. Understanding Flynn’s Taxonomy is crucial because it lays the foundation for comprehending the intricate relationships between threads, processes, and the overall structure of parallelized systems. A clear understanding of these concepts is essential for understanding how to create multi-threaded applications. This article marks the beginning of a blog series on multi-threading, covering all the theoretical and programming aspects of multi-threading. We’ll dig deep into threading APIs and libraries (POSIX Threads, C++ threads) and OpenMP in the upcoming blogs. Without further ado… Flynn’s Taxonomy In 1966 [ ], computer scientist Dr. Michael J. Flynn proposed a classification of computer architectures based on their capability to process the number of instruction streams and data streams concurrently. This classification is still relevant and known as Flynn’s taxonomy. 1 In his initial paper, Flynn proposed four classifications: Single instruction stream, single data stream (SISD) Single instruction stream, multiple data streams (SIMD) Multiple instruction streams, single data stream (MISD) Multiple instruction streams, multiple data streams (MIMD) Flynn published three further classifications of SIMD in a subsequent paper published in 1972[ ]: , , and . 2 Array Processors Pipelined processors Associative Processors “Stream” refers to the sequence of data or instructions as seen by the machine during the execution of a program. [ ] 1 A single stream issues one element in each time unit, resembling a queue. Conversely, multiple streams typically issue numerous elements per time unit, akin to managing multiple queues simultaneously. Consequently, a single instruction stream (SI) issues only one instruction within a time unit, whereas a multiple instruction stream (MI) issues multiple instructions during the same period. Similarly, a single data stream (SD) issues a single data element per time unit, while a multiple data stream (MD) issues multiple data elements within the same time frame. Flynn’s taxonomy can be visually represented as: Before proceeding, it's important to recall how instructions are executed (the so-called instruction cycle). The instruction cycle consists of following steps: Fetch instruction - read the instruction from the memory. Decode instruction - decode to interpret the requested action. Fetch Data (if needed) - get the data for processing the instruction. Execute instruction (process data) - execute the instruction/process the data. Write-back data - save/store the results. Let’s try to understand what these different classifications mean. Single instruction stream, single data stream (SISD) SISD represents a computing architecture where a uniprocessor machine executes a single instruction stream, operating on a single data stream. This architectural design corresponds with the principles of the von Neumann model. The control unit retrieves a sequence of instructions from memory, decodes them, and dispatches them to the processing unit, which handles the data fetched from memory one unit at a time. Consequently, only one instruction is executed at any given time, reflecting a sequential execution of instructions. It is crucial to note that SISD processors may exhibit limited concurrency. For instance, the execution phases of sequential instructions can overlap, facilitated by an additional functional unit. SISD processors achieve concurrent processing through techniques like * *or . pipelined superscalar processors Pipelined processors involve executing multiple instructions in the same execution unit concurrently by segmenting the execution unit into different phases, while superscalar processors execute multiple instructions in parallel using multiple execution units. Before the 2000s, most single-core processors adhered to the SISD architecture, encapsulating the foundational structure of early computing systems. Single instruction stream, multiple data streams (SIMD) In a SIMD system, a single instruction stream is broadcast to all the processing elements (PE), and each element independently processes its assigned portion of the data. This allows for efficient parallelization of tasks and can result in significant speedup for specific computations. SIMD is well-suited for tasks involving performing the same operation on a large data set in parallel, such as graphics processing, scientific simulations, or certain mathematical computations. An instruction is supplied to numerous data elements concurrently, following a synchronized execution pattern. All instructions are queued in this “lockstep” process, and the associated data is distributed across distinct compute units. Each compute unit executes the initial instruction in the queue simultaneously, proceeds to execute the subsequent instruction in sync, and continues this synchronized execution for following instructions in the queue. SIMD architecture finds its most prevalent application in Graphics Processing Units (GPUs). In 1972, Flynn published another research paper classifying SIMD into three further classifications: Array Processors (SIMT) An array (or vector) processor executes an instruction set optimized for efficiently processing large one-dimensional arrays called vectors. In this subtype of SIMD, each parallel processing element has its distinct memory and register file. However, they receive the same instructions. Nvidia’s concept, implemented in GPGPUs, is the most common example of array processors. SIMT combines SIMD with multi-threading and is the basis of most modern GPUs. SIMT (Single Instruction, Multiple Threads) Pipelined Processors (packed SIMD) Just like other SIMD variants, pipelined processors receive the same instructions. However, they read data from a central resource and process fragments of that data independently. Finally, they write back the results to the same central resource. Modern SIMD variations, such as Intel’s AVX or ARM’s NEON, use register files as the shared resource (instead of main memory). This register-based SIMD is commonly called , underscoring its efficient use of register files for parallel computation. packed SIMD Associative Processors (predicated/masked SIMD) Each parallel processing unit receives the same instruction, but the decision to execute or skip the instruction is made independently based on local data within the unit. Modern terminology characterizes this as “ ” or “ ” SIMD, showcasing the ability to conditionally execute operations based on specific criteria within individual processing units. Modern SIMD ISAs such as AVX-512 and GPGPUs use predication to improve cache usage efficiency and ensure effective pipelining. predicated masked Most modern GPUs utilize a combination of these methods, most commonly SIMT with predication. Multiple instruction streams, single data stream (MISD) In MISD systems, multiple instruction streams operate concurrently on a single data set. This type of architecture is less common than other categories in Flynn’s Taxonomy and is often considered more theoretical than practical. In an MISD system, different processors or processing elements receive distinct instructions, but they all operate on the same data stream. Each processor performs a unique computation on the shared data stream based on its assigned set of instructions. The results from each processor can be compared or combined to achieve a final output. One possible use case of MISD systems is creating fault-tolerant systems or specialized applications where redundant computations are performed simultaneously for error checking or reliability. For instance, some earlier MISD systems were designed for fault-tolerant space mission systems. Multiple instruction streams, multiple data streams (MIMD) In MIMD systems, multiple processors or processing elements operate independently, each with its own set of instructions and data. It is well-suited for applications that require parallelism on different tasks or where the workload can be divided into independent subtasks. MIMD systems are highly flexible and represent the most widely used class of computer architecture. Depending on how MIMD processors access the memory, they are classified into two categories: . Shared and distributed memory Examples Let’s try to understand these classifications using a simple example. Suppose we have two 1-D vectors, and , each having an equal number of single-precision floating-point numbers (floats). A B We have to multiply all the elements in A with a scalar and then add them to every corresponding element in B. The result must be stored in a third array called . c result Thus, we have: Vector A: [a1, a2, a3, ...]
Vector B: [b1, b2, b3, ...]
Scalar: c

result: [c * a1 + b1, c * a2 + b2, c * a3 + b3, ...] SISD For the given task, scalar addition and multiplication would involve processing each element of the vectors sequentially. The processor performs the addition of corresponding elements from the two vectors, followed by the multiplication with a scalar, and stores the results in a third vector. The operations are carried out one at a time. For example, iterating through every iteration of this for loop without any optimizations would be the SISD way of getting the vector. result for(int i = 0;  i < n; ++i){
  result[i] = c* A[i] + B[i];
} SIMD Suppose our machine supports Advanced Vector Extensions (AVX) SIMD instructions. Thus, a single SIMD register (YMM) can store 8 single-precision floats. Therefore, each element-wise operation is performed in parallel while using SIMD, enhancing efficiency. The same scalar addition and multiplication are applied to multiple elements concurrently. For example, let us assume we have n elements in A and B. We can perform 8 data operations simultaneously, significantly improving our efficiency. Only n%8 iterations will be done sequentially. In fact, those n%8 elements can also be efficiently performed using mask registers. For example, a C++ code using SIMD intrinsics can really do wonders. // SIMD code (with intrinsics)
int ub = n - (n % 8); // 8 floats per SIMD register
__m256 x, y, z, s, temp; // vectors of 8 float entries
s = _mm256_set1_ps(c); // store the scalar c
for(int i = 0;  i < ub; i += 8){ // notice the increment of 8
  x = _mm256_loadu_ps(&A[i]); // load 8 elements of A in x
  y = _mm256_loadu_ps(&B[i]); // load 8 elements of B in y
  z = _mm256_loadu_ps(&result[i]); // load 8 elements of result in z
  temp = _mm256_mul_ps(s, x); // multiply elements of A with s and store in temp
  z = _mm256_add_ps(temp, y); // add elements of temp with y and store in z
  _mm256_storeu_ps(&result[i], z); // copy the elements to the result vector
} 
 /* Reminder loop for remaining iterations
  * Masked registers can also be used instead of this
*/
for(int i = ub;  i < n; ++i){
  y[i] = a * x[i] + y[i];
} Don’t worry if all the SIMD jargon feels a bit heavy. SIMD intrinsics deserve an article of their own, and I’ll cover them in a future article. offers detailed documentation on how to use different intrinsics. Intel intrinsics guide Modern compilers also enable vectorization if the user gives relevant optimization flags. Feel free to check my article on to learn more about such compiler flags. Compiler Optimizations MIMD In MIMD, multiple processors operate independently, each executing its instructions on its data set. In the given task, different processors might perform various operations on different elements of the vectors simultaneously. Depending on the shared or distributed memory model, several ways exist to solve the given task. For instance, if working with a shared memory programming model like OpenMP, different threads may work together and distribute the loop iterations (See__ __). OpenMP scheduling Similarly, for a distributed memory programming model like MPI, the vectors may be distributed to different processors such that each calculates their respective chunk of result. One of the processors may work as a coordinator, distribute the data, and even collect the final result from all the processors. For example, if p processors exist, the data may be divided into chunks. n/p // Each processor works on n/p data elements
Processor 1: [c * a1 + b1, ....] -> Result1
Processor 2: [c * ae + be, ....] -> Result2 // e = n/p +1
Processor 3: [c * af + bf, ....] -> Result3 // f = 2*n/p + 1 What next? This brings us to the end of the first part of this blog series on multi-threading. In the next part, we’ll dig deep into SIMD and MIMD systems, mainly introducing the programming models used to exploit them. References [1] Very high-speed computing systems | IEEE Journals & Magazine | IEEE Xplore [2] Some Computer Organizations and Their Effectiveness | IEEE Journals & Magazine | IEEE Xplore [3] Intro to Parallelism with Flynn’s Taxonomy (youtube.com) Also published here. Featured Photo by on . JACQUELINE BRANDWAYN Unsplash

Flynn’s Taxonomy and the Concept of Multithreading

When I was little, programming was simple. My friend had a computer and there were Basic and Assembly. You could write your program in Basic, which was easier to do but your program would be slow, or you could write something in Assembly, which was harder, but your program would run significantly faster. The explanation for this was simple too. Basic was an interpreter, in order to run your program, it had to go through your code every time you invoke it, and interpret it line-by-line. If it says "PRINT X", the interpreter had to find a variable named “X”, find a routine that does printing, and call the found routine for the found variable. Assembly was, well, assembly. It did interpret your program in a way too, but only once when you run the assembler. After that, your program would be executable without interpretation. Programs that need interpretation run slower than programs that don't need interpretation. If, of course, they are equivalent programs. And in Basic and Assembly, they usually were. Basic is an imperative language, not even too friendly to structural programming. Even a thing as staple as a "function" is not a built-in language construct there but a pattern: "GOSUB ... RETURN", pretty much like "call ... ret" in Assembly. Now fast forward 30 years. Languages are plenty. Computers are everywhere. Programming is not simple anymore. My department earns its bread by rewriting researcher's code originally written in Python in C++ for performance because common knowledge is that Python is interpreted and slow, and C++ is compiled and fast. But somehow, every year this rewrite gets harder and harder to win any performance from. Something changes and changes fast. The common knowledge however doesn't change so we keep rewriting. But now we're forced to optimize everything like crazy just to justify what we do. An algorithm comes in in Python, we rewrite it in C++ equivalently, and suddenly it becomes 3 times slower. That's... not what we're here for. So we reengineer the algorithm to push and get the performance boost we promised. And most of the time, since researchers don't care about performance at all and algorithmic-wise they do leave some low-hanging fruits behind, this works. Still, this whole business now looks like a scam. We're making code slower by rewriting it in C++ just so we could make it faster by reengineering the code. Why don't we re-engineer it directly in Python then? Ah! The thing is, we don't know Python. We know a little Python, enough to read and understand, but not enough to make ultra-fast programs in it. So what's there to know? Libraries Most of the Python libraries are written in C or Fortran. core is written in C; - in Cython and C; - in Fortran, C, and partially C++. They have no reasons to be slower than whatever was written in C++, Rust, or Julia. They can be faster though. In our company, we cater to both cloud services and desktop applications. And desktop users are getting angry when the new version of their favorite application stops working on their hardware for apparently no reason. So we keep our desktop builds target old. Really old, like pre-Nehalem old. This way, nobody gets angry but nobody gets to enjoy SSE3 either. NumPy Pandas SciPy Of course, a computational library built for a proper target will be generally faster than an equivalent library built for a generic 15-year-old computer with limited superscalar capabilities. The good news is if you're building for a cloud, you can set your target build platform to be exactly the machine you procure, and then your C++ libraries will run at the same speed as Python ones and maybe even a little bit faster. Compilers To be honest, the whole argument about which language is faster is ridiculous. A language is not a compiler or an interpreter, it is what it is – a language: a set of rules that specify how we should tell a computer what we want if to do. A language is just a set of rules, a specification. And nothing else. The very distinction between interpretation and compilation is something from the last century. Nowadays, there are C interpreters like , , or , and there are Python compilers. JIT compilers such as [PyPy] and classic compile-before-you-run compilers such as (that also has JIT capability if you only want part of your code to be compiled). IGCC PicoC CCons Codon Codon is built upon LLVM, the same infrastructure Rust, Julia, or Clang are built upon. The code built with Codon runs, give or take, at the same performance levels as built with any of those. There might be performance disadvantages due to Python’s garbage collection or large native data types but we’re not talking about 100x or 10x anymore. LLVM does its magic. it turns Python code into machine code for you. There are also myths about just-in-time compilation or JIT. Some say that it is superior to the usual compile-before-you-run technique because it always compiles for the architecture users have, and thus exploits it optimally. Some say that there is still compilation overhead that with just-in-time compilation also falls on users. This makes programs run slowly since they have to both run and compile themselves at the same time. The problem with both myths is that they are both true and both unhelpful. Yes, JIT generally compiles to a better machine code unless you build your binaries explicitly for the target machine, which, by the way, happens quite regularly when you deploy in a cloud. And yes, there is a compilation penalty in runtime, but it is negligible if your runtime is measured in months, which, again, when you deploy in a cloud, is not something unheard of. So there are pros and cons. What’s important, Python (Codon specifically) supports both compile-before-you-run and JIT modes so you can choose what will suit your needs the most. Traditional compilers, such as Clang, do not have the JIT option. Numba and its kernel model Speaking of JIT, is probably the most game-changing technology in the world of ultra-fast Python programming. It is a compiler but it only targets selected kernels, not the whole program. You, of course, get to select what should be compiled and for which platform. In this setup, you can run pieces of your code on the CPU, and others - on . Numba GPGPU Technically, one can create backends for other specialized devices such as Google’s or even Lightmatters’ . There is no such backend yet, those guys decided to roll out their own library instead. But, what’s symptomatic, they also choose to provide the interface for the photonic computer in Python so you could interact with Pytorch, Tensorflow, or ONNX seamlessly. TPU photonic accelerator So Lightmatter is not yet there. But NVidia is. They did provide their CUDA backend for Numba and you can now write kernels in Python, and run them on NVidia hardware with maximum efficiency. C++ doesn’t have that. There is, however, a CU dialect, coming from , of course, that extends C++ for this very matter. In Python, you don’t have to extend the language itself. Since Numba works as a JIT kernel compiler, adding a backend is just a matter of patching a library. NVidia So, the kernel model is targeting heterogeneous computing. You can run pieces of code on different devices, which is nice by itself. But there is another dimension of heterogeneity you might have not thought about. With the kernel model, you can target different kernels for different computation contexts and not necessarily hardware devices. This means that if you want one kernel to be fast but not particularly precise, you can build it with a “-fast-math” option. But if, in some other context, you want that kernel to be precise rather than fast, you can rebuild the very same code without the trade-off. This is something that is hard to achieve with traditional compilers where you can’t change compilation options in the middle of a translation unit. Well, with the kernel model, every kernel is its own translation unit. Conclusion Python is not slow. Neither it is fast. It is just a language, a set of rules and keywords. But there are lots of people who got used to these rules and these keywords. They feel comfortable writing in Python and they are interested in making Python better for them. This user base is large enough to attract both new startups with revolutionary technologies such as Lightmatter and their photonic computers, and well-established companies with decades of expertise in high-performance computing such as NVidia. All these people are vastly invested in making Python a better... not language, of course, but environment. The environment in which writing ultra-fast programs is just marginally harder than writing slow throwaways. Altogether they are making huge progress. Python is getting faster every year. At this point, forget about photonic computers if you can, programs written in Python often run on par with the ones written in Julia, C++, or Rust. But Python wouldn’t stop there. It is getting faster than traditional compilers are getting user-friendlier. Be prepared to see that in a few years Python compilers: PyPy, Numba, or something completely new, will borrow techniques from or to generate code so much more effectively that no traditional compiler could possibly come close. After all, writing a new JIT backend in Python is way easier than reimagining the whole LLVM infrastructure. Spiral Herbie

“Python Is Slow” and Other Myths of a Dying Era

“Python es lento” y otros mitos de una era moribunda

"Python est lent" et autres mythes d'une époque mourante

"पायथन धीमा है" और मरने वाले युग के अन्य मिथक

「Python は遅い」など、滅びゆく時代の神話

“Python é lento” e outros mitos de uma era agonizante

“Trăn chậm chạp” và những huyền thoại khác về một kỷ nguyên đang hấp hối

“Python 很慢”和垂死时代的其他神话

📡 Radar for the crypto market Dappradar It is an application that allows you to keep track of such things as viewing statistics and details of different , viewing ratings and statistics of projects, viewing statistics of different collections on different blockchains, tracking your or someone else’s NFT or cryptocurrency in 3 networks (Ethereum, BNB Chain, Polygon), tracking cryptocurrency statistics and many more statistics and ratings in all areas from crypto games to trading platforms. dapps DeFi NFT portfolio So I think this is one of the most useful tools for researching different projects + most of the functionality that you may need is completely free. ⛽ Websites to know the price of gas - are two of my favorite sites that I use to find out the price of gas, these sites will tell you how much it will cost you to approve NFT, wrap ETH -> WETH, create a collection, etc. , gwei blocknative 👛 Transaction tracking - is one of the best trackers for your wallets, you can add all your wallets in one place and track changes in these wallets + they can be divided into groups, for example, create two groups the first will be a group of wallets from different influencers, and in the second group all your wallets and you can not track each wallet separately, and all the wallets together and will see the total balance and more. 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Mintyscore Of course, these stats don’t have a 100% chance to come true, but the higher the rating the project has on this site, the faster it usually sells out and the more people are interested in buying it on the secondary market. 👁️ Tracking activity in the NFT market — this site will allow you to keep track of such things as the sharp change in price or in the NFT collections, that are actively buying (for this there is an additional tool ), in which collections buy , what buy with large balances and of course track the wallets you need and their activity. Moby activity mints whatsminting many at once NFT wallets 🔎 Finding upcoming collections with good activity - is a site where you can look at upcoming mints with a large audience, explore these projects, and maybe find a future collection you would like to mint. Nftgo 💎 Find the best offer from the collection - is a tool that will allow you to make a better choice when buying NFTs from any collection, you go to Rarity sniper, find a collection where you want to buy NFTs, and set BUY NOW and now you can see the best deals by rarity. Rarity sniper 🛠 The best extension for working with OpenSea - is, in my opinion, the top 1 free extension that you should download if you use OpenSea. It will give you such features as total inventory cost calculation on OpenSea, and the ability to see all NFTs before anyone else after revealing (this is made by the fact that the normal user has to wait for the OpenSea cache update, but this extension takes pictures for NFTs from the source), warn you if the collection is trying to cheat you, mass-queue items for OpenSea metadata refresh after a reveal, View real-time activity. SuperSea https://twitter.com/subliminoid/status/1439280006945988611 https://twitter.com/subliminoid/status/1450451179377504258 https://twitter.com/subliminoid/status/1439672750730354693 https://twitter.com/subliminoid/status/1450451150050930694 https://twitter.com/SuperSeaNFT/status/1513804908557971459 There are of course also paid features such as Notifications of the price you want, quick buy, mass offer sending, and color highlighting of different rarities. https://twitter.com/SuperSeaNFT/status/1532330609389015041 https://twitter.com/subliminoid/status/1450451116173500425 https://twitter.com/subliminoid/status/1490656856733372417 🧭 Top NFT aggregators - two top aggregators for NFT on Ethereum, they will allow you to see all NFT collections from different trading platforms, this is very useful because usually NFT collections are selling at once on very many platforms and this creates a problem that if you need to find some good price you need to sit in several tabs and update different pages and compare, but here sites have collected everything in one place with comfortable filters and fast buy, It’s a hell of waste not using such tools. , Gem Genie 🔄 Useful site for the NFT flip - is a site that will give you statistics such as the number of NFTs for sale and removed from sale, price changes, changes in the number of owners, percentage of NFTs for sale, and some other statistics that may be useful. flips finance 🪜 Help me grow up Thank you all for your attention and I hope you like this compilation, I am waiting for comments and feedback (most likely this article will be updated as I find new tools). Also published . here

Top 17 Useful Tools for Ethereum

You probably have heard of the idea of out-of-order execution, and that it breaks some common-sense assumptions about writing programs, and software has to insert barriers to make things right. That was a baffling concept to me at least when I heard of it. At the very least, What's the point? why would the hardware want to execute out of order only to have the software correct the behavior back? Is the software taking care of all the quirks for me?  And if not, where's the catch? Well,  all this conundrum comes from the evolution of processor design to create parallelism and the software efforts to harness the power while reining in the unwanted impact. Let's start with a tiny bit of history. Sequential consistency on uniprocessor Going back to the good old days when your CPU was just single-issue (meaning executing one instruction at a time), and there's no multi-threading programming to speak of, the programming model is straight forward -- there's memory and a stream of instructions executing in program order, doing reading/writing to the memory. For as long as the program lives, memory will reliably hold results written to it, and  reproduce the same values when read afterwards. That is, a read after a write to the same memory location will faithfully return the value written. A write after a write to the same memory location will overwrite the old value with the new. To illustrate, look at this simplistic : example1 { i = ; :: << i << ; j = ; :: << i << ; :: << j << ; i = ; :: << i << ; :: << j << ; ; } 1 # include <iostream> 2 3 int main () 4 int 1 5 std cout ' ' 6 int 5 7 std cout ' ' 8 std cout ' ' 9 2 10 std cout ' ' 11 std cout '\n' 12 return 0 13 Easy to predict, the result will be . At first variable assumes the value . Up until , where the value's updated, no matter how many times it's read the location will always return the same value. Right after 2 is written to it, reading that variable will invariably return . 1 1 5 2 5 i 1 line 9 2 There's also the variable . Since it's a different location, what value is written to it doesn't concern the output of whatsoever. j i Now let's look at a 2-threaded example to add a bit of nondeterminism. Keep in mind we're still executing on a single CPU. Imagine we have two functions and executed by two separate threads, in the following : func0 func1 example2 i = ; j = ; output[ ]; {
  i = ;
  output[ ] = j;
} {
  j = ;
  output[ ] = i;
} int 0 int 0 int 2 void func0 () 1 1 void func1 () 1 0 After the program finishes what are the valid outputs? There are roughly four instructions here. Two reads and two writes. Depending on how they are mixed, and are both likely -- if one function happens to execute before another then these will ensue. is possible too, so long as the reads are both performed after the writes are done. 01 10 11 You probably have noticed is not possible. No matter how you execute, the first instruction will be writing a to either of the output elements. 00 1 When you make this conclusion, you are of course intuitively making assumptions that the programming model is . To summarize, this means that even though the instructions from different threads may be interleaved in a nondeterministic way, each thread's execution is in program order. Also the operations are not overlapped. One memory access is done before another begins. sequential consistency Over the years, processor design has improved drastically, and computer architects play all kinds of tricks to extract parallelism from executions. For example, here in our example you may notice that in , the two operations can be performed in parallel, so long as they both execute before . Given a possible instruction sequence, we'll be free to reorder the instructions to maintain the impression that the program still runs with sequential consistency, as long as we maintain the order of memory operations to the same variable! func0 func1 Computer architects actually went ahead and took advantage of this to implement that can execute multiple instructions at a time. They tracked the operation dependencies using algorithms like and . In the history of processor you will see the architects playing nasty and nastier tricks, such as speculative execution, which caused a ton of now in the cloud business. superscalar processors Scoreboarding Tomasulo trouble But I have digressed here. The point is no matter how the system evolves, has been the contract between programmers and system builders. sequential consistency de facto Consistency model on multi-processors In the early to mid 2000's, the idea of SMP (symmetric multiprocessing) really caught on. The reason was that computer architects found it more and more difficult to dissipate the amount of heat generated by a single powerful processor. Using the same die area, they'd rather build multiple smaller "cores". However, like the uniprocessor days, in each core, instructions may still be executed out-of-order. The read-write dependencies are still tracked by the same algorithms so as to maintain the illusion that it's as if the program was still executed in-order. However, this dependency tracking won't be available across cores. Now looking back on our : example2 i = ; j = ; output[ ]; {
  i = ;
  output[ ] = j;
} {
  j = ;
  output[ ] = i;
} int 0 int 0 int 2 void func0 () 1 1 void func1 () 1 0 Suppose the two functions are executed on two separate processors, processor-0 will be free to think is all it's executing. So it's possible for this processor to read the and write the output first. Processor-1 is in the same boat! So under this paradigm, the output is actually possible! func0 j 00 To illustrate this, let's write a full-fledged program that can reproduce this: i; j; output[ ]; ::atomic< > ready; { (!ready.load()) {
  }

  i = ;
  output[ ] = j;
} { (!ready.load()) {
  }

  j = ;
  output[ ] = i;
} {
  i = ;
  j = ;
  ready.store( ); :: ; :: ; ::this_thread::sleep_for( ::chrono::milliseconds( ));
  ready.store( );

  t0.join();
  t1.join(); :: << output[ ] << output[ ] << :: ;
} { (;;)
    run_test();
} # include <atomic> # include <iostream> # include <thread> int int int 2 std bool void func0 () while 1 1 void func1 () while 1 0 void run_test () 0 0 false std thread t0 (func0) std thread t1 (func1) std std 5 true std cout 0 1 std endl int main () for Don't mind the boiler plates for now. The sleep in and the atomic are to make the two threads start roughly at the same time. run_test ready On my x86 processor, is not only possible, it's the dominant output (perhaps because reading is cheaper than writing with cache coherency protocols, which we will cover in later posts). In fact and combined only account for about 25% of the output. 00 01 10 For some reason is really rare, next to nonexistent, but that is just because of the timing of the execution. If you tell both threads to yield between the read and the write, then you will see most of the time. 11 11 Also, to illustrate this won't be a problem on uniprocessors, we can pin the two threads onto the same core by adding the following snippet right after you create the threads. Afterwards you will see output completely disappear. 00 cpuset;
  CPU_ZERO(&cpuset);
  CPU_SET( , &cpuset);
  pthread_setaffinity_np(t0.native_handle(), ( ), &cpuset);
  pthread_setaffinity_np(t1.native_handle(), ( ), &cpuset); cpu_set_t 0 sizeof cpu_set_t sizeof cpu_set_t How do we fix the broken consistency I don't know about you, but when I first saw this result my mind was blown. I knew some RISC processors can go aggressive on relaxing memory models, but I had always thought my old buddy x86 is a dependable fellow. X86 in fact, implements a memory model called TSO, or total store ordering. As the name suggests, the stores (writes) to all locations are globally ordered. More on the relaxed memory model later, but the tl;dr here is that you can imagine TSO places write results in a buffer called LSQ, or load-store-queue, before they go out to the cache. Each read afterwards will in turn check this queue for an early hit, before they themselves go out to the cache. The reason is that cache access takes much longer than register access, and it's valuable to keep a buffering structure on-core. But of course the consequence is that your read may bypass previous writes to different locations, as long as you can find a result in the LSQ, sometimes breaking sequential consistency. The fix is to enforce sequential consistency by removing the reordering. C++ gives you a standard way of doing this. Instead of declaring and as plain integers, we'll wrap them around a type, and the reads and writes will be through the and methods. i j std::atomic load() store() By default these methods will , even when these variables are accessed across different processors. So our new program will look like this: enforce sequential consistency ::atomic< > i; ::atomic< > j; output[ ]; ::atomic< > ready; { (!ready.load()) {
  }

  i.store( );
  output[ ] = j.load();
} { (!ready.load()) {
  }

  j.store( );
  output[ ] = i.load();
} {
  i.store( );
  j.store( );
  ready.store( ); :: ; :: ; ::this_thread::sleep_for( ::chrono::milliseconds( ));
  ready.store( );

  t0.join();
  t1.join(); :: << output[ ] << output[ ] << :: ;
} { (;;)
    run_test();
} # include <atomic> # include <iostream> # include <thread> std int std int int 2 std bool void func0 () while 1 1 void func1 () while 1 0 void run_test () 0 0 false std thread t0 (func0) std thread t1 (func1) std std 5 true std cout 0 1 std endl int main () for As promised before, we have now converted and into atomics. And voilà, the pattern is eliminated even if we're running on 2 cores, fully bringing us to the sequential consistency land! i j 00 Underlying the hood, the compiler inserts memory barriers so that the processors will coordinate to observe a global order on memory operations. Later on we'll get a chance to look at how the barriers are actually inserted at the assembly level. This enforcement is quite slow. If all variables are protected by memory barriers, it kind of defeats the purpose of having multiple cores. But for now, we see it solves the problem of correctness. Recapitulation and what's to come Here we have covered: Sequential consistency is the programming model computer systems strive to deliver. It's always been maintained on uniprocessor programming. When programming on multiple processors, at times programmers need to explicitly enforce sequential consistency on their own. There are language constructs programmers can take advantage of. In the next episode, we'll dig deeper into some of the more relaxed memory models and how they are supposed to be used to maintain the illusion of sequential consistency and at the same time produce as much parallelism between multiple cores as possible. Disclaimer : Compilation in this article is done with clang++ 10, with compiler flags "-O2 -std=c++20 -lpthread". YMMV as to the execution results.

Everything You Need to Know About Multithreading: Sequential Consistency Concept [Part 1]

If you’re new to the world of IoT, chances are you’ve heard a number of terms and acronyms thrown around and wondered about their differences or whether you should purchase a Raspberry Pi or an ODROID. That main reason I became curious about how interchangeably these boards were being used was because some of the main intro to embedded systems labs at MIT (6.08, 6.111, 6.115) teach , PSoCs, and FPGAs. programming microcontrollers I first started hearing about the different types of boards around 2012, around the time Arduino and Raspberry Pi were becoming popular. I’ve mostly used microcontroller development boards due to their ease of integration into smaller projects (usually small IoT devices won’t require high computing power or peripherals like RAM). Another thing that I didn’t realize at first was that the terms referred to the integrated circuits (ICs), and that a development board had to be built around the IC to make them useable or to interface external components. Microcontroller (MCU) Atmel ATMEGA32U4-AU AVR Microcontroller from Tindie A small computer on a single integrated circuit. They are generally less complicated that SoCs (a SoC can have a microcontroller as one of its components). They contain at least one CPU (processor core), memory (RAM, flash, ROM), and programmable I/O peripherals. They are designed for embedded applications, as opposed to microprocessors used in personal computers or other chips used for more general purpose applications. Many IoT devices that are automatically controlled (using some form of a feedback controls system) use microcontrollers. Their main advantage is that they reduce the size and cost of a similar system using separate microprocessors/memory/input/output devices. They can be used for data collection, sensing, actuating physical devices, and low-energy applications, among other uses. Some common microcontrollers at the Atmel AT89, MSP430 from Texas Instrument, and ESP32/ESP8266. You can usually find development boards that include a microcontroller (such as the Arduino Uno, Intel Galileo, TI Launchpad, or BeagleBone Black), thus removing the necessity of designing a PCB for the microcontroller. Microprocessor Intel Core i7 microprocessor A microprocessor is a device that incorporates the functions of a CPU on a single IC (or a few). It is a clock-driven, register-based digital integrated circuit that accepts binary data as input, processes it according to an instruction set stored in its memory, and provides output. Different instrument sets determine the different architectures of microprocessors (AMD x86 and x86–64, PowerPC, ARM). More common architectures today are generally 32-bit or 64-bit, with some reduced instruction set computing (RISC) microprocessors that — as expected — have reduced instruction sets. There are other devices that are multi-core (Intel Xeon processors, AMD Phenom II). Historically, AMD and Intel are known as the companies with the fastest CPUs with their dual-core and quad-core CPUs. Nowadays, CPUs are almost all implemented on microprocessors, causing the two terms to be practically interchangeable. When comparing the microprocessors in laptops, you generally consider the number of cores, the clock speed, the cache size (like RAM, but the memory is stored on the CPU itself so it’s faster), and other aspects like hyperthreading (running multiple threads simultaneously from a single CPU, which is something you can do nowadays) or dynamically increasing clock speed when the need arises. System on a Chip (SoC) AMD Am286ZX/LX, SoC based on Intel 80286 (from Wikipedia) An integrated circuit with all the components of a computer, including a CPU, memory, I/O ports, and secondary storage. They consume less power and take up less space than a multi-chip design, and are common in embedded systems. Compared to the motherboard-based architecture, the CPU, graphics, hard-disk, USB-connectivity, ROM/RAM, and memory interfaces are all integrated (a motherboard would connect all of the components separately or as expansion cards). SoCs are usually built around a microcontroller, built around a microprocessor (found in mobile phones), designed for a specific application, or designed as a programmable SoC with some reprogrammable aspects, similar to FPGAs. Compared to microcontrollers, SoCs usually have far more pins and more systems integration of different peripherals. Note that system-on-a-chip can refer to many things on the market, and it generally refers to a single chip that does everything that used to take up multiple chips (some still require peripheral such as RAM or flash, so the name may be misleading). Some common ones are the Cypress Semiconductor Programmable System on a Chip (PSoC) and Altera System on a Programmable Chip (SOPC). On that topic, PSoC solely refers to the family of microcontroller-integrated SoCs produced by Cypress Semiconductor, although there are other programmable SoCs that exist. has some useful knowledge on current conventions within SoCs. This StackExchange thread Field-programmable gate array (FPGA) Xilinx Spartan FPGA from Wikipedia An FPGA is an integrated circuit that can be configured by a consumer after manufacture (“field-programmable”). Its configuration is specified using a hardware description language (HDL). They contain programmable logic blocks (like AND and XOR) and a hierarchy of reconfigurable interconnects that “wire together” the blocks. These blocks can also contain memory elements (flip-flops, etc.) The biggest companies for delivering FPGAs include Altera and Xilinx. Compared to microcontrollers, they are faster, parallelizable, and have more design flexibility (trading companies often hire FPGA programmers to program their trading strategies in order to beat out competing firms). They are generally more expensive and more difficult to use, but contain the same ICs, logic components, and programmability. Common FPGAs are the Xilinx Spartan and Virtex Series and the Altera Stratix and Cyclone Series. (More info at ) this StackExchange thread Complex Programmable Logic Device (CPLD) An Altera MAX 7000-series CPLD with 2500 gates from Wikipedia A programmable logic device that’s slightly less complex than an FPGA. It primarily consists of a macrocell with logic expressions and operations. It doesn’t require an external configuration ROM (needed by FPGAs) and has less internal state storage and layered logic than FPGAs. CPLDs have thousands to tens of thousands of logic gates, while FPGAs can have up to several millions. They are primarily manufactured by Altera, Atmel, Cypress Semiconductor, Lattice Semiconductor, or Xilinx, and are programmed in VHDL, Verilog HDL, or Standard Test and Programming Language (JAM/STAPL). Programmable Logic Device (PLD) This is a more generic terms that refers to an electronic component used to build reconfigurable digital circuits — they differ from logic gates, which have a fixed function, as PLDs don’t have functions after being manufactured. Different types of PLDs (in approximate order of complexity from least to most) include the programmable logic array (PLA), programmable array logic (PAL), generic array logic (GAL), complex programmable logic device (CPLD), field-programmable gate array (FPGA), and other variants. Some of the terms used to describe PLDs are trademarked by specific companies (PAL was owned by Monolithic Memories, Inc. (MMI) and currently held by Lattice Semiconductor, for example). Low-complexity devices are programmed in PALASM, ABEL, or CUPL, while higher-complexity devices are programmed in the higher level Verilog and VHDL languages. Digital Signal Processor (DSP) Texas Instruments DSP processor from Analog Devices A specialized microprocessor optimized for digital signal processing. While other microprocessors may have general functionality for executing digital signal processing algorithms, they aren’t usually optimized for continuous processing in real-time. DSPs usually have better power efficiency and are used in mobile phones or devices with power consumption constraints. The algorithms used require a large number of mathematical operations to be quickly performed (FIR filters, Fast Fourier transforms (FFTs), matrix operations). Instruction set architectures commonly used to increase parallelism in DSPs include SIMD, VLIW, and superscalar architecture. Integrated Circuit (IC) Erasable Programmable Read-Only Memory (EPROM) ICs from Wikipedia A set of electronic circuits integrated into one small flat chip of semiconductor material (usually silicon). This results in a chip that is orders of magnitude smaller than a circuit that would have to be built by hand using separate electronic components. These chips are usually mass-produced, and are used in tons of electronics. ICs can be classified into analog, digital, and mixed signal ICs. Digital integrated circuits can contain logic gates, flip-flops, multiplexors, and other circuits, and are usually produced as MCUs, DSPs, or microprocessors. The most advanced ICs are generally microprocessors (“cores”). They can also be classified as logic ICs, memory chips, interface ICs, power management ICs, and programmable devices. Analog ICs can include sensors, power management devices, RF chips, and operational amplifiers (op-amps), which process continuous signals and perform functions such as amplification, active filtering, mixing, and demodulation. They ease the burden on circuit designers by providing an efficiently designed analog circuit that can be difficult to design from scratch. Mixed-signal ICs can create functions such as analog-to-digital converters (ADC) or digital-to-analog converters (DAC), digital potentiometers, as well as clock/timing ICs. Application Specific Integrated Circuit (ASIC) Bitfury 55nm ASIC H-Board from Cryto Store An integrated circuit customized for a particular use (as the name implies). For example, some ASICs are designed for digital voice recording or parallel computing. In Bitcoin (or altcoin) mining, ASICs can be designed to solve specific hashing algorithms. Under some definitions, you can consider a GPU an ASIC, such as NVIDIA GPUs, which are meant for graphics computing. They perform better than CPUs in this specific task due to a compact design (less space and material used), power dissipation (battery life), and speed of computation. Usually the discourse between GPUs vs. ASICs is within the altcoin mining community, which usually discusses ASICs in the context of chips designed to solve a specific complex algorithm. Application-specific standard products (ASSPs) are between ASICs and industry-standard circuits. While ASICs combine a collection of functions designed for a single customer, ASSPs are used as off-the-shelf components. Their uses include USB interface chips, controller chips for a PC, and a chip for a modem. Electronic design automation (EDA) A simple PCB designed in EAGLE A category of software tools for designing electronic systems for integrated circuits (ICs) or printed circuit boards (PCBs). The tools create a design flow for chip designers to create entire semiconductor chips with billions of components. Their simulation software includes transistor, logic, behavior, hardware emulation, and field solvers. For design, they usually inplement schematic capture, layout, and logic synthesis. Analysis includes formal verification to prevent deadlock, equivalence checking, static time analysis for worst-case scenarios, physival verification to prevent physical defects and to check for physical manufacturability, and clock domain crossing verification (like linting) to check for issues in clock domains. Major companies producing EDAs are Altium (Altium Designer), Cadence Design Systems (Verilog, OrCAD), Autodesk (EAGLE), National Instruments (Multisim), and WestDev (Pulsonix). There is also KiCAD, an open-source EDA software, as well as many web-based EDA tools, some of which are web-based versions of the aforementioned EDAs. In terms of some of the differentiations between commonly purchased development boards, knowing the difference in the hardware makes purchasing a board a lot easier. If you’re just starting out, you usually don’t care too much about specifics, and you’ll want a board that’s easy to interface with pin-outs that make sense for whatever project you’re building (you’ll also want systems compatibility with whatever peripherals may be relevant to you). ODROID ODROID C-2 from Wikipedia A series of single-board computers capable of running Android and Linux distributions (the name comes from “open” + “Android”). The hardware features a SoC with a ARM CPU and an on-chip GPU. SD cards are used to store the operating system, and the outputs include USB 2.0, USB 3.0, HDMI, and a 3.5 mm jack. There are also lower-level output pins with general-purpose input/output (GPIO) pins supporting common protocols such as I2C. There are also some models with an 8P8C Ethernet port and an eMMC port. Common models are the ODROID-C1, ODROID-C2, and the ODROID-XU4. Raspberry Pi Raspberry Pi Zero from Wikipedia A series of single-board computers developed for the purpose of teaching basic computer science in schools. The models all featured a Broadcom SoC, with an ARM CPU and an on-chip GPU. SD cards are used to store the operating system, and the outputs include USB ports, HDMI ports, a 3.4 mm jack for audio, and lower-level GPIO pins supporting I2C and other protocols. There are also some models with an 8P8C Ethernet port and an eMMC port. Common models are the Model A, Model B, and Zero. Arduino Arduino Uno from Wikipedia An open-source hardware and software company and user community that produces single-board microcontrollers and microcontroller kits. Their hardware contains an Atmel 8-bit AVR microcontroller with flash memor and a single or double row of pins that can connect to add-on modules (“shields”) and can be addressable via a I2C serial bus. Most models are pre-programmed with a boot loader for uploading programs to the on-chip flash memory. Some of the I/O pins can produce pulse-width modulated signals and receive analog inputs. Common boards are the Arduino Pro, Arduino Mega, and Arduino Yun. As it turns out, there isn’t a ton of standardization in terms of how IoT systems are built and maintained — at least not universally. There are certainly standards that have been set over the past few decades with regards to the protocols and operating systems used that are optimized for smaller devices, but there’s enough variability that how you choose to build your system is dependent on what you value — CPU efficiency, memory, cost, etc. As for the devices themselves, there’s a reason why Arduino is so popular — it takes away all of the mess of having to choose a processor and figuring out layouts for a board and figuring out the programming environment and presents everything neatly packaged such that a 9-year-old child can program an Arduino to turn on an LED. Sticking with the familiar environment that Arduino presents removes a lot of headache, but it doesn’t allow you to explore the greater possibilities of integrating devices that aren’t in the Arduino ecosystem — which is very much a possibility. Sites like Digi-Key (and to varying degrees SparkFun, Adafruit, and Tindie) have a huge array of embedded devices and integrated circuits (ICs) far beyond the scope of Arduino. I’d recommend checking out similar sites if you’re working on a larger scope project. If you liked this post and want to hear more about my thoughts on hardware, computer science, or even life as a university student, feel free to follow me or find me elsewhere online! @sharontlin Twitter @sharonlinnyc Facebook @sharon-lin GitHub

FPGAs, SoCs, Microcontrollers— A Quick Rundown of IoT Devices

As published in VRScout on 4th September 2017 While we were distracted with VR hype cycles and the “when will VR go mainstream?” debate, the understated and overlooked snuck up on us. WebVR If you are wondering why you’ve started seeing more and more discussion of WebVR in the headlines, you’re not alone. This year alone, we’ve seen major announcements from and , as well as the continued growth of independent WebVR communities, such as JanusVR: Google Firefox This is just the beginning; there will be much, much more from here. The roots of WebVR actually started with the conceptualization of Virtual Markup Reality Language (VRML) which ultimately did not take off, but paved the way for the burgeoning VR web as we are coming to know it. 23 years ago What is WebVR? is a JavaScript API for rendering 3D graphics within a browser, meaning that, if you punched in a link in the browser for a 3D website, you would find yourself in a 3D space (whether on your computer browser or within a VR headset). In laymen’s terms, WebVR is a low-barrier entry point to develop VR for the web without having to be familiar with WebGL, which can be frustrating for beginners to grasp. WebVR In its ease of use and lack of friction for consumers, WebVR presents a glimmer of hope for the mainstream adoption of virtual reality (VR) and augmented reality (AR). While there is certainly a place for high-end experiences and hardware, not all VR needs to function as a “high-end” experience. In fact, for many use cases (things like going to basic websites), it’s much more important that these experiences be accessible than it is that they sport cutting-edge graphics. Ultimately, it’s these experiences that will drive mass-adoption of immersive technologies. The ‘Casual VR’ Phase Why and how? While we move through what some call the “trough of disillusionment,” for VR, WebVR — what we can call “Casual VR” — paves a way for consumers without state-of-the-art hardware to experience VR, through the web on multiple platforms. In fact, this could prove to be the best way to democratize VR in the long run. WebVR developers at a WebVR xLab. Copyright: Vanessa Radd. Web developers have been quietly developing and building this new Web for the past few years. The beauty of building without the hype is that developers get to focus. WebVR is a “code once, work on multi-platforms answer” to VR, hence it has proven itself to be a cost-efficient tool for developers to build quick VR or AR prototypes and launch products. Technology Agnostic “The importance of the term ‘ agnostic’ is something I have been stressing for many years, based on the early experiences of the 1990s [developing for VR] when we saw various organizations suffer — fail even — from standardizing on one rendering or modeling toolkit solution,” said Professor Bob Stone, Director of the Human Interface Technologies Team at the University of Birmingham (UK) and a VR pioneer of over over 30 years. technology With the software platforms such as , Division’s dVS/dVise, Superscape’s VRT and Sense8’s WorldToolKit back in those days, VR developers had to go about creatively creating and porting their assets to ensure the longevity and staying power of what they were building. Virtuality “Even today, we still maintain the technology-agnostic policy,” said Stone. “[We archive] our models, textures, code and so on in open or industry-standard formats so that, should one company disappear, our sponsors and collaborators will not be left empty-handed.” A British Nuclear Fuels PLC control room modeled and rendered in the 1990s using Superscape’s VRT platform. The same BNFL control room model hosted under Division’s dVS/dVISE platform, but using the same assets as in the image above. The actual BNFL Control Room, built following early prototyping and human factors trials using VR. Looking Forward Mozilla took the lead in pioneering WebVR, launching its VR content authoring tool in 2015, along with its resource. A-Frame MozVR “What we’re seeing is lots of companies, big and small, using A-Frame as a rapid content authoring tool and publishing it on the web as a low-friction content distribution method,”said Dietrich Ayala, Technical Evangelist for Mozilla. “Our position is that tools like A-Frame lower the barriers to entry for content authors and ease distribution for everyone.” Besides A-Frame, other WebVR frameworks include React VR, Argon.js, PlayCanvas, JanusVR and Primrose. , and also published their WebVR and open-source developer resources. Google Oculus Samsung Internet A WebVR Community Group to decide on WebVR standards was formed by Mozilla, Google, Samsung, Facebook, Intel, Microsoft and other major tech players, with Apple recently joining the community in . July this year “The more web there is in the VR ecosystem, the better the chances that VR will thrive and grow as a place where creativity and innovation can happen,” said Ayala. “With most major browsers implementing WebVR and Apple signaling some interest, the standards side looks really positive.” Browsers On the browser front, browser vendors are developing or enhancing their to make them VR-friendly: there’s Chrome for Android, Firefox Nightly, Samsung Internet, Microsoft Edge, Chromium, Servo and . All you need is a desktop or mobile browser and Internet connection to view a VR-enabled website with a VR headset or just view the VR content casually in 2D, without a headset. browsers Oculus Carmel Mozilla recently announced a new VR enhancement: it’s the first desktop browser for all users. to support WebVR Content and Education In the midst of a dearth of content for VR, WebVR content creators are coming together to create and collaborate. Over a million creators are sharing their 3D models on Sketchfab’s 3D/VR art community platform. Virtuleap also organized the first global WebVR hackathon. “For application domains such as education and heritage, developing VR scenes and experiences for the Web is highly important,” said Stone. “[This] promotes accessibility by many beneficiaries without the need (necessarily) for expensive and sophisticated computing or human interface hardware.” This democratized approach opens up possibilities in education far beyond what we’re seeing today. “I also think that WebVR, as a JavaScript API, also enables a wide range of future students and young developers to ‘dip their toes into the water’ and start to build portfolios demonstrating their capabilities, ultimately to future employers,” said Stone. “I remember the promise of the days of VRML and products such as SGI’s Cosmo and Cortona3D (which still available today, of course). But the ability to be able to open up interactive — and quite impressive — demos of VR experiences that existed in higher quality form on specialised platforms, became an amazing marketing tool in the late 1990s and 2000s.” Creating VR/AR content for WebVR vs native apps So you saw the ‘ ’ portal demos that have taken the internet by storm. Similarly, for WebVR, Mozilla’s A-Frame allows for that albeit with ‘ ’. Here, you can create separate universes to explore, go in and out of it on the mobile or desktop web — no app is needed. The best part is you get to stay within the website itself. This is ‘universe’ (or portal) traversal on the open web. With an app, you have to leave the constraints of the app to open a new one. Made with ARKit link traversal With ARKit, there has been a mindshift as to the possibilities of content creation for VR and AR, making content creation powers accessible to the masses. It has unlocked more creative minds to explore creating VR and AR for the web. It was only a few weeks before we saw people hacking ARKit into WebViews. There are a number of other experimentations around AR on the web: the integration of Argon.js into A-Frame, a Chromium extension that works on Tango (now ARCore) devices, and AR.js. Another exciting development for WebVR was Google’s announcement of ARCore . this week “By the end of the year all key global mobile phone brands would have announced their support for WebVR, and will have devices to support it out by Q4 2017 or Q1 2018,” said Christopher Gomez, XR industry advisor and angel investor. “And with ARKit and ARCore in the mix, we will have a vibrant mix of platforms and ideas. Immersive technologies are here to stay — it will be a reality for everybody. The ‘Metaverse’ — it’s real.” Experimentation is Key Experimenting is needed in this phase of VR — the creative power of communities is huge. And if you are a Unity developer, it is fairly seamless to integrate and embrace developing for WebVR. “I like the workflow of Unity, there are lots of support and tutorials online and if you get stuck searching any question about it will probably give you a useful answer,” said Uve, Samsung Internet developer advocate. “A-Frame, one of the popular WebVR frameworks has a similar object component system to Unity and good documentation so most Unity developers will be able to switch over and get to grips with it relatively fast, if they want to try it out.” Unity as an integrated development environment (IDE) and graphic engine is a versatile tool that enables various types of experiences built quickly. Developers are able to compile or export assets into different native or web platforms. “Nowadays it is possible to use WebVR in Unity with a , and support for WebVR will be integrated into Unity WebGL, making Unity projects work without modifications,” said Diego González-Zúñiga, developer advocate for Samsung Internet_._ “It is a matter of time until the engine will be able to create VR experiences running on the WebVR implementation of a browser.” plugin “Augmented City” #ARCity As WebVR content creators and developers start to converge worldwide, in Singapore, the community assembled at an ‘Augmented City’ experiential xLab in July. This xLab, organized by the ( ) in partnership with Mozilla and the in Asia Society, is the first of its kind globally with the kickoff of this first xLab installment supported by . XR Alliance @xrforce VR AR Women Intel Disclaimer: I’m a founding member of the XR Alliance and founder of VR AR Women in Asia Society. WebVR content creators in Singapore Using A-Frame and the prompt: “How can we augment or virtualize our experience of Singapore in ways that are interesting, engaging and fun?” participants, comprised of technical designers and developers, came together to prototype and build VR/AR content for the web, telling the history and their experience in the city in an engaging way. Creating an AR Tracker The projects, built by the workshop participants, the and the and , like any WebVR builds, allow for anyone on the web to take the code from the and build and mix on top of it for their own creations. AsiaXR group WebVR Asia community group repository “The xLab was developed to encourage creative exploration of tools in the immersive technologies space,” said Gomez. “The lab itself was designed for portability as plans on expanding it is to other cities globally, is underway. In this series, WebVR tools for VR and AR content creation were the focus. Both Mozilla and Intel have been great partners in this, and with their continued support, we know the future for VR and AR is a bright one for all of us in the industry.” Projects churned out from the one-day lab (which included an ideation session followed by a hard coding session) include a VR/AR simulation of a flood in a city (featured image), building of bucket lists, experimenting rotating 360 images in VR and AR, tracking objects in AR, and “We Built This City” — metatags of people’s memories of the city, among others. We are moving towards experiencing everything in 3D: the web, entertainment, apps, global meetings and the like — hence, companies and developers need to get up to speed or be left behind. WebVR allows for exploration of imagination, experimentation and collaboration. Content creators, independent artists and developers are hungry to create and waiting out the VR tech to develop would just not be feasible. WebVR allows for creativity of expression, democratization of VR, and an app-free experience. At least for the foreseeable future, it’s the path of least resistance that allows for the propagation of VR and AR content and a vibrant, democratized ecosystem. — — Vanessa Radd @vanradd is a founding member of the XR Alliance, president of the VR AR Association Singapore, and founder of the VR AR Women in Asia Society. Radd is ranked as the global top three augmented reality influencer.

WebVR: Taking The Path Of Least Resistance To Mainstream VR

A few months ago, I decided that I wanted to pursue a career in data engineering. I was fascinated with data because of all the big data being used, like Spark, but besides that, I knew very little. This is what I learned in the past 4 months. engineering tools Data The first thing you need to grok is what is the point of all the data? The data ultimately helps the people that are making decisions make better decisions. For instance, if you sell T-shirts and you find that most of your customer’s are between 18–25, then you can put Justin Bieber’s face on the T-shirts and all of sudden your sales will go through the roof. Once you have the data, you can do some statistics on it, make fancy visualizations, run some SQL, and as a whole the organization can make better decisions. Big Data Big data, in the most general case refers to a quantity of data, that either can’t be stored on a single server, or computing something over this data using a single server, would take too long (what is too long depends on the application). Historically, the scale of data was small enough to fit on one server. As a result, the tools that developed, e.g. Relational Database Management Systems (RDMS) were largely meant for single node deployments (this isn’t really true since distributed computing models existed since the 1960s and distributed databases like Teradata existed since the 1970s- but these were very niche). Even though our compute and storage capacities were increasing exponentially, the data generated was growing even faster. MapReduce and Hadoop Although, some solutions existed, the modern era of distributed computing and storage started when in 2004 Google published . The key proposition here along with their 2003 paper, , was how to perform distributed and fault-tolerant computing and storage on top of commodity servers. MapReduce: Simplified Data Processing on Large Clusters The Google File System Around the same time Doug Cutting was working on Nutch, an open source web crawler. Doug was facing problems of distributed storage and processing and when Google’s papers were released. He decided to implement them as part of Nutch. Eventually, these would become Hadoop and HDFS. (If you are more interested in the history, see for an excellent writeup on the topic). https://medium.com/@markobonaci/the-history-of-hadoop-68984a11704 Other Parts of Ecosystem I covered two aspects of the big data ecosystem, namely distributed computing and distributed storage. However, the ecosystem is much more diverse and has many more moving parts. Another important component is the ingestion layer. Why do we need an ingestion layer? Let’s say we have logs being generated on many nodes, and we want to store these in HDFS and run a Hadoop job on them. The ingestion layer basically acts as a fault-tolerant, distributed middle layer between the producers of data on one side, and consumers on the other. Players in the Game Now, that we have some sense of what the big data ecosystem looks like, let’s consider the two key players in the game: data scientists and data engineers. Data Scientist The job of the data scientist is to turn the big data into actionable insights. This requires them to apply machine learning, statistics, and analytical thinking, to the data. Some data scientists will have strong programming skills and some will have a deep understanding of the ecosystem of tools. However, their primary job is to run experiments, develop algorithms and get insights. Data Engineer Data engineering is a new enough role that each organization defines it a little differently. However, broadly speaking their job is to manage the data and make sure it can be channeled as required. In some companies, this means data engineers build the underlying system that allows data scientists to efficiently do their job, e.g. at Netflix data engineers may build and maintain the infrastructure that allows data scientists to experiment with recommendation algorithms, and in other companies, the data engineering is the whole shebang, e.g. at Twitter, the biggest challenge is how to make data flow as quickly and efficiently as possible. Typically, a data engineer will have strong programming skills and a deep understanding of the big data ecosystem, and distributed systems in general. A data engineer will perform one of the following: Designing, implementing, and maintaining data warehouses Designing, implementing, and maintaining ETL pipelines DevOps Working on infrastructure tools Running statistical experiments, implementing machine learning, etc. 1. Data Warehouse I mentioned before that sometimes the job of the data engineer is to make data accessible, so for instance the data scientist can run queries on top of this data. However, this leads to the question of how this data should be stored. The answer to this question is very nuanced and this is where the concept of data warehouse comes into play. A data warehouse is a consolidation of all the data in an organization in order to make it easier to analyze. The raw data is often heterogenous in its format and location. For instance, you may have some data coming from log files and other from transactional databases, e.g. a MySQL database that stores the order quantity and order amount. To give you a sense of some of the subtleties in working with data warehouses, consider that for transactional databases, data is stored in normalized form, so that the data does not become anomalous. To perform complex queries on normalized data, requires joins. Joins, in general ,are expensive, and at the big data scale, when the data is located on multiple servers, across the network, joins become prohibitively expensive. A solution to this is to denormalize the data. However, denormalizing the data, typically leads to increased storage space. 2. ETL (Extract, Transform, Load) ETL is related to the concept of Data Warehouses. Essentially, ETL refers to moving the data from some initial source, to a different location. For instance, if we have CSV log files and we want to put them in our data warehouse, we must first read them from the source (extract), then strip the commas, and remove malformed lines (transform), and put them in the final data warehouse (load). 3. DevOps In some cases, data engineers will have to work with the raw compute and storage nodes, e.g. installing the software, etc. This is more typical in smaller organizations where the roles are not clearly delineated. Larger organization will have specialized DevOps roles. 4. Working on Infrastructure Tools In some cases, data engineers will work on infrastructure tools, e.g. adding an extension to Hadoop. Again, this is more typical of smaller organizations, with larger organizations having separate tooling teams. 5. Running statistical experiments, implementing machine learning, etc. This is closer to the role of data scientist, but depending on your background and interests you may be more or less close to the data science side of things. Again, this varies from organization to organization. Data engineering is a very exciting field, and as data driven decision making becomes more central to our businesses so will the challenges and opportunities of data engineering. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

What is Data Engineering?

Groundbreaking research on LK-99, a potential room-temperature superconductor, and its staggering implications for our future. From superfast travel to quantum computing, we delve into a world where the line between sci-fi and reality blurs. When , a physicist at Lawrence Berkeley National Lab, casually dropped her recent research on Twitter with an audacious mic-drop meme of Obama, the internet took a collective gasp. Sinéad Griffin https://twitter.com/sineatrix/status/1686182852667572224?s=20&embedable=true Why the buzz? Her findings potentially catapult us into a future we've only seen in sci-fi movies. With the wave of a tweet, Griffin declared, in no uncertain terms, that , the material at the center of her study, could very well be the much-anticipated 'holy grail' of modern material science and applied physics. LK-99 In layman's terms, imagine a world where your morning commute is reduced to mere minutes because . Or a place where puzzle. Imagine our . Yes, we're talking about a world powered by room-temperature superconductors - a world that could make possible. trains can travel at speeds previously unthinkable energy could be stored and transferred with near-zero wastage, solving a substantial piece of the climate change computers and technologies operating at speeds so fast that 'instant' seems slow LK-99 Let's explore how this material might transform our world and how we might respond to the upcoming wave of innovation. Stick around, and you might witness a revolution in the making. No big deal, right? Unveiling the Marvel of LK-99 We are in the midst of a profound scientific shift. The innovative work carried out by researchers around the globe is reshaping our understanding of materials and their potential. LK-99, the new kid on the block, is challenging our expectations with its unique properties. According to the referenced simulations, LK-99 achieves its extraordinary capabilities through a fascinating mechanism. Copper atoms percolate into a crystal structure, replacing lead atoms, causing the crystal to strain slightly and contract by 0.5%. This unique restructuring enables a paradigm shift in superconductivity. Making the Complex Understandable Griffin investigated what would happen to the 'electronic structure' of this material. In layman's terms, she studied the available conduction pathways in the material. Turns out, there are conduction pathways for electrons under just the right conditions, enabling superconductivity. Intriguingly, these pathways are situated close to the 'Fermi Surface' – a level equivalent to “sea level” in the realm of electrical energy. This scientific breakthrough is a testament to the extraordinary potential of LK-99. Experts argue that this discovery could usher in a new age of technological innovation, with LK-99 at the helm. https://twitter.com/Andercot/status/1686286684424691712?s=20&embedable=true The Voice of Expertise Alex Kaplan further elucidated the findings of this groundbreaking research. Highlighting key parts of the computational results and crediting the team behind this success, Kaplan encouraged the scientific community and enthusiasts alike to scrutinize the discussion section of the research paper. It holds invaluable insights into the intricacies of LK-99 and the potential it offers. https://twitter.com/alexkaplan0/status/1686244162767552512?s=20&embedable=true The Future Unfolds The implications of LK-99 becoming a viable room-temperature superconductor cannot be overstated. Akin to the invention of the transistor, the successful application of LK-99 could redefine technological innovation, making what once seemed like science fiction a tangible reality. At the heart of quantum computers are qubits, which often need to be cooled to extreme temperatures close to absolute zero to function efficiently. With room-temperature superconductors, we might be able to devise a new class of quantum computing systems that are easier to scale and maintain, leading to quantum computers becoming more accessible and widespread. Quantum Computing: Superconducting maglev (magnetic levitation) trains, like those already in operation in some parts of the world, could become more efficient and commonplace. These trains, levitating over their tracks due to magnetic repulsion between superconducting magnets on the trains and coils in the track, could potentially reach even higher speeds, reducing travel times dramatically. High-Speed Trains: The most direct application of room-temperature superconductors would be in power transmission. Currently, a significant amount of energy is lost due to the resistance in wires. Room-temperature superconductors would virtually eliminate these losses, leading to a significant increase in the efficiency of electrical power grids and renewable energy systems. Energy Infrastructure: Medical devices such as MRI machines currently require cooling to enable their superconducting magnets. With room-temperature superconductors, these devices could become cheaper, smaller, and more energy-efficient, potentially revolutionizing medical diagnostics. Medical Technology: The efficiency of data centers could be substantially improved with the use of room-temperature superconductors, reducing energy costs and enabling faster, more reliable data transmission. This could revolutionize the IT industry, making cloud services more affordable and efficient. Data Centers: Room-temperature superconductors could make electric propulsion systems for spacecrafts more viable, possibly leading to more efficient and faster space travel. This could transform space exploration, making missions to distant planets and stars more feasible. Space Exploration: The advent of room-temperature superconductors like LK-99 could indeed bring about a transformation across many sectors. This could revolutionize how we compute, travel, consume energy, and even explore the cosmos. However, the path to LK-99’s broad adoption will not be without challenges. The material's unique properties hinge on copper atoms percolating into specific locations in the crystal lattice, a feat not easily achieved. But overcoming these hurdles could unlock a future we've only begun to imagine. The Verdict The verification of LK-99 as a potential room-temperature superconductor is a game-changer for humanity. With further research, experimentation, and refinement, the possibilities are endless. Isn't it electrifying to witness the dawn of a new technological epoch? The thrill of being part of this incredible journey is indescribable. Let's continue to push boundaries, defy norms, and redefine what's possible; let’s ACCELERATE together! The future beckons! Also published . here

Revolutionary Potential Superconductor LK-99: Could It Usher Us Into A New Era?

The Noonification: Bitcoin is a Force Only for Good, Like Superman (7/14/2023)

Imagine Superman absorbed the energy not of the sun, but of people’s gratitude and belief in his powers. And kryptonite was people’s disbelief, discouragement, and hate. I just described Bitcoin. You have heard it said that technology is a double-edged sword. Recently, ChatGPT. It can harm as well as uplift a student. Or humanity in general. But after some thought, I posit that some technologies, like Bitcoin, are a net-good technology. Much less harmful than useful and instead, very vulnerable to being attacked, stained, ridiculed, and greatly misunderstood. My idea could be taken with a pinch of salt. Because of its vulnerabilities, Bitcoin is one technology that quite early on, many famous people have said is a scam, a Ponzi scheme, money used by criminals, ‘stupid’ and ‘evil’. To be fair, though, others have praised it. , said Bill Gates, while the billionaire investor Michael Saylor quit his CEO job last year to focus on singing Bitcoin’s praises. And buying more Bitcoin. “It is a technological tour de force” The trouble is, Bitcoin is sometimes complicated, like an action novel - Fast paced action featuring Japanese, Chinese, and American corporations, financial mind games with people spending billions of dollars of money that ‘does not exist’, innocent victims who get ‘rekt’. Other times, it is mundane and in a little corner of the world for my neighbors right now hardly know it exists. Or that it is a real thing with real power. In other places, it is high philosophy dealing with human action (praxeology), divine conceptions, and so on. But it is good. Only good. The fastest good on Earth. And this might be appreciated if we ponder, as we shall do today, how people would react to its gradual disappearance, its relation to Facebook or Nuclear power, and the core architecture built mostly on cooperation and not competition. The Last Bitcoin Currently, there are 19.6 million bitcoins in existence, and they will soon reach the hard cap limit of 21 million. To get a feel for where Bitcoin stands in the eyes of the world, imagine this. Imagine Bitcoins’ greatest weakness  - the ability of keys to get lost - has affected all the coins except one. Of the 21,000,000 Bitcoin that can exist, 20,999,999 get lost, and only 1 Bitcoin is left in the entire world. Would this be a good thing or a bad thing? Some people would say it would be a bad thing but for different reasons. A Bitcoiner today would definitely say this would be a bad thing to happen, as Bitcoin would be one thread away from being lost forever. Though, chances are high that Bitcoiners in that world with only one coin left would be very humble people. True believers in a lost technology especially if lost coins had never been salvaged before. A no-coiner (non-Bitcoiner) today who has brushed against some very toxic maximalists, would likely say it is bad because it would lead to wars. ”, they would argue. With rage-fuelled toxic humans in their mind’s eye. “People would wring each other’s throats for this worthless single thing A no-coiner in that world with 1 coin would likely be somebody who doesn’t lose any sleep over “Bitcoin”. Hard to think they would even know what it is, as lost technology is usually sought out by archaeological types. But one thing I believe is nobody who learns of this one bitcoin would be outraged that only 1 exists. That would be absurd. They would be saddened at learning of the history of this great technology, now reduced to a single event in space and time. : Definitely, all altcoins would be goners. Not even one would be left Aside However, instead, this tragic event might then become the most uplifting event in the world. After moping around over lost bitcoins, this one coin could quickly become a beacon of hope. A unifier to put ahead of us, as we remember all the likenesses that went before it into the deep dark void of cyberspace. Preserved with the collective will of everybody on Earth like Da Vinci’s Mona Lisa. And maybe, if quantum computing is mature by then, there might be a way to actually map and treasure hunt for these lost coins in the depths of cyberspace. today’s no-coiner insists. Zapping me back to reality. “How about having a World War 3 because of this coin” “Frankly, it would be much easier for a few people to destroy the last few nodes holding up this last Bitcoin than to bother waging a world war over it at all”. : People will treasure Bitcoin’s Scarcity. Like they would Superman. They just haven’t realized it yet. Lesson Types of Technologies Physical Technology As already posited, most technology is a double-edged sword. That means, it could benefit you, but it could hurt you. Fire was the first example of highly-destructive, highly-beneficial tech. Thousands of years later, circa 1938, Nuclear power became the best example. In a few hours, the nuclear weapons we humans have created could totally destroy the planet, ushering all surviving creatures back to the Stone Age. Or rather, back to a post-CT-extinction world. But wielded with care and the will to create a great civilization, together, the energy abundance that our nuclear fission and fusion resources can unleash would solve world hunger, climate change, desertification, and global electrification, with enough to spare for space exploration. Physical technologies like nuclear power thus depend on humanity having enough heart to care about the common good for us all, more than the clouding squabbles over race, gender, religious affiliation, cultural predispositions, or economic interests. To use nuclear power for good means sacrificing the chance to exploit or step over others, for the opportunity to sit on the round table and work together. Social Technology Another type of technology is social technology. It is . Social technologies like telephones, the internet, and social media, have exponentially multiplied our capability to harness physical technologies like nuclear power, and we would be severely limited as a species without them. a way of using human, intellectual, and digital resources in order to influence social processes - Wikipedia Knowledge would travel at a snail’s pace, and some places would be dramatically better worse, in many ways, than others because of the highly difficult flow of technological progress and the unique cultural situations and environments of each place. and Now unlike physical technologies like fire, nuclear power or knives, social technologies are mostly a force for good, but there is a caveat. Being information-collecting entities, they can easily decouple from their place of serving people to making people serve them. The power shifts to reside not with the users anymore but with the central authorities i.e. the corporations owning the IP of these websites. Plus the websites themselves when the AIs get sentient! And because human habits are mathematically predictable (lunch is between 11 am and 2 pm everywhere), these sites e.g. Google or Facebook, have amassed so much data on their users that they can send targeted ads with precision, making money from us before we know we want to buy a product. This is no problem until data breaches happen, or until artificial intelligence sitting on top of this technology acquires the capability to undermine the efforts of us, the people, who trained it in the first place (by competing with us for pennies). As is happening today which is fuelling online rage and clickbait. Fiat currencies are another form of social technology with a central authority called the Central Bank, and altcoin cryptocurrencies are a form of social technology with a central authority called the ICO company. Synergistic social technology The third technology I know is Bitcoin. A synergistic social technology. A synergistic social technology works unlike any other social technology. First, it does not harvest people’s data so it then becomes even more powerful than the individual person. It grows only if people believe in it, protect it, and use it consistently. Any slight disinterest, distrust or dissatisfaction, and its value collapses (these are all those spiky swings of the Bitcoin price. It is frantically trying to satisfy everybody). Not like Google stock which is rock solid and stable. And goes up relatively predictably. in real-time When BTC grows in value, the people get stronger because the users are the owners. When data breaches happen, it suffers because people jump ship like * that. They are very picky when it comes to their monetary self-sovereignty. snap* So it can only be a force for good because bad people creeping in will scare away the good people who actually do the hard work of loving, sharing, building, maintaining, and innovating around the technology. This is easy to see with the Silk Road incident of 2013. Negative connotations of Bitcoin with criminal activity crashed the price faster than a meteorite. From $266 to $50. Did any illegal trade in drugs and whatnot proliferate with this crash? No. It quickly dried up. If anything, the US government greatly profited after the seizure of Bitcoin worth billions of dollars while US citizens were lost in FUD, faithless and not sure whether the dream of monetary sovereignty was over this quickly. Interestingly, some people tried creating Silk Road 2.0, but the FBI got them so fast that they hardly made headlines. : Criminal activity in a currency like Bitcoin = You will get caught and Bitcoin suffers. Lesson Fraudsters look for Thriving Environments Every Bitcoiner got on board this global social ecosystem by listening to a few sound rules of money and making the bold choice to live unapologetically truthfully. Bitcoin is good because fixed rules of money, including the ultimate rule on quantity, mean a simple order everyone can follow and make wealth. Work hard and buy or mine some Bitcoin satoshis. Hold for at least 5 years. You’ll make a profit more than anything else. How hard is that? How non-evil. Good money is not controlled by anybody but is protected and used by everybody. Alas, whenever good people connect to the wisdom that is Bitcoin and join the party, the number goes up and fraudsters get on board as well. Fraudsters are predators, and predators eat the meatiest prey in the Jungle. Despite being only a modest $600 billion net worth right now, because it is such a sound and positive force of good for people, the scammers are plentiful, and they want to take your cash and disappear. And each time good people’s money is taken, they get rekt (financially ruined) and the price crashes. The scammers go into hiding, to wait for you and me to pick ourselves up again, so they can strike again. Many of these scammers and fraudsters hide behind the thin veil of ‘cryptocurrencies’ so the next time you read a toxic maximalist calling an altcoin a “sh**coin”, understand they are frustrated and just want to protect you and themselves from the sweet words of scam artists. Pumping and dumping of crypto, even bitcoin “investments”, is fraud and hurts both the naive people who opt into these scams, and the bitcoin community at large when the exit is made and people’s morale crashes into millions of tiny pieces. : Bitcoin is not evil. It is the easiest technology for corrupt and evil people to ruin. That is why it sounds so evil when anything bad happens by its hand, yet the majority of the ills in the financial world happen via fiat currencies. Lesson Cooperation outshines Competition What’s in it for poor people? - Anonymous There are people who feel that Bitcoin has been an entirely useless innovation as far as getting poor people out of poverty is concerned, while billionaires like Michael Saylor keep buying all the bitcoins. They can make millions in profit in months when the price goes up, while a modest earning pleb can only Dollar Cost Average a few sats every month and hence needs to wait all 5 years to make a modest enough profit to cash out. But here are some facts; Logically, nobody can own all bitcoins cause not everybody wants to sell at any moment in time. If the price of Bitcoin goes up you can’t buy more with the stock you already have. People have to spend bitcoins on food and other things no matter how ‘hodledor’ they would like to be. Most importantly, Bitcoin is about the people. The common man, woman, and child. on how spending and replacing, vs hodling, is how we spread Bitcoin adoption and ultimately, its utility in more sectors around the world. Carl Menger penned a great piece a few days ago Cooperation outshines competition. Consensus and collective agreement is built into the nature of the protocol, not one-upmanship. If Bitcoin gets too valuable and transaction fees balloon, I believe there will be another fork to break down satoshis into even smaller amounts. Or we shall find ways to make bigger and cheaper coinjoins. Because the most valuable Bitcoiner to the network is every Bitcoiner. Not only Mr. Saylor. More important than competition, Bitcoin thrives on cooperation. Think of everybody; some work as miners, others run nodes, some code LN apps, others orange-pill. It is impossible for us to all stack as much as the other because different work earns differently in the Bitcoin ecosystem. In the world. And aside from the Bitcoin activities, some people are CEOs of big companies creating all the other wonderful stuff we want to buy with Bitcoin while some are students, still grappling with what they should learn to make it in this fast-changing world, et cetera. Bottom line - Together is how it works. Thinking of how each Bitcoin node runs can show us the importance of sticking together. The one lesson we should steadfastly hold onto, as taught to us by Bitcoin itself, is using and running Bitcoin just like Bitcoin nodes. Together. As a family. Some nodes (people) are big miners with 10,000 ASICs minting entire bitcoins on a daily basis while others are laptop nodes. It does not matter. All need to DYOR (Do Their Own Research) and verify transactions themselves. If a transaction (a deal in the real world) is no good, they should do everyone else a favor and block it. Fast. If Bitcoin goes to $1 million and Michael Saylor becomes the richest man in the history of our planet, I believe I will benefit because Mr. Saylor will spend his coins investing in pro-people companies all over the world. Poor or financially desperate people in Africa, Asia, or anywhere right now should stack as a good habit for financial discipline, the way exercise is good for your body even when you are not preparing for a world-championship fighting match (like yours truly). When the price gears up, opportunities will open up. And Bitcoiners are hardly underpaid despite great work ‘cause honest payments flow best through Bitcoin as well. One more thing, the halving is near. Get ready.

Bitcoin is a Force Only for Good, Like Superman

বিটকয়েন একটি শক্তি শুধুমাত্র ভালোর জন্য, যেমন সুপারম্যান

Bitcoin ist eine Kraft, die nur dem Guten dient, wie Superman

Bitcoin es una fuerza solo para el bien, como Superman

Bitcoin est une force uniquement pour le bien, comme Superman

बिटकॉइन सुपरमैन की तरह केवल भलाई के लिए एक शक्ति है

ビットコインはスーパーマンのように善のためだけに力を発揮する

비트코인은 슈퍼맨처럼 선을 위한 힘입니다

Bitcoin é uma força apenas para o bem, como o Super-Homem

Bitcoin Süpermen Gibi Sadece İyiliğe Yönelik Bir Güçtür

Bitcoin là một lực lượng chỉ dành cho những điều tốt đẹp, giống như siêu nhân

比特币是一股正义的力量，就像超人一样

Launching a startup is one thing; finding an investor is another. Most founders know that searching for the right investor can make or break a company’s ability to pivot and scale. From business decision-making to coaching, these VCs are the guiding force that every entrepreneur needs to make big swings and stay on track. On today’s episode of Startups On Demand, I am joined by , Principal at , where he brings experience in partnering with startups as well as mentoring and helping teams to scale up. Aviad Ben-laish StageOne Ventures We talk about the importance of speed in early-stage , the involvement of investors in helping founders pivot their company, and the 3 main criteria StageOne is looking for in an entrepreneur. startups Omri: How did your experience in consulting help you in your VC career? I think it helped with the way I think about investments. I think one of the things that you realize in consulting is that clients, especially large enterprises, don’t necessarily care about how things are done. Technology can be X, Y, and Z. As long as they get the value, and as long as it does what you told them it’s supposed to do, then that’s good enough. For me, technology for technology’s sake is very interesting, but I try to leave that for universities and research. But if you’re looking at it from a business perspective, if a solution does what it promises, then the client won’t necessarily care if it’s the most cutting-edge technology or if it’s done by a team of 50 outsourced people. For me, it’s important because we see startups who put too much focus on “this is the best technology ever” but why should the client care? At the end of the day, it’s the use case. It’s the value for the client, then technology comes second. It’s the enabler, but it’s not necessarily the driver. Aviad: Omri: Is speed something you look for in entrepreneurs? Yes, it’s definitely part of the things we’re looking for because it’s a comparative market, and there are almost no blue oceans out there. You can always face competition, even with incumbents or startups. So speed is definitely important. But you need to do it in the right way, otherwise, you’ll stumble and fall when you don’t have to. So I think for us when we’re trying to help founders, we always tell them to take a step, make sure they know where they’re going, and move ahead. When founders don’t usually have a very strong business background, it’s in the value that you try to provide. But at the end of the day, it’s important for us to find a founder that we don’t necessarily need to babysit. It’s okay to help and give directions, but we’re not gonna run the company for the startup. They need to prove that they’re able to build the company. Aviad: https://youtu.be/5mep2kPntSU?embedable=true Omri: How involved are you with your portfolio companies? That depends on the company and its stage. In the early stages, sometimes you need to be more involved, especially with the introduction to potential clients and other investors. But if the startup is still building the code and working on the technology, there’s not much that you can do. It’s totally up to them. There are areas and periods where there’s definitely a spike in our involvement, but most of the time, it depends on the startup. Naturally, as the company grows, raises a couple of dozen million dollars, and hires several employees, it becomes more structured and there’s less for us to do. Aviad: It really varies from one startup to another, especially the more deep-tech the technology is, probably the fewer things we have to do early on because it takes longer to get the technology to the point where it could sell. Omri: Do you think your main value is in the early stages of the startup where there’s less structure with helping the founders cope with what they do next, and once they raise their seed, then you guys still take an active part of the board but not as much previously? No, I think that’s a bit too early, but probably once they get to series C, there’s a bit less involvement, because the company’s ball is already rolling. Early on, there’s definitely more to do, especially in the business and coaching side. When you pick an investor, it’s kind of like a marriage – an early-stage fund, we’re there for about 10 years; investments are usually about 7 to 10 years. So we get to build relationships with our founders, and being one of the first investors, that’s the kind of relationship that lasts even much later on. So even though we have much less to do on the business side, they will still come to us for advice. Aviad: Omri: What are your 3 criteria for investing in a founder? Initially, on a personal level, there has to be a good connection between you and a founder, because you’re gonna work together for a long time. You need to know if you’re able to give them advice and if they can take advice. In the first few meetings, although it’s not professional sounding, you need to like the person and you need to see that they like you back. You don’t want to be just a checkbook. Aviad: The second part is there is a connection or experience of that specific founder of that area that they’re building. It’s not a must for an investment, but if the founder is looking to solve a problem based on a pain that they’ve experienced personally, I think it’s a much bigger draw for us and it makes the case much stronger because being able to relate to that market will really help them later on. Lastly, it’s about the vision. We do look for founders with a big, long-term vision, and not looking to sell early. A multi-billion dollar company, ideally. Also published here.

Unleashing Early Stage Potential: Superstar Founders and Measuring KPIs

Hello World! This is Sidra, Business Development Representative from Hacker Noon's Sales team. Today, I'm here with exciting news - (again!) v. smart WE ARE HIRING Yes! Here is your chance to be a part of a . Hacker Noon has marked 2021 as the yet. Yup - we are growing, and we need more energy, more talent, more teammates. diverse, fun, and LEGENDARY team most successful business year As we double revenue five years in a row with a historically and intentionally lean team, we are looking to have two more people: a and a . are on track to Sales Representative Customer Success Manager But first things first: ? WTF is HackerNoon Hacker Noon is built for technologists to read, write, and publish. We are an open and international community of 15k+ contributing writers publishing stories and expertise for 3M+ curious and insightful monthly readers. Learn more at . about.hackernoon.com How HackerNoon Makes Money We will never put up a paywall, charge readers, run a pop-up ad, or pay for content to get published on Hacker Noon. However, we do make money through : sponsorship and partnership Companies can advertise through our beautiful and lean site-wide sticky . Top Navigation Billboard Banner Ad We have options of targeted marketing through our offering. Ad-by-Tag Ad Organizations can share their stories through our program. Brand as author We hold , generating hundreds of stories for them. fantastic writing contests for companies Then, there are . tech company news pages Sponsors can advertise through our daily newsletters (150k+ subscribers!) - Noonification! Also, organizations . sponsor the HackerNoon podcast Now, about the job roles - let's see what we need and what we offer. Sales Representative (Remote, Part-time) We are open to training someone with less experience, but we also expect YOU to be more of a do-er than being just an idea person. Ideally, you are someone who's passionate about tech with all its flaws and beauties. You are also someone who is a compassionate listener and has the empathy and ability to build long-term relationships with customers. With intuition and experience, you can identify customers' pain points and solve their problems with our multiple offerings. Like any other position at HackerNoon, priorities will be given to candidates with excellent writing and communication skills. Your main responsibility is to . You will be finding and reaching out to potential leads (that you help organize) with provided (and eventually your own) pitches to onboard them. sell some of to Hacker Noon's target customers our standard offerings The performance will ultimately be judged by the number of meetings taken and the percentage of deals closed You will need to to your colleagues. self-organize, be self-motivated, and be a nice human being Our main Sales Manager is based in Asia and often takes meetings during Asia hours - so we are looking to someone who is ideally . available in Europe or North America timezone Through a process of trial and error, depending on types of clients and the problems they are trying to solve document perfect pitch for each offering, Beyond sales, you should also be able to like the back of your hand. explain the publishing process and HackerNoon value prop Though these things change fast around here — don't say you weren't warned. You need to be agile and experienced with G-suite, Slack, Notion, and other remote-first tools. Communication is key everywhere, especially in the remote environment. Everything else you need to know, we'll teach you. Our team is (and will always be) working remotely. Customer Success Manager In this role, your primary objective is to alleviate the workload of our Sales team in the Customer Success division, be it be their point of contact through various means of communications, reviewing customer journey, identifying how it is supported, and helping clients overcome issues and achieve their goals using a consultative approach. Your main responsibility is to be before their campaign starts (to make sure every detail is in place), during, and after their campaign ends. the main point of contact between HackerNoon and our clients You need to demonstrate superb communication and people skills through different platforms such as Gmail, Telegram, and WhatsApp. You are to be able to uncover the root cause of a potential churn and quickly formulate timely solutions. highly self-organized and attentive to details You are expected to constantly check in with the clients about the status of their campaign, provide qualitative and quantitative results, and eliminate any potential roadblocks. You will be designing customer surveys to understand their experience with HackerNoon and possibly expand their partnerships with the company. You will also help our Business Analyst, , with post-campaign reports and generate client case study for future sales. Hang Ngo . You have to be agile and experienced with remote-first tools such as Slack, Notion, and GSuite (we use those a lot around here) You are an overall nice human being to people around you and able to show empathy to others’ hard times and struggles. Last but not least, . Since HackerNoon is, and will always be a remote-first company, we need to be as transparent as possible with our team. communication is key Anything you need to know, we will help. You just need to ask. What Does Hacker Noon Offer: Every single Hacker Noon team member started out as a part-time contractor first, including the CEO. After a mutually agreed upon trial period and compensation amount, there will be an evaluation between you and Hacker Noon to find the best fit: full-time, part-time, or no time. A handsome compensation. Hear it from the insider (ahem, that's me). You cannot have a better working environment than this. Thoughtful, compassionate leadership, great learning opportunities, and flexible working timings - The best work culture. WFA FTW! You will be working directly with the leadership. Not only that, you'll also feel heard and valued. How awesome is that! Chance to learn and grow. Apply Today! Please fill out , which asks for this information: this form Your CV URLs of your Linkedin, personal site, or any showcase of your work on the interwebs Why you are a fit for this job How you know about this job If you don't hear back from us within 4 weeks, please assume your application has been unsuccessful and try again, but better.

HackerNoon is Looking for Sales Superstars. Apply Now!

Image via Nintendo's Official Website Following 11 mainline titles and numerous spinoffs, the latest title in the Mario Party series - Mario Party Superstars - is set to release very soon. It borrows elements from the oldest titles in the franchise, acting somewhat like a “best of” collection of boards and minigames. This might come as a surprise to some, especially when the recent Super Mario Party title ended up as the . best-selling game of the series The Mario Party series has gone through many changes over the years. After the developers of the first 8 mainline titles were , later games attempted to do their own thing in the form of ‘innovating’ the base gameplay. These changes make one thing clear; Mario Party Superstars will likely be the best game in the series since 2007. absorbed by Konami Before Mario Party Superstars: Gameplay Changes Throughout the Franchise Image via official press release The Mario Party series started on the Nintendo 64, providing a baseline for the gameplay style used by most of the series. Mario characters move around a board, trying to collect coins and stars to achieve victory. After each character moves once, a minigame is played to signify the end of a turn, allowing players to compete in order to improve their standing. Each title had its own extra features and gimmicks between the boards you could play on. Some were linear, only allowing players to grab stars at the end, while others featured unique ways of obtaining those stars in the first place. Players could focus on stealing stars from opponents, or simply opt to collect enough coins to achieve bonuses at the end of the game. The base gameplay was simple, yet it allowed for immense creativity. This changed after the release of Mario Party 8. With the aforementioned absorption of Hudson by Konami, all mainline Mario Party titles were developed by , the minds behind the Wii Party titles. This was the moment the series was changed, for better or for worse...but in my personal opinion, it’s hard to argue for the former. Nd Cube Minor Disappointments, Major Mishaps Image via official press release The first Mario Party developed by Nd Cube, Mario Party 9, was also the first mainline title to alter the base gameplay completely. Boards Rather than moving around a board on their own to collect stars and coins, all players are forced to move together using a vehicle. Every board is incredibly linear to account for this, and the only way to win is by collecting mini-stars scattered around each board. There are no coins and no regular stars; no matter what strategies you might use, there’s only one way to progress. Minigames Even the minigames were altered. The actual variety and fun of these minigames hasn’t diminished, but there’s a lot less charm to them. At the end of each minigame, all characters are simply put in a lineup where they play a happy or a sad animation, depending on whether they won or lost. This is quite different to how previous titles worked; characters would pose in the minigame’s area, sometimes having unique victory or defeat animations. Even with the addition of boss minigames in Mario Party 9, all players face off against an enemy together. Enemies can’t even harm the players; their attacks just cause a loss of points. The boards and the minigames are the heart of Mario Party. While a deeper change to the formula could have been welcome, the changes brought in by Mario Party 9 erased a lot of the unique ways of play. Less strategies could be used since players all shared the same space, and even minigames felt less competitive to a point. This altered style of gameplay was brought over for Mario Party 10, a title that garnered mixed reviews . Even though that title had numerous gamemodes for players to enjoy, it was hard to deny that the series lost the spark that made it so great in the first place. from both players and critics Super Mario Party was a New Beginning with Familiar Elements Image via official press release This might’ve been the start of the series’s downfall, but that changed with Super Mario Party. Abandoning the numbering system used up to then, this mainline title was almost a sort of reboot for the franchise, taking elements from many previous games to make something new out of them. The result is a return to form for the series, bringing the classic coins-and-stars gameplay of the series back in full. It showed up in previous titles and even spinoffs, but Super Mario Party was the first time in 10 years that this style returned as the main focus of a game. New Elements Super Mario Party included further elements to differentiate itself as a new title. Players can gather partners as they travel through boards, giving them access to alternate dice blocks they can use. Alternate modes make for more team-based gameplay, allowing players to move around boards much more freely. Even the roster is unique, being the first Mario Party title to make Bowser a fully playable character rather than giving him his own gamemode or gimmick. Lack of Boards Unfortunately, even its return to form isn’t perfect. Super Mario Party only has four boards available for players, the lowest of any mainline Mario Party. The boards themselves are also much smaller than usual, with some of them struggling to even surpass 30 spaces. These restrictions most likely come from a simple gameplay change - the default dice block rolls 1 through 6, like in Mario Party 9 onwards. Not as much space is moved during a turn as a result, possibly warranting a reduced size for the boards. This size limits the secrets and gimmicks found in boards, and I’ve personally found the overall variety between rounds of Super Mario Party to be very lacking compared to older titles. Minigames This game does bring back some of the charm from minigames, where characters will sometimes pose in unique ways in a minigame’s arena. But such a change is superficial compared to the alterations the boards themselves have gone through. Super Mario Party was unable to capture the heart of Mario Party that the older games were known for. And that is where Mario Party Superstars succeeds. The Best Answer: Mario Party Superstars Image via Nintendo's Official Website While I might sound like I’m simply advocating for the newest game in the Mario Party series, I’m also writing as a confirmation that those older N64 titles really are as good as fans of them remember. Sure, future games had their own gimmicks and changes, but because the series stuck with its formula - up until 9 - it managed to keep a sense of familiar-yet-unique fun. Mario Party Superstars realizes this, and ports over the gameplay of those older titles in full - as well as numerous boards and minigames across the series. While Superstars doesn’t really do anything unique for the gameplay, it also doesn’t really need to. By returning to the old series formula, the game can focus on that style that made Mario Party so great in the first place. If the developers do decide to further ‘innovate’, their experience in working on this new title should certainly show them why the older games are still so beloved - and how further experiences could be improved. There’s very little I can say about Superstars that would be considered bad. My greatest complaints would be that the minigame choices are a little skewed, with only two minigames returning from Mario Party 8, and that there’s only five boards. Certainly a number greater than Super Mario Party, but most titles featured six or more. Even so, there is always the possibility of updates or hidden unlockables; there will likely be quite a bit to look forward to as the game nears its launch date. Not every change or innovation is a good thing, and sometimes it can be wonderful to simply return to your roots. Mario Party Superstars decides to go back on the changes introduced in more recent Mario Party titles, opting to regain the focus of being an intricate, fun, competitive party game. I’m personally very happy to see this happen, and I wonder how the future of Mario Party will play out...and if Nintendo will decide to follow suit with some of their other series. More in Gaming Is Monster Hunter Rise Better or Worse than Capcom’s Previous Installments? How to 'Git Gud' at Elden Ring Before its Release Deltarune Chapter 2: Secrets and First Impressions

Mario Party Superstars: Why a Return to Form is the Best Thing for the Series

Hiring people is always a stressful but interesting process. Hiring good employees is mostly hard. There are lots of flags that can show if you will be able to work together productively and for a long time or not. Outside of the standard sessions, test tasks, etc. never forget about your gut feel . If something doesn’t feel right or you’re not absolutely sure about hiring a person, then say no, just do it. interview It will take longer to hire the right employees, but you want to build a company full of people that are right for their roles and are interested in the project, instead of people that came along at the right time. So, how how to tell if you’re hiring the right people? Ask these six questions: 1. What do you like about our business? If there’s something you don’t like, how’d you change it? This question shows two things. 1. It gives you insight into how candidates will describe verbally things they don’t like. Will it be the fast answer or not. Will he/she talk about problems too much or just the right amount of time. Always look for people who talk just a little bit about the problem and mostly about the solution. 2. Which book are you currently reading? It’s a great question to check if the person is interested in reading at all (we live in the era where it’s a questionble). Moreover, it shows which books the person likes the most (if a candidate thinks too much — the answer probably won’t be honest). Passionate people tend to read books or listen to audio books to improve their skills. Whether the books are specific to a skill such as sales, front-end development, etc. or they’re reading a book focused on self-development doesn’t matter, it’s a good sign. I’m all in with fiction books but if a candidate hasn’t read an educational book for a while, that’s a bad sign. are always looking to improve their skills, find out more useful information. Superstars 3. Tell me about a problem you were tasked with solving in your previous job. What did you do? With this question you’ll be able to see problem solving and creative thinking skills or lack of it. Was the approach thoughtful or did they pass it on to someone else? 4. What’s the one thing you’ve accomplished in your career that you’re most proud of? This gives you insight into how they define success. If they were promoted several times in their previous role during a two-year period, then you may have met a potential superstar. 5. Have you played any team sports before? You all know what is it about. Be aware of which role the person plays in a team is crucial for businesses. People who play team sports such as basketball are driven, focused on achieving goals and physically fit, which helps keep their mind in peak condition. Generally, they will also be great communicators, cope well under pressure and perform well during team events. 6. What do you do for fun? Balance is an important part of success, and physical fitness, relationships contribution, travelling, and learning are important too. As an example, if someone goes to the swimming pool two days a week, volunteers on Saturdays and is learning French “just for themselves,” then it’s fair to say they value achievement, goal setting and are continually looking to improve themselves. This will translate into their job. All in all, look at every aspect of candidates’ lives to see what they like to do and what they hate — and decide whether these factors matter to your business or not. Pay attention not only on the words they say, but on their behaviour during interview too: calm and appropriately relaxed voice and posture can say a lot. Where to find great specialists? There are actually much more options than usual Linkedin posting. For modern and proactive people who have their hands on the pulse with technology and are ready to take the next step in their careers we created — the platform that helps to find the most suitable company and job position based on their professional background, work-related skills, and achievements. Aworker users are no longer tied up to one job, company, city or even continent so all your connections from all over the world will be counted. The catch here is that you have to prove the data you write about by attaching certificates or other verifying documents. Aworker Anyone can get a reward by recommending a friend or acquaintances for open positions (they’ll be rewarded even if they won’t get a job after the interview). All your acquaintances from internships, part-time jobs, webinars, and other professional events will be able to prove your skills and achievements at work. To find out more about the project, visit . this page I wish you all to find your Superstars. If you are the one — you’re awesome! Keep improving!

Job Interview: Ask These 6 Questions to Find a SuperStar

Being the most adopted Version Control System (VCS), Git makes contributing to open-source and teamwork as easy as a walk in the park. If you are a developer, you should know it. definitely As software developers, many people tell us that we to learn X or Y. This can quickly get overwhelming for new developers who are seeking for advice. The advice we get is not always right. However, there are cases when a technology is a , and you will find that the majority of developers use it and advocate learning it. Version control systems (VCS) are such a technology. Everyone is using them (if not, they definitely should). Git is one of the most used version control systems. Let’s dig deeper into VCSs and Git and see why they are considered a when developing. have must must The problem If you have developed applications without using VCS, you most probably have had bad experiences synchronizing the files between the team members. I myself have had this problem, and I used to name files with a version number so that I differ between the files. Manual Version Control, source: imgur Besides looking bad, it also has many usability problems: finding the version you need, remembering the changes you made in each version, and sharing the files with the team. As a solution, there have been many systems which intend to make this process easier and safer. These systems are called Version Control Systems. Version Control System(VCS) For solving the mentioned problems, and many more, different types of VCSs were developed. Let’s talk about them briefly. Local VCS This type of VCSs enables users to store a history of the changes they made to their files locally, eliminating the problems regarding file history, but still leaving a big problem unsolved: the team collaboration. Local VCS, source: Git Centralized VCS Centralized VCSs solve the problem of team collaboration by centralizing the history and storing the versioned files in a server, where clients can access the files and get the latest snapshot of the history. For a long time, this has been the go-to VCS. While it offers many advantages compared to Local VCSs, it still has a major issue, which is also found in Local VCSs: there is a single point of failure — if the central server fails, the whole file history is gone for good. Centralized VCS, source: Git Distributed VCS Distributed VCSs bring all the good parts of the Centralized VCSs, and more. They eliminate the single point of failure by sharing the file history with each team member and not only get a snapshot, but get the full mirror of the repository. If any server dies, the history does not die with it, it can be restored from the clients, who have a full mirror of the repository (i.e. a full backup of the data). Being distributed, they offer a great possibility of developing in a non-linear manner and choosing one of several workflows. Distributed VCS, source: Git Git source: codereviewvideos Git is a distributed VCS which was born as a need of the Linux development team. It is an open-source project started by Linus Torvalds (who also started Linux). Getting Started with Git **Disclaimer** This will not be an in-depth tutorial about git, it will rather be a short tutorial about the basic git commands. Starting the work If you are starting a new project, or have an existing project that you would like to add to git, run inside the project’s folder. git init If you want to work on a project which is already on git, get the git repository , and run . URL git clone UR Git directories Git organizes your repository in three directories: Working Directory, which contains the files, Index, which is the staging area and HEAD, which points to the last commit. Git add & commit To send your changes in a file to the Index, you run . After adding all the changes, if you want to send them to the HEAD, you run . But, the changes are still in your local repository. git add fileName git commit Pushing your changes To push the changes that you’ve committed, you run . git push origin branchName Branching (GitHub Workflow) Branches, as the name suggests, allow developers to work on different features independently. They help developers organize their workflow with ease. One of the popular workflows is GitHub workflow, which suggests that each time you want to work on a feature, you a branch, the changes, and then merge the branch to the branch. You create a branch by running , which is a shortcut, explained . create commit master git checkout -b branchName here Staying up-to-date Since git gives each person involved in the project a local copy of the repository, this copy has to be updated after someone pushed to the remote repository. You do this by running . At times, this process is where problems may rise. As many people may be working on the same files, there may be some , which you will have to resolve. Resolving a conflict can be explained as telling git which changes to keep. You will then have to and the resolved conflicts. git pull conflicts add commit Log To view the history for your repository, run . git log Discard changes If you have made some changes that you are not proud of, you can discard them by running ; you can discard ALL the changes by running , which you should only use when you are sure that you want to discard all the changes. If the changes are in the Index (i.e. you ran ), you can run , which will remove the directory from the staging area(Index), but it will not discard the changes (you will have to run ). git checkout fileName git checkout . git add fileName git reset HEAD -- <directoryName> git checkout Advantages FOSS(Free Open-Source Software) Git is an open-source and free software, meaning that not only you are permitted to use it to fulfill your needs, but you can also configure it to your preference. Fast and small Being distributed, it does not interact with the central server all the time, resulting in a very fast and efficient VCS. Although it mirrors the central repository locally, the clients’ file size is quite small Distributed VCS Being a Distributed VCS, Git ensures that no data is lost, as the central repository is mirrored in each client, which enables restoring files from the client to the central server. Secure For naming and identifying objects in its database, Git uses SHA1, which is a cryptographic hash function. Each file and commit has a checksum, which ensures that there is no change from the Git database without Git being aware of it. Flexible This advantage comes as a result of Git being a Distributed VCS. Git enables users to choose whatever workflow they prefer, be it , or other. centralized, feature, git, forking Git hosting services The most popular service is (find me on GitHub: ) where you can find many interesting open-source projects. Alternatives to GitHub include , , etc. GitHub DhurimKelmendi Bitbucket GitLab Helpful learning resources There are many awesome sites that help you learn git. Here are some of them: You can find many useful articles in the site. learn-anything website git git’s Getting Started Book Atlassian’s git tutorial GitHub’s interactive tutorial Codecademy’s free course Udacity’s free course GitHub Workflow by GitHub Another tutorial by Atlassian Git cheatsheet by Atlassian The next step After learning git, make sure you contribute to open-source projects and help the world while coding. Feel free to if you have any questions, challenging opportunities, or you just want to say hi. email me Finally, be sure to so that you do not miss any new stories I publish. follow me

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Alumni Series Between January and March 2015, we ran out second startup incubator programme in London. One of the team’s, Double, are the first of our alumni series, where we catch up with the startups that have been through one of our incubator programmes. The founders Gary MacDonough, Ben Greenock and Loren Gould took part in the BBC Dragons Den where they pitched a panel of investors for funding to continue and accelerate the growth of their double dating app, Double. “By the time we went to film Dragons’ Den we only had £2.36 in our bank account and owed about £7,000” said co founder Gary. “We weren’t taking salaries either so just living was getting quite difficult. For quite some time, Ben was even living under my stairs in true Harry Potter style!” We caught up with them to discuss their back story, their time on IncuBus, Dragons Den and what’s next! What was the background of the founding team before starting Double? Gary has always worked for himself doing a variety of things. He started working full time Double in September 2014. Ben worked for a beauty company up in Scotland and quit his job in January to go full time on Double. He moved down to London and lived under Gary’s stairs for 6 months. Loren worked at M&C Saatchi and quit his job in May (He had a 3 month notice period). He quit his job the day before we went on Dragons’ Den. What is Double? Double is a location based dating app for double dates. You sign up with a friend and get matched with other pairs nearby. What was the inspiration? Our friends were frequently using Tinder but never actually went on dates. One evening my friend Amy got a match with a hot guy and chatted to him excitedly. When I asked if she would meet him she exclaimed it would be ‘too awkward and potentially unsafe’. She turned to my other friend present Sarah and said ‘if only you could come too!’ That was the eureka moment. I remember meeting the team for the first time and it was obvious from the start how enterprising they were. Previous to Double, the three founders had a previous venture selling ugly Christmas jumpers. They used the £30k they made from that venture to invest in something less seasonal. What have been your main accomplishments since IncuBus Class 2? Releasing a lite version of the Android app. Opening a second office in Edinburgh. Bringing Michael Birch, founder of Bebo on as an investor and advisor. Getting a double date from the app! What was the most useful part of your time on IncuBus class 2? Introductions and networking in the London tech scene. As we had just moved to London and didn’t know anyone this was very valuable. For example The Dragon’s Den opportunity can be traced back to the meeting at the IncuBus hosted . Silicon Drinkabout What had been your biggest challenge to date? Finding a technical co founder… let us know if you hear of anyone :) How do you plan on finding that person? More networking and attending events. By far the best way to meet you people. Sounds obvious, but it is. What was the Dragons Den experience like? The pitch itself was certainly not the hardest we had done. We prepared and practiced a lot. We also had professional pitching advice from (highly recommended). Metaspeech The hardest part was having to be on set at 6am then not knowing what time we were pitching at… in the end we waited 12 hours and our pitch lasted 2 hours! I couldn’t feel my legs by the end of it haha. How did that opportunity come about? We met a guy called Michael Raven at the IncuBus hosted Silicon Drinkabout in Camden who introduced us to the BBC. Coincidentally my mum had encouraged me to apply a few days before… I filled out the application but never actually submitted it but when the opportunity arose we were ready. You can watch them pitch the Dragons in their suits of ‘ ’ below. You’ll have seen the trio who were seeking £75,000, were offered all the money for 15 per cent stake (negotiating it down from 20%) in the startup. After turning down offers from dragons Deborah Meaden and Touker Souleyman, they accepted the offer from Nick Jenkins. amour Now you’ve successfully raised on Dragons Den, what is the funding going to go towards? Improving the iOS and Android versions. Introducing monetisation features. Launching in NYC!!! Bebo founder and investor in Double, Michael Birch, said: “The Double guys have an awesome team and great product execution. They are also fun people to work with!” Double is available to download from the Apple App Store & Google Play For more information about Double please visit: www.joindouble.com Post originally appeared on the IncuBus London Blog as part of our alumni series

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