The Magic Store is a Web3 solution where the community vets, ranks, and prioritizes the apps via a DAO mechanism, with clear earning metrics to incentivize the participation of validators, creators, and users

Magic Square

In this exclusive interview, Andrey Nayman, co-founder of Magic Square, shares his journey in the blockchain industry and the inspirations behind creating a community-driven App Store. Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Andrey Nayman: I've been in the blockchain industry since 2013. My experiences leading various tech companies inspired me to start Magic Square. We're building a community-driven App Store that makes using decentralized apps simpler and safer. I’m passionate about using my blockchain knowledge to create something valuable and innovative for users worldwide. Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Andrey Nayman: Magic Square is all about putting users first. Imagine it like a big online store, but instead of selling physical items, we present Web3 Apps. Magic Store is a way for users to discover all trusted Dapps and engage with them in one place. What makes us different is that we check every app really carefully before putting it in our store. This makes our platform safe and easy to use, whether you're new to Web3 or already an expert. Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Andrey Nayman: Magic Karma and Hot Offers are innovative features designed to enhance the user experience on Magic Square. Magic Karma serves as our reputation system – users receive daily rewards for contributing to the Magic Ecosystem, such as trying out new apps or participating in community activities, earning them points. These points can then be exchanged for exciting rewards. On the other hand, Hot Offers form our engagement layer, where users can complete tasks provided by the listed projects and receive real payments for their efforts. Together, these features keep our users happy and engaged while using our platform. Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Andrey Nayman: The $SQR token is like the heart of Magic Square. Users earn $SQR as a reward for doing things on our platform every day. You can stake $SQR to unlock special benefits if you're a member. You'll get paid in $SQR when you earn rewards through Hot Offers. We also use $SQR to reach the right people in our marketing efforts. To join Launchpad sales, you must stake even more $SQR. So, $SQR is important for everyone using Magic Square. It's what keeps everything running smoothly and helps our community grow. Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Andrey Nayman: Community is at the core of every crypto project. At Magic Square, we believe in bringing the crypto community together in one place. Here, users can decide which projects make it onto our platform, shaping their future. Our community moderators actively gather feedback, which our product team uses to create a roadmap aligned with their needs. To make our community feel valued and engaged, we offer daily rewards through Karma Points and exciting opportunities through Hot Offers. We also host regular surveys where users can share their ideas and feedback, ensuring our platform evolves based on their input. Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Andrey Nayman: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Here's how it works: Users can easily monetize their communities by sharing affiliate offers from listed projects. With one click, they share these offers on social media. When their friends engage and convert, users receive instant payouts. This benefits the user by allowing them to earn effortlessly while supporting their favorite projects. For developers, Magic Boost expands their reach and boosts user acquisition, enhancing app visibility and credibility. Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Andrey Nayman: In the near future, our Launchpad will be a big focus. It's designed to help projects grow steadily and connect with our community. We'll customize our approach, using both public and private sales, to give projects the best chance of success. With our active community, we'll make sure these projects get plenty of attention. Stay tuned for more updates as we work together to shape the future of Web3! Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. In this exclusive interview, Andrey Nayman, co-founder of Magic Square, shares his journey in the blockchain industry and the inspirations behind creating a community-driven App Store. Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Ishan Pandey: Hi Andrey, great to have you here. To start off, could you share a bit about your background and what inspired you to co-found Magic Square? Andrey Nayman: I've been in the blockchain industry since 2013. My experiences leading various tech companies inspired me to start Magic Square. We're building a community-driven App Store that makes using decentralized apps simpler and safer. I’m passionate about using my blockchain knowledge to create something valuable and innovative for users worldwide. Andrey Nayman: Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Ishan Pandey: Magic Square offers a unique ecosystem for discovering Web3 projects. Can you explain what sets Magic Store apart from other platforms in the space? Andrey Nayman: Magic Square is all about putting users first. Imagine it like a big online store, but instead of selling physical items, we present Web3 Apps. Magic Store is a way for users to discover all trusted Dapps and engage with them in one place. What makes us different is that we check every app really carefully before putting it in our store. This makes our platform safe and easy to use, whether you're new to Web3 or already an expert. Andrey Nayman: Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Ishan Pandey: Given the diverse offerings within the Magic Square ecosystem like Magic Karma and Hot Offers, how do these features enhance the user experience and engagement? Andrey Nayman: Magic Karma and Hot Offers are innovative features designed to enhance the user experience on Magic Square. Magic Karma serves as our reputation system – users receive daily rewards for contributing to the Magic Ecosystem, such as trying out new apps or participating in community activities, earning them points. These points can then be exchanged for exciting rewards. On the other hand, Hot Offers form our engagement layer, where users can complete tasks provided by the listed projects and receive real payments for their efforts. Together, these features keep our users happy and engaged while using our platform. Andrey Nayman: Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Ishan Pandey: The $SQR token plays a central role in the Magic Square community. Could you discuss how it integrates into the ecosystem and what its benefits are for users? Andrey Nayman: The $SQR token is like the heart of Magic Square. Users earn $SQR as a reward for doing things on our platform every day. You can stake $SQR to unlock special benefits if you're a member. You'll get paid in $SQR when you earn rewards through Hot Offers. We also use $SQR to reach the right people in our marketing efforts. Andrey Nayman: To join Launchpad sales, you must stake even more $SQR. So, $SQR is important for everyone using Magic Square. It's what keeps everything running smoothly and helps our community grow. Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Ishan Pandey: Community-driven platforms depend heavily on user participation. How does Magic Square encourage community involvement in project voting and validation? Andrey Nayman: Community is at the core of every crypto project. At Magic Square, we believe in bringing the crypto community together in one place. Here, users can decide which projects make it onto our platform, shaping their future. Our community moderators actively gather feedback, which our product team uses to create a roadmap aligned with their needs. To make our community feel valued and engaged, we offer daily rewards through Karma Points and exciting opportunities through Hot Offers. We also host regular surveys where users can share their ideas and feedback, ensuring our platform evolves based on their input. Andrey Nayman: Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Ishan Pandey: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Can you elaborate on how this program works and what its advantages are for both users and developers? Andrey Nayman: Affiliate programs are common in Web3, but Magic Boost offers a unique twist. Here's how it works: Users can easily monetize their communities by sharing affiliate offers from listed projects. With one click, they share these offers on social media. When their friends engage and convert, users receive instant payouts. This benefits the user by allowing them to earn effortlessly while supporting their favorite projects. For developers, Magic Boost expands their reach and boosts user acquisition, enhancing app visibility and credibility. Andrey Nayman: Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Ishan Pandey: Looking forward, what are some key developments or milestones that the Magic Square community can anticipate in the near future? Andrey Nayman: In the near future, our Launchpad will be a big focus. It's designed to help projects grow steadily and connect with our community. We'll customize our approach, using both public and private sales, to give projects the best chance of success. With our active community, we'll make sure these projects get plenty of attention. Stay tuned for more updates as we work together to shape the future of Web3! Andrey Nayman: Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Andrey Nayman Reveals the Vision Behind Magic Square's Unique Web3 Ecosystem

Welcome to Tech News Playlist, a recap of the latest news, trends, products, and solutions from the most sought-after tech startups in the industry. For all things business and tech, we’ve got you covered. Today’s series of news includes the foundation of Venture Studio by a Nigerian entrepreneur, the startup that won the 2022 Startup World Cup, as well as the latest updates on aerospace, cybersecurity, and crypto. Tech News Playlist brings you into the know with the fast-paced digital space from all corners of the industry – all in one platform. We’re giving you the latest updates, including news, funding announcements, product launches, events, campaigns, and more. Leading Stories Nigerian Serial Entrepreneur Launches Venture Studio Opeyemi Awoyemi, , has Fast Forward Venture Studio, which builds and backs companies to unlock prosperity in Africa via cutting-edge technology. He and his Omolara Awoyemi launched this operational expertise to scale a venture studio, a rare move, as most Africans either launch syndicates or venture capital funds post. one of Nigeria’s serial entrepreneurs founded co-founder Retail Tech Startup Wins Ignite 2022 Packworks, a that provides a B2B platform for neighborhood store owners, has won the top prize at the Ignite 2022’s Startup World Cup (SWX) x Wildfire Pitch competition. The company will represent the Philippines at the 2023 SWC Grand Finale of Pegasus Tech Ventures and compete for the $1 million prize. retail tech startup Daily News AeroSpace Space Force to be the first customer of new space gas station Orbit Fab is set to deliver hydrazine propellant to the Space Force satellite. This transpired after Orbit Fab won a $13.3 million USSF 4-year-contract to service geostationary (GEO) military satellites, to begin in 2025. Colorado-based space startup Cybersecurity Microsoft launches Defender Cloud Security Posture Management and Defender for DevOps Microsoft to protect code in the cloud: Defender Cloud Security Posture Management and Defender for DevOps. It aims to manage software development and runtime security across multi-cloud, multiple-pipeline environments. introduces new security services Crypto Magic Square builds a web3-friendly app store for developers Magic Square aims to help developers list projects that are vetted by the community. 250,000 users have signed up to test its upcoming beta version. This traction is an aftermath of Apple’s 30% tech tax on developers, which antagonized web3 startups, with major NFT marketplaces like OpenSea and Magic Eden noticeably only allowing users to browse listings on their iPhone apps without enabling trading to avoid the steep fees. Nascent startup AI Swiss startup Jua launches AI-powered weather predictor Jua introduces a new AI-powered platform to give meteorologists more data-driven insights to understand current uncontrollable weather conditions and patterns. This weather prediction model updates every 15 minutes and predicts more than 15 weather-based parameters, which include air pressure, wind direction, wind speed, and precipitation, improving extreme weather forecasts. Zurich-based tech startup Funding Stability AI Raises $101 Million in Funding Round Stability AI announced that it $101 million in a funding round headed by Coatue and Lightspeed Venture Partners and O’Shaughnessy Ventures LLC. The company is behind the development of Dance Diffusion and Stable Diffusion, open-source music, and image-generating systems. raised Also published . here

Tech News Playlist: Nigerian Founder is Back, Retail Startup Wins Ignite 2022

This paper is under CC 4.0 license. available on arxiv Authors: (1) Macoto Kikuchi (菊池誠), Cybermedia center, Osaka University, and Department of Physics, Osaka University and kikuchi.macoto.cmc@osaka-u.ac.jp. Table of Links Introduction Model and Methods Results Discussion and Acknowledgment The mutation-selection mechanism of Darwinian evolution gives rise not only to the adaptation to environmental conditions but also to the enhancement of the robustness against mutation. Suppose more than one phenotypes share the same fitness value. The robustness distribution should differ for different phenotypes. Thus, we expect that some phenotype is favored in evolution and some is hardly selected as a consequence of the selection bias for mutational robustness. In this paper, we investigated this selection bias on phenotypes for a model of gene regulatory networks (GRNs) by numerical simulations. The model we used exhibits three types of response to the change of input signal; monostable, toggle switch, and one-way switch. We regarded these three response types as three phenotypes. We constructed the randomly generated set of GRNs using the multicanonical Monte Carlo method and compared it to the outcomes of evolutionary simulations. The results suggest that the one-way switches were strongly suppressed in evolution because a majority of the one-way switches were not mutationally robust. I. INTRODUCTION Living systems have developed through Darwinian evolution, a process in which a genotype changes so that the phenotype fits the environmental conditions. This process consists of the repetition of a mutation of genotype and a selection of individuals. This mutation-selection mechanism of evolution not only gives rise to the adaptation to the environment; it also causes a selection bias in evolution. One of the best-known such biases is the bias for mutational robustness.[1–3] Mutational robustness is a characteristic trait of living systems that they do not readily lose their functionality by a genotype mutation. The fact that the existing organisms possess this trait can be confirmed experimentally, especially for microorganisms, for example, by the comprehensive gene knock-out or rewiring of the gene regulatory networks.[4– 7] However, how such robustness has developed through evolution is difficult to investigate by experiments. Thus, theoretical studies or computational experiments provide indispensable information. A theory based on population dynamics has shown that mutational robustness evolves in neutral evolution.[8] Since this selection of mutationally robust genotypes is a selection irrespective of fitness, it is called “second-order”.[2] The second-order selection is considered to enhance the evolvability in the cases of non-neutral evolution because the mutationally robust genotypes have a higher possibility of having offspring with high fitness. We note here the usage of the word “fitness.” Conventionally, fitness is defined based on the number of offspring, and the effects of all the selection biases are taken into account. In this paper, however, we define fitness by a value calculated from a specific fitness function and consider selection biases separately. Suppose there are more than one phenotypes that take similar values of fitness. Their robustness distributions of the corresponding genotypes should be different in general. Thus, we expect that second-order selection affects the appearance probabilities of these phenotypes; the phenotypes with relatively robust genotypes will be favored in evolution; in contrast, the phenotypes with relatively fragile genotypes will be suppressed. We expect this phenotype selection that originates from the difference in mutational robustness of genotypes to occur in general situations in evolution. In this paper, we deal with a model of the gene regulatory networks (GRNs) as an example and investigate the enhancement of mutational robustness in evolution and the phenotype selection induced by the robustness using computational methods. We usually conduct evolutionary simulations (ES) to investigate Darwinian evolution numerically. However, ES alone is not enough for discussing selection biases in evolution because the outcomes of ESs already involve the effects of the selection biases. For exploring characteristic properties of evolution, we need a reference to compare with ES. In the case of GRNs, an appropriate reference system is a randomly generated set of GRNs. If there is no selection bias, evolutionary obtained GRNs should coincide with that picked up from this set having the same fitness. Ciliberti et al. conducted a random sampling of GRNs in a different context to investigate the structure of the neutral space.[9, 10] However, simple random sampling does not work for the present purpose because GRNs with high fitness are rare. Thus, we need some rare-event sampling method. We have proposed to use the multicanonical Monte Carlo (McMC) method for this purpose. McMC has been proposed originally for investigating phase transitions of spin systems.[11, 12] In those cases, McMC enables us to sample energy evenly in its entire range. After that, it has been recognized that McMC can also be used for rare-event sampling of non-physical systems by regarding any quantity as energy.[13] Counting the number of large magic squares is an example.[14] It was used to investigate biological networks by Saito and the author.[15] Nagata and the author applied it to GRNs by regarding fitness as energy.[16] In this case, McMC enables us to sample GRNs evenly in the entire range of fitness. The method of comparing the outcomes of ES and the reference set obtained by McMC has been proposed by Kaneko and the author.[17]. A comparison of ES and McMC was conducted recently to discuss the evolution of the genetic code.[18] In the previous paper, we showed for a model of GRN that the mutational robustness is enhanced by evolution, and the appearance of bistability is delayed at the same time.[17] These two phenomena are closely related to each other; our analysis suggested that the reason for the delay in the appearance of bistability was attributed to the fact that the bistable GRNs were less mutationally robust than the monostable GRNs. In the present paper, we further explore this phenomenon of phenotype selection due to mutational robustness. We use a different definition of fitness in the present work. In this definition, the GRNs exhibit two types of bistability at high fitness adding to monostability: the toggle switch and the one-way switch.[19] The one-way switches of GRNs realize irreversible changes in the cell states in living cells and are relevant to processes such as cell maturations and cell differentiations; an example is the maturation of Xenopus oocytes.[20] Although each fixed point of GRN corresponds to a cell state in living systems, in this paper, we regard three distinguishable stabilities, monostable, the toggle switch, and the one-way switch, as three phenotypes for discussing phenotype selection. These three stabilities exhibit different robustness distributions. We will show that the appearance of the one-way switch, which is the least robust among the three, is suppressed in the evolution.

Phenotype Selection due to Mutational Robustness: Introduction

It happens all the time—you search for anything in Google or Apple Store and get millions of suggested results as if you had time to go one by one and find what’s really best for you. Sometimes they even get it right, provided that you are in the right country, but it may not always be the case. Meanwhile, as the crypto-verse evolves, major search engines have become blockchain play stores as the main source for DeFi apps listings. But, is that what crypto users need? Is a generic search engine good enough to satisfy the sophisticated needs of DeFi users? People tend to believe that search engines have all the answers. A search engine, be it a web page or an application, often leads to millions of results here and there, seemingly good enough for us to conclude that we are using the right tool—one that provides us with all the options we need. This is enhanced by the immediacy with which we operate on a daily basis, surrounded by an infinite number of tailored ads, instilling in us the presumption that what is suggested at the top of the search results should be exactly what we are looking for. And if it isn't, those who are navigating in the DeFi ecosystem would often assume that it's probably not worth investing much more time in finding the right dApp mobile app or tool, simply, because it most likely hasn’t been created yet. Users should be aware that if they expect the first result to be the correct one, then they should be ready to be misled. In a way, this idea seems to corroborate Segal's law: “A man with a watch knows what time it is. A man with two watches is never sure.” The problem is that when looking for a dApp, you not only want the right one but you need the perfect one—not something that is just okay. What a Trendy App for Crypto Is Not With an ecosystem that is just evolving and becoming mainstream, how can we be sure that the dApp listing we find at first is the right one? The problem with being sure about things is not knowing whether you have asked yourself the right questions or if you have questioned yourself enough. Search engines are designed to deliver results. That is, they deliver suggestions to you and visible monetary results to the company paying to be shown first. Not to forget the financial gains a search engine company earns through the sale of advertising positioning. This conflict becomes even more evident when the subject of our query is very specific and not a generic search, as we meant to address in the example above. In this case, the list of results is made with the highest bidders and not the best possible results. Interesting, but probably unfair to users who are exposed mainly to paid positioning listings. Trusting a search engine, such as a dApp store, that works around a conflict of interest seems a really bad idea. You Are Ranked Numbers speak for themselves. The search engine industry generated $1.3 trillion dollars in 2020 alone. This amount explains why companies get remunerated for ranking results. However, justifying this to the user who receives nothing for this transaction is far more complicated. Every click is money except for the person clicking the mouse. Everyone knows that anyone can promote anything with enough money, and because of how unfair this is to startups, a parallel industry has emerged. We are talking about Search Engine Optimization techniques that enable projects to be featured as relevant in dApp listings even if they aren’t blessed with substantial funding. This is very common in emerging sectors such as DeFi. And this takes us a step further—how reliable a search result is for a user looking for projects to invest in? In this sense, no one in their right mind would let marketing campaigns or a sacrosanct algorithm drive their investments. This modus operandi cannot replace a thorough analysis made of impartial, external audits and the convincing criteria provided by like-minded, real users who endorse a project’s functionality. A Crypto App Store as It Should Be It is in this scenario when lists of dApps ranked by real people become useful. This is the approach taken by MagicSquare, a crypto app store made for and by the crypto community. The MagicSquare platform aims to provide users of any level of expertise with guidance to choose projects that are tested and ranked by the community and not listed first because of the money companies can pay. In that way, MagicSquare ensures that the marketing budget is not all a dApp can have. By allowing the community to validate the solutions dApps intend to provide, MagicSquare lets users find promising DeFi tools based on proven functionality. The applications listed in this DeFi search engine are also verified by the platform itself, thus ensuring that they are always legit tools for users. As a validated app store, such an initiative is likewise positive for the integral development of the DeFi ecosystem as it supports and highlights the solutions and projects that really bring value to the sector. Community Chosen Good Crypto Applications In a way, replacing a traditional search engine system with one based on the criteria of thousands of users might seem to complicate things. Referring to our initial analogy, it is like pretending that a person with many watches could be more sure of what time it is. In reality, a person with a thousand clocks has more sources to base his criteria on and decide what the time really is for them, whereas the person who has only a single clock is left to rely solely on the time that the clock says it is. DeFi users, especially the new ones, shouldn’t be taken hostage by major search engines that have not yet collected all the necessary input to truly offer the best results. There’s no doubt that only a dedicated blockchain play store could do that.

Who Gets to Decide What Good is?

I’m running , a Telegram channel about and in general. Here are the best posts of September 2018. @pythonetc Python programming Overriding vs overloading There are two concepts with similar names that can be easily confused: overriding and overloading. Overriding happens when a child class defines a method that is already provided by its parents effectively replacing it. In some languages you have to explicitly mark the overriding method (C# requires the modifier), in some languages it's optional (the annotation in Java). Python doesn't require any special modifier nor does it have a standard way to mark such methods (some people like to use a custom decorator that does virtually nothing, just for the sake of readability). override @Override @override Overloading is another story. Overloading is having multiple functions with the same name but different signatures. It’s supported by languages like Java and C++ and is often used as a way to provide default arguments: class Foo {    public static void main(String[] args) {        System.out.println(Hello());    }

public static String Hello() {        return Hello("world");    }

public static String Hello(String name) {        return "Hello, " + name;    }} Python doesn’t support finding functions by their signatures, only be their names. You can write code that analyzes the types and number of arguments explicitly. That usually looks clumsy and generally is not a nice thing to do: def quadrilateral_area(*args):    if len(args) == 4:        quadrilateral = Quadrilateral(*args)    elif len(args) == 1:        quadrilateral = args[0]    else:        raise TypeError()

return quadrilateral.area() If you need type hints for this, the module can help you with the decorator: typing @overload from typing import overload

@overloaddef quadrilateral_area(    q: Quadrilateral) -> float: ...

@overloaddef quadrilateral_area(    p1: Point, p2: Point,    p3: Point, p4: Point) -> float: ... Autovivification allows you to create a dictionary that returns the default value if the requested key is missing (instead of raising ). To create a you should provide not a default value but a factory of such values. collections.defaultdict KeyError defaultdict That allows you to create a dictionary that virtually contains infinite levels of nested dicts, allowing you to do something like . d[a][b][c]...[z] >>> def infinite_dict():...     return defaultdict(infinite_dict)...>>> d = infinite_dict()>>> d[1][2][3][4] = 10>>> dict(d[1][2][3][5]){} Such behavior is called “autovivification”, the term came from the Perl language. Object instantiation An object instantiation includes two significant steps. First, the method of a class is called. It creates and returns a brand new object. Second, Python calls the method of that object. Its work is to set up the initial state of the object. __new__ __init__ However, isn't called if returns an object that is not an instance of the original class. The reason for this is that it was probably created by another class, hence was already called for that object: __init__ __new__ __init__ class Foo:    def __new__(cls, x):        return dict(x=x)

    def __init__(self, x):        print(x)  # Never called

print(Foo(0)) That also means that you should not ever create instances of the same class in with a regular constructor ( ). It could lead to the double execution or even infinite recursion. __new__ Foo(...) __init__ Infinite recursion: class Foo:    def __new__(cls, x):        return Foo(-x)  # Recursion Double : __init__ class Foo:    def __new__(cls, x):        if x < 0:            return Foo(-x)        return super().__new__(cls)

    def __init__(self, x):        print(x)        self._x = x The proper way: class Foo:    def __new__(cls, x):        if x < 0:            return cls.__new__(cls, -x)        return super().__new__(cls)

    def __init__(self, x):        print(x)        self._x = x [] In Python, you can override square brackets operator ( ) by defining magic method. This is how you create an object that virtually contains an infinite number of repeated elements: [] __getitem__ class Cycle:    def __init__(self, lst):        self._lst = lst

    def __getitem__(self, index):        return self._lst[            index % len(self._lst)        ]

print(Cycle(['a', 'b', 'c'])[100])  # 'b' The unusual thing here is that the operator supports a unique syntax. It can be used not only like this — , but also like this — , or , or , or even . The semantic is , but you can use it any way you want for your custom objects. [] [2] [2:10] [2:10:2] [2::2] [:] [start:stop:step] But what gets as an index parameter if you call it using that syntax? The slice objects exist precisely for that. __getitem__ In : class Inspector:...:     def __getitem__(self, index):...:         print(index)...:In : Inspector()[1]1In : Inspector()[1:2]slice(1, 2, None)In : Inspector()[1:2:3]slice(1, 2, 3)In : Inspector()[:]slice(None, None, None) You can even combine tuple and slice syntaxes: In : Inspector()[:, 0, :](slice(None, None, None), 0, slice(None, None, None)) is not doing anything for you except simply storing , and attributes. slice start stop step In : s = slice(1, 2, 3)In : s.startOut: 1In : s.stopOut: 2In : s.stepOut: 3 Coroutine cancellation Any running coroutine can be cancelled via the method. will be thrown into the coroutine that will lead for it and all wrapping coroutines to be terminated, unless the error is caught and suppressed. asyncio cancel() CancelledError is a subclass of that means that it can be accidentally caught by that is meant to catch “any error”. To safely do this within a coroutine, you stuck with something like this: CancelledError Exception try ... except Exception try:    await action()except asyncio.CancelledError:    raiseexcept Exception:    logging.exception('action failed') In , the common practice to schedule execution of some code at a later time is to spawn a task that does : asyncio await asyncio.sleep(x) import asyncio

async def do(n=0):    print(n)    await asyncio.sleep(1)    loop.create_task(do(n + 1))    loop.create_task(do(n + 1))

loop = asyncio.get_event_loop()loop.create_task(do())loop.run_forever() However, creating a new task may be expensive and is not necessary if you aren’t planning to any asynchronous operations (like the function in the example). Another way to do this is to use and functions that schedule an asynchronous callback to be called: do loop.call_later loop.call_at import asyncio                                                        def do(n=0):                           print(n)                           loop = asyncio.get_event_loop()    loop.call_later(1, do, n+1)        loop.call_later(1, do, n+1)                                       loop = asyncio.get_event_loop()    do()                               loop.run_forever()

Python stories, September 2018

Spencer Randall asked me. It is a compelling question, one of the challenges preventing mass cryptocurrency adoption. “People don’t understand Bitcoin and other cryptocurrencies. That’s why we’re here to share our insights, analysis, and make more people aware of this asset class.” Houston, Texas “Would you ever invest in something you don’t understand?” Almost a year ago, a friend introduced me to the founding team — Spencer Randall, Michael Thoma and Brooks Vaughn. CryptoEQ is a startup in Houston, Texas becoming the “trusted source for cryptocurrency analysis” for novice investors, launched July 2019. “Crypto is complex. We make it simple,” Spencer says. “We offer technical, fundamental and sentiment analysis, along with proprietary trading algorithms and systems.” CryptoEQ.io Spencer highlighted the difference between traditional investors, like Warren Buffet calling crypto “rat poison,” and crypto advocates, like Jack Dorsey integrating crypto into the payment platform service he founded, Square. “What is complex and disruptive is often controversial and misunderstood. We saw that with the internet bubble, file sharing, decentralization of media. My convictions for this company and this industry draw from these patterns.” Spencer was an early adopter. “I went to crypto meetups all through Texas. I was in Austin, San Antonio, Dallas, all around Houston. The meetups felt more like mixers. We would discuss crypto with three or four people at a time, but there wasn’t any structure. So, I founded a meetup here in Houston facilitating curated discussions on crypto. We compared notes on assets, investing, and trading in crypto. Everyone had their own system. After six months, we identified the problem we wanted to solve: we wanted to create a single system to help everyone trade and invest in cryptoassets.” Spencer met his co-founders, Michael Thoma and Brooks Vaughn, at these meetups. “We got to know each other for months before we realized we were going to be business partners,” Spencer laughed. “Brooks, Mike, and I were together by the summer of 2018. When I built out the team, I sought people good at what I was not. It turns out, Mike, Brooks, and I balanced out each other’s strengths and weaknesses. There’s a lot of harmony in building a team that way. I’ve done the same thing with our marketing team, our development team. It’s a really healthy way to start a team.” Spencer takes the role of “The Engineer,” Brooks as “The Innovator” and Michael as “The Scientist.” Spencer continued, “It’s not like day one we had this mapped out, no, but when you have a culture built around trust, it shakes out. It all starts with trust, and the right roles and responsibilities fall in place.” The team has a collective 12 years of crypto experience. Spencer envisions CryptoEQ as a lighthouse shepherding in newcomers to crypto. “Think, Morningstar and TradingView utility meets Apple product simplicity. There’s nothing in crypto that has emerged as a household name prioritizing usability like Apple.” Spencer shared with me the guiding principles for CryptoEQ’s products: , , and . “The quality piece is most important. There are more than 5,000 cryptoassets. We want to help people vet, trade and invest in the assets that matter.” Signal over Noise Direction over Data Quality over Quantity Usability over Complexity There are a multitude of crypto analysis and content engines. But one critical ingredient is lacking: “Trust. Our analysis is independent and objective. We don’t accept outside consultation to influence what we list, what we rate or how we rate our assets.” CryptoEQ is a progressive, web-based app accessible through any browser. I was curious why the startup has not published an app on the App Store. “An app store, like Apple’s or Google’s, is a gatekeeper. Sticking with a simple, intuitive interface accessible from any device is the future of app development and aligns with crypto fundamentals,” Spencer explained. Spencer noted competitors focused each on a single element of analysis: technical, fundamental and sentiment. “Our magic is a 360 degree market analysis combined with trading signals and sentiment analysis. We haven’t seen a product tackle all three and wrap them into one interface.” Spencer defined each analysis type for me. “Technical analysis is tracking market price action and issuing signals — tops, bottoms, entry and exit points. Fundamental analysis is applying a conceptual and objective framework to asset research and analysis. Our research is by humans for humans. Sentiment analysis is interpreting emotions — fear, greed, happiness, etc. — from text scraped from any internet source and digested using natural language processing. I’m really excited to launch this. Folks are really interested, it’s really sticky. For now, we offer trend analysis: observing search engine volume to identify irregularities leading to trade opportunities.” The delivers the promised concise and simple interface: technical trading panel, overview fundamental analysis and trend analysis. website home page The technical trading panel recommends entry/exit points. Spencer noted about the trading performance, “Four out of five trades were winning trades in the last six months. We are really excited about that, it’s incredible in a volatile market like this.” But the team wants to improve the results. “How can we get to 90%? We revisit our algorithms regularly and we want to bring in folks specializing on machine learning.” The team’s fundamental analysis considers ten objective criteria to award one of five ratings — , , , and , , , , , , , , and “An immense amount of work goes into rating any one asset and evergreening the report to incorporate latest developments. Mike and Brooks have created a great system and standard to evaluate any asset,” Spencer reports, “and we’re excited about the results. In 2019, an equally weighted portfolio of our favorably rated assets outperformed a comparable portfolio of unfavorably rated assets by 61%.” Gold Silver Bronze Neutral Deficient — evaluated on 10 criteria — Use Case Technology Economics Governance Vulnerabilities Network Effects Team User Experience Regulation Roadmap . Spencer explained how they protect their brand. “All of our algorithms are trade secret, anyone we’ve worked with that has helped us with development has signed a non-disclosure. The fundamental ratings and due-diligence framework Mike and Brooks pioneered is also protected by non-disclosures.” Spencer shared with me exciting upcoming developments. “Next month, we’ll be launching the v3 website and an additional 10 assets. Some of the top sites in the space receive millions of visits per year, and our improvements will be a big boost on our journey to that volume. We’re targeting mid-year for v4, including display advertising and sentiment analysis. v5 early next year will launch enterprise products.” The team is also actively growing. “Our core team is doubling to 10. We have business and software developers, marketing strategists and the cofounders. There is also a network of contractors around the globe.” The growth continues, even amidst an unprecedented global medical epidemic, Covid19. “Our team structure and business adapts well to full remote work, and we are proud to offer our new hires opportunities in these challenging times.” We conclude the interview with a discussion on CryptoEQ’s culture. “Mike, Brooks and I talked about brand identifiers and the way we’d like to work together as team. Over time, I realized that they’re the same thing. Our mission as cofounders is to embody these principles. Our top three are , and . In entrepreneurship, to get anything off the ground, you have to own what you do. You must have an open mind and really consider people’s ideas when embracing something that’s completely disruptive and against the grain. And it all starts with trust. The cofounders trust each other, our team members trust each other; all our other values stem from this.” Drive Maverick Trust I noticed one of their other values, . Since I first met up with CryptoEQ, the team has done exactly what they said they would do, when they said they would do it. The Wild West of crypto needs objective, trustworthy analysis to cut through the clutter and scams. The team that will make good on this will also live it themselves. Integrity The author, Evan Crain, is excited to share the link to the IOTA core report he wrote for CryptoEQ and announce an upcoming crypto dictionary he is editing with CryptoEQ, intended for novice investors. The author occasionally completes paid editing and writing assignments and provides unpaid advice for CryptoEQ. This article was unpaid and initiated by the author from personal interest to learn more about CryptoEQ. The author hopes you learned as much about CryptoEQ as he did in writing the article! The content of this article is journalistic only, and is not intended to provide tax, legal, accounting, financial, investment, trading, or professional advice. Please also see CryptoEQ’s full disclaimer at CryptoEQ.io .

"Our magic is 360-degree Market Analysis, Trading Signals & Sentiment Analysis" - Spencer Randall

A gif demonstrating how a Convolutional Neural Network can be used for a VR level editor type interface. Seriously … It’s like Harry Potter. TLDR; video . here A while back, I wrote my in series about the intersection of AI, Creativity, and 3D content generation. This blog post is a continuation of that series. first blog post My dream VR application is ultimately a seamless extension of my imagination. Sure it’s a lofty goal, but it is my intuition that techniques can help make this idea a reality. machine learning Specifically, this blog post explores the use of convolutional neural networks to dramatically change interaction design in VR. Designing VR in VR Anyone that has designed a VR application will tell you that working in traditional 2D mediums will only get you so far. In order to make VR, you need to be in VR. However, since VR is such a new medium, the industry supporting VR creation is also new. Companies like Unity and Epic are creating VR level editors, however they end up feeling just like VR desktops, menus upon menus entrenched in design principles for 2D mediums. A demonstration of a VR editor. For me, the process of creating a VR environment in real time needs to be fast and effortless. But how do we create such a system without making use of 2D menus? What if we could draw what we wanted? What if instead of having to navigate through a series of options, the system can understand what I need? Enter Machine Learning If you think about it, by entering into a VR environment, everything you do within that environment is being translated into data. Everything you look at, every twitch of your arm, every action you take can potentially be recorded. Dystopian cyberpunk ramifications aside, this is quite compelling from a ML standpoint because what any ML model needs is lots of quality data. For the purposes of this blog post we’ll be looking at how convolutional neural networks (CNN) can be used for gesture recognition designed to replace current 2D menu design principles. CNN’s have a remarkable ability to recognize data that has any sort of spatial relationship, i.e. images. What if we could use this ability to create interfaces that figure out what you want?. For instance, instead of picking a prop from a long list of items, what if I just sketched it out? I’m definitely not the first person to think of this. I’m essentially talking about Google or in VR. In fact companies like Adobe are already exploring this type of effect in their products. Check out this recent demonstration of . While I’m not actually sure that these implementations use a CNN or something else, functionally it is the same. Quick Draw AutoDraw ProjectQuick3D My First Model As a first step, I took three classes from the , and used them to train a simple CNN. For the architecture, I took a basic MNIST example since the data for MNIST and Quickdraw is very similar. Quick Draw dataset Drawing a circle creates a sphere, while drawing a triangle creates a cube. For those that are detail oriented, I’m sorry, drawing a triangle on a trackpad is much easier than drawing a square :( As a proof of concept, I started with 2D interface on my MacBook Pro. The is trained on circles, squares, and triangles. The idea being that when a user draws a shape, an associated shape will be instantiated in 3D. network Knowing that the basic principle worked, it was time to take the whole thing into VR! I decided to go with Leap Motion because I wanted the interactions to feel natural and effortless. While the tracking of leap motion wasn’t as precise as say a Vive controller, I found that once I accounted for Leap’s tracking quirks, the interactions felt very fluid. A simple model with classes for “tree”, “bush”, and “flower” So this was super cool, and it was quite surprising how well it worked. But here’s where I ran into the first problem with my assumptions. Drawing was a very frictionless interaction, however only the first time you do it. For instance, imagine having to draw a tree sketch for every tree in a virtual forest. Some additional UI thinking was required. What if instead of drawing a tree, I drew a square? In other words what if I mapped each object to a primitive shape? “Circle” = “Bush”, “Square” = “Tree”, “Triangle” = “Flower” This made the interaction easier, quicker, and very satisfying. However it’s easy to imagine how you could run out of primitive shapes quickly. Perhaps you could use numbers, but then we’re moving away from the idea of an effortless conjuring of a 3D object. If you have to remember an arbitrary mapping, it’s too hard. My next step was to separate the drawing and placing mechanic. Switching modes is done by tapping different spheres aligned with my wrist. Above my hand is an icon indicating which object I have currently selected. This allows me to quickly and intuitively choose a new object and quickly and effortlessly place it. Switching things up with a very hacky interface. Here you can see drawing and placement as two separate interaction modes. It was time to really dive in CNN’s and start making a custom model that could use more classes. Since I’m still a ML newb, I used a relatively small data set of 11 classes. After manually playing around with the architecture for a day or two, I stumbled upon , a library that would help automate my architecture optimization. The final model I used was this: Hyperas This resulted in above 95% accuracy for evaluation set. Now I had more classes, meaning I could magically pluck more objects out of thin air. However, since the classes from Quick Draw are seemingly arbitrary, I was left with a rather random model. This place could use a house! and some chairs! we definitely need a pirate ship (sailboat)! These kids aren’t going to drive themselves to school! How about some flying lessons? For a more fluid view check out the screen capture on youtube: Conclusion The experience of drawing objects out of thin air is quite magical. But is it useful? Honestly, in its current form with only a few classes, no. Yes it’s more intuitive, but it’s not faster then a standard but well designed menu system. What this system requires to be superior is a larger model, something that could recognize around 1000 classes. That’s a bit out of my reach unfortunately. The ability to just draw up any object out of 1000, would indeed be faster then finding that same object in a standard menu system. However at this point, you could just use a voice command like “house”, and perhaps that would be more intuitive. A drawing based system becomes the best solution for objects whose granularity is defined by simple visual features. Perhaps it’s easier and quicker to draw a dandelion or a sunflower, than it is for a speech recognition systems to process the audio of “dandelion” or “sunflower”. Or even a large and complex model that has granular classes like 20 varieties of flowers. Perhaps you can’t remember the name of something? Or what if you’re in a busy office setting and your audio samples become too noisy? It feels good to draw objects vs just saying words. Maybe a combination of the two works best? In any case, while this prototype falls short of replacing a menu system, I think it succeeds in demonstrating the power of machine learning in a creative VR context or in the context of VR UI. A 3D environment allows us to evolve past desktop based design and machine learning is definitely a powerful tool for user interaction. In the next few blog posts, I’ll explore other, potentially more powerful implementations of creative ML in VR. 👋🏾 Get to know the people and ideas shaping the products we use every day. Subscribe to Noteworthy — the product & design newsletter written by the Journal team.

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