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EasyTdd 0.5.0: Streamlining Mocking with Incremental FluentMock and Other Updates

Introducing Builder: Your Buddy in Test-Driven Development (TDD)

নির্মাতার সাথে পরিচয় করিয়ে দেওয়া হচ্ছে: টেস্ট-ড্রিভেন ডেভেলপমেন্টে আপনার বন্ধু (TDD)

Wir stellen vor: Builder: Ihr Kumpel für testgetriebene Entwicklung (TDD).

Presentamos Builder: su aliado en el desarrollo basado en pruebas (TDD)

Présentation de Builder : votre compagnon dans le développement piloté par les tests (TDD)

बिल्डर का परिचय: टेस्ट-ड्रिवेन डेवलपमेंट (TDD) में आपका साथी

Builder の紹介: テスト駆動開発 (TDD) の相棒

Builder 소개: 테스트 주도 개발(TDD)의 친구

Apresentando o Builder: seu amigo no desenvolvimento orientado a testes (TDD)

Представляем Builder: ваш помощник в разработке через тестирование (TDD)

Builder'ı Tanıtıyoruz: Test Odaklı Geliştirmede (TDD) Arkadaşınız

Giới thiệu Builder: Người bạn đồng hành của bạn trong Phát triển theo hướng kiểm thử (TDD)

Builder 介绍：测试驱动开发 (TDD) 的好帮手

In this blog post, I want to discuss the importance of starting with a red test. The red-green-refactor cycle is a well-known mantra in test-driven development. To recap: Red - write a piece of test code for the functionality you want to implement. It must be red since the functionality is not there yet. One of the main ideas is that the tests should guide the development process. Green - write a piece of code for the test to pass. Refactor - eliminate code duplicates, enhance readability, improve aesthetics, possibly extract some methods or create new classes, and optimize. Don't forget to refactor both the production code and the test code. Why red? I very rarely do post-code testing (perhaps there is a better-known expression for writing tests after writing production code). I do that when I encounter a piece of code not covered with tests that I need to put my hands on. And I'm happy when the test is green, making me feel the code works as expected. Test-driven development is not only about writing tests to make sure the production code works as expected, but it is rather (as the name suggests) a process of development. A couple of things pop into my head when I think about Red in . First is demand; I produce the production code when it is needed (red test), and I need to meet the demand. Second - YAGNI (You Aren’t Gonna Need It), I do not produce the code that is not needed, I do not code too much, because it might not be needed. TDD I'm just a human after all I'm only human, and I make mistakes. I make mistakes in production code and test code alike. The idea for this blog post came last week when I encountered situations where the test was not red, and it was very tempting to leave it as is because it was GREEN. This was especially true when there were already several tests, leading me to think that I might have already covered the case. It may sound like an oxymoron, but a GREEN test in the first step of TDD is a RED flag. In the following sections, I will provide cases that I encountered last week where I made a mistake in the test, causing it to be green, but it was incorrect. it vs if I'm a backend developer and use TDD a lot. However, sometimes I have to work with front-end and do . I do my best to practice TDD while doing JavaScript development as well. JavaScript development For JavaScript testing, I use as a test runner and as a test framework. In , the function sets a test case, and the function defines an individual test. I want to share how I mistyped as multiple times, leading to a false impression of all passing tests. karma jasmine jasmine describe it it if it('Seat map is selected when person of the seat map is selected', 
    function () {
        let seatMaps = result.Seating.SeatMaps;
        expect(seatMaps[0].Selected).toBeTrue();
        seatMaps[2].Passengers[1].select();
        expect(seatMaps[2].Selected).toBeTrue();
    }
); and if('Seat map is selected when person of the seat map is selected', 
    function () {
        let seatMaps = result.Seating.SeatMaps;
        expect(seatMaps[0].Selected).toBeTrue();    
        seatMaps[2].Passengers[1].select();
        expect(seatMaps[2].Selected).toBeTrue();
    }
); In all modern development environments and text editors for developers, is usually highlighted in a different color. I'm not colorblind, but it is still easy for me to miss it. Additionally, I find that my finger muscle memory is used to typing a two-letter word starting with as . It just happens automatically. As a result, there are no errors, and it is a valid sentence. All tests appear green when I run them. It is easy to notice when it is the first test because it will show that 0 tests have been run. However, it is more difficult when there are more tests. In this particular situation, my initial thought was, Fortunately, my experience with treating green tests as a red flag saved me. if i if "aha, I already covered this functionality.". Missing the attribute This example is in C#. While it's not taken from production code, it captures the essence: [DataRow(1, 2, 3)]
[DataRow(2, 3, 5)]
public void SomeFakeTest(int a, int b, int c)
{
    Assert.AreEqual(c, a + b);
} In the MsTest test framework, the attribute alone is insufficient to mark a method as a test. The or attribute is required for the method to be recognized as a test. I have found myself forgetting this attribute a few times, which resulted in tests not being run and creating a false impression of all tests passing. DataRow TestMethod DataTestMethod Missing assertion The following example, which I adapted from my previous blog post, demonstrates the situation. I recall the instances when I write a test - I think of a test case, a test name, arrange it, then act, and then - get distracted. When I return to see where I left off, I usually run the tests to see the red ones and continue from there. But in this particular case, all are green. This case is tricky; it's easy to forget that I left the test unfinished, and it's green because it doesn't have an assertion.ertions. [TestCaseSource(typeof(UnexpectedPaymentMessageIsSentWhenInvoiceIsOverpaidOrUnknownCases))]
public async Task UnexpectedPaymentMessageIsSentWhenInvoiceIsOverpaidOrUnknown(
    Invoice invoice,
    string message)
{
    _invoiceRepositoryResult = invoice;

    await CallCallback();
} Misread Recently, I needed to modify the functionality of a DTO converter to return passengers in order by , rather than in a somewhat random manner. I'm using , which allows passing configuration in the method. I simply typed , and Visual Studio's IntelliSense suggested a list of options. I chose the first one that started with and ended with . sequenceNumber FluentAssertions BeEquivalentTo config.With With Ordering seatMapDto
    .Passengers
    .Select(x => x.FirstName)
    .Should()
    .BeEquivalentTo(
        new[]
        {
            "GUDMARIN",
            "GUDMARIANA",
            "TOM"
        },
        config => config.WithoutStrictOrdering(),
        "should be ordered by sequenceNumber"
    ); I ran the test, and it was green. Since I expected the test to be red, I began by making sure that the input data was unordered in the arrange part to create a nonsequential order for the test to be red. Only later did I realize that I had chosen the configuration instead of . WithoutStrictOrdering WithStrictOrdering Wrapping it up Being human and making mistakes is not easy :). In this blog post, I wanted to showcase how sometimes silly mistakes can give the impression that tests are green, and emphasize the importance of starting with a red test first. Have you encountered similar situations where a mistake in a test led you to believe that production was working as expected? Please share your experiences in the comments. If you enjoyed this post, please click "like" and "follow". Feel free to explore my other blog posts, where I write about test-driven development and my pet project EasyTdd, a Visual Studio extension that makes test-driven development simpler. Also published here.

Why You Should Start With a Red Test in Test-Driven Development

The Easy Way to Begin Test-Driven Development

“A wise ninja does not seek out an enemy who he does not fully understand." - Splinter from Teenage Mutant Ninja Turtles As a technical writer passionate about science fiction, one of the most exciting parts of my job at Sonatype is when I have the opportunity to collaborate with the security research team. Ever since we found that correspond to what we call RAT mutants—novel malware that leverages the capabilities of remote access trojans and information stealers—our security researchers have had a nagging sense of déjà vu. a series of Python packages last month It’s not a rare occurrence: they constantly assess hundreds of suspicious packages from open-source registries to determine if they’re truly malicious. During this procedure, they identify familiar patterns between the packages, and sometimes, upon recognizing strong similarities, they conduct further research to track down the actor/campaign behind them. Between the last week of 2022 and the beginning of 2023, our AI system flagged suspicious packages uploaded to the PyPI registry including and . Our security researchers looked at them and confirmed they were indeed malicious. And after further analysis, they noticed these packages added a series of features not often seen in Python malware. pyrologin , easytimestamp , discorder , discord-dev , style.py , pythonstyles In what has become a de facto strategy, bad actors hide a payload in the file so developers only have to use a simple pip install to get infected. In this case, they’d be inadvertently launching a PowerShell script that downloads a ZIP file to install libraries that let the attacker control the victim’s mouse, the keyboard, take screenshots, and create hidden remote connections. And on top of that, these packages also exfiltrate sensitive information like passwords, cookies, and cryptocurrency wallet data, and even try to install a tool for remote access. Our findings were in line with of the very same packages. setup[.]py Phylum’s technical report Nevertheless, security researcher Carlos Fernandez was having a haunting sense of déjà vu. During the week after we disclosed the packages, memories of having seen earlier versions of this malware flashed through his mind. A threat actor might have been evolving their malware through time, but we needed to make sure, so I put my OSINT hat on to help him see if there was any truth to his intuition. The Origin Story Looking back on our database, on September 25, 2022, a package called was uploaded to the PyPI repository. Our AI flagged it as suspicious and our security researchers confirmed that it contained malware and that it bundled the sources of another package called . Both libraries were authored by , aka BillyTheGoat, aka BillyV3 (with contributions from loTus04 and BlueRed). This author from France (at least that’s what his location in GitHub and the language spoken through his Discord channels suggest) is credited as the person behind , a Python obfuscator that we’ve found often in recently uploaded malware, as well as one of the creators of , an information stealer that’s been active since July in an to open registries. pygradient pystyle billythegoat356 Hyperion W4SP stealer ongoing supply chain attack W4SP stealer boasts persistency (it gets reactivated every time users restart the PC), obfuscation (the author promises it’s fully undetectable), and steganography techniques to hide polymorphic and highly-obfuscated payloads within an image file. The malware sends all the victim’s Discord accounts, credit cards, passwords, and crypto-wallets back to the attacker through a hard-coded Discord webhook address. Bad actors interested in this malware have purchased it by paying about 20 USD in crypto. Originally, did not have a malicious nature—a harmless package to style and color console output to enhance readability and user experience. But according to Phylum research, once it became popular (the package is still active on PyPI and it accumulates more than 40,000 downloads every month), they decided to add W4SP malware in a couple of releases. pystyle Open-source packages like can act as sleeper agents for months only to be activated later by their own author for malicious purposes. A risky move, since their popular package can be discovered by security researchers like us who’ll report it as malicious and have it be taken down forever. That’s why they often create new packages instead where they include the source code of an innocuous library, add some type of malicious payload, and upload it under a new name: something generic, benign-sounding, innocent-looking. A name such as . pystyle pygradient After taking a closer look with Carlos, we found this proto-RAT mutant malware lacked some of the features of the December versions: even though it also launched a PowerShell script and exfiltrated sensitive data, it didn’t use base64 encoded payloads or an allowlist txt file to avoid self-infection. In a way, it was a milder, less sophisticated variant. But then we looked at early-November uploaded packages, including , , and , and after diving deeper, we found confirmation of our suspicion: the malware was truly evolving. paintpy devicespoof devicespoofer A Copycat Chasing Malware Even though BillyTheGoat hasn’t been active on GitHub since November, we’re still finding malware that’s based on his creations or stealing directly from them, mostly by a user that’s apparently based in Portugal called . zeeckt This user uploaded a recently removed package in PyPI called , falsely credited to BillyTheGoat in what looked like —a technique to increase the reputation of their new package by linking it to a popular GitHub library, thus getting associated with its hundreds of stars and forks. pystilez StartJacking Checkmarx this bad actor after finding a W4SP attack in a package called (along with similarly-named variations) that included a Discord server link within their malicious code. The administrator profile linked to a verified Steam account and the name “zeeckt” appeared as one of the attacker’s Steam aliases. has been tracking apicolor also reported that this threat actor was creating fake users by copying the profiles of popular GitHub accounts to increase legitimacy. And one of their victims decided to create warning people about this attack after falling for one of their fake profiles. Check Point a YouTube video A security researcher posted one of zeeckt’s payloads for educational purposes and he received an interesting reply from the co-author of W4SP stealer: How ironic for your stealer to be stolen. The same author, apparently a 17-year-old Python developer, reiterates his frustration in a README file on his GitHub profile: We don’t know exactly what malicious code zeeckt has stolen from other bad actors, but one thing is certain: that name continues to pop up in malware campaigns across open-source registries. Sneakier Variants Gnaw Away at Your Crypto Wealth Carlos recently found four more packages by : , and . After further investigation, he tagged them as malicious and reported them to PyPI to be taken down. The fastest case took about 20 minutes from publication to deletion. zeeckt forenitq forenith , forenity forenitz Looking at in Carlos found the following first-stage payload: setup[.]py forenitq , The attacker creates three temporary files that download and execute Windows binaries from specific URLs using the "start" command. Even though the slugs and are suggestive of malicious intent, we couldn’t confirm that assumption without looking under the hood. /rat /clip At the time of this writing, the page hosted at is still active and it only shows a link to a Discord invite that’s already expired or private. hxxp://20[.]226[.]18[.]203 Carlos noticed that the package was posing as the popular package, using their metadata for a possible StarJacking attempt. Colorama After deobfuscating the RAT file, he found a line that loads a possible clipboard hijacker, but the specifics of it were base64 encoded: Upon decoding, we get a Python code designed to hijack a victim's clipboard to replace the intended cryptocurrency wallet address with the attacker's address: It looks for specified patterns like (bc1), (0x), Monero (4), and l (L or M or 3), and when a pattern is found, it replaces the intended address with the attacker's cryptocurrency wallet address. bitcoin Ethereum itecoin The code uses the library to copy and paste clipboard data. It will install the library if it’s not already installed and then copy the cryptocurrency wallet address to the clipboard. The code is then set on a continuous loop to monitor the clipboard for wallet address patterns. pyperclip Additionally, we uncovered more techniques this RAT mutant uses to evade detection: a first-stage payload added to the file instead of and a second-stage polymorphic payload that changes every time you run the binary. forenitq/ansi[.]py setup[.]py The attacker also added a new command and control with a very complete help menu—and this time se fala português: As you can see, malicious actors are eager to steal your crypto funds and system credentials to further penetrate your infrastructure. Their RAT malware keeps mutating to be more evasive and damaging to developers. So, if you're not safeguarding your software supply chain today, consider taking action to do so as soon as possible. IOCs (Indicators of Compromise) hxxp://20[.]226[.]18[.]203/inject/tCxFLYLT6ViY9ZnP hxxp://20[.]226[.]18[.]203/clip hxxp://20[.]226[.]18[.]203/rat

How RAT Mutants, in Python, Steal Data and Evade Detection

কিভাবে RAT মিউট্যান্ট, পাইথনে, ডেটা চুরি করে এবং সনাক্তকরণ এড়িয়ে যায়

Wie RAT-Mutanten in Python Daten stehlen und sich der Erkennung entziehen

Cómo los mutantes RAT, en Python, roban datos y evaden la detección

Comment les mutants RAT, en Python, volent des données et évitent la détection

पायथन में आरएटी म्यूटेंट कैसे डेटा चोरी करते हैं और जांच से बचते हैं

RATミュータントがPythonでデータを盗み、検出を回避する方法

Python에서 RAT 돌연변이가 데이터를 훔치고 탐지를 회피하는 방법

Como os mutantes RAT, em Python, roubam dados e evitam a detecção

Python'da RAT Mutantları Verileri Nasıl Çalıyor ve Tespitten Nasıl Kaçıyor?

Cách RAT Mutants, trong Python, đánh cắp dữ liệu và trốn tránh sự phát hiện

RAT 突变体如何在 Python 中窃取数据并逃避检测

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Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! 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Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! 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Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

In the first two months of the pandemic, we saw two years’ worth of digital transformation. And it’s just one of the examples of how fast the world around us is moving. To keep up with competition and ever-changing user needs, businesses and their IT teams need to rethink the way they’ve been creating software for decades. This is where rapid application development (RAD) comes into play. In this article, we’ll take a closer look at this concept, assess rapid application development advantages and disadvantages, and investigate RAD’s connection to low-code and no-code app development. Let’s dive in! What is Rapid Application Development Exactly? The rapid application development concept was invented in the 80s and has since become one of the most popular Agile approaches. Essentially, RAD aims to create applications faster. This is achieved through frequent interactions and continuous feedback analysis. Unlike the Waterfall methodology, which dictates that software teams need a detailed roadmap for their project, RAD does not rely on heavy planning. On the contrary, its goal is to release a working application (or a small piece thereof!) faster, present it to stakeholders, test the application on real-world data and tasks, and tweak the software based on what’s been discovered so far. Another core difference from the traditional software development methodologies is that RAD is driven by interface rather than coding requirements, which helps create applications with a particular user in mind. Rapid Application Development Stages The rapid application development model incorporates four phases: Requirements analysis. At this stage, a business analyst sits down to talk with stakeholders to loosely define software requirements. This is a sharp contrast with Waterfall projects, which rely on thorough requirements elicitation and leave little room for adjustments and change requests once the project scope has been agreed upon. Iterative prototyping. A software development team creates a proof of concept (POC) version of an application containing its essential features. The working prototype is then demonstrated to stakeholders, and critical changes are made. The iterative prototyping approach helps software engineers evaluate the feasibility of a complex app or functional component early on and rule out possible technology roadblocks. Actual development. The development (aka “construction”) stage encompasses coding, application testing, and feedback collection. Since most of the user problems and change requests are addressed during the prototyping stage, software engineers can create applications faster. Product finalization. The RAD paradigm revolves around faster development cycles, meaning developers might have to cut corners to put up an app in a tight timeframe. As a result, technology debt may accumulate — and that’s one of the issues RAD teams address during the product finalization phase. Additionally, they conduct full-scale testing, write technical documentation, and onboard users. How RAD and Low-Code App Development Complement Each Other As we mentioned earlier, rapid application development is interface-driven. Since no-code and low-code app development platforms incorporate well as complete back-end infrastructures, RAD advocates often use them to prototype and build applications. Additionally, most low-code and no-code platforms now offer pre-packaged templates to fast-track app development. builders as drag-and-drop app To choose the optimum technology stack for your RAD project, you should compare the advantages of low-code and no-code application development tools and study the on product review websites like G2. platforms’ ratings Rapid Application Development: Pros and Cons Different approaches to software development have their advantages and downsides, and RAD is no exception. Advantages of Rapid Application Development** The major advantage of rapid application development is that software teams can change the key features and functionality of an application at any given moment. Other reasons to embrace RAD include: Stakeholders communicate frequently, solving issues together and creating a product that best meets the business/technology needs of a company With RAD, you can manage your resources appropriately and avoid running over budget without seeing a glimpse of real value from your new technology RAD helps reduce your time to market, which is key for innovative technology startups and companies undergoing ad-hoc digital transformation In RAD projects, user feedback takes center stage, increasing customer satisfaction levels Disadvantages of Rapid Application Development The core disadvantage of the RAD concept stems from software projects’ shorter lifespan and user-centricity. The larger and more complex your project is, the harder it becomes to collect stakeholder feedback and coordinate developer activities. ** Here’s what you should consider before opting for RAD methodology: To sail through the rapid application development stages, you need a strong, inherently Agile software team whose members collaborated on similar projects in the past and have direct contact with each other. This makes RAD hard to implement in enterprise-grade settings and outsourced projects with many developers scattered around the globe RAD is interface-driven, meaning developers often cut corners during the prototype stage to get the green light from the stakeholders. These workarounds can lead to a huge technical debt on the back-end level, which needs to be addressed later in the development process and may eventually affect release deadlines Unlike Waterfall, RAD requires stakeholder commitment, which seldom suits executives and senior project/product managers at large enterprises due to scheduling conflicts To reap RAD’s benefits, your team needs to choose suitable app building software that combines the power of low-code development with the simplicity of no-code tools When to Use Rapid Application Development Now that we’ve assessed rapid application development’s advantages and disadvantages, it’s time to define instances when RAD should be the go-to methodology to coordinate your project. Creating an MVP to Raise Funding After a turbulent 2020, startup funding is on the rise again, with it’s 1999 all over again. To avoid missing out on the investment frenzy, a software startup must develop a prototype or minimum viable product (MVP) version of their applications faster  — and that’s where RAD comes in handy. checks like investors writing huge In addition to speeding up the development cycle, the RAD approach helps put up a decent-looking, full-featured prototype on a shoestring budget and refine the product once you raise funding. Getting Buy-In from a Company’s C-Suite Although pandemic has given a second wind to their digital transformation initiatives, very few companies splurge on a new technology solution with only vague ROI potential. believe the 77% of executives might seem like a waste of money if you go the traditional route, building separate apps for iOS and Android users and sucking the company’s annual IT budget dry. But if you develop a cross-platform prototype using a no-code or low-code app builder, test the software among sales representatives working in the field, and present the results of your experiment to your CEO, your chances of getting a budget will increase exponentially. Creating a mobile version of an agricultural company’s CRM system Prototyping Applications at Hackathons Hackathons are where bright tech ideas take shape and rising stars are discovered. If you don’t believe me, just ask the founders of EasyTaxi and Appetas. What makes hackathons challenging is that you only get a couple of days to __ __or asset management system.** create a novel meditation app With RAD and the underlying technology stack, including visual development tools and website/application templates, it is totally possible to turn your idea into a prototype in mere days. Rapid application development is a powerful approach that helps exercise greater risk control and ensure greater user satisfaction with the final product. When used appropriately and backed up by a suitable technology stack, RAD can work wonders for your business — especially if you’re working on a novel product or toying with the idea of implementing new software in the workplace.

Advantages and Disadvantages of Rapid Application Development 

A facial recognition demonstration using , , python and the drone , from . Keras Tensorflow Tello DJI here: Source code https://github.com/cleuton/drone-facerec This project is based on two other Github projects: , created by ; FaceREC - CNN implementation of facial recognition me , created by . EasyTello Ezra Fielding Overview I ran this using (Linux). To run using or some fixes must be done. Be my guest to convert it and submit a pull request! Ubuntu MS Windows MacOS This software controls a Tello drone, capturing the video streaming generated by its onboard camera. He intercepts each frame and tries to recognize faces that appear on the image, using a that I trained, based on images from the dataset , and some pictures of mine. Convolutional Neural Network Model Labeled Faces in the Wild It is a proof of concept of the use of artificial intelligence with drones and, why not, Internet of Things (IoT). Note that performance can be low, after all, I am using a laptop and a cheap and simple drone. With more resources, better performance of the solution is possible. To use this solution, you will need: A , with Firmware 1.4 or higher; drone Tello A facial recognition model, generated by the project ; FaceRec_CNN The Dlib face prediction file: (see ); shape_predictor_68_face_landmarks.dat FaceRec_CNN I will teach you how to do these 3 things. Before using There are a few things you need to do before using this application. First it was made exclusively for Drone Tello, but it applies to any other drone that has a python programming API. The second thing to do is to be able to control your drone using only the original project and then try to use this project here. Why? Well, it's not as simple as it looks ... easyTello If you just bought your Tello drone, you need to check which version of it has. This can be done through the app (if you haven't downloaded it, it's better to do it now). Firmware Tello Then, turn on the drone, connect to his WiFi network with your Smartphone (TELLO ...), open the app and click on the settings button, as in the image: Then, click on : More Click on the three dots button: Now, check the firmware version number. If it's 1.3... then you'll need to update it! The process is done in two parts: Download the firmware to the Smartphone and update on the drone. To download the new firmware version, disconnect from the drone's WiFi and connect to the Internet. Using the Tello app, click the button to download the firmware. After downloading, connect to the drone's WiFi and click the button again. Update Update After the process, check the new firmware version. : An indication that the API is out of date is when the drone does not recognize the command ! Warning streamon If you have updated the firmware correctly, I recommend that you try to run the original project, following the instructions in that repository. This confirms that the tracking API is working correctly. Then, you can download and run this repository here. easyTello That done, clone this repository and create an environment using the ( : Anaconda script ./ conda-env.yml) conda env create -f conda-env.yml Activate that environment before running this project: conda activate drone-facerec Creating a model file To do facial recognition, it is necessary to train a neural network. I didn't insert the original project here, not even the model file that I trained. I recommend that you clone the original project and train your own model, and then copy the file created to the folder, within this project. FaceREC_CNN HDF5 easyTello Download and extract the , and place it in the FaceRec_CNN project and also inside the folder for this project! file shape_predictor_68_face_landmarks easytello Generating a model is simple: 1. Get multiple photos of the same person's face Use and download several photos of the same person, copying to the folder the FaceRec_CNN project; Labeled Faces in the Wild raw Name the files in this pattern: john-doe.nnnn.jpg (do not use spaces, number photos of the same subject, separating them with dots). For example: "bill-clinton.0001.jpg"; Take several photos of your face (and whoever else you want to recognize) and save it in the folder following the same nomenclature as in step "1/2"; raw Make sure that there is only one face on each training photo! If there is more than one face, crop the photo; 2. Convert the photos The script will rotate and crop the faces, transforming them into monochrome images. It will separate in training and test photos (folders "train" and "test") according to the variable . If it stays at 30%, then 70% of the images will be for training and the others for testing; trainCNN.py train_test_ratio = 0.3 Write down the categories found! The program will display a vector with the names found. Note to change in the prediction script( ) and in the recognition script for this project ( ); predict.py faceprocessor.py ./ easytello / faceprocessor.py If there are 4 people in your folder, it has to separate 4 people in and 4 people in . With few images, fewer people can be in and this will result in error. raw train test test 3. Get people's names and model file People's names are displayed on the console after training. Copy this vector and change it in ; faceprocessor.py The model file will be named copy it to the folder of this project; 'faces_saved.h5' easytello Before trying with the real drone, I suggest that you run a local test using your webcam: ffmpeg -f video4linux2 -s x480 -r -i /dev/video0 -vcodec libx264 -f  h264 -an udp: 640 15 //localhost:11111 Use the program that I included ( ) to capture the images and do the recognition. If everything works, if he recognizes you, then everything is ready to run on the Drone. facetest_ffmpeg.py ./ facetest_ffmpeg.py If you want, you can run this project with FFMPEG: python teste.py Controlling the drone : To do facial recognition, do not use the original Easytello project! I modified the project to communicate with my facial recognition script ( ). Warning faceprocessor.py ./ easytello / faceprocessor.py The ( ) script controls the drone. It basically does 2 things: puts it in command mode and starts capturing video. But you can do so much more! You can make the drone take off, go forward, or turn around and then land. There are some commented commands that you can use. If you want to know more about the commands that Tello accepts, . teste.py ./ teste.py see the documentation : Do not try to connect your smartphone to the drone, as it only serves a single connection! Warning Make sure you have placed the file in the easyTello folder! Make sure the name is the same as it is in the file ( ) h5 faceprocessor.py ./easytello/faceprocessor.py : If you make the drone take off, make sure there is room for it! If it hits the ceiling or walls, it could be damaged! And be careful when trying to use Tello outdoors (not recommended). If he moves more than 10 meters, he may be out of range of WiFi and collide with something. Theoretically, if he loses contact, he lands automatically. Warning Previously published at https://github.com/cleuton/drone-facerec/blob/master/english_version.md

Facial Recognition On Drone

Even if all centralized exchanges are closed, leaving us with no option but the decentralized ones (DEXs), can these platforms handle the high liquidity that Binance, Coinbase, or Bithumb are facing today? We’re going to take a deeper look into this question and identify the requirements for DEXs to achieve high liquidity, while at the same time try to spot the potential candidates that can bring a workable solution to the table. Liquidity is a challenge for the entire cryptocurrency market In the cryptocurrency market, refers to the difficulty of an asset to be bought and sold at a fair price. Lower liquidity creates a more volatile market, where prices change substantially in a shorter period of time. Higher liquidity makes for a stable, less volatile market where prices do not fluctuate as significantly. High liquidity is always desirable, not just in cryptocurrency but in any market. liquidity https://www.coindesk.com/price/bitcoin Low liquidity causes price slippage. In a volatile market, the delay between placing a trade and having it executed could be enough for the price to go up or down significantly. If the movement is against your desired trade, you could lose money. Ideally, the order price (the one you asked for) should be close to the executed price (the one you actually get). In an exchange with low liquidity, however, this isn’t possible due to the lack of traders, thereby with every trade. increasing the risk Currently, the most liquid asset is . The market is able to easily absorb any transaction without causing a drastic change in the value of the dollar. There’s almost no risk involved for either the buyer or the seller. The value of the currency (the US dollar, euro, yen, etc.) is known at the time of the transaction along with the costs associated with it. fiat currency For example, one million US dollars will keep its value before and after the transaction, and the commission is calculated based on the intermediary (bank, broker, etc.) in the same stable currency. However, for that same transaction, using one million dollars worth of Bitcoin, or any other cryptocurrency, in a low liquidity market not only has a greater effect on the cryptocurrency’s price but it could end up costing even $100,000 more (or less) than the original price when making the trade. The risk is on both sides. Centralized exchanges In the past few years, the liquidity of the cryptocurrency market had its . In 2017, before the “hype” started, the total market capitalization of all cryptocurrencies (including Bitcoin) was only $16 billion. In a single year, it went up to over $800 billion. Currently, we are perceiving the prices being at an all-time low even if we are sitting on a and a 24-hour transacted volume of $16 billion. This amount was representative of the entire cryptocurrency market two years ago and is now being traded on a daily basis! ups and downs $120 billion market cap https://coinmarketcap.com/charts/ Who is handling the liquidity of this transaction volume? Mostly centralized exchanges with an astonishing of all market transactions. The other 0.2% is on the DEXs. 99.8% With most of the centralized having been in the past, we can’t overlook the benefit of using a decentralized exchange where there is no need to put any trust in the exchange platform itself, as the funds are held by each user in their own personal wallet. Not to mention the increased privacy and the reduced risk of server downtime, which makes DEXs the clear winner between the two. hacked So what keeps traders from jumping ship? The on DEXs. low liquidity DEXs: a deserted market What do the decentralized counterparts need in order to increase their liquidity? There are three requirements and none of them can’t work without the other: Usability Traders Scalability Let’s figure out how close are we to having a solution that checks them all and offers high liquidity. https://www.stateofthedapps.com/ Offering a seamless user experience The easiest issue to solve is offering, at the very least, the same user experience as their centralized counterpart. If they want traders and average users to consider using their platform, the “cost” of switching to a new platform should be reduced as much as possible. There are a few essential points to be consider: The interface should be familiar (the (Binance, Bittrex, and Bitfinex) are currently setting the standard) market leaders Offering the same features for any trader (conditional orders, stop-loss, access to reports, etc.) Integrating trading analysis tools (having their tools incorporated directly into the exchange is a must) Once these points are settled, there are things to consider in order to remain competitive in the market, such as offering lower fees, support for other cryptocurrencies, and various trading pairs. solution was to offer as many tokens as possible by coming up with a permissionless token-listing system to simplify the token submission process. Even if this approach is aligned with their community-driven, open-source vision, the market didn’t embrace the other shortcomings coming with the use of this decentralized exchange. The promise of a new user interface has been around since the beginning of last year, but there are no signs of them being close to implementing it. The liquidity remains low on ForkDelta and reinforces our belief that user experience aspects are an important factor. ForkDelta’s has also had a great evolution, reaching while having every aspect decentralized; from placing the order to matching or executing that order. The ones who will be able to lower the barriers of switching from one exchange to another (allowing users to exchange any token within one platform) can score high marks in overall usability. Unfortunately, BitSquare is not there yet, as they have only sorted these tokens into 11 tradable pairs. The delay of selling one currency for BTC to then use that BTC to buy another token can cost a trader a lot of money. BitSquare 126 tradable cryptocurrencies What looks promising is the solution coming from , which not only eliminates specific trading pairs with cross-chain liquidity pools and comes up with a modern color-driven user interface but, being part of a more complex DAE (digital assets ecosystem), they are making a step forward by offering an analytics engine, , to their users. This engine displays a list of all available coins and a selection of the top-ranked coins based on fundamental, technical, and market sentiment analysis data that a trader can use to their advantage. It’s the first time we’re seeing an intuitive UI interaction in accessing and viewing data for a user’s interest in an exchange and, in terms of user experience, this might just push the standards even higher. VDEX Vespucci https://icons8.ru/ouch/illustration/marginalia-payment-processed Bringing a lot of traders Having all the features in place and offering a seamless user experience could be part of the second requirement: having as many traders as possible on their DEX so the liquidity increases organically. But there is another way that programmatically solves the liquidity problem by sharing it. One solution comes from , which is a service that searches multiple exchanges, protocols, and market makers while trying to ensure the best available price. They are able to achieve this by aggregating the data from other platforms through their APIs (application programming interface). Leveraging their open trades, it might offer the trader the experience of trading against all other users, thus faking internal liquidity. It might sound ideal in theory, although the best price is not always the best available in the open cryptocurrency market. It’s just the best price between the aggregated sources. Dexdex This very approach was also adopted by , pooling orders from multiple DEXs. They actually automated the process for the trader: the user indicates the number of tokens he or she wishes to buy or sell and the AI will execute the order at the best rates from the other exchanges. However, the transaction time is the highest among other DEXs. Having the transaction guaranteed is an important thing for anyone who just wants to buy some tokens at the best rate, but for a professional trader, the delay is unacceptable. Easytrade In this case, we’re seeing the solution somewhere in between. Pooling liquidity from the other DEXs, sharing the traders that are scattered between the multitude of available choices, all while making sure there are enough traders on the platform. Between these platforms we can mention , who are offering a white-labeled solution for any DEX or dApp willing to join their pool, along with , who will use the price discovery formula in order to augment liquidity and bring stability to their tradable assets. altcoin.io VDEX Bancor algorithm Being able to scale Top-notch user experience will bring a high volume of traders which should bring forth the missing liquidity. What’s missing though? It’s not that there’s something missing, it’s more about what’s required when the traded as volume increases way over the DEX’s capacity. A decentralized exchange allows for securely matching and handling order books in a decentralized manner. This is done (on the blockchain). A big volume of transactions means that not all of the orders will fit in the next block. Waiting for the next one can create delays that would bring the price change problem back. We might get the liquidity, but if the placed order is not executed in the shortest time possible then we’re still facing an issue. on-chain One way to fix this is by using “ ” for order matching. An atomic swap is when a trade, instead of being made into two separate transactions (buyer sends first, seller sends second), the order is filled in a single (atomic) operation. Technically, a smart contract acts as a trustless escrow that holds onto one currency until the other user sends their currency. This increases transaction speeds. atomic swaps , a DEX running on the Komodo platform, recently achieved 60,000 atomic swaps, but their trading volume is still low and we’re not sure if their technology will scale by itself. has a similar approach running atop of the Blocknet Protocol, allowing users to maintain control of their tokens and private keys during the atomic swap. Between the examples previously mentioned, is leveraging the same atomic swap technology along with the , using EOS.IO contracts as nodes that allows them to scale off-chain while maintaining the on-chain advantages. BarterDex BlockDX VDEX loopring protocol https://dilbert.com/ Conclusion The major challenge with most DEX’s is liquidity. The one who will have the trading volume of Binance or Coinbase will set the standard. It doesn’t matter if the final solution will be sharing a pool as altcoin.io proposes or combining multiple technologies as VDEX does. In any case, the latter has been rather interesting in our research. Not only is the UI up to today’s standards, but in terms of solving the liquidity problems, they are building an ecosystem with interoperable elements, supporting each other. For example, the comprehensible multi-blockchain information extracted via the Vespucci analytics engine aggregates blockchain timelines to determine block production coincidences between chains to access greater liquidity. How smart is that? Anyway, 2019 could be the year when DEXs will flourish. It’ll be good to keep an eye on the best contestants!

What are the Requirements for Decentralized Exchanges to Achieve High Liquidity?

TL;DR: There are now over 250 decentralized exchanges in the world today. These are the hurdles of what it will take to be #1. , the largest fee-based centralized cryptocurrency exchange by volume ($1.07b per ) its intention to get into the ultra-competitive decentralized exchange (DEX) market. Despite its size and name recognition Binance will not be the leading DEX of 2019. Binance day recently announced Why? Binance will certainly invite its existing user base to its new DEX to help kickstart liquidity. This will not be a competitive advantage however. The liquidity problem is already being solved today by DEX aggregators like (Dexdex) that pools orders from multiple DEXs. According to the site: dexdexplatform Easytrade.io “Dexdex searches multiple exchanges, protocols and market makers ensuring you always get the best available price. It also provides tools for anyone to generate their own liquidity.” Their technology is also open-source and on GitHub. Enter the DEX Aggregators This is important because Dexdex actually aggregates some of the orders of 0x relayers to add to its own virtual liquidity pool. 0x is a protocol for trading Ethereum (ERC20) digital tokens and assets and pooling liquidity. Many of today’s top DEXs use its technology. RadarRelay (runs on the 0x protocol) and ForkDelta are amongst its sources of liquidity. ForkDelta already gets most of its liquidity from EtherDelta.​ So Dexdex is like an aggregator of aggregators. Imagine being able to aggregate the liquidity of all DEXs. It’s not exactly a zero-sum game but an ever expanding pie: The ultra-competitive race for the DEX market (by % of trade volume among DEXs). Liquidity Is a Slippery Slope The two major challenges with most DEX’s today are liquidity and UX. The DEX that will win over the masses in the next couple of years is going to be the one that has the liquidity of Binance and the UX of Coinbase. The problem is that there is a lack of standardization across DEXs — no common trading and liquidity protocol. This is the reason that most DEXs have low liquidity and volume — they simply do not share a common liquidity pool. There are many projects trying to solve this protocol problem today. The leader by number of DEX integration partners in this space currently is the and has a great chance of becoming that standard in 2019. 0x protocol Why? I analyzed a of 258 known DEXs in existence today (yes there are 258!) and removed the 70 DEXs without a known or publicly published protocol leaving 188 DEXs. I then plotted this in the pie chart below for a better visualization. The lack of a standardized protocol for these exchanges is staggering. There are over two dozen different protocols! The leader is 0x which powers of these DEXs today: Github database over 19% DEXs by protocol (Source: ) https://github.com/distribuyed/index Most people don’t want to do their own due diligence when it comes to trusting the underlying settling mechanism. Trade settling is how trades are confirmed and committed to the Ethereum blockchain). does this seamlessly for any of its relays that use the 0x protocol for off-chain orders. In other words instead of hundreds of future DEXs each with their own trade settlement technology the 0x protocol standardizes this “DEX-as-a-service” functionality across the board. This puts 0x in a position to be to a DEX what Ethereum is a smart contract. 0x The Challenges Ahead One major challenge I see with this Dexdex/EasyTrade model is the same problem Expedia had when it became the go-to for finding the price rates on hotels. Anyone can create an aggregator but unless you have millions of dollars in marketing budget you have to add some other value otherwise anyone can become an aggregator. I believe UX will be one of several key differentiators.​ If you have never traded on a DEX its not exactly user friendly. and I would not recommend it if you’re new to the cryptocurrency trading world. Binance will still be a formidable player however as it forays into the DEX space. It plans to monetize by owning many of the nodes that will power its own blockchain. Binance has the name recognition, marketing dollars and trust (if we can define trust being that they already process 1.4m TPS for its users). I believe that in 2019 the DEX that pulls ahead of the pack will: Aggregate liquidity. Have a killer UX. Be trustless. Have low to no fees. Have automatic order matching and instant trades. Leverage network effects for growth. 0x provides that battle-tested protocol and allows the aggregation of liquidity from its relayers. This puts Dexdex (I have no stake or relationship with them) in a position to compete with any “DEX” that Binance creates granted it can get more exposure. Many of today’s DEXs’ fees are two-fold — first a Gas fee to confirm the transaction on the Ethereum network and the DEX fee. The Binance DEX will require fees in BNB token to pay for transactions on its blockchain. ForkDelta charges .03%. Radar Relay just charges for Gas although states that it will charge fees in the future once it gets more traction. The successful and sustainable DEX of the future will charge no user fees just as Expedia charges no user fees. Once an exchange or DEX gets enough volume new monetization opportunities will emerge. In the meantime to avoid massive Gas fees key components of the orders (bid matching, balance inquiries, transaction histories, limit orders) can be side-chained (blockchain side-chain technologies like ZombieChain would be a great fit). Compounded with the lack of speed and functionality for advanced features such as algorithmic or bot trading most DEXs are probably not yet an option for high-frequency or day traders with more sophisticated needs than your average retail HODLer. Loom Network Its still early in the evolution of DEXs and major players such as IDEX, Komodo and Stellar are already breaking ground with clever alternative solutions such as and . hybrid-exchanges atomic swaps Which DEX will get the lion’s share of the market in 2019? Its still a bit early in the game to tell. Tech break-thoughs in DEXs are now becoming the new normal. For now perhaps the best that we can do to predict the DEX of the future is to follow the money. Better yet follow the liquidity.

I Analyzed 258 Decentralized Exchanges and This Is What I Found

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