A spatial sandbox for creative teams. Playbook is an XR design tool that allows for code-free creation, iteration, and export of 3D scenes into your game engine of choice so you can spend less time writing code and more time building your experience.

Playbook

playbook

Revolutionizing Healthcare: Andy Schoonover's Playbook for Disrupting Entrenched Industries

বিপ্লবী স্বাস্থ্যসেবা: প্রবেশ করা শিল্পগুলিকে ব্যাহত করার জন্য অ্যান্ডি শুনোভারের প্লেবুক

Revolutionierung des Gesundheitswesens: Andy Schoonovers Strategie zur Umwälzung etablierter Branchen

Revolucionando la atención médica: el manual de Andy Schoonover para revolucionar industrias arraigadas

Révolutionner les soins de santé : le manuel d'Andy Schoonover pour perturber les industries bien établies

स्वास्थ्य सेवा में क्रांति: स्थापित उद्योगों में बदलाव के लिए एंडी स्कूनओवर की कार्यपुस्तिका

ヘルスケアの革命: 定着した業界を変革するためのアンディ・シューノーバーの戦略

의료 혁신: 고착화된 산업을 혁신하기 위한 Andy Schoonover의 플레이북

Revolucionando a saúde: o manual de Andy Schoonover para desestabilizar indústrias entrincheiradas

Революция в здравоохранении: книга Энди Шуновера по разрушению устоявшихся отраслей

Sağlık Hizmetinde Devrim Yaratmak: Andy Schoonover'ın Yerleşik Endüstrileri Yıkmaya Yönelik Başucu Kitabı

Cách mạng hóa hoạt động chăm sóc sức khỏe: Sách hướng dẫn cách phá vỡ các ngành công nghiệp cố thủ của Andy Schoonover

医疗保健革命：安迪·斯库诺弗颠覆根深蒂固行业的策略

United States v. Apple INC Court Filing, retrieved on March 21, 2024 is part of . You can jump to any part in this filing . This part is 11 of 25. HackerNoon’s Legal PDF Series here B. Apple has every incentive to use its monopoly playbook in the future 136. Apple’s conduct does not just impact the past and present but poses significant risk to the development of new innovations. Apple may use its smartphone monopoly playbook to acquire or maintain power over next-frontier devices and technologies. As Apple grows its dominance, Apple may continue delaying or stifling the innovations of cross-platform companies, in order to lock users into Apple devices. 137. Apple has countless products and services—AirPods, iPads, Music, Apple TV, photos, maps, iTunes, CarPlay, AirDrop, Apple Card, and Cash. These provide future avenues for Apple to engage in anticompetitive conduct and the ability to circumvent remedies. Appropriate forward-looking remedies are necessary to ensure that Apple cannot use these products and services to further entrench its monopoly power. 138. Apple’s conduct extends beyond just monopoly profits and even affects the flow of speech. For example, Apple is rapidly expanding its role as a TV and movie producer and has exercised that role to control content. 139. Apple has also attempted to use its monopoly to collect user data and stifle innovation in the automotive industry by, among other things, impeding the development of digital key technologies by requiring them to be offered in Apple’s proprietary wallet product and creating new single points of power over emerging uses of the iPhone. These acts further reinforce Apple’s power in the iPhone by locking in Apple’s services and excluding other alternative technologies that have the potential to disintermediate Apple’s iPhone. 140. Finally, Apple’s monopolization of smartphone markets gives it tremendous power over the lives of millions of Americans. Today, Apple uses that power to undermine rival smartphones, suppress innovative technologies, and stymie consumer choice. Tomorrow, Apple may use its power to force its own users (and their data) to become its next profitable product. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 21, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. justice.gov

Why Apple Has Every Incentive To Use Its Monopoly Playbook in the Future

United States v. Apple INC Court Filing, retrieved on March 21, 2024 is part of . You can jump to any part in this filing . This part is 4 of 25. HackerNoon’s Legal PDF Series here A. Apple launched the iPod, iTunes, and the iTunes Store against the backdrop of United States v. Microsoft 26. When Apple began developing mobile consumer devices, it did so against the backdrop of United States v. Microsoft, which created new opportunities for innovation in areas that would become critical to the success of Apple’s consumer devices and the company itself. For example, the iPod did not achieve widespread adoption until Apple developed a cross platform version of the iPod and iTunes for Microsoft’s Windows operating system, at the time the dominant operating system for personal computers. In the absence of the consent decree in United States v. Microsoft, it would have been more difficult for Apple to achieve this success and ultimately launch the iPhone. 27. On May 18, 1998, the Justice Department and the attorneys general of 19 states and the District of Columbia filed United States v. Microsoft, an antitrust lawsuit against Microsoft alleging that the company had violated Section 2 of the Sherman Act by monopolizing the market for Intel-compatible personal computer operating systems. At trial, the government successfully established that Microsoft took steps to undermine the competitive threats posed by “middleware,” such as web browsers like Netscape, after recognizing that if users could use middleware to access a variety of content and services via remote servers, over the internet, they might be less reliant on Windows. 28. Microsoft also took steps to undermine cross-platform technologies like QuickTime, a software architecture developed by Apple to play multimedia content (e.g., music and videos) on Apple’s Mac computers and Microsoft’s Windows PCs. In particular, Apple’s then-Senior Vice President of Software Engineering testified that Microsoft “[wrote] steps into its operating system to ensure that a QuickTime file will not operate reliably on Windows,” “trick[ed] the user into believing that QuickTime technology is part of the problem actually caused by the Windows operating system,” and “introduced greater technical incompatibilities between QuickTime and Microsoft products.” 29. In April 2000, the trial court ultimately found that Microsoft’s conduct violated Section 2 of the Sherman Act. An appeals court upheld the district court’s findings of liability regarding middleware. 30. In January 2001, Apple introduced iTunes, software built on Apple’s QuickTime architecture, and advertised it as “Jukebox Software” for organizing and listening to music. The initial version of iTunes was only compatible with Apple’s Mac computers. 31. Later that same year, Apple debuted the iPod, a portable digital audio player that worked alongside iTunes to “let[] you put your entire music collection in your pocket and listen to it wherever you go.” Like iTunes, the initial iPod was only compatible with Mac computers. 32. On November 1, 2002, the trial court accepted a proposed consent decree in . Among other things, the consent decree prohibited Microsoft from retaliating against companies for developing or distributing products such as browsers and media players. The consent decree also required Microsoft to make various APIs available to third-party developers, including Apple. United States v. Microsoft 33. Following that consent decree in October 2003, Apple launched a cross-platform version of iTunes that was compatible with the Windows operating system. As a result, a much larger group of users could finally use the iPod and iTunes, including the iTunes Store. The iTunes Store allowed users to buy and download music and play it on their iTunes computer application or on the iPod. Apple benefited substantially from this new customer base. In the first two years after launching the iPod, Apple sold a few hundred thousand devices. The year after launching a Windows-compatible version of iTunes and gaining access to millions more customers, Apple sold millions of devices. Apple went on to sell hundreds of millions of iPod devices over the next two decades. Moreover, iTunes became the market leader in online music services. At an event in 2007, Apple’s then-CEO said of the iPod, “it didn’t just change the way we all listened to music, it changed the entire music industry.” At the same event, he announced that the company would change its name from Apple Computer, Inc. to Apple, Inc. in light of its shifting focus to consumer electronics rather than computers. 34. The ubiquity of iPod and iTunes on Windows, in part because of a successful antitrust enforcement action against Microsoft, contributed to the development and success of Apple’s next flagship product—the iPhone. But after launching the iPhone, Apple began stifling the development of cross-platform technologies on the iPhone, just as Microsoft tried to stifle cross-platform technologies on Windows. 35. In January 2007, Apple debuted the first-generation iPhone, describing the device as “an iPod, a phone, and an internet communicator,” and touting the fact that users could “sync[] content from a user’s iTunes library on their PC or Mac.” Apple marketed the iPhone as a smartphone that was easy to use. Reflecting on the company’s learning from the iPod, Apple’s then-CEO announced, “iTunes is going to sync all your media to your iPhone—but also a ton of data. Contacts, calendars, photos, notes, bookmarks, email accounts.” 36. The original iPhone cost approximately $299—approximately $450 in 2024 dollars adjusted for inflation—with a two-year contract with a phone carrier. 37. At launch, nearly all native apps for the iPhone were created by Apple. There were only about a dozen apps overall, including Calendar, Camera, Clock, Contacts, iPod, Messages, Notes, Phone, Photos, Safari, Stocks, Voice Memos, and Weather. 38. Within a year of launching the iPhone, Apple invited third-party developers to create native apps for the iPhone. Apple released its first software development kit—essentially the digital tools for building native apps on Apple’s operating system (iOS)—to encourage and enable third-party developers to create native apps for the iPhone. Apple also offered developers ways to earn money by selling apps and later in-app purchases and subscriptions. By 2009, Apple was running marketing campaigns highlighting the value that third-party apps provide to iPhone users with the trademarked slogan: “There’s an app for that.” 39. Apple’s decision to invite third-party participation on its iPhone platform benefited Apple, too. The proliferation of third-party apps generated billions of dollars in profits for Apple and an iPhone user base of more than 250 million devices in the United States. Apple’s market shares—over 70 percent of the performance smartphone market and over 65 percent of the broader smartphone market—likely understate its monopoly power today. 40. While Apple profits from third-party developers that increase the iPhone’s value to users, Apple executives understand that third-party products and services can, in their own words, be “fundamentally disruptive” to its smartphone monopoly, decreasing users’ dependence on Apple and the iPhone and increasing competitive pressure on Apple. Apple therefore willingly sacrifices the short-term benefits it would gain from improved products and services developed by third parties when necessary to maintain its monopoly. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 21, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. justice.gov

How Apple Stole Pages from Microsoft's Monopoly Playbook

allow you to declare tasks to be performed on remote hosts. Ansible playbooks Overview of Playbook Structure: specifies a group of tasks to be executed on a set of hosts. Play: an action to be performed on the host. Task: a piece of code for a specific task. Module: What are Task Control & Handler? These are methods of controlling how and when your automation scripts are executed. Task control lets you specify conditions for running tasks while handlers are a form of tasks that you trigger only when changes need to be applied. Task Control Overview Task control in Ansible allows you to state the conditions under which your scripts should run. This can be implemented using conditionals such as: When This conditional allows you to execute a task only if it evaluates to true. Ansible Facts are variables containing information like network interfaces, operating system, IP addresses, and more, which are gathered from host machines and can be used in conditionals. ansible_facts Task Handlers Overview Handlers are tasks set to run only when certain changes occur, such as when a service's configuration file is updated by another task. Notify The directive in a task triggers a handler if the task results in any changes to the host. notify Listen The directive allows multiple tasks to notify the same handler even if they use different names. listen notify Practice Exercises We'll start with two pre-configured inventory files. contains two hosts while contains one host. Inventory-webserver1.yaml inventory-webserver2.yaml Clone the repo git clone https://github.com/perplexedyawdie/ansible-learn.git 2. Change the directory to playbooks-task-control-handlers cd ansible-learn/playbooks-task-control-handlers Spin up the environment using docker-compose docker compose up -d --build 4. SSH into the Ansible server ssh -o StrictHostKeyChecking=no -o NoHostAuthenticationForLocalhost=yes root@localhost -p 2200
# password: test123 Working with Playbooks Create playbook file cd ansible_learn

touch install_nginx.yaml

nano install_nginx.yaml Add the following config to install nginx # ansible_learn/install_nginx.yaml
# setting the state param to "present" will ensure that it is installed

- name: Install NGINX Web Server
  hosts: webserver1
  tasks:
    - name: Install NGINX
      ansible.builtin.apt:
        name: nginx
        state: present Run the linter ansible-lint install_nginx.yaml Execute the playbook on inventory-webserver1.yaml ansible-playbook --key-file /root/.ssh/id_rsa_ansible -u root -i inventory-webserver1.yaml install_nginx.yaml Confirm installation on server1 & server2 then check if it's installed on server3 ssh -i /root/.ssh/id_rsa_ansible root@server1 nginx -V
ssh -i /root/.ssh/id_rsa_ansible root@server2 nginx -V


ssh -i /root/.ssh/id_rsa_ansible root@server3 nginx -V Task Controls & Handlers We want to create a playbook that installs Apache on servers that do not have a specific file (e.g., ), and then use a handler to restart Apache if it is newly installed or updated. /var/www/html/index.nginx-debian.html Create & open the file install_nginx.yaml touch conditional_install_apache.yaml

nano conditional_install_apache.yaml Add a condition to install Apache if doesn't exist then add a handler to restart Apache if it is newly installed. /var/www/html/index.nginx-debian.html - name: Conditional Install and Restart Apache
  hosts: all
  tasks:
    - name: Check if NGINX Default Page Exists
      ansible.builtin.stat:
        path: /var/www/html/index.nginx-debian.html
      register: nginx_page

    - name: Install Apache
      ansible.builtin.apt:
        name: apache2
        state: present
      when: not nginx_page.stat.exists
      notify: Restart Apache

  handlers:
    - name: Restart Apache
      listen: "Restart Apache"
      ansible.builtin.service:
        name: apache2
        state: restarted

# The stat module checks for the existence of the default NGINX page. The result is registered in nginx_page.
# The installation task uses the when condition to install Apache only if the file does not exist.
# If the installation task runs, it notifies the handler to restart Apache.
# The handler listens for the notification and uses the service module to restart Apache. Run the linter ansible-lint conditional_install_apache.yaml Execute the playbook on & inventory-webserver1.yaml inventory-webserver2.yaml # webserver1
ansible-playbook --key-file /root/.ssh/id_rsa_ansible -u root -i inventory-webserver1.yaml conditional_install_apache.yaml

#webserver2
ansible-playbook --key-file /root/.ssh/id_rsa_ansible -u root -i inventory-webserver2.yaml conditional_install_apache.yaml Confirm installation on server3 then check if it's installed on server1 & server2 ssh -i /root/.ssh/id_rsa_ansible root@server1 apache2 -V
ssh -i /root/.ssh/id_rsa_ansible root@server2 apache2 -V


ssh -i /root/.ssh/id_rsa_ansible root@server3 apache2 -V Recap Awesome effort! We just learned how to define Ansible playbooks and how to control the flow of execution using conditionals. Next, we'll look on variables, gathering facts about host machines and how to manage config files using templates. Also published here.

Mastering Ansible Playbooks: A Guide to Task Controls & Handlers

The Noonification: Pythons Testing Playbook: Building Bulletproof Code (1/21/2024)

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The TechBeat: Python's Testing Playbook: Building Bulletproof Code (1/18/2024)

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The TechBeat: Python's Testing Playbook: Building Bulletproof Code (1/13/2024)

Among the plentitude of modern programming languages, stands out for its elegance and power; a preferred tool for everything from web development to data science. Yet writing Python code is only half of the work. The other, equally crucial half, lies in rigorous testing. Python Think of testing as safety net of software development: it ensures that your code is not only operational but resilient to unforeseen challenges. Given Python's extensive libraries and varied applications, the importance of testing is paramount. Poor testing practices can render a robust project vulnerable to minor bugs. In this article, I'll provide practical strategies and tools to empower your code against real-world challenges. It's a guide for both novices and seasoned Python developers, aimed at enhancing your understanding of Python testing. Let's begin! Content Overview Foundations of Testing in Python Understanding Testing Principles From Start to Finish: Testing in the Software Lifecycle Unit Testing in Python: Strategies, Frameworks, and Tools How to Write Robust Unit Tests Python's Testing Toolkit: Frameworks Compared Tools for Enhancing Unit Testing Integration Testing and Advanced Testing Methods Integration Testing: Ensuring Component Cohesion Test-Driven and Behavior-Driven Development: The Python Way Foundations of Testing in Python Understanding Testing Principles is much more than mere bug detection. It's one of the cornerstones of the development process, pivotal in ensuring your code is not only working but also maintainable and scalable. The key to effective testing lies in understanding two fundamental concepts: test coverage and automated testing. Testing shows how much of your code is covered by tests; however, more coverage doesn’t always mean better quality. In my experience, it’s about striking a balance – ensuring thorough test coverage without overburdening your project with excessive tests. Under-testing leaves gaps in your safety net, while over-testing can clutter your project and waste valuable resources. Test coverage Then there’s , and this is where Python shines. Automation allows you to run tests quickly and consistently, identifying issues early on, and saving you time to focus on actual coding. Python offers several frameworks for this, like and , both of which we'll explore in detail later. automated testing pytest unittest Understanding these testing principles is the foundation of building robust, reliable Python software. As we progress, keep these principles in mind – they're the cornerstone of everything we're going to discuss below. From Start to Finish: Testing in the Software Lifecycle Testing is not a discrete step but a continuous thread running through the software development lifecycle. It plays a crucial role at every stage, from conception to deployment. In the early stages, testing acts as a guide for your design decisions, influencing the architecture and functionality of your code. It encourages proactive thinking about potential use cases and edge cases to consider. As development progresses, testing shifts to a preventive role, identifying and resolving issues before they escalate. This phase benefits the most from automated testing, a method that efficiently validates your code throughout its creation. Upon reaching the deployment stage, the focus pivots to integration testing. It's crucial to confirm that all components of your system interact flawlessly in the intended environment. And even after deployment, testing isn't finished. Continuous testing in operational settings is key to uncovering any issues that didn’t surface during the development, maintaining the reliability of your application under real-world conditions. Unit Testing in Python: Strategies, Frameworks, and Tools How to Write Robust Unit Tests Unit testing – testing the smallest code pieces – is the backbone of a solid testing strategy. Good practices in unit testing start with clarity and simplicity. Each test should focus on aspect, ensuring that the unit performs its intended function – this makes it easier to identify the issue when a test fails. For this article, let's create a versatile function named . one process_data The function takes a dictionary as input. The keys of the dictionary are string identifiers, and the values are either integers, floats, strings, or lists of integers and/or floats. The function processes each item based on its type: For numbers, it squares the number. For strings, it reverses the string. For lists, it returns the sum of the elements. def process_data(data):
    processed = {}
    for key, value in data.items():
        if isinstance(value, (int, float)):
            processed[key] = value ** 2
        elif isinstance(value, str):
            processed[key] = value[::-1]
        elif isinstance(value, list):
            if not all(isinstance(x, (int, float)) for x in value):
                invalid_types = set(type(x) for x in value if not isinstance(x, (int, float)))
                raise ValueError(f"List at key '{key}' contains unsupported data types: {', '.join(invalid_types)}")
            processed[key] = sum(value)

        else:
            raise ValueError(f"Unsupported data type at key '{key}'. Expected: int, float, str, or list; got {type(value)}")
    return processed Let's use to write a simple unit test that will check if the output is correct for given inputs pytest import pytest

def test_process_data_basic():
   input_data = {"a": 2, "b": "hello", "c": [1, 2, 3]}
   expected_result = {"a": 4, "b": "olleh", "c": 6}
   assert process_data(input_data) == expected_result A common mistake here is over-complicating tests, checking for too many things in one test or creating overly complex setups. This can make tests hard to understand and maintain, defeating the very purpose of testing. Here is an example of a that should be improved: not optimal test case def test_process_data_over_complicated():
   input_data = {"a": 2, "b": "hello", "c": [1, 2, 3], "d": 4}
   expected_result = {"a": 4, "b": "olleh", "c": 6, "d": 16}
   assert process_data(input_data) == expected_result


   with pytest.raises(ValueError):
       process_data({"a": {"nested": "dict"}}) Here we test exception handling as well as normal functionality in the same test. These tests must be separated into two different functions. Additionally, our contains - having two different integers doesn't significantly add value to the test since they are both testing the same functionality. One integer would be sufficient to test this case. input_data {"a": 2, "d": 4} Another important aspect is to test . Instead of limiting tests to ideal scenarios, challenge your code with atypical and extreme inputs, like zero, negative numbers, or very large values. This approach ensures your code is robust under unexpected conditions. edge cases def test_process_data_edge_cases():
   edge_case_data = {
       "empty_string": "",
       "empty_list": [],
       "large_int": 10**10,
       "mixed_list": [0, -1, -2, 3],
       "special_chars": "!@#$%^"
   }

   expected_result = {
       "empty_string": "",
       "empty_list": 0,
       "large_int": 10**20,
       "mixed_list": 0,
       "special_chars": "^%$#@!"
   }
   assert process_data(edge_case_data) == expected_result It's also essential to balance thoroughness with maintainability. Sometimes, less is more: it's important to cover various scenarios, yet too many tests can slow down the development. Choose tests that provide the most value and cover the critical functionalities of a unit. Python's Testing Toolkit: Frameworks Compared There are numerous Python unit testing frameworks available, but we will focus on two key players: and . Both are powerful tools with distinct features that cater to different testing needs. pytest unittest unittest: The Traditional Approach , embedded in Python's standard library, follows an object-oriented methodology. This framework suits those who favor a structured, traditional approach to crafting tests. unittest One standout feature of unittest is its ability to automatically detect test files and methods, as long as they adhere to a predefined naming convention. While making organizing large test suites more straightforward, it can also be seen as a limitation. import unittest


class TestProcessData(unittest.TestCase):
   def test_process_data_normal(self):
       input_data = {"a": 2, "b": "hello", "c": [1, 2, 3]}
       expected = {"a": 4, "b": "olleh", "c": 6}
       self.assertEqual(process_data(input_data), expected)


   def test_process_data_invalid_input(self):
       with self.assertRaises(ValueError):
           process_data({"a": {"nested": "dict"}})


if __name__ == '__main__':
   unittest.main() pytest: Enhanced Flexibility and Features pytest, on the other hand, is renowned for its simplicity and ease of use; it shines with a less verbose syntax and greater flexibility in writing tests. Unlike unittest, doesn't require classes or naming conventions for test discovery, which allows for a more intuitive and less rigid testing process. pytest Another advantage of pytest lies in its robust fixture system, streamlining setup and teardown phases in testing. This feature aids in crafting reusable test components, minimizing repetition and enhancing the maintainability of tests. Comparing Assertions The assertion styles between these two frameworks also differ. In , you use specific assertion methods like assertEqual and assertTrue. In , you can simply use Python's built-in assert statement, which makes the tests more readable and natural, and writing them – faster. unittest pytest # unittest assertion
self.assertEqual(process_data({"a": 2}), {"a": 4})


# pytest assertion
assert process_data({"a": 2}) == {"a": 4} The choice between unittest and pytest boils down to your preference and the specific needs of your project. I prefer the ease of use and robust capabilities of pytest, however, unittest remains a dependable and solid choice. Tools for Enhancing Unit Testing pytest: Fixture System Among pytest's most versatile features is its fixture system, offering a streamlined approach to setting up and tearing down resources required for testing. For example, you can easily establish a test database before each test and discard it subsequently, ensuring a clean testing environment. @pytest.fixture
def sample_data():
   return {"a": 2, "b": "hello", "c": [1, 2, 3]}


def test_process_data_with_fixture(sample_data):
   expected = {"a": 4, "b": "olleh", "c": 6}
   assert process_data(sample_data) == expected pytest: Parameterized Testing Parameterized testing allows you to run the same test function with different sets of data, reducing redundancy and making tests more comprehensive. With parameterization, you can test a range of inputs and scenarios without writing multiple test functions. @pytest.mark.parametrize("input_data, expected_output", [
   ({"a": 3}, {"a": 9}),
   ({"b": "world"}, {"b": "dlrow"}),
   ({"a": 2, "b": "hello", "c": [1, 2, 3]}, {"a": 4, "b": "olleh", "c": 6}),
   ({"d": []}, {"d": 0}),
])
def test_process_data_normal_parameterized(input_data, expected_output):
   assert process_data(input_data) == expected_output


@pytest.mark.parametrize("input_data, exception_type, exception_msg_match", [
   ({"a": 2, "b": ["str", 2, 3]}, ValueError, "List at key"),
   ({"a": 2, "b": (2, 3)}, ValueError, "Unsupported data type at key")
])
def test_process_data_invalid_input_parameterized(input_data, exception_type, exception_msg_match):
   with pytest.raises(exception_type, match=exception_msg_match):
       process_data(input_data) unittest: Mocking Although I prefer pytest, it’s sometimes useful to integrate it with certain features from unittest, like Mock. Mocking is handy when you want to isolate your tests from external dependencies or side effects. For example, if your function sends an email, you can use Mock to simulate the email-sending process without actually sending one. Here is a toy example of a simple mock test: class NotificationService:
    def send(self, message):
        # Simulate sending a notification
        print(f"Notification sent: {message}")
        return True

def send_notification(service, message):
    return service.send(message)


import unittest
from unittest.mock import MagicMock

class TestSendNotification(unittest.TestCase):
    def test_send_notification(self):
        # Create a mock object for the NotificationService
        mock_service = MagicMock()

        # Call the function with the mock object
        send_notification(mock_service, "Test Message")

        # Assert that the send method was called with the correct parameters
        mock_service.send.assert_called_with("Test Message")

if __name__ == '__main__':
    unittest.main() Integration with Tox is another great tool for enhancing your Python testing. Tox automates testing in multiple environments, ensuring that your code works across different versions of Python and different configurations. Tox [tox]
envlist = py37, py38, py39, py310


[testenv]
deps = pytest
commands = pytest Integration Testing and Advanced Testing Methods Integration Testing: Ensuring Component Cohesion The next critical practice in Python development is integration testing. This phase shifts the focus from individual units to the interaction between them, making sure they work well together, not just in isolation. It’s a common mistake to assume that well-functioning parts of a system will automatically integrate well. Issues often arise precisely at the junctures between units. For instance, a perfectly working function that passes data to a database might fail when the database schema changes. For integration testing in Python, tools like pytest can be used to test broader interactions. The goal here is to simulate real-world usage as closely as possible, including the edge cases. Test-Driven and Behavior-Driven Development: The Python Way Test-Driven Development is a transformative approach, emphasizing writing tests before the actual code. This methodology reverses traditional development practices, fostering more organized development practices. The essence of TDD is simple: Write a test for a new feature before implementing it. Initially, this test will fail, as the functionality it tests doesn't exist yet. Then, write the minimal amount of code needed to pass the test. Once the test passes, refactor the code to optimize it, making sure that it always passes the test. TDD ensures that your codebase has comprehensive test coverage, as you’re developing tests and features simultaneously. Behavior-Driven Development builds on the foundations of TDD, placing an emphasis on cooperation between developers, testers, and non-technical stakeholders. In testing, BDD focuses on devising tests that assess more than just code functionality but also its alignment with the expectations and understanding of everyone involved. The process starts with defining the expected behavior of a feature in plain language accessible to all team members. These specifications guide the development of tests. After writing the test, you develop the feature to meet the specified behavior. This method aids in crafting features that match user expectations and also enhances communication within the development team. This inclusive approach is a defining characteristic of BDD, guaranteeing that the development process resonates with the requirements and anticipations of the end-users. Adopting TDD and BDD requires a notable shift in perspective and could initially reduce the pace of development. Nonetheless, the long-term benefits they bring make the investment of time and effort worthwhile. Effective testing is fundamental to building robust, reliable, and maintainable Python applications. From the basics of unit testing to the collaborative approach of Behavior-Driven Development, each methodology and tool we've explored above contributes to a stronger, more resilient codebase. Mastering these testing techniques is crucial, whether you are just starting with Python or have years of experience. Testing forms a fundamental aspect of the development cycle, pivotal in creating software that adheres to the highest quality benchmarks. Let's continue to expand the limits of our capabilities with Python, supported by robust testing strategies!

Python's Testing Playbook: Building Bulletproof Code

It looks like gone are the days when a new generation of hardware actually meant a new generation of hardware. Take the recent launch of the Apple iPhone 15, for example. Despite the fanfare surrounding the launch, is it really all that much better than its predecessor? Or, Nvidia's 40 series graphic cards, which weren't necessarily the generational leap the company made it out to be. And then, of course, you have AMD's 7000 series cards, which were found to perform just as well, if not poorer, than the older generation 6000 series cards. This is a troubling pattern; yet, it hasn't stopped any of the tech majors from stepping in and bringing their house into order. With others behaving this way, can you really blame Intel for joining the list of companies that simply slap on a new code to hardware and call it a day? Yes, Team Blue launched their 14 series of processors last week, and boy, where do we even begin with this one. As Gamer Nexus jokingly called it, "The new generation-less generational improvement.. Generationally," the 14-series processors are simply a refresh of the preceding 13-series processors. Not only do they perform just the way their preceding processors did, they also cost just about the same, with one key difference: they chug a LOT of power. So what even is the point of these "new" product launches anymore? In Intel's case, it just might be its swan song for the LGA 1700 platform as it gears up for a new class of processors produced at scale using extreme ultraviolet (EUV) lithography machines at its $18.5 billion plant in Ireland. Whether the 15-series processors will be good, only time will tell. Tesla, X, and Everything in Between 🚗 It was Musk season again this past week, with one or the other of the billionaire's companies in the news. Kicking off with some earnings news this past week, Tesla reported a decline in its third-quarter 2023 earnings: 66 cents per share versus the $1.05 per share it reported in the same quarter a year ago. The carmaker saw its profit decline despite generating more in sales during the three month period ending Sept. 30, 2023: $23.4 billion versus the $21.5 billion it generated in the third quarter of 2022. Tesla blamed an increase in expenses for the drop in profits. A quick glance of the company's income statement showed Tesla's operating expenses went up 43% year over year to $2.41 billion as the company ramped up costs due to the Cybertruck, AI, and other R&D projects. The Cybertruck is supposedly scheduled to begin deliveries on Nov. 30 but Musk has been a little shifty on the whole thing, telling investors that Tesla had dug its own grave with the vehicle presumably because of how soon it could ramp up production. Meanwhile, Musk's X is planning to test out a basic subscription model requiring users to pay $1 annually for basic functionalities like the ability to like, repost or quote other accounts' posts, and bookmarking posts on the web version of the platform, Reuters reported. The company also plans to launch two new tiers of premium subscriptions as it figures out ways to monetize the platform. Of the two tiers, one would be a lower cost with all features but no reduction in ads, while a more expensive version will come with no ads baked in. Tesla ranked #3 on HackerNoon's tech company rankings this week. X, formerly known as Twitter, ranked #20. 👋 You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. In Other News.. 📰 Tinder now lets mom pick your next date — via The Verge.
Crypto's role in terrorist financing — via Reuters.
OpenAI Is in Talks to Sell Shares at $86 Billion Valuation — via Bloomberg.
Humanoid robots face a major test with Amazon’s Digit pilots — via TechCrunch.
Nokia says it will cut up to 14,000 jobs — via CNN.
Anthropic tests AI rules for the people, by the people — via Axios.
Okta shares fall 11% after company says client files were accessed by hackers via its support system — via CNBC. And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. It looks like gone are the days when a new generation of hardware actually meant a new generation of hardware. meant Take the recent launch of the Apple iPhone 15, for example. Despite the fanfare surrounding the launch, is it really all that much better than its predecessor? Or, Nvidia 's 40 series graphic cards, which weren't necessarily the generational leap the company made it out to be. Apple Apple Nvidia Nvidia generational leap generational leap And then, of course, you have AMD 's 7000 series cards , which were found to perform just as well, if not poorer , than the older generation 6000 series cards. AMD AMD 7000 series cards 7000 series cards if not poorer if not poorer This is a troubling pattern; yet, it hasn't stopped any of the tech majors from stepping in and bringing their house into order. With others behaving this way, can you really blame Intel for joining the list of companies that simply slap on a new code to hardware and call it a day? Intel Intel Yes, Team Blue launched their 14 series of processors last week, and boy, where do we even begin with this one. As Gamer Nexus jokingly called it , "The new generation-less generational improvement.. Generationally," the 14-series processors are simply a refresh of the preceding 13-series processors. jokingly called it jokingly called it Not only do they perform just the way their preceding processors did, they also cost just about the same, with one key difference: they chug a LOT of power . So what even is the point of these "new" product launches anymore? In Intel's case, it just might be its swan song for the LGA 1700 platform as it gears up for a new class of processors produced at scale using extreme ultraviolet (EUV) lithography machines at its $18.5 billion plant in Ireland. LOT of power LOT of power using using Whether the 15-series processors will be good, only time will tell. Tesla, X, and Everything in Between 🚗 Tesla, X, and Everything in Between 🚗 It was Musk season again this past week, with one or the other of the billionaire's companies in the news. Kicking off with some earnings news this past week, Tesla reported a decline in its third-quarter 2023 earnings: 66 cents per share versus the $1.05 per share it reported in the same quarter a year ago. Tesla Tesla The carmaker saw its profit decline despite generating more in sales during the three month period ending Sept. 30, 2023: $23.4 billion versus the $21.5 billion it generated in the third quarter of 2022. Tesla blamed an increase in expenses for the drop in profits. A quick glance of the company's income statement showed Tesla's operating expenses went up 43% year over year to $2.41 billion as the company ramped up costs due to the Cybertruck, AI, and other R&D projects. blamed blamed The Cybertruck is supposedly scheduled to begin deliveries on Nov. 30 but Musk has been a little shifty on the whole thing, telling investors that Tesla had dug its own grave with the vehicle presumably because of how soon it could ramp up production. dug dug Meanwhile, Musk's X is planning to test out a basic subscription model requiring users to pay $1 annually for basic functionalities like the ability to like, repost or quote other accounts' posts, and bookmarking posts on the web version of the platform, Reuters reported. test test The company also plans to launch two new tiers of premium subscriptions as it figures out ways to monetize the platform. Of the two tiers, one would be a lower cost with all features but no reduction in ads, while a more expensive version will come with no ads baked in. Tesla ranked #3 on HackerNoon's tech company rankings this week. X, formerly known as Twitter, ranked #20. Tesla Tesla 👋 You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. 👋 You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. You’re reading part 2 of HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Part 1 went live yesterday. Prefer reading the whole thing a day early AND in one go? No problemo! Just subscribe here to receive the complete newsletter in your inbox every Tuesday. part 2 Part 1 Part 1 here complete In Other News.. 📰 In Other News.. 📰 Tinder now lets mom pick your next date — via The Verge. Crypto's role in terrorist financing — via Reuters. OpenAI Is in Talks to Sell Shares at $86 Billion Valuation — via Bloomberg. Humanoid robots face a major test with Amazon’s Digit pilots — via TechCrunch. Nokia says it will cut up to 14,000 jobs — via CNN. Anthropic tests AI rules for the people, by the people — via Axios. Okta shares fall 11% after company says client files were accessed by hackers via its support system — via CNBC. Tinder now lets mom pick your next date — via The Verge . The Verge The Verge Crypto's role in terrorist financing — via Reuters . Reuters Reuters OpenAI Is in Talks to Sell Shares at $86 Billion Valuation — via Bloomberg . Bloomberg Bloomberg Humanoid robots face a major test with Amazon’s Digit pilots — via TechCrunch . TechCrunch TechCrunch Nokia says it will cut up to 14,000 jobs — via CNN . CNN CNN Anthropic tests AI rules for the people, by the people — via Axios . Axios Axios Okta shares fall 11% after company says client files were accessed by hackers via its support system — via CNBC . CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings

With New Product Launch, Intel Takes a Page from the Nvidia Playbook 🐒

নতুন পণ্য লঞ্চের সাথে, ইন্টেল এনভিডিয়া প্লেবুক থেকে একটি পৃষ্ঠা নেয় 🐒

Mit der Einführung neuer Produkte orientiert sich Intel am Nvidia-Playbook 🐒

Con el lanzamiento de un nuevo producto, Intel toma una página del Nvidia Playbook 🐒

Avec le lancement d'un nouveau produit, Intel s'inspire du Playbook de Nvidia 🐒

नए उत्पाद लॉन्च के साथ, इंटेल ने एनवीडिया प्लेबुक से एक पेज लिया

新製品の発表で、インテルが Nvidia ハンドブックの 1 ページを取り上げます 🐒

신제품 출시로 인텔은 Nvidia 플레이북에서 한 페이지를 차지합니다 🐒

Com o lançamento de um novo produto, a Intel tira uma página do manual da Nvidia 🐒

Выпуская новый продукт, Intel берет пример с Nvidia Playbook 🐒

Yeni Ürün Lansmanıyla Intel, Nvidia Playbook'tan Bir Sayfa Alıyor 🐒

Với việc ra mắt sản phẩm mới, Intel đã kế thừa một trang từ Nvidia Playbook 🐒

随着新产品的发布，英特尔效仿 Nvidia 的做法🐒

FTC v. Amazon Court Filing, retrieved on Sep 26, 2023, is part of . You can jump to any part in this filing . This is part 38 of 80. HackerNoon’s Legal PDF Series here c. Amazon’s anti-discounting strategy prevents rivals and sellers from offering lower prices and deprives rivals of scale necessary to compete 305. By suppressing competition in the online superstore and marketplace services markets, Amazon’s anti-discounting strategy artificially inflates prices. Shoppers and sellers pay more, and Amazon reaps the benefits. 306. Amazon’s one-two punch of high fees and seller threats forces sellers to use their inflated Amazon prices as a price floor everywhere else they sell online. As a result of Amazon’s conduct, shoppers often have no choice but to pay at least the price in Amazon’s Buy Box even when they buy online somewhere other than Amazon. 307. Sellers generally price their goods to at least cover their costs, including fees charged by online marketplace services providers (such as those discussed in Part IV, above). Thus, the seller’s shopper-facing price depends on the amount of fees charged by different marketplaces. 308. As discussed in Part V.D.3, above, the cost of doing business is higher on Amazon than on other marketplaces—and Amazon has steadily hiked the fees it charges sellers, (redacted) 309. Because Amazon has steeply raised its fees, sellers need to charge higher prices on Amazon than they would on a less-costly marketplace to make the same per-unit profit. Amazon’s high fees should present other online superstores with an opportunity that would make shoppers, sellers, and themselves better off: if those superstores can offer sellers lower fees, sellers could offer shoppers lower prices while making the same or a higher profit margin, which should cause shoppers and sellers alike to flock to the less-costly online store. 310. Amazon has destroyed this competitive dynamic by algorithmically forcing sellers to ensure that their prices off Amazon are no lower than their prices on Amazon, regardless of the relative costs. Similar anticompetitive effects flow from ASB, which contractually prevents brands from offering lower prices elsewhere online even when it would be profitable for them to do so, including on their own websites. 311. Amazon internally recognizes that (redacted) 312. (Redacted) For instance, one seller told Amazon that (redacted) Sellers have also complained to Amazon “that [Buy Box disqualification] encourages Sellers to raise their prices on competitor websites.” 313. One Amazon seller (redacted) 315. Amazon understands that its anti-discounting strategy generally does not have the effect of lowering prices on Amazon because sellers must pay the high fees charged by Amazon. A 2017 Amazon internal memo observed that Buy Box disqualification “has not led Sellers to lower their prices” and “has not motivated Sellers to reduce prices.” (redacted) 316. The primary and intended effect of Amazon’s anti-discounting strategy is that sellers do not offer lower prices off Amazon even if other online marketplaces offer sellers lower costs. 317. This effect is intensified for sellers subject to ASB. While Amazon’s algorithmic anti-discounting punishment focuses on individual products, Amazon’s enforcement of ASB threatens an ASB seller’s ability to sell anything at all as a third-party seller on Amazon’s Marketplace. ASB’s threatened contractual punishments could therefore effectively cut off a huge channel for sellers. In that way, ASB is broader in scope than any particular instance of Amazon’s algorithmic third-party punishment, making it even more likely that Amazon’s punitive program deeply chills discounting by ASB sellers off Amazon. 318. (Redacted) The force and fear of Amazon’s tactics are so strong that actual punishment is often not necessary. The threat alone can be enough. 319. For example, (redacted) Amazon’s anti-discounting punishments also limit the extent to which sellers sell on other online marketplaces, where sellers can control the final prices offered to customers. (redacted) 320. Amazon’s anti-discounting conduct reverberates throughout both relevant markets because of Amazon’s dominance in each market. For example, (redacted) However, (redacted) 320. (Redacted) 321. (Redacted) 322. (Redacted) This is not a healthy, competitive market. 323. (Redacted) (This info is Redacted) 324. In total, Amazon’s anti-discounting conduct helps maintain Amazon’s monopolies by stifling competition relevant markets, denying scale to rivals, harming sellers, and depriving shoppers of lower prices. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 2:23-cv-01495 retrieved on October 2, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ftc.gov

No Discounts Allowed: Reviewing Amazon's Price Control Playbook 

Your Playbook for Navigating Life's Paradoxes 

জীবনের প্যারাডক্স নেভিগেট করার জন্য আপনার প্লেবুক

Ihr Playbook zur Bewältigung der Paradoxien des Lebens

Su manual para navegar las paradojas de la vida

Votre manuel pour naviguer dans les paradoxes de la vie

जीवन के विरोधाभासों से निपटने के लिए आपकी प्लेबुक

人生のパラドックスを乗り越えるためのハンドブック

인생의 역설을 탐색하기 위한 플레이북

Seu manual para navegar pelos paradoxos da vida

Ваш учебник по преодолению парадоксов жизни

Hayatın Paradokslarını Çözmek İçin Başucu Kitabınız

Playbook của bạn để điều hướng những nghịch lý của cuộc sống

应对人生悖论的手册

’s new AI project NotebookLM is moving away from General AI models, and towards specified AI models that will allow you to interact with specific sources, and perform tasks related to the source content. Google Google’s recent interest in AI Since November 2022, the rapid proliferation of Generative AI technology and subsequent inflated commercial potential, have many Tech giants eager to get in on the ground floor of this emerging technology, hoping to capitalize on its future potential. Google has recently begun the rollout of a new player, beginning the introduction of their new Generative AI model called NotebookLM. Publicly available only through a waitlist, Google is testing the AI with a select group of people, either specifically appointed to do so or chosen from NotebookLMs’ . waitlist Google, being a leading tech giant, has the resources and expertise likely to propel its AI model beyond what we have previously thought capable. While there isn’t much information available about the new tool, for those of us enthralled by the development of AI, the tectonic plates of the tech industry have already begun to shift. Sifting through the available information surrounding NotebookLM, this article covers all angles surrounding the product’s development, accessibility/pricing, and features. Unveiling NotebookLM Beginning in Late 2022. Google’s $300 million investment in generative AI developer Anthropic, was the beginning of a partnership involving the sharing of Google’s Cloud computing service Google Cloud, as well as Anthropic’s sharing of AI knowledge and expertise, which presumably led to the development of NotebookLM. NotebookLM is unlike anything we have seen on the market yet; it’s a specialized language model that operates and interprets sources on the fly, interacting and analyzing the data using only the sourced information. Everything we have seen so far, Open AI’s Chat GPT, and Google’s Bard, have been general Language Models that interact with a vast amount of knowledge to answer general questions, but NotebookLM offers users a way to learn about a specific topic using multiple credible sources in a much more time efficient manner. Notebook LM is intended to be able to take a couple of sources on similar topics, quickly analyze the information, and be able to interact with it, providing a summary and overview of key points, and also allowing users to interact with and question specific aspects of the data like any other AI would. Still in its development phase, the AI can currently only work with 5 sources under 10,000 words, however, we can expect the final product to allow users to input different types of sources containing larger sets of data. The Technological Leap For those who need to learn and familiarize themselves with large amounts of specified and credible data, generalized AI models like Claude, Chat GPT, and , can only give them general overviews of broad topics based on basically the whole internet’s worth of information. Bard For students, researchers and professors, an AI that allows you to input selected sources surrounding a very specific topic, that will process the data, and allow you to ask specific questions while only pulling from the input sources, NotebookLM is an absolute game changer. Say you’re a student, who needs to study for an important test, complete an assignment, or even do the reading and preparation for a class. I can open NotebookLLM and simply input sources relating to a specific topic, whether entire textbooks, research articles, or even books, and get a summary of the topic that the sources discuss, and ask questions about the specific sources’ different perspectives and nuances, and come up with potential questions to ask your professor. Based on the input sources, I could even ask the AI to help me outline an essay around a specific question or ask it to come up with the top 20 most likely essay questions for an exam regarding that information, to help me prepare for a test. It could also create video scripts, pitch decks, analysis, and who knows what more. This is an absolute game changer when it comes to academia, and many students will find a tool like this essential to functioning when their workload is high. Content Regulation If we consider Anthropic and Google’s partnership and sharing of expertise in the development of this AI, it’s safe to assume that the principles of Anthropic’s regulation would apply similarly to NotebookLM. Anthropic was founded by several former Open AI employees, who fundamentally disagreed with the ethics of Open AI’s billion-dollar partnership with Microsoft, raising flags related to the readiness of AI to produce virtuous and non-harmful content. Unlike Open AI’s Chat GPT, Anthropic’s AI Claude, is governed by a constitutional AI model whose sole purpose is to regulate the output of another Language Model, based on a set of principles and values. On Top of already programming the AI to ethically regulate its output, Anthropic prides itself on adding an extra layer that prevents anything from slipping through the cracks. Given their partnership on this project, it’s safe to assume that NotebookLM will have the same or a similar approach in terms of regulation. Academic Ethics Even with the rollout of the first set of AI LLM models such as GPT-1 in November 2022, the academic community immediately reacted by trying to place strict regulations on the usage of AI by students. My college banned Chat GPT on the school Wi-Fi within 2 days of its first release! The strict regulation is in response to concerns that students would use AI to write their essays and do their assignments, but as we got familiar with Language Models like Chat GPT, it was clear that the writing was not of significant substance and lacked writing skills as well as specificity. As this new AI model gets closer to release, we are sure that it will revolutionize the intake and processing of credible information, and allow users to consume larger amounts of information in less time, but we are unsure of its capability to generate well-written academic essays. If AI is developed enough to produce complex, well-structured, well-written essays that are cited, it would completely flip academia on its head, especially considering its been proven that ‘AI detectors’ have significant flaws and are able to accurately ascertain whether something is written by AI or not. It will certainly be interesting to see how the academic community will adjust its policies and regulations to better regulate the use of NotebookLM, but I suspect that like the current popular AI models, it will not replace human ingenuity, but actually democratize the intake of information and allow neurodivergent people to better access complex information, as well as those who are unable to do so due to time constraints. Perspective as a student; Academic ethics and AI usage Through my online presence, I have and will continue to advocate for better usage of AI as an academic tool, and I cannot stress enough that AI is a tool to enhance your academics, NOT to replace labor. However, I still think that AI is an extremely useful tool, and everyone should be able to use it responsibly to increase productivity. Using AI unethically will not only probably get you a bad grade, but it can and will get you permanently dismissed from an academic institution. I have seen it happen with one of my classmates. IT IS NOT WORTH IT. With that being said, I want to quickly discuss some things you need to keep in mind as a student who employs AI ethically. Firstly, at the beginning of a class, discuss AI usage with your professor and find out their specific regulations and guidance when using AI. Professors are usually reasonable human beings, and being transparent with them is always in your best interest. Secondly, be extremely transparent with your AI usage. If you can, try and use a writing document that tracks your progress in writing an assignment with time stamps, and with whatever AI language model make sure you keep track of the specific tasks and commands you use. If you are smart and proactive, before an assignment is due go to a professor and let them know you used AI for one, two, or three things, and assure them that everything you did was within guidelines, and always offer to show them specific proof of everything that AI helped you to do. It’s the best way to ensure you are compliant with your institution’s regulations. Thirdly, be smart about your usage. The most I will ever take AI as a tool is to use it as a spell checker or to help me outline an essay. Using it to rephrase entire paragraphs is where you start to trek in dangerous territory. Lastly; Make no mistake, your future is on the line. If for some reason it comes out that you used AI unethically(and it very much can), you will likely lose any position and degree you have, even if it is well into your career. Final thoughts; What to expect NotebookLM is still currently available to a select few who are being used to test and train it, but as it rolls out, we can expect it to be as accessible and free as the rest of the Google suite. As with other AI models, it will not be perfect and will continue to improve and new versions will be released over time. We have seen the capability of Anthropic’s vast knowledge of AI, and we can expect to see a complete game changer. It is also important to note that Google is simply not as ethical as many of us would like to believe, and it is not far-fetched to think that NotebookLM may be more profit-driven than we anticipate. With all being said, look out for the release of this exciting new platform, as we wait to see how this affects academia, keep in mind how information absorption and retention could be transformed. Also published here.

Google’s New AI Model, NotebookLM, will Rewrite the Academic Playbook Forever

The Ultimate Playbook for Getting More GitHub Stars

The Gamification Playbook:  Mastering the Art of Engaging Product Building

দ্য গ্যামিফিকেশন প্লেবুক: আর্ট অফ এনগেজিং প্রোডাক্ট বিল্ডিং আয়ত্ত করা

Das Gamification-Playbook: Die Kunst der ansprechenden Produktentwicklung beherrschen

El libro de jugadas de la gamificación: dominar el arte de crear productos atractivos

Le manuel de jeu de la gamification : maîtriser l'art de créer des produits engageants

गेमिफ़िकेशन प्लेबुक: आकर्षक उत्पाद निर्माण की कला में महारत हासिल करना

ゲーミフィケーション プレイブック: 魅力的な製品構築の技術を習得する

게임화 플레이북: 매력적인 제품 구축 기술 익히기

O manual da gamificação: dominando a arte de engajar a criação de produtos

Oyunlaştırma Başucu Kitabı: İlgi Çekici Ürün Oluşturma Sanatında Ustalaşmak

Cẩm nang Gamification: Làm chủ nghệ thuật xây dựng sản phẩm hấp dẫn

游戏化手册：掌握参与产品构建的艺术

If we can trust a quick Search, the average software engineer stays at their job for . That also means every 1-2 years, developers need to set themselves up in a new project, with a new team, computer, and tech stack. For contractors and freelancers, that period might be even shorter. Google 1-2 years The first weeks at a new gig can be a bit daunting and intimidating. You want to leave a good first impression, get ready to contribute as soon as possible, all the while insane amounts of information are thrown at you from all sides. Let us, therefore, look at a few ways you can make your life easier and onboard like a pro. ⛵ Content Overview Get Your Dev Environment Ready Understand the Release Process Ask All the Questions Document Everything Schedule Lots of Coffee Chats Shadow Your Peers Get Involved in Support Tasks ] Take on Small Impromptu Tasks Do Code Reviews Get Your Dev Environment Ready The sooner you have your development environment set up, the sooner you can jump into the code and start making meaningful contributions. Building and running the software locally on my machine is usually the first goal I set for myself when starting a new job. Depending on the complexity of the setup and the number of meetings and intro sessions you have, this might take a day or a couple of weeks. To make things easier, I’d recommend keeping a list of tools you use in your day-to-day work (not on a company laptop, so can you can check the list when you start a new gig) - this should include your favorite IDE, terminal, build tools, API clients but also email clients, office programs, internet browsers, clipboard tools… Whatever tool or software you use that does not come pre-installed, put it on your list. Next, ask your co-workers what tools they use. You’ll usually need some extra tools for each specific project and tech stack. Also, make sure you get access to shared collections for e.g. clients. API Finally, a big time-saver is syncing your settings with a non-company account, so your IDE settings and keybindings will work exactly how you like them on a new machine. I sync my VS Code settings with my personal GitHub account. That way, whenever I need to set myself up on a new computer, all I need to do is log in to GitHub, and my IDE will look and feel exactly how I left it on the last computer. My VS Code color scheme is a work of art, I won’t risk losing that. 🎨 IDE Understand the Release Process Every company (and sometimes even every team within the company) has its own release process. Before you push changes to the branch for the first time, you want to understand what exactly happens afterward. main Will your changes be deployed directly to production? Do you need to deploy a feature branch first and get the product owner’s approval? Is there a deadline by which you need to merge your changes for them to be included in the next release? When does the release happen and how? Do you need to provide release notes or a change log? If your team has CI/CD in place, make sure you understand the steps of the pipeline. If your team doesn’t have CI/CD in place, you’re doomed and should hand in your letter of resignation immediately. 🥲 (Just kidding, of course. There are still valid reasons for a manual release process. I think.) Ask All The Questions People say there are no stupid questions but let’s be real here, if a developer asked which database the project uses after having worked on the backend team for two years, they wouldn’t exactly come across as the smartest. However, when you are just joining a new team, there is no question too dumb to ask. Everyone knows you’re new, and it’s in their best interest to get you up to speed as quickly as possible. Whenever there is anything you don’t understand, ask your peers - and make sure you write down the answer. really Document Everything The first few weeks on a new job can be exhausting and you’ll be bombarded with new information constantly, so don’t rely on your brain to remember everything. Instead, document everything, from the steps necessary to build the software to how the team does sprints and code reviews. Ideally, you won’t have to start from scratch and there will already be documentation available. When you work your way through existing documentation and How-To guides, make sure to update any information that might be outdated or unclear. These documents go out-of-date quickly, so your first contribution can be improving them. If you run into any sort of problem while you’re setting up, make sure you document the issue and the solution (which you found, hopefully) and share it with the team. You might be the first person facing a particular problem but you probably won’t be the last one. Schedule Lots of Coffee Chats Whether you work in an office or remotely, whether it’s a small start-up or a big corporation, make sure you get to know your peers. Even if most processes should be streamlined in any company, there will always be cases where pinging Jose from Accounting or Sarah from IT might significantly speed up your request. Make sure you know who does what in the company. I would also recommend getting to know people from as many different departments as possible. Chances are, you will bond with your dev colleagues over an intense pair programming session sooner or later. But being a new joiner is the perfect opportunity to talk to your non-tech colleagues and get their perspective on what’s working well and what could be improved in the company. If you dare, ask them how they like collaborating with the engineering department and if there is anything that could be improved. Shadow Your Peers Ask your peers if you can shadow them for whatever they are working on. It’s a great way to learn and even though it might slow down the other person in the short term, long term it will take some load off their shoulders. Onboarding a new team member requires time and should be considered when planning capacities. Don’t feel bad about asking other people for their time. Get Involved in Support Tasks Most software engineers need to do some sort of support shift every once in a while and it’s usually no one’s favorite thing to do. Depending on the setup, taking over the support shift might not even require deep knowledge of the code base. The sooner you are up to speed with the support process, the sooner you’ll be able to make your colleagues’ lives a bit easier. I would recommend shadowing the person doing the support shift for a few weeks and then switching seats, so you’d take over the support shift with someone in the background who might give you additional context and answer any questions. Take On Small Impactful Tasks I’ve seen teams where it’s common for new engineers to take weeks, if not months, before they open their first Pull Request. That’s usually the case when the code base is very complex or the engineer is completely new to the tech stack. But I guarantee you, in every team, there will be smaller tasks that you can tackle without knowing the code inside and out. While you get onboarded onto the project, maybe you can fix some linter warnings, upgrade a library to the latest version, fix typos, or increase the test coverage. These tasks can have a big impact but are often neglected in everyday work. Your team members who are busy finishing features and fixing bugs will be grateful and you’ll feel amazing if you’re able to open your first Pull Request after a few days - even if all it does is change to . accuontNumber accountNumber Do Code Reviews It’s tempting to neglect to do code reviews at the beginning because you feel like you don’t know the code well enough to leave suggestions. I’ve certainly been guilty of that in the past. However, reviewing Pull Requests is a great way to familiarise yourself with the code. No one will expect you to get involved in discussions on the business logic in the first days but you might be able to catch some careless slips. You will also get an idea of how your team does Pull Requests - how big are the changes usually, how many reviewers get involved, which quality gates have to be passed, what’s the tone like in the comments? I hope these tips will help you when starting your next job or project. Please leave a comment with anything you do to make the first few days a success! You’ve got this, and I’m rooting for you. ✨Now go build things. 🚀

A Software Engineer's Playbook for Starting a New Gig

We are far from a world where people only engage with and consume pictures, videos, and text only created by professionals. TikTok, for example, has created some of the biggest names in the entertainment industry today from people that picked up a camera and pressed record. It has created an incredible opportunity for both consumers and brands. Brands continue to see the value in user-generated content and its use to build trust with their audience, spread their name, and grow their revenue. If you haven’t started already, collecting this valuable content being posted on social media or shared with your business and starting to repurpose it to benefit your business is crucial. What is UGC? User-generated content, or , is videos, pictures, texts, and other mediums created by your customers and published across social media or other channels. Here are some of the more common forms of UGC that can be found: UGC Reviews Videos i.e, unboxing videos or even product demonstrations Pictures i.e, wearing a brand’s clothing or using it in action Testimonials Podcasts Blog posts Why is UGC important? UGC content can be effectively used across all the stages of the buyer's journey to help engage them and build credibility with the brand. Competition amongst brands is fierce, and one of the best ways to attract more customers is to stand out from the rest and build trust with your prospects. agree that consumers trust content created by customers more than content created by a brand. Therefore, encouraging UGC ultimately helps your brand to grow a community, motivating your customers to share their thoughts and experiences with the product. 93% of marketers UGC can be a significant driver in the success of your brand by prompting purchasing decisions and increasing overall conversion rates. Don't miss out on the opportunities UGC offers, and start utilizing the methods we suggest below to collect your own. What GoPro has taught us about UGC is famous for its small, portable video cameras that thrill-seekers and adventurers love. GoPro is one of the most successful brands in taking advantage of the benefits of user-generated content. In doing so, they have amassed a large following of loyal customers that look forward to sharing their stories and videos to be posted across their social channels. GoPro With three million+ YouTube followers, GoPro constantly uploads user-generated content showing their customers using the product in action, whether it be surfing, traveling, race car driving, or even a wedding! GoPro has captured the hearts of its audience, inspiring them to purchase the product and create their own stories and videos to be shared with the like-minded community they’ve created. Tips for catching UGC content Gathering UGC content doesn’t have to be difficult. There are many different ways that you can try to encourage your pre-existing customers to create their own and share them. Here are some tips for catching UGC from customers: Ask for reviews and feedback Simply asking your customers for a review or feedback can go a long way towards gathering user-generated content and strengthening their relationship with the brand. Send emails, text messages, or make a post asking your customers to review your product or service and post it online. Many of your brand loyalists will often share their thoughts online for others to read. Customers will often be happy to support your business and provide feedback on their experiences. People like to have their voices heard, and it is up to you to provide the space for it. Start a #hashtag Hashtags are a great way to encourage customers to share content about your brand and its products. A great example of a successful hashtag is campaign. It urged users to post a picture wearing their iconic Mickey Ears with the hashtag in their caption– for every photo posted, Disney donated $5 to the Make-A-Wish Foundation. Disney’s #ShareYourEars The campaign was a huge success, with nearly 2 million people posting photos wearing the ears and sharing their thoughts on the brand’s positive contribution to society. As a result, Disney collected great user-generated content and made waves worldwide due to their charitable donation. Launch a contest or challenge Contests and challenges are another great way of motivating customers to share content about your brand online. For example, the contest can ask customers to post a photo to enter and win a prize chosen by your company. Sabra Hummus did a great example of this for . They asked people to share their favourite flavour of Sabra hummus, which took off on TikTok, with creators sharing videos about their favourite Sabra hummus, some videos professionally filmed, allowing Sabra to collect user-generated content they could continue to use across their channel for years to come! National Hummus Day Collecting UGC with Moast Moast enables brands to build communities of like-minded people that share content about their products to be viewed and discussed amongst prospective customers. With Moast, brands onboard customers to the platform so that prospective customers can ask questions, arrange meetups, and learn more about the company and its products. A brand's customers are asked to create profiles with a short bio and stories on how they use your product. They then can add posts, videos, and thoughts about their products and the brand. The result is a comprehensive collection of a brand’s best customers and their “user stories” featured on an interactive map. Moast is the perfect platform to collect user-generated content for your business. Customers can share their content, and you can repurpose it across your brand's channels. It's a great way to engage with your audience and share their experiences with others.

Acquiring UGC From Customers: A 2023 Playbook

of this article, we described how to quickly and easily build an infrastructure for running UI tests on Android using Appium and Selenoid. We are continuing our story to explain how we incorporated the launch of UI tests in iOS into the process. In the first part Scaling with GGR The maximum number of parallel workflows within a host is limited by its resources. Thus, we needed a tool for combining multiple hosts into one cluster. For this, we use the (GGR) from the folks at Aerokube. Based on the description in the documentation, GGR is a load balancer used for creating scalable and highly available Selenium clusters. Go Grid Router The project with the tests runs a query in GGR as part of the process used. It polls the Selenoid parameters specified in its configuration and distributes the load among them based on the platform used, the availability of free flows, and the predefined specific weight of each Selenoid. Deploying GGR and GGR UI is easy: Install . Docker Create a directory for GGR configuration files . mkdir -p /etc/grid-router/quota Create a file . users.htpasswd $ htpasswd -bc /etc/grid-router/users.htpasswd test test-password Create a quotas file where you specify the address of the deployed Selenoid as the host: $ cat /etc/grid-router/quota/test.xml
<qa:browsers xmlns:qa="urn:config.gridrouter.qatools.ru">
<browser name="android" defaultVersion="10.0" defaultPlatform="android">
  <version number="10.0">
    <region name="1">
      <host name="0.0.0.0" port="4444" count="1"/>
    </region>
  </version>
</browser>
</qa:browsers> Run the GGR container: docker run -d \
  --name ggr \
  -v /etc/grid-router/:/etc/grid-router:ro \
  --net host aerokube/ggr:latest-release \
  -listen=:4445 -guests-allowed In the tests project, change the Appium port to the port of the running GGR: val driver = AndroidDriver(URL("http://localhost:4445/wd/hub"), capabilities) Run the GGR UI container: docker run -d \
  --name ggr_ui \
  -p 8888:8888 \
  -v /etc/grid-router/quota:/etc/grid-router/quota:ro \
  aerokube/ggr-ui:latest-release Run the Selenoid UI container where we pass the GGR UI port via : selenoid-uri docker run -d \
  --name selenoid-ui \
  -p 4446:4446 \
  --link selenoid:selenoid \
  aerokube/selenoid-ui:1.10.4 \
  --selenoid-uri "<http://ggr-ui:8888>" Our Selenoid UI should now display the status of all Selenoid clusters connected to GGR. Now, we proceed to the running of tests on iOS We use our own Mac mini farm to run UI tests on iOS. The farm can similarly be assembled from decommissioned but operational MacBooks. Alternatively, they can be . The following needs to be installed on each host: rented Appium (Appium 2.x must be used when one is working with iOS≥16). Xcode. Some early problems We were unable to replicate the structure used in running Android tests because we couldn’t find a way to run iOS simulators in Docker containers. One option that we considered was running , but we ran into doubts about the legality of its use for any purposes unrelated to . So, we decided to go a different route. Docker-OSX OS X Security Research Iteration #1: GGR→Appium We added Appium (port 4723) as a Selenoid host for iOS tests in the previously created GGR config file: <qa:browsers xmlns:qa="urn:config.gridrouter.qatools.ru">
<browser name="android" defaultVersion="10.0" defaultPlatform="android">
  <version number="10.0">
    <region name="1">
      <host name="0.0.0.0" port="4444" count="1"/>
    </region>
  </version>
</browser>
<browser name="iPhone 14" defaultVersion="16.2" defaultPlatform="iOS">
  <version number="16.2">
    <region name="1">
      <host name="0.0.0.0" port="4723" count="1"/>
    </region>
  </version>
</browser>
</qa:browsers> In such a case, the iOS scheme looks like this: The structure used in this iteration is operational. The problem is that, in this case, we can only run tests in one workflow on each Mac mini, which is wasteful. Also, the cluster will not be displayed in the Selenoid UI. Iteration #2: GGR→Selenoid→Appium Selenoid allows you to work . The above problems informed our decision to use Selenoid when running iOS tests as well, though as an executable file: with more than just containers Download Selenoid ( / ). amd64 arm64 Create a configuration file. browsers.json Be sure to specify Appium and startup settings in the configuration file: {
  "iPhone 14": {
    "default": "16.2",
    "versions": {
      "16.2": {
        "image": ["appium", "--log-timestamp", "--log-no-colors", ...]
      }
    }
  }
} Give permission to execute the Selenoid file. In our case, we used . chmod 755 Run Selenoid via the terminal. We used the following parameters: . selenoid -conf ~/browsers.json -disable-docker -capture-driver-logs -service-startup-timeout 4m -session-attempt-timeout 4m -timeout 6m -limit 2 The specified timeouts are necessary because standard timeouts may not be sufficient for downloading the app from the cloud storage and launching the simulator. The parameter was used to set the maximum number of running simulators. This is the reference value to be used by the GGR in the future. The performance of the host is used as a guide for setting the parameter. -limit You can read more about startup settings in the Selenoid . documentation If need be, a PLIST file can be created on each Mac mini to autorun Selenoid in case of a sudden system restart. Now, the process of the cluster looks like this: With this approach, we partially achieve Selenoid UI functionality and the ability to run tests across multiple flows on the same host. The downside is that, on each Mac mini, you have to manually carry out a multitude of routine tasks to create a simulator and link it with Appium by specifying the UUID and port assignment. This can become a problem if you need to upgrade to a new iOS version later. Iteration #3: GGR→Selenoid→Bash→Appium We have a large Mac mini farm that will continue growing as time goes on. With this in mind, we were looking for a way to make scaling easier so that we wouldn’t have to create simulators by hand and then connect them to Appium. With the previous schema in place, Appium and simulators would have had long lifetimes, which could have led to unpredictable consequences. Searching for a solution, we discovered that a bash script can be specified as a host in the Selenoid configuration file: {
  "iPhone 14": {
    "default": "16.2",
    "versions": {
      "16.2": {
        "image": ["~/bin/config/start_appium.sh", "iPhone 14"]
      }
    }
  }
} This is what ours looks like: #!/bin/bash

set -ex

DEVICE_NAME=$1
APPIUM_PORT=$(echo $2 | cut -d '=' -f 2)

function clean() {
  if [ -n "$APPIUM_PID" ]; then
      kill -TERM "$APPIUM_PID"
  fi
  if [ -n "$DEVICE_UDID" ]; then
      xcrun simctl delete $DEVICE_UDID
  fi
}

trap clean SIGINT SIGTERM

# Each simulator has a udid, so to run the same devices in parallel - clone and run
# only clones. You cannot clone a running device. After closing the session, delete the clone.
cloned_device_name="[APPIUM] ${DEVICE_NAME} ($(date +%Y%m%d%H%M%S))"
DEVICE_UDID=$(xcrun simctl clone "$DEVICE_NAME" "$cloned_device_name")

# https://github.com/appium/appium-xcuitest-driver#important-simulator-capabilities
WDA_LOCAL_PORT=$(($APPIUM_PORT+1000))
MJPEG_SERVER_PORT=$(($WDA_LOCAL_PORT+1000))
DEFAULT_CAPABILITIES='"appium:udid":"'$DEVICE_UDID'","appium:automationName":"'XCUITest'","appium:wdaLocalPort":"'$WDA_LOCAL_PORT'","appium:mjpegServerPort":"'$MJPEG_SERVER_PORT'"'

appium --base-path=/wd/hub --port=$APPIUM_PORT --log-timestamp --log-no-colors --allow-insecure=get_server_logs,adb_shell \
       --allow-cors --log-timestamp --log {choose_directory_for_logs} \
       --default-capabilities "{$DEFAULT_CAPABILITIES}" &
APPIUM_PID=$!

wait If the script is used, pay close attention to the stated capabilities and Appium startup settings. These are set up here assuming that Appium 2.x is used for the run ‒ Appium 1.x does not require the vendor to be specified in capabilities, and there is no option of specifying --base-pat. The script solves the problem of simulators running in parallel: When multiple Appiums are directly connected to GGR from one Mac mini, , because you cannot run the same emulator with identical UDIDs. You have to manually duplicate and hardcode the UDID each and every time. (For example, if you need to change the iOS version or the simulator model.) a problem arises with the emulators . It is necessary to run Appium manually every time, and regularly check it for conflicts with ports and simulators. Poor scalability The use of Selenoid makes it possible to simplify this process down to a single script that does not create conflicts between multiple Appium + Simulator pairs within a single host. It launches Appium and kills it when receiving a corresponding signal from Selenoid, and it dynamically clones the simulators at startup and deletes them once the session ends. The process we developed looks like this: Next, we add the Selenoid addresses of each Mac mini to the configuration file of the deployed GGR, merging the Android and iOS structures: Bottom-line summary The assembled infrastructure allows us to run a total of about 500 UI tests per hour across 36 workflows on both platforms. Addition of a new host for Android tests can be fully automated by using the workflow on GitHub Actions, and takes about two minutes. In the very near future, we plan to automate the deployment of the Selenoid cluster on a Mac mini as well. Further down the road, we would like to try out running containers on a Mac mini with Linux to unify all the processes and make it easier to deploy to them without breaking any MacOS usage rules. If you have had any related experience, we'd love for you to share it in the comments. Docker-OSX Posted by . Ivan Grigoriev

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Her Gün Mobil Cihazlarda 5000'den Fazla Test Nasıl Çalıştırılır; inDrive'ın Başucu Kitabı'ndan (Bölüm 2)

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