Author: HQW

is Sonnet 3.5 better than chat gpt for the coders?

it seems Sonnet is better than the chatgpt version, so Anthropic is winning the race here.

For more info  on 12 days with open ai wait, explore here later

Beginner Level (2-4 weeks)

• Learn the basic syntax and concepts of Python programming language
• Understand the fundamentals of web automation and testing
• Get familiar with the Playwright library and its features
• Write simple scripts to automate basic web interactions

Intermediate Level (1-2 months)

• Gain in-depth knowledge of Playwright’s API and capabilities
• Learn advanced techniques like handling dynamic content, iframes, and shadow DOM
• Implement robust error handling and logging mechanisms
• Integrate Playwright with testing frameworks like pytest
• Understand the Page Object Model (POM) design pattern

Advanced Level (3-6 months)

• Develop reusable and maintainable Playwright automation frameworks
• Implement parallel and cross-browser testing
• Integrate Playwright with Continuous Integration (CI) and Continuous Deployment (CD) pipelines
• Create comprehensive reporting and dashboarding solutions
• Stay updated with the latest Playwright updates and best practices

Factors Affecting the Timeline

• Prior experience in Python and web automation: Developers with existing Python and web testing knowledge can learn Playwright more quickly.
• Complexity of the application under test: More complex web applications may require more time to automate effectively.
• Dedication and learning approach: Learners who dedicate more time and follow structured learning resources can progress faster.
• Access to mentorship and guidance: Working with experienced Playwright practitioners can accelerate the learning curve.

Conclusion

Becoming a “decent enough” professional in Python automation using the Playwright tool typically requires 2-6 months of focused learning and practice, depending on your starting point and learning approach. Continuous upskilling and staying updated with the latest Playwright developments are essential to maintain and expand your expertise over time.

API automation involves writing scripts or programs to automatically perform various test scenarios on APIs. This includes tasks like sending requests, validating responses, and checking the behavior of the API under different conditions.

Why Use Python for API Automation?

Python is a popular choice for API automation due to its simplicity, readability, and the availability of numerous libraries and frameworks that make API testing more efficient. Some key reasons to use Python for API automation include:

• Ease of Use: Python has a clean and concise syntax, making it easy to learn and implement for API testing.
• Rich Ecosystem: Python has a vast ecosystem of libraries and frameworks, such as requests, pytest, and allure, that simplify API testing and reporting.
• Cross-Platform Compatibility: Python scripts can be run on Windows, macOS, and Linux, making it a versatile choice for API automation.
• Integration Capabilities: Python can be easily integrated with other tools and systems, allowing for comprehensive test automation.

Key Steps in API Automation with Python

1. Set up the Environment: Install Python and the necessary libraries like requests, pytest, and allure (if using for reporting).
2. Understand the API: Study the API documentation to understand the endpoints, request/response structures, and expected behaviors.
3. Write Test Cases: Create Python scripts that send various HTTP requests (GET, POST, PUT, DELETE) to the API endpoints and validate the responses.
4. Implement Assertions: Use Python’s assertion functions to check if the API responses match the expected data, status codes, and other criteria.
5. Handle Authentication: If the API requires authentication, use techniques like API keys, OAuth, or Basic Authentication in your Python scripts.
6. Manage Test Data: Store and manage test data, such as valid and invalid input, in a separate file or database.
7. Generate Reports: Use reporting frameworks like Allure or pytest-html to generate comprehensive test reports.
8. Integrate with CI/CD: Incorporate the API automation scripts into your continuous integration and continuous deployment (CI/CD) pipeline to ensure ongoing API quality.

Example: Simple API Automation with Python

Here’s a basic example of API automation using the requests library in Python:import requests # Set the API endpoint URL url = "https://api.example.com/users" # Send a GET request to the API response = requests.get(url) # Check the response status code assert response.status_code == 200 # Validate the response data data = response.json() assert len(data) > 0 assert "name" in data[0]

This script sends a GET request to the /users endpoint, checks if the response status code is 200 (OK), and then validates that the response data contains at least one user with a “name” field.

Conclusion

API automation using Python is a powerful technique that can help you streamline your testing efforts, improve the reliability of your APIs, and ensure they continue to function as expected over time. By leveraging Python’s simplicity and the rich ecosystem of libraries, you can create robust and maintainable API automation scripts.

Quick Summary

As a QA automation engineer, there are several legal ways to supplement your daily income through side projects, freelancing, and monetizing your expertise. This can provide financial flexibility and help you grow your career.

Who

• This guidance is for experienced QA automation engineers looking to increase their daily earnings.
• Key beneficiaries include those seeking additional income streams or wanting to expand their skillset.
• Entrepreneurs, freelancers, and consultants may also find these opportunities appealing.

What

• Common daily income opportunities for QA automation professionals include:
• Freelance software testing and automation services
• Developing and selling automation frameworks or tools
• Providing training and mentorship in automation technologies
• Consulting on test automation strategy and implementation
• Monetizing technical blogs, YouTube channels, or online courses

When

• These income opportunities can be pursued at any stage of a QA automation career.
• They work best when done consistently, in parallel with a full-time job.
• Timing is important, such as offering training before major software releases.

Where

• Freelance work can be found on platforms like Upwork, Fiverr, or through personal networks.
• Automation tools and frameworks can be sold on marketplaces like GitHub, CodeCanyon, or personal websites.
• Online training and mentorship can be hosted on platforms such as Udemy, Coursera, or your own website.
• Consulting services are often engaged directly with clients or through referrals.

Why

• Additional daily income can provide financial security and flexibility.
• Monetizing your QA automation expertise allows you to leverage your skills.
• It keeps you engaged with the latest tools and technologies.
• Diversifying income streams reduces reliance on a single employer.

How

• Identify your unique automation skills and experience to offer services.
• Create a portfolio of your work to demonstrate your capabilities.
• Establish an online presence through a professional website or social media.
• Optimize your offerings for discoverability and make them easy to purchase.
• Manage your time effectively to balance your day job and side projects.
• Provide exceptional service to build a positive reputation and repeat business.

Best Practices

• Focus on high-demand automation skills and technologies.
• Price your services competitively but don’t undervalue your expertise.
• Maintain strict separation between day job and side activities.
• Reinvest some earnings back into professional development.
• Diversify your income streams to reduce risk.

Examples

• Developing and selling a comprehensive Selenium WebDriver framework.
• Providing on-demand automation script maintenance and debugging services.
• Teaching an online course on Cypress.io for end-to-end testing.
• Consulting with startups on setting up their QA automation infrastructure.

References

• Freelance QA Automation Engineer Rates
• Monetizing Technical Expertise
• Side Hustles for Software Engineers

Who writes better software is the race motive between these 2 AI leaders

Amount of $ invested in research and the actual may decide who does it first! Looks like ChatGPT is I’ll not make it easy for Anthropic!!

What is anthropic

What is ChatGPT

What is Gemini ai

Are there any other competitors

What is the cost per month

How to increase your value by using it a

Quality increased or decreased for the license version?

How it helps each profession?

1. Doctors
2. Software developers
3. Teachers
4. Research scientists
5. Data scientists
6. Data analysts
7. Writers

This app will make a big advancement in the way the price jump of the realists market happens..

Stay tuned

Claude

ChatGPT

Gemini

So on

Which one is better for your skill set

Try asking questions like you are intervythise, you may be feeling astonished to see ChatGPT being better than Gemini on most conversations

Myth of Google has more data so it will be better in the generation of the answer token from the sample token will be challenged!

Claude

ChatGPT

Gemini

So on

Which one is better for your skill set

Try asking questions like you are intervythise, you may be feeling astonished to see ChatGPT being better than Gemini on most conversations

Myth of Google has more data so it will be better in the generation of the answer token from the sample token will be challenged!

To know more about this topic we need see what each tool responds to your ask?

Let’s ask the same questions for both tools and see which one gives the relevant information..

Example: prompts

Consider your self as a trip advisor, provide me a 2 day trip to Niagara Falls Ontario in a weekend,

The available time for looking around is from morning 8am to noon being 8 pm

ChatGPT

Gemini