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Medium

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"Boost Your YouTube SEO: How Chapter Markup Optimizer Can Increase Watch Time"

Chapter markers on YouTube videos help improve navigation and search visibility.
Optimized chapter markers enhance user experience and improve engagement metrics.
Chapter titles indexed by YouTube's algorithm lead to better discoverability in search results.
The Chapter Markup Optimizer simplifies the process of creating and optimizing video chapters.

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PlanetPython

371

PyCon: What to expect at PyCon US sprints

PyCon US sprints include topic-specific summits, sponsor presentations, and deep-dive tutorials days before the first official talk, extending for four days after.
Attendees organize the sprints, working on open-source and community projects with provided resources and support.
Participants include project maintainers, seasoned contributors, community organizers, and first-time contributors, with no requirement to attend all four days.
Choosing a project you're familiar with enhances sprint participation, with some projects offering dedicated spaces for collaboration.
Sprint attendees are advised to have experience with the project they contribute to, while newcomers may find guidance in dedicated spaces for first-time contributors.
Finding issues to work on may involve tackling known problems, addressing personal needs, or inquiring with maintainers for tasks to engage with.
Pair programming, seeking help when stuck, and preparing the work environment ahead of time are recommended strategies for fruitful sprint participation.
Stay safe during the event by backing up your system, using unique identifiers for your hardware, and practicing caution with network security and device interactions.
Sprints are highlighted for their productivity, camaraderie, and potential for significant contributions, making them a valuable part of the PyCon US experience.
Participating in sprints fosters collaboration, learning, and networking opportunities that extend beyond the event, enhancing the overall community engagement.

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Medium

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Mastering Python ElementTree’s iter() with XPath: Efficient XML Parsing

ElementTree is a built-in Python library that simplifies XML parsing and manipulation.
Using XPath with ElementTree enables targeted selection of XML elements.
Optimizing ElementTree and XPath usage is crucial for efficient XML processing.
ElementTree's iterparse() method allows iterative parsing of large XML files.

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Medium

368

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How to use Web Search in VS Code Github Copilot

Microsoft has created a hidden tool called 'Web Search for Copilot' to enhance agentic GitHub Copilot in VS Code.
The tool, an official extension developed by Microsoft, is designed to work with GitHub Copilot's Agent Mode.
By installing the 'Web Search for Copilot' extension and switching to Agent Mode, users can access web search capabilities.
To use web search, users need to register with Tavily and create an API key, which is prompted during the first search request.

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Javacodegeeks

253

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Java Stream vs. Flux.fromIterable()

Java Stream and Flux.fromIterable() are used in Java for processing sequences of data in a functional style but differ in design and behavior, with Flux being asynchronous and supporting multiple subscribers.
Java Stream is synchronous, designed for CPU-bound, in-memory transformations, and can be consumed only once, while Flux fromIterable() is reactive, asynchronous, and supports backpressure.
Flux allows for multiple subscriptions and is non-blocking, making it suitable for high-throughput, event-driven data processing, unlike Java Stream which is limited to a one-time consumption.
Java Stream handles errors traditionally with try-catch blocks, while Flux provides operators like onErrorResume for declarative error handling without halting the stream.
Both Java Stream and Flux support declarative operations like map and filter, but Flux is more suitable for reactive, IO-bound applications requiring non-blocking execution.
Key differences include execution model (synchronous vs. asynchronous), blocking behavior, backpressure support, consumption rules, use cases, and error handling.
Java Stream is preferred for simple, sequential operations on finite datasets, while Flux is recommended for asynchronous, non-blocking processing of potentially infinite or slow-producing data.
Java Stream lacks reactive features like backpressure and multiple subscriber support, while Flux excels in reactive web applications, event-driven systems, and error recovery.
In conclusion, Java Stream offers simplicity in synchronous processing, while Flux provides advanced features for reactive, asynchronous data handling in applications with complex requirements.
Overall, understanding the differences between Java Stream and Flux helps in selecting the appropriate tool based on the nature and demands of the data processing tasks.

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15 Likes

Medium

416

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No Coding Skills? No Problem — How I Built a Full Business Tool With AI.

This article discusses how a complex business tool was built using AI and logical thinking without extensive coding skills.
The tool had features such as demand forecasting, stock level calculation, trend visualization, and product group management.
By leveraging ChatGPT and understanding the business requirements, the author successfully developed a tool with around 1000 lines of Python code.
The author emphasizes that although AI can assist with coding, the creativity and understanding of code architecture are still crucial.

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23 Likes

PlanetPython

277

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PyPodcats: Episode 8: With Mojdeh Rastgoo

Mojdeh Rastgoo, an active member of the open-source community, is the newest host of PyPodcats.
She has been involved in the Python ecosystem since discovering it in 2016 and has given tutorials and contributed to various initiatives.
Mojdeh is a co-organizer of PyLadies Paris and a member of the PSF Code of Conduct Working Group.
In this episode, Mojdeh discusses her journey, her involvement in the community, and the future plans for PyPodcats.

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Dev

102

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AI in Software Testing: How CoTester Outperforms Other Platforms

The adoption of AI in Software Testing has transformed QA operations, offering faster test execution, predictive defect detection, self-healing automation, enhanced test coverage, and reduced manual effort.
CoTester from TestGrid stands out for combining AI in software testing with strong automation capabilities, improving efficiency, accuracy, and scalability.
Key reasons why AI is crucial in software testing include faster test execution, predictive defect detection, self-healing automation, enhanced test coverage, and reduced manual effort.
CoTester's unique features include AI-powered self-healing scripts, smart test case generation, integration with CI/CD pipelines, comprehensive test coverage, defect prediction, test execution optimization, and cloud-based execution.
CoTester stands out with its comprehensive AI-driven approach compared to competitors like Testim Copilot, Mabl AI, aqua AI, Kane AI, and Tosca Copilot by offering scriptless automation, dynamic test case generation, debugging, and root cause analysis.
The future of AI in testing will focus on greater adoption of AI-powered test automation, advanced machine learning models, self-healing capabilities, and deeper integration with DevOps and Agile workflows.
Choosing CoTester from TestGrid ensures access to advanced AI-powered test automation, seamless integrations, intelligent defect detection, and scalability, essential for competitive software testing.
Adopting tools like CoTester is vital for staying competitive in the software industry, optimizing QA processes, reducing testing costs, and accelerating software releases.
AI in software testing will continue to evolve, making tools like CoTester a smart investment for teams looking to leverage AI-driven testing.
Source: This article was originally published on Medium.

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Dev

389

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🚀 Building Fast, Scalable, and Robust CRUD APIs with FastAPI & PostgreSQL

FastAPI has quickly become one of the most loved web frameworks in the Python ecosystem. Combine it with PostgreSQL and SQLAlchemy for a fast, scalable, and production-ready backend stack.
The stack offers speed, reliability, developer experience, and scalability, making it an ideal choice for building modern APIs with performance and clean code in mind.
The guide covers project structure for scalability, database setup with PostgreSQL and SQLAlchemy models, and full CRUD operations with FastAPI routers.
If you're looking to build powerful CRUD APIs in Python, this tutorial provides valuable insights and practical examples.

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Softwaretestingnews

166

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Redefining Software QA in 2025

The software testing landscape is set to transform in 2025 due to economic pressures, complex application ecosystems, and rapid technological advancements.
Quality assurance (QA) teams will confront increasing demands for strategic foresight and innovative tools to release faster without compromising quality.
Software quality intelligence using AI will highlight quality risks, reducing the need for mass testing and focusing on impactful areas under tight deadlines.
Test automation will help manage frequent deployments efficiently, integrating into DevOps pipelines for streamlined processes.
Cloud adoption will lead to hybrid testing environments, demanding seamless management of diverse deployment setups for consistent performance.
Organizations transitioning to the cloud will need to embrace automation and AI at scale to meet SaaS provider release cycles and customer demands.
The European Accessibility Act will drive accessibility integration, requiring QA teams to incorporate accessibility tests into DevOps workflows.
Lean, clean software code will become essential for efficiency and environmental benefits as companies adopt a shift-left approach in development.
AI-powered tools will democratize quality assurance across organizations, enabling non-technical users to participate in testing with user-friendly platforms.
Success in software testing in 2025 will involve integrating automation, AI, and agile cloud solutions to align with rapid release cycles and quality expectations.

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Dev

123

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Headless Vue + WordPress Boilerplate

A headless Vue + WordPress boilerplate that allows using Vue as the front-end for WordPress theme in a headless architecture.
Key Features: Uses Vue 3 with Vite for fast development, connects to WordPress REST API for dynamic content, has a modular folder structure for components, pages, APIs, and stores, supports Vue Router and Pinia, and allows styling with standard CSS or preferred CSS preprocessor.
Folder Structure: Consists of functions, public, and src directories with subdirectories for different functionalities, such as APIs, assets, components, pages, router, and stores.
Getting Started: Download and place the files in the themes folder, install dependencies, run the development server, and build for production.

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7 Likes

Medium

312

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Zero to Mastery (ZTM)Academy FREE Week 2025

Zero To Mastery (ZTM) Academy is offering a Free Week from April 14 to April 21, providing access to all courses and resources for free.
Participants can explore courses, career paths, projects, and community on ZTM platform without any cost or obligation.
This initiative allows individuals to learn in-demand tech skills and prepare for their careers, catering to beginners and professionals alike.
ZTM's Free Week offers a guided and engaging learning experience, with no credit card requirements or hidden fees.
Participants can access courses such as 'Master the Coding Interview', 'The Complete Career Toolkit', and 'Complete Web & Mobile Designer'.
ZTM provides role-based career paths for disciplines like Full-Stack Development, Machine Learning, UI/UX Design, and more.
Courses on ZTM are taught by industry professionals, emphasizing practical skills and project-based learning for portfolio building.
Participants also gain access to a supportive Discord community for guidance, collaboration, and networking opportunities.
The Free Week encourages focused learning, real project application, and participation in group activities for a more immersive experience.
Don't miss this chance to explore ZTM's offerings for free, with the opportunity to kickstart a new career trajectory or enhance existing skills.

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Dev

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Getting Started with Docker: A Beginner's Guide

Docker is an open-source platform that allows you to automate the deployment of applications inside lightweight, portable containers. Docker containers share the host OS kernel, making them faster and more efficient.
Installing Docker: Windows/macOS users can download Docker Desktop, and Linux users can use the package manager. Verify the installation after installation.
Running Your First Container: By pulling and running an Nginx web server, you can see the Nginx welcome page on http://localhost:8080 in your browser.
Why Use Docker? Docker provides consistency, isolation, scalability, and CI/CD friendliness, making it a great tool for development and deployment.

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Medium

Top 8 Must-Have Skills for QA Engineers to Succeed in 2025

Proficiency in automation testing tools like Selenium, Appium, and TestNG is essential for QA engineers.
Understanding Continuous Integration and Continuous Deployment (CI/CD) and being able to integrate test scripts in CI pipelines is crucial for successful QA engineers.
API testing using tools like Postman, REST Assured, and SoapUI has gained importance in the era of microservices and distributed systems.
Skills in performance testing using tools like Apache JMeter, LoadRunner, and Gatling are becoming mandatory as user expectations for application performance increase.

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Hackernoon

350

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How to Setup a CI/CD Pipeline with GitHub Actions

Continuous Integration and Continuous Deployment (CI/CD) helps automate repetitive tasks like testing and deploying, allowing developers to focus on writing features.
GitHub Actions is recommended for setting up CI/CD workflows within a GitHub repository.
The article guides on setting up a simple CI/CD pipeline using GitHub Actions to reduce manual errors, ease deployment, and save time.
Prerequisites include having a GitHub account and basic knowledge of YAML syntax and GitHub.
Create a YAML file in the .github/workflows folder, defining triggers, setting up Python, installing pylint, and configuring pylint checks.
The pipeline checks for docstrings in the codebase with specific pylint configurations.
After committing and pushing changes, the pipeline runs automatically on the main branch showing yellow or green dots based on success or failure.
Testing the set pipeline involves checking GitHub Actions and detailed results to ensure correct configuration and execution.
GitHub Actions automates checks on code commits or pull requests, aiding in identifying errors early in the development cycle.
Implementing CI/CD with GitHub Actions enhances development efficiency by automating checks and ensuring smoother code deployments.

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