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Building a Robust JS/TS Monorepo: Best Practices with Yarn, NX and Changesets

Monorepos have become a popular approach to managing complex software projects by unifying the codebase for enhanced collaboration and code reuse.
Yarn Workspaces allow easy management of multiple packages within a monorepo, enabling sharing dependencies, simplifying management, and improving control over the repository.
Setting up Yarn involves installing it and configuring it with plug’n’play for IDE recognition of dependencies.
Organizing the monorepo structure involves categorizing code into Apps, Features, and Libs for better navigation and code reuse.
NX offers efficient script execution in parallel, identification of dependencies, and caching results to optimize the build process.
NX helps manage circular dependencies by generating a dependency graph to detect and prevent issues.
Yarn Constraints enforce rules for packages in the monorepo, ensuring consistency in dependencies, Node versions, and dependency versions.
Changesets aid in version management by tracking changes in small, human-readable documents, allowing granular version control and automated releases.
GitHub Actions integration automates tasks like PR quality checks, version releases, and application deployments, enhancing workflow efficiency.
By implementing best practices and tools like Yarn, NX, Changesets, and GitHub Actions, teams can enhance collaboration, streamline releases, and improve project consistency in a monorepo setup.

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Hackernoon

276

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C++ Metaprogramming: Compilation of Calculations, from Basic Techniques to Advanced Methods

Compile-time calculations in C++ provide advantages such as data and logic validation, reduced execution time, and enhanced code safety.
Templates and constexpr are key compilation mechanisms for computations in C++.
Historically, metaprogramming in C++ emerged through templates, allowing complex computations using recursive templates.
Examples like factorial and Fibonacci calculations showcase template metaprogramming but can lead to code complexity and debugging challenges.
Debugging complexity, increased compilation time, and template limitations are drawbacks of template metaprogramming.
The introduction of constexpr in C++11 provides a modern approach for compile-time computation, offering simplicity and ease of use.
constexpr functions and variables allow evaluation at compile time, simplifying code and improving readability.
Modern constexpr features include conditional operators, loops, and constexpr classes for more advanced compile-time computations.
Useful features like static_assert and if constexpr further enhance compile-time checks and conditional operations.
Pitfalls of extensive metaprogramming include increased compilation time, expression limitations, potential hidden copies, and debugging complexity.

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Digitaltrends

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Vibe coding: What it is, and why you should give it a miss

Vibe coding refers to creating software without writing any code by using AI tools and natural language prompts.
It is becoming popular among non-technical users but poses challenges in terms of accuracy and security.
Vibe coding tools generate auto-complete suggestions, summarize code snippets, and create code based on natural language prompts.
For non-technical users, interpreting the output accurately can be difficult and may lead to security risks.
Efficiency and data costs become issues when using vibe coding for software development, as generated code may be inefficient.
The solution proposed is to emphasize learning coding rather than relying solely on vibe coding tools for software development.
Learning programming skills is crucial for building safe and efficient software, regardless of the availability of AI-powered coding tools.
Vibe coding may not be a sustainable solution in the long run for non-technical individuals looking to create software products.
It is recommended to prioritize understanding the fundamentals of software development over quick solutions like vibe coding.
Consumers are advised to be cautious with independently developed vibe-coded products due to potential security risks.

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Javacodegeeks

276

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Mock JWT Decoding with JwtDecoder in JUnit Tests

JWTs are commonly used for microservices and Spring security authentication, requiring mocking of JWT decoding for testing purposes.
JWT (JSON Web Token) is used for securely transmitting information, structured as Header.Payload.Signature.
JwtDecoder in Spring Security decodes JWTs to extract claims, providing essential functionality for authentication.
JwtDecoder verifies a JWT's signature, decodes claims like subject and expiration time, and works with different key types.
Advantages of JwtDecoder include secure JWT verification, support for various signing mechanisms, and seamless integration with Spring Security.
To mock JwtDecoder in JUnit tests, Mockito is used to simulate behaviors for valid, expired, and invalid JWT tokens.
Unit tests demonstrate creating and testing JWT decoding scenarios like valid tokens, expired tokens, and decoding failures.
JUnit provides detailed output on test results, indicating success or failure of assertions during testing.
Mocking JWT decoding with JwtDecoder streamlines secure and efficient testing of authentication logic in Spring Security applications.

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PlanetPython

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Python Bytes: #425 If You Were a Klingon Programmer

The episode of Python Bytes #425 covered various topics including the benefits of using uv, Python Developer Tooling Handbook, blacken-docs tool for doc writers, and reinventing notebooks as reusable Python programs.
The discussion about uv prompted mixed responses from people, with some being reluctant to switch from existing tools like pip and pip-tools.
The Python Developer Tooling Handbook focuses on enhancing Python development productivity by understanding tools related to packaging, linting, formatting, and dependency management.
Blacken-docs is a tool that allows running black on Python code blocks in documentation files, supporting Markdown, reStructuredText, and LaTex files.
Marimo enables storing notebooks as plaintext Python files, making them Git-friendly, easy to read, importable as Python modules, and executable as Python scripts.
Marimo notebooks can be tested using pytest, leveraging the interoperability with other Python tools for better testing practices.
Extras included announcements like PyConUS refund policy for international attendees, new format for The Complete pytest Course Bundle, and PyCon Taiwan calling for proposals.
The episode also featured a humorous segment about the top 12 things likely to be overheard if you had a Klingon Programmer.

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PlanetPython

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Talk Python to Me: #498: Algorithms for high performance terminal apps

Will McGugan is back to discuss algorithms for high performance terminal apps.
Will's latest article focuses on revolutionizing what's possible in the terminal.
He discusses smooth animations, dynamic widgets, and TUI frameworks.
Will takes a modern, web-inspired approach to old-school terminals.

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Dev

235

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How to Build a Successful DApp: Step-by-Step Guide and Best Practices

Decentralized applications (DApps) are revolutionizing technology by leveraging blockchain for transparency and security.
Building a successful DApp involves careful planning, design, and execution, as outlined in this step-by-step guide.
Understanding the fundamentals of DApps and the Web3 landscape is crucial for effective smart contract development.
Planning your DApp involves defining goals, creating a detailed roadmap, and managing risks effectively.
Designing an intuitive interface is essential for user-friendly DApps, focusing on simplicity and device compatibility.
Developing core features and smart contracts requires choosing the right technology stack and iterative development based on feedback.
Testing, auditing, and ensuring security play a critical role in the deployment and success of a DApp.
Deployment best practices include selecting a reliable network, gradual rollout, and real-time monitoring post-launch.
Maintaining, updating, and scaling your DApp are ongoing processes for long-term success and user satisfaction.
Choosing the right Web3 agency can streamline the development process and ensure expertise in smart contract development.

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Dev

135

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What is Decision Table Testing? A Beginner’s Guide

Decision table testing is a black-box technique that ensures comprehensive coverage of input conditions and outcomes in a structured tabular format.
It consists of conditions, actions, and rules defining decision logic in a visual way, aiding in catching defects before impacting users.
Creating a decision table involves identifying input conditions, defining possible outcomes, constructing a table, and optimizing test cases.
Decision table types include extended entry, limited entry, and cause-effect, catering to different testing needs.
Benefits of decision table testing include covering all input combinations, improving communication, and reusability.
Challenges include binary output, lack of subjective insights, and the need for skilled testers to handle complex tables.
Best practices suggest prioritizing test cases, utilizing automation, refreshing tables, maintaining a consistent format, boundary testing, and starting simple.
Tools like DecisionRules.io, Google Sheets, and CoTester by TestGrid can assist in creating decision tables based on complexity and automation needs.
Decision table testing simplifies complex decision-making processes, enhances software reliability, and minimizes oversights by providing a clear, structured testing approach.
Following best practices and leveraging appropriate tools can streamline the testing efforts and optimize quality assurance in software development.

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Medium

167

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What Is Vibe Coding, Really? Why Letting AI Do Everything Might Be Holding You Back

Vibe coding has gained popularity in the coding community, but its meaning has evolved over time.
Originally, vibe coding referred to writing code in a flow state, relying on intuition and working fast.
However, many people now use vibe coding to describe relying too much on AI coding assistants and not actively learning.
The author emphasizes the importance of using AI as a creative partner to enhance coding skills and achieve real growth.

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Dev

163

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Supercharge Your Coding Mojo: Connecting This FREE MCP Server to Cursor

The integration of Apidog MCP Server with Cursor transforms how developers work with API documentation and coding.
MCP Server enables AI coding assistants like Cursor to access and understand API documentation seamlessly.
Setting up the integration involves obtaining an Apidog access token, finding the Project ID, and configuring Cursor.
This integration empowers developers to generate code, handle API changes, improve documentation incorporation, and build complete API clients efficiently.
Tips include managing multiple API projects, utilizing standard OpenAPI specs, and securing access tokens.
Specific AI prompting enhances the accuracy of results in generating code or explanations.
The integration signifies a shift towards AI-enhanced development, eliminating manual translation of API specs.
Tasks that once took days now require hours, and the quality of API implementations has significantly improved.
This seamless integration promises increased productivity, better code quality, and time saved previously spent on API documentation.
The future is here with this efficient integration, offering a streamlined API development workflow with AI assistance.

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Dev

339

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What Is Self-Healing Test Automation and How It Works?

Self-healing test automation automatically detects and fixes test scripts when the software application changes, reducing manual effort and improving test reliability.
It adapts to code-level changes, ensuring test scripts remain accurate without requiring manual updates.
Benefits of self-healing test automation include preventing object locator flakiness, improving test coverage, lowering test maintenance efforts, minimizing test failures, and providing a faster feedback loop.
Self-healing test automation integrates AI technologies to make testing smarter and more efficient.
An example of self-healing test automation is adjusting test scripts to identify updated elements like class names in a web application.
The process involves steps like identifying elements, performing test execution, identifying issues, and implementing self-healing updates in the test scripts.
KaneAI by LambdaTest is an AI testing tool that offers self-healing features to identify and update broken locators automatically.
KaneAI facilitates intelligent test generation, multi-language code export, 2-way test editing, smart versioning, and bug reproduction.
Self-healing test automation simplifies test maintenance, saves time, boosts test coverage, and enhances software quality.
Integrating self-healing test automation with AI technologies improves testing efficiency and ensures smoother software development processes.

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Dev

312

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Implementing Stripe Subscriptions with Firebase Cloud Functions and Firestore

Integrating Stripe subscriptions with Firebase applications involves creating several Cloud Functions to handle functionalities like product information, checkout sessions, webhooks, and more.
Steps include setting up a Stripe account, creating Cloud Functions for product and pricing information, and exposing this data to the frontend.
Cloud Functions are created to handle Stripe checkout sessions, connect the frontend to Stripe for subscription management, and process subscription events via webhooks.
A function is implemented to allow users to manage their subscriptions through a Customer Portal session.
Cloud Functions are used to retrieve subscription data and integrate it into the frontend for users to access and manage their subscriptions.
Firestore rules are updated to ensure secure access to subscription data within the user document.
After thorough testing in test mode and ensuring all functionalities work as intended, the system can be switched to live mode for processing subscriptions.
The guide concludes with a reminder of the minimal Cloud Functions needed for integrating Stripe with Firebase applications and encourages sharing project successes.
Overall, the process involves leveraging Firebase Cloud Functions to streamline the integration of Stripe subscriptions, enhancing user experience and subscription management within Firebase applications.
Implementing and testing each step meticulously ensures a smooth transition to live mode for processing subscriptions and successful application launch.

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Medium

276

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Did You Know These Wild Facts About Blockchain?

Understanding the technical concepts of blockchain is critical for security researchers.
Each operation in the Ethereum Virtual Machine (EVM) has a gas cost, and the gas limit determines the number of transactions in a block.
Mitigations for gas exhaustion include increasing gas costs for certain opcodes and batch processing transactions with priority mechanisms.
A security risk in smart contracts is allowing access to uninitialized storage, which can be exploited by attackers.

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Medium

108

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5 AI Prompts That Will Transform Your Writing Forever

AI technology can transform writing by assisting writers in organizing ideas, improving structure, and enhancing critical assessment.
AI-powered tools like ChatGPT and Gemini can be used to enhance writing skills and creativity.
The prompts given to AI systems play a significant role in achieving desired results.
Providing detailed prompts can help writers effectively utilize AI tools for effective and personalized writing.

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PlanetPython

367

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PyBites: Case Study: Developing and Testing Python Packages with uv

Structuring Python projects properly, especially when developing packages, can often be confusing, and developers encounter common questions about organizing project folders, including test placement and package directory location.
Python offers two ways to organize code: modules and packages. With Python versions 3.3 or higher, importing code from different directories works smoothly due to implicit namespace packages introduced in Python 3.3.
However, challenges arise when modules are moved into subdirectories, leading to potential import errors. The article discusses the significance of __init__.py files in resolving these issues.
uv is a Python package and project manager that streamlines package resolution and environment management. It simplifies setting up and managing Python projects, offering a unified approach.
uv's 'uv init' command efficiently sets up projects, creating essential project files like README.md, pyproject.toml, and a virtual environment lock file. It recognizes projects based on the pyproject.toml file.
By running 'uv venv' and 'uv sync', developers can create virtual environments and synchronize dependencies, allowing seamless management of project environments.
With uv, testing package functionalities using pytest and importing packages into scripts becomes straightforward. Changes in source code reflect immediately without requiring package reinstallation due to an editable install feature.
The article concludes by highlighting the feasibility of using uv for monorepo setups to manage multiple packages within the same repository.
uv simplifies Python project structuring and dependency management, making it a valuable tool for Python developers seeking efficient project organization.
uv removes the complexities of project configuration and offers seamless virtual environment management, enabling developers to focus on writing high-quality code.
The use of uv in Python projects enhances development efficiency and promotes best practices in project setup and package management.

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