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What's new in Yumma CSS 3.0?

Yumma CSS 3.0 is the biggest release that introduces new features, improvements, and fixes.
The new version offers an all-new CLI for easier CSS management and removal of unused CSS.
Over 50 new utility classes have been added, including box shadow, margin, padding, interactivity, positioning, transforms, and more.
The release focuses on improving build performance, making changes to color utility, and offers a migration guide for projects.

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How to Convert Markdown Files to PNG Images

A Python script that converts Markdown files to PNG images has been created.
The script utilizes Python libraries such as Markdown and Selenium with ChromeDriver.
It automatically converts the Markdown file to HTML and then captures a screenshot in PNG format.
The resulting script allows developers to transform documentation into visually appealing formats.

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Dzone

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Introducing SmallRye LLM: Injecting Langchain4J AI Services

Langchain4J is a tool for Java developers to easily add advanced language AI capabilities to their applications.
It provides a unified interface, built-in tools, and easier integration with AI providers.
LangChain4J AI Services automates the steps to integrate large language models into Java applications.
The framework creates a proxy implementation of defined interfaces to communicate with LLMs.
Langchain4J Microprofile (SmallRye LLM) brings Langchain4J AI services to Jakarta EE, MicroProfile, and Quarkus ecosystems.
It leverages CDI, Microprofile Config, Fault Tolerance, and Telemetry for streamlined integration.
Langchain4J Microprofile enables developers to build and configure AI services easily using familiar features.
Developers can create AI services, configure models, and inject them into enterprise Java applications seamlessly.
SmallRye LLM simplifies the integration process and allows developers to focus on enhancing business logic with AI capabilities.
By leveraging SmallRye LLM, developers can create modular AI-powered applications with minimal configuration.

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I Ignored Firebase Costs for 6 Months — Here’s What It Did to My App

Firebase made it easy to build and deploy the app, providing real-time functionality and smooth user experience.
The developer ignored the increasing Firebase costs, not realizing that every change triggered the app to pull the entire document tree, leading to unnecessary data transfer.
After ignoring the costs for 6 months, the developer received a surprising bill of $48.67 from Firebase.
The experience served as a lesson for the developer to be mindful of the potential costs associated with Firebase and other cloud services.

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Crypto-News-Flash

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If SWIFT Joins Ripple This Week, XRP Price Could Skyrocket—Here’s How High

April 16 could mark the end of Ripple's legal battle with the SEC, while April 21 could be the start of a partnership with SWIFT.
SWIFT and Ripple's collaboration could become a reality after being an active participant in an interoperability pilot program.
XRP has the potential to disrupt SWIFT and the traditional financial system in terms of transaction speed, transparency, and interoperability.
If Ripple wins the legal battle, XRP could operate legally and openly in the largest financial market, signaling the growth of the cryptocurrency.

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133

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The Dark Side of Spring Boot Nobody Talks About

Spring Boot applications, with their opinionated configuration, may experience slower startup times due to default configurations for libraries and services.
To improve startup times, lazy initialization of beans can be enabled using the spring.main.lazy-initialization property.
Reducing unnecessary dependencies or using profiles to load only required beans can also help in optimizing startup times.
Spring Boot applications, with their numerous dependencies, may consume more memory than necessary, often becoming an issue for large-scale applications or resource-constrained environments.

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Mastering PDF Annotation with Go: A Developer’s Guide

PDF annotation with Go and UniPDF simplifies adding extra information without altering the original content of a PDF.
Go's advantages for PDF processing include its concurrency model for efficient handling of large files and clean syntax for both beginners and experienced developers.
UniPDF, a Go library, offers efficient tools for PDF tasks like annotation, extraction, and creation, making annotating PDFs simple and well-suited to Go's strengths.
To begin annotating PDFs with UniPDF, developers need to install the library, and then they can easily start adding text comments, images, highlights, shapes, and attachments.
Go's speed, concurrency model, static typing, and error handling make it a strong choice for annotating PDFs, especially in fields like legal, healthcare, and education.
Annotating PDFs in Go allows developers to enhance collaboration, make documents interactive, and streamline document management processes.
By leveraging Go's capabilities and libraries like UniPDF, developers can create more user-friendly and interactive document experiences, transforming the way they work with PDFs.
Go's simple syntax, powerful concurrency model, and scalability make it ideal for building scalable PDF annotation solutions for various industries.
Exploring Go's potential in PDF annotation can lead to improved workflow efficiency and enriched document context for a more engaging user experience.
In conclusion, annotating PDFs in Go with tools like UniPDF empowers developers to not only read documents but also enhance them with context, clarity, and interactivity.

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The Evolution of Data Query Languages: From Imperative Code to Declarative SQL and Beyond

Imperative vs. declarative languages are at the core of the evolution of data query languages.
Imperative languages provide step-by-step instructions for performing a task, while declarative languages describe what needs to be achieved.
Declarative languages, like SQL, focus on the desired outcome of data queries without specifying execution details.
The evolution of data query languages has shifted towards more declarative approaches, promoting simplicity and efficiency in querying data.

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CAP Theorem Made Easy: Choosing Between Consistency and Availability

The CAP Theorem provides a framework for understanding trade-offs in distributed database architecture.
According to the CAP theorem, distributed databases can prioritize consistency, availability, or partition tolerance, but not all three simultaneously.
CP systems prioritize consistency and partition tolerance, while AP systems prioritize availability and partition tolerance.
Modern distributed systems often offer adjustable variables to balance between availability and consistency.

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Dev

310

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Unlocking Developer Productivity: Orchestrate Workflows with Built-in System Tasks

Orchestration using workflow engines like Orkes Conductor can significantly enhance developer productivity by automating task execution and abstracting concerns like retries and error management.
System tasks within Orkes Conductor are reusable components that simplify the creation of complex workflows, enabling developers to focus on task dependencies without worrying about resilience.
Benefits of system tasks include standardized structure for clarity and consistency, ease of expressing complex operations, and enterprise-grade resilience without additional effort.
System tasks facilitate time-saving advantages by decoupling execution code from business logic, making tasks and workflows reusable, scalable, and maintainable.
System tasks handle common operations like HTTP calls, webhook integrations, and AI tasks within the Conductor's JVM, streamlining development and eliminating the need for custom infrastructure management.
Inline tasks within Orkes Conductor enable lightweight JavaScript code execution directly in workflows, whereas worker tasks provide flexibility for complex logic but require external setup and logging.
System tasks offer declarative, reliable workflow building, reducing development effort, enhancing clarity, ensuring consistent behavior, and enabling faster time-to-market for common tasks.
Orkes Conductor provides both system tasks for common use cases and customization options through inline and worker tasks, catering to varying complexity levels in workflow implementations.
System tasks run within the Conductor JVM for speed and availability guarantees, while worker tasks offer full control over complex logic implementations but require additional infrastructure management.
The choice between system tasks and worker tasks depends on requirements, with system tasks offering speed and availability advantages, and worker tasks providing flexibility and customizability.

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Alvinashcraft

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Dew Drop – April 14, 2025 (#4403)

Various articles cover topics such as choosing a framework for building native apps on Windows with .NET, key features of .NET Aspire 9.2, hosting remote MCP servers in Azure Container Apps, and understanding MCP and its significance.
Web and Cloud Development articles discuss topics like showing Toast and swapping content with HTMX, implementing Vertical Slice Architecture in .NET, creating a custom 404 page in Blazor Web Apps, and introducing Spring Cloud Azure Starter Key Vault JCA.
Visual Studio & .NET insights include using a private NuGet feed for building and restoring .NET apps for Docker deployment, exploring the reasons for using Rust as a C# developer, and discussing issues related to UI thread hanging in a kernel call.
AI-related articles talk about VS Code's competition with Cursor & Windsurf, evaluation of agentic AI systems, understanding Vector Search, and discussions on GenAIScript and Copilot Agent mode.
Design, Methodology & Testing articles touch on topics such as the comparison between the Modern Coder and Vibe Code, tips for introverted engineering managers, creating content, and focusing on UX design for agents.
Mobile, IoT & Game Development articles provide insights into setting up a Raspberry Pi 500 home recording studio and a command for keeping phones awake while plugged in.
Screencasts and Videos cover topics ranging from writing high-performance C# and .NET code to creating SaaS applications using Claude Code and 3.7 Sonnet, along with various podcasts on AI, data engineering, and leadership.
Lastly, various community event highlights, database insights, updates on Microsoft Copilot, PowerShell compatibility issues with Azure Runbooks, Apple Maps launching on the web, and announcements related to Windows 11 Insider Preview builds are also discussed across the articles.

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Dev

383

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Understanding RAG: Retrieval Augmented Generation Essentials for AI Projects ✅

Retrieval-augmented generation (RAG) enhances large language models by incorporating external data fetching to improve accuracy of generated answers by providing context from retrieved information.
RAG's retrieval step searches for relevant information before the language model generates an answer, augmenting the original query with retrieved context for greater accuracy.
RAG addresses limitations of standalone LLMs like fixed knowledge cutoff and hallucination issues by grounding responses in retrieved documents.
Advantages of RAG include access to current, specific information, reduced hallucinations, trust in answers, and cost-effective knowledge updates.
Techniques to provide information to an LLM for RAG include semantic search with vector databases, using APIs and external tools, and querying structured databases.
RAG distinguishes itself from Model Context Protocols (MCPs) by actively retrieving external knowledge to augment responses, addressing the need for contextual information.
Long Context AI models like the Llama 4 series complement RAG by providing larger text spans for processing and reasoning, enhancing the model's outputs.
RAG's importance lies in providing relevant context to LLMs, transforming them into reliable tools for real-world applications requiring specific and current data.
RAG is a pivotal component for making LLMs more practical and accurate, bridging the gap between general knowledge and specific information needs.
Future articles may delve into coding RAG solutions using mentioned tools and frameworks, providing readers with hands-on guidance for implementing retrieval-augmented generation mechanisms in projects.
Additional resources like research papers on retrieval-augmented generation and knowledge-intensive NLP tasks are provided for further exploration into the topic.

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Dev

100

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How to Evaluate a System Design?

When evaluating system design, consider fidelity, simplicity, and cost-effectiveness.
Fidelity: Focus on whether the design meets the requirements and solves the right problem.
Simplicity: Look for clear separation of concerns, minimal moving parts, and logical naming and documentation.
Cost Effectiveness: Assess if the design is practical to implement and maintain without excessive cost.

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Dev

302

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Why Python is the Perfect Programming Language for Beginners in 2025

Python is a high-level, interpreted programming language known for its simplicity and readability.
Python remains the #1 language in the AI and machine learning space.
Python has a beginner-friendly syntax, making it easy to learn and use.
Python offers versatile use cases, including web development, data analysis, game development, cybersecurity scripting, and more.

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Medium

137

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Send Automated Audio Messages Using WhatsApp API

Automated audio messages can be sent using the WhatsApp API.
The Wassenger API provides a step-by-step guide on setting up and using automated WhatsApp audio messages.
Ready-to-use code examples are available in various programming languages.
The API allows sending audio messages to phone numbers, group chat lists, and WhatsApp channels.

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