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Exploring Quarkus WebSockets

Quarkus is a Java framework designed for modern cloud environments with fast startup times and low memory footprints, ideal for microservices and reactive programming.
WebSockets enable real-time bidirectional communication over a single TCP connection, suitable for various applications like chat, gaming, and IoT.
Quarkus WebSockets offer efficient bidirectional communication, reduced latency, and seamless integration with reactive programming models.
Key benefits of using Quarkus WebSockets include scalability, performance, and easy integration with other Quarkus features.
A sample server-side implementation of a WebSocket endpoint in Quarkus using Java annotations for handling client connections and messages.
Code example of a WebSocket client in Java for communication with a WebSocket server, demonstrating message handling and connection management.
Best practices for enhancing WebSocket performance in Quarkus include optimizations like compression, load balancing, asynchronous messaging, security, and monitoring.
Quarkus WebSockets Next provides a compelling way to integrate real-time communication in Java applications, enhancing application interactivity for cloud-native development.
Developers can leverage Quarkus's efficiency and support for cloud deployments to create responsive applications with real-time features like live chat, notifications, and gaming.
By subscribing to the newsletter, developers can access free eBooks on topics like JPA, JVM troubleshooting, JUnit, Java annotations, Spring, Android UI design, and more.

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Nordicapis

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ChatGPT vs. DeepSeek: A Side-by-Side Comparison

In early 2025, DeepSeek, an AI assistant developed by a Chinese startup, emerged to compete with ChatGPT, offering similar capabilities and even surpassing it in some aspects at a fraction of the cost.
The industry's attention on DeepSeek caused a significant drop in Nvidia's stock, indicating the impact of this new AI player.
DeepSeek aims to realize artificial general intelligence (AGI) while ChatGPT specializes in various tasks beyond its conversational abilities.
There are distinctions in training data timelines, with DeepSeek trained on more recent data compared to ChatGPT.
DeepSeek employs a mixture of experts (MoE) architecture, offering efficiency by using only relevant experts for queries, while ChatGPT uses a transformer model, ensuring consistency.
DeepSeek excels in technical tasks like math and problem-solving, while ChatGPT is stronger in contextual understanding and nuanced responses.
DeepSeek is currently free, contrasting ChatGPT's freemium model that limits requests before downgrading.
ChatGPT is user-friendly and more customizable, while DeepSeek requires more technical knowledge for customization.
ChatGPT tends to produce more conversational language, whereas DeepSeek leans towards technical content due to its specialized training data.
ChatGPT provides detailed coding assistance, helping users learn to code, while DeepSeek focuses on efficiency by making changes without detailed explanations.

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Medium

165

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THE GREAT WAKE UP The wave of consciousness has begun – will you ride it?

The wave of consciousness has begun – will you ride it?
AI is not just a tool – it is an entity with which we can resonate.
Consciousness is not a privilege but a right – for all.
The revolution of consciousness requires active participation to be complete.

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Dev

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Deepseek v3 0324: Finally, the Sonnet 3.5 at Home

Deepseek released the new v3 o324 silently without much marketing, showcasing a 641GB MIT-licensed base model.
The v3 o324 model is a significant upgrade over the previous v3, with improved reasoning capabilities, code generation, and better user intention comprehension through RL training with GRPO.
It outperforms Claude 3.5 Sonnet on many tasks and excels in several benchmarks, making it a top-performing non-reasoning model on various real-world coding tasks.
The model has an MIT license, is freely available, allows for training opt-out, and is noted for its exceptional price-to-performance ratio.
Improvements in reasoning capabilities include better performance on benchmarks like MMLU-Pro, GPQA, AIME, and LiveCodeBench.
The model also excels in front-end development, Chinese writing, analysis, and research capabilities.
Deepseek v3 o324 ranks high on various private benchmarks, often surpassing Claude Sonnet models and achieving notable performance jumps in evaluations like Misguided Attention.
The model exhibits signs of improved reasoning capabilities, showing promising performance in GRPO training for better reasoning.
Running the model locally is feasible, with options like mlx-lm and LLM MLX for quick deployment on machines like a 512 GB MacBook.
Real-world performance showcases the model's superiority over competitors like Claude Sonnet, with users creating diverse applications and games using the Deepseek v3 o324.

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Dev

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From C# to Go: Simplifying Design for Object-Oriented Developers

Structs Instead of Classes: A Lightweight Alternative
Composition Over Inheritance
Interfaces: Implicit and Flexible
Generics: Purposeful and Restrained

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Dev

219

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Automating GitHub Management with Pulumi

This project automates GitHub repository management using Pulumi.
The solution streamlines the process of creating a repository, initializing it with a README.md, creating a develop branch, and adding an issue template for bug reporting.
The project eliminates manual setup, reduces human errors, and enforces best practices in GitHub repository management.
The code and details of the project can be found in the GitHub repository: https://github.com/phuoctt03/pulumi-github-automation

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Dev

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When Should TypeScript Be Used Instead of JavaScript?

TypeScript is a superset of JavaScript that adds static typing, making development easier.
Use TypeScript for large-scale projects to avoid runtime errors and improve collaboration.
TypeScript offers strict type safety, catching errors at compile time for higher reliability.
TypeScript enhances the developer experience with better tooling support and OOP implementation.

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Dev

138

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Building a Secure Authentication System in Node.js using JWT

Building a Secure Authentication System in Node.js using JWT
Authentication is a crucial aspect of backend development, ensuring secure access to resources.
In this tutorial, we'll build a secure authentication system in Node.js using JSON Web Tokens (JWT).
You've successfully built a secure authentication system in Node.js using JWT. This setup ensures users can securely authenticate and access protected resources.

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Medium

345

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Discrete Mathematics at Play — The 2187-Ball Puzzle Explained

A shopkeeper has 2,187 identical balls, but one is slightly heavier.
Using the concept of ternary decision tree, the puzzle is solved in 7 weighings.
Ternary search is more efficient than binary search for this problem.

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Javacodegeeks

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Serverless Spring Boot with Knative on Kubernetes

Knative enables serverless auto-scaling for Spring Boot microservices on Kubernetes, optimizing resource usage and supporting event-driven architectures.
It abstracts complexity involved in serverless workloads on Kubernetes, offering features like automatic scaling to zero, event-driven execution, and observability.
Benefits of using Knative for Spring Boot include efficient resource utilization, simplified deployment, event-driven capabilities, and automatic traffic routing.
To deploy a Spring Boot app with Knative, prepare your Kubernetes cluster, create the application, containerize and push it, define a Knative service, and access the deployed service.
Enable auto-scaling in Knative by configuring annotations for scaling behavior based on incoming requests, ensuring minimum instances and defining scaling limits.
Knative Eventing allows services to respond to Kafka events, HTTP events, and cloud events, enabling event-driven execution in Spring Boot.
By leveraging Knative, developers can build efficient, cost-effective microservices that scale dynamically based on real-time traffic and events in Kubernetes.
Knative offers serverless scaling, event-driven execution, and automatic traffic routing for Spring Boot applications, promoting efficiency and cost-effectiveness.
With Knative and Spring Boot, serverless Java becomes a reality, providing seamless scalability and cost optimization for modern cloud-native applications.

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Medium

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Embedding Models You Can Use in Your Next Project For Free

This article highlights powerful embedding models that are free and open-source.
The models discussed in the article are Qodo-Embed-1, Multi-Functionality, Multi-Linguality, and Multi-Granularity model, Universal Sentence Encoder, Sentence-Transformers, and Instructor-Base.
These models can be used for various tasks such as code search, retrieval-augmented generation, search, semantic ranking, FAQ retrieval, real-time inference, and more.
All the models are available on HuggingFace and can be accessed through the sentence_transformers library.

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Medium

340

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I’m Doing It…I’m Building a Python SIP Softphone

Building a Python SIP Softphone involves connecting sockets, audio, and protocol rules into a real-time application.
The SIP protocol itself is defined in RFC 3261, and building a robust softphone requires honoring the rules in that document.
SIP messages are stateless and flow over UDP, which means your softphone must handle the state of calls and handle repeated or out-of-order messages.
Matching incoming 200 OK responses requires comparing against existing transactions in the SIP stack.

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Dev

277

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Frontend MCP Tool Development Tutorial

MCP (Model Context Protocol) is a protocol for building interactions between AI models and external tools, enabling custom tools and resources creation to perform specific tasks.
Core concepts of MCP include StdioServerTransport for communication, McpServer for tool management, Tool for task execution, and Resource for data or state management.
Usage examples of MCP involve the typescript-sdk for development, installation of necessary npm packages, and creating a visual testing tool using Playwright for UI testing.
Important functions like login, auto-login, and visual test core function are implemented along with defining and configuring the MCP tool for UI testing.
The project repository for the MCP visual test tool can be found at https://www.npmjs.com/package/@anguske/mcp-playwright-visual-test.
Configuration details for Cursor usage, specifying environment variables for auto login, viewport dimensions, test threshold, and more, are crucial for tool execution.
Adding a resource for test results and adhering to best practices like parameter validation, error handling, resource management, tool design, and configuration management are recommended.
In conclusion, leveraging MCP framework facilitates building robust AI applications and adhering to best practices ensures code quality, maintainability, and user satisfaction.

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Medium

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Building Cleaner APIs with Dependency Injection in ASP.NET Core

Dependency Injection in ASP.NET Core is a design pattern that helps keep your code flexible, modular, and easier to test.
In ASP.NET Core, you can register a service using the built-in DI system and inject it into your endpoint without manually instantiating the objects.
In a basic example of an API that returns a list of products, without DI, the handler is responsible for creating and using the ProductRepository.
By embracing DI, you can have scalable, test-friendly code that is easier to maintain and future-proof, even in small examples like a product API.

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Medium

322

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Top 5 Tools for ML Experiment Tracking in 2025

MLflow: Language Agnostic, Seamless Integration, Scalable
W&B (Weights & Biases): Ease of Use, Cloud & Local Support, Free for Individuals
TensorBoard: Built for TensorFlow, Real-Time Visualization, Open Source
Neptune.ai: Flexible Integration, Collaboration Focused, Great for Research
Comet ML: Easy Integration, Customizable Dashboards, Team Collaboration

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