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Dev
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I built an AI Agent that can control a smart-home and you can too.
- The article discusses the evolution of AI agents in controlling real-world connected devices in smart homes, paving the way for enhanced automation and interaction through APIs.
- The Matter-MCP Server, an open-source tool, simplifies the integration of AI agents with Matter devices, offering a more straightforward approach to control through natural language.
- It addresses the challenge of communication with Matter devices for AI-driven automation by acting as a bridge through the MCP (Model-Context-Protocol), enabling structured communication interfaces.
- The Matter-MCP Server empowers AI to commission new devices, read/update attributes, send commands in natural language, monitor status, and access protocol documentation, enhancing home automation's intuitiveness.
- Setting up the Matter-MCP Server involves steps like cloning the repository, setting up a Python virtual environment, installing dependencies, and integrating UV for AI integration.
- Configuration with AI-powered assistants like Claude involves adjustments in desktop settings and adding MCP server configuration to facilitate interaction with the Matter-MCP Server.
- The article details the installation process in Claude Code, usage of Python Matter Server, testing with Matter Virtual Devices, and debugging methods for the AI-powered IoT setup.
- Expanding AI agent capabilities by adding more MCP servers enables dynamic searching for relevant Matter documentation and device commands without manual intervention.
- The Matter-MCP Server streamlines AI-driven IoT automation, offering a quick integration of Matter devices with AI assistants, saving developers time on dealing with protocols and debugging.
- By eliminating complex coding and debugging, developers can leverage the Matter-MCP Server to create AI-powered IoT solutions efficiently, making smart home automation more seamless.
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Dev
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AI Model Achieves 99% Accuracy in Satellite Image Analysis Using Channel-Aware Learning
- ChA-MAEViT unifies masked autoencoder pretraining with multi-channel vision transformers
- Introduces two key innovations: channel-aware masking and channel-specific embedding layers
- Achieves state-of-the-art performance on hyperspectral image classification and remote sensing
- Reduces the need for extensive labeled data through effective self-supervised pretraining
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Medium
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302
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Implementing Dependency Graph Analysis in PHP to Predict Breaking Changes Before They Happen
- Implementing Dependency Graph Analysis in PHP to Predict Breaking Changes Before They Happen
- PHP has come a long way since the spaghetti-code era.
- With frameworks like Laravel, Symfony, and Slim pushing boundaries, developers are wiring up complex ecosystems of internal modules, third-party libraries, and shared services.
- Dependency graph analysis in PHP helps map out how classes, functions, packages, and modules interact, providing foresight to avoid fragile setups.
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Medium
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279
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Hull MA Alone Isn’t Enough — Here’s How to Filter It for Cleaner Trends
- This article discusses how to filter Hull Moving Average (HMA) for cleaner trends.
- The article suggests combining the HMA with other indicators to improve its performance.
- By subtracting 'slow' from 'fast' and smoothing the result, the HMA can closely track price without lagging behind.
- This approach reduces false signals and provides clearer directional cues.
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Javacodegeeks
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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
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166
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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
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220
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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
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139
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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
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346
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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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4
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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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324
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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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