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Where Java Stores Static vs. Instance Data in Memory

Static fields in Java belong to the class, shared across all instances, and are stored in a memory region such as the method area or Metaspace.
Static fields are initialized once per class and are directly tied to the class object, not to any specific instance.
Instance fields in Java belong to individual objects and are stored in the heap memory, separate for each object.
Local variables in Java are short-lived and reside in the stack memory, created and removed with each method call.
Objects in the heap, including instance fields, are managed by the garbage collector when no longer referenced.
Static fields, being part of the class loading process, tend to stay in memory for the entire application lifetime.
Memory layout for Java objects includes an object header, followed by instance fields arranged based on source code ordering and type.
Java treats static fields separately, storing them in data structures tied to the class object, accessible without object instantiation.
Shadowing occurs when child classes have fields with the same name as parent classes, where the child's field takes precedence.
Inheritance in Java impacts object memory layout, with subclass memory including fields from the superclass.

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Medium

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I built my own SSH Manager with Go after rage-quitting an Udemy course

Built own SSH Manager with Go after rage-quitting an Udemy course.
Used package named 'tablewriter' to make it beautiful and readable.
Implemented 'bufio' library to enable user input and server selection.
Connected to the server successfully by making ssh host.com call from the project.

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PlanetPython

189

Eli Bendersky: Sparsely-gated Mixture Of Experts (MoE)

In transformer models, the attention block is typically followed by a feed forward layer (FF) with a hidden layer and ReLU activation.
The feed forward layer holds most of the weights in the transformer due to its large hidden dimension, playing a major role in token reasoning.
Transformers repeat blocks multiple times, leading to challenges in total layer size, paving the way for efficiency improvements like sparsely-gated mixture of experts (MoE).
MoE architecture involves experts, a router or gate, and selecting top experts based on scores to process tokens effectively through weighted averaging.
MoE aims to increase model capacity without significantly raising computational costs by using a subset of parameters for each token.
The experts not among the top K for a token are disregarded, enhancing computational efficiency by eschewing unnecessary computation.
MoE allows for an overall model size increase without a proportional rise in computational expense.
Efficient MoE implementation may pose challenges related to load balancing among experts, addressing through methods like adding noise or special losses.
The MoE architecture offers specialization among experts, with each focusing on different model capabilities, optimizing token flow through the model.
Considerations like the Shrinking Batch Problem in MoE papers emphasize challenges associated with expert batch sizes and computational efficiency.

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Logrocket

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React-Toastify (2025 update): Setup, styling & real-world use cases

Toast notifications are in-app pop-up messages that provide users with feedback about an operation without needing user action to dismiss.
React-Toastify is a popular package in the React ecosystem that provides toast notifications for React applications.
To set up React-Toastify, you need to install the package and import the necessary components like ToastContainer and toast.
Styling toast messages can be done by importing the CSS file provided by React-Toastify and customizing the styles as needed.
React-Toastify allows for positioning of toast messages in different locations on the webpage such as top-right, bottom-left, etc.
You can differentiate between types of toast messages like success, error, or warning by using specific functions like toast.success, toast.error, and toast.warning.
Customizing toast messages allows you to style them according to your brand colors and application theme.
React-Toastify v11 introduces updates focused on accessibility, keyboard navigation, custom progress bars, and more.
Real-world use cases of React-Toastify include handling API notifications, integrating with authentication flows, and providing feedback on async operations.
FAQs cover installation, positioning, animation, use cases, updating React-Toastify, and the benefits of using the library for toast notifications in React applications.

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Medium

350

AI Grows More Human

Nuclear fusion achieves a new record for sustained power, bringing us a step closer to a fusion-fueled future.
Apple releases the iPhone 14 Pro with an Always-On Display and a Dynamic Island UI, making it feel revolutionary.
NASA’s James Webb Space Telescope delivers stunning images of the universe, while DART showcases planetary defense capabilities.
Self-driving technology expands, EV adoption increases, and the metaverse seeks to gain mainstream acceptance.

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Dev

232

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From Skepticism to System: How We Transitioned to Full-AI Dev.

Since October 2024, we’ve been running Easylab AI on a fully AI-assisted development stack, where human engineers no longer write production code themselves.
The initial project, Linkeme, helped automate social media communications and proved the value of full-AI development. It showed raw velocity and the ability to get a working version in hours.
The team iterated, built internal tools, and learned which AI models were best for different tasks. They developed role-based agents and structured workflows to improve the system.
Benefits of full-AI development include faster delivery, less fatigue among engineers, a scalable internal knowledge architecture, and a culture of systems thinking and speed.

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Dev

409

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✨ Java Stream Operations Overview ✨

Java Streams introduced in Java 8 offer a declarative way to process elements.
Streams have two types of operations: Intermediate and Terminal.
Intermediate operations are lazy and return another Stream.
Common intermediate ops include filter, map, flatMap, distinct, sorted, peek, limit, skip, takeWhile, and dropWhile.
Terminal operations produce a non-stream result and trigger the execution of the entire pipeline.
Examples of terminal ops include forEach, toArray, reduce, collect, min, max, count, anyMatch, allMatch, noneMatch, findFirst, and findAny.
Intermediate operations are lazy and execute only when a terminal operation is invoked.
Various stream creation methods include using empty stream, single element, array, Collection, Stream.builder(), iterate, and generate.
Intermediate examples cover filter, map, flatMap, mapMulti, distinct, sorted, peek, limit, skip, takeWhile, and dropWhile.
Terminal examples demonstrate forEach, toArray, reduce, collect, min, max, count, anyMatch, allMatch, noneMatch, findFirst, and findAny.

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Dev

384

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Annotation Management Simplified: Remove Annotations from PDFs in .NET

GroupDocs.Annotation Cloud .NET SDK simplifies the process of removing unwanted annotations from PDF documents in .NET applications.
Developers can remove highlights, arrows, strikeouts, or comments from PDFs through straightforward REST API calls.
The SDK offers cloud-based design, eliminating the need to handle complicated PDF libraries or server-side infrastructure.
With the GroupDocs.Annotation Cloud .NET SDK, users can manage annotations programmatically, leading to improved workflow and secure user experiences.

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Medium

236

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A Comparative Analysis of Leading Large Language Models (LLMs) in Early 2025

Key findings suggest continued dominance of models from major tech firms like OpenAI (GPT-4o), Google (Gemini series), and Anthropic (Claude series) alongside Meta (Llama series) and specialized AI companies like DeepSeek and xAI in the LLM landscape.
Strategic approaches vary between proprietary and open-source models, with the former focusing on performance and the latter on flexibility and cost-effectiveness.
Major trends include the adoption of massive context windows exceeding one million tokens, advancing 'reasoning' capabilities, and a need for complex benchmarks covering extensive tasks.
LLMs have transitioned from research to practical applications, impacting sectors like software, customer service, content creation, and scientific research.
Standardized benchmarks are crucial for evaluating LLM capabilities, with a shift towards evaluating reasoning, coding, safety, and multimodality.
Top 10 LLMs are analyzed based on criteria such as context window size, API costs, parameter counts, developer organization, and license type for a comprehensive view.
Models differ in access costs, with premium proprietary models like OpenAI's GPT-4.5 having higher pricing compared to more cost-effective options like DeepSeek V3 and Alibaba's Qwen models.
The availability of models with million-token context windows reshapes data processing possibilities but presents challenges in latency, cost, and information utilization.
LLM API pricing varies widely, reflecting performance, features, and competitive strategies, with premium models coexisting with lower-cost alternatives.
Transparency varies between proprietary and open-source models, with licensing models playing a crucial role in the competitive positioning and adoption of LLMs.
The field of LLMs in early 2025 is dynamic, marked by fierce competition, diverse offerings, and a blend of proprietary and open-source solutions catering to varied user needs.

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PlanetPython

304

Ned Batchelder: Regex affordances

Python regexes have powerful features for text manipulation.
Python regexes can be constructed and used in creative ways.
Using regular expressions, environment variables can be expanded.
The real code example in coverage.py demonstrates these regex features.

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Javacodegeeks

223

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Monitor Non-Heap Memory in the Java Virtual Machine

Non-heap memory in the Java Virtual Machine (JVM) is crucial for performance and stability, handling class metadata, compiled bytecode, and thread stacks.
Monitoring non-heap memory is essential to prevent issues like Metaspace OutOfMemoryError and performance degradation from growing code caches.
Creating a sample Java application can demonstrate non-heap memory usage through activities like loading custom class loaders and spawning threads.
Tools like VisualVM help monitor non-heap memory areas like Metaspace, Code Cache, and thread stacks by providing insights into usage and performance metrics.
Using VisualVM's MBeans plugin, deeper access to JVM internals like Metaspace and loaded classes can be gained for efficient monitoring.
JConsole, another tool, offers graphical monitoring capabilities for observing heap and non-heap memory usage, CPU, threads, and class loading.
jcmd, a command-line utility, enables inspecting memory usage and offers detailed statistics, including Metaspace and Code Cache metrics.
Native Memory Tracking (NMT) with jcmd provides insights into non-heap memory usage categories like Metaspace and Code Cache.
Additional tools like Java Mission Control (JMC) and Prometheus + Grafana can enhance JVM memory monitoring for performance optimization in distributed systems.
Monitoring non-heap memory is vital for JVM performance optimization and application stability, ensuring efficient resource utilization.

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Johndcook

257

Topological Abelian Groups

A topological group is a set with a topological structure, and a group structure.
An Abelian topological group is a topological group whose group structure is Abelian.
The category of Abelian groups is much nicer than the category of groups.
The category of topological Abelian groups is not an Abelian category.

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Medium

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Setting Up a Modern TypeScript Project with Parcel as Bundler (No Framework)

Parcel offers a zero-config experience for compiling TypeScript, bundling assets, and optimizing output.
This guide covers setting up a TypeScript project with Parcel, focusing on hot module reloading, CSS, images, and production builds.
You don't need additional loaders for CSS, images, or HTML with Parcel, as it handles them intelligently.
With TypeScript and Parcel dependencies, you're all set for a modern project without complex setups.
While Parcel requires no initial configuration, customization can be done through a .parcelrc file if needed.
Configuring TypeScript compilation can be done through a tsconfig.json file for finer control.
Parcel automatically detects entry points, handles CSS, images, bundles TypeScript, and injects assets into HTML.
To optimize image handling, a type declaration file for assets can be added in a TypeScript project.
Package.json scripts can be defined for development and production builds, made easier by Parcel's capabilities.
Development server and production builds in Parcel are straightforward, with scripts that perform auto-detection.

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Dev

181

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Everything about AI Function Calling (MCP), the keyword for Agentic AI

The article discusses AI Function Calling and the Agentic AI framework specialized in this area, emphasizing the importance of function schemas for accurate implementation.
It compares OpenAPI and Model Context Protocol (MCP) for function calling, noting the differences in JSON schema specifications and ecosystem sizes.
The role of compiler-driven development in generating AI function schemas automatically is highlighted, reducing errors and improving efficiency.
Document-driven development is crucial for creating reliable AI agents through detailed function documentation and descriptions.
The orchestration strategy involves selecting candidate functions to reduce context and validation feedback for correcting argument errors.
The Describer agent helps describe return values in markdown content or UI components, enhancing visualization in function calling.
Agentica, the Agentic AI framework, utilizes LLM function calling and offers flexibility, scalability, and productivity in developing agents.
The framework supports TypeScript Class, Swagger/OpenAPI Document, and MCP for efficient agent development.
Overall, the article emphasizes the significance of accurate function schemas, documentation, and orchestration in successful AI function calling and agent development.

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Crypto-News-Flash

183

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XRP News: Expert Says Ripple Could Rule Out SWIFT—Here’s Why It Matters

Market expert and trader Arthur dismisses the idea of a potential collaboration between Ripple and SWIFT.
Ripple's recent acquisition of Hidden Road is seen as a move to accelerate its vision of sovereignty, not a bridge to SWIFT.
Ripple aims to become a gateway for major institutions, offering solutions like tokenized Real-World Assets and smart liquidity routing.
Opinions are divided among experts, with some suggesting potential collaboration between Ripple's XRPL and SWIFT, while others believe Ripple cannot replace SWIFT.

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