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How to Fix Bootstrap Buttons for Expanding Forms Properly?

Developers commonly face issues with Bootstrap buttons not functioning correctly when expanding or collapsing comments within forms, especially when dealing with multiple forms on the same page.
The problem arises from Bootstrap associating collapse actions with all elements sharing a common class, causing buttons to trigger visibility changes in all elements instead of the targeted one.
To correct this, it is essential to ensure that each 'Show All' button interacts solely with the comments within its respective form by updating the HTML structure, ensuring unique IDs, and testing the functionality.
By following the suggested steps, developers can manage Bootstrap button interactions within multiple forms effectively, enabling independent expansion and collapse of comments for a better user experience.

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Dev

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Escaping the DOM with React Portals: Practical UI Rendering Techniques

React Portals allow rendering components outside the normal DOM hierarchy for easier handling of z-index, positioning, and layering.
Portals are useful for modals, tooltips, dropdowns, and overlays that require breaking out of typical layout constraints.
Creating a modal with React Portals involves adding a target in index.html, creating the modal component, and using it in the app.
Portals maintain React tree structure, allowing components to retain access to context, hooks, and state.

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Medium

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Check Nested Relations with One Method Call

Laravel 12.10 now allows developers to check nested relationships using dot notation with the relationLoaded() method.
Previously, verifying nested relationships required additional code, but the new enhancement simplifies this process.
The update eliminates inconsistencies in loading and verifying relationships, making code more intuitive and error-resistant.
This improvement in Laravel 12.10 streamlines the process of checking and loading nested relationships for better application performance.

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Medium

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The Meaning of Volatile Variables in the Java Memory Model

In Java, each thread has its own working memory, which can cause discrepancies when sharing variables across threads.
Without the 'volatile' keyword, threads may not see the most recent value of a shared variable due to caching.
Using 'volatile' ensures that every read and write operation on a variable is visible to all threads.
Volatile fields in Java help in synchronizing the visibility of shared variables across threads.
Volatile doesn't provide atomicity; it only guarantees the visibility of the most recent value.
Volatile is useful for simple communication between threads, such as signaling the status.
Memory barriers are introduced by the JVM when dealing with volatile variables to control memory reads and writes.
Volatile alone is not sufficient for complex operations that require atomicity; atomic classes or locks are needed.
For scenarios where the whole process from read to write needs to be uninterrupted, volatile is not suitable.
In cases where atomicity is crucial, such as incrementing shared variables safely, volatile is not the appropriate solution.

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Dev

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What is a Large Language Model (LLM)? A Comprehensive Guide for Developers

Large Language Models (LLMs) are powerful AI tools used for understanding and generating human-like text in applications like chatbots and content generation.
LLMs are trained on vast textual datasets and built on transformer architectures, enabling tasks such as text generation, translation, and sentiment analysis.
They have billions of parameters, offering capabilities for text completion, question answering, and more.
Based on the transformer model with self-attention mechanisms, LLMs excel at capturing language context and relationships.
Applications of LLMs span content generation, customer support, translation, education, healthcare, and data collection.
LLMs use components like embedding layers, positional encoding, multi-head self-attention, and feedforward networks in their architecture.
In modern development, LLMs find use in chatbots, content generation, code completion, language translation, and sentiment analysis.
Visualizing LLM architectures involves input embedding, positional encoding, stacked transformer blocks, and output layers for predictions.
Integrating LLMs into applications can be done through APIs provided by platforms like OpenAI, Hugging Face Transformers, and AWS SageMaker.
LLMs offer developers innovative solutions and the potential to create advanced applications, emphasizing the importance of staying informed and ethical.

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Dev

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PocketBase + React Native

The individual has been exploring ways to quickly launch apps by creating fully functional MVPs and proofs of concept in less than a day.
While PocketBase has been favored for the backend, the user is still searching for an efficient frontend solution, considering options like Quasar and React Native with Expo's plugins.
Encountered challenges with file attachments using PocketBase and React Native on mobile devices due to issues with multipart form data handling and networking stack in React Native.
Made adjustments to work around the file attachment issue by using FormData and fetch for React Native while highlighting the differences in implementation between web and mobile.

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Dev

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The One Trait All Truly Great Engineers Share

Great engineers, from juniors to seniors, share the trait of being endlessly curious.
Curiosity differentiates engineers by driving them to understand why and how things work, rather than just settling for surface-level knowledge.
Following curiosity leads engineers to learn beyond conventional roadmaps, uncovering undocumented features and deepening their understanding.
Curiosity can be trained by consistently questioning and exploring deeper into technical concepts, enhancing an engineer's problem-solving abilities.

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Risingstack

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Benchmarking LLMs: How We Actually Know What’s Good

Benchmarks are essential for comparing large language models (LLMs) to determine which models excel in various tasks such as math, code, and multilingual support.
Common benchmarks for text-based LLMs include MMLU for academic knowledge, GSM8K for math reasoning, ARC for science questions, and HumanEval for code generation.
Models like GPT-4 perform well in various languages, but smaller models struggle outside of English, emphasizing the importance of multilingual testing.
Vision benchmarks like VQAv2, MMMU, and MathVista assess LLMs' ability to interpret images and visual data, highlighting the models' progress but also their limitations.
For audio tasks, metrics like WER measure speech-to-text accuracy, with models like Whisper showing promising results.
Leaderboards such as Chatbot Arena, Hugging Face Open LLM Leaderboard, and HELM by Stanford offer insights into LLM performance across various tasks and metrics.
Metrics like accuracy, BLEU, WER, Elo Rating, and Robustness convey different aspects of LLM performance and capabilities.
While benchmarks help evaluate LLMs, questions around true intelligence versus memorization remain, emphasizing the need for ongoing research in AI.
To make informed choices in LLM selection, understanding benchmarks and considering factors like generalization, consistency, and reasoning steps is crucial.
In the absence of a universal IQ test for AI, benchmarks serve as a critical tool for developers and researchers to assess the capabilities of large language models.
Ensuring models excel in specific areas like accuracy, code generation, or multilingual support requires thorough examination of benchmarks before implementation.

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Dev

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Ditch the Log: Supercharge Your Debugging Game with These Modern Tricks

Console.log, while useful, can be messy, slow, and limited, prompting the need for modern debugging techniques.
Breakpoints allow pausing of code execution for inspection and variable manipulation, akin to freezing time.
console.table() presents data in an organized table format, enhancing readability and structure.
Conditional breakpoints enable code pausing based on specific conditions, improving efficiency in debugging.
console.trace() provides a function call trace, aiding in understanding code execution flow more clearly.
The debugger statement serves as an emergency brake for code execution, halting it for thorough inspection.
Chrome DevTools offer various features like the Network Tab, Performance Tab, and Application Tab for in-depth debugging insights.
Utilizing modern debugging techniques can significantly enhance the debugging process and make issue resolution more efficient.
A personal anecdote emphasizes the effectiveness of these modern techniques in quickly identifying and resolving bugs.
The debugging toolkit now includes tools like breakpoints, console.table(), console.trace(), the debugger statement, and Chrome DevTools superpowers.
Effective debugging is about using the right tools at the right time, and developers are encouraged to embrace these modern debugging practices.

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Dev

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This Week In React #233: RSC, Next.js, Relay | Expo, WebGPU, Skia, Apple fees, Reanimated, Fragment Refs...

This week's React updates include Expo 53, Skia Graphite, Reanimated Shared Elements Transitions, and the Apple fees court injunction.
ui.dev launches the final content of their react.gg course, designed to teach new React 19 concepts.
React Compiler team discussions about integrating with Vite and Oxc/Rolldown and making React components compatible with MCP.
Dan Abramov's blog posts cover topics like Functional HTML, RSC for Astro Developers, and the future of AI interaction.
Various React packages such as Relay 19.0, Mantine 8.0, React Admin 5.8, and React Error Boundary 6.0 have been updated.
Expo SDK 53 brings upgrades, background tasks, and Expo Maps; Expo Router v5 and Game On introduce innovative features.
A new Skia Graphite backend based on WebGPU enables performing 2D/3D experiences with Reanimated integration on various platforms.
Epic Games wins against Apple to comply with app store anti-steering injunction, impacting Fortnite and Kindle app.
Exciting upcoming features in React include Fragment Refs with intersection observer support and Reanimated PR for shared elements transitions.
New releases like Compose Multiplatform 1.8, Zustand 5.0.4, and Nitro Audio Manager enhance cross-platform development.

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Medium

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Model-View-Tool (MVT): A New Architecture for Agent-Powered Web Apps

The Model-View-Tool (MVT) architecture is proposed as a new approach for web applications, emphasizing the separation of concerns: data, presentation, and logic.
Initially, websites followed a structured hierarchy with clear navigation bars and content areas, aligning with the MVC model that handled input, data, and presentation.
With the rise of Large Language Models (LLMs), user interaction has shifted towards input-based interactions, prompting a need for new design paradigms like MVT.
MVT introduces the concept of an agent handling user prompts and directing requests to Tools that bridge the gap between Model and View.
The seamless experience in MVT involves users directly inputting requests, which are intelligently processed by the agent to fetch relevant information.
This new architecture minimizes complex navigation and distractions, delivering requested content immediately and adapting to user preferences over time.
Users are increasingly valuing personalized and efficient interactions on the web, which the MVT framework aims to facilitate through responsive and user-centric design.
Agents are expected to play a central role in enhancing user experiences by guiding users effectively, focusing on user needs, and streamlining interactions.
The MVT model aligns with the evolving user expectations of natural language interactions over menu-driven navigation, simplifying interfaces and enhancing user satisfaction.

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Dev

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Convert DOCX to HTML in .NET with Cloud REST API

The GroupDocs.Conversion Cloud .NET SDK facilitates transforming Word (DOCX) documents into HTML within .NET applications with structured and high-quality content.
It offers a simple API for converting DOCX files into clean, web-ready HTML, suitable for various applications like document viewers, content management systems, and web-based editors.
Developers using the SDK have control over the output structure, ensuring text, formatting, images, and styles are preserved during the conversion process.
The provided C# code snippet demonstrates how to convert Word to HTML in .NET using the REST API, making the process efficient and customizable for different platforms.

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Dev

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REST Easy: Building Bulletproof APIs with Go Fiber

Go Fiber is a powerful and simple-to-use tool for building REST APIs with high performance and efficiency.
Setting up a Go Fiber application is made easy with its clean, Express-inspired API and fast Fasthttp foundation.
Crafting routes and handlers in Fiber is efficient due to its radix tree algorithm for fast route matching.
Implementing validation, error handling, and JSON responses in Fiber ensures bulletproof APIs.
Fiber's middleware aids in request validation, maintaining consistent responses throughout the API.
Global error handling in Fiber centralizes error responses for better API maintenance.
Fiber's support for WebSocket, HTTP/2, and Server-Sent Events adds versatility to real-time applications.
Fiber promotes consistency in JSON responses through a standardized response structure.
Go Fiber balances performance and developer experience, making it a preferred framework for building robust REST APIs.
Migrating an existing REST API to Fiber and benchmarking the performance can showcase significant improvements.

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Dev

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From Code to Cash: Monetizing Python AI Agents ⚡

The journey from creating a successful AI agent to monetizing it involves challenges like client convincing and deployment complexities.
Outcome-based pricing, where customers pay based on results achieved, aligns interests and provides transparency in ROI.
Evidence shows outcome-based pricing can lead to shorter sales cycles and higher conversion rates.
Implementing outcome-based pricing requires precise metering and analytics tools like Stripe.
An example lead enrichment agent showcases the process of enriching leads using Google Sheets and Python scripts.
Setting up Stripe involves creating products, adding customers, and setting up subscriptions for billing.
Preparing AI agents for deployment involves wrapping core logic in a Flask endpoint and defining type-safe models for data extraction.
Deploying agents as API services using platforms like Itura simplifies execution monitoring, GitHub integration, and billing integration.
Customers trigger the agent with a unique URL, and usage is metered for billing through Itura's integration with Stripe.
Transitioning a Python AI agent into a consistent revenue stream involves challenges but can be overcome with outcome-based pricing and proper tools.

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Infoq

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JBang Jash Brings Streamlined Process Execution to Java

JBang Jash, introduced by the JBang team, is a Java library that simplifies the execution of external processes and shell commands through a fluent and predictable API.
It aims to address complexities faced by developers when using standard Java APIs for process execution, providing chainable process execution, automatic input/output stream handling, and customizable handling of non-zero exit codes.
JBang Jash also supports piping commands in a pipeline, detects the appropriate shell environment, and offers an intuitive interface for working with shell processes and streams in Java 17+.
The project, originally named 'fluent-process' by OnGres, Inc. in 2020, was forked and renamed to 'jash' in 2025 to provide a more idiomatic Java interface for shell processes. Jash can be integrated into any Java project without the need for the JBang CLI tool.

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