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The Silent Revolution: How Edge Computing Is Transforming Modern Technology

Edge computing has emerged as a game-changer in modern technology, addressing the need for faster processing and real-time data analysis by processing data closer to the source.
It drastically reduces latency, bandwidth usage, and security risks compared to traditional centralized cloud systems.
Edge computing enables real-time data analysis for connected devices, from smart homes to industrial sensors, without overwhelming cloud servers.
Despite its benefits like ultra-low latency, improved security, and reduced bandwidth usage, edge computing also poses challenges that businesses need to address for successful implementation.

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Medium

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Multithreading in Node.js: Approaches and Best Practices

Node.js uses a single-threaded event loop model for asynchronous operations like API requests but can face performance degradation with CPU-bound tasks.
To handle CPU-bound tasks efficiently, Node.js provides Worker Threads and child processes for multithreaded or parallel execution.
Node.js developers can use worker_threads module for parallel execution introduced in Node.js v10.5.0, offering the ability to run JavaScript in multiple threads.
Node.js also utilizes a thread pool via libuv for asynchronous I/O operations, providing developers more control over performance with tools like worker_threads and child processes.

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TechBullion

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Ultimate Guide to Xfinity Customer Service: Contact Information, Support, and Troubleshooting

This ultimate guide provides comprehensive information on contacting Xfinity customer service at 1-844-513-2433 for various types of support.
Methods covered include phone, live chat, mobile app, social media, and in-store visits, ensuring fast and effective assistance.
The guide includes tips for quicker help, the best times to call, and advice on issue escalation if necessary.
Common problems are addressed, offering troubleshooting steps before reaching out for support.
Different support options are detailed, such as the Xfinity app, live chat, virtual assistant, and in-person visits to Xfinity Stores.
Special services like accessibility support, multiple language assistance, and equipment returns are also highlighted.
Customers can access billing help, outage information, cancellations, and equipment assistance through various channels.
Additional features like Xfinity Rewards, service agreement access, and complaint filing are explained for a comprehensive customer experience.
Understanding support options, utilizing the Xfinity App, and checking for outages are recommended for efficient issue resolution.
Key FAQs about Xfinity customer service, technician visits, outages, equipment returns, and service upgrades are answered in the guide.
By leveraging the range of support tools provided by Xfinity, customers can ensure a smooth and hassle-free service experience.

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Towards Data Science

281

Agentic AI 101: Starting Your Journey Building AI Agents

The AI industry is evolving rapidly, with the development of Generative AI being a key focus.
The process of building AI Agents has progressed from simple chatbots to advanced agents capable of handling various tasks.
To create an AI Agent, an API is used to interact with a Language Model (LLM) like OpenAI, Gemini, etc.
Python packages such as Agno, Langchain, and CrewAI facilitate the creation of AI Agents.
Agents are structured with components like model, description, instructions, expected output, and tools for real-world interaction.
Tools enhance AI Agents' capabilities, from generating content to summarizing YouTube videos.
Reasoning and Knowledge tools allow agents to analyze queries and retrieve information from external sources.
Agents with memory can personalize responses based on past interactions, learning user preferences over time.
Building AI Agents step by step with packages like Agno and progressing to advanced agents offers valuable hands-on learning.
Continuous exploration and experimentation with AI Agents can lead to innovative applications and solutions.

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Towards Data Science

380

Rust for Python Developers: Why You Should Take a Look at the Rust Programming Language

The programming language Rust is gaining popularity due to its performance and security features, making it an interesting alternative for Python developers, especially in the field of Data Science.
Rust, developed by Mozilla, focuses on memory management control and an ownership model to prevent errors at compile time, offering advantages in performance over languages like Python.
Rust is a compiled language that translates code into native machine code before execution, leading to faster execution and early error detection during compilation.
Rust's memory safety without a garbage collector and ownership model prevent errors like NullPointer exceptions and race conditions, making it ideal for memory-intensive applications.
Rust's tooling includes 'cargo' for managing code processes, and its growing community and high performance make it a favored language among programmers.
Rust differs from Python in type specification, speed, memory usage, multithreading, error handling, and syntax, with Python being easier to start with but Rust offering higher performance and control.
Rust complements Python by providing more control, high performance, and robustness for tasks that require parallelism and performance optimization, making it a valuable addition to a developer's toolset.
In a comparison of opening a CSV file and counting rows, Python's code is straightforward but may have memory issues with large files, while Rust's code is more robust, efficient, and memory-saving.
Rust shines in data science for tasks like data preprocessing with Polars for faster processing, compute-intensive tasks with PyO3 for integration with Python, and web backends with actix-web for high-performance APIs.
Rust is recommended for developers interested in low-level programming, seeking improved performance over Python, working on backend services or CLI tools, understanding memory interactions, and developing robust, maintainable tools.

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Cryptonews

166

MapleStory Enters Web3: Nexon-Backed Nexpace to Launch Blockchain Game on May 15

MapleStory N, the web3-integrated game, is set to launch on May 15, 2025.
The game will offer in-game asset trading and ownership through the Avalanche blockchain.
Developed by Nexpace and backed by Nexon, MapleStory N is the first game in the MapleStory Universe on PC.
The game's mechanics include player-to-player trading, NFT ownership of items, and a Reward Experience 2.0 program for managing in-game items.

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Medium

252

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Welcome to the Embr Testnet User Survival Guide

The Embr Testnet is a memecoin launch experience focused on meme-fueled volatility and gamified trading.
To begin, visit https://testnet.embr.fun/ and connect to the Embr Testnet network on your EVM wallet.
Users can create, trade, and play with tokens on the Embr platform, boosting their Capy for battles using specific memes.
The Embr Testnet Bug Bounty Program has been launched to refine functionality and improve security, with bounties paid in USDC for reported bugs.

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Idownloadblog

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Nugget v5.2 released, now codesigned on macOS & Windows, added PosterBoard improvements & bug fixes

Nugget, an iPhone customization utility, updated to version 5.2 with new features and bug fixes.
PosterBoard templates and Live Photo wallpapers added.
Nugget is now codesigned for macOS arm and Windows.
Bug fixes include improved error messages, optimized GUI backend, and more.

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Towards Data Science

A Farewell to APMs — The Future of Observability is MCP tools

The evolution of generative AI technologies is fundamentally changing how software is written, leading to a shift in how software is monitored, understood, and optimized.
Application Performance Monitoring tools will need to adapt to leverage AI agents for real-time analysis and improvements in the Software Development Lifecycle.
The Model Context Protocol (MCP) acts as a communication tier between AI agents and applications, enabling new use cases and integrations.
MCPs open up opportunities for agents to act autonomously and intelligently across different domains, transforming how applications are built and deliver value.
The limitations of a human-centric model include fixed interfaces, cognitive load, and limited scope in solving specific requirements for end users.
With agent-centric MCPs, developers can focus on AI-driven processes instead of predefined interactions, enhancing productivity and addressing emergent use cases.
Observability data structured for machines, not humans, allows AI agents to consolidate data across applications and domains, automating analysis.
By utilizing AI agents with MCP data, tasks such as code reviews can be autonomously handled, improving efficiency and providing actionable insights.
Emergent use cases powered by AI and observability data can enhance user productivity by detecting issues, selecting areas for refactoring, and preventing errors.
Products need to evolve to support AI agents effectively, by preparing and structuring data to cater to the limitations and capabilities of the agents.

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Towards Data Science

320

Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit

The article provides a detailed guide on building and deploying a chat powered by LLM Gemini in Streamlit and monitoring API usage on Google Cloud Console.
It covers steps like setting up a new GitHub repository, cloning it locally, creating a virtual environment, defining the project structure, and getting an API key.
The article explains the importance of storing API keys securely and provides code snippets for setting up the project structure and obtaining the API key.
It discusses prompt engineering, model selection, generating content parameters like temperature and maxOutputTokens, and displaying chat history within the app.
The guide includes setting system prompts, setting context for the chat with memory, adding a reset button, and deploying the chat app on Streamlit.
It also explains how to monitor API usage on Google Cloud Console by checking metrics like requests, errors, and latency.
The article aims to help developers create interactive chat applications using Streamlit and LLM technology, emphasizing security and efficient API management.

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Knowridge

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Scientists develop new technique to make fusion power plants safer and last longer

Engineers at the University of Surrey have developed a new technique to enhance the safety and durability of nuclear fusion power plants.
Through the PFIB-DIC imaging technique, researchers studied P91 steel welds in fusion reactors to identify stress-induced strength changes.
At high temperatures in fusion plants, the steel can become brittle and lose over 30% of its strength due to welding-induced stress.
This discovery will aid in improving materials testing, advancing reactor design, and accelerating the development of commercial fusion power plants.

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The Apple Post

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Tim Cook says Apple is ‘making progress’ on delayed Apple Intelligence features

Apple is making progress on its delayed Apple Intelligence features, including a more advanced, personalized version of Siri, according to Apple CEO Tim Cook.
Cook mentioned during Apple's fiscal Q2 2025 earnings call that the company needs more time to ensure the AI features meet their high quality standards.
Delayed features like Personal Context, Onscreen Awareness, and Deeper App Integration aim to enhance Siri's capabilities in understanding and assisting with various tasks across apps.
Apple had confirmed in March that these key AI capabilities would be postponed with a vague timeline of arriving 'in the coming year.'

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Medium

202

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A Practical Guide to Elixir’s Task Module — Let’s create a Cron Job

Task module in Elixir is often underutilized compared to GenServer for concurrent operations.
Tasks are lightweight processes for performing defined actions without much inter-process communication.
They serve well in transforming sequential code into concurrent code, handling tasks like large file uploads concurrently.
Task processes are preferred for short-lived operations without state management, unlike GenServer.
Task.start/1 is used for side-effects, while Task.async and Task.await are for received results.
A simple cron job for clearing expired tokens is an example of Task module practical usage.
The code snippet showcases a background process for token cleanup at specified intervals.
By adding the module to the supervision tree, the task starts with the app and gains fault tolerance.
Supervised modules need to implement start_link/1 function to receive a single argument.
With the added supervision, Elixir's supervisor ensures automatic restarts on task crashes for improved fault tolerance.

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Medium

427

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Ancient Purity Laws and Software Development Team Health

Ancient laws about ritual purity, as described in the Torah portions Tazria and Metzora, focus on identifying, isolating, and reintegrating individuals in a state of impurity back into the community through purification.
The laws entail living in isolation outside the camp, followed by purification rituals to address spiritual impurity and seek reintegration, not as a punitive measure but as a process of restoration.
In the context of software development, the principles from these ancient laws can be applied to address interpersonal issues like gossip, arrogance, and lack of collaboration within development teams, emphasizing the importance of reflection, responsibility, and team healing.
Modern purification rituals in software development may include temporary isolation and reintegration processes like mentoring on best practices, assigning unfamiliar code tasks, and collaborative decision-making workshops to strengthen team communication and trust.

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Towards Data Science

195

How AI Is Rewriting the Day-to-Day of Data Scientists

The article discusses various AI tools used in data science, such as OpenAI API, ChatGPT, Gemini, Glean AI, Cursor, Copilot, Hex Magic, and Snowflake Cortex.
It focuses on two main aspects: eliminating low-value tasks and accelerating high-value work in data science.
In terms of eliminating low-value tasks, AI plays a role in empowering stakeholders for self-serve analytics and reducing time spent on repetitive reporting.
AI tools are aimed at enhancing query data with natural language, implementing chat-based BI assistants, and improving data catalog tools for better documentation.
To accelerate high-value work, AI assists in coding and debugging, automating EDA and analysis processes, and enhancing NLP capabilities for data analysis.
AI tools like Google Data Science Agent, ChatGPT's Data Analyst tool, and OpenAI API are mentioned for their contributions in streamlining data analysis workflows.
The article emphasizes the importance of using AI to eliminate barriers to productivity and to focus on driving impactful data projects efficiently.
It advises data scientists to be pragmatic in adopting AI tools that work, staying updated on emerging technologies, and prioritizing the core purpose of data science.

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