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Medium

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Speed Up Web Scraping with Concurrency in Python

Implementing concurrency in web scraping projects can significantly reduce scraping time from hours to minutes or seconds.
Concurrency is particularly effective in web scraping as it is primarily I/O-bound, and the Global Interpreter Lock (GIL) in Python is not a significant limitation for I/O-bound tasks.
Python offers libraries like asyncio, aiohttp, concurrent.futures, multiprocesssing, and Scrapy for implementing concurrency in web scraping.
To avoid issues in concurrent scraping, it is important to control and manage concurrency, handle errors properly, debug asynchronous code, and be respectful to websites by following scraping best practices.

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Medium

243

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Is the Full-Time Developer Job Winding Down? Nah, It’s Just Leveling Up!

The traditional developer career is evolving rather than winding down, emphasizing specialization over being a jack of all trades.
Specialist roles like AI/ML wizards, cloud architects, cybersecurity experts, data engineers, DevOps engineers, front-end developers, and back-end developers are gaining prominence.
The gig economy and freelance opportunities provide flexibility and innovation in the tech industry, enabling unique career paths for developers.
Low-code/no-code tools are simplifying app development, but the need for skilled developers persists for complex and secure solutions.
Foundational skills like problem-solving, algorithms, architecture, communication, and continuous learning remain crucial in the ever-changing tech landscape.
Adapting to specialization, mastering new technologies, and taking control of one's career are crucial for success in the evolving software development field.

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Towards Data Science

285

The Ultimate AI/ML Roadmap For Beginners

AI and machine learning skills are in high demand, offering lucrative career opportunities.
Key roles in AI/ML include Machine Learning Engineer, Data Scientist, and AI Engineer.
Foundational knowledge in mathematics, particularly linear algebra, calculus, and statistics, is essential.
Python is the preferred programming language for AI/ML, with NumPy, Pandas, and scikit-learn as essential libraries.
Understanding data structures, algorithms, and basic ML concepts is crucial for aspiring AI/ML professionals.
Learning machine learning involves grasping supervised and unsupervised learning, evaluation metrics, and feature engineering.
Deep learning covers neural networks, convolutional and recurrent models, transformers, and reinforcement learning.
MLOps focuses on deploying machine learning models into production efficiently using cloud technologies and tools like Git.
Staying updated on research papers in AI/ML is essential to keep abreast of the latest developments.
Breaking into AI/ML may take about a year following a structured roadmap, focusing on gradual skill development.

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Logrocket

130

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Leader Spotlight: Cybersecurity products for enterprises vs. consumers, with Amit Sharma

Amit Sharma, VP of Product Management at Gen, discusses managing cybersecurity products for enterprises and consumers, drawing on his experience at companies like Symantec and NortonLifeLock.
In the enterprise market, cybersecurity solutions include endpoint, network protection, and data protection with cloud components, necessitating integration and role-based access.
Consumer cybersecurity products are sold through various channels like retail partners and employee benefit programs, requiring ease of use, centralized management, and adherence to differing regulatory environments.
Product strategies must evolve to address the changing attack surface, operating environments, and consumer needs, while complying with evolving privacy and regulatory standards.
Balancing security, compliance, and user experience in cybersecurity products involves privacy by design, accessibility compliance, data anonymization, encryption standards, and third-party certifications.
Cybersecurity products for enterprises prioritize technical understanding, while consumer products focus on ease of installation, engagement through gamification, automation for seamless updates, and personalization based on user behavior.
Amit Sharma's motivation at Symantec and NortonLifeLock stemmed from diverse roles, geographic experiences, and shifts in product lines, enabling a comprehensive understanding of the organization and its markets.
Approaching new product verticals involves identifying customer problems, market dynamics, aligning with corporate strategy, and coaching team members on financials, product functions, customer feedback, and strategy definition.
Understanding financials, products, and customer feedback are crucial for product managers transitioning between product lines to gain a comprehensive view and drive success in their new roles.

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Dev

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ServBay Launches Educational Program for Students and Teachers

ServBay has launched an Educational Program to provide students and teachers with quality tools and resources for learning and innovation.
The program offers exclusive discounts on ServBay Pro purchases and renewals for students and educators.
ServBay aims to foster innovation and facilitate a smooth transition to professional careers for program participants.
To apply for the educational support, users need to log in using their .edu email address and place an order for ServBay Pro.

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VentureBeat

388

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Microsoft infuses enterprise agents with deep reasoning, unveils data Analyst agent that outsmarts competitors

Microsoft has announced significant additions to its Copilot Studio platform, unveiling deep reasoning capabilities and specialized agents like Researcher and Analyst for Microsoft 365 Copilot.
The Analyst agent functions as a personal data scientist, processing diverse data sources and generating insights without technical expertise from users.
Microsoft has leveraged its understanding of Excel workflows to create the Analyst agent, making it valuable for financial analysis and operational reporting.
The deep reasoning capability enables agents to tackle complex analytical work by invoking advanced reasoning models like OpenAI's o1, allowing agents to handle ambiguous business problems more methodically.
Microsoft's agent flows combine rule-based workflows with AI reasoning, enabling scenarios like intelligent fraud prevention and optimization in companies like Pets at Home and Dow Chemical.
Microsoft's enterprise data integration through the Microsoft Graph provides agents with contextual awareness, enhancing relevance based on workplace relationships.
Microsoft aims to make these capabilities accessible to organizations of varying technical resources, with over 100,000 organizations using Copilot Studio and creating more than 400,000 agents in the last quarter.
While competition in the enterprise agent space is intensifying with companies like Google, OpenAI, Salesforce, Oracle, SAP, and AWS entering the market, Microsoft's advantage lies in its comprehensive approach and strong coupling with OpenAI's reasoning models.
Microsoft focuses on business outcomes rather than raw AI capabilities, offering a wide range of agents tailored for specific business processes and individual work patterns.
Enterprise decision-makers are advised to consider integration with existing tools and data when selecting an agent platform, where Microsoft's strong user base in tools like Excel and Power Automate provides an advantage.
Microsoft's ecosystem combines personal copilots and specialized agents to drive practical business applications with measurable ROI, highlighting the maturity of agent technology in delivering business results.

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Dev

429

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Re-Revisiting Performance in Ruby 3.4.1

The article discusses revisiting performance in Ruby 3.4.2 and addresses feedback received on a previous article focusing on the usage of Structs and Classes.
Initial observations favored Structs over Classes in some scenarios, but further benchmarking revealed that Classes are now faster than Structs in most cases.
Benchmarking code from the Alchemist's article was used to compare performance between different data structures like Arrays, Hashes, Classes, Structs, and Data.
Results showed that in real-life scenarios, Classes outperformed Structs, with Data objects being more performant than both when dealing with 10 attributes.
Stringified Hashes were noted to perform well under the frozen string literal comment, and using primitive data objects like Arrays and Hashes is recommended for larger datasets.
The article emphasized that Structs and Data objects serve as value objects, allowing for direct comparison, unlike Class instances.
The conclusion highlights the performance priority order of data structures and advises using frozen string literal comments and prioritizing Arrays or Hashes for larger datasets.

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Dev

225

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How to use gdb to debug Golf applications

To include debugging information in your application executable, use the "--debug" option when building the application using gg.
Debugging Golf programs with gdb is similar to debugging plain C programs, as Golf programs are technically C programs.
The debugging ecosystem for Golf programs is well-developed, allowing the use of tools like Valgrind and Google ASAN.
Debugging Golf programs requires debug information either during compilation or through included debugging information.
An example of debugging in Golf involves creating a parsing application, compiling it with debug information and running it with gdb.
Golf programs can be executed as web application servers or command-line executables, requiring appropriate environment variable settings.
Using gdb for debugging Golf programs involves setting breakpoints, running the program step-by-step, and examining variables and loop iterations.
Golf programs can be debugged at the C code level by compiling with the '--c-lines' flag and examining the generated C code in gdb.
Debugging Golf programs with gdb provides insights into program execution at both the Golf and C code levels.
Learning to debug Golf programs with gdb can help in understanding program behavior and resolving issues efficiently.

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Medium

275

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Jupyter Kernel Selection in VSCode: Docker & WSL Setup Guide

Docker provides a consistent environment for applications, ensuring code runs identically regardless of the OS.
Creating a Docker image for a Python project involves writing a Dockerfile to define dependencies and run commands.
Combining Jupyter Notebooks with Docker in VS Code allows for a consistent and isolated environment to simplify dependency management.
Integrating Docker, VS Code, Jupyter Notebooks, and WSL creates a robust and efficient development environment for Python projects.

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Medium

117

Behind the 8-Ball: Is Humanity Sleepwalking Into an AI-Controlled Economy?

By 2030, artificial intelligence may become the invisible hand behind our markets, policies, and personal decisions.
Institutions meant to safeguard society are lagging behind AI development in terms of control and infrastructure centralization.
There is a risk of entering a digital feudalism, where access to intelligence is leased and controlled by corporations, leading to economic concentration and job loss.
Immediate intervention is needed to democratize AI access, legislate algorithmic transparency, protect digital rights, and introduce economic adaptation tools.

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Medium

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Let the Network Decide: The Channel-Wise Wisdom of Squeeze-and-Excitation Networks

The Squeeze-and-Excitation Network (SENet) was implemented to address the issue of treating all input features equally in traditional models.
The SENet adaptively recalibrates feature channels based on relevance, leading to high accuracy and interpretability in wildfire risk assessment.
Using a simulated wildfire dataset, the SENet achieved an accuracy of 95.8% with precise and recall for fire events.
Feature importance analysis highlighted the significance of temperature, pressure, and solar radiation in wildfire prediction.

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Idownloadblog

438

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Nugget SparseRestore-based iPhone customization utility updated to v5.0.1 with more bug fixes

The Nugget SparseRestore-based iPhone and iPad customization utility has been updated to version 5.0.1.
The update introduces bug fixes, improvements, and an option to disable the Windows path fix.
Existing users are strongly encouraged to download and use the latest release for maximum bug prevention.
Nugget allows users to customize their iOS or iPadOS device without the need for a jailbreak.

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Dev

356

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Let's Build a Full-Stack App Using the MERN Stack! Part 3: React NextJS UI

To create a Next.js application, run the following command: npx create-next-app@latest
Start the Development Server: Navigate into the ui folder and run the app: cd ui npm run dev
Install Axios: Next, install Axios to handle API requests: npm install axios
Now, let's set next.js to 'use client', add imports, and create our interfaces.

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Dev

280

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Sets are the Data Type Your Code Needs

A set is a unique unordered collection, which provides efficient item existence checks compared to arrays.
Using sets can enhance computational efficiency and reduce memory overhead by storing only unique elements.
Sets in Ruby are implemented using hashes, offering constant time lookup regardless of set size.
Sets ensure guaranteed uniqueness, offer performance advantages, and support mathematical set operations.
To start using sets in Ruby, you need to add the 'set' library, as sets are part of the standard library.
Sets can be created from arrays, and duplicates are automatically removed upon set creation.
Operations in sets like union, intersection, difference, and symmetric difference are available for efficient data manipulation.
Sets support methods for adding, removing, and checking membership of elements.
Use sets when uniqueness and fast membership checks are crucial, while arrays are preferred for ordered data and when duplicates hold significance.
Additional resources provided for further learning on sets and Ruby programming.

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Dev

153

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Day 1126 : Laser

A professional worked on coding, adding functionality to a project and testing it. Managed to make Web Components work in a Vue application.
Research on laser engraving 3D printed parts led to ordering a laser module for the large format 3D printer and organizing items for the shed.
A new 3D printer was purchased, which will expand project possibilities. Feedback on a test pressing for vinyl records was positive.
To save money, the professional utilized a 30-day price match guarantee for discounted purchases and planned to go through tracks for a radio show.

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