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7 Bash Scripts for Quick Troubleshooting in Production Environments

When a server slows down, the first thing to check is resource utilization. This script provides a quick snapshot of CPU, memory, and disk usage.
If a server is slow, finding which processes are consuming the most resources is very important. This script helps identify CPU spikes, memory leaks, and low disk space, which are common reasons for system slowdowns.
Quickly identifies which processes are hogging system resources, which lets you know what action to take quickly(e.g., restarting a stuck process).
This script helps check connectivity and latency because network issues can both degrade performance and cause downtime.

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Zerynth

Image Credit: Zerynth

Smart Industrial Energy Monitoring: Sustainable Production Powered by Data

Industrial energy monitoring is vital for assessing sustainability in production plants by analyzing energy sources and their impact.
Focus on economic costs, total usage, and environmental impact drives the need for smarter energy monitoring strategies.
Transition from basic consumption tracking to predictive management using IoT and AI technologies revolutionizes energy monitoring.
IoT facilitates real-time data collection from connected machinery while AI analyzes patterns for optimization.
Predictive intelligence aids in anticipating consumption peaks, reducing waste, and enhancing production efficiency.
Correlating energy consumption with productivity helps in optimizing efficiency and minimizing waste.
Benefits of implementing predictive energy monitoring include cost reduction, improved efficiency, failure prevention, and sustainability.
Energy monitoring system provides control over consumption, enables cost savings, ensures production continuity, and supports strategic decision-making.
Zerynth's solution offers real-time monitoring of electrical parameters to deliver actionable insights for efficient energy management.
Monitoring energy flows helps in identifying inefficiencies, avoiding downtime, and making informed decisions for enhanced business outcomes.

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3 Likes

Medium

337

Image Credit: Medium

Coding Without Thinking Is Slowly Killing Software Engineering

Coding Without Thinking is becoming a dominant approach in tech.
Shallow solutions that involve copying code snippets without understanding don't scale and fail to solve the real issue.
Algorithmic thinking is the foundation of good software engineering and is essential for problem-solving.
AI-assisted coding tools should augment thinking, not replace it.

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20 Likes

Medium

341

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This is to Build Models That Actually Work Without High Performance Metrics

Data leakage can lead to models that fail when applied to new data.
Preprocessing the entire dataset before splitting and using features closely tied to the target variable can cause data leakage.
To prevent data leakage, it is important to split the data first, then preprocess, and double-check for any target leakage.
Building models that perform well on unseen data is more valuable than achieving perfect metrics on known data.

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Medium

319

Image Credit: Medium

How to Choose the Perfect Programming Language for Your Next Project

Key considerations for selecting a programming language based on project needs are discussed.
For mobile development, you can choose between native or cross-platform development.
Frontend development choices depend on factors like project complexity, team expertise, and performance requirements.
Backend development choices depend on performance, scalability, and language compatibility with the frontend.

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19 Likes

Medium

153

Image Credit: Medium

HTTP, DNS, CDN, Load Balancing | System Design Basics

HTTP is the rulebook for sending and receiving messages on the internet.
HTTP is stateless, which means it doesn't remember you after your request.
TCP is used for sending important data with tracking and signature.
UDP is like yelling across a room, used for less critical communication.

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9 Likes

Medium

109

Image Credit: Medium

Is Java Still Worth Learning in the Age of AI?

Java is a powerful, object-oriented programming language that is over 25 years old.
Big companies like Google, Amazon, Netflix, and Spotify use Java for building scalable applications.
Java has the advantage of stability, portability, a huge ecosystem, and a strong community.
While Python dominates AI and machine learning, Java is still widely used in backend systems, banking software, and e-commerce platforms.

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6 Likes

Medium

381

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AI Tools Every Developer Must Master in 2025 — Stay Ahead or Get Left Behind!

AI-powered tools are game-changers for developers, offering speed and efficiency.
Perplexity AI fetches real-time answers, saving time on debugging.
OpenAI Codex automates repetitive coding tasks, accelerating development.
Hugging Face provides pre-trained AI models for faster development.

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22 Likes

Silicon

170

Image Credit: Silicon

The Microsoft Milestone: 50 Years On, It’s Personal

Microsoft celebrates its 50th anniversary, marking half a century of shaping the future of personal computing and technology.
Microsoft has played a significant role in the lives of many people, shaping how they work, learn, and communicate with its products and services, such as Windows, Microsoft Office, and Internet Explorer.
After facing criticism in the early 2000s, Microsoft underwent a transformative shift, focusing on software, services, and cloud infrastructure, and becoming more self-aware and adaptive to the changing times.
Microsoft's investments in artificial intelligence, accessibility, and sustainability have helped the company stay relevant and become a leading player in the tech industry.

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10 Likes

Javacodegeeks

236

Image Credit: Javacodegeeks

How to Handle Generic List Matchers in Mockito

Mockito is used for creating mock objects in unit tests to replace object behavior with a simulated version for easier testing of code interactions.
When dealing with generic lists in Mockito, unchecked warnings may arise, and this article explores effectively handling generic list matchers to avoid compiler warnings.
To set up Mockito in a Maven project, include the mockito-core dependency in the pom.xml file with the appropriate version and scope.
Using Matchers.any(List.class) with generic lists can trigger unchecked warnings due to the lack of type information, as demonstrated in a PaymentService example.
Enabling compiler warnings in Maven can reveal unchecked method invocation and unchecked conversion warnings when working with generic lists in Mockito tests.
To address unchecked warnings in Java 8+, anyList() matcher can be utilized to match any List without triggering warnings and ensuring type safety in tests.
For Java 7 and earlier versions, specifying the generic type with ArgumentMatchers.anyList() can help in maintaining type safety and avoiding unchecked warnings.
By applying these techniques, developers can effectively handle generic list matchers in Mockito across different Java versions, ensuring cleaner and type-safe tests.
The article concludes by offering solutions for Java 7 and Java 8+ developers to handle generic list matchers in Mockito and emphasizes maintaining type safety in tests.
Downloadable source code related to Mockito generic list matchers is available for further reference and exploration.

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Hackernoon

170

Image Credit: Hackernoon

How To Introduce a New API Quickly Using Spring Boot and Maven

The article discusses how to introduce a new API quickly using Spring Boot and Maven, following a project structure similar to a previous article using Gradle.
The post walks through creating the Motivational Quotes API with an OpenAPI specification in YAML format.
Assumptions include using Java 17, Spring Boot 3, and Maven for build automation.
Steps to create a new Spring Boot Service using Maven are outlined, emphasizing the use of the Spring Boot CLI.
The article shows how to set up the project structure using Maven and editing the pom.xml file to adopt the API-First approach.
Business logic is added to the service based on the OpenAPI contract, creating a fully functional Motivational Quotes API.
Deployment to Heroku is detailed, including creating a system.properties file and a Procfile for customization.
Commands to push the code to Heroku and test the deployment using curl requests are demonstrated.
The article compares Gradle and Maven, highlighting their strengths and suitability for different project requirements.
It concludes with a reflection on leveraging frameworks and services to focus on delivering features efficiently.

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10 Likes

Medium

Image Credit: Medium

50 Scenario-Based Interview Questions and Answers

The most valuable interviews aren’t just about knowing commands, but about understanding real-world scenarios and how to solve them.
The article presents 50 scenario-based DevOps interview questions, organized into key areas of DevOps expertise.
Scenario 1: Broken Build - Sarah suggests checking for newly added dependencies, verifying build server internet access, and updating package versions if necessary.
Scenario 2: Deployment Rollback - Sarah explains initiating a rollback procedure, redeploying the previous stable version, ensuring system recovery, and conducting a root cause analysis.

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1 Like

Medium

146

Automating Bank Statement Analysis with Azure Document Intelligence

This article demonstrates how to automate bank statement analysis using Azure Document Intelligence.
The code sample utilizes the Azure AI Document Intelligence client library to extract transaction details from bank statement PDFs.
The workflow involves setting up Azure credentials, installing Python libraries, and processing the bank statements.
This automated approach streamlines financial and administrative operations, reducing manual effort.

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8 Likes

Medium

205

Image Credit: Medium

LLD/Machine Coding w/ Implementation: Rate Limiter

Users can have different rate limits based on their tier (e.g., Free vs. Premium users).
The system allows different rate-limiting algorithms to be plugged in and swapped quickly.
Examples of rate limiting algorithms include counter-based and sliding window approaches.
The implementation emphasizes extensibility, performance, and adherence to SOLID principles.

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12 Likes

Dev

Image Credit: Dev

RIP RAG? Gemini 2.0 Flash Just Changed the Game Forever!

Google's Gemini 2.0 Flash is set to transform cross-platform mobile app development and web application development services.
Gemini 2.0 Flash is an AI model that can process multimodal inputs, simplifying processes and enhancing user experiences.
It replaces the need for Retrieval-Augmented Generation (RAG) models, making development pipeline more streamlined.
Gemini 2.0 Flash improves web application development services, making it easier to integrate AI capabilities and enhance user experiences.

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