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15 bash scripts to automate boring DevOps tasks so you can go play Elden Ring instead

DevOps often involves repetitive tasks, and bash scripting can automate these processes efficiently.
The article unveils 15 battle-tested bash scripts for beginner DevOps engineers to streamline tasks.
Bash scripts provided are practical tools that can save time and make automation easier.
Using bash scripts can reduce errors, save hours, and free up time for other tasks like playing Elden Ring.
By leveraging bash, DevOps professionals can quickly address common issues without complex setup.
Bash scripting enhances understanding of system components and their interactions in Linux environments.
It is recommended to integrate bash scripts into cron jobs for scheduled automation and convenience.
Ensuring proper checks and precautions are essential before running scripts in production environments.
Bash scripts can be employed for various use cases, such as monitoring, backups, and system maintenance.
Advanced bash usage includes integrating with CI/CD pipelines and external services for seamless operations.

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Dev

375

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🔐 Stop hardcoding secrets: generate `.env` files from AWS SSM with a simple CLI

Envilder is a CLI tool built by M47 Labs for managing secrets and configurations stored in AWS SSM securely.
It reads a mapping file linking environment variables to AWS SSM parameter paths and generates a .env file with the values.
Envilder helps in onboarding new team members, keeping environments in sync, updating CI/CD pipelines, and maintaining centralized configuration.
Features of Envilder include compatibility with SecureString and plain parameters, CLI-first approach, support for any CI system, and multiple AWS profiles.

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Dev

172

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Deploying and Exposing Go Apps with Kubernetes Ingress, Part 1

Kubernetes Ingress is crucial for managing external access to services, offering advanced traffic control features like path-based routing, SSL, and load balancing.
Ingress vs. Services: Ingress improves routing capabilities over Services by enabling path-based and host-based routing for more sophisticated traffic management.
Introduced in Kubernetes v1.1, Ingress became stable in v1.19, providing a crucial tool for modern web apps needing smarter routing and security.
The tutorial demonstrates building Go services (API and Web) deployed with Kubernetes Ingress for routing external HTTP traffic based on URL paths.
API and Web services are containerized, Kubernetes cluster is set up using Kind, NGINX Ingress controller is deployed, and Ingress rules are defined for routing.
NGINX Ingress controller is implemented for managing routing and load balancing in the Kubernetes cluster.
Deployment configurations for API and Web services are created along with respective Service definitions for internal access.
Ingress resources are set up using defined routing rules based on URL paths to direct traffic to the appropriate services.
Verification steps ensure all components are running, and testing with cURL confirms the expected service responses through Ingress routing.
The project showcases the power of Ingress in managing complex traffic patterns in microservices architectures, enhancing external access and routing capabilities.

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Dev

367

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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

221

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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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Medium

354

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How to Build Wealth in Your 20s: Habits Over Hustle

Develop a growth mindset and focus on long-term wealth building rather than getting rich quick.
Live below your means, save early, and prioritize needs over wants to multiply your savings through compounding.
Start investing early with small amounts and develop high-income skills to increase job opportunities and business potential.
Avoid bad debt, build multiple streams of income over time, and prioritize health and long-term financial goals.

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Alvinashcraft

Dew Drop – May 9, 2025 (#4416)

The Dew Drop for May 9, 2025 (#4416) features top links and updates in various tech domains.
Highlights include discussions on Vibe Coding in Hanselminutes Podcast and C# 14 extension members.
Web & Cloud Development updates cover AI features in JavaScript, Kubernetes 1.33, and Node.js security releases.
AI segment includes topics on multi-agent apps with A2A protocol, Model Context Protocol, and AI code editors.
Visual Studio & .NET section discusses VS Code April 2025 release and ReSharper 2025.1.2 bug fixes.
Articles on design, methodology, testing, mobile development, and game development are also featured.
Podcasts like InfoQ and UX delve into culture, AI, and designer burnout, while Miscellaneous covers various news stories.
Events, community updates, database insights, and SharePoint/M365 news are part of the compilation.
The Dew Drop shares an array of curated content, podcasts, and community events in the tech landscape.
For a detailed overview of the latest in technology and development, access the full article on the respective sources.

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Hackernoon

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Cloud Sprawl Is Real. Continuous Discovery Is Your Best Defense

Cloud sprawl is a real issue for cloud engineers given the difficulty in tracking all applications running across environments.
Lack of knowledge about running applications leads to difficulties in managing and securing them, posing real-time security risks.
Continuous discovery is crucial to address cloud sprawl, especially in multi-cloud environments with various deployment options.
Identity fragmentation adds complexity with apps authenticating against different Identity Providers, making auditing challenging.
Traditional audits are outdated once completed, and narrow in scope, unable to keep up with constantly changing environments.
Continuous discovery, involving cloud-native scanning, identity correlation, CI/CD monitoring, and tagging, provides real-time application visibility.
A real-world example shows the challenges of estimating the number of applications without a reliable application registry.
Continuous discovery helps engineers and IAM teams ensure application security, compliance, and governance by providing real-time insights.
Discovery plays a critical role in identifying hidden risks, ensuring security, compliance, and cost-effectiveness across cloud environments.
Continuous discovery is essential to proactively manage security posture, compliance, and incident response in dynamic cloud-based environments.
It is imperative for organizations to adopt continuous discovery practices to stay ahead of the evolving cloud landscape and mitigate security risks.

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Bigdataanalyticsnews

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Modern Data Engineering Services in 2025: Key Trends Fueling Enterprise-Wide Digital Transformation

By 2025, data engineering has become essential for extracting insights from massive data volumes and driving digital transformation.
Global data creation is projected to reach 175 zettabytes by 2025, necessitating modernized, cloud-centric data engineering practices.
Modern data engineering emphasizes real-time processing, modular architectures, and serverless solutions for scalability and efficiency.
Key trends in data engineering for 2025 include cloud-native platforms, AI/ML-driven automation, DataOps, data quality focus, and self-service analytics.
Data engineering drives faster decision-making, personalized customer experiences, and operational efficiency through predictive analytics.
Security, compliance, and governance are integrated into data pipelines to ensure trust and compliance at scale.
Challenges faced by data engineering teams include talent shortages, tooling complexity, and legacy system integration.
The future of data engineering involves automation, edge processing, low-code tools, and adaptable platforms to meet evolving business needs.
Modern data engineering services are strategic enablers of enterprise growth, demanding a cultural shift towards data-driven strategies and insights.
Key advancements in data engineering involve AI for automation, real-time processing at the edge, and composable architectures for agility.

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Medium

412

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The Broken TGW Attachment That Taught Us Everything About Route Table Ownership

A cloud engineer encountered a strange issue with a Transit Gateway (TGW) VPC attachment in a pendingAcceptance state without an 'Accept' button or approval process.
The engineer discovered that the TGW route table linked to the attachment was deleted but still referenced, leading to a cross-account TGW mystery.
After conducting CLI detective work, it was revealed that the issue stemmed from a deleted route table in a centralized network account, leaving dangling references in the spoke (development) account.
To address the problem and make troubleshooting easier for similar scenarios, a guided shell script named tgw_check.sh was developed to help navigate and resolve issues related to TGW ownership and route table management.

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Dev

155

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esProc SPL vs DuckDB: Which is more Lightweight for In-Application Computation

DuckDB and esProc SPL are compared for in-application computation, focusing on lightweight aspects.
Both DuckDB and esProc SPL can be embedded in applications, with DuckDB being convenient for Python integration and Java ecosystem compatibility via JDBC, while esProc targets the Java ecosystem primarily.
esProc SPL exhibits lightweight characteristics with a small installation package, support for hot deployment, and compatibility with various data sources through a native interface approach.
esProc SPL's inclusion of flow control capabilities within its data processing language makes it stand out against DuckDB, which lacks such capabilities, necessitating external languages like Python for complex computations.

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9 Likes

Dev

141

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DevOps in 2025: the future is automated, git-ified, and kinda scary but fun.

DevOps in 2025 has evolved to focus on automation, collaboration between humans and AI agents, and self-healing platforms.
Platform engineering has emerged as a key concept, enabling teams to create reusable infrastructure layers for faster development.
AI-driven processes such as BotOps are becoming standard, with AI handling tasks like testing, deploying, and incident management.
GitOps has shifted from a buzzword to a crucial operating mode, emphasizing automation with version control for deployments.
Containers are still relevant, with Kubernetes being essential, while WebAssembly (WASM) is gaining popularity for faster app execution.
Security is integral at every stage, with DevSecOps practices ensuring security from code commits to production behavior.
The DevOps culture in 2025 emphasizes autonomy, asynchronous communication, and improved work-life balance for remote-first teams.
Skills needed in 2025 include platform thinking, interaction with AI tools, system design, security practices, and strong soft skills.
Legacy practices like manual configurations, Bash scripts, and legacy CI tools are being replaced by modern, automated solutions.
DevOps engineers are transitioning to roles like Platform Engineer, SRE, and DevEx Engineer, focusing on broader aspects of platform management.
The DevOps landscape in 2025 is characterized by automation, collaboration, and a shift towards sustainable learning and personal growth.

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Dev

124

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10 open-source MCPs that make your AI agents smarter than your team lead

AI agents, although smart, often lack direction and coordination, leading to inefficiencies in tasks.
Mission Control Platforms (MCPs) act as a strategy layer to provide structure, memory, and coordination to AI agents.
This article introduces 10 open-source MCPs that can transform AI agents into synchronized operational teams.
Examples include CrewAI for organizing agents into crews, LangGraph for graph-based orchestration, and AutoGen for chat-based collaboration.
SuperAgent offers a user-friendly web UI for agent orchestration, while Camel focuses on role-playing interactions between agents.
Other MCPs like AgentVerse, MetaGPT, and Langroid provide various features such as communication mechanics, role assignments, and fine-grained control over agent behavior.
Tips are provided on choosing the right MCP based on UI/CLI preferences, memory, logic control, and project requirements.
Potential use cases range from content pipelines and research assistants to autonomous coding agents and scenario simulations.
It is advised not to use an MCP for simple projects where memory, tool use, or multi-agent coordination are not necessary to avoid unnecessary complexity.
Additional tools like vector databases, agent toolkits, and observability plugins are recommended to enhance agent capabilities.
In conclusion, embracing MCPs can empower AI agents to perform tasks efficiently, collaborate effectively, and fulfill specific roles within different workflows.

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7 Likes

Medium

372

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Backup your OpenSearch indices with manual snapshots

Taking OpenSearch snapshots is essential to backup and restore data in case of indices deletion, destruction, or corruption in OpenSearch clusters.
To manually take snapshots, one needs admin access to the OpenSearch service API and must create a role allowing the service to write snapshots to an S3 bucket.
Creating a snapshot repository using an S3 bucket, checking for ongoing snapshots, and then taking a snapshot are crucial steps in the backup process.
After taking a snapshot, verifying its progress, restoring data indexes, and managing snapshots through AWS IAM permissions are recommended practices for data protection.

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Medium

403

From EC2 to EKS: How Application Deployment Has Evolved on AWS

Managing application deployments on AWS has evolved from using EC2 to EKS (Elastic Kubernetes Service).
In the scenario of deploying a website starting with a single EC2 instance and later managing multiple EC2 instances manually for different parts of the application, the process becomes cumbersome and inefficient.
Transitioning to EKS involves launching a Kubernetes cluster that automates the management of resources on EC2 instances, treating the infrastructure as a unified entity.
EKS with Kubernetes simplifies deployment by packaging services as Docker containers, allowing Kubernetes to efficiently allocate resources, handle failures, and enable automatic scaling.

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