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📝 Blog Draft: Day 30 – “How Linux Rewired My Mind in 30 Days”

The author embarked on a 30-day challenge to learn Linux from scratch, sharing their journey and learnings with over 1300 dev.to friends and the CloudWhislter Community.
Favorite projects included Git & Terraform for version control and infrastructure as code, automating health checks with AI diagnostics, and email integrations for notifications.
The author explored dangerous Linux commands, SSH key authentication, and used Linux as a learning playground by teaching Linux to a 10-year-old, creating a command-line Hunt Game, and summarizing commands in a script.
They also built impressive projects like a Raspberry Pi Smart Cat Feeder, a Terminal Weather App, a CI/CD pipeline, and a secure banking portal simulation, while delving into Linux history and lessons learned forever.

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Dev

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✅ Day 29 Theme: "Linux Portfolio Builder + Reflections CLI Style"

Created a script named 'Day29.sh' as an interactive Linux portfolio script to summarize a 30-day Linux journey.
Highlighted achievements including working with Git, Terraform, CI/CD, AI-powered health checks, email alert automation, and more.
Displayed most used commands and favorite projects upon user input.
Expressed gratitude to the Linux community and followers, concluding with readiness for the last day of the challenge.

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Dev

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A Practical Guide to Deploying Frontend Apps on Vercel Using GitHub Actions

Deploying frontend applications quickly and reliably is essential for fast iteration and consistent user experience. Setting up a custom CI/CD pipeline using GitHub Actions alongside Vercel allows for more control over deployments.
GitHub Actions offer more flexibility for complex workflows than Vercel's default Git integration, making it ideal for tailored deployment processes.
Prerequisites include a TypeScript-based frontend app, a GitHub repository, a Vercel account with needed tokens, and Node.js/npm installed locally.
The deployment process involves cloning the repository, understanding the frontend application structure, setting up GitHub Actions workflow, obtaining Vercel tokens, and deploying the application.
The frontend app interacts with an AI-powered backend through the Chat component, managing message states, structure, sending messages, and rendering UI elements.
The GitHub Actions workflow file automates the deployment process to Vercel by handling dependency installation, building the project, and deploying it using the Vercel CLI.
To securely deploy using GitHub Actions, obtain a Vercel token via the Vercel dashboard, then add it as a secret in your GitHub repository.
Deployment is triggered on push to the main branch, and the workflow stages are monitored on the GitHub Actions tab for successful deployment.
Post-deployment, Vercel provides a dashboard for production deployment preview, status, activity logs, and environment settings for monitoring and managing the app.
By following this guide, you create a streamlined CI/CD pipeline for your frontend app, ensuring automated releases and reducing manual deployment errors.

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Dev

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A Simple AWS-Powered Clinic Appointment System App Using Docker and SNS

The article discusses the creation of a Clinic Appointment Management System using Docker, FastAPI, and AWS services for patients to book appointments online, receive real-time notifications, and enable admin data management.
The architecture includes components like VPC setup with public and private subnets, Dockerized microservices handling frontend, patient, doctor, appointment, and notification services, PostgreSQL database, OpenVPN for admin access, and AWS SNS for real-time notifications.
Security measures like IAM roles, security groups, private subnets, and OpenVPN are implemented. Monitoring with CloudWatch and scalability features like load balancer and Docker microservices updating independently are included in the design.
Future enhancements include adding HTTPS support, using AWS RDS, setting up CI/CD pipeline, switching to ECS or EKS, domain-level routing with Route 53, advanced security measures like WAF, GuardDuty, Inspector, and implementing alerting mechanisms.

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Kubernetes

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Kubernetes v1.33: In-Place Pod Resize Graduated to Beta

In-place Pod resize feature in Kubernetes, introduced in v1.27 alpha, has graduated to Beta in v1.33.
It allows changing CPU and memory resources without restarting the Pod, reducing disruptions for stateful services.
Desired resources can now be updated in Pod spec for in-place resizing.
Resize can be triggered using the 'resize' subresource in the Pod spec.
Benefits include reduced disruptions, improved resource utilization, and faster scaling for verticle scaling.
Changes from Alpha to Beta include resize via 'resize' subresource and status tracking with conditions.
Enhancements in stability, reliability, sidecar support, and CRI integration have been made.
Future focus includes stability, addressing limitations, VPA integration, and user feedback incorporation.
Community feedback is essential for further enhancements and bug fixes as the feature progresses through Beta.
In-place Pod resize can now be experimented with in Kubernetes v1.33, offering more efficient resource management.

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Dev

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Deploying LLMs on Amazon EKS using NVIDIA GPUs

Deployed an LLM inference solution using NVIDIA GPUs on Amazon EKS while attending an AWS hands-on workshop.
Utilized Ray Serve and vLLM for deploying the Mistral 7B Instruct v0.3 model on Amazon EKS.
Deployed components like Ray, Ray Serve, and vLLM for building and managing generative AI applications on Amazon EKS.
The deployment included using the kuberay operator for handling Ray complexity, utilizing Ray dashboard for cluster visibility, and installing NVIDIA DCGM exporter for GPU monitoring.

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Dev

307

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How AI Is Reshaping Web Development in 2025: Trends You Can’t Ignore.

AI and web development are intersecting to introduce groundbreaking trends in 2025.
AI-powered web design tools like Uizard and Framer AI are accelerating UI creation and making it more accessible.
Advanced AI code assistants like ChatGPT + Code Interpreter and Tabnine are enhancing code generation and debugging processes.
AI is revolutionizing SEO strategies with tools like SurferSEO and NeuronWriter, emphasizing original content and semantic keywords.

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Medium

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Alternative Applications of SDLC: Flexible, User-Centric, and Real-Life Aligned Approaches

In today's digital world, traditional software development life cycle (SDLC) models are no longer enough for rapidly changing user expectations and diverse products.
Alternative approaches like DevOps with CI/CD, Design Thinking, Discovery and Delivery workflows, Lean UX with Agile, no-code/low-code tools for MVPs, and open-source community-driven projects are gaining popularity.
Real-life examples include Netflix utilizing CI/CD for seamless deployments, Airbnb boosting bookings with Design Thinking, and Spotify balancing testing new features with implementing approved ones in sprints.
These alternative SDLC models prioritize user feedback, rapid iteration, and collaboration, shaping the future towards purposeful, empathetic, and agile software development.

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Dev

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Keep It Small, Keep It Moving: The Real Power of Tiny PRs

Keeping pull requests (PRs) small and committing often can significantly benefit development teams.
Advantages of small PRs include easier review processes, helping junior developers contribute, reducing review time, and making bug spotting more efficient.
Small PRs also lead to fewer merge conflicts, clearer Git history, better CI/CD feedback, improved code practices, increased team velocity, and enhanced trust within the team.

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Siliconangle

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Winds of change: Dell Technologies retools its portfolio to meet enterprise AI’s full stack demands

Dell Technologies is retooling its portfolio to fulfill the demands of enterprise AI, positioning it as the backbone of new infrastructure from datacenter to edge.
With a history of adapting to industry changes, Dell is now focused on addressing complex enterprise needs for scaling AI workloads, efficient compute, and hybrid cloud integration.
The emergence of a new computing era driven by AI is reshaping the technology stack and Dell aims to be a leader in AI solutions for the full stack.
Dell's enterprise AI infrastructure strategy includes disaggregated architecture to support expanding AI workloads and the deployment of the PowerEdge XE8712 liquid-cooled server.
Collaborating with Nvidia, Dell offers the AI Factory solution for end-to-end AI enterprise implementation, enabling scalable and flexible deployment options.
Dell's open portfolio emphasizes customer choice, essential for AI deployments, and its PC enhancements support on-device AI processing, catering to edge applications.
Dell's NativeEdge platform aids in managing edge applications, exemplified by its use in cold chain applications for large grocery store businesses.
Partnering with Red Hat Inc., Dell's integration of RHEL AI with PowerEdge servers simplifies containerized applications management for AI development.
Dell's cloud-native solutions, like the Data Lakehouse and Storage Services for Public Cloud, aim to streamline AI deployment and provide flexibility and control for customers.
Dell's focus on multicloud solutions aligns with its 'ground-to-cloud' vision, enabling seamless transitions and operations across diverse computing environments.

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Dev

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Kubernetes 1.33: Resizing Pods Without the Drama (Finally!) 🎉

Kubernetes 1.33 brings in-place pod vertical scaling, allowing seamless CPU and memory adjustments without pod restarts.
This feature enables changing resource allocations on running pods, enhancing flexibility and resource efficiency.
By making resources mutable and introducing the resize subresource, Kubernetes simplifies resource tuning with minimal disruption.
Benefits include improved performance tuning, cost optimization, handling spiky workloads, and supporting stateful applications.
Resizing pods involves updating resource specs, validating against node capacity, dynamically reconfiguring cgroups, and updating pod status.
A sample code snippet demonstrates how to leverage in-place resizing for CPU changes without restarting the pod.
resizePolicy allows controlling behavior, specifying whether changes require a restart, offering flexibility based on specific needs.
Limitations include no Windows support, only resizing CPU and memory, and restrictions on decreasing memory limits without restarts.
In-place resizing sets the stage for VPA integration, paving the way for efficient resource management and scaling strategies.
Kubernetes 1.33's in-place pod resizing feature revolutionizes vertical scaling workflows, eliminating disruptions and enhancing resource management.

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The New Stack

410

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OrbStack: A Deep Dive for Container and Kubernetes Development

OrbStack is a Docker Desktop alternative for macOS, offering faster performance and tighter integration with the platform.
It runs Docker containers and Linux VMs on macOS with lower overhead using native virtualization features and a custom integration stack.
OrbStack's architecture centers on a lightweight Linux VM and custom services built in Swift, Go, Rust, and C.
Filesystem integration in OrbStack improves file I/O performance significantly compared to Docker Desktop.
OrbStack provides bidirectional and real-time file sharing, making container data easily accessible from the host side.
The platform emphasizes fast I/O, enhancing tasks like package installs, builds, and database operations on mounted volumes.
OrbStack includes a Docker engine, GUI, and CLI options, with seamless container networking, port forwarding, and volumes integration for macOS.
OrbStack features a lightweight single-node Kubernetes cluster with deep integration, enabling immediate use of locally built container images.
OrbStack's Linux machines support various init systems and provide file sharing, networking, and SSH editing capabilities.
The platform also offers automatic domain names for containers, simplifying local web application development.

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Dev

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Goodbye ClickOps, Say Hi to Terraform

A software developer shares their journey of moving from running HashiStack on AWS to a new automated cloud setup on Google Cloud Platform using Terraform.
The decision to use Terraform was influenced by its ease of defining infrastructure as code, support for multiple cloud providers, and the reliability of spinning up infrastructure with a single command.
Terraform enables reusability, idempotency, version control, and state tracking, providing consistency across environments and teams.
The developer first experimented with Terraform using Docker locally before transitioning to migrating their infrastructure to GCP, highlighting the ease and efficiency of the process.

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Dev

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Implementing and Managing Security Protocols in Linux

Managing a Linux system involves implementing security protocols to safeguard the environment from threats.
Essential security measures for Linux systems include securing SSH access, managing firewalls with Firewalld, enforcing policies with SELinux, and controlling access with users, groups, and permissions.
For SSH security, best practices include disabling root login, using key-based authentication, and considering changing the default SSH port.
Firewalld is crucial for controlling network access and defining trusted services like SSH and HTTPS.
SELinux provides mandatory access control to prevent unauthorized programs from accessing system files.
User management tools like chmod, chown, and sudo help control access to files and directories.
Keeping the system updated with regular software updates and monitoring and auditing activity are vital for maintaining a secure Linux environment.
Regularly reviewing systems and staying consistent with security protocols ensures a strong security posture for Linux systems.

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Dev

410

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5 Developer Pain Points Solved by Internal Developer Platforms

Developers often spend a significant amount of time managing tools instead of coding, with up to 75% of time wasted on toolchain maintenance.
Platform engineering simplifies development by providing clear instructions and modular components, akin to official LEGO kits.
Internal Developer Platforms (IDPs) help solve common pain points like deployment bottlenecks, context switching costs, and environment inconsistencies.
IDPs offer self-service templates for deployments, reducing the need for specialized knowledge and manual steps.
They also address context switching costs by creating unified interfaces and standardized workflows, minimizing interruptions.
Standardized environment templates and self-service provisioning through IDPs ensure consistency across development stages.
By reducing cognitive load from managing multiple tools, IDPs streamline development with uniform interfaces and workflows.
IDPs embed security into workflows, automating security scanning and compliance checks to provide real-time feedback on issues.
Teams can leverage existing tools like Git workflows, Docker containers, and CI/CD pipelines to implement platform engineering practices.
Implementing IDPs can enhance productivity, reduce development friction, and create a more efficient development lifecycle.

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