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Getting Started with GitHub Actions and Workflows for Terraform Deployment with Azure Cloud

GitHub Actions is an automation tool that allows developers to create CI/CD pipelines.
A GitHub Workflow is a set of automated tasks defined in a YAML file.
GitHub Actions and Workflows automate software development, saving time and improving productivity.
GitHub Actions and Workflows can be used to automate Terraform deployments on Azure.

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Medium

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Is Your Business Ready for Kubernetes?

Kubernetes is powerful, flexible, and scalable, but not a one-size-fits-all solution.
The appropriate time to consider Kubernetes depends on the size and stage of your business.
For small businesses and simple tech stacks, Kubernetes is overkill and better alternatives exist.
For businesses with distributed teams, numerous microservices, and serious uptime requirements, Kubernetes is a must-have.

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Dev

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Observability 2.0: The Future of Monitoring with OpenTelemetry

Observability 2.0, powered by OpenTelemetry, is redefining how DevOps teams collect, analyze, and act on telemetry data.
Observability 2.0 provides end-to-end visibility, contextual insights, automated anomaly detection, and vendor-agnostic data collection.
OpenTelemetry is an open-source observability framework that standardizes data collection and integrates with various backend observability platforms.
OpenTelemetry offers features like automatic instrumentation, multi-language support, distributed tracing, and cloud-native readiness.

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Dev

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Building a Feature Flag System in Node.js with Redis and Middleware

Feature flags (also known as feature toggles) allow you to enable or disable functionality without deploying new code.
This article explains how to implement a feature flag system in Node.js using Redis and Express middleware.
It provides step-by-step instructions for project setup, Redis client setup, middleware implementation, and usage in routes.
The article also suggests enhancements like enabling features for specific user IDs or roles, implementing flag variants for A/B testing, creating a dashboard for toggling flags, and using JSON structures in Redis for more complex rules.

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Dev

410

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Linux in Action - File Structure Hierarchy and Its Role in Modern Business

Linux is a powerful and versatile operating system crucial for businesses due to its customization and flexibility.
The article covers Linux fundamentals, kernel, distributions, file hierarchy, and its role in tech and business.
Linux's stability and file structure consistency across distributions make it ideal for various applications.
Popular Linux distributions include Ubuntu, Debian, Fedora, CentOS, RHEL, Arch, and SUSE, catering to different needs.
Linux is extensively used in AWS for services like EC2, Lambda, and ECS, providing a secure cloud platform.
Businesses leverage Linux for server management, system administration with Bash, and DevOps automation.
Linux file structure showcases key directories starting from the root directory, aiding in system organization.
RHEL 9 is favored in industries for its stability, security, support, scalability, and compatibility features.
Understanding Linux enhances efficiency, security, collaboration, and simplifies debugging for personal and professional tasks.
Mastering Linux empowers users in navigating systems, automating tasks, and excelling in DevOps and server management.

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Dev

104

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Linux Essentials: Taming Files & Directories Like a Terminal Boss

Creating directories is simple with commands like 'mkdir' to create a single directory or 'mkdir -p' to create nested directories.
To create files, use the 'touch' command, such as 'touch /Luna.py' to create a single file or 'touch /Luna1.py /Luna2.py /Luna3.py' to create multiple files.
The 'cat' command is useful for viewing or writing file contents, allowing you to create and write to a new file or append content to an existing one.
To delete files or directories, you can use the 'rm' command, like 'rm -rvf /Luna1.py' to delete a single file or 'rm -rvf /Luna2.py /Luna3.py /Luna4.py' to delete multiple files.

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Medium

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Beyond Frameworks: The Real Weight of Leading Transformation at Scale

The article discusses the challenges of leading through change and transformation fatigue, emphasizing the importance of alignment in leadership philosophies.
Transformation fatigue arises from the erosion of alignment over time, not just from stalled progress.
The article reflects on the impact of leadership divergence on team autonomy and trust in transformation initiatives.
It highlights the need to defend the transformation mindset amid leadership shifts and misalignment at scale.
Leading Agile, DevOps, or product transformation requires a focus on mindset more than just adopting frameworks.
The journey of transformation involves continuous effort to maintain alignment as leadership evolves and ownership changes.
The fatigue in transformation does not stem from lack of progress but from the ongoing effort to sustain alignment amid shifting leadership philosophies.
The article shares personal experiences of navigating leadership philosophies and alignment challenges during a transformation journey.
It underscores the importance of trust in team autonomy and the complexity of defending it across various layers of leadership.
As organizations grow, sustaining alignment becomes more complex, and the risk of fatigue increases with expanding teams and diversifying roles.

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Siliconangle

269

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On theCUBE Pod: Tariffs, tech volatility and the race for AI-driven cloud leadership

Artificial intelligence is reshaping cloud computing with AI infrastructure playing a crucial role in enterprise strategies.
Hyperscalers are focusing on building full-stack AI solutions and developer-friendly tools.
Google Cloud's growth is driven by AI infrastructure, highlighting the shift in cloud computing dynamics.
The importance of delivering full-stack AI solutions is transforming cloud providers' strategies and partnerships.
AI-first approach is influencing cloud platforms to prioritize real-world AI applications and developer experience.
Market volatility due to geopolitical tensions and tariffs is impacting tech manufacturing and market stability.
Security concerns are rising in the era of AI-powered advancements, necessitating robust security frameworks.
Hyperscalers need to balance growth with security to drive future of enterprise cloud services.
Success in cloud computing is now defined by trusted AI infrastructure, ecosystem depth, and strong security frameworks.
Podcast discussed the impact of AI, security, and geopolitical factors on cloud evolution and future competitiveness.

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Openstack

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OpenStack Delivers Efficiency, Cost Savings for Powering AI Workloads

FPT Smart Cloud, a Silver Member of OpenInfra Foundation, uses OpenStack to power AI workloads in Vietnam, compared with proprietary solutions like VMware and Microsoft Azure.
The company has built a significant OpenStack footprint for AI workloads and high-performance computing, leveraging features like flexibility for customization and mature cloud ecosystem.
FPT Smart Cloud utilizes OpenStack for managing AI factories in Vietnam and Japan, offering services like bare metal, GPU cloud instances, and Kubernetes provisioning.
For storage, they employ SAN storage, Ceph, and local NVME, with performance improvements seen compared to VMware.
In another use case, FPT AI eKYC offers secure digital onboarding and authentication to Vietnamese banks, demonstrating scalability and agility with OpenStack.
By optimizing existing AI/HPC workloads with OpenStack, FPT achieved a 7% GPU efficiency increase and multi-million dollar savings.
Technologies like NUMA, HugePage, CPU pinning, and NVMe storage contribute to GPU optimization and network/storage capacity enhancements.
FPT leverages OpenStack features like DPDK, SR-IOV, and vGPU live migration to improve high availability for critical AI and HPC workloads.
The 'OpenInfra for AI' Working Group, including members like FPT Smart Cloud, aims to promote OpenStack as the go-to open-source infrastructure technology for AI workloads.
Collaboration within the OpenInfra community focuses on enhancing infrastructure for AI workloads and addressing software gaps to support AI scenarios.
Joining the OpenInfra Foundation can support the efforts to advance OpenStack adoption for AI workloads.

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Medium

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Why Traditional Docker Images Are Dead: Distroless Is the Future for Secure Spring Boot…

Distroless Docker Images are a new approach to containerization.
They strip away unnecessary elements like shell, package manager, and OS-level utilities.
Traditional Docker images are bloated and insecure due to containing a full operating system.
Distroless images are gaining popularity for their minimalistic and secure nature.

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Medium

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Is Docker Dead for Spring Boot in 2025? Better Alternatives You Need to Know

Docker's viability in 2025 is questioned as new tools steal the spotlight.
Docker revolutionized development workflow with its simplicity in packaging Spring Boot apps.
The container ecosystem has evolved, with Kubernetes moving away from Docker.
Developers face confusion with the shift towards containerd and CRI-O.

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Dev

283

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Day 4 of 30 Days of Linux Mastery: Getting Comfortable with Vim

vim is a powerful text editor that lives in almost every Unix-based system.
Vim operates in three modes: Command Mode, Insert Mode, and Extended Mode.
Some core Vim commands include: entering Insert Mode, exiting to Command Mode, saving files, quitting Vim, copying lines, pasting, deleting lines, undoing changes, and navigating to specific lines.
Learning Vim is a valuable skill for Linux users and offers speed and efficiency in editing files.

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Medium

233

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Downstream impact of AI on Engineering Analytics and DORA

AI tools can positively impact developer productivity by improving code reviews, diagnostics, and workflows.
DORA metrics, including deployment frequency, lead time for change, change failure rate, and mean time to restore, measure software development and deployment effectiveness.
Deployment frequency indicates the efficiency of the development process, while lead time for change helps set realistic deadlines.
Mean Time to Restore and Change Failure Rate provide insights into issue resolution and code quality.

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The New Stack

365

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Build Scalable LLM Apps With Kubernetes: A Step-by-Step Guide

Large language models (LLMs) like GPT-4 have revolutionized AI applications in various sectors by enabling advancements in natural language processing, conversational AI, and content creation.
Deploying LLMs effectively in real-world scenarios poses challenges due to their demanding computational requirements and the need for scalability and efficient traffic management.
Kubernetes, a leading container orchestration platform, offers a dynamic framework for managing and scaling LLM-based applications in a cloud-native ecosystem, ensuring performance and flexibility.
This step-by-step guide focuses on deploying and scaling LLM-powered applications using Kubernetes, essential for transitioning AI models from research to production environments effectively.
The process involves containerizing LLM applications, deploying them on Kubernetes, configuring autoscaling for fluctuating demands, and managing user traffic for optimal performance.
Key prerequisites include basic Kubernetes knowledge, Docker installation, a Kubernetes cluster set up, and OpenAI and Flask installation in the Python environment to create LLM applications.
Steps include creating an LLM-powered application in Python, containerizing it using Docker, building and pushing the Docker image, deploying the application to Kubernetes, configuring autoscaling, and monitoring/logging the application.
Further enhancements for handling advanced workloads include using service mesh, implementing multicluster deployments, and integrating CI/CD automation.
Overall, building and deploying scalable LLM applications with Kubernetes can be complex but rewarding, empowering organizations to create robust and production-ready AI solutions.
Kubernetes' features like autoscaling, monitoring, and service discovery equip applications to handle real-world demands effectively, allowing for further exploration of advanced enhancements.

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Dev

210

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Service Mesh Explained: What's a service mesh?

Service mesh is an abstract infrastructure layer that manages service-to-service communication, providing traffic control, security, observability, and service discovery.
In a microservices architecture, services communicate through APIs, and a service mesh enhances the communication process.
The Control Plane is the core of the service mesh, managing proxies, defining network structure, updating routing tables, and enforcing network policies.
The Data Plane, within a service mesh, processes communication based on decisions made by the Control Plane to ensure efficient data movement.
The sidecar proxy in a service mesh handles tasks like service discovery, health checking, routing, load balancing, security, and observability.
Benefits of using a service mesh include centralized traffic management, enhanced security, resilience, fault tolerance, and improved observability in microservices environments.
While powerful, service meshes come with drawbacks like increased complexity, performance overhead, steep learning curve, and potential debugging challenges.
Using a service mesh is not mandatory and should be considered based on specific needs for advanced features like routing, authentication, and observability.
The service mesh world offers solutions for building smarter, composable infrastructure, emphasizing benefits in resilience, security, and visibility.
Exploring service mesh concepts can be beneficial for those working with distributed systems, offering insights into its applications and practical use cases.
Service meshes are part of a broader trend towards more efficient infrastructure, providing value in managing complex microservices architectures.

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