thehardwarestore.ai

This repository is a curated 'awesome list' of OpenTofu resources, tools, and community links. It is not a software application or data pipeline; it is a static markdown document (README.md) that aggregates official OpenTofu links, community channels, feature highlights, tools (environment managers, wrappers, CI, tests, state management, providers, platforms, registries, helpers), learning materials, media, and podcasts. The repository includes standard awesome list infrastructure: a Jekyll-based GitHub Pages site (with custom CSS and a CNAME for awesome-opentofu.com), CI workflows for link checking and markdown linting, and a contribution guide. There is no executable code, no API, no data transformations. The value lies in the curated, up-to-date collection of external resources, not in any software logic. The repo is actively maintained with recent commits and automated dependency updates via Renovate and Dependabot.

This repository is a companion to the Korean book '컨테이너 인프라 환경 구축을 위한 쿠버네티스/도커' (Kubernetes/Docker for Container Infrastructure). It provides a collection of Vagrant-based lab environments, shell provisioning scripts, Kubernetes YAML manifests, Helm charts, and CI/CD examples (Jenkins) to help readers build and manage Kubernetes clusters on VirtualBox. The codebase includes single-master and multi-master (via Kubespray) cluster setups, along with demonstrations of core Kubernetes concepts (Pods, Services, Ingress, Storage, Monitoring). While the repository is actively maintained with yearly architecture documents, the actual scripts and configurations rely on outdated software versions (e.g., Kubernetes 1.18, Docker 18.06, CentOS 7) and external URLs that may become stale. The code is functional for educational purposes but is boilerplate-heavy and repetitive across chapters, limiting its reusability in production environments.

This repository is a comprehensive DevOps reference guide and cheatsheet collection, not a production application. It contains hundreds of markdown files covering topics such as Docker, Kubernetes, CI/CD tools (Jenkins, GitHub Actions, CircleCI), infrastructure provisioning (Terraform, Ansible, Puppet), cloud providers (AWS, Azure, GCP), networking, Linux, observability (Prometheus, Grafana), and interview preparation. The codebase includes a few illustrative examples (a Node.js Express app with Dockerfile, a bash system management script, Apache configuration snippets) but is overwhelmingly documentation. The content is organized by tool with consistent subdirectories (concepts, commands, tutorials), though many files are stubs or incomplete. The repository serves as a learning roadmap and quick reference, but suffers from outdated references (e.g., Node 10, Python 2), broken links, and inconsistent quality. It is not a runnable system and has no data pipelines or analytical models.

This is not a production application but an educational repository for Microsoft's AZ-104 Azure Administrator training. It contains laboratory instructions, demonstration guides, and infrastructure-as-code templates (ARM JSON and Bicep) for provisioning Azure resources such as virtual networks, virtual machines, load balancers, and storage accounts. The repository provides step-by-step exercises for learners to practice Azure administration tasks. While the templates are functional and follow best practices, the repo's primary purpose is instructional, not to serve as a reusable software package. The codebase is essentially documentation with supporting automation scripts, and its architecture is not applicable to a running system.

This repository is purely educational documentation for a 60-day cloud and DevOps learning curriculum. It contains no executable code, APIs, services, or data processing components. The content consists of structured weekly learning paths (Week1-World8) with reading resources, hands-on exercises, and conceptual explanations covering Linux, networking, cloud platforms, IaC, containers, CI/CD, security, monitoring, and final projects. While well-organized as learning material, it has zero reusable engineering components since it contains only markdown files and images. The repository serves as a study guide rather than a functional software system.

This repository is a personal portfolio and link aggregation page for Anton Babenko, a well-known figure in the Terraform and AWS DevOps community. It contains no source code, application logic, or data pipelines. The sole content is a README.md file that lists links to the author's social media profiles, media projects (YouTube channel, newsletter), major open-source projects (terraform-aws-modules, pre-commit-terraform, serverless.tf, compliance.tf), utilities, and abandoned projects. It serves as a central directory for discovering the author's work, but it is not a functional software repository. There are no executable files, tests, configuration files, or any code that can be run or reused. The repository is essentially a static documentation page with no technical depth.