This repository is the Pulumi Infrastructure as Code (IaC) platform. It provides a CLI (`pulumi`) and multi-language SDKs (TypeScript, Python, Go, .NET, Java, YAML) that allow users to define cloud infrastructure using general-purpose programming languages. The architecture is a monorepo containing a core engine (`pkg/engine`) that orchestrates resource lifecycle management, deployment steps, and state operations. Language plugins (`pulumi-language-nodejs`, `pulumi-language-python`, `pulumi-language-go`) host user programs and communicate with the engine via gRPC. Code generators (`pkg/codegen`) produce SDKs from provider schemas, enabling a pluggable provider model. The CLI entry point is `pkg/cmd/pulumi/main.go`, dispatching to subcommands for stack management, deployment operations (up, preview, destroy, refresh), policy-as-code, config, secrets, and ESC (Environments, Secrets, Config). External dependencies include cloud provider APIs (AWS, Azure, GCP, Kubernetes) accessed through plugins, and state backends (local filesystem, S3, Azure Blob, GCS, PostgreSQL, or Pulumi Cloud). The system uses protobuf for serialization and gRPC for inter-process communication between engine, language hosts, and providers.
This repository is a comprehensive Chinese-language Docker tutorial and technical book ('Docker ไปๅ ฅ้จๅฐๅฎ่ทต') rather than a traditional software service. The dominant content is structured Markdown chapters covering Docker introduction, installation, images, containers, registries, Dockerfile best practices, Compose, Kubernetes, cloud integrations, security, observability, and practical case studies. It also contains validated example code such as Dockerfiles, Compose files, and Kubernetes manifests, plus a custom toolchain for building static HTML and PDF output. The repository operates as a documentation publishing pipeline. Markdown sources are consumed by npm scripts and Python tools: prepare_pdf_sources.py inlines images as data URIs for PDF generation; build_html_reader.py creates a single-file offline HTML reader with Mermaid and dark-mode support; verify_artifacts.py checks generated artifacts and produces SHA256 manifests. CI workflows on GitHub Pages build and deploy the site, run link checks, validate examples with native tools such as kubeconform and actionlint, and enforce CJK typography rules. Architecturally, this is a collection of small, mostly standalone Python and Node scripts plus CI workflow orchestration; there is no long-running backend or service. Key entry points are package.json scripts and the tools/ and tests/ directories. The data flow is document transformation: Markdown to HTML and PDF, with example validation performed during CI. External dependencies include mdPress, mermaid-cli, pandoc, and container runtime tooling for validating the examples. Maintenance is active and changelog-driven, with frequent updates tracking Docker and Kubernetes versions. Some mirror lists and example image tags inevitably become stale as the ecosystem moves, but the CI and test layers keep the content and build pipeline in good shape. The most reusable value lies in the documentation tooling and validation patterns, not in the tutorial prose itself.
Hermit-rs is a Rust-based unikernel (library OS) that enables applications to run directly on hardware or hypervisors without a traditional operating system. The repository provides the build infrastructure (the `hermit` crate), the system call ABI (`hermit-abi`), and a collection of example applications demonstrating networking, file systems, TLS, WASM execution, and more. The actual kernel is maintained in a separate submodule. The project is actively developed with CI testing across x86_64, aarch64, and riscv64 architectures, and supports running under QEMU, Uhyve, and KVM. It is a hybrid of a system library and an application framework, but its dominant character is an application platform for building unikernel images.
This repository is a curated, actively-maintained personal knowledge base of academic paper reading notes, focused on systems for machine learning, cloud computing, and related areas. It is organized by conference venue (e.g., OSDI, SOSP, MLSys) and by topic (e.g., LLM systems, GPU sharing, memory disaggregation). The core content consists of markdown files with structured summaries (TL;DR, background, design, evaluation) and cross-linked topic aggregation pages. The repository is the source for a GitBook static site and includes scripts for building and serving that site, as well as utilities for PDF text extraction. While not a data pipeline or software application, it functions as a high-quality research resource with consistent formatting, curation guidelines, and reusable templates for note-taking and paper list organization.