thehardwarestore.ai

Netis Cloud Probe is a production-grade network packet capture and forwarding system designed to monitor traffic without requiring dedicated TAP or SPAN hardware. It consists of a high-performance C capture engine (cpworker), a Go daemon (cpdaemon) that manages worker processes and communicates with a central policy manager (CPM), and a Go CLI (cpctl) for manual control. The system supports multiple capture sources (libpcap, DPDK, pcap files) and output sinks (GRE, VXLAN, ZMQ, rotating files) with a configurable execution model (real-time or pipelined using a lock-free SPSC ring buffer). Configuration reload is handled via a state-machine protocol, enabling hot-swapping of tasks without packet loss. The codebase is multi-language (C, Go) with CMake and Mage build systems, comprehensive unit and integration tests, and bilingual documentation (English, Chinese). It is actively maintained with a recent commit and continuous integration.

This repository provides a Helm chart for deploying Uptime-Kuma, a self-hosted uptime monitoring tool, on Kubernetes. The chart is well-structured with support for Deployment or StatefulSet, persistent storage, ingress, external database (MariaDB/MySQL), Prometheus ServiceMonitor, and network policies. It includes comprehensive helm-unittest tests, CI/CD pipelines for linting, testing, and releasing, and automated dependency updates via Renovate. The chart is actively maintained and follows Helm best practices.

Custom Pod Autoscaler is a Kubernetes operator that provides custom autoscaling functionality. It runs as a pod in a Kubernetes cluster, periodically gathering metrics (via custom scripts or Kubernetes metrics server), evaluating those metrics to determine desired replica count, and scaling target resources accordingly. The application features a REST API for manual interaction, supports hooks for custom logic at various points in the process, and is designed to work with any language or framework through shell commands or HTTP requests. The architecture follows clean separation of concerns with distinct packages for configuration loading, execution abstraction, metric gathering, evaluation, and scaling logic.

This repository is a comprehensive personal R&D platform implementing a polyglot microservices architecture. It hosts a personal website/web application (React/Vite), multiple API servers in different languages (Rust/Axum, Go/Gin/gRPC, Python/FastAPI, Node.js/Express), mobile applications (iOS/Android/React Native), and desktop applications (Qt). The platform includes extensive data processing infrastructure (Kafka, Pulsar, NATS, Spark, Flink), machine learning capabilities (model training/serving/fine-tuning), monitoring and observability tools, and infrastructure as code (Terraform/OpenTofu, Kubernetes). Services communicate via messaging systems and APIs, with a clear separation of concerns and standardized patterns for configuration, health checks, logging, and error handling across language implementations.

Yggdrasil is an Envoy control plane that watches Kubernetes ingress resources across multiple clusters and generates dynamic configuration for Envoy proxies. It implements a modular architecture with separate packages for Kubernetes integration (pkg/k8s) and Envoy configuration generation (pkg/envoy). The system uses Kubernetes informers to watch resources, aggregates events from multiple clusters, translates ingress objects into Envoy listeners and clusters, and serves the configuration via xDS gRPC protocol. It also provides metrics and health endpoints for monitoring. The code demonstrates good separation of concerns, proper use of Kubernetes client-go patterns, and clean abstraction for handling multiple Kubernetes API versions.

ToolJet is a comprehensive low-code/no-code application development platform designed for building internal tools, workflows, and AI agents. The system features a visual drag-and-drop editor (AppBuilder) that allows users to construct UIs using a library of widgets, connect to diverse data sources via a plugin architecture, and manage application logic through a backend NestJS service. It supports multi-tenancy, version control (Git), and enterprise-grade security features like SSO and RBAC. The architecture is a monorepo with a React-based frontend and a NestJS-based backend, utilizing PostgreSQL for persistence and Redis for caching.

Portainer Community Edition is a full-stack container management platform providing a unified web UI and REST API for Docker, Docker Swarm, Podman, Kubernetes, Azure Container Instances, and edge computing environments. The backend is written in Go, using BoltDB for storage, gorilla/mux for routing, and includes proxy layers for Docker/Kubernetes/Azure APIs, Helm chart management, GitOps workflows, LDAP/OAuth authentication, RBAC, and backup/restore functionality. The frontend is a hybrid of legacy AngularJS (1.8) and modern React with TypeScript, using React Query, Zustand, Formik, Yup, and a custom UI component library with Storybook. Key entry points are the Go main command (api/cmd/portainer/main.go) and the webpack-compiled React/AngularJS app (app/app.js). The system manages endpoints, stacks, services, containers, volumes, networks, images, registries, edge devices, user teams, and access controls through a layered architecture of HTTP handlers, data services, and middleware. External dependencies include Docker SDK, Kubernetes client libraries, Helm SDK, and various third-party registries.

This is the Kubernetes Autoscaler monorepo, containing multiple production-grade autoscaling components for Kubernetes: Cluster Autoscaler (horizontal node scaling), Vertical Pod Autoscaler (vertical pod resource scaling), Addon Resizer (resource adjustment for addons), and Balancer (workload distribution). The architecture is a collection of independent Go applications and libraries, each with its own entry points (main.go) and Kubernetes API extensions. The codebase is heavily multi-cloud, with vendored SDKs for over 15 cloud providers (AWS, GCE, Azure, OCI, Huawei, Alicloud, etc.) and extensive generated client code for custom CRDs. The core autoscaling logic is well-engineered, but the repository is bloated with auto-generated SDKs and boilerplate, leading to moderate technical drift risk. It is actively maintained with frequent commits and CI pipelines.

Parca is a continuous profiling platform that collects, stores, queries, and visualizes pprof profiles in real time. The Go backend ingests profiles via gRPC (both pprof and OpenTelemetry OTLP), normalizes them into Apache Arrow columnar format, and stores them in either FrostDB (default) or ClickHouse. It provides a rich gRPC+JSON API for querying profiles (flamegraph, table, source view, callgraph, pprof download), scraping Prometheus-style targets, managing debug information for symbolization, and sharing profiles via links. The TypeScript/React frontend delivers an interactive profiling UI with flamegraphs, tables, source views, metrics graphs, sandwich views, and comparison mode, all backed by efficient Arrow-based querying. The codebase demonstrates strong engineering with reusable patterns (generic caching, signed request abstraction, Arrow compaction), but carries some drift risk from dual storage backends, generated protobuf code, and a complex UI state layer built with multiple state management strategies.

Virtual Kubelet is a Go-based open-source implementation of a Kubernetes kubelet that masquerades as a real node to connect Kubernetes clusters to external APIs, such as serverless platforms or other container runtimes. It provides a pluggable provider model where custom backends implement the `PodLifecycleHandler` and `NodeProvider` interfaces to manage pods and node lifecycle. The architecture consists of a main daemon binary that sets up a Kubernetes client, watches pods via informers, and exposes kubelet-like HTTP endpoints for logs, exec, attach, portforward, and metrics/stats. Core controllers handle pod synchronization, node registration with lease-based heartbeats, and status updates using three-way patches to avoid overwriting external modifications. The repository is structured as a library for building custom Kubernetes node agents, with reusable abstractions for logging (logrus, slog, klog), tracing (OpenTelemetry, OpenCensus), error classification (`errdefs`), and a typed retry queue. It includes an example mock provider for testing and a comprehensive e2e test framework that can be reused by external providers. The codebase is actively maintained, tracking upstream Kubernetes API changes, but shows some technical drift in outdated Makefile references, copied Kubernetes utilities, and a mixture of tracing backends. Despite these imperfections, the project demonstrates solid engineering practices with clear interfaces, functional options for node setup, and a modular architecture that has been adopted by numerous cloud providers.

Timoni is a Kubernetes package manager CLI tool powered by the CUE configuration language. It provides a workflow for defining, packaging, and deploying Kubernetes applications as OCI-distributed modules with typed schemas, health checks, and server-side apply. The architecture is a single binary CLI implemented in Go using Cobra for commands, with core logic in internal packages for module building (CUE compilation with overlay injection), OCI artifact management (push/pull/tag/sign), instance reconciliation (dry-run, diff, apply, wait, prune), and bundle composition for multi-instance multi-cluster deployments. The system relies on Flux CD's ssa package for Kubernetes server-side apply and inventory tracking via Secrets, and integrates with Cosign for signing and verification. The codebase is well-structured with a clear separation of concerns across engine, reconciler, runtime, and oci packages, and benefits from extensive integration tests using envtest and an in-memory OCI registry. While tightly coupled to the Kubernetes and CUE ecosystems, several internal components demonstrate high craftsmanship and potential for reuse.

CloudAI Fusion is a cloud-native control plane for managing multi-cloud AI infrastructure, emphasizing verifiable auditability and cryptographic evidence. It unifies GPU scheduling, security red-teaming, FinOps, and GitOps under a single evidence ledger that records tamper-evident receipts for all control-plane actions. The architecture is polyglot, using Go for the high-performance control plane (API Server, Scheduler, Agent) and Python for AI/ML agents (anomaly detection, LLM orchestration). It integrates deeply with Kubernetes, supporting multi-tenancy, edge-cloud federation, and WebAssembly-based serverless workloads. The system distinguishes itself through a 'Verifiable Moat' architecture where subsystems are unified under a cryptographic ledger using Ed25519 signatures and Merkle transparency logs. It enforces 'honesty' by explicitly reporting backend modes (real vs. simulated) via a capability registry, preventing production deployments from relying on simulated backends. Key functionality includes verifiable model provenance, authorized red teaming with reproducible exploit witnesses, and cross-cluster failover attestation. The platform supports major cloud providers (AWS, Azure, GCP, Aliyun, Huawei, Tencent) through a standardized abstraction layer. While primarily an application, it contains significant data engineering and ML components, particularly in the `ai/` directory for RL-based scheduling and anomaly detection. These are integrated as features of the platform rather than standalone research artifacts. The codebase demonstrates high craftsmanship in distributed systems patterns, including Raft consensus, event-driven messaging, and plugin-based extensibility. However, the complexity of the cryptographic evidence layer and the mixed language stack introduce maintenance overhead.

VolSync is a Kubernetes operator for asynchronous replication of persistent volume data between clusters or to object storage. It implements a pluggable data mover architecture with backends for rsync, rclone, restic, syncthing, and rsync-tls. The operator watches ReplicationSource and ReplicationDestination custom resources, orchestrates mover jobs (typically as Kubernetes Jobs or StatefulSets), and manages the lifecycle of replication: trigger scheduling, synchronization, cleanup, and optional snapshot handling. A kubectl plugin provides CLI commands for managing replication relationships and migrations. The codebase is built with controller-runtime, includes a Helm chart, Prometheus metrics, and extensive test coverage (unit, integration, e2e). Major dependencies include vendored copies of restic and minio-go for the restic-based mover, which introduce maintenance risk but enable FIPS-compliant encrypted backups to S3-compatible storage.

This is a Go-based Kubernetes agent that watches cluster events (Kubernetes Event objects and Pod status changes) and sends them to Sentry for error tracking and monitoring. It runs as a read-only sidecar inside a Kubernetes cluster, using client-go informers and watchers to observe Jobs, CronJobs, Deployments, ReplicaSets, Events, and Pods. When a warning or error event occurs, the agent enriches it with Kubernetes metadata (owner references, node names, namespace) and captures it as a Sentry error event. It also supports Sentry Crons monitoring for CronJobs, custom DSN per resource via annotations, GKE integration for cluster metadata, and client-side filtering by event reason or source. The architecture is cleanly separated into watchers, enhancers, filters, and backoff logic, with comprehensive unit tests and CI/CD pipelines.

Luet is a container-native package manager for building, installing, and managing software packages. It uses Docker/buildah as build backends, treats packages as container images, and employs SAT solving (with optional Q-learning) for dependency resolution. The application features a CLI-driven architecture with commands for package operations, repository management, and system maintenance. Key architectural elements include a modular design with pluggable solvers, compiler backends, and repository clients, plus a context-based dependency injection system for logging, configuration, and garbage collection. The system stores package metadata in BoltDB or in-memory databases and supports templating via Sprig for dynamic package definitions.

Khue's Homelab is a GitOps-driven infrastructure-as-code framework that automates a complete self-hosted Kubernetes platform from bare metal to user-facing applications. The codebase is organized into five layers: metal (Ansible roles that PXE-boot Fedora Server, bootstrap a k3s cluster with kube-vip HA, and install Cilium for networking/load balancing), system (bootstrap Helm charts for ArgoCD, cert-manager, ingress-nginx, Rook Ceph, and monitoring), platform (Gitea, Woodpecker CI, Grafana, Kanidm identity, Dex SSO, zot container registry), apps (Jellyfin + the *arr stack, Matrix, Paperless, Ollama, and more), and external (Terraform-managed Cloudflare DNS, tunnel, and ntfy alerting). ArgoCD ApplicationSets watch the git repository and deploy every Helm chart, making the repo itself the source of truth; a single top-level `make` drives the entire pipeline. The project demonstrates strong IaC craftsmanship: idempotent Ansible roles with checksum-verified downloads and run_once state handling (k3s token genesis, kubeconfig extraction), an ephemeral PXE server with per-MAC kickstart files and DHCP-proxy mode, declarative random secret generation via a small Go tool combined with the External Secrets Operator for cross-namespace secret sync, and a Terraform module that creates least-privilege Cloudflare API tokens injected directly into the cluster. It also includes a Nix dev shell for reproducible tooling, pre-commit hooks, Woodpecker CI that renders Helm diffs on pull requests, and Terratest-based smoke/integration/version tests. The author explicitly labels the project ALPHA and recommends forking for personal use. It is best treated as a reference architecture and a collection of reusable provisioning patterns rather than a turnkey product; some portions (scripts/hacks, take-screenshots, the gitea config job) are acknowledged work-in-progress, and a few pinned versions lag the ecosystem (Go 1.19 in-cluster compile jobs, k3s 1.30, several stale container image tags).

Kata Containers is an open-source secure container runtime that runs each container inside a lightweight virtual machine (microVM), providing strong workload isolation while maintaining container-like performance and orchestration. The project provides a containerd shim-v2 implementation, a guest-side agent, and supports multiple hypervisors including QEMU, Cloud Hypervisor, Firecracker, and its own built-in VMM (Dragonball). It integrates deeply with Kubernetes (runtime classes, CSI, mutating webhooks, policy generation via Rego) and supports confidential computing features (TDX, SEV-SNP, IBM Secure Execution, ARM CCA). The codebase is dual-language: the primary runtime is written in Go, with a newer Rust rewrite (runtime-rs) that shares core libraries with the agent and Dragonball VMM. The project is actively maintained with extensive CI, multi-architecture support, and a large ecosystem of tools (agent-ctl, kata-ctl, log-parser, trace-forwarder, genpolicy). While the architecture is modular and many components (e.g., safe-path, dbs-allocator, retry library) are independently reusable, the coexistence of two runtime implementations, generated API clients, and architecture-specific code introduce some technical drift and maintenance overhead.

PortKiller is a cross-platform desktop application for developers to monitor, manage, and kill processes listening on TCP ports. It provides a unified interface across macOS (SwiftUI), Windows (.NET 9 / WinUI 3), and Linux (Python GTK3 + Rust core) with features including real-time port scanning, process type detection, favorites and watched ports with notifications, two-stage process termination (SIGTERM → SIGKILL), Kubernetes port-forward management with auto-reconnect, and Cloudflare tunnel integration (quick and named tunnels). The application also includes an onboarding wizard, sponsor/contributor display, Sparkle-based auto-updates (macOS), and extensive keyboard shortcut support. The architecture is platform-native with shared patterns: each platform implements its own UI layer, port scanner, and process killer, while a Rust core library (`portkiller-core`) provides scanning and process termination for Linux. The macOS version uses an actor-based `PortScanner` and observable state management (`AppState`), the Windows version follows MVVM with a system tray mini-window, and the Linux version uses a GTK3 system tray indicator. All platforms share a common feature set but with varying completeness (macOS is the most feature-rich). External dependencies include `lsof`/`ss` (Linux), `kubectl`, `cloudflared`, `socat`, and system utilities for process enumeration. Key data flows: port scanning via platform-specific commands (lsof, ss, GetExtendedTcpTable) → parsing into `PortInfo` models → filtering/searching → display in tree/table views → user actions (kill, watch, favorite, forward, tunnel). Kubernetes port-forwards and Cloudflare tunnels are managed as subprocesses with monitoring loops for reconnection and log streaming. The application persists user preferences (favorites, watched ports, customizations) using platform-native storage (Defaults on macOS, JSON on Windows/Linux).

DefectDojo is a Django-based vulnerability management platform that aggregates, deduplicates, and tracks security findings from over 200 security scanners (SAST, DAST, SCA, container, cloud, infrastructure). The system ingests scan reports via a standardized parser interface, normalizes findings into a core data model (Product, Engagement, Test, Finding, Location), and provides advanced features such as risk-based prioritization, SLA management, JIRA/GitHub integration, notifications (Slack, Teams, email), role-based access control, and customizable reporting. The architecture is a Django monolith with a REST API (v2 using DRF, v3 using Ninja in alpha), Celery for async task processing (imports, deduplication, JIRA sync, SLA expiry), PostgreSQL with full-text search and audit logging via pghistory, and a containerized deployment with Docker Compose. Key entry points include the parser factory (dojo/tools/factory.py), API viewsets, and management commands. The codebase demonstrates strong software engineering patterns such as a modular parser contract, an abstract location manager for feature-flag-driven migration, and an authorization filter registry, but carries technical debt from a dual API surface and an incomplete migration to a new location model.