thehardwarestore.ai

NeoHtop is a modern, cross-platform system monitor built with Svelte, Rust, and Tauri. It provides real-time monitoring of system processes, CPU usage, memory usage, network I/O, and disk statistics. The application features a beautiful, customizable UI with dark/light theme support, advanced process search and filtering (including regex support), process pinning, and the ability to kill processes. The architecture cleanly separates the Rust backend (which uses the sysinfo crate to gather system information) from the Svelte frontend (which displays the data and handles user interactions), communicating via Tauri's IPC mechanism.

Glances is a cross-platform, real-time system monitoring tool written in Python. It collects system metrics (CPU, memory, disk, network, processes, sensors, containers, GPU, NPU, etc.) via the `psutil` library and optional hardware-specific backends, and renders them through multiple interfaces: a curses-based terminal UI, a web UI (Vue.js + FastAPI REST API), a pure JSON/CSV stdout mode, a Model Context Protocol (MCP) server for AI assistant integration, and exports to 20+ time-series databases and message brokers (InfluxDB, Prometheus, Kafka, Elasticsearch, etc.). The architecture is modular and plugin-based: each metric domain is a self-contained plugin inheriting from a common base class, and each export target is a separate module. Glances can run standalone (local monitoring), in client/server mode (XML-RPC or REST), or as a central browser that discovers and displays multiple Glances instances via Zeroconf. The project is production-grade, with extensive test coverage, CI/CD across multiple OSes, Docker images, and active security maintenance. Its primary use case is real-time monitoring of single or distributed systems, with optional historical data export for long-term analysis.

The system-monitoring-center is a Linux desktop application for real-time system resource monitoring and management. It provides a tabbed interface displaying CPU, memory, disk, network, GPU, sensors, processes, services, users, and system information. The application allows users to monitor performance metrics, manage processes and services, and view detailed hardware information through a modular architecture. Built with Python and Tkinter using the Sun Valley theme, the application follows a modular structure where each tab (CPU, Memory, etc.) implements a consistent interface (tab_gui, initial_func, loop_func). Core system data is gathered by the Libsysmon module via /proc, /sys, and system commands (dmidecode, lspci, etc.). The GUI features interactive charts, process/service management capabilities, and supports internationalization across 15+ languages. Configuration is managed through a version-aware system in Config.py. Key reusable components include Common.py for generic GUI utilities (sortable treeviews, search entries, charting canvases) and Config.py for persistent settings management. The application demonstrates good engineering practices with clear separation of concerns, active maintenance, and low technical drift despite Linux-specific dependencies.

LibreHardwareMonitor is a free, open-source hardware monitoring application for Windows (with partial Linux support) that reads temperatures, fan speeds, voltages, loads, clock speeds, and other sensor data from CPUs, GPUs, motherboards, storage devices, network adapters, power supplies, batteries, and various fan/water cooling controllers. The architecture is a Windows Forms desktop application (LibreHardwareMonitor.Windows.Forms) backed by a reusable .NET library (LibreHardwareMonitorLib) that abstracts hardware access through interfaces (IHardware, ISensor, IControl) and a visitor pattern for traversal. Key entry points include the MainForm UI, the Computer class that orchestrates hardware enumeration, and an embedded HTTP server providing REST/JSON and Prometheus endpoints. Data flows from low-level hardware interfaces (LPC I/O, MSR, SMN, SMBus, HID, NVAPI, ADL, WMI, IPMI, SMART) through sensor objects with min/max tracking and history compression, to the UI tree view, system tray icons, logging, and web clients. External dependencies include the Aga.Controls tree view library, HidSharp for USB HID, PawnIO kernel driver for low-level I/O, and several platform-specific native APIs. The codebase is large and well-structured with clear separation of concerns, but contains some monolithic files, outdated JavaScript libraries (jQuery 1.7.2, Knockout 2.1.0), and hardcoded hardware-specific mappings that require ongoing maintenance.

This repository is an educational reference and study guide for system design interviews. It is not a deployable application or data pipeline. It provides a structured index of system design topics, solution sketches for common design problems (e.g., pastebin, Twitter, web crawler), and Python code snippets illustrating key patterns like LRU caching, hash tables, BFS on social graphs, and MapReduce. The material is oriented toward preparing engineers for technical interviews, with back-of-the-envelope calculations, REST API designs, and scalability discussions. The code examples are minimal, self-contained, and demonstrate fundamental computer science patterns, but are not production-grade software. The repository also includes Anki flashcard decks for spaced repetition.

This repository is a curated suite of 152 Claude AI skills (76 builder + 76 paired checklist reviewer archives) for generating structured Excel workbooks across the automotive engineering lifecycle. Domains include ISO 26262 functional safety, ISO/SAE 21434 cybersecurity, ISO 21448 SOTIF, ASPICE, AUTOSAR, SysML/MBSE, diagnostics (UDS, DBC, ODX), calibration (A2L), quality tools (APQP, PPAP, FMEA), and program management. Each `.skill` file is a ZIP archive containing SKILL.md, Python generators/reviewers, references, and shared utilities. Builders produce multi-tab xlsx workbooks from JSON input; reviewers validate existing workbooks and emit compliance checklists with dashboards. The suite is maintained via an autonomous weekly cycle (plan, polish, docs, release), with status tracking, chain-contract audits, and known issue catalogs. While the concept is robust, many shipped skills suffer from documentation/code drift (advertised check counts mismatch actuals), silent failures (empty workbooks certified as compliant), and duplicated shared scripts across 152 archives.

This repository is a curated reading list organized as a GitHub Pages site using Jekyll. It contains no application logic, no data pipelines, and no executable code. The entire substantive content is a single README.md file with approximately 1,500 categorized links to external articles, talks, and case studies about scalable system design. The repository's value lies entirely in its curation quality and organization, not in any software it produces. The architecture is a static Jekyll site served via GitHub Pages, configured through _config.yml with the 'jekyll-theme-modernist' theme. The only other files are standard GitHub community templates (CONTRIBUTING.md, SECURITY.md, issue templates, CODE_OF_CONDUCT.md, LICENSE, FUNDING.yml). There are no tests, no CI/CD pipelines, no package manifests, and no source code in any programming language. The last commit was in January 2026, indicating the repository is still receiving updates. The content covers distributed systems patterns across major tech companies (Netflix, Uber, Facebook, Google, LinkedIn, Pinterest, etc.) organized into categories: principles, scalability, availability, stability, performance, intelligence/ML, architecture, interview prep, organization, and talks. The breadth of coverage is impressive but the curation is entirely manual link collection with no original analysis or tooling.