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.
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.
CADAM is a production-grade, open-source web application that converts natural language descriptions into fully parametric 3D CAD models. It operates in two primary modes: parametric (generating OpenSCAD code via an AI agent that can iterate with live previews) and creative (producing 3D meshes via external providers like Meshy and Tripo). The application is built with React 19, TypeScript, TanStack Start, Vite, and Tailwind CSS, with a Supabase backend for authentication, database, and file storage. AI integration spans multiple providers (Anthropic, OpenAI, Google, xAI, and others) via the Vercel AI SDK, and the generated OpenSCAD code is compiled in-browser using a vendored WebAssembly build of OpenSCAD. The system includes a token-based billing model, real-time preview updates, parameter editing, and export to STL, OBJ, GLB, and other formats. Although primarily an application, the benchmarks directory serves as a research record of the quality of generated parametric models, covering complex assemblies like a V8 engine and turbofan.
macroquad is a Rust game engine and graphics library inspired by raylib, providing a simple, immediate-mode API for 2D and 3D rendering, audio, input handling, and UI. The codebase is structured as a monolithic core library with several subcrates (particles, tiled, profiler, physics-platformer, macroquad_macro) that add specialized functionality. The architecture centers on a single-threaded game loop orchestrated by the `#[macroquad::main]` proc macro, with drawing batched via an internal OpenGL abstraction (miniquad). Key data flows include texture and font loading, immediate-mode UI widget rendering, and an experimental scene/coroutine system for game logic. The library is cross-platform (Windows, Linux, macOS, Web, Android, iOS) and emphasizes minimal dependencies and ease of use. While the core is tightly coupled, several subcrates and subsystems are well-encapsulated and demonstrate high craftsmanship.
This is a Zellij plugin that provides quick filtering and switching between terminal tabs and panes, inspired by the original harpoon for Neovim. It maintains a user-defined list of 'bookmarked' panes (identified by session-unique IDs) and allows adding, removing, and navigating to them via keyboard shortcuts. The plugin persists the bookmarked pane list to disk using shell commands so that the list survives session restarts. The architecture is a minimal single-threaded state machine that reacts to Zellij events (key presses, tab/line updates, session changes). All logic resides in two Rust source files: main.rs for the plugin lifecycle and event handling, and persistence.rs for loading/saving bookmarks via JSON files. The code is well-commented with references to the official Zellij API documentation. The codebase is small and focused, and demonstrates disciplined use of the plugin framework.
Kalker is a multi-platform mathematical calculator implemented primarily in Rust. It provides a CLI with a REPL, a web component compiled to WebAssembly, and an Android mobile app via Capacitor. The core library (`kalk`) supports user-defined variables and functions, units, differentiation, integration, root finding, equation systems, matrices/vectors, complex numbers, and multiple number bases. The codebase is well-structured with a clear separation between parsing (lexer/parser), evaluation (interpreter), and a custom value system. It features extensive test files and integration tests for numerical methods. While the core numerical routines (tanh-sinh quadrature, Newton's method, Gaussian elimination) are tightly coupled to the interpreter, they are implemented with care and demonstrate non-trivial numerical analysis. The repository also includes build scripts for multiple targets and CI/CD pipelines.
DWARF Explorer is a cross-platform GUI utility for visualizing DWARF debugging information in executable files. Built on PyQt6, pyelftools, and filebytes, it allows users to open ELF, Mach-O, PE, WASM, and ar files to browse their DWARF debug information tree. The application provides views for DIEs (Debugging Information Entries), attributes, references, and includes specialized tools for analyzing locals at addresses, function maps, data sections, frame information, and unwind data. The architecture follows a modular design with clear separation between the GUI layer (PyQt6-based interface in __main__.py and ui.py) and the DWARF analysis logic. Core functionality includes parsing various binary formats via formats.py, building and navigating the DIE tree through tree.py and die.py, and performing specialized analyses using utilities in dwarfutil.py, locs.py, and other modules. The application supports features like following DIE references, searching by conditions, highlighting DIEs, and exporting C skeletons. Key strengths include robust error handling with crash reporting, support for multiple binary formats and DWARF versions (including experimental v1 support), and thoughtful UI design with keyboard navigation, bookmarking, and detailed views. The code demonstrates good practices in separating concerns, with reusable components like the Mach-O unwind parser that could be extracted for use in other projects.
This repository is a Chinese-language translation and expansion of the Learn wgpu tutorial series, teaching the wgpu Rust implementation of the WebGPU graphics API through a Cargo workspace of ~25 runnable example crates. The curriculum progresses from window/surface setup (tutorial1-2), render pipelines and buffers (tutorial3-4), textures/uniforms/instancing/depth (tutorial5-8), through OBJ model loading, Blinn-Phong lighting, normal mapping, camera control, compute-shader terrain generation, HDR rendering, and particle systems. Each example is a small but complete winit application that runs natively and as WebAssembly via wasm-bindgen, sharing a common utils crate that provides the WgpuAppAction lifecycle trait, node-based render abstractions (ViewNode, ComputeNode, BufferlessFullscreenNode), and buffer/bind-group helpers. A VitePress documentation site embeds live WASM demos of the examples, and showcase crates demonstrate a Pong game, offscreen GIF encoding, multithreaded model loading, and compute-shader tangent generation. Although primarily a tutorial codebase, it is genuinely hybrid: it doubles as a deployable documentation site and a reference implementation of modern wgpu patterns.
This repository is a curated 'awesome list' of programming languages that compile to WebAssembly or have their VMs in WebAssembly. It is not a software application or data pipeline; it is a markdown-based directory organized by maturity levels (in production, stable, unstable, work in progress, unmaintained). The README.md is the sole source of content, listing over 60 languages with links to their respective projects and brief descriptions. The repository includes standard community files (CODE_OF_CONDUCT.md, CONTRIBUTING.md) but contains no executable code, build scripts, or data transformations. It serves as a reference guide for developers exploring WebAssembly language support. The list is actively maintained (last commit 2026-05-22) and well-structured with clear categories.
sax-wasm is a high-performance SAX-style XML/HTML/JSX parser written in Rust and compiled to WebAssembly, with JavaScript/TypeScript bindings for Node.js and browsers. It provides streaming parsing of byte streams, emitting events for tags, attributes, text nodes, processing instructions, comments, CDATA, and doctype declarations. The parser is non-validating and focuses on raw speed, using SIMD instructions and optimized grapheme cluster handling. It includes a comprehensive TypeScript API with async generator support for streaming reads from ReadableStream. The repository contains the full Rust source, JS/TS bindings, build tooling (wasm-bindgen, wasm-opt), and extensive test suites covering XML, JSX, Angular templates, edge cases with multi-byte characters, and write-boundary splitting. While the parser is highly optimized and well-tested, the project appears to be in a stale maintenance state with no recent commits (last commit dated 2026-09-13, likely erroneous) and a small community following.
This repository implements the Dusk Network blockchain node (Rusk), a full Proof-of-Stake node with privacy-preserving transactions (Phoenix) and public transactions (Moonlight). The system is a Rust workspace containing a consensus engine (succinct attestation consensus with proposal/validation/ratification steps), a WebAssembly-based smart contract VM (Piecrust), a RocksDB/SQLite ledger, a mempool, an HTTP/GraphQL API server (Axum), and a CLI/TUI wallet. It also includes a JavaScript SDK (w3sper.js) for browser/Deno wallet interaction and a wallet-core library compilable to WASM. The architecture is modular: the node binary (rusk/src/bin/main.rs) composes services via a builder pattern (RuskNodeBuilder), supporting both full-chain and HTTP-only modes. Key data flows include transaction submission via RUES (Rusk Event System) or GraphQL, consensus message propagation over Kadcast P2P, block execution through the VM with hard-fork feature gating, and state persistence via RocksDB column families. External dependencies include dusk-plonk, dusk-poseidon, jubjub-schnorr, wasmtime, rkyv, and sqlx. The codebase demonstrates high craftsmanship in areas like deterministic committee sortition, zero-knowledge proof verification caching, and a reusable data-driver pattern for contract encoding/decoding.
WebDNN is a browser-based neural network inference engine that executes ONNX models directly on the client side. It provides four backends—CPU, WebAssembly, WebGL (1/2), and WebGPU—allowing models to run on a wide range of hardware. The repository includes a Python graph transpiler for offline model optimization (shader generation, operator fusion, 8-bit quantization) and a TypeScript runtime with a modular operator registration system. While the runtime is application-oriented (library for web ML inference), the Python transpiler contains research-grade optimization passes and weight compression algorithms, giving the project a hybrid character. The codebase is well-structured with clean abstractions for backends and operators, but some backends (WebGPU, WebAssembly) are partially complete and require specific build toolchains.
This is a monorepo for the Ember.js-based encrypted chat application 'emberclear', which provides end-to-end encrypted messaging with no server-side message storage. The architecture is modular, consisting of multiple Ember addons (crypto, networking, local-account, UI, test-helpers, tracked-local-storage) and a separate card game app (pinochle) that reuses the same networking and crypto infrastructure. The chat app uses public-key cryptography (tweetnacl, libsodium) via web workers, relays messages through Phoenix WebSocket relays, and persists data locally via IndexedDB and localStorage. It includes features like QR code identity sharing, channel voting with cryptographic chains, and a state-machine-driven setup wizard. The codebase is well-tested with QUnit, acceptance tests, and smoke tests, but shows signs of technical drift with outdated dependencies (Node 14, Ember 3.26.1), many TODO comments, and incomplete features (e.g., voting, QR login transfer). The pinochle game app appears experimental but demonstrates sophisticated state machine usage and animation systems.
This repository is a curated collection of Docker Compose samples demonstrating how to integrate various services (web frameworks, databases, proxies, etc.) using Docker Compose. It serves as a reference for developers to quickly set up development environments. The samples cover a wide range of stacks including Angular, Django, Flask, Go, Rust, and more. Notable examples include WebAssembly-based microservices using WasmEdge. The repository is not a single application but a library of examples, each with its own compose.yaml and application code. It is actively maintained by Docker.