LiveSplit One is a cross-platform speedrun timer application, the successor to the classic LiveSplit timer. It runs as a Progressive Web App and as a Tauri desktop application, targeting Windows, Linux, and macOS. The core timing logic is compiled to WebAssembly via the 'livesplit-core' Rust library, while the UI is built with React 19, TypeScript, and Vite. The application provides a rich set of features for speedrunners: highly customizable split timers, real-time comparisons, layout editor with drag-and-drop, integrated leaderboards from Speedrun.com, global hotkeys (desktop only), always-on-top mode, WebSocket server for remote control, and support for multiple timekeeping methods (real time vs game time). Data persistence is handled via IndexedDB in the browser and the filesystem in Tauri. The codebase is well-structured with a clear separation between core logic (WASM), UI components, and platform-specific code, though some central components like 'LiveSplit.tsx' are large and could benefit from further decomposition.
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.
This repository contains a CLI tool for interacting with SoloKeys Solo 2 security keys. The application enables users to list connected devices, interact with various onboard applications (Admin, FIDO2, NDEF, OATH, PIV, Provision, QA), update firmware, and switch between regular and bootloader modes. The architecture features a clean separation of concerns with distinct layers for CLI parsing, library functionality, transport abstraction (CTAP/PCSC), device management, and application-specific implementations. The code demonstrates solid Rust engineering practices with proper error handling, user experience considerations, and adherence to security best practices.
This repository is a structured, comprehensive AI engineering curriculum spanning 20 phases and over 500 lessons, from math foundations and ML fundamentals to deep learning, computer vision, NLP, speech, transformers, generative AI, reinforcement learning, LLMs from scratch, agent engineering, multi-agent systems, production infrastructure, and ethics/safety. It is an educational resource, not a production application or data research project. The code consists of self-contained pedagogical implementations (primarily Python, with some TypeScript, Rust, and Julia) that demonstrate core algorithms from scratch—such as attention mechanisms, BPE tokenization, diffusion models, RLHF, and agent loops—using synthetic data and deterministic stubs. Capstone projects include small web servers (Hono/TypeScript) and test harnesses. The curriculum is actively maintained with CI tooling, certification prep materials, and translation support. While the code is simplified for learning, it exhibits clean patterns, consistent lesson structure, and testing practices that make individual components (e.g., tool registries, state machines, evaluation harnesses) reusable in other contexts.
Tabby is a self-hosted AI coding assistant that provides code completion, chat, and code search capabilities through multiple interfaces: a web dashboard (Next.js/React), IDE plugins (VS Code, IntelliJ, Eclipse, Vim), and a Language Server Protocol (LSP) agent. The backend is primarily written in Rust, using Axum for HTTP services, Tantivy for indexing, and llama.cpp for local model inference. It supports enterprise features like SSO (OAuth, LDAP), team management, access policies, and background job scheduling for repository indexing, web crawling, and third-party integration (GitHub/GitLab). The system is designed to be self-contained, running without external cloud dependencies, and can operate on consumer-grade GPUs. The architecture follows a monorepo pattern with Rust crates for core services (inference, indexing, git operations, download) and TypeScript/JavaScript for the agent and UI. The web dashboard uses GraphQL (Juniper) for API communication, while IDE plugins communicate via LSP. Data flows include: code completion requests from editors → agent → backend model server → response with postprocessing; chat queries → retrieval-augmented generation (RAG) pipeline using indexed code and documentation; and background jobs for syncing repositories, crawling web docs, and indexing commits/issues/PRs. External dependencies include SQLite (via SQLx), llama.cpp, tree-sitter, and various cloud provider APIs for OAuth. The codebase demonstrates strong engineering practices with modular design, extensive testing (unit tests, golden tests), and reusable patterns such as a background job abstraction, config merging system, and a postprocessing pipeline for code completions. However, the polyglot nature (Rust, TypeScript, Java, Kotlin) and multiple client implementations introduce maintenance complexity and potential drift. The repository is actively maintained with frequent releases and a large community (33k+ stars).
Keyviz is a free, open-source desktop application built with Tauri v2 (Rust backend) and React 19 (TypeScript frontend) that visualizes keyboard and mouse actions in real-time. It is designed for content creators, streamers, and educators who want to display keystrokes and mouse clicks during tutorials, presentations, or live streams. The application runs as a system tray utility with two windows: a transparent, always-on-top visualization window that renders animated keycaps and mouse indicators, and a separate settings window for customization. State is synchronized between windows via Tauri events using a custom Zustand middleware. The core input pipeline relies on a vendored fork of the `rdev` crate, which provides cross-platform global input listening (Windows low-level hooks, macOS CGEventTap, Linux X11 via XRecord). A background Rust thread captures keyboard and mouse events, detects a configurable toggle shortcut (default Shift+F10), and emits structured `input-event` payloads to the frontend. The frontend then displays animated key overlays with multiple keycap styles (minimal, laptop, lowprofile, pbt), mouse click rings, scroll indicators, and configurable animations (fade, zoom, float, slide). Users can filter which keys are shown, adjust alignment, margins, monitor selection, and customize colors, gradients, borders, and backgrounds. The settings UI provides a sidebar with tabs for General, Appearance, Keycap, Mouse, and About. It includes a theme toggle (light/dark), import/export of style configurations as JSON, and a Pro upsell. Persistence is handled via Tauri's plugin-store, with a custom storage adapter for Zustand. The project also includes a large set of reusable UI primitives built on Base UI, Radix UI, and React Aria Components, such as color pickers, gradient pickers, number scrubbers, shortcut recorders, and alignment selectors. While the codebase is well-architected and feature-rich, it carries some technical debt from the vendored rdev fork and a mix of UI libraries.
Weylus is a cross-platform application that turns tablets and smartphones into graphic tablets/touch screens for computers. It operates by running a local web server that serves a web page to the tablet's browser. The web page captures pointer, touch, stylus, keyboard, and wheel events via JavaScript PointerEvents and sends them over a WebSocket connection to the server. The server then simulates these input events on the host machine using platform-specific mechanisms: on Linux it uses the uinput kernel module for full stylus/touch/mouse/keyboard support; on macOS and Windows it falls back to the autopilot library for mouse/keyboard simulation and Windows-specific synthetic pointer injection for touch/pen. Simultaneously, Weylus captures the screen (or a selected window/monitor) using platform-native APIs: X11 shared memory (XShm) on Linux X11, PipeWire/GStreamer on Wayland, CoreGraphics on macOS, and DirectX via captrs on Windows. The captured frames are encoded into a fragmented MP4 H.264 video stream using a statically linked ffmpeg (libx264) with optional hardware acceleration (VAAPI, NVENC, VideoToolbox, MediaFoundation). The video stream is sent to the browser via the same WebSocket connection and played using the Media Source Extensions API. The application provides a GUI (built with FLTK) for configuration and a headless mode for automation. It supports access codes, custom input areas, and multiple simultaneous clients.
Psst is a native desktop Spotify client written in Rust, designed as a fast, lightweight alternative to the official Electron-based client. It implements the proprietary Spotify session protocol (Shannon cipher, Diffie-Hellman key exchange, Mercury messaging) to stream audio directly, bypassing the Web API for playback. The application is structured as a workspace with three crates: `psst-core` (session management, audio pipeline, caching, Last.fm scrobbling), `psst-gui` (Druid-based GUI with controllers, widgets, and Web API integration), and `psst-cli` (simple command-line player). The GUI features a full browsing experience (home, search, library, artist/album/playlist/show details, lyrics, recommendations, credits) with async data loading via a Promise pattern and remote image caching. The audio pipeline includes decryption, decoding (symphonia), resampling, normalization, and output via cpal or cubeb. Authentication uses OAuth2 with PKCE and supports user-provided Spotify Client IDs. The project is actively developed with rolling releases and cross-platform builds (Windows, macOS, Linux).
Gifski is a polished macOS application that converts video files into high-quality animated GIFs. The application uses a hybrid architecture: a SwiftUI frontend handles the user interface, video decoding (via AVFoundation), real-time preview compositing (via Metal shaders), and system integration (Share Extension, Shortcuts), while the core GIF encoding is performed by a companion Rust library (gifski-api) exposed through a C API. Key data flows involve decoding video frames, optional cropping/trimming/resizing, temporal denoising and palette quantization in the Rust engine, and finally writing the GIF output. The codebase demonstrates solid engineering with actor-based concurrency, comprehensive unit tests, and reusable UI components. However, it carries technical drift from macOS version workarounds and deprecated Rust API usage, and targets a future macOS version (26+) not yet generally available.
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.
C2Rust is a compiler toolchain that translates C99 code into unsafe Rust, then applies static analysis and refactoring to produce safer, more idiomatic Rust. The pipeline consists of four main stages: transpilation (c2rust-transpile) using a Clang-based AST exporter to produce initial Rust code; static analysis (c2rust-analyze) that infers pointer permissions and rewrites raw pointers to safe references using a Polonius-based borrow checker; refactoring (c2rust-refactor) which applies dozens of AST transformations (e.g., ownership splitting, macro collapsing, control flow reconstruction) to improve code quality; and postprocessing (c2rust-postprocess) that leverages large language models to transfer C comments and align definitions. The project also includes a cross-checking framework that instruments both C and Rust code to compare runtime behavior via hashing, a program dependence graph (PDG) builder for memory safety analysis, and dynamic instrumentation for event logging. It is primarily a CLI tool (c2rust) that orchestrates these components, with extensive integration tests on real-world C projects (nginx, curl, sqlite, ruby, python2, libxml2, zstd, lua). The codebase is substantial and actively maintained, but relies heavily on unstable rustc internal APIs and LLVM/Clang, introducing maintenance risk. Several components exhibit high craftsmanship and are reusable in isolation.
ggez is a lightweight, cross-platform 2D game framework for Rust, inspired by LÖVE. It provides a comprehensive set of subsystems for graphics, audio, input, filesystem, and timer management, all accessible through a central `Context` object. The framework abstracts over `winit` for windowing and events, `wgpu` for GPU rendering, `rodio` for audio playback, and `gilrs` for gamepad support. The architecture is modular, using sub-contexts (e.g., `GraphicsContext`, `AudioContext`) that can be individually accessed via `Has`/`HasMut` traits, allowing for custom context composition. The graphics pipeline supports 2D and optional 3D rendering (behind a feature flag) with shaders, meshes, images, instanced drawing, and text rendering via `glyph_brush`. The filesystem uses a virtual filesystem (VFS) layer with physical directories and zip archives, and configuration is handled via TOML serialization. Key entry points include `ContextBuilder` for initialization, `EventHandler` trait for the game loop (with `update`, `draw`, and input callbacks), and the `event::run()` function to start the application. Data flows are event-driven: user input is collected by `winit` and forwarded to the event handler, while rendering commands are batched and submitted to the GPU each frame. The framework also includes built-in coroutines for asynchronous tasks without futures. The repository contains over 40 examples demonstrating all major features, from simple drawing to complex 3D scenes and shader effects. ggez is actively maintained with a stable API, though some parts (e.g., 3D support, coroutine implementation) are less mature. The codebase demonstrates good engineering practices: builder patterns, resource caching, buffer sub-allocation, and a clear separation of concerns. It is suitable for hobby game development, educational projects, and rapid prototyping. The heavy reliance on evolving crates like `wgpu` and `winit` introduces some technical drift, but the framework keeps pace with updates.
Dura is a background process (daemon) that automatically snapshots uncommitted changes in Git repositories. It runs as a CLI tool with subcommands: `serve` (daemon loop), `watch`/`unwatch` (manage watched repos), `capture` (one-shot snapshot), `status` (daemon state), `kill` (stop daemon), and `metrics` (post-process logs with git diff stats). The architecture is a single Rust binary. On every polling cycle (5 seconds), it iterates watched repos, uses an optimized file-timestamp check (`PollGuard`) to detect changes efficiently, then creates a commit on a separate `dura/<HEAD_hash>` branch without modifying the working tree or HEAD. Snapshots include all tracked and untracked files. Configuration is stored as TOML in the user's config directory; runtime state (PID) is stored as JSON. Logging is structured JSON via a custom tracing layer. The design emphasizes non-disruptive operation and data safety.
Feldera is a real-time streaming SQL analytics platform built on the DBSP (Differential Dataflow) incremental computation engine. It compiles SQL into efficient incremental circuits that process streaming data with sub-millisecond latency, supporting complex queries including joins, aggregations, window functions, and recursive queries. The system is deployed as a pipeline manager (Rust) with a REST API, a web console (Svelte), a Python SDK, a dbt adapter, and a CLI tool. It integrates with numerous data sources and sinks (Kafka, Postgres, Delta Lake, Iceberg, HTTP, NATS, Redis, etc.) and provides fault tolerance via checkpointing and storage backends. The architecture is polyglot: a Java-based SQL compiler (using Apache Calcite) translates SQL to Rust code that runs on the DBSP runtime; the pipeline manager orchestrates compilation, deployment, and lifecycle; the web console offers a full UI for pipeline management, monitoring, and profiling. The codebase also includes a Spark SQL translator (felderize) and extensive benchmark suites (Nexmark, LDBC). While primarily an application, it has strong research roots in incremental view maintenance and formal monotonicity analysis, reflected in the compiler's optimization passes and the DBSP runtime's operator design. Key reusable components include the adapter library (connector traits), the Feldera types crate (shared configs and errors), the common UI component library, and the Python client SDK. The project is actively maintained with frequent commits, comprehensive CI/CD, and a growing test suite covering SQL semantics across many data types and edge cases.
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.
SongRec is a Linux desktop application (with CLI counterpart) that recognizes songs by sending audio fingerprints to Shazam's proprietary API. It is written in Rust and uses GTK4/libadwaita for the GUI. The architecture is a multi-threaded monolith with dedicated threads for microphone capture, audio processing (fingerprinting), HTTP communication with Shazam, and the GUI event loop. External dependencies include Shazam's servers (reverse-engineered endpoint), FFmpeg for audio file decoding, and platform audio backends (CPAL, PulseAudio, ALSA). The application provides persistent song history with CSV export, favorites, MPRIS integration, system tray icon, and configurable request intervals to handle rate limiting. The codebase shows good engineering practices with trait abstractions for audio backends, a typed channel message system, and versioned preferences. A separate Python reference implementation exists in the `python-version/` directory but is not the primary codebase.
Vigil is a microservices status page application written in Rust. It monitors distributed infrastructure services via multiple probe modes (HTTP, TCP, SSH, ICMP, push, script, local), aggregates their health status, and serves a public status page with real-time updates. It also sends alerts through various channels (Slack, Email, Twilio, Telegram, etc.) when services go down or recover. The architecture is a single binary with separate threads for probing, aggregation, and HTTP serving, using a shared in-memory state protected by RwLock. The codebase is well-structured, with clear separation of concerns: config reader, prober, notifier, aggregator, and responder modules. It includes an MCP server for AI agent integration. The project is actively maintained with recent commits and CI/CD pipelines.
Nova is a custom ECMAScript (JavaScript) engine implemented in Rust, designed as a modular, embeddable runtime. It features a full bytecode compiler and virtual machine, a custom mark-compact garbage collector with typed arena allocation, and a shape-based (hidden class) object model for property access optimization. The engine closely follows the ECMAScript specification, implementing built-in objects (Promise, Array, Map, Set, Proxy, Temporal, etc.), module loading, and concurrent agents. Its architecture emphasizes data-oriented design and safe GC interaction through Rust-specific patterns like `GcScope`/`NoGcScope` and the `Bindable` trait. The project includes a standalone CLI (`nova_cli`) for running scripts and a test262 harness, currently passing approximately 80% of the official test suite. While actively developed, the engine has known limitations: many features are incomplete or behind feature flags (e.g., RegExp lacks full compliance, Temporal has placeholders, and several built-ins have `todo!()` stubs). The codebase exhibits high craftsmanship in its GC safety system, bytecode compiler, and object shape mechanics, but relies heavily on unsafe Rust and manual lifetime management. Nova is primarily a research-grade implementation aimed at embedding and exploration, not yet production-ready for general use.
Proxelar is a high-performance, scriptable local traffic workbench for HTTP, HTTPS, and WebSocket debugging. It functions as a man-in-the-middle (MITM) proxy that can capture, inspect, intercept, modify, replay, and export traffic across multiple protocols (HTTP/1.1, HTTP/2, HTTP/3, WebSocket, SOCKS5, DNS, UDP, and WireGuard). The system is built as a Rust workspace with four crates: `proxelar-cli` (binary with TUI, terminal, and web GUI interfaces), `proxyapi` (core proxy engine with TLS interception, Lua scripting, addon system, route rules, and session export/import), `proxelar-proto` (transport-neutral HTTP core with sans-I/O parsers for HTTP/1 and HTTP/2), and `proxyapi_models` (shared data types for headers, sessions, and captured exchanges). It supports forward and reverse proxy modes, declarative JSON routing rules, Lua addons with SHA-256 verification, and exports to HAR, curl, and raw formats. The architecture is modular with clean dependency direction, extensive integration tests, performance benchmarks, and property-based testing. It is a production-quality developer tool comparable to mitmproxy or Charles Proxy, with a focus on performance, correctness, and extensibility.
Tempo is a custom Ethereum-compatible L1 blockchain built in Rust, offering a full node client with validator and follower modes. It diverges from standard Ethereum by introducing account abstraction (AA) as the default transaction type, 2D nonces, fee token mechanisms, and a novel consensus protocol using the Commonware framework with BLS threshold signatures and distributed key generation (DKG). The execution layer is based on revm (Rust EVM) with extensive use of hardfork-gated precompiled contracts (TIP20 tokens, stablecoin DEX, fee manager, storage credits, etc.), replacing many ERC standards. The architecture is actor-based, with separate crates for consensus, execution, payload building, transaction pool, and node orchestration. It includes a rich CLI, localnet dev environment, and extensive benchmarking and testing infrastructure. While primarily an application, it has strong research elements in consensus design, gas modeling, and economic experiments (e.g., fee AMM arb bot).