The Firmware Modification Kit (FMK) is a CLI-based application toolkit designed for the extraction, analysis, and rebuilding of embedded Linux firmware images, primarily for routers and IoT devices. It functions as a monolithic suite of shell scripts, C/C++ binaries, and Python utilities that orchestrate a workflow: ingest a firmware binary, extract its filesystems (SquashFS, CramFS, YAFFS2), allow modification of contents or web UIs, and rebuild the image. The architecture relies heavily on bundled legacy versions of open-source tools, specifically Binwalk 2.1.1, multiple versions of SquashFS tools (2.0 through 4.2), and the 7-Zip/LZMA SDK. While functionally effective for its niche in embedded security and reverse engineering, the codebase exhibits significant technical debt due to massive code duplication across versioned forks of the same libraries and reliance on outdated dependencies (Python 2, legacy Windows API calls).
This is a Torch7 implementation of the seminal neural style transfer algorithm (Gatys et al., 2015). It provides a command-line interface for artistic style transfer: given a content image and one or more style images, it optimizes a target image to simultaneously match the content of the content image and the statistical style (Gram matrix features) of the style image, using a pre-trained VGG-19 CNN. The single-file script (`neural_style.lua`) defines custom nn modules for content loss, style loss, Gram matrix computation, and total variation regularization. It supports L-BFGS and Adam optimizers, multiple backends (nn, cuDNN, clnn), multi-GPU splitting, and various tunable parameters (layer selection, style scale, weight blending). The code is well-structured for its era and demonstrates solid understanding of Torch7's autograd mechanics, but it is a monolithic research prototype with no test suite and heavy dependency on now-deprecated libraries (Torch7, loadcaffe, cudnn.torch). Its architecture is not modular enough for easy extraction as a library. The repository is historically significant but effectively obsolete for practical use in modern deep learning ecosystems.
Baton is a CLI tool for controlling Spotify playback with an integrated TUI for browsing and selecting music. The application uses Cobra for command structure, Viper for configuration, and the gocui library for the terminal interface. It interacts with the Spotify Web API to manage playback, search for content, and handle user library operations. The architecture separates API interactions (api package), command-line interface (cmd package), and TUI components (ui package). While functional, the codebase shows signs of aging due to outdated dependencies and tight coupling in some areas, particularly the API package's reliance on Viper for token management.
This repository serves as the issue tracker for the Postico macOS PostgreSQL client application. It contains minimal source code: a Python script for exporting Postico's query history from its SQLite database, plus issue templates and documentation. The export_query_history.py script connects to Postico's local database, retrieves query history with connection nicknames and timestamps, handles both raw and compressed query text, and outputs formatted SQL to stdout. The repository is primarily used for community bug reports and feature requests, with the export script provided as a utility for users to share their query history when reporting issues.
The resume-cli is a command-line tool for managing JSON Resume files. It provides commands to initialize, validate, export (to HTML/PDF), and serve a resume locally. The codebase follows a modular structure with separate modules for each command (init, validate, export, serve) and shared utilities for input handling, schema loading, validation, and theme-based rendering. The application uses the commander package for CLI argument parsing, relies on external themes for rendering, and supports multiple input formats (JSON, YAML, directory via quaff). Despite being archived per the README (development moved to a monorepo), the code demonstrates clean separation of concerns, proper error handling, and test coverage for core functionalities.
ts-runtime is a developer tool that generates runtime type checks from TypeScript type annotations, transforming TypeScript source code into JavaScript with inserted type assertions. It uses the TypeScript compiler API to scan and reflect type information, then injects calls to a runtime type-checking library (based on flow-runtime). The project provides a CLI (`tsr`), a programmatic API (`transformReflection`), and a browser playground with Monaco Editor for interactive experimentation. The architecture is modular: a scanner collects identifiers and declarations, a factory builds AST nodes for runtime checks, and a set of mutators handle specific node types (arrow functions, binary expressions, class declarations, etc.). The runtime library (`ts-runtime.lib.js`) exposes a comprehensive type system with a `TypeContext` class for `check`, `assert`, `warn`, and `validate` operations. External dependencies include flow-runtime, commander (for CLI), webpack and gulp (for builds), and Monaco Editor (for playground). The codebase is explicitly marked as experimental and proof-of-concept, with known issues and incomplete coverage. It is not production-ready.
This repository contains a monolithic Python CLI tool for downloading Udemy courses, including DRM-protected video content. The core logic in main.py orchestrates authentication (bearer token or browser cookies), fetches course curriculum via Udemy's private API, and downloads lectures, subtitles, assets, quizzes (normal and coding assignments), practice assignments, and role-play exercises. It relies heavily on external tools: yt-dlp for DASH/HLS segment downloading, aria2c for accelerated downloads, ffmpeg for muxing and video transcoding, and shaka-packager for DRM decryption. The code is functional but lacks modularity, with global state, sparse error handling, and a monolithic main script. The standout component is mp4parse.py, a well-structured MP4 box parser that extracts PSSH headers for Widevine key identification, which could be extracted and reused independently. The project is no longer actively maintained as stated in its README, and some dependencies may be outdated.
The Web Tooling Benchmark is a performance measurement suite designed to evaluate JavaScript-related workloads in common web developer tools. It executes benchmarks for tools like Babel, TypeScript, UglifyJS, and others, measuring their throughput in runs per second. The architecture consists of a Node.js CLI interface and a browser-compatible bundle, both driven by a benchmark suite that runs each tool's core functionality on standardized third-party code samples. Key entry points are src/cli.js (for Node.js) and src/bootstrap.js (for browser). The suite reads input files from the third_party directory, processes them via each tool's API, and aggregates results using geometric mean. External dependencies include the benchmarking framework, various JavaScript tools (acorn, babel, typescript, etc.), and utilities for file system virtualization and output formatting.
PatchaPalooza is a CLI tool for fetching, storing, and analyzing Microsoft Patch Tuesday security update data from the MSRC CVRF API. It downloads monthly bulletins as JSON files into a local cache (msrc_data/) and provides commands to query CVEs, filter by CVSS score, and display statistics with colored output. The repository also bundles a large historical dataset of MSRC bulletins (1999–2026) including both Windows and Azure Linux (Mariner) advisories. The tool is a single monolithic Python script with no tests, no modularization, and minimal error handling. The README explicitly states it is no longer actively maintained and has been superseded by a web application. While functional for basic vulnerability lookup, the code is tightly coupled to the MSRC API schema and lacks reusable components. The bundled data is valuable for security research, but the tool itself is not production-grade.
Wraith is a Ruby CLI tool for visual regression testing. It captures screenshots of web pages at multiple viewport widths using headless browsers (PhantomJS, CasperJS, or Chrome via Selenium), compares them with ImageMagick to generate pixel-level diffs, and produces an HTML gallery of results. The tool supports two primary workflows: comparing two different domains (capture mode) and tracking changes over time on a single domain (history mode). It can also spider a site to automatically discover paths. The architecture is a monolithic Ruby gem with a Thor-based CLI, where each step (screenshot capture, cropping, comparison, thumbnail generation, gallery creation) is handled by separate classes. The codebase is functional but tightly coupled to its specific workflow and external dependencies like ImageMagick and PhantomJS. The repository is explicitly deprecated and no longer maintained, with outdated dependencies (Ruby 2.4.1, PhantomJS) and no recent activity.
This repository is a CLI tool (@webdevtoday/claude-agents) that enhances Claude Code with specialized AI agents for software development tasks. It provides 16 domain-specific agents (code-reviewer, test-runner, debugger, devops-engineer, etc.) that can be executed independently or chained together in workflows. The system integrates with Claude Code through hooks, commands, and MCP tools, featuring a memory system for agent coordination, voice notifications for task completion, and context-aware adaptation to project structures (particularly context-forge projects). The architecture follows a modular command pattern with core utilities for configuration, TTS, hooks, and agent management. The project has been explicitly archived as of June 2026 because Claude Code now provides native subagents, skills, hooks, and plugins functionality that replicates this tool's features. While the code demonstrates solid engineering patterns in several areas, it is no longer maintained and has been superseded by native capabilities in the platform it was designed to enhance.
This repository contains a monolithic Python 2 CLI application for brute-force cracking of Facebook accounts. It provides a menu-driven interface that allows a user to log in with a Facebook access token or credentials, then attempt to crack the passwords of friends or specified user IDs using a hardcoded set of common passwords (e.g., '786786', 'Pakistan', first_name+786). The tool also includes features for extracting friend IDs, emails, and phone numbers via the Facebook Graph API, as well as automating post deletion, friend request acceptance, and unfriending. The architecture is tightly coupled, with global variables, repetitive code, and no separation of concerns. The main entry point is `wasi.py`, which calls `tlogin()` for authentication and then delegates to `.wasii.py` for the main menu and cracking logic. A secondary module `.opt.py` offers a variant with user-specified password lists. The code relies on deprecated Python 2 libraries (mechanize, urllib) and hardcoded Facebook API tokens, making it fragile and insecure. There are no tests, no modular design, and the code exhibits poor craftsmanship with minimal error handling and no reusability. The repository is a typical example of a low-quality hacking script popular in underground communities, with high technical drift due to Python 2 deprecation and likely outdated API endpoints.