Milo is a self-hosted Discord bot designed for community operations, support, and lightweight moderation. It features a modular architecture using discord.py cogs to separate concerns like AI chat, economy, farming, games, moderation, and utility functions. The bot uses SQLite for persistent storage of per-guild data (economy balances, farming progress, reminders) and includes mechanisms to prevent race conditions in economy operations via per-guild locking. Configuration is managed through environment variables and a local config.json file, with support for guild-specific overrides for features like AI chat API keys. The bot includes AI-assisted chat with optional web search, a per-guild economy with jobs and gambling, a farming progression system, mini-games (Tic-Tac-Toe, guess the number), social interaction commands (hug, slap), media fetching (memes, cat/dog pictures), and moderation tools (warnings, automod for invites/links/bad words). Utility commands cover reminders (with recurring and snooze options), AFK status, server information, and more. Milo emphasizes privacy and control for community owners, with all data stored locally and no required external services beyond Discord for core functionality.
Alumnet is a full-stack, AI-powered alumni exhibition and management system designed for school history museums. It consists of three workspaces: a touch-screen kiosk frontend (client) built with Vue 3, Three.js, and Tailwind CSS; an admin dashboard (admin) using Vue 3 and Element Plus; and a backend server (server) built with Express, TypeScript, PostgreSQL (with pgvector), and Qdrant for vector search. The system provides alumni CRUD, AI-powered natural language search via RAG, 3D galaxy visualization, timeline corridor, interactive message boards, video greetings, donation management, and comprehensive admin controls including content audit, backup/restore, and data import. It features JWT authentication with role-based access, privacy data masking, rate limiting, and a layered architecture (routes → services → repositories). The codebase includes extensive testing (unit, integration, property-based with fast-check) and is deployable via Docker Compose or PM2. While primarily an application, it incorporates data engineering patterns through its RAG pipeline and knowledge base management.
CtxPort is a browser extension and companion documentation website that lets users copy AI conversations (from ChatGPT, Claude, Gemini, DeepSeek, Grok, Doubao, GitHub) as structured Markdown Context Bundles. It solves 'context rot' by enabling portable, lossless migration of conversations between AI tools without uploading data to third parties. The architecture is a modern monorepo using pnpm workspaces and Turborepo, with separate packages for schema validation (Zod), markdown serialization, plugin-based extraction, and shared UI components. The browser extension (Manifest V3, WXT, React 19, Tailwind CSS 4) injects copy buttons and sidebar list icons into supported platforms, while the documentation website (Next.js + Nextra) provides multilingual (en/zh) user guides and feature descriptions. All processing is client-side; no data is uploaded. The codebase demonstrates strong engineering practices including a plugin system for extensibility, a strategy pattern for content flatteners, clean separation of concerns, and comprehensive CI/CD pipelines. Some fragility exists in DOM selectors and platform APIs, but the architecture is well-structured for adaptation.
GeminiProChat is a minimal web UI for interacting with Google's Gemini Pro API. It is built as a server-side rendered application using Astro with SolidJS for interactive components. The application provides a chat interface where users can send messages and receive streaming responses from the Gemini model. It supports features like system role settings, temperature control, message history persistence, password protection, and PWA capabilities. The architecture consists of Astro pages (index, password), API endpoints (auth, generate), and SolidJS components for the chat generator, message rendering with markdown and code highlighting, and a theme toggle with view transitions. External dependencies include the Google Generative AI SDK (patched to use v1beta API), markdown-it for rendering, and Zag.js for slider components. The application can be deployed on Vercel, Netlify, Railway, Zeabur, or via Docker, with environment variables for API key, base URL, password, and model name.
PandaWiki is a production-ready, self-hosted AI knowledge base and wiki platform. The actual codebase implements document management with versioned releases, RAG-based Q&A and search, AI-assisted document summarization and editing, multi-source content ingestion (URL, RSS, Sitemap, Notion, Feishu, DingTalk, Confluence, EPUB, Wiki.js, Yuque, Siyuan, etc.), a customizable public landing page builder, analytics, comments, contribution review, and multi-channel bot integrations for WeChat, WeCom, DingTalk, Feishu/Lark, and Discord. It is not a data research repository, but it contains substantial document-ingestion and analytics pipelines that feed the RAG system. The architecture is a full-stack monolith-style application split across a Go backend (Echo + GORM + PostgreSQL + Redis + MinIO/S3 + NATS JetStream), a React admin panel (Vite/MUI/Redux), a Next.js public site, and a pnpm workspace of shared UI/icon/theme packages. The Go backend follows a layered design with handlers, use cases, repositories, middleware, and dependency injection via generated Wire code. A standalone MQ consumer process handles async RAG document updates, crawler exports, and cron-driven statistics aggregation. The public app is served separately from the admin panel, with an embeddable widget and an H5 chat interface. In production, documents are imported through crawler tasks or direct upload, converted to HTML/Markdown, chunked, and upserted into a configurable vector/RAG service. User chat requests rewrite the query, retrieve ranked chunks, and stream LLM responses via SSE with citations. Statistics are rolled up hourly into PostgreSQL. Authentication supports local accounts, JWT sessions, GitHub OAuth, LDAP, CAS, and enterprise SSO. Feature availability is gated by commercial license editions, with a private pro submodule adding enterprise functionality.
DesignFlow Kit is an open-source, full-stack web application that provides an AI-powered UX/Product Design toolkit. It features 8 AI chat assistants for various design workflows (research, personas, journey maps, wireframes, testing, workshops, information architecture, and general analysis), a Kanban-style sprint board with drag-and-drop and voice recording, a dashboard for progress tracking, a catalog of 41+ integrations, and pre/post-design kits. The frontend is built with React 18, TypeScript, Tailwind CSS, and shadcn/ui components, using Wouter for routing and TanStack Query for data fetching. The backend is an Express.js server with Drizzle ORM and PostgreSQL (with in-memory fallback), integrating with OpenAI GPT-4o for streaming chat, Whisper for transcription, and Resend for email-based OTP authentication. The application is deployed as a monolith via Replit-specific integrations, though the core stack is portable. The codebase is well-structured with clear separation of concerns, but suffers from significant duplication across the 8 chat page components and relies heavily on static data files for tool/integration catalogs. No automated tests are present, and the OTP flow includes a visible demo code in development mode, signaling some immaturity. Overall, it is a functional and feature-rich tool with moderate reusability of its core components.
KEA Research is a full-stack multi-provider AI research chat application. The backend is a FastAPI service backed by SQLite/SQLAlchemy, exposing chat, admin, health, user, and TTS endpoints. The frontend is an Astro + TypeScript single-page app that stores chat history and pipeline data in IndexedDB, with a Bootstrap-based UI. Deployment is containerized via Docker Compose with nginx handling TLS and SSE-friendly proxying. The core product logic is a four-step research pipeline: initial multi-provider responses, Mixture-of-Agents refinement, peer evaluation, and a final KEA synthesis with facts, consensus, flagged claims, rankings, and confidence scores. A web admin panel manages providers, models, provider sets, users, and app settings, while the frontend supports research layers, bookmarks, notes, drafts, export, attachment ingestion, and text-to-speech via a Kokoro ONNX model.
This repository contains Toxic-MD, a WhatsApp multi-device bot built on Node.js using the Baileys library (reverse-engineered WhatsApp Web API). It is a plugin-based application that handles incoming WhatsApp messages, processes commands from over 200 plugins organized by category (General, Groups, Owner, AI, Downloads, Editing, Settings, etc.), and executes actions such as group administration (anti-link, anti-badword, welcome messages, warn/kick), media downloads (YouTube, TikTok, Instagram), AI chat (via Groq/Llama), image/sticker editing, and privacy toggles. The bot maintains persistent settings in a PostgreSQL database (with a JSON file fallback), including global and per-group configurations, warn limits, banned users, and conversation history for AI context. It runs as a long-lived process with auto-reconnection, an Express health server, and supports deployment on Heroku, Railway, and traditional Node.js hosts. Architecturally, the bot is a monolith with a central message router in `src/toxic.js`, a dynamic plugin loader in `handlers/commandHandler.js`, and a set of background features (`features/`) that listen to events like group joins, sticker sends, and anti-spam triggers. The codebase is moderately complex, using global state objects (e.g., `_toxicMsgCounts`, `lidPhoneCache`) and patching the Baileys library at startup to adjust timeouts and event buffering. External dependencies include Express, Groq API, various download services (Nexray, qu.ax, uguu.se), and media utilities (ffmpeg, sharp, wa-sticker-formatter). The bot's command structure is consistent: each plugin exports `name`, `aliases`, `description`, and a `run(context)` function, enabling extensibility. However, the code exhibits several quality issues: no test suite, heavy use of global variables, hardcoded developer phone numbers, repetitive toggle commands, and obfuscated API keys in `keys.js`. Despite these shortcomings, the bot is functional and widely forked (1587 forks), indicating practical utility in the WhatsApp bot community.