The awesome collection of OpenClaw Skills. Formerly known as Moltbot, originally Clawdbot.
Moltbot is a self-hosted AI assistant that runs on your machines, connects to messaging platforms, performs actions, and maintains persistent memory. It was renamed from Clawdbot due to trademark concerns.
* **What it is:** Moltbot is an AI assistant designed to run locally on your machines (macOS, Windows, Linux) offering privacy and customization. It differs from cloud-based services.
* **How it works:** It connects to various messaging platforms (WhatsApp, Telegram, Slack, etc.) allowing interaction via chat.
* **Capabilities:** Moltbot can perform actions beyond answering questions – automating tasks, running scripts, scheduling jobs, browsing the web, and integrating with other services via plugins.
* **Key Feature: Persistent Memory:** Unlike many bots, Moltbot remembers past interactions, providing a tailored and consistent experience.
* **Name Change:** The project was renamed from Clawdbot to Moltbot due to trademark concerns with Anthropic’s Claude.
* **What it is:** Moltbot is an AI assistant designed to run locally on your machines (macOS, Windows, Linux) offering privacy and customization. It differs from cloud-based services.
* **How it works:** It connects to various messaging platforms (WhatsApp, Telegram, Slack, etc.) allowing interaction via chat.
* **Capabilities:** Moltbot can perform actions beyond answering questions – automating tasks, running scripts, scheduling jobs, browsing the web, and integrating with other services via plugins.
* **Key Feature: Persistent Memory:** Unlike many bots, Moltbot remembers past interactions, providing a tailored and consistent experience.
* **Name Change:** The project was renamed from Clawdbot to Moltbot due to trademark concerns with Anthropic’s Claude.
This article details how to combine Clawdbot with Docker Model Runner (DMR) to build a privacy-focused, high-performance personal AI assistant with full control over data and costs. It covers configuration, benefits, recommended models, and how to get involved in the ecosystem.
Documentation and downloads for Proxmox Mail Gateway, including admin guides for versions 9.x, 8.x, and 7.x, as well as a datasheet.
This article details how to set up a custom voice pipeline in Home Assistant using free self-hosted tools like Whisper and Piper, replacing cloud-based services for full control over speech-to-text and text-to-speech processing.
A tutorial on building a private, offline Retrieval Augmented Generation (RAG) system using Ollama for embeddings and language generation, and FAISS for vector storage, ensuring data privacy and control.
1. **Document Loader:** Extracts text from various file formats (PDF, Markdown, HTML) while preserving metadata like source and page numbers for accurate citations.
2. **Text Chunker:** Splits documents into smaller text segments (chunks) to manage token limits and improve retrieval accuracy. It uses overlapping and sentence boundary detection to maintain context.
3. **Embedder:** Converts text chunks into numerical vectors (embeddings) using the `nomic-embed-text` model via Ollama, which runs locally without internet access.
4. **Vector Database:** Stores the embeddings using FAISS (Facebook AI Similarity Search) for fast similarity search. It uses cosine similarity for accurate retrieval and saves the database to disk for quick loading in future sessions.
5. **Large Language Model (LLM):** Generates answers using the `llama3.2` model via Ollama, also running locally. It takes the retrieved context and the user's question to produce a response with citations.
6. **RAG System Orchestrator:** Coordinates the entire workflow, managing the ingestion of documents (loading, chunking, embedding, storing) and the querying process (retrieving relevant chunks, generating answers).
1. **Document Loader:** Extracts text from various file formats (PDF, Markdown, HTML) while preserving metadata like source and page numbers for accurate citations.
2. **Text Chunker:** Splits documents into smaller text segments (chunks) to manage token limits and improve retrieval accuracy. It uses overlapping and sentence boundary detection to maintain context.
3. **Embedder:** Converts text chunks into numerical vectors (embeddings) using the `nomic-embed-text` model via Ollama, which runs locally without internet access.
4. **Vector Database:** Stores the embeddings using FAISS (Facebook AI Similarity Search) for fast similarity search. It uses cosine similarity for accurate retrieval and saves the database to disk for quick loading in future sessions.
5. **Large Language Model (LLM):** Generates answers using the `llama3.2` model via Ollama, also running locally. It takes the retrieved context and the user's question to produce a response with citations.
6. **RAG System Orchestrator:** Coordinates the entire workflow, managing the ingestion of documents (loading, chunking, embedding, storing) and the querying process (retrieving relevant chunks, generating answers).
Mail-Archiver is an open-source, self-hosted email archiving system that allows you to take back ownership of your communication data. It's built with .NET and PostgreSQL, offering a responsive web interface for managing and backing up emails from multiple accounts.
Inference Snaps are generative AI models packaged for efficient performance on local hardware, automatically optimizing for CPU, GPU, or NPU.
SPINQ Triangulum Ⅱ is a cost-effective, maintenance-free, and highly stable 3-qubit desktop NMR quantum computer for quantum computing education, demonstrations, and research. It supports any 3-qubit quantum algorithm, open hardware-level pulse sequence editing, and features classical-quantum hybrid programming.
This article details how the author created a dashboard to manage their self-hosted applications, focusing on the use of Homepage and its benefits for organization and access to service information.
