OpenTag is an open-source, self-hosted alternative to Claude for Slack. It enables users to run AI agents directly within Slack threads that can read conversations, answer queries, execute tools, and render rich, generative UI elements like tables and bar charts. Built on the CopilotKit SDK, it allows for complete ownership of the runtime and model without per-seat pricing or vendor lock-in.
- Open-source Slack AI agent
- Generative UI for rich message rendering
- Human-in-the-loop approval gates
- Multi-platform support via adapters (Discord, Telegram, WhatsApp)
- Self-hosted architecture for privacy and control
- Open-source Slack AI agent
- Generative UI for rich message rendering
- Human-in-the-loop approval gates
- Multi-platform support via adapters (Discord, Telegram, WhatsApp)
- Self-hosted architecture for privacy and control
Most users treat self-hosted large language models like a simple chat interface, effectively limiting their potential to basic question-and-answer tasks. The author suggests moving beyond this ChatGPT clone approach by integrating local AI as an always-on intelligence layer within your digital workflow. By treating the LLM as a backend engine rather than just a website, you can gain superior privacy and control while automating complex tasks across your files and devices.
"Prove AI is a self-hosted solution designed to accelerate GenAI performance monitoring. It allows AI engineers to capture, customize, and monitor GenAI metrics on their own terms, without vendor lock-in. Built on OpenTelemetry, Prove AI connects to existing OpenTelemetry pipelines and surfaces meaningful metrics quickly.
Key features include a unified web-based interface for consolidating performance metrics like token throughput, latency distributions, and service health. It enables faster debugging, improved time-to-metric, and better measurement of GenAI ROI. The platform is open-source, free to deploy, and offers full control over telemetry data."
Key features include a unified web-based interface for consolidating performance metrics like token throughput, latency distributions, and service health. It enables faster debugging, improved time-to-metric, and better measurement of GenAI ROI. The platform is open-source, free to deploy, and offers full control over telemetry data."
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.
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).
DispatchMail is an open source locally run (though currently using OpenAI for queries) AI-powered email assistant that helps you manage your inbox. It monitors your email, processes it with an AI agent based on your prompts, and provides a (locally run) web interface for managing drafts/responses, and instructions.
This article details how to enhance the Paperless-ngx document management system by integrating a local Large Language Model (LLM) like Ollama. It covers the setup process, including installing Docker, Ollama, and configuring Paperless AI, to enable AI-powered features such as improved search and document understanding.
