This article details the process of running a personal AI assistant on a low-cost microcontroller. It covers the use of Ollama for running large language models (LLMs) locally and MimicLaw for optimizing the model for resource-constrained devices. The author shares their experience with porting and running the models, along with the challenges and solutions encountered.
This article discusses how to effectively prompt local Large Language Models (LLMs) like those run with LM Studio or Ollama. It explains that local LLMs behave differently than cloud-based models and require more explicit and structured prompts for optimal results. The article provides guidance on how to craft better prompts, including using clear language, breaking down tasks into steps, and providing examples.
This post reviews two LLM options in Emacs - Ellama and gptel - and how to set them up, including adding models from OpenRouter and Ollama.
A "Clawdbot" in every row with 400 lines of Postgres SQL. An open-source Postgres extension that introduces a claw data type to instantiate an AI agent - either a simple LLM or an "OpenClaw" agent - as a Postgres column.
This article details the setup and initial testing of Goose, an open-source agent framework, paired with Ollama and the Qwen3-coder model, as a free alternative to Claude Code. It covers the installation process, initial performance observations, and a comparison to cloud-based solutions.
This article provides a comprehensive guide on implementing the Model Context Protocol (MCP) with Ollama and Llama 3, covering practical implementation steps and use cases.
The ollama 0.14-rc2 release introduces experimental functionality allowing LLMs to use tools like bash and web searching on your system, with safeguards like interactive approval and command allow/denylists.
This article details how to build powerful, local AI automations using n8n, the Model Context Protocol (MCP), and Ollama, aiming to replace fragile scripts and expensive cloud-based APIs. These tools work together to automate tasks like log triage, data quality monitoring, dataset labeling, research brief updates, incident postmortems, contract review, and code review – all while keeping data and processing local for enhanced control and efficiency.
**Key Points:**
* **Local Focus:** The system prioritizes running LLMs locally for speed, cost-effectiveness, and data privacy.
* **Component Roles:** n8n orchestrates workflows, MCP constrains tool usage, and Ollama provides reasoning capabilities.
* **Automation Examples:** The article showcases several practical automation examples across various domains, from DevOps to legal compliance.
* **Controlled Access:** MCP limits the model's access to only necessary tools and data, enhancing security and reliability.
* **Closed-Loop Systems:** Many automations incorporate feedback loops for continuous improvement and reduced human intervention.
**Key Points:**
* **Local Focus:** The system prioritizes running LLMs locally for speed, cost-effectiveness, and data privacy.
* **Component Roles:** n8n orchestrates workflows, MCP constrains tool usage, and Ollama provides reasoning capabilities.
* **Automation Examples:** The article showcases several practical automation examples across various domains, from DevOps to legal compliance.
* **Controlled Access:** MCP limits the model's access to only necessary tools and data, enhancing security and reliability.
* **Closed-Loop Systems:** Many automations incorporate feedback loops for continuous improvement and reduced human intervention.
This article details how to build a 100% local MCP (Model Context Protocol) client using LlamaIndex, Ollama, and LightningAI. It provides a code walkthrough and explanation of the process, including setting up an SQLite MCP server and a locally served LLM.
The series of articles by Adam Conway discusses how the author replaced cloud-based smart assistants like Alexa with a local large language model (LLM) integrated into Home Assistant, enabling more complex and private home automations.
1. **Use a Local LLM**: Set up an LLM (like Qwen) locally using tools such as Ollama and OpenWeb UI.
2. **Integrate with Home Assistant**:
- Enable Ollama integration in Home Assistant.
- Configure the IP and port of the LLM server.
- Select the desired model for use within Home Assistant.
3. **Voice Processing Tools**:
- Use **Whisper** for speech-to-text transcription.
- Use **Piper** for text-to-speech synthesis.
4. **Smart Home Automation**:
- Automate complex tasks like turning off lights and smart plugs with voice commands.
- Use data from IP cameras (via Frigate) to control external lighting based on presence.
5. **Hardware Recommendations**:
- Use Home Assistant Voice Preview speaker or DIY alternatives using ESP32 or repurposed microphones.
1. **Use a Local LLM**: Set up an LLM (like Qwen) locally using tools such as Ollama and OpenWeb UI.
2. **Integrate with Home Assistant**:
- Enable Ollama integration in Home Assistant.
- Configure the IP and port of the LLM server.
- Select the desired model for use within Home Assistant.
3. **Voice Processing Tools**:
- Use **Whisper** for speech-to-text transcription.
- Use **Piper** for text-to-speech synthesis.
4. **Smart Home Automation**:
- Automate complex tasks like turning off lights and smart plugs with voice commands.
- Use data from IP cameras (via Frigate) to control external lighting based on presence.
5. **Hardware Recommendations**:
- Use Home Assistant Voice Preview speaker or DIY alternatives using ESP32 or repurposed microphones.
