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.
This article explains how MQTT (Message Queuing Telemetry Transport) can be used to create a more streamlined and organized smart home by enabling local communication between devices, reducing reliance on cloud services, and simplifying automation.
Seeed Studio has introduced two new mmWave sensor kits, designed for edge AI detection of falls and heartbeats respectively. These kits are powered by the company's XIAO ESP32C6 development board and pre-installed edge-AI algorithms.
