Featured image of post Tasmotizer: Try to Flash a WiFi LED Light with a Custom Firmware

Tasmotizer: Try to Flash a WiFi LED Light with a Custom Firmware

My IOT enabled home grows sensor by sensor. In one of the last articles, I explained which new devices I want to integrate: Power switches and Wi-Fi lightbulbs. Starting with power switches, I made the experience of disassembling, soldering connection lines, and manual flashing. It required some dexterity to solder the connecting pins to the board, but it was possible. Then, manually flashing them with Tasmotizer, and they were available in my Home Assistant application.

Featured image of post Tasmotizer: How to Flash a Sonoff S26R2 Power Switch with Custom Firmware

Tasmotizer: How to Flash a Sonoff S26R2 Power Switch with Custom Firmware

In your smart home, controlling and access sensors and devices is an essential capability. From a functional viewpoint, smart power switches just offer a remote control for enabling or disabling power. But from a smart home perspective, these devices allow powerful and creative use cases, like time-based scheduling of other sensors, or the activation and deactivation based on events.

Featured image of post Home Assistant: Overview about Smart Light and Smart Power Protocol

Home Assistant: Overview about Smart Light and Smart Power Protocol

What started with a ESP8266 board and DHT11 temperature sensor to store data in a timeseries DB evolved to a Home Assistant based environment with air quality, temperature, and movement sensors and more than 5 boards. In my journey to extend the home automation systems and further learn about the fascinating and rich Home Assistant ecosystem, I want to add smart lights and smart power switches too. But which protocols do these (proprietary) devices use? Can they be integrated with home assistant?

Featured image of post Home Assistant and ESP Home: How to use MQTT Integration for Dynamic Device Configuration

Home Assistant and ESP Home: How to use MQTT Integration for Dynamic Device Configuration

An MQTT message broker is the standard way to transfer information between different IOT devices. Home Assistant has a powerful MQTT integration for receiving, sending, and processing data. One powerful feature is to use MQTT messages as configuration instructions. The basic idea: Retained messages on the queue are read by devices to change their configuration.

Featured image of post IOT: Dynamic Deep Sleep Configuration for ESP8266 and ESP32 Boards

IOT: Dynamic Deep Sleep Configuration for ESP8266 and ESP32 Boards

Especially when running your ESP boards on battery, conserving energy is an important aspect. ESPHome controlled boards can be configured for deep sleep, in its most simple form two values for sleep duration and run duration. Yet there are some subtleties to regard: Ensure the run time is appropriate for making WIFI connections, activate all connected sensors, and send data e.g. via MQTT. Once configured, the deep sleep behavior repeats. What will you do if you need to change it? Do you get the sensor, connect it via USB, and upload a new program? Do you hope for an OTA update during the limited time that the sensor is online?

Featured image of post Home Assistant: How to Fix API Disconnection and False Positive Sensor Readings with ESP8266 Boards

Home Assistant: How to Fix API Disconnection and False Positive Sensor Readings with ESP8266 Boards

When using Home Assistant to integrate with ESPHome managed devices, connection timeouts can occur. Normally this means that the device could not be reached temporarily only, and will eventually get connected again. However, a particular behavior creates quite a disturbance: Upon reconnection, all configured sensors are activated immediately. This triggers for example motion sensors, sending a false alert. If you have sophisticated automations running, working with false positives is quite a nuisance. So, how can we prevent this?

Featured image of post Universal Personal Assistant with LLMs

Universal Personal Assistant with LLMs

Large Language Models are a unique emergent technology of 2024. Its fascinating capabilities to produce coherent text influences many areas and use cases, especially pushing the boundaries of classic natural language tasks.

Featured image of post LLM Evaluation: Which LLM to use for developing a personal assistant?

LLM Evaluation: Which LLM to use for developing a personal assistant?

In my blog series, I covered several aspects of LLMs: Understanding their evolution, investigating libraries, researching, and trying how LLMs can be fine-tuned. Ultimately, I want to use LLMs for a personal assistant that has access to documents and databases, providing a natural language interface to books and sensor data. The explosion of open-sourced LLMs beginning in 2023 lead to exponential growth that now in earl 2024 culminates into one question: Which model to choose for which application type?

Featured image of post Fine-Tuning LLMs: Comparison of Collab, Kaggle and 6 other Platforms

Fine-Tuning LLMs: Comparison of Collab, Kaggle and 6 other Platforms

Fine-Tuning and evaluating LLMs require significant hardware resources, mostly GPUs. Building an on-premise machine learning computer is always an option. But unless you are running this machine 24-7, rented infrastructure for a short period of time may be the better option. And additionally, you get access to scalable hardware for the workload type: Why stop with a single 24GB GPU when you can have 10?