I’ve been playing around a lot with Ollama, an open source project that allows one to run LLMs locally on their machine. It’s been fun to mess around with. Some benefits: no rate-limits, private (e.g., trying to create a pseudo therapy bot, trying to simulate a foul mouthed smarmy sailor, or trying to generate ridiculous fake news articles about a Florida Man losing a fight to a wheel of cheese), and access to all sorts of models that get released.
I decided to try my hand at creating a simplified interface for interacting with it. The result: Super Simple ChatUI.
It seems like every year, late in the summer or early in the fall, the air in the Bay Area fills with thick smoke from raging infernos happening around northern California. The air is hazardous to breath, preventing you from taking kids to the park, walking your dog or even opening your windows.
Last year, we made the wise decision to purchase an air purifier, which admittedly, looks like a giant iPod shuffle.
As fires continue to burn around these parts, we’ve started to rely on air quality data from PurpleAir, which monitors air quality data from a series of IoT sensors that people can purchase for their homes or businesses.
You can view a map featuring realtime data collected from their sensors. Here in the Bay Area, the sensors are quite ubiquitous and can give a more realistic pictures of air quality near your home.
For example, here is the current air quality around the Bay Area from PurpleAir while I write this post.
For comparison, here is the current air quality map for Bay Area Air Quality Management District (BAAQMD), which we used to reference when trying to determine local air quality.
The fidelity you get from PurpleAir is pretty amazing.
Knowing this, I decided to write a Node app that periodically queries PurpleAir for air quality data from a sensor located a few blocks from our house. It continuously runs on a Raspberry Pi setup in our entertainment center and sends me a text message to close our windows when the AQI crosses above 100.
I’ve made the source code available on Github. Check it out!
