36 lines
2.3 KiB
Markdown
36 lines
2.3 KiB
Markdown
This side project log covers work done from 3/27/2023 - 4/29/2023
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This side project log is a bit late due to it being a busy month of school, but today is my last day!
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## SEPTA Site
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This week, I published SEPTA Site on Github, you can find it here: [github.com/nickorlow/septa-site](https://github.com/nickorlow/septa-site).
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I made a few tweaks to it in terms of styling and also wrote a descriptive README to give people instructions on how to run it as I don't want
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to host it myself since it handles credentials from another service.
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## SQUIRREL
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SQUIRREL, short for SQL Query Util-Izing Rust's Reliable and Efficient Logic, is a SQL database that I am writing in Rust. Currently, it can
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parse CREATE TABLE commands, and works with the data types varchar and int. I plan to implement basic CRUD operations, then add JOINs, and
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then try to make it wire-compatible with Postgres.
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This project is currently not open-sourced as I am waiting to add more features and polish it up more.
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## Swole Control
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This one isn't a *personal* project, however it is a project that I worked on with a group. We began working on it in February as a part of a
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club at UT called Texas Convergent. We recently presented it at the club's demo day and won the prize for having the best business.
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Swole Control is an app that monitors machine usage at a gym on a machine-by-machine level, providing gym goers with information about what machines
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are free (this is a major pain point as a gym goer myself). It also provides gym owners with statistics on which machines are most popular, providing
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them valuable insights into their business.
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To achieve this, we built hardware that consisted of an ESP-32 micro controller and an ultrasonic distance sensor. This hardware is mounted on a gym machine
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and it measures the distance to the nearest object. It then sends this measurement to a Rust backend which stores it in a Firestore database (although we had
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a fork of it that worked with Postgres). The backend then uses these measurements and compares them to a baseline to determine if there is a user at a machine.
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Our mobile app then reads this from the Firestore database (it's planned to have it read this from the API to have a better-defined application boundary). The
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frontend is written in React Native.
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---
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**These projects had minimal/no work done on them:** RingGold, and NWS Container Deployment Service
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