Here you will find my open source code and tech blog.

Code

Telegraf Clients

I have used Influx DB and the TICK stack in my work. It’s a great toolset to measure, monitor, and alert on near real time events. Influx DB is a time series database.

It’s important to have a simple, reliable, and unobtrusive way of emitting metrics to Influx DB. Telegraf is a tool written to collect metrics and send them to Influx. If you want to log and graph custom application specific measurements, then your program has to emit metrics to Influx. My preferred way of doing so is by running an instance of telegraf along side my application and letting it collect and emit those events to Influx for me. That means that you have to emit the measurements to telegraf instead. There is a text line specification for Influx measurements. In order to allow my applications to easily emit measurements to telegraf, I wrote a few libraries in languages I frequently use.

Bamboo, a Redis-backed Job Queuing System

At Wanderu, I built and open-sourced a job queuing system built on top of Redis. The system itself is a reliable distributed redis-backed job queuing system with ack/fail semantics and support for multiple languages.

There were a bunch of reasons to build a custom solution with some required properties for our use cases. Existing Python and Node solutions were missing properties that we needed, so I built and open-sourced this.

offero.tech

I am using this site to prototype NextJS, React, Material-UI, and deployment using Zeit now.sh.

Project here: offero.tech

Diffusion of Innovations Simulation

This project was completed while attending George Mason University and accomplishing a Graduate Certificate in Computational Social Science. The project aimed to reproduce the results of simulations performed by researchers on the spread of information through social networks.

Project here: DISim

I hosted it originally on SourceForge and the full documentation is still hosted there DISim Docs

Product Match

This project is an example web scraper and NLP classifier using BeautifulSoup and NLTK. It’s an example of how to collect data via web scraping and use it to produce a classifier that can predict the category for a given text description.

It’s a good example of how to do this kind of stuff with Python.

Project here: Product Match

Algorithms

I also like to improve and test my skills on algorithm problems and I keep my solutions to problems in a few repositories on github for reference. ALL code is my own.