I built Aliasbot [www.aliasbot.com]. A Slack app that let's you create custom teams of people on Slack and tag them all at once. Think "@devops the server is down!@!1!".
I did this while finishing my Master's degree and then working full time. It's been a great experience building it and I'm excited to see the growth in 2018!
Launched my side project Alias, a Slack bot that let's users define custom aliases and use them to type less. [0]
I managed to build it and launch it while finishing my Masters in CS, then working full time, while also being in a relationship. So I had to use any spare time I had to work on the app.
I learned a lot about time management and how to get into a flow state, fast. I tried doing a little bit of work every day. No matter how little, every day I tried doing some work on the app.
This quote stuck with me as I was working on the app: “It gets easier… Every day it gets a little easier… But you gotta do it every day — that’s the hard part. But it does get easier.”
Goal for 2018: Focus on marketing and bizdev to keep growing. I don't have much experience in this area so it'll be a challenge but I'm sure I'll learn a ton!
I build this app to help myself type less in Slack. I found myself typing very long phrases (chrome extension, facebook messenger etc) over and over again and so I build this tool.
I can just make an alias, "ce" and have it be seen by my recipient as the full "chrome extension" phrase.
Please leave any feedback or questions in the comments and I'll be around to answer them!
I focus chatbots and conversational agents, and my last project was a Messenger healthbot for symptoms diagnosis. I have a Masters Degree in Machine Learning and specialize in NLP and Data Science. I have been an Android and SQL dev in the past.
Can you elaborate a little bit more on this? Was it tough because you couldn't find clients and had to live off savings or was it tough because you were working long hours + stress. Or was it something else entirely?
Do you mind expanding a little bit on the "few types of patterns in ML and Big Data". Do you mean models, pipelines etc or something different. I am studying ML in grad school and I'm seeing way more patterns than just a few, almost too many to be honest if by patterns you mean models. I'd love to hear your view and what you meant by only few types of patters.
My last project was a Facebook Messenger healthcare chatbot. I
was an Android and SQL dev in the past. I have a Masters Degree in Machine Learning and specialize in NLP and Data Science.
