Initially, we thought about making a ChatBot that would help the user create rhymes and find words that were "on the tip of their tongue". Then we analyzed the usefulness of the ChatBot, and realized that we should try to solve problems that actually irritate us, and one such issue was the situation described as "tl;dr" (too long; didn't read), where a busy group chat accumulates enormous amounts of messages over short periods of time. In this situation, it is troublesome for the user to read what transpired in the chat over a span of time.
What it does
The TL;DR ChatBot summarizes a lengthy conversation in a group chat. It prioritizes messages based on the number of reactions, the length of the messages, time stamps and keywords.
How we built it
Challenges we ran into
Accomplishments that we're proud of
We are proud that we were not only able to successfully complete each filter but also combine them together into one simplified piece of code. Working efficiently by dividing up the workload according to our strengths allowed us to complete the project in time, and create a working MVP (Minimum Viable Product).
What we learned
What's next for TL;DR Bot
Making it available on more social media platforms for the public to use; making it more customizable with respect to parameters; making it into an app that manages multiple social media platforms; monetizing the ChatBot for business purposes; develop a public user base, for absolutely free.
The API token in code posted to Github is revoked, the version demonstrated at the hackathon included a token that was never uploaded to Github. In TLDR-Bot/stdlib/MaanavD/slack-app/functions/commands/tldr.js, lines 15 and 16 must be changed accordingly.