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

The program is based on Slack API. We used JavaScript code to filter the messages based on the above conditions.

Challenges we ran into

The JavaScript algorithm was difficult to organize so that it ran multiple filters on the same input, without overlap and redundancies. Working with the framework (node.js) was also challenging because it was completely new and it took a while to get used to. Many native javascript functions were not present in node.js, and we had to find imports for all of them to serve our purposes.

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

JavaScript was a new language for some of us and so we had to learn most of the basics on Google, like making functions, outputting the code using "console.log" and testing the code with the Google Chrome developer tools. Establishing the basics allowed us to make the code more complex and actually serve our purposes.

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.

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