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Ayush Raj
Ayush Raj

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Sloth-Digest: Turn 500 Unread Messages into a 15-Second Brief

#mlhacks #devchallenge #hackathon

This is a submission for the MLH x DEV Writing Challenge

What I Built

Ever opened WhatsApp after a few hours and found 300+ unread messages? Or returned to a Slack channel only to discover that an entire incident, three decisions, and five action items happened while you were away?

That's why I built SlothDigest — a privacy-first chat backlog analyzer that helps you catch up without reading every single message.

🦥 The idea is simple: read less, understand more.

SlothDigest turns lengthy, messy chat exports into a compact, actionable briefing. It supports WhatsApp, Slack, Discord, Telegram, and plain-text conversations.

Features:

  • ⚡ 15-Second Executive TL;DR: a quick overview of the most important conversations.
  • 🌋 Drama & Chaos Meter: visualizes chat tension using conflict-related words, ALL-CAPS messages, and excessive punctuation.
  • 🎯 Action Items & Task Tracker: surfaces tasks, assignees, and urgency, and tracks completion.
  • 🚨 Personal Alerts: flags messages that mention you or assign you urgent work.
  • ⚖️ Decision & Deadlock Radar: finds decisions, pending approvals, and discussions going in circles.
  • 👥 Yap Leaderboard: shows who is talking the most.
  • 🎧 Voice Briefing: a spoken summary using the browser's built-in Web Speech API.
  • 🍿 Sloth's Savage Roast: a humorous take on your group's dynamics.
  • 📄 Digest Export: export summaries and reports as Markdown.
  • 🔒 Privacy by Design: analysis happens locally in your browser, so chat contents are never sent to a remote AI service.

How I built it: SlothDigest uses JavaScript, Vite, HTML, and CSS, and runs entirely in the browser with no backend.

  1. Chat Parser: recognizes different message formats and extracts senders, timestamps, and content.
  2. Analysis Engine: uses local heuristics and pattern matching to identify tasks, highlight conversational signals, generate summaries, and produce the drama score.
  3. Interactive Dashboard: brings the results together with visual indicators, searchable messages, task checkboxes, participant stats, and export options.
  4. Voice Narration: the Web Speech API turns the briefing into audio so you can catch up while doing something else.

A note on transparency: SlothDigest currently uses rule-based, client-side analysis, not a generative AI model. Privacy, low setup requirements, and transparency were central to the design.

What I learned: Not every useful tool needs a giant model, a cloud backend, or another API subscription. A thoughtful local-processing pipeline, a focused interface, and a real problem can be enough. I also learned how much messy variation exists between chat export formats, and how far simple heuristics can go. (Add any specific lessons from your build here.)

Demo

🔗 GitHub Repository: https://github.com/ayushraaj-01/Sloth-Digest

🌐 Live Demo: (add your deployed URL here if available)

🎥 Demo Video / Screenshots: (embed a video or add screenshots here. MLH highly encourages this!)

The project includes four ready-to-use sample scenarios, so you can explore it without importing your own chats:

  • A chaotic family group chat
  • A production incident in Slack
  • Neighborhood group drama
  • The never-ending struggle to schedule a D&D session

Partner Technologies

(Fill this in only if you used an MLH partner technology. Name it, explain how you integrated it, and describe what it was like to work with, including what worked well and what you'd improve. More depth is better.)

If you didn't use a partner technology, you could instead write a short, honest section like:

SlothDigest doesn't currently use an MLH partner technology. It relies on the browser's built-in Web Speech API for voice briefings and runs fully client-side. I'd love to explore partner tools in the future, for example for deployment, hosting, or optional open-weight model support.

Hackathon Experience

(Tell us which hackathon you attended, what the atmosphere was like, who you met or teamed up with, and what you'll remember most.)

Here's a starter you can edit:

I built SlothDigest during [hackathon name]. The atmosphere was [describe it], and my favorite moment was [a memory, a person you met, or a bug you finally fixed at 3 AM]. What I'll remember most is [your takeaway].

🦥 Stay lazy. Stay informed. Let SlothDigest do the scrolling.

Repository: https://github.com/ayushraaj-01/Sloth-Digest

If you're interested in improving chat parsing, task extraction, accessibility, or local-first tools, check out the repo and contribute!

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