This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
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 exactly 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 transforms lengthy, messy chat exports into a compact, actionable briefing. Instead of scrolling through hundreds of messages, you get the important information in one place.
Here's what it does:
⚡ 15-Second Executive TL;DR: Get a quick overview of the most important conversations.
🌋 Drama & Chaos Meter: Visualize chat tension using signals such as conflict-related words, ALL-CAPS messages, and excessive punctuation.
🎯 Action Items & Task Tracker: Surface tasks, identify assignees, highlight urgency, and track completion.
🚨 Personal Alerts: Quickly identify messages that mention you or assign you urgent work.
⚖️ Decision & Deadlock Radar: Find decisions, pending approvals, and discussions going in circles.
👥 Yap Leaderboard: See who is talking the most and explore different participants' contributions.
🎧 Voice Briefing: Listen to a spoken summary using the browser's built-in Web Speech API.
🍿 Sloth's Savage Roast: Get a humorous take on your group's conversation dynamics.
📄 Digest Export: Export summaries and reports in Markdown.
🔒 Privacy by Design: Analyze conversations locally in your browser without sending chat contents to a remote AI service.
It supports chat formats from WhatsApp, Slack, Discord, Telegram, and plain-text conversations.
I wanted to build something that solves a familiar problem: information overload. Whether you're a student catching up on a college group, a developer following a busy Slack channel, or someone trying to survive a chaotic family WhatsApp group, SlothDigest helps you focus on what matters.
Demo
GitHub Repository:
👉 https://github.com/ayushraaj-01/Sloth-Digest
The project includes four ready-to-use sample scenarios, so you can explore the experience without importing your own conversations.
These cover a chaotic family group chat, a production incident in Slack, neighborhood group drama, and the never-ending struggle to schedule a D&D session.
Live Demo: Add your deployed application URL here if available.
Code
Explore the source code, inspect the implementation, or contribute to the project:
👉 SlothDigest on GitHub
The project is organized into small modules for parsing chat messages, analyzing conversations, presenting the results, and generating voice briefings.
The core analysis includes heuristic-based summaries, task extraction, conversation-pattern detection, and humor-driven commentary. The voice feature uses the browser's native speech capabilities.
How I Built It
SlothDigest is built with JavaScript, Vite, HTML, and CSS.
I focused on creating a lightweight application that runs directly in the browser rather than requiring a backend server.
Here's how the main pieces fit together:
- Chat Parser
The parser recognizes different message formats and extracts information such as senders, timestamps, and message content.
- Analysis Engine
The analysis engine examines parsed messages using local heuristics and pattern matching. It identifies potential tasks, highlights conversational signals, generates concise summaries, and produces the drama score.
- Interactive Dashboard
The interface brings the results together with visual indicators, searchable messages, task checkboxes, participant statistics, and export options.
- Voice Narration
The Web Speech API turns the briefing into audio, making it possible to catch up while doing something else.
- Privacy-First Architecture
The application processes conversation data locally. The analysis does not depend on sending private chat exports to an external AI API.
An important distinction: SlothDigest currently uses rule-based, client-side analysis rather than an open-weight language model. It is designed around local processing and open-source development, but it does not claim to use generative AI for its summaries.
That made privacy, low setup requirements, and transparency central to the implementation.
Why Does Open Innovation Matter?
For a tool that handles private conversations, I believe the ability to inspect and control how your data is processed matters just as much as the features themselves.
Chat exports can contain personal information, private workplace discussions, names, and sensitive details. Sending all of that to an external service introduces another layer of trust and dependency.
SlothDigest takes a different approach.
Local processing: Conversations stay in the browser during analysis instead of being sent to a remote AI service.
Inspectable implementation: Developers can explore the parsing and analysis logic, understand how the heuristics work, and improve them through contributions.
Fewer dependencies: The application does not need an AI API key, a model-hosting subscription, or a dedicated backend to perform its current analysis.
Room to experiment: An open codebase makes it possible to experiment with better parsers, improved task extraction, additional languages, and even optional open-weight models in the future.
Open innovation isn't only about using a particular model or framework. It's also about making tools accessible, understandable, and adaptable.
For SlothDigest, that means making chat analysis useful without requiring users to hand their conversations to another service.
My Agent Session
I don't have a shareable agent session link to include yet.
If a DevRelay session is available, it can be added here to show how the project was developed and how an agent contributed to the implementation.
The Lazy Payoff 🦥
Imagine returning to a group chat with 300 unread messages.
The traditional approach is to scroll, skim, jump between conversations, and hope you don't miss the one message that actually requires your attention.
With SlothDigest, you can import a supported chat export and review a condensed briefing, potential action items, conversation patterns, and important mentions.
The goal is to reduce the effort spent catching up and make it easier to identify what deserves your attention.
The biggest payoff isn't simply reading faster. It's avoiding the need to understand every message before knowing what happened and what needs to happen next.
No more spending 20 minutes scrolling through memes to find the one message that says the meeting moved to 10 AM.
That's the kind of laziness I can get behind. 😴
Prize Categories
Primary submission: Hacktoberfest Open-Source AI Challenge — Week 3: Slow and Steady.
Additional partner categories: Include any applicable categories listed on the challenge page that your project genuinely qualifies for.
Final Thoughts
Building SlothDigest was an opportunity to explore how much value can come from making existing information easier to understand.
Not every useful AI-adjacent tool needs a giant model, a cloud backend, or another API subscription. Sometimes a thoughtful local-processing pipeline, a focused interface, and a very real problem are enough to build something useful.
I'm continuing to explore how SlothDigest can become better at understanding conversations while keeping its privacy-first approach.
If you're interested in improving chat parsing, task extraction, accessibility, or local-first tools, check out the repository and contribute!
🦥 Stay lazy. Stay informed. Let SlothDigest do the scrolling.
Repository: https://github.com/ayushraaj-01/Sloth-Digest
Top comments (0)