This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Ever opened a college WhatsApp group and found hundreds of messages, only to discover that the important assignment deadline was hidden somewhere in the middle? 😅
That's the problem I wanted to solve with InboxSloth AI — Smart Message Summarizer.
InboxSloth AI is an AI-powered application designed to turn long message conversations into short, useful briefings. Instead of reading every message manually, users can get a summary of important updates, actionable tasks, deadlines, and potential warnings.
🦥 What can it do?
- 📝 Summarize conversations: Turn lengthy discussions into concise summaries.
- ✅ Extract action items: Identify assignments, tasks, and follow-ups.
- 📅 Find deadlines: Detect dates and times mentioned in messages.
- 🚨 Highlight important updates: Help users prioritize urgent announcements.
- 🔁 Reduce repetition: Identify duplicate messages and repeated information.
- 🌐 Handle mixed-language messages: Aim to support English, Marathi, and Marathi-English conversations.
- 🔒 Run AI locally: Use an open-weight model through Ollama instead of depending entirely on a hosted AI API.
The goal is simple: spend less time scrolling through messages and more time getting things done.
My main target users are students, project teams, and anyone overwhelmed by busy group conversations.
Demo
GitHub Repository: InboxSloth AI
The project uses Ollama to run the Gemma 3 1B model locally.
To try the model:
ollama pull gemma3:1b
ollama run gemma3:1b
The web application can be run locally after installing its Python dependencies and configuring the project.
Note: A public video or deployed demo link will be added when available.
Code
🔗 Source code: [(https://github.com/MrAtharvaB/InboxSloth-AI)]
The project is built with an open-source-friendly stack:
- Python for application logic
- Flask for the backend
- Ollama for local model inference
- Gemma 3 1B for AI-powered text processing
- HTML, CSS, and JavaScript for the web interface
The repository contains the project code and setup instructions as development progresses.
How I Built It
I chose a local AI approach because I wanted to explore how a small language model could solve an everyday productivity problem without requiring a paid inference API for every request.
1. Local AI with Gemma
I use Gemma 3 1B through Ollama as the intended language model for summarizing conversations and extracting useful information.
The model can be instructed to identify key announcements, tasks, deadlines, and uncertain information from message text.
2. Python and Flask
Flask provides the foundation for connecting the user interface with the AI processing pipeline.
The intended workflow is:
- The user provides a conversation or message dump.
- The application prepares the text for processing.
- The local model generates a summary and extracts relevant information.
- The application organizes the results into a readable format.
3. A focus on useful output
A summary alone isn't always enough. Knowing that an assignment is due on Friday is more useful than simply knowing that an assignment was discussed.
That's why InboxSloth AI focuses on action items and deadlines alongside the summary.
An important design goal is to avoid inventing dates or treating conflicting announcements as confirmed facts.
4. Learning through experimentation
Building this project has also helped me explore local model setup, prompt design, Python application development, and the practical challenges of making AI outputs useful.
Rather than building another generic chatbot, I wanted to experiment with AI as a tool that reduces repetitive work.
Why Does Open Innovation Matter?
Open innovation makes projects like InboxSloth AI possible for individual developers and students.
Using an open-weight model through Ollama gives me an opportunity to experiment with local inference, understand how the model fits into an application, and explore AI development without making a hosted API the foundation of every interaction.
It also creates opportunities to learn from the broader developer community.
For a student developer, this matters because experimentation should not stop simply because an API has usage limits or an application needs a different deployment strategy.
Open tools make it easier to learn, adapt, test ideas, and build projects that others can inspect and improve.
Local inference can also reduce the need to transmit conversation text to an external model provider, although privacy still depends on how the complete application handles its data.
For me, open innovation is about making AI development more accessible and giving developers the freedom to understand and improve the tools they use.
My Agent Session
I am developing InboxSloth AI as an experiment in practical local AI.
A DevRelay agent session can be added here if I capture and publish one for this project.
The Lazy Payoff 🦥
Imagine spending 10 minutes scrolling through a group chat just to find three important announcements.
InboxSloth AI aims to turn that process into a quick review of a concise summary and a task list.
The potential payoff is less repetitive reading, fewer missed announcements, and a clearer view of what needs attention.
I am treating this as a productivity goal to validate through testing, rather than claiming a measured time saving before collecting real usage data.
Prize Categories
I am submitting InboxSloth AI to the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
I will add any applicable partner prize categories after verifying the official challenge requirements.
Final Thoughts
InboxSloth AI started with a simple idea: important information shouldn't get lost in a sea of messages.
This project is my opportunity to explore local AI, open-weight models, and practical productivity tools while continuing to improve my development skills.
If you have ideas for improving message summarization, multilingual support, or local AI workflows, I'd love to hear your suggestions!
⭐ Check out the project: InboxSloth-AI on GitHub
Less scrolling. More doing. 🦥
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