This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
I built LazyDesk AI 🦥, an AI-powered document summarisation and productivity assistant that helps people spend less time reading lengthy documents and more time focusing on what matters.
Have you ever opened a long PDF or report just to find one important deadline, a few key points, or something you need to act on? That's the problem I wanted to tackle.
LazyDesk AI allows users to upload PDF and plain-text (TXT) documents and receive structured summaries containing an overview, key points, important dates or deadlines, and recommended next actions. Users can also download their summaries in Markdown format.
I built it for students, professionals, and anyone who regularly deals with information-heavy documents. Instead of manually going through every paragraph to understand the essentials, users can start with a concise summary and return to the original document whenever they need more detail.
The idea behind the name is simple: let AI handle some of the reading while you focus on what matters.
Demo
The project currently runs locally through Streamlit.
To try it out, follow the setup instructions in the GitHub repository:
🔗 GitHub Repository: https://github.com/Nijel20/LazyDesk-AI
A hosted demo or recorded walkthrough would make the project easier to explore without installing it. For now, the repository provides the code and instructions needed to run the application.
Code
🦥 Explore LazyDesk AI on GitHub:
https://github.com/Nijel20/LazyDesk-AI
The project is built with Python and Streamlit, with separate modules for document reading, AI communication, summarisation, and configuration.
The repository includes the application source code, dependency list, environment configuration example, and README with setup instructions.
How I Built It
I built LazyDesk AI using Python, Streamlit, and the Ollama Cloud API.
The application follows a straightforward workflow:
Upload: The user selects a PDF or TXT document.
Extract: Python extracts readable text using pypdf for PDFs and standard text decoding for TXT files.
Analyse: The application sends a summarisation prompt and the extracted text to a configured model through the Ollama Cloud API.
Present: The AI returns a structured summary with key points, deadlines, and recommended next actions.
Save: The user can download the summary as a Markdown file.
I separated the application into modules so that document processing, AI requests, and summarisation logic are easier to maintain and extend.
For AI inference, I used Ollama Cloud with a configurable model identifier. This made it possible to integrate an AI model into a working application without requiring me to host a large model on my own computer.
One important detail is that my current implementation uses hosted inference rather than local inference. Document text included in requests is sent to Ollama Cloud for processing, so this version requires an internet connection.
Why Does Open Innovation Matter?
Open innovation matters because it gives developers opportunities to experiment, learn, and build useful applications without having to create every component from scratch.
For this project, Python and Streamlit provided accessible building blocks for developing the application, while Ollama made AI model integration practical for a student project.
Using an open-weight model through an accessible model ecosystem also creates room for experimentation. I can explore alternative supported models, compare their summarisation quality, and potentially investigate local inference in a future version.
A closed API can also make AI applications possible, but open-weight models offer a different kind of flexibility: developers can explore model choices and, where the model licence and deployment setup permit, gain more control over how inference is run.
LazyDesk AI is still an early-stage project, but it demonstrates how these tools can turn a simple productivity idea into a working application.
For me, open innovation means making experimentation more accessible and giving more people the opportunity to turn ideas into practical tools.
I'd love to hear your feedback and suggestions for what LazyDesk AI should do next!
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