LazyDigest：讓 AI 替你閲讀長篇內容

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

Ever opened a long document, a pile of notes, or a lengthy block of text and thought, “I'll read this later”?

That's exactly the problem LazyDigest is designed to solve.

LazyDigest is a local-first AI summarizer that turns lengthy text into something you can understand at a glance. Instead of manually going through every paragraph, you can paste text or upload a .txt or .md file and get:

Short summary: Understand the main idea without reading everything.

Key points: Find the most important information quickly.

Action items: Identify tasks and next steps mentioned in the text.

Important dates: Surface dates and deadlines when they appear in the content.

It also handles longer inputs by splitting the text into smaller chunks, summarizing them, and combining the results into a final digest.

I built it for students, developers, and anyone who deals with too much text and too little time.

The idea is simple: spend less time extracting information and more time actually using it.

Code

GitHub repository: LazyDigest

How I Built It

LazyDigest uses Gemma, an open-weight AI model, running locally through llama.cpp's OpenAI-compatible server interface.

The application is built with:

Python and FastAPI for the backend and API endpoints.

HTML, CSS, and JavaScript for the user interface.

HTTPX to communicate with the local model server.

Gemma to generate summaries, key points, action items, and dates.

Chunk-based summarization to handle longer text without sending the entire input in one request.

The workflow is straightforward:

The user pastes text or uploads a supported text file.

The backend validates the input.

Short inputs are sent for summarization, while longer inputs are processed in chunks.

The model generates structured results.

The interface presents the digest in a readable format.

I kept the initial version focused on doing one thing well instead of adding unnecessary infrastructure. There is no database storing uploaded documents, and the application does not need a separate hosted AI API.

The project is intentionally small enough for someone to understand, run, and build upon.

Why Does Open Innovation Matter?

For a tool that processes personal notes, study material, work documents, or private text, where the data goes matters just as much as the result.

Using an open-weight model locally gives users more control over their data and how AI is run.

With LazyDigest, the text can be processed on the user's own machine instead of being sent to a third-party AI service. Once the model and dependencies are installed, summarization can work without an internet connection, provided the local inference server is running.

Open models also give developers the freedom to experiment with different models, adjust prompts, inspect the surrounding implementation, and extend the project without being locked into one hosted API.

That flexibility is what makes this approach exciting to me. AI should not always require sending your data elsewhere or paying for every request.

Sometimes, the best AI tool is one you can run yourself.

The Lazy Payoff

Imagine receiving a long set of notes before an exam, opening a lengthy project document, or reviewing a large collection of text.

Instead of starting by reading every line, you can start with the summary, scan the key points, and check the action items and dates. You can then return to the original text when you need more detail.

I haven't measured the actual time saved yet, so I won't claim a specific percentage. The goal is to reduce the effort required to understand lengthy text and identify what deserves attention.

The next steps I'd like to explore include PDF support, scheduled folder digests, and optional voice input or speech output.

For now, LazyDigest is a small step toward making everyday information overload a little less overwhelming.

Prize Categories

Best use of Gemma