This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
The Problem: I Can't Listen, Understand, and Write Everything at Once
Have you ever sat through a lecture where the lecturer is explaining something important, the slides are packed with information, and you're desperately trying to write everything down before the next slide appears?
Yeah. That's the problem.
You want to pay attention to the explanation, but you also don't want to miss the definition on the slide. You try to write a summary, only to realize that the lecturer has already moved on to another topic. Later, when you review your notes, you find incomplete sentences, missing explanations, and that one crucial detail you remember hearing but somehow never wrote down.
And if you record the lecture, congratulations! Now you have a two-hour audio file to revisit, alongside dozens of slides and whatever notes you managed to write.
The information is there. The problem is connecting it all together.
I wanted to build something that could help with that.
What I Built
Meet SlideScribe, an AI-assisted lecture companion that transforms lecture slides and audio recordings into structured, editable study notes.
Instead of treating slides, recordings, and notes as separate materials, SlideScribe brings them together into one workflow.
Here's what it can do:
- Import lecture materials: Upload PDF slide decks and audio recordings.
- Generate structured notes: Combine extracted slide content with the lecture transcript to create organized notes.
- Trace information to its source: Include references to slide numbers and transcript timestamps, making it easier to find the original explanation.
- Edit and save your notes: Refine the generated content in a rich-text editor.
- Study actively: Generate quizzes and flashcards from your notes to prepare for exams.
The goal isn't to replace paying attention in class or blindly trust whatever an AI model generates. It's to reduce the tedious work between attending a lecture and actually being ready for an exam.
A Quick Look at SlideScribe
Live demo: https://slidescribe-ak1j.onrender.com
Source code: https://github.com/edgiaxel/SlideScribe
Youtube Demo: https://youtu.be/Q6fHZHcGJug
1. Your Study Workspace
The workspace is where your notes live. Rather than jumping between different documents and tools, you can keep your lecture materials and study notes organized in one place.
2. Notes You Can Actually Work With
The generated notes are editable, so you can correct a sentence, add your own explanation, or reorganize a section without starting from scratch.
The important part is traceability. References such as [Slide 14] or [18:40] are intended to help you locate the original source when you want to verify a detail or revisit the lecturer's explanation.
AI-generated notes should be a starting point for learning, not an excuse to stop checking the source.
3. From Lecture Materials to Notes
Import your PDF slides and lecture audio, then let the processing pipeline extract the information needed for note generation.
PDF extraction preserves page references, while audio transcription produces timestamped segments. These become the source material for the generated notes.
4. From Reading to Practicing
Reading your notes repeatedly isn't always enough. SlideScribe also generates quizzes and flashcards to help you test what you've understood.
You can review concepts, check your answers, and return to the notes when you need clarification.
5. A Simple Settings Interface
The settings interface provides a place for basic user preferences as the application develops.
6. Database Integration
SlideScribe integrates with MongoDB Atlas to persist application data. The backend also has an in-memory fallback for local development when a database connection isn't configured.
7. Deployment
The application is deployed on Render, making the project accessible through a public URL rather than requiring every reviewer to clone the repository just to see the interface.
Note: AI inference and audio processing depend on the configured backend services. The live deployment's available functionality may differ from the fully configured local development environment.
How I Built It
SlideScribe combines a modern web interface, a Python backend, document and audio processing, and open-weight AI models.
Frontend
- React 19
- TypeScript
- Vite
- Tailwind CSS v4
- Tiptap for rich-text editing
- Lucide Icons
The frontend provides the workspace, note editor, material import interface, study tools, and settings interface.
Backend
- FastAPI for the API
- Python for processing and orchestration
- pypdf for extracting text from PDF slides
- faster-whisper for audio transcription
- Motor for asynchronous MongoDB integration
- MongoDB Atlas for persistent storage
Open-Weight AI
For local AI inference, SlideScribe uses Gemma 3 4B through Ollama.
The general processing workflow looks like this:
- Extract text from each PDF page while retaining its page number.
- Transcribe the lecture recording into timestamped segments.
- Combine the available source material and pass it to the note-generation pipeline.
- Generate structured notes with source references.
- Use the resulting notes as input for quiz and flashcard generation.
The intention is to make the generated content easier to inspect and verify, rather than presenting an unexplained block of AI-generated text.
Why Open-Source AI Matters Here
For a project aimed at students, the cost and accessibility of AI processing matter.
Using open-weight models and local inference gives developers more control over where processing happens, which models they use, and how they manage inference costs. Tools such as Ollama and faster-whisper also make it possible to experiment with the pipeline without depending entirely on paid proprietary inference APIs.
There are trade-offs, of course. Local inference requires suitable hardware, and deploying AI workloads can introduce memory, performance, and hosting constraints. Open-source software doesn't magically make those problems disappear.
Still, it gives projects like SlideScribe a practical starting point for building and experimenting with AI-powered study tools.
Testing and Verification
I also worked on automated checks to verify the backend and frontend.
The backend has a pytest test suite covering application behavior and processing endpoints. The frontend can be built with Vite, and a browser smoke-test workflow was added to exercise important UI interactions and capture screenshots.
These checks help catch regressions, but they aren't a substitute for testing every feature in a real deployment. Database connectivity, model availability, and resource-intensive audio processing need to be verified in the environment where the application actually runs.
Who Is This For?
SlideScribe is designed for:
- College and university students dealing with dense lecture materials.
- Bootcamp participants who need to review technical explanations.
- People preparing for professional certifications.
- Anyone who learns from a combination of presentation slides and spoken explanations.
The potential side-hustle opportunity is straightforward: students already spend time and money organizing study materials and preparing for exams. A tool that reduces the friction of turning lectures into useful revision material could provide real value.
A future business model could include a free tier for basic note generation and paid tiers for larger uploads, more processing capacity, advanced study tools, or hosted AI inference. Pricing would need to be validated with actual users rather than assumed from the beginning.
What's Next?
SlideScribe is a starting point, not a finished commercial product. There are several areas I would like to improve:
- Exporting flashcards to tools such as Anki.
- Improving the mobile study experience.
- Supporting longer recordings without losing important context.
- Expanding quiz customization and study controls.
- Improving deployment reliability for AI inference and audio processing.
- Gathering feedback from more students and testing the workflow with real lecture materials.
The most important next step is seeing how people actually use it and identifying which parts save them the most time.
Try It Yourself
Live application: https://slidescribe-ak1j.onrender.com
GitHub repository: https://github.com/edgiaxel/SlideScribe
If you try SlideScribe, I'd be interested in hearing which part of your study workflow you find most frustrating: taking notes during a lecture, organizing materials afterward, or reviewing everything before an exam.
Final Thoughts
I built SlideScribe around a problem that feels familiar to students: there's a difference between having all the lecture materials and having notes that genuinely help you understand them.
Slides contain the visual information. Recordings contain the spoken explanations. Personal notes contain whatever you managed to capture in real time. Getting those pieces to work together takes time and effort.
SlideScribe is my attempt to make that process less painful, while keeping the original materials close enough that students can check the information for themselves.
The bigger idea is simple: AI should help us spend less time wrestling with study materials and more time actually learning from them.
Thanks for checking out SlideScribe!
Built for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle.
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