This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend I built StudyForge, an open-source AI study assistant for my friend, who was struggling with one surprisingly simple problem: he was too lazy to even type prompts into an AI tool to get notes, explanations, and quizzes.
So instead of asking him to figure out the right prompt every time, I built a study workflow around the task itself.
StudyForge lets a student upload course material such as PDFs, PowerPoint presentations, and Word documents and turn that material into structured study content including topic explanations, expected questions, and quizzes.
Core features:
Live app: https://studyforge-8cdo.onrender.com Open-source AI study assistant — upload your lecture slides and get exam-ready notes, expected questions, and an interactive quiz tailored to your university and course.
No account required. Works offline with a local Ollama model. Self-hostable.
The public StudyForge demo is deployed on Render:
https://studyforge-8cdo.onrender.com
The hosted deployment uses Google Gemini 3.8 Flash (gemini-3.8-flash) for AI generation.
StudyForge also supports Ollama with configurable local models for self-hosted and offline use.
Every student uploads lecture slides to ChatGPT and asks "make me notes". StudyForge does what that prompt can't.
| Tool | Problem |
|---|---|
| ChatGPT / Gemini (raw) | Generic output — knows nothing about your university, syllabus, or exam style |
| Notability AI, Adobe AI | Requires an account and subscription; sends your documents to a third-party cloud |
| Manual prompt engineering | Produces notes, questions, or a quiz — never all three in one structured pass |
StudyForge is different…
StudyForge is built on Next.js 16 + React 19, with Supabase for persistence and
a multi-provider AI waterfall at its core. The open-source AI stack is what makes the
whole thing work:
Universal (Groq) → OpenAI → Ollama → Gemini → Claude
The first working provider wins. The key open-source piece is Ollama — it runs entirely on the user's machine, with no internet required after the model is downloaded.
The default local model is Qwen3:30B — an open-weight model that produces genuinely academic-quality output for this task.
Open innovation makes StudyForge much more flexible than building the entire application around one closed AI API.
With Ollama and open-weight models, users can run models locally, choose models that fit their hardware and requirements, and experiment with different models without rebuilding the application.
It also means the AI layer can evolve independently of the rest of the product. A student can use a local model, while a hosted deployment can use a compatible cloud provider.
For a tool built around education, that flexibility matters: the goal is to make AI-assisted studying more accessible and adaptable rather than tying the entire experience to one proprietary model. The full development history is transparent via the commit log:github.com/RishavRajSingh44/StudyForge/commits