Inspiration:
Every student knows the "study panic" of having a 500-page textbook in one hand, a notebook in the other, and 20 hours of recorded lectures on their laptop. These materials never "talk" to each other. I built The Infinite Syllabus AI to bridge that gap, turning a mountain of scattered files into a single, interactive knowledge base.
What it does
Itβs a multimodal academic hub. You can drop a 50-page PDF, a photo of a whiteboard from class, and a voice memo of a lecture into the app. Because of Gemini's massive context window, the app remembers everything. You can ask: "What did the professor say about the diagram on page 12 of the textbook?" and it will answer by comparing the audio and the PDF.
How I built it
Gemini 1.5 Flash: Used for its speed and 1-million token context capacity.
System Prompting: I designed a "Traceability Engine" via system instructions that forces the AI to cite sources (e.g., "See PDF page 4" or "Mentioned at 02:15 in your audio").
Google AI Studio: Used for rapid prototyping and deploying the multimodal interface.
Challenges I ran into
The hardest part was ensuring the AI could handle "Handwritten-to-Text" conversion accurately. I had to refine the prompt to instruct Gemini to look specifically for mathematical symbols and diagrams in images before comparing them to the clean text in the PDFs.
What's next for The Infinite Syllabus AI
I plan to add "Exam Mode," where the AI generates personalized practice tests by finding the contradictions between your notes (what you understood) and the textbook (the facts).
Built With
Gemini API
Google AI Studio
Multimodal AI
Large Language Models (LLMs)
Built With
- css3
- google-cloud-platform-(gcp)-cloud-services:-google-cloud-run-apis:-gemini-api-(gemini-1.5-flash)
- google-search-grounding-ai-architecture:-multimodal-rag-(retrieval-augmented-generation)
- html5
- languages:-javascript-(es6+)
- python-frameworks:-react
- streamlit-platforms:-google-ai-studio
- system-instruction-engineering
- vibe
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