What if your lecture notes could do more than just sit in a folder waiting for exam season?
That was the idea behind Recallix, a local AI-powered study assistant I built for one of my Friend as part of the DEV Community challenge.
Instead of manually summarizing lectures, extracting important concepts, and figuring out what to revise, Recallix helps turn raw notes into structured, actionable study material.
Lecture notes can become overwhelming, especially when students have to manage multiple subjects, lengthy explanations, and revision schedules.
The challenge isn't always finding study material. Sometimes, it's making that material easier to understand, organize, and revise.
I wanted to build something that could simplify this process without adding another complicated tool to a student's workflow.
Recallix takes lecture notes or transcripts and uses a locally running language model to process them.
Here's what it does:
The goal is simple: spend less time organizing notes and more time learning.
The project includes a dashboard for managing lectures and tasks, an interface for adding lecture notes, and a contextual Q&A experience.
The screenshots are available directly in the GitHub repository, inside the docs/screenshots/ directory and README.
I built Recallix using:
The architecture keeps the application straightforward:
Frontend → FastAPI → Ollama/Gemma → SQLite-backed lecture data
One of the key decisions was to use local AI rather than depend on a paid cloud API. This keeps the application accessible without API keys and avoids sending study notes to an external AI provider.
I approached Recallix as a full-stack application rather than just an AI wrapper.
The backend handles lecture creation, persistence, AI processing, task management, and question answering. The frontend provides a dashboard to interact with those capabilities.
I also focused on what happens when things don't go as planned.
For example, if Ollama is offline, lecture notes are still saved. The application shows a clear warning instead of breaking completely. I tested the core workflows, including lecture processing, grounded question answering, unsupported questions, and AI failure handling.
The backend test suite passed 27 tests, and the frontend linting and production build completed successfully.
Building Recallix helped me explore several practical aspects of AI-powered application development:
It also reinforced an important lesson: a useful AI application isn't just about getting a model to generate text. Reliability, usability, and knowing when the model doesn't have enough information matter just as much.
Recallix is currently a local application. There are several directions in which it could grow:
These are possible future improvements, not features currently implemented.
Recallix runs locally and requires Ollama with the Gemma 3:1B model.
The repository contains the installation instructions, setup commands, project structure, and screenshots.
GitHub: https://github.com/namandeeptripathi/Recallix There is currently no public hosted demo because the application relies on local model inference.
Built with curiosity, local AI, and the goal of making studying a little more organized.