Recall: A Private AI Memory Companion That Runs Locally

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built Recall, a local-first AI memory companion for a friend who constantly has the same problem: they remember that they saw, wrote, saved, or discussed something somewhere, but can't remember where.

Recall turns their personal data — notes, documents, conversations, bookmarks, and other files — into a private, searchable memory.

Instead of manually searching through folders and chat histories, they can ask questions like:

• "Where did I save that document about my project?"
• "What did I say about our Goa trip?"
• "What were the things I wanted to learn this year?"
• "Find everything related to my internship applications."

The important part is that their personal memories stay on their own machine.

How I Built It

Recall is built around open-source AI and local inference.

The core stack includes:

• Ollama for running open-weight language models locally
• Qwen / other open-weight models for reasoning and response generation
• BGE-M3 for local embeddings
• Qdrant for vector storage and retrieval
• Whisper for processing voice notes
• FastAPI for the backend
• Next.js for the interface

The pipeline looks roughly like this:

Personal Data
     ↓
Document / Text / Voice Ingestion
     ↓
Chunking + Metadata Extraction
     ↓
Local Embeddings
     ↓
Vector Database
     ↓
Memory Retrieval
     ↓
Local Open-Weight LLM
     ↓
Answer

Rather than simply building "chat with your files", Recall treats the information as a persistent personal memory that can be retrieved using semantic search, metadata, entities, dates, and relationships.

Everything important happens locally.

Why Does Open Innovation Matter?

Personal memories are some of the most sensitive data someone can give an AI system.

For Recall, sending that data to a third-party API would defeat one of the main reasons for building the product in the first place.

Using open-weight models and local inference means the system can run without sending my friend's private data to an external AI provider.

It also makes the system replaceable and hackable. I can swap the model, change the retrieval pipeline, modify the memory representation, fine-tune components, or run the entire system on different hardware without rebuilding the product around a closed API.

That flexibility is what makes open innovation particularly useful here.

The AI isn't just an API call inside the application.

The open AI stack is what makes the private memory system possible.

Prize Categories

• Open Source AI — Recall is built around open-weight models and local inference using Ollama, with the AI stack running on the user's own machine.
• Build for a Friend — The project was built for a real friend to solve a genuine problem with finding and remembering their personal information.

Team Submission

This is a solo submission, built entirely by me.