This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
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:
The important part is that their personal memories stay on their own machine.
Recall is built around open-source AI and local inference.
The core stack includes:
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.
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.
This is a solo submission, built entirely by me.