Description of the stack:
- The frontend is a TypeScript Next.js app, with Prisma and RDS Postgres for persistence, and S3 for storing files uploaded by users.
- On the backend I have a BullMQ worker for handling asynchronous tasks such as audio/video transcription, backed by Redis.
- A Telegram bot that allows quickly adding resources from a phone and for having practice conversations with AI.
- Everything is deployed via a Github Actions pipeline, with images hosted on ECR and other AWS resources deployed using Terraform.
Preferably the solution would allow me to reuse at least parts of the existing CI/CD pipeline and Terraform for managing resources. Some of the suggestions by commenters such as running this on a machine in my home, while may be optimal from a cost minimization perspective, compromise a bit too much on quality-of-life for me—not simply from a developer perspective, but I also live somewhat of a nomadic lifestyle and prefer to have few physical possessions.
I already tried applying for AWS Activate but was rejected due to not meeting their criteria. I do not have a company or non-profit entity formally set up for this project. It initially started as a research project purely for personal interest, to explore how LLMs can be used for memory augmentation (in general, not just for language learning). I’ve been a user of conventional SRS tools such as Anki for many years, and have been frustrated by certain UX limitations that hindered consistent long-term use. Initially, I built it as a tool for myself, with me as the sole target audience. Onboarding foreign monks in Thailand was a fortuitous occurrence that I had not initially anticipated.