I have just implemented chunking with overlap for larger documents to split texts into smaller chunks and ensure access to all documentation in your RAG. It's currently in the testing phase, and I’d like to experiment with different models to optimize the process. Once I confirm that everything is working correctly, I can merge the PR into the main branch, and you’ll just need to update Rlama with `rlama update`.
This is my next step. Currently, I’ve built an MVP to test the features, integrations, and see how far I can go with rLlama. I’m already developing a RAG on my end by chunking the data, adding overlap, and using metadata to retrieve the best possible context. This should be deployed soon. The version on GitHub has been pushed for days now, and it was only a version to showcase the features. I can’t wait to improve it and make it useful for everyone!
I've already made some examples, even with my own codebase, to see how it can be used to understand projects, and I want to show how it can be used with documentation or studies. I will publish them next week.
Hey! Yes, that's something I was planning to do—a complete documentation on the code, its architecture, and the entire stack to allow others to develop alongside me. I just deployed a functional version, and soon, the website will have documentation with its architecture and a visualization of the entire code.
but for now here is the stack used:
Core Language: Go (chosen for performance, cross-platform compatibility, and single binary distribution)
CLI Framework: Cobra (for command-line interface structure)
LLM Integration: Ollama API (for embeddings and completions)
Storage: Local filesystem-based storage (JSON files for simplicity and portability)
Vector Search: Custom implementation of cosine similarity for embedding retrieval
I thought about adding an API interface for it. It is on my to-do list of things that could be good to add. For now, I'm gathering feedback to see what people like about it or not.
Imagine one day you did something on your laptop two months ago and you want to find out where you did it. If you don't remember, it will do it for you. If you want a detailed daily summary of your activities, it will do that for you. If you want to summarize a meeting or your university lectures in detail, it will do that for you. People were afraid of this technology because it wasn't private, and that's completely understandable. Now, imagine having the same technology but completely local. All your data would remain confidential and encrypted, and you could ask it any question about your digital activity.
Yes, it's similar to RewindAI. I was inspired by them :) I mentioned this and listed them as inspirations on Product Hunt. However, I'm aiming to create a local ecosystem, and this is just the beginning. I have a lot of ideas in mind (but little time), so I'm progressing day by day.
Honestly, I don't even know if it's a coincidence or done on purpose. Initially, I'm just a simple developer who shared my bugs on Reddit and other platforms to find solutions to my problems. When I saw that Microsoft had the same functionality and the same name, my first thought was that there was no point in continuing this project because I would never be able to compete with them. Originally, I wanted to launch on June 27th, and I talked about this situation on Reddit. That's when I received a lot of feedback, which really warmed my heart. So, I decided to open-source my project, create a landing page in two days, and now I'm gradually building the project and making it publicly available for everyone.
Originally, Remind was supposed to be called Recall AI, a project I have been working on since last December. However, last week I was watching a video about the new Copilot+ laptops and noticed they featured the same functionality with the same name. So, I decided to open-source the project, rename it to Remind, and publish the landing page as quickly as possible. The reason I believe Remind will be better than other solutions is that it will be completely local, free, and private for users. I provided a demo, a link to the GitHub repository with all the code, and some information on how I implemented the solution. Since I launched it quickly, the site is obviously rushed, but you can check the public code—it is not a spyware at all. Although I'm just at the beginning, it already looks quite promising compared to existing paid and non-private solutions.
Hey everyone,
I wanted to share a bit of my journey with you all and maybe get some advice or find some folks interested in collaborating.
About six months ago, I was really inspired by RewindAI and decided to create my own local memory app called "Recall". The idea was to build something that could capture and index digital activity, making it easy to remember and find information later.
After months of hard work, I finally got Recall to a point where it was working well. I've managed to ask him for information on my activity from a week ago with details and all that locally with local llms! It was exciting to see it come together and actually do what I hoped it would.
But then, I found out yesterday that the new Copilot+ laptops have a feature called "Recall" that does pretty much the same thing but better. It was a bit of a shock. Here I was, thinking I had something unique, only to see it already out there in a big way.
So, now I'm not sure what to think. Instead of giving up, I decided to open-source Recall and rename it reMind. I believe that by sharing it, maybe some of you might have ideas or want to help improve it. If you’re interested, I’d love to hear from you!
I wanted to share a bit of my journey with you all and maybe get some advice or find some folks interested in collaborating.
About six months ago, I was really inspired by RewindAI and decided to create my own local memory app called "Recall". The idea was to build something that could capture and index digital activity, making it easy to remember and find information later.
After months of hard work, I finally got Recall to a point where it was working well. I've managed to ask him for information on my activity from a week ago with details and all that locally with local llms! It was exciting to see it come together and actually do what I hoped it would.
But then, I found out yesterday that the new Copilot+ laptops have a feature called "Recall" that does pretty much the same thing but better. It was a bit of a shock. Here I was, thinking I had something unique, only to see it already out there in a big way.
So, now I'm not sure what to think. Instead of giving up, I decided to open-source Recall and rename it reMind. I believe that by sharing it, maybe some of you might have ideas or want to help improve it. If you’re interested, I’d love to hear from you!