L88-Full

Hi HN, I recently created a project called *L88-Full* and published it on GitHub: https://github.com/Hundred-Trillion/L88-Full I'm looking for feedback from the community. If you have time to check the repository, I would really appreciate: * Code reviews * Suggestions for improvements * Bug reports or fixes * Any ideas on how the project could be improved or expanded If you're interested in contributing, feel free to open an issue or submit a pull request. Even small feedback would help a lot. Thanks for taking a look and supporting the project!

I’ve been developing a small RAG pipeline and ran into a specific technical issue involving OCR. I’m using PyMuPDF for extraction, and whenever a PDF contains a centered watermark on each page, the OCR becomes noisy—text breaks, artifacts show up, and the output degrades enough that it affects chunking and retrieval accuracy downstream. The document is otherwise clean, so I’m trying to understand whether this is a known limitation of PyMuPDF or if there are better approaches for handling watermarked PDFs before OCR. I’m working with an RTX 4000 (8GB VRAM), so I’m also trying to stay within reasonable GPU constraints. I’d really appreciate any ideas on: more robust OCR libraries or models that handle watermarks well preprocessing strategies to suppress watermark text better extraction pipelines for RAG use cases or any general advice on im

Hey everyone, I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement. Repo: https://github.com/Hundred-Trillion/L88-Full I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation. I’d really appreciate feedback on: Better architecture ideas for small-VRAM RAG Splitting evaluator/generator roles effectively Improving the LangGraph pipeline Any bugs or design smells you notice Ways to optimize the system for local hardware I’m 18 and still learning a lot about proper LLM architecture, so any te

Hey everyone, I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement. Repo: https://github.com/Hundred-Trillion/L88-Full I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation. I’d really appreciate feedback on: Better architecture ideas for small-VRAM RAG Splitting evaluator/generator roles effectively Improving the LangGraph pipeline Any bugs or design smells you notice Ways to optimize the system for local hardware I’m 18 and still learning a lot about proper LLM architecture, so any te