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GeezerVison is showing github.com — cpetersen - Overview

@cpetersen cpetersen Follow
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Chris Petersen cpetersen

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42 followers · 14 following
  • Scientist
  • Encinitas, CA
  • http://petersen.io

Achievements

Achievement: Pair Extraordinairex4Achievement: YOLOAchievement: QuickdrawAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx4Achievement: Starstruck

Achievements

Achievement: Pair Extraordinairex4Achievement: YOLOAchievement: QuickdrawAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx4Achievement: Starstruck

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@scientist-hq @boxen @scientist-labs
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  1. scientist-labs/red-candle scientist-labs/red-candle Public

    Ruby gem for running state-of-the-art language models locally. Access LLMs, embeddings, rerankers, and NER models directly from Ruby using Rust-powered Candle with Metal/CUDA acceleration.

    Rust 204 6

  2. scientist-labs/parsekit scientist-labs/parsekit Public

    Ruby document parsing toolkit with zero runtime dependencies. Parse PDFs, DOCX, XLSX, and images (with OCR) using a single, lightweight gem. Statically links MuPDF and Tesseract at compile time for…

    Ruby 49

  3. scientist-labs/lancelot scientist-labs/lancelot Public

    Ruby bindings for the Lance columnar data format. Built on the Lance Rust crate, Lancelot brings high-performance vector search, full-text search, and hybrid retrieval to Ruby applications with a n…

    Ruby 10

  4. scientist-labs/ragnar-cli scientist-labs/ragnar-cli Public

    Ragnar is a pure Ruby command-line RAG (Retrieval-Augmented Generation) tool with zero external dependencies. It provides local document indexing, semantic search, and LLM-powered query processing.…

    Ruby 12 2

  5. scientist-labs/clusterkit scientist-labs/clusterkit Public

    High-performance UMAP dimensionality reduction for Ruby, powered by the annembed Rust crate. Fast, memory-efficient manifold learning with model persistence.

    Ruby 9 3

  6. scientist-labs/topical scientist-labs/topical Public

    Ruby library for fast, flexible topic modeling — built on modern embeddings and clustering techniques to uncover themes in text.

    Ruby 14

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