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Semantic IDs: How to train an LLM-Recommender Hybrid with steerability and reasoning on recommendations.
Environments for LLM Reinforcement Learning
A dotfile manager and templater written in rust 🦀
Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
OODA Loop AI Agents - Observe, Orient, Decide, Act framework for systematic problem-solving. Built by AstroMVP to help startups ship AI products fast 🚀
Discovers and download via torrent new music. Based on Last.fm listening history and automatically queues downloads via Lidarr or Headphones API
The pretty much "official" DSPy framework for Typescript
A configuration framework that enhances Claude Code with specialized commands, cognitive personas, and development methodologies.
A Model Context Protocol (MCP) server that provides tools for fetching Reddit content, including frontpage posts, subreddit information and hot posts, post details, and comments.
Ungreedy subword tokenizer and vocabulary trainer for Python, Go & Javascript
A self-hosted dashboard that puts all your feeds in one place
Superlinked is a Python framework for AI Engineers building high-performance search & recommendation applications that combine structured and unstructured data.
High-performance runtime for multi-agent systems. Build, run and manage secure multi-agent systems in your cloud.
Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
An extremely fast Python type checker and language server, written in Rust.
Demo of a customer service use case implemented with the OpenAI Agents SDK
An on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking toolkit. (https://idp-leaderboard.org/)
A lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.
🥷 Cracking the MAANG Interviews | Google & Bloomberg | Algorithms and Data Structures | Tips and Resources
Official Implementation of "Can Large Language Models Understand Preferences in Personalized Recommendation?"
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph