You probably could choose your own adventure! . Eg @_nerdai_ coded the first book in Jax https://
github.com/nerdai/llms-fr
om-scratch-rs
…).
OPEN SOURCE
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Choose Your Own Adventure: Building LLMs with JAX
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2025 LLM Roadmap: From Fundamentals to Production Applications
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Here’s the 2025 LLM roadmap 1. Code and train your own LLM to really understand the fundamentals
2. Train models more conveniently using production-ready libraries
3. Learn about the big-picture considerations for real-world LLM/AI apps -
Embedding Fine-Tuning Guide for RAG Systems
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RAG Embedding Fine-Tuning Guide A practical guide demonstrating embedding model fine-tuning for RAG systems, featuring LangSmith monitoring and RAGAS metrics for performance evaluation. Check out the complete implementation https://
github.com/apatti/AIEBoot
camp/blob/main/09_Finetuning_Embeddings/Fine_tuning_Embedding_Models_for_RAG_using_RAGAS.ipynb
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PyTorch Democratizes Fast Kernel Writing Challenge
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we've been working on democratizing fast kernel writing on the @PyTorch team. try the challenge, either you or your AI!
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Building IntelliJ AI Assistant with Deepseek and LangChain
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IntelliJ AI Assistant Plugin Learn to build a coding assistant that integrates Deepseek AI into IntelliJ IDEA using LangChain. The plugin generates code and explains it in real-time, featuring Java Plugin DevKit and native IDE integration. Watch the tutorial now
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Open-Reasoner-Zero: Efficient RL Framework Outperforms DeepSeek-R1
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7). Open-Reasoner-Zero – an open-source large-scale minimalist RL framework to enhance reasoning. Achieves significant scalability requiring only 1/30th of the training steps of DeepSeek-R1-Zero-Qwen-32B to outperform it on GPQA Diamond.
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Complete RAG Guide: LangChain Implementations and Advanced Retrieval
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bRAG: Complete RAG Guide A comprehensive project showcasing RAG implementations with LangChain – from basics to advanced features like multi-query retrieval, ColBERT indexing, and RAG-Fusion. Check out this 1.7K+ starred guide https://
github.com/bRAGAI/bRAG-la
ngchain/
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Closed Source Licensing Challenges in AI-Driven Technology Success
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How closed source licensing makes it harder for technologies to succeed in an AI-driven world (that depends on open training data)
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LangChain Korean Tutorial: RAG, Agents and Core Technologies
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LangChain Korean Tutorial A comprehensive guide combining documentation, videos, and hands-on examples for mastering LangChain in Korean. Features practical implementations of RAG, agents, and core LangChain technologies. Check it out here https://
github.com/teddylee777/la
ngchain-kr
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RAG Tutorial: Enhancing LLMs with Retrieval-Augmented Generation
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RAG Tutorial Dive into Retrieval-Augmented Generation with this tutorial using Hugging Face and OpenAI. Build powerful RAG systems with LangChain's vector stores to enhance LLM capabilities and reduce hallucinations. Watch the tutorial: https://
youtube.com/watch?v=i0wcc8
qjfJw
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