AI Engineering Toolkit! I have curated list of 100+ LLM libraries and frameworks for training, fine-tuning, building, evaluating, deploying, RAG, and AI Agents! 100% Open Source

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This repo covers everything you need to learn about MLOps! Made with ML is a comprehensive guide that combines machine learning with software engineering to design, develop, and deploy production-grade applications. It focuses on the full lifecycle of building an end-to-end

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If you’re into ML, LLMs, and AI agents, join AI Engineering (it’s free): https://t.co/3A8sO2Nz1A Github Repo:

RAG engine for deep document understanding! RAGFlow is an open-source RAG engine for deep document understanding and streamlined knowledge workflows from complex data formats. Key features: • Template-based chunking that understands document structure • Citation-backed

NaiveRAG is fast but dumb. GraphRAG is smart but slow. This open-source solution fixes both. RAG systems have a fundamental problem: They treat documents as isolated chunks. No connections. No context. No understanding of how things relate. Graph RAG addresses this, but

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Build a Large Language Model from scratch! This repository contains the code examples for developing, pretraining, and finetuning a LLM from scratch. It is the official codebase for the book Build a Large Language Model (From Scratch). Notebook examples are included for each

Turn High-Volume PDFs into LLM-Ready data with Vision-First Agentic Document AI! LandingAI has released Agentic Document Extraction (ADE) DPT-2 Mini, a lightweight variant of the Document Pretrained Transformer 2 (DPT-2) designed for high-volume document workflows. It’s ideal

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Try out DPT-2 mini in the Playground here: https://t.co/pxilG9eRSX ADE Github Repo:

Turn High-Volume PDFs into LLM-Ready data with Vision-First Agentic Document AI! LandingAI has released Agentic Document Extraction (ADE) DPT-2 Mini, a lightweight variant of the Document Pretrained Transformer 2 (DPT-2) designed for high-volume document workflows. It’s ideal

Fine-tune LLM agents without fine-tuning LLMs! Memento is a memory based continual learning framework for LLM agents that lets them learn from experience over time without touching model weights. It maintains a Case Bank of past trajectories including tasks, step sequences,

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Paper: https://t.co/2KuBvJLJ8o Github Repo:

Fine-tune LLM agents without fine-tuning LLMs! Memento is a memory based continual learning framework for LLM agents that lets them learn from experience over time without touching model weights. It maintains a Case Bank of past trajectories including tasks, step sequences,

Finally, a practical, open project structure for building AI agents! Better Agents is a CLI tool and standards kit for building production-ready agent projects. Most agentic projects start without a real structure. Testing, evaluation, and prompt versioning get added only when

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