Екатерина Масайло @matprogramm

If you’re building AI systems in 2025, there are only two tools worth learning: LangGraph and n8n. The choice you make here will define how far you can actually scale. Here’s everything you need to know (and what nobody is telling you):

All-in-One RAG System! RAG-Anything is a unified framework with a multi-stage multimodal pipeline that extends traditional RAG architectures. It handles diverse content through intelligent orchestration and cross-modal understanding. 100% Open Source

100 page prompt is crazy

I finally understand how large language models actually work After reading the 2025 textbook “Foundations of LLMs” It blew my mind and cleared up years of confusion Here’s everything i learned (in plain english):

Github 👨‍🔧: Awesome-GraphRAG A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation. github .com/DEEP-PolyU/Awesome-GraphRAG

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad…

Google just released a python library for data extraction! LangExtract is a python library that extracts structured information from unstructured text documents with precise source grounding and interactive visualization. 100% Open Source

Anyone looking to get started with PyTorch - there is this really cool article by Sebastian Raschka.

one of the most exciting LLM posts i’ve read in a while read it. your future self will thank you

Join @Redisinc and @LangChainAI on July 23 for a webinar to learn how LangGraph + Redis make it easy to build AI agents with real memory. In the live webinar, you'll get demos, performance tips, and hear from the engineers behind the integration. 👉 RSVP here:…

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Context Engineering Guide I'm writing a detailed guide on context engineering for AI devs. v1 is out now! (bookmark it) I use a concrete deep research multi-agent example to show what context engineering involves.

LangChain now supports Claude Sonnet 4's citable search results - bringing proper source attribution to your RAG apps. ✨ You can now: • Get natural source attribution with proper titles and sources automatically included in search results • Have citations seamlessly linked…

‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module for asymmetric models & much more. Sparse + Dense = 🔥 hybrid search performance! Details in 🧵

🛑Prevent Hallucinated Responses Our integration with @CleanlabAI allows developers to catch agent failures in realtime To make this more concrete - they put together a blog and a tutorial showing how to do this for a Customer Support agent Blog: cleanlab.ai/blog/prevent-h…

Hugging Face 🤗 released a new chapter to their LLM course which is a masterclass on fine-tuning. It covers: > using Hugging Face ecosystem > understanding loss and metrics > pytorch implementation > hands out certificate

Investment research, accelerated. @MorningstarInc built Mo, their AI research assistant, using LangGraph — helping users cut through massive datasets to surface precise, verifiable investment insights. Aisis Julian (Senior Software Engineer) and her team at Morningstar have…

📄🔍 GraphRAG Contract Analysis A powerful solution combining GraphRAG and LangGraph agents to analyze legal contracts using Neo4j knowledge graphs, featuring benchmarks across multiple LLMs. Check out the full implementation guide ➡️ towardsdatascience.com/agentic-graphr…

Python is insane for time series. Case in point: Pytimetk 📈

This is the ultimate guide to Fine-Tuning LLMs