🤗 Mellum is now open source on Hugging Face! It’s a focal model that is small, efficient, and made for one thing: code completion. ⚙️ Trained from scratch by JetBrains. 🌱 First in a growing family of dev-focused LLMs. 🔗 jb.gg/Mellum_XOS

What if your LLM could actually take action—call functions, query real data, and handle logic? Alex Gilmore walks through building agentic workflows using #LangChain, #LangGraph, and #Neo4j: ✅ Smart tool selection ✅ Validated inputs (Pydantic FTW) ✅ Parallel task execution

5 AI to convert APIs to MCP servers in just a few minutes. 1. FastAPI-MCP exposes your FastAPI endpoints as MCP servers in one line of code with native auth support. 100% opensource.

OpenAI just released a 32-page masterclass on how to build AI agents.

🔌📊 MCP Server Tutorial Learn to build MCP servers that process stock market data using FastMcp and LangChain. This comprehensive guide demonstrates how to create ReAct agents with LangGraph for standardized data access. 📺 Check out the tutorial: youtube.com/watch?v=3K39NJ…

Better prompts = better #Cypher queries. 🔥 Learn how smarter schema representation can boost #Text2Cypher accuracy, cut token costs, and make your #LLMs actually useful, NOT overwhelmed. Full breakdown 👉 bit.ly/4d4fop5 #Neo4j #GraphDatabases #GenAI

Apache Camel 4.10.4 (LTS) is now available for download: buff.ly/H0M2ZxF Camel is an #opensource integration framework that empowers users to quickly and easily integrate various systems consuming or producing #data.

🔬 DeerFlow: Deep Research Framework An open-source framework for conducting systematic deep research through coordinated LangGraph agents. Enables comprehensive literature analysis, data synthesis, and structured knowledge discovery. GitHub Repository: github.com/bytedance/deer…

Turn messy files into queryable knowledge graphs — fast. ⚡ Part 4 of the Neo4j LLM Knowledge Graph Builder series shows how the back-end architecture and APIs make it happen. Build smarter, scale faster 👉 bit.ly/4k0lRnF #Neo4j #KnowledgeGraphs #LLMs #GenAI

How does Google DeepMind's AlphaEvolve "evolve" code? Luciano Abriata breaks down the intelligent prompting, creative mutation, and survival-of-the-fittest approach that allows AI to discover optimized solutions and even rediscover known mathematical theorems.

Agentic Document Extraction just got much faster! From previous 135sec median processing time down to 8sec. Extracts not just text but diagrams, charts, and form fields from PDFs to give LLM-ready output. Please see the video for details and some application ideas.

🚀Excited to share our latest work: LLMs entangle language and knowledge, making it hard to verify or update facts. We introduce LMLM 🐑🧠 — a new class of models that externalize factual knowledge into a database and learn during pretraining when and how to retrieve facts

Nemotron-CORTEXA just reached the top of the SWEBench leaderboard for using LLMs to solve software engineering problems, solving 68.2% of SWEBench GitHub issues! It does so by using a multi-step problem localization and repair process, generating multiple proposal candidates

analyzed common prompt engineering strategies from several of the major AI assistants' system prompts for 18 cents with DocETL it is insane how instructions like "Your goal is to leave the user feeling like no stone has been left unturned. Responses that are too short are lazy."

Introducing Codestral Embed, the new state-of-the-art embedding model for code.

Test it out at app.emergent.sh. It's worth giving it a test-drive.

The blog is available here: huggingface.co/blog/linuxfoun…

🤖 VeRL + LangGraph A seamless integration combining VeRL's reinforcement learning with LangGraph for enhanced multi-turn RL agents. Streamlines training-to-production workflows with automated tool handling and conversation management. 🔍 Explore the implementation:

Fixed link: github.com/qodo-ai/pr-age…