How Exa built a Web Research Multi-Agent System with LangGraph and LangSmith
See how Exa used LangGraph and LangSmith to build a multi-agent web research system to process research queries
GenAI
Deep Search
An Engineer's Guide to Fine-Tuning LLMs, Part 2: The Execution Playbook
A deep dive into the methods, fine-tuning pipeline, and operational risks of building specialised models.
Fine-Tuning
Tutorial
Carlos E. Perez (@IntuitMachine) on X
OpenAI self-leaked its Deep Research prompts and it's a goldmine of ideas! Let's analyze this in detail!
GraphRAG
Building Knowledge Graphs With Claude and Neo4j: A No-Code MCP Approach
Extract and generate a knowledge graph from educational curriculum information without writing a single line of code.
Evaluating Long-Context Question & Answer Systems
Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.
Evaluation
More efficient multi-vector embeddings with MUVERA | Weaviate
Weaviate `1.31` implements the MUVERA encoding algorithm for multi-vector embeddings. In this blog, we dive the algorithm in detail, including what MUVERA is, how it works, and whether it might make sense for you.
Vector Searchrecipes/weaviate-features/multi-vector/reason_moderncolbert_comparison.ipynb at main · weaviate/recipes
This repository shares end-to-end notebooks on how to use various Weaviate features and integrations! - weaviate/recipes
Reason ModernColBert
Agents
LoRA Hyperparameters Guide | Unsloth Documentation
Best practices for LoRA hyperparameters and how they affect the fine-tuning process.
Build an AI Domain Deep Research Agent
Fully functional agentic deep research app with step-by-step instructions (100% opensource)
fine-tuning-magistral.ipynb · kingabzpro/Magistral-Small-Medical-QA at main
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Magistral
Fine-Tuning Magistral: A Step-By-Step Guide
Step-by-step guide to fine-tuning the Mistral reasoning model on a medical MCQs dataset using the Transformers framework.
Enterprise Agentic AI Hierarchy of Needs
The crucial layers of infrastructure that make up a production grade Agentic AI system.
Which agent framework should you use? I tried 7. The winners will surprise you 🤯
I rewrote my "tech writer" agent in 7 frameworks: Agno, Autogen, Google ADK, Atomic Agents, DSPy, Langgraph, and Pydantic AI. You'll NEVER guess the winners.
Vector Search Explained | Weaviate
Learn about vector search, a technique that uses mathematical representations of data to find similar items in large data sets.
HNSW
SmolAgents— Grocery Finder
A complete walkthrough for building a code-generating AI agent that recommends grocery items by querying SurrealDB's HNSW vector index.
NirDiamant/agents-towards-production: This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.
This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for re...
Computer Use
Agentic GraphRAG for Commercial Contracts | Towards Data Science
Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent
Solutions
Mastering RAG: How to Select A Reranking Model
Choosing the best reranking model for your RAG-based QA system can be tricky. This blog post simplifies RAG reranking model selection, helping you pick the right one to optimize your system's performance.
Reranker
How we built our multi-agent research system \ Anthropic
On the the engineering challenges and lessons learned from building Claude's Research system
Jason Zhou (@jasonzhou1993) on X
After 1 hr research,
Here are the best open source 'General agent' projects:
- Suna: https://t.co/BRsQToXL9P
- Deer-flow from Bytedance: https://t.co/4zwuRKaNFZ
- Google-gemini-search: https://t.co/iFIMgBxfeg
- Langchanin open deep search:
Cognition | Don’t Build Multi-Agents
Frameworks for LLM Agents have been surprisingly disappointing. I want to offer some principles for building agents based on our own trial & error, and explain why some tempting ideas are actually quite bad in practice.
Comprehensive Guide on Reranker for RAG
Explore how reranker for RAG systems by refining results, reducing hallucinations, and improving relevance and accuracy.