GenAI

TL;DR Agents need context to perform tasks. Context engineering is the art and science of filling the context window with just the right information at each step of an agent’s trajectory. In this post, we break down some common strategies — write, select, compress, and isolate — for context engineering

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

See how Exa used LangGraph and LangSmith to build a multi-agent web research system to process research queries

A deep dive into the methods, fine-tuning pipeline, and operational risks of building specialised models.

OpenAI self-leaked its Deep Research prompts and it's a goldmine of ideas! Let's analyze this in detail!

📦 Package Links & Code Examples:- 🐍 PyPI: https://pypi.org/project/graph_nd/- 📁 GitHub: https://github.com/zach-blumenfeld/graph-nd- 📚 Docs: https://grap...

Extract and generate a knowledge graph from educational curriculum information without writing a single line of code.

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Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.

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.

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations! - weaviate/recipes

This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design...

Best practices for LoRA hyperparameters and how they affect the fine-tuning process.

In-depth tutorials on LLMs, RAGs and real-world AI agent applications. - patchy631/ai-engineering-hub

Fully functional agentic deep research app with step-by-step instructions (100% opensource)

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Step-by-step guide to fine-tuning the Mistral reasoning model on a medical MCQs dataset using the Transformers framework.

The crucial layers of infrastructure that make up a production grade Agentic AI system.

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.

Learn about vector search, a technique that uses mathematical representations of data to find similar items in large data sets.

A complete walkthrough for building a code-generating AI agent that recommends grocery items by querying SurrealDB's HNSW vector index.

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...

Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent

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.

Learn how to build better retrieval augmented generation (RAG) pipelines for LLMs, search, and recommendation. In this chapter we explore two-stage retrieval and the incredible accuracy of reranker models.

On the the engineering challenges and lessons learned from building Claude's Research system

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: