Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.

Agent skills shift AI agents toward procedural tasks with skill.md steps; progressive disclosure reduces context window bloat in real use.

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Library Futures Academy, an open-source retrieval-augmented generation (RAG) pipeline is being developed using historic newspapers held in the archives. This combined with optical character ...

Why Most Retrieval-Augmented Generation Systems Fail Online Retailers — And the Practical Fixes That Turn Customer Interactions Around ...

Using an AI coding assistant to migrate an application from one programming language to another wasn’t as easy as it looked. Here are three takeaways.

A Python library for interacting with the LandingAI Agentic Document Extraction REST API, designed for flexibility, reliability, clarity, and performance. Built for Python 3.9+ and generated with ...

This is a learner-focused project where you'll build a complete research assistant system that automatically fetches academic papers, understands their content, and answers your research questions ...