This GitHub repository directory contains resources for evaluating Large Language Models (LLMs), including a Jupyter Notebook demonstrating how to use LLM Arena as a judge and a Python script for the same purpose. It also includes a README file with instructions on how to view the notebook if it doesn't render correctly on GitHub.
An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
Nvidia’s NeMo Retriever models and RAG pipeline make quick work of ingesting PDFs and generating reports based on them. Chalk one up for the plan-reflect-refine architecture.
This repository contains the source code for the summarize-and-chat project. This project provides a unified document summarization and chat framework with LLMs, aiming to address the challenges of building a scalable solution for document summarization while facilitating natural language interactions through chat interfaces.
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
A blog post comparing when to use regular Google search versus LLMs for research, outlining the strengths and weaknesses of each. It details scenarios where search engines excel (facts, current events, specific sources) and where LLMs shine (analysis, synthesis, creative thinking). It also lists tasks LLMs struggle with, such as complex reasoning, real-time information, and fact verification.
This article discusses the challenges of assembly planning in manufacturing, highlighting its complexity and the need for AI-powered solutions. It explains the gap between 'as-designed' and 'as-manufactured' views of a product and how AutoAssembler aims to bridge this gap with a 'virtual build' approach. It details why classic approaches to assembly planning have stalled and how recent advancements in compute power, AI, and data models are making industrial-scale assembly planning tractable.
A detailed comparison of the architectures of recent large language models (LLMs) including DeepSeek-V3, OLMo 2, Gemma 3, Mistral Small 3.1, Llama 4, Qwen3, SmolLM3, and Kimi 2, focusing on key design choices and their impact on performance and efficiency.
Sam Altman discusses the imminent arrival of digital superintelligence, its potential impacts on society, and the future of technological progress. He highlights the rapid advancements in AI, the economic and scientific benefits, and the challenges of ensuring safety and equitable access.
MarkItDown is an open-source Python utility that simplifies converting diverse file formats into Markdown, designed to prepare data for LLMs and RAG systems. It handles various file types, preserves document structure, and integrates with LLMs for tasks like image description.
