Ecosia and Qwant are developing a web index for Europe to challenge the dominance of Google and Bing.
Gemma Scope is a research tool for analyzing and understanding the inner workings of the Gemma 2 generative AI models, allowing examination of individual AI model layers during request processing.
This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
Simon Willison reviews the new Qwen2.5-Coder-32B, an open-source LLM by Alibaba, which performs well on various coding benchmarks and can run on personal devices like his MacBook Pro M2.
The article describes how the author used Amazon Q Developer to write code for a virtual Commodore 64, starting with a simple BASIC program and then converting it to 6502 assembler for improved performance. The author also shares resources and tips for working with retro computing.
A collection of lightweight AI-powered tools built with LLaMA.cpp and small language models.
This paper explores the structure of the feature point cloud discovered by sparse autoencoders in large language models. It investigates three scales: atomic, brain, and galaxy. The atomic scale involves crystal structures with parallelograms or trapezoids, improved by projecting out distractor dimensions. The brain scale focuses on modular structures, similar to neural lobes. The galaxy scale examines the overall shape and clustering of the point cloud.
The article discusses the emerging role of AI agents as distinct users, requiring designers to adapt their practices to account for the needs and capabilities of these intelligent systems.
- Agents are becoming active users in systems, requiring designers to extend UX principles to include both humans and A and agents.
- The future of UX lies in understanding and designing for Agent-Computer Interaction.
Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.
A comparison of frameworks, models, and costs for deploying Llama models locally and privately.
- Four tools were analyzed: HuggingFace, vLLM, Ollama, and llama.cpp.
- HuggingFace has a wide range of models but struggles with quantized models.
- vLLM is experimental and lacks full support for quantized models.
- Ollama is user-friendly but has some customization limitations.
- llama.cpp is preferred for its performance and customization options.
- The analysis focused on llama.cpp and Ollama, comparing speed and power consumption across different quantizations.