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- 2024-12-04
2024-12-04
HOT Andrew Ng release AISuite, Funding opportunities, GPT competitor
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Enhancing AI Agents: A Reflection on Pranav Raja's Six-Step Guide
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Enhancing AI Agents: A Reflection on Pranav Raja's Six-Step Guide
The article offers a non-technical framework aimed at founders for enhancing AI agents by focusing on evaluation and iterative refinement. It emphasises establishing a comprehensive evaluation strategy that assesses both final outputs and individual components of the agent's workflow. The article highlights the importance of human oversight through auditing and “human-in-the-loop systems” to ensure quality and facilitate continuous learning.
📰 TRENDING NEWS
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Giving you the latest and best resources to keep your tool sets sharp.
⭐️ 20.1k stars |
GraphGrag offers language-agnostic, structured interactions to build solutions using a pattern language. The platform aims to enable developers to build software systems through shared pattern languages and codifying guides, and the repository has plenty of examples of different GraphGrag 'statements' - to help users familiarise with their syntax.
MODEL |
A Google DeepMind, Columbia University, and UC San Diego group effort working on realistic 4D render of imagery. CAT4D offers a novel approach to generating dynamic 4D scenes from monocular video using a multi-view video diffusion model. CAT4D's technology can be utilised in fields like augmented reality with special interest in robotics simulation environment to dynamically model captured footage.
View the paper or its model on HuggingFace
⭐️ 6.9k stars |
Andrew Ng's AISuite is a Python library offering a unified interface to multiple Generative AI providers, including OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, Hugging Face, and Ollama. It simplifies interactions with various Large Language Models (LLMs) through a standardised interface, allowing developers to seamlessly switch between providers without altering their code. AISuite focuses on chat completions and facilitates easy comparison of responses across different LLMs.
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