This article discusses methods to measure and improve the accuracy of Large Language Model (LLM) applications, focusing on building an SQL Agent where precision is crucial. It covers setting up the environment, creating a prototype, evaluating accuracy, and using techniques like self-reflection and retrieval-augmented generation (RAG) to enhance performance.
A collection of lightweight AI-powered tools built with LLaMA.cpp and small language models.
All Hands AI has released OpenHands CodeAct 2.1, an open-source software development agent that can solve over 50% of real GitHub issues in SWE-Bench. The agent uses Anthropic’s Claude-3.5 model, function calling, and improved directory traversal to achieve this milestone.
This paper describes a computational cognitive model of instrument operations at the Linac Coherent Light Source (LCLS), a leading scientific user facility.
- The model simulates aspects of human cognition at multiple scales, ranging from seconds to hours, and among agents playing multiple roles.
- The model can predict impacts stemming from proposed changes to operational interfaces and workflows, and its code is open source.
- Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output.
The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
Conclusions:
1. The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
2. The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
3. The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
4. Future work includes extending the model to capture more detailed measurements of individual and team behavior, inter- and intra-team communications, and learning at multiple scales.
- The model simulates aspects of human cognition at multiple scales, ranging from seconds to hours, and among agents playing multiple roles.
- The model can predict impacts stemming from proposed changes to operational interfaces and workflows, and its code is open source.
- Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output.
The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
Conclusions:
1. The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
2. The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
3. The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
4. Future work includes extending the model to capture more detailed measurements of individual and team behavior, inter- and intra-team communications, and learning at multiple scales.
Mistral AI has introduced two methods for creating custom AI agents: La Plateforme Agent Builder, a user-friendly interface, and Agent API, a programmatic solution. This allows users to create and configure agents using Mistral's AI models or fine-tuned models.
This article explores how to run an agent on a federated learning architecture, discussing the benefits, challenges, and steps involved.
Reworkd is a platform that simplifies web data extraction, using LLM code generation to help businesses scale their web data pipelines. No coding skills required.
A website for the Seeed Watcher, a physical AI agent for space management, with features like product catalog, ecosystem, support, and company information.
Explores recent trends in LLM research, including multi-modal LLMs, open-source LLMs, domain-specific LLMs, LLM agents, smaller LLMs, and Non-Transformer LLMs. Mentions examples such as OpenAI's Sora, LLM360, BioGPT, StarCoder, and Mamba.
The future of iOS apps might be services that just tie into Apple Intelligence, with little to no interface of their own.
