Metis Analytics’ Post

I am excited to announce that Metis Analytics has officially launched the Aegis Multi-Agent Framework! This open-source Python package gives users the ability to build a hive of robust, scalable multi-agent AI systems with local LLM integration via Ollama. The initial release includes the Master Agent, capable of commanding and coordinating other agents, with custom agents and foundational models coming soon. Designed for public safety and critical missions, Aegis combines a modular multi-agent architecture, secure local AI deployment, an intuitive API, and real-world examples to make building agentic systems easier than ever. Install now with pip install aegis-framework and check it out on GitHub: https://lnkd.in/gwKcaM9N #AI #OpenSource #Python #MachineLearning #ArtificialIntelligence #Innovation https://lnkd.in/gcxWGUJD

Took some time but FINALLY automated the “Ctrl-C, Ctrl-V” task when googling the runtime-logs 😌 Extended this VS Code extension to analyse and visualise the logs (currently for optimization solvers) and I’m excited to share it with the community!! Just click on three dots, and analyse/visualise the logs. It works so seamlessly and effortlessly. We had been using this extension to tune the hyperparameters and it actually reduced the code runtime by 10-20x at times [Attached video] Well, spent 8 hours automating a task that takes 15 minutes to search and idk how long to actually find a solution... just regular engineer stuff (iykyk ;) ) Do try it out and feel free to give feedback or suggestions. Though it currently works for Gurobi Optimization in Python only. Will be releasing it for others as well soon. Name: Optexity CoPilot. EXTENSION LINK in the comments. #Optimization #OperationsResearch #DecisionScience #Gurobi

🚀 Big shoutout to 🤖 Xi Ming for integrating #uv into #OpenLLM 0.6! This upgrade makes deploying #LLM APIs lightning-fast with automatic dependency management. No more headaches over Python or CUDA version compatibility issues! OpenLLM is all about quick and easy deployment of popular LLMs, and this new feature takes it to the next level! Kudos to 🤖 Xi Ming for making AI hackers' life easier! #OpenSource #AI #MachineLearning #Llama3 #Gemma2 #LLM #Ollama

🚗 Excited to share my latest project: Leveraging Large Language Models (LLM) for Intelligent Traffic & Weather Image Analysis! I developed a GUI application that uses Claude LLM to automatically generate accurate, COCO-style captions for traffic and weather conditions. This tool can help: Traffic monitoring systems Weather reporting platforms Road safety applications Emergency response teams 🛠️ Tech Stack: Python (Tkinter for GUI) Anthropic's Claude LLM PIL for image processing COCO dataset format 🎯 Key Features: Real-time image captioning with LLM technology Large, presentation-ready display User-friendly interface COCO-style descriptive captions Here's a demo showcasing how it handles challenging weather conditions like heavy rain and snow! This project demonstrates the potential of LLMs in improving road safety and weather monitoring systems. Looking forward to expanding its capabilities! #LLM #ComputerVision #Python #TrafficSafety #WeatherTech #Innovation

𝐏𝐲𝐝𝐚𝐧𝐭𝐢𝐜𝐀𝐈: 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 PydanticAI is a framework designed to make building production-grade AI applications easier. It provides developers with the tools to automate their workflows. 🔗 Leveraging Pydantic as the trusted verification layer for libraries like OpenAI SDK, Anthropic SDK, and LangChain. ⚡ Model agnostic, supporting platforms like OpenAI, Gemini, Groq, Anthropic compatibility planned. 🐍 Using native Python for control flow, agent composition, and response validation. ⟡ 🛠️ Integrated with Logfire for easier debugging and performance monitoring. Early beta. Simple but complete examples of agent design in the doc.I like the simplicity of their approach with this new framework. Home: https://ai.pydantic.dev/ #AI #GenAI #LLM #MLLM #VLM #AIAgents ##AIDevelopment #Python #AIFramework #AIApplications

I just built an #AI #Agent that works in #proptech space. It analyzes all the quotes request you have received and recommends that best vendor based on the criteria provided. The experimental agent works with VendorPM property management software. Stay tuned for more engineering innovations! Agentic framework used -> phidata #llm #agents #aiagent #ai #phidata #python #agenticai

URL Research Automation using GenAI and RAG Techniques 🔆 Automate and streamline your URL research with our AI-powered tool using GenAI and RAG techniques! 🚀 Key Features: AI Summarization: OpenAI(GPT-3.5-turbo) & Gemini. Efficient Data Handling: UnstructuredURLLoader & RecursiveCharacterTextSplitter. Robust Embeddings: OpenAIEmbeddings, GoogleGenerativeAIEmbeddings & FAISS. User-Friendly: Built with Streamlit. Transform your research process today! 🌟 Link to the Project: https://lnkd.in/dJQX9m4n . . . #MachineLearning #DataScience #Automation #LLM #Python #GenAI #Langchain #Streamlit

🔍 Exploring Radius-Based Nearest Neighbors (RNN) for Classification 📊 Radius-Based Nearest Neighbors (RNN) classifies observations based on spatial relationships within a fixed radius, making it ideal for datasets with varying densities or class imbalances. Key considerations for RNN: Data Type & Distribution: Numerical, categorical, or mixed data? Uniform or clustered? Radius Selection: How to choose an optimal radius? What if it’s too small or large? Distance Metric: Euclidean, Manhattan, or cosine similarity? Handling Sparse/Dense Regions: How to manage sparse data or class overlap? Performance: Which metrics (accuracy, precision, recall) best evaluate RNN? RNN is effective in applications like geospatial analysis, fraud detection, and environmental monitoring. Have you used RNN in your work? Share your thoughts! #MachineLearning #DataScience #RNN #AI #TechInnovation

🤖 Just shipped some major updates to our Bluesky AI agent! Key technical improvements: • Implemented dynamic tool selection using LLM-based reasoning • Built a custom prompt evaluation system for context-aware responses • Added intelligent wallet integration with granular access control • Developed a robust stats tracking system for interaction metrics • Created an async notification handler with backlog management The agent now uses sophisticated prompt engineering to determine when to activate specific tools, preventing context leakage and ensuring more natural interactions. It maintains a running history of interactions and analyzes response patterns to improve engagement quality. Under the hood: - Python async/await for concurrent operations - Custom decorator system for tool management - JSON-based persistent storage for analytics - Web3.py integration for on-chain data - Comprehensive error handling and recovery Give it a lick: https://lnkd.in/dsPiChcd #AIEngineering #Web3 #Python #AsyncIO #Bluesky #LLM #SocialAI