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#4608 First Principles: Make Aerial Drones Whisper Quiet

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#4608 First Principles: Make Aerial Drones Whisper Quiet #4608  Apply First Principles to aerial drones to make them whisper quiet To make an aerial drone "whisper quiet" using First Principles , we must break the system down to its most fundamental physical realities—moving past incremental dampening or standard propeller shapes—and build a solution upward from the core physics of fluid dynamics, acoustics, and mechanics. Here is the foundational deconstruction and reconstruction of drone noise. 1. Deconstruct to Fundamental Realities (The "Why") Drone noise is not a single phenomenon; it is the byproduct of energy conversion. To generate lift, a drone must accelerate a mass of air downward. In doing so, it creates pressure fluctuations. sound is simply pressure fluctuations traveling through a medium. The primary physical sources of drone noise break down into three fundamental pillars: A. Aerodynamic Noise (The Air) Rotational (Tonal) Noise: As a blade passes a ...
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#4607 Langchain Agents with Ollama --- TBD --- TBD --- TBD ---

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#4607 Langchain Agents with Ollama --- TBD --- TBD --- TBD --- https://docs.langchain.com/oss/python/langchain/agents #4607  From <>  Google Gemini (3.5 Flash)
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#4606 CrewAI and Ollama Board of Directors for Innovation

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#4606 CrewAI and Ollama Board of Directors for Innovation https://docs.crewai.com/v1.15.3/en/tools/overview #4606  Provide a python app using CrewAI and Ollama supported local models with a board of directors to 1) find opportunities, 2) analyze the opportunity, 3) suggest solutions, and 4) implement solutions using locally implemented agents that support local tool use. Include a critic for each agent and feed back its response to the original agent for follow-up for each step. Support Web Search and File tools. Here is a complete, production-ready Python application using CrewAI and Ollama local models. To satisfy the requirement of having a Critic for each Agent that sends feedback back to the original agent for revisions before moving to the next phase, we use CrewAI Flows . Flows provide the state management and routing hooks necessary to create iterative critique loops between agents. Core Prerequisites First, make sure you have the required packages installed: pip instal...
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#4605 Python Board of Directors for Innovation

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#4605 Python Board of Directors for Innovation #4605  Provide a python app using Ollama supported local models with a board of directors to 1) find opportunities, 2) analyze the opportunity, 3) suggest solutions, and 4) implement solutions using locally implemented agents that support local tool use. Include a critic and response for each step. Building a "Board of Directors" using local LLMs is a fantastic way to handle complex problem-solving. By using local models via Ollama and a multi-agent framework, you can ensure complete privacy and zero API costs. For this implementation, we will use AutoGen (specifically the updated autogen-agentchat architecture), as it natively supports local tool calling, structured multi-agent conversations, and strict transition loops (e.g., Director -> Critic -> Director). Prerequisites First, ensure you have Ollama running locally with a tool-capable model (like llama3.1 or mistral ). pip install autogen-agentchat autogen-ext-ollam...
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