A study companion that lives on the macOS desktop, with chat, word lookup, live captions and notes after class. This post covers the scope decisions, the model comparisons and a few things I learned along the way.
https://docs.openclaw.ai/ Deploying OpenClaw Server and system setup Tencent Cloud Lighthouse instance: 4 CPU cores, 4GB RAM, 70GB system disk Ubuntu 22.04.5 LTS 64-bit — ChatGPT recommended this as the most compatible option. Installing OpenClaw Update the system and install dependencies # Update the system sudo apt update && sudo apt upgrade -y # Install base dependencies sudo apt install -y git curl wget unzip build-essential # Install nvm curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash source ~/.bashrc # Install Node.js nvm install --lts nvm use --lts # Verify the install and check versions node -v npm -v Install OpenClaw npm install -g openclaw@latest Start OpenClaw # Initialise config and install openclaw onboard --install-daemon # install-daemon registers the OpenClaw Gateway as a background daemon: it runs # silently, starts on boot, and restarts itself if it crashes From ChatGPT: ...
AI Agents, From Chatbot to Production An AI agent is the LLM application taken further — something that can plan on its own, carry out tasks, and interact with its environment. Foundational papers: Yao, S., et al. (2022). “ReAct: Synergizing Reasoning and Acting in Language Models.” ICLR. [paper] — the ReAct framework, the core pattern behind most agents Wei, J., et al. (2022). “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” NeurIPS. [paper] Wang, G., et al. (2024). “A Survey on Large Language Model based Autonomous Agents.” arXiv. [paper] — a survey of the agent landscape 1. The Overall Progression Chatbot → Tool Use → Agent → Production Agent What each stage means: ...