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: ...
Prompt Engineering https://www.coursera.org/learn/chatgpt-prompt-engineering-for-developers-project/home/welcome Prompt engineering is the practice of designing and refining the input you give a large language model so it produces the output you actually want. It took off after GPT-3 came out, and it’s basically the bridge between what a user wants and what the model can do. Key papers: Brown, T. B., et al. (2020). “Language Models are Few-Shot Learners.” NeurIPS. [link] — the GPT-3 paper, the first to really demonstrate the potential of prompt engineering Wei, J., et al. (2022). “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” NeurIPS. [link] 1. Prompting Principles Principle 1: Write clear and specific instructions Clarity and specificity are the two things that matter most when writing a prompt. Vague instructions tend to produce unpredictable outputs, while specific ones noticeably improve output quality. ...
RAG RAG (Retrieval-Augmented Generation) combines information retrieval with text generation — it pulls in an external knowledge base to make an LLM’s answers better. Foundational papers: Lewis, P., et al. (2020). “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” NeurIPS. [paper] — the paper that started RAG Guu, K., et al. (2020). “REALM: Retrieval-Augmented Language Model Pre-Training.” ICML. [paper] Izacard, G., & Grave, É. (2021). “Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering.” EACL. [paper] — a different take on RAG 1. Context Window What it is The context window is the maximum amount of text an LLM can read and reason over in a single pass. ...
Transformer: From First Principles to a Product Lens 📚 Why does the Transformer matter so much? The Transformer architecture came out of Vaswani et al.’s 2017 paper “Attention Is All You Need.” It changed NLP completely and is now the backbone behind every major LLM — GPT, BERT, LLaMA, you name it. The core idea: drop CNNs and RNNs entirely and rely only on the attention mechanism to capture dependencies across a sequence. That single move solved two problems at once: Parallelism: RNNs process tokens one at a time; attention lets you process the whole sequence at once, which speeds up training enormously Long-range dependencies: RNNs/LSTMs tend to lose the signal over long sequences (vanishing gradients); attention doesn’t have that problem ...
Tensors Tensor = container Tensors are the foundation everything in modern machine learning sits on. At its core a tensor is just a container — usually of numbers, occasionally of strings, though that’s rarer. Think of it as a bucket of numbers. A 0-D tensor is a scalar; a 1-D tensor is a vector; a 2-D tensor is a matrix; a 3-D tensor (and up) is what you use for shared data storage — things like color images. ...