👋 Welcome to my log!

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 ...

1. Comparing the mainstream payment methods 1. Business-to-business cash transfer How it works: an initiated transfer from a bank counter or corporate online banking, or a payment through a bank’s fund-splitting system with a receipt. Limits: large transfers are possible. Anything above RMB 50,000 goes through the PBOC’s large-value payment system, which doesn’t run on weekends and holidays, and can be affected accordingly (on weekdays it runs from 20:30 the previous evening through 17:15 the same day). ...

I’ve recently been working on some anti-fraud and anti-money-laundering projects, and it’s given me a much more concrete sense of how telecom fraud and money laundering actually work. E-commerce platforms are an easy target for criminals, and as a product manager you have to design with that risk in mind from the start — if crime ends up happening on your product, the best case is you feel terrible about it, and the worst case is regulatory penalties. Under China’s “dual penalty” system, that liability now runs from the platform all the way down to the individuals responsible. Fighting this kind of crime is everyone’s job. ...

Notes from working through Cross-Border E-Commerce Payment and Settlement, my starting point for getting into cross-border payments. A few basic concepts Foreign exchange (FX) Foreign exchange is a claim held by a country’s monetary authorities — the central bank, currency management bodies, exchange stabilization funds, and the treasury — in the form of bank deposits, treasury bills, and short- and long-term government securities, that can be drawn on to cover a balance-of-payments deficit. It covers foreign currency itself, foreign-currency deposits, foreign-currency securities (government bonds, treasury bills, corporate bonds, stocks), and foreign-currency payment instruments such as drafts, bank certificates of deposit, and postal savings certificates. ...

Wrapping up what to prepare for when you’re new to a job, and the mistakes I made so I don’t forget them. 1. Get your tools set up Find out what software the company runs on day to day. If IT already has it installed for you, great. If it’s something you’ve never used, learn the basics when you have a spare moment, or just ask a colleague. 2. Learn how the place actually works ...