Technology on Xiangtao's log

DesktopPet: A Desktop Pet I Built to Help Me Study in English

15989119660@163.com (Xiangtao XIAO) — Tue, 29 Sep 2026 00:00:00 +0800

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.

Deploying and Using OpenClaw

15989119660@163.com (Xiangtao XIAO) — Sat, 11 Apr 2026 00:00:00 +0000

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

15989119660@163.com (Xiangtao XIAO) — Sat, 14 Mar 2026 00:00:00 +0000

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

15989119660@163.com (Xiangtao XIAO) — Thu, 12 Mar 2026 00:00:00 +0000

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 (Retrieval-Augmented Generation)

15989119660@163.com (Xiangtao XIAO) — Sun, 08 Mar 2026 00:00:00 +0000

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.

Understanding the Transformer, From a Product Manager's Angle

15989119660@163.com (Xiangtao XIAO) — Fri, 20 Feb 2026 00:00:00 +0000

The Transformer reshaped NLP and became the backbone of every modern LLM. This post walks through its core ideas from a product perspective.

Getting the Basic Concepts of Machine Learning Straight

15989119660@163.com (Xiangtao XIAO) — Sat, 16 May 2020 00:00:00 +0000

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.