1. Preparing the Node Environment Install nvm 1 curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.4/install.sh | bash Set the nvm environment variables You need to edit Bash’s config file and add the following: 1 2 3 export NVM_DIR="$HOME/.nvm" [ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh" [ -s "$NVM_DIR/bash_completion" ] && \. "$NVM_DIR/bash_completion" Install Node 1 nvm install 22 default 2.

The same applies to a desktop environment. 1. Prepare the node.js environment Install nvm 1 curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash 1 2 echo 'export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"' >> ~/.bashrc echo '[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"' >> ~/.bashrc 1 source

1. Get the Project ID and API Key Visit https://aistudio.google.com/apikey Get the project ID and API key, and set them as environment variables 1 2 echo "export GOOGLE_CLOUD_PROJECT=" >> ~/.bashrc echo "export GEMINI_API_KEY=" >> ~/.bashrc 1 source ~/.bashrc 2. Prepare the Node.js Environment Install nvm 1 curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh |

1. LLM Deployment Tool llama.cpp Research on large models is split into two parts: training and inference. The training process is essentially the process of finding model parameters that minimize the model’s loss function and optimize the inference results. Once training is complete, the model’s parameters are fixed, and at

1. What Documentation as Tooling Means “Documentation as tooling, tooling as a product” is a slogan I have repeated many times in my previous blog posts. A good document is no match for a tool that is pleasant to use. A script or a single command is more direct than reading documentation and can solve the problem faster.