LLM
LiteLLM - Providing a Unified Model API Format
· ☕ 2 min read
1. What LiteLLM Is LiteLLM is an open-source LLM adapter: call 100+ APIs (OpenAI, Anthropic, Gemini, Bedrock, Azure, Ollama, vLLM, Qwen, and more) using the standard OpenAI format. Two ways to use it: Python SDK — called directly from your code AI Gateway — a team-level proxy with authentication, billing,

OpenClaw Installation and Configuration
· ☕ 3 min read
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.

You Can Also Implement AI Agents This Way - The MCP Edition
· ☕ 5 min read
1. What Is MCP MCP is short for Model Context Protocol, a protocol for passing context between models and applications. The greatest value of MCP is that, as the model wars are winding down and AI applications are about to explode, it gives developers a unified standard for invoking capabilities beyond the model itself.

You Can Also Land AI Agents This Way - The Operations Events Edition
· ☕ 6 min read
1. Why Operations Events Less invasive Operations teams usually already have observations accumulated in the Metrics and Log dimensions. Operations events can be a new entry point: introduce new operational capabilities without affecting the stability of existing systems. Better suited to automation Metrics represent system state, Log represents specific code behavior, while Event represents changes in components.

Using LLaMA-Factory to Fine-Tune the Qwen3 Model
· ☕ 6 min read
1. Testing the Original Model Start the environment 1 2 3 4 5 6 7 8 9 10 11 12 nerdctl run -it \ --security-opt apparmor=unconfined \ --security-opt seccomp=unconfined \ --gpus all \ --ipc=host \ -p 8000:8000 \ --ulimit memlock=-1 \ --ulimit stack=67108864 \ --name vllm \ --volume /data/models:/data/models \