Still programming with traditional methods? Try the free AI programming assistant, OpenCode, with fully localized configuration

OpenCode is the ideal open-source alternative to Claude Code—completely free, supports local models, and eliminates reliance on costly cloud APIs. Developers can finally escape exorbitant token fees while maintaining full data privacy and enjoying a powerful, flexible AI coding experience. Table of Contents Introduction LLM Configuration Docker Deployment: Web UI / CLI Service Terminal CLI & VS Code Plugin Integration Comparison: Web UI vs Terminal CLI Common Issues & Security Recommendations Summary & Best Practices Introduction ✨ Core Features of OpenCode ✅ Fully Open Source — MIT License, 109k+ GitHub Stars ✅ Zero-Cost Model Access — Official free models + support for any local or cloud LLM ✅ Fully Local Execution — Data never leaves your network; meets enterprise security & compliance requirements ✅ Modern Web UI — No command line needed; ready to use out of the box ✅ Broad Compatibility — Works with llama.cpp, Ollama, vLLM, LM Studio,…

It took 2 hours and 58 minutes to deploy the ideal AI programming assistant, Claude Code, and configure the local self-hosted model

Deploy Claude Code (by Anthropic) and connect it to a self-hosted large language model (e.g., Qwen, Llama series, etc.), completely bypassing Anthropic's official API, enabling secure offline/intranet development assistance. Table of Contents Preface 🔧 1. Install the Claude Code CLI ⚙️ 2. Global Configuration File Setup 💻 3. VS Code Extension Integration ⚠️ 4. Common Issues and Solutions ✅ 5. Summary and Recommendations Preface Introduction to Claude Code Claude Code is Anthropic’s intelligent programming assistant that supports code understanding, generation, debugging, and refactoring. Through its OpenAI-compatible API interface, Claude Code can seamlessly integrate with any locally hosted LLM service that supports this protocol (e.g., llama.cpp, vLLM, Ollama, etc.)—without relying on Anthropic’s official API. 📖 Official documentation: https://code.claude.com/docs Self-Hosted Large Language Models The previous article, “Outperforming 235B-parameter models: Single-GPU private deployment of OpenClaw,” described how to deploy a local LLM service using llama.cpp. This guide uses that setup as the backend…

Its performance outperforms that of 235B, enabling single-card privatized deployment of OpenClaw

Complete Deployment Guide for a Local AI Agent Platform Based on Docker + llama.cpp This solution has been validated on a single GPU with 22GB VRAM (e.g., RTX 2080 Ti), achieving an optimal balance between performance and functionality. It is well-suited for private AI agent scenarios requiring long context, low concurrency, and high accuracy. Table of Contents Solution Overview Deploying llama.cpp Local Model Service OpenClaw Deployment Guide Common Issues and Notes Summary and Recommendations Preface Why Choose Local Deployment Over Cloud APIs? Advantage Description Data Security All project code, files, and interaction records remain within your internal network, preventing sensitive data leakage. Cost Control Eliminates expensive token-based cloud fees—especially beneficial for high-context, high-interaction platforms like OpenClaw. Full Autonomy Enables free selection of open-source models and full customization of context length, concurrency, quantization precision, and more. Why Qwen3.5 Series Models? Qwen3.5 adopts a hybrid architecture that effectively addresses inference bottlenecks in…