Claude Code is Anthropic’s terminal‑based AI coding assistant that now runs on self‑hosted models through Ollama. By pairing the CLI with a local LLM such as GLM 4.7 Flash, developers can generate, refactor, and debug code directly from their terminal without internet access, keeping source code and data fully on‑premise.
What Is Claude Code?
Claude Code brings conversational AI to the command line, allowing developers to request new functions, refactor modules, generate documentation, or troubleshoot failing tests without leaving the project folder. The assistant reads surrounding files and markdown, then produces the next token in the code stream, delivering instant, context‑aware completions.
Local Model Support with Ollama
Integrating Ollama transforms Claude Code from a cloud‑dependent tool into a fully local solution. Ollama simplifies the deployment of open‑source LLMs on macOS, Linux, and Windows Subsystem for Linux, enabling Claude Code to route prompts to a locally hosted GLM 4.7 Flash model that runs efficiently on consumer‑grade GPUs.
How Ollama Enables On‑Premise Execution
- Runs models offline, eliminating the need for API keys or internet connectivity.
- Keeps all code and proprietary data inside the developer’s environment, enhancing privacy.
- Reduces latency by processing prompts locally, delivering faster responses.
Simple Installation Across Platforms
Anthropic provides a unified installer script that detects the operating system, downloads the correct binaries, and registers Claude Code as a shell command. The same script works on macOS, Linux, and Windows WSL, streamlining setup for any development workstation.
Installation Steps for macOS, Linux, and Windows WSL
- Run the installer script with administrative privileges.
- Verify that the Claude Code command is available in the terminal.
- Configure the shell to support multi‑line input (Shift + Enter) and optional Vim‑style keybindings.
- Test the installation by asking Claude to generate a simple function.
Real‑World Use Cases
Claude Code’s terminal workflow enables rapid prototyping of complex applications. One example is building an AI Study Assistant that ingests PDF textbooks, extracts key concepts, and generates exam‑ready multiple‑choice questions—all orchestrated from the command line.
Building an AI Study Assistant from the Terminal
- Use Claude to parse PDFs and create structured summaries.
- Prompt Claude to formulate concise questions and answer keys.
- Leverage generated code to scaffold a lightweight web UI for students.
- Iterate entirely within the terminal, reducing context switches.
Benefits for Developers and Enterprises
Running Claude Code locally addresses two major concerns: data security and operational cost. Enterprises can keep proprietary codebases off external servers, complying with strict data‑handling policies, while developers avoid per‑token cloud pricing, making AI assistance more affordable.
Data Security and Cost Efficiency
- All processing occurs on the developer’s machine, eliminating data exfiltration risks.
- No recurring cloud fees; only hardware and electricity costs apply.
- Enables use of internal models that align with corporate compliance standards.
Performance Considerations
The quality of Claude‑generated code depends on the clarity of the surrounding codebase and the specificity of prompts. Well‑structured projects and detailed instructions yield more accurate completions, while ambiguous contexts may reduce output relevance.
Practitioner Insights
Full‑stack engineer Jordan Liu reports that running Claude with GLM 4.7 Flash on a laptop reduced routine refactor times from minutes to seconds. He emphasizes the confidence gained from knowing that no proprietary modules ever leave the machine, and highlights the natural feel of multi‑line input and Vim‑mode bindings.
Future Outlook for On‑Premise AI Coding Assistants
Anthropic’s move to support local models via Ollama reflects a broader industry shift toward on‑premise AI tooling. As open‑source LLMs mature and hardware acceleration becomes commonplace, the distinction between “AI‑assisted” and “AI‑driven” development will continue to blur, positioning tools like Claude Code as essential bridges for modern software engineering.
