Offline Agentic Coding (www.williamangel.net)

In a recent announcement, Ollama has introduced the capability to use its service as a backend for Claude code, enabling offline agentic coding. This means developers can leverage local machine learning models for coding tasks without requiring an internet connection, a significant advancement for environments with limited connectivity. This feature was tested on an airplane, highlighting its practical utility for developers on the go. However, while this offline coding capability is groundbreaking, the overall performance of local models has raised some concerns. Among various models tested, Gemma4:e2b did not complete any tasks despite its speed, while qwen3-coder-next:q4_K_M delivered reasonable results but was notably slow and resource-intensive, consuming 50-60GB of memory. Although the prospect of self-programming machines is enticing, the feedback suggests that serious hardware is necessary to run these models effectively, which might not yield superior results compared to traditional coding methods. While it's a remarkable step forward, the technology still needs refinement to be more practical for everyday use.