A local-model setup angle for advanced Hermes users
The goal is to connect local or to an AI harness and run them without internet access. Online provider models such as Claude, OpenAI, Mistral, Perplexity, xAI Grok, and Gemini are outside the target. The focus is model choice and training, not , file use, or MCP setup.
Many current AI harness setups depend on online providers, which can lead to token costs and too many tool calls. The desired direction is to reduce those costs and wasted calls in work environments such as VSCode, Cursor, OpenCode, OpenClaw, and Hermes. The setup already has a working AI harness and access to strong hardware, so space, power use, and heat matter more than budget.
Past experience with OpenAI Operator and Claude Code, , and showed that computer use worked fairly well before workflows moved more toward API-only access. and MoE models have produced usable results, even with the usual drift in tool and function use.
Key points
- Hermes is mentioned alongside VSCode, Cursor, OpenCode, and OpenClaw as part of a coding-.
- The target setup uses offline local models instead of major online provider models.
- The main motivation is to cut token costs and reduce excessive tool calls.
- The focus is model and training behavior, not browser use, file handling, or MCP.
- Large models and MoE models have shown usable results despite tool-use drift.