FAQ
Questions that matter before adopting a local AI coding workflow.
These answers stay inside the boundaries of what the current LordCoder project actually documents: local execution, model fit, workflow discipline, guided setup, and a cross-platform core with a Windows-most-polished setup reality.
LordCoder is a local AI coding workflow built around the native LordCoder core, with Ollama support, saved model selection, generated runtime config, practical launchers, and a stronger emphasis on structured developer workflows.
Yes. The core local story is that developers can run AI-assisted coding on their own machines, especially when using local Ollama-backed models.
The native core is now designed with cross-platform use in mind, but the most polished setup experience is still on Windows. The right message is cross-platform product direction with uneven setup maturity.
The product now includes guided launchers, saved model choice, and generated effective config so users are not forced to hand-tune every runtime detail before they can start.
Yes. The positioning is explicitly about multi-file reasoning, repository-aware workflows, testing, and git discipline rather than isolated code snippets.
The default setup includes a pytest command, and the product narrative keeps verification close to editing so AI output stays accountable to the codebase.
The current guidance centers on 16GB RAM as a practical minimum and 24GB as a healthier target, with 7B, 14B, and 32B models positioned by speed and memory budget.
No. The value proposition in the current project is a local, privacy-first coding assistant workflow rather than a cloud-hosted service.