blog: Built-in Local Inference blogpost. - #8808
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Pull request overview
Adds a new documentation blog post introducing goose’s built-in local inference (llama.cpp) and guiding users through setup, model selection, and practical expectations for running fully local open models.
Changes:
- Introduces a new blog article explaining how built-in local inference works in goose and how to enable it in the desktop app.
- Compares built-in inference with the Ollama provider and outlines tradeoffs (performance, context window, tool calling).
- Provides a curated “featured models” list plus usage tips and social metadata.
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Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
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* main: (24 commits) fix: copy and content improvements in goose2 (#8886) feat: make ollama host configurable in goose2 (#8912) polish sidebar navigation and project icons (#8896) fix: model picker stays usable during provider loading (#8900) feat: update provider row after saving credentials (#8914) feat: support google model inventory refresh (#8913) chore: Added goose 2 UI refactor review skill (#8903) blog: goose with peekaboo (#8884) blog: Built-in Local Inference blogpost. (#8808) perf: parallelize provider resolution and eagerly init SQLite pool (#8899) refactor: update goose2 credential management behind provider-scoped ACP/core API (#8887) fix: handle acp requests concurrently (#8781) build: set LLAMA_STATIC_CRT for Windows CUDA (#8901) perf: deduplicate _goose/providers/list RPC call at startup (#8873) chore: add a bit more instructions in the release pr (#8890) chore: disable spellcheck in model search (#8889) add skills to the chat composer (#8881) mergeable configs + cleanup (#8378) refactor: agent provider to use explicit type states (#8879) [goose2] MCP Apps: hydrate and replay app payloads in Goose2 (#8632) ...
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
This pull request adds a comprehensive new blog post to the documentation, providing a practical guide on running goose with local open models. The article covers the benefits, available models, configuration options, practical tips, and a quick reference for different setups, helping users run goose entirely free on their machines.