Peter Smith
⬤ LM Studio just dropped version 0.4.1, and it's a game-changer for developers who want to keep their AI workflows private. The update adds an Anthropic-compatible endpoint that lets you connect Claude Code to models running locally on your own hardware. You can now run GGUF and MLX models without touching remote servers—everything stays on your machine.
⬤ Getting it running is surprisingly straightforward. You fire up a local server, tweak a few environment variables, and suddenly your dev tools are talking to your own models instead of pinging the cloud. Claude Code treats your local setup just like it would a remote API, with full support for streaming responses and all the interactive features you'd expect. The whole AI coding assistants adoption trend is picking up steam, and this update makes it way easier to jump in without sending your code off-site.
⬤ LM Studio backs this up with tool usage support through the same interface, so your local models can execute actions and handle workflows just like their cloud-based cousins. Whether you're working in the terminal or VS Code, everything operates with local inference while keeping feature parity. Research shows that AI tools impact on productivity in significant ways, and local execution adds a privacy layer that many teams need.
⬤ This release signals something bigger: a real shift toward private AI workflows where standardized APIs give you the freedom to choose between local and remote execution. You're not locked into one approach, and switching between them doesn't mean rebuilding your entire toolchain. The compatibility with existing dev tools means you can move workloads to local models without losing the features that make modern coding assistants actually useful.
Peter Smith
