Most open-source LLM chat interfaces follow the exact same blueprint: a heavy Python or Node.js backend, a Postgres/Redis database, and a multi-container Docker Compose setup just to pass a JSON prompt to an API. If you just want a clean, responsive workspace to inspect token counts, test system prompts, or chat with your local Ollama instance, deploying 2GB+ of Docker containers is massive overkill.
Most open-source LLM chat interfaces follow the exact same blueprint: a heavy Python or Node.js backend, a Postgres/Redis database, and a multi-container Docker Compose setup just to pass a JSON prompt to...
Chats, model configs, system prompts, and API keys are persisted locally in your browser using Dexie.js.
If you just want a clean, responsive workspace to inspect token counts, test system prompts, or chat with your local Ollama instance, deploying 2GB+ of Docker containers is massive overkill.
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Most open-source LLM chat interfaces follow the exact same blueprint: a heavy Python or Node.js backend, a Postgres/Redis database, and a multi-container Docker Compose setup just to pass a JSON prompt to an API. If you just want a clean, responsive workspace to inspect token counts, test system prompts, or chat with your local Ollama instance, deploying 2GB+ of Docker containers is massive overkill. a lightweight, completely serverless, local-first web environment for LLMs.
Everything executes entirely on the client side in your browser.
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