An author describes the process of replacing various daily browser extensions—such as Grammarly and video summarizers—with a single local Large Language Model (LLM). By setting up Ollama to run models like Qwen or Llama on a local machine, they use extensions like PageAssist to bridge the gap between the model and the web interface. This setup enables advanced document analysis and agentic tasks through automation tools rather than simple one-to-one extension replacements.
- Using Ollama as a backend for browser communication via CORS configuration.
- Running models directly in browsers using WebGPU or WebAssembly.
- Integrating LLMs with Python scripts and workflow automation for custom tasks like price tracking.
- Using Ollama as a backend for browser communication via CORS configuration.
- Running models directly in browsers using WebGPU or WebAssembly.
- Integrating LLMs with Python scripts and workflow automation for custom tasks like price tracking.
