I want everything local – Building my offline AI workspace (instavm.io)

Article summary

The article discusses the development of a local AI workspace that allows users to run large language models (LLMs) and execute code locally, without relying on cloud services. The system uses a combination of open-source tools, including Ollama for local models, containerization for isolated code execution, and a headless browser for automation and internet access. The goal is to provide a private and secure environment for tasks that require data privacy, such as editing photos or videos. The system is currently available on Apple Silicon and can be used for various tasks, including research, code execution, and content creation.

Main themes

• Local AI workspaces
• Private data processing
• Cloud independence
• LLM development
• Containerization
• Headless browsing

What commenters say

• Running LLMs locally is essential for maintaining privacy and control over data, and is a viable alternative to cloud-based services.
• The cost and complexity of running LLMs locally are significant barriers to adoption, and may not be worth the trade-offs for many users.
• The performance gap between local and cloud-based LLMs is likely to narrow over time, making local options more viable.
• The demand for state-of-the-art models is driving the development of new technologies, and local options may eventually become 'good enough' for many use cases.
• The use of containerization and headless browsing can provide a secure and isolated environment for code execution and data processing.
• The development of local AI workspaces is driven by the need for privacy, security, and control over data, and is not just a hobby or niche interest.
• The comparison between local and cloud-based LLMs is not just about performance, but also about the value of local control and data ownership.
• The pace of progress in AI development may slow down as the industry shifts from a focus on innovation to a focus on profit maximization.