Source URL: https://simonwillison.net/2025/Jun/3/run-your-own-ai/
Source: Simon Willison’s Weblog
Title: Run Your Own AI
Feedly Summary: Run Your Own AI
Anthony Lewis published this neat, concise tutorial on using my LLM tool to run local models on your own machine, using llm-mlx.
An under-appreciated way to contribute to open source projects is to publish unofficial guides like this one. Always brightens my day when something like this shows up.
Via @anthonyllewis.bsky.social
Tags: open-source, llm, generative-ai, mlx, ai, llms
AI Summary and Description: Yes
**Summary:** The text discusses a tutorial by Anthony Lewis on utilizing the llm-mlx tool for running local LLM models, emphasizing the value of contributing to open source through unofficial guides. This insight is particularly relevant for AI professionals looking to enhance their understanding and practical applications of local model deployment.
**Detailed Description:**
The content highlights several key points, making it significant for professionals dealing with AI and generative AI technologies:
– **Tutorial Focus:** Anthony Lewis’s tutorial specifically targets the use of the llm-mlx tool, which allows users to run local large language models (LLMs) on their own machines. This has important implications for developers and AI practitioners who want to leverage LLM capabilities without relying on cloud services.
– **Open Source Contribution:** The text underscores an often-overlooked method of contributing to the open-source community—by creating unofficial guides that can assist others. This approach helps to demystify complex technologies and foster a collaborative environment.
– **Local Deployment:** Running AI models locally can enhance data privacy and security, as it mitigates concerns related to data exposure in cloud environments. It is also relevant to discussions around AI security because local execution of models can help in better control of the operational parameters and sensitive data handling.
– **Community Engagement:** The mention of sharing useful resources on platforms like bsky highlights the importance of community engagement in the tech world, particularly within open-source projects, where knowledge-sharing has a significant impact.
Overall, this tutorial acts as a practical resource for professionals seeking to expand their skill set in AI and contributes positively to the open-source ethos, encouraging wider community involvement.