Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama
This article argues that users should avoid relying on frontier AI providers for sensitive work due to risks of data theft and model training on user activity. The author advocates for self-hosting models via tools like Ollama to ensure privacy and data sovereignty.
Why it matters
It highlights the growing tension between AI users and providers regarding data privacy, intellectual property, and the security of proprietary workflows.
Maybe you’re a Claude code/codex user diligently avoiding uploading personal data to LLM providers. Is it possible that the most valuable information isn’t your data- but the metadata about your sessions? The intuitions you apply in coming up with ways to coax the ai into solving problems might actually be special. It’s statistically improbable, but Claude might not be gaslighting you. It may be that you’ve actually got a real insight! Your agent sessions are transcripts of the hardest problems you work on. What would it cost you if someone had copies of them?
Last week there was public drama that shines a light on the risk that inference providers are training on user activity with the intent of delivering new discoveries. The mathematicians affected have published concerns about the ethics of frontier providers. If you missed it: https://www.theverge.com/ai-artificial-intelligence/991710/openai-navier-stokes-solution
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