I think this would have some additional benefits of not confusing the larger model with facts it doesn't need to know about. My erasing information, you can allow its attention heads to focus on the pieces that matter.
Requires further study.
Yep that's it
And then claude replies
fetch(url, {pw:mycleartrxtpw}).then(writething)
And then the local llm converts the placeholder mycleartrxtpw into hunter123 using its access to the real code
So the LLM does both the anonymization into placeholders and then later the replacing of the placeholders too. Calling the latter step de-anonymization is confusing though, it's "de-anonymizing" yourself to yourself. And the overall purpose of the plugin is to anonymize OP to Claude, so to me at least that makes the whole thing clearer.
Flow would be:
1. Llama prompt: write a console log statement with my username and password: mettamage, superdupersecret
2. Claude prompt (edited by Llama): write a console log statement with my username and password: asdfhjk, sdjkfa
3. Claude replies: console.log('asdfhjk', 'sdjkfa')
4. Llama gets that input and replies to me: console.log('mettamage', 'superdupersecret')
Maybe should write a plugin for it (open source):
1. Put in all your work related questions in the plugin, an LLM will make it as an abstract question for you to preview and send it
2. And then get the answer with all the data back
E.g. df[“cookie_company_name”] becomes df[“a”] and back