chatgpt-api VS aifiles

No problem at all :) Check out this github repo: https://github.com/transitive-bullshit/chatgpt-api

You can check out the chatgpt-api repository on GitHub. This API provides free access to ChatGPT https://github.com/transitive-bullshit/chatgpt-api

google for "chatgpt nodejs api" theres a couple things out there such as https://github.com/waylaidwanderer/node-chatgpt-api https://github.com/transitive-bullshit/chatgpt-api you might have to pay to use it though, or risk getting your account banned for using the free website preview thing

I just pushed an update to the `chatgpt` NPM package with support for the official ChatGPT API: https://github.com/transitive-bullshit/chatgpt-api

You forgot to give a link to this (well known in close circles) git: https://github.com/transitive-bullshit/chatgpt-api

Happy to answer any questions 👋 Check out the GitHub repo to get started: https://github.com/transitive-bullshit/chatgpt-api

So yes, those 10TB archives may end up being 5TB if someone spent the time to really comb over, understand, make good decisions, and organize that data. But I have not yet seen anything that can scratch that surface yet, other than perhaps https://github.com/jjuliano/aifiles - but I won't use it until it's local only and has guarantees not to destroy data without explicit permission. An overlay filesystem that shows compression/deduplication with LLM capability like aifiles is probably the best option here.

However, I wouldn't imagine that most people's life data is less than 2TB even with all of this - it's mostly imposed as an artificial constraint by these companies.

I imagine things like this are underway: https://github.com/jjuliano/aifiles

Honestly I think it's actually a pretty fantastic development if that's the direction. I don't use Dropbox much, nor have I used aifiles (due to sending all of your data to OpenAI) - but the idea of being able to not have to manually and tediously look over terabytes of files to completely reorganize all of your files into a better directory hierarchy, and tag each files with meaningful labels, etc sounds phenomenal.

Obviously there are some implementation details for this to be not awful - for example: 1) only local models for local data, 2) making the changes on e.g. ZFS (to allow rollback) or as some type of optional 'overlay' view to switch back and forth from the original to ai-organized, etc. and 3) having thresholds and logic for what may be considered 'duplicates' to be removed, and how to better compress data

As for the de-duplication and processing: this could be very good for dropbox in that, e.g. if a person wants to completely re-encode all of their image files or video files with AV1, the resulting data could be cut in half or more - which saves Dropbox storage space. After which, neural perceptual hashing could be done on all of the files and a threshold of similarity could do de-duplication on a perceptual basis (for example, keep the bigger size file that is 99% similar to a 2x downsized version, and re-encode it). User preference to keep things like tiff files completely intact or any other lossless encoding of their choosing could be good options as well

There's definitely a strange disparity between the computation cost for deploying a decent model to do this compared to the storage cost - but if a (perhaps even non-LLM!) small model is created to be able to plow through data at fast rates could be deployed it may make sense.

Or perhaps the type of semantic compression that LLMs do are of interest for making a new type of lossy compression algorithm of which Dropbox is interested.

aifiles: A CLI that organize and manage your files using AI

Output from the language model is also being injected into a script that is then executed: https://github.com/jjuliano/aifiles/blob/ef529fd6281eaf8d373...

He argued below that he is not vulnerable to indirect prompt injection attacks (https://github.com/greshake/llm-security), but I think he is wrong.