Can someone explain how you accidentally rack up such a bill?
For example: You can deploy your Python script as a Lambda. Imagine somewhere in the Python script you'd call your own lambda - twice. You basically turned your lambda into a Fork Bomb that will spawn infinite lambdas
A lot of the times this comes down to a user error.
For example, very similar to your case, I knew someone that enabled Cloudtrail, and configured some things to have Cloudtrail logs dumped on S3. Guess what? Dumping things on S3 also creates a Cloudtrail that gets logged to S3 that Cloudtrail logs. Etc
Doing things like that and creating a loop can get you massive bills
Many people believe that the ToS was added to make Mozilla legally able to train AIs on the collected data.
"Don’t attribute to malice what is easily explained by incompetence"
So yea Mozilla wrote some terms that where ambiguous and could be interpreted in different ways, and 'many people believed' that they did this intentionally and had the worst intentions possible by their interpretation of the new ToS
And yea, now 'many people will believe' that 'Mozilla revised their decision to do this after the backslash' - OR, it was never their intention and now phrased it better after the confusion
People just want to get their pitchforks out and start drama at any possible opportunity without evidence of wrongdoing... Mozilla added stupid stuff to the ToS, ok yea fair enough - but if they actually did "steal user data" - this would be very easily detectable with Wireshark or something
Also some feedback, a bit more technical, since I was trying to see how it works, more of a suggestion I suppose
It looks like you're looping through the documents and asking it for known tags, right? ({str(db.current_library.tags)}.)
I don't know if I would do this through a chat completion and a chat response, there are special functions for keyword-like searching, like embeddings. It's a lot faster, and also probably way cheaper, since you're paying barely anything for embeddings compared to chat tokens
For example, with the phrase “My favorite tropical fruits are __.” The LLM might start completing the sentence with the tokens “mango,” “lychee,” “papaya,” or “durian,” and each token is given a probability score. When there’s a range of different tokens to choose from, SynthID can adjust the probability score of each predicted token, in cases where it won’t compromise the quality, accuracy and creativity of the output.
So I suppose with a larger text, if all lists of things are "LLM Sorted", it's an indicator.
That's probably not the only thing, if it can detect a bunch of these indicators, there's a higher likelihood it's LLM text
Since others already suggested mostly on-topic suggests, here's an alternative suggestion:
Instead of looking specifically for a mentor - look for an open source project that you can help with. Ideally one with a discord or something to it's easy to be in contact the the lead dev. A lot people don't mind mentoring juniors, but in my experience it doesn't happens that explicitly - "be my mentor" - and it might sound like you're asking them a lot.
If you invert it into "Hey I wanna help you with your open-source project, but I don't really know what to do, what your expectations are, how to implement a specific feature" - then you're offering to do work them, instead of asking for something. And implicitly you'll get mentorship in return.
And "real" projects probably also look better on your github / portfolio than only some dummy projects for learning purposes
Documentation? Maintainable? Test cases? You're too attached to old paradigms in a new vibe based world.
Why do you need any of those? If you need any new features, you just re-engineer your prompt and ask the AI to rebuild it from scratch...