• 3 Posts
  • 64 Comments
Joined 1 year ago
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Cake day: June 17th, 2023

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  • Usually it doesn’t solve my problems but it gives me a few places to start looking. I know some models are capable of this but to get a perfectly accurate and useful response would probably require it to recall a specific piece of input it was given and not just an “average” of the inputs.



  • Don’t remember the tool, maybe someone here does, but there’s some web service out there that boasts a “no storage” approach. You provide some URI and some other value (maybe username) and it makes a password for you, but it’s always the same for a given combination. Basically it’s a purely functional generator.

    Downside would be forgetting a minor detail (Did it end with a slash or not? What was the username?) or the site going down. You can achieve the same thing yourself with a hash calculator but those passwords are a bitch to type in.

    tl;dr just use KeePass







  • This is the kind of AI stuff that really annoys me. Looking at one of the mutation examples I didn’t see anything that wouldn’t normally be tested by a typical mutation tool. You took a simple, idempotent process and you got an llm to do it slower, less accurately, and using more resources.

    If you wanted to marry the two in a new and possibly useful fashion I would say use an llm to analyze the results of a standard mutation test and give guidance on what issues should be acted upon first. An off-by-one calculation could mean somebody loses a million dollars or it could mean a button is grayed out. Standard mutation tools don’t give you that context.