cheetah | a very fast brute force webshell password tool | Hacking library

I have a set of legacy data that has aimed to match companies together (based on a number of different factors). However, I would like to do a bit of QA on the matches just based on the names that came from the different profiles (and may have small differences).

Assuming a dataset as follows:

Is their an algorithmic or pythonic way to know for example that 1 and 2 are probably well matched and 3 badly matched? This, without any ML etc.

I would imagine I need to create a score somehow and knock out on this. However, would love some input if their is best practice.

Some ideas I have had is to look through patterns, compare letters in sets etc. However, at a lost to execute them.

I am looking for an easy, concise way to use dplyr::select without rearranging columns.

Consider this dataset:

I'm trying to set up an object instance that will provide values to the Cheetah3 text templating engine.

This is my text template script...

My program is a guessing game that has three questions the user tey to guess the answers of the 3 questions and they have only three chances if they did make three mistakes the program restart again but if they answer the 3 questions before their 3 chances finished they get their score at the end.

The problem

When the user enters a wrong answer from their first try it automatically goes to the second question.

I have a table in psql db like below.

I am trying to make an application with python. But when I try to run it in VS code, it throws an error with VS code as follows:

But when I run the code outside VS code, it works perfectly fine. see below:

The problem that I get is related to file directories. I can say that because it also happens with my audio files. I am not sure what is happening here or what I am doing wrong.

The weird thing about this is that when I enter my main folder's name that has the python script in the directory like this:

I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language

by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.

code:

I am trying to import data from a JSON file (a list of animals) into a React component.

I am getting this error:

TypeError: Cannot read property 'map' of undefined.

I can't figure out why though, because it is defined in my Animal component.

Here is my json file:

I'm doing this "quiz" and I'm trying to add like a scoring system