sherlock VS fastapi

In the interest of cleaning my digital life a bit I really want to delete all of my old accounts that I no longer use. The terminal application "Sherlock" on github can search for instances of a username you input and find associated websites. Sherlock

1.) Search for the username that you use on YouTube on other sites, to find additional information. This could literally involve putting the username in to different search engines, or using tools such as Sherlock.

How can I move and manage large numbers of virtual environments? The most of them is based on GitHub tools, for example: https://github.com/sherlock-project/sherlock. Can I move created virtual environments between the same OS (Ubuntu > Ubuntu or Fedora > Fedora)?

If you're tech savy, you could try running their Internet handles through a software called Sherlock. It will search different social media platforms. I'm not sure if there is a web alternate tp Sherlock, but using different search engines is also worth a shot (like bing).

Met this tool thou so many patches it worked https://sherlock-project.github.io/

Sherlock-Project is the most efficient way to hunt down social media accounts by username across social networks. This is great if your target uses the same username in most media platforms.  https://github.com/sherlock-project/sherlock

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include: