Zbigniew Łukasiak zby

## dyson_fission.py
import math

# Constants
SUN_RADIUS = 696340000  # meters
SUN_POWER = 3.828e26  # watts
DYSON_SPHERE_THICKNESS = 1  # meter
DYSON_SPHERE_STANDOFF = 5  # meters
METAL_DENSITY = 7874  # kg/m^3 (assuming iron)
NUCLEAR_PLANT_POWER = 1000e6  # watts (1000 MW, typical large nuclear plant)
NUCLEAR_PLANT_MASS = 200000000  # kg (estimated mass of a nuclear power plant)

## gist:e8a0652b196068f1492f68ff79fee31e
(test_env) zby@zby-Z4:~/llm/litellm$ pip install -e .
Obtaining file:///home/zby/llm/litellm
  Installing build dependencies ... done
  Checking if build backend supports build_editable ... done
  Getting requirements to build editable ... done
  Preparing editable metadata (pyproject.toml) ... done
Collecting importlib-metadata>=6.8.0
  Downloading importlib_metadata-8.2.0-py3-none-any.whl (25 kB)
Collecting openai>=1.40.0
  Downloading openai-1.40.3-py3-none-any.whl (360 kB)

## gist:838e7d8fb27051cfd063af0534ee596f

The answer to the question:"
How is transparency defined in the AI Act and what transparency requirements apply to low-risk Ai systems?
" is:
Transparency in the EU AI Act is not explicitly defined as a single concept. Instead, it is addressed through various obligations for providers and deployers of AI systems to ensure that users and affected individuals are informed about the nature and capabilities of the AI systems they interact with.

For AI systems that are not classified as high-risk (which could be considered lower-risk), the key transparency requirements include:

1. Informing users of AI interaction: Providers must ensure that AI systems intended to interact directly with natural persons inform people they are interacting with an AI system, unless it's obvious.

## gpt_google.py
import requests
import json
from string import Template


#question = input("Enter your question: ")
question = "What was the first major battle in the Ukrainian War?"

# load the api key from a file
with open("config.json", "r") as f:

## object_detection_setup.sh
# tested on Ubuntu 20.04 and 21.04

sudo apt-get -y update
sudo apt-get -y upgrade
sudo apt-get -y dist-upgrade

# UNZIP INSTALLATION
sudo apt-get -y install unzip

# PYTHON 3 INSTALLATION

## session-test
(ns login-app.ex)

(use 'ring.middleware.session
     'ring.util.response)

(defn handl [{session :session uri :uri headers :headers}]
    (let [body
          (str
            "<html><body>"
            session "<br>\n"

## gist:4564733
$ cat src/login_app/core.clj
(ns login-app.core)

(defn handler [request]
  {:status 200
     :headers {"Content-Type" "text/html"}
        :body "Hello World"})


[ snip ]

## gist:4509425
sub frobnicate {
  my( $self, $data ) = @_;
  ...
  $self->twiddle( $data );
  ...
}

sub twiddle {
  my( $self, $data ) = @_;
  ...

## gist:4509300
while ( my $data  = $self->next ) {
    my $featues = Text::FeatureCount->new->analyze($data->{content});

## gist:3986332
sub frobnicate {
    #...
    my $dbh = MyApp->dbh;
    #...
}

# or

sub frobnicate {
    #...
	import math

	# Constants
	SUN_RADIUS = 696340000 # meters
	SUN_POWER = 3.828e26 # watts
	DYSON_SPHERE_THICKNESS = 1 # meter
	DYSON_SPHERE_STANDOFF = 5 # meters
	METAL_DENSITY = 7874 # kg/m^3 (assuming iron)
	NUCLEAR_PLANT_POWER = 1000e6 # watts (1000 MW, typical large nuclear plant)
	NUCLEAR_PLANT_MASS = 200000000 # kg (estimated mass of a nuclear power plant)
	(test_env) zby@zby-Z4:~/llm/litellm$ pip install -e .
	Obtaining file:///home/zby/llm/litellm
	Installing build dependencies ... done
	Checking if build backend supports build_editable ... done
	Getting requirements to build editable ... done
	Preparing editable metadata (pyproject.toml) ... done
	Collecting importlib-metadata>=6.8.0
	Downloading importlib_metadata-8.2.0-py3-none-any.whl (25 kB)
	Collecting openai>=1.40.0
	Downloading openai-1.40.3-py3-none-any.whl (360 kB)

	The answer to the question:"
	How is transparency defined in the AI Act and what transparency requirements apply to low-risk Ai systems?
	" is:
	Transparency in the EU AI Act is not explicitly defined as a single concept. Instead, it is addressed through various obligations for providers and deployers of AI systems to ensure that users and affected individuals are informed about the nature and capabilities of the AI systems they interact with.

	For AI systems that are not classified as high-risk (which could be considered lower-risk), the key transparency requirements include:

	1. Informing users of AI interaction: Providers must ensure that AI systems intended to interact directly with natural persons inform people they are interacting with an AI system, unless it's obvious.
	import requests
	import json
	from string import Template


	#question = input("Enter your question: ")
	question = "What was the first major battle in the Ukrainian War?"

	# load the api key from a file
	with open("config.json", "r") as f:
	# tested on Ubuntu 20.04 and 21.04

	sudo apt-get -y update
	sudo apt-get -y upgrade
	sudo apt-get -y dist-upgrade

	# UNZIP INSTALLATION
	sudo apt-get -y install unzip

	# PYTHON 3 INSTALLATION
	(ns login-app.ex)

	(use 'ring.middleware.session
	'ring.util.response)

	(defn handl [{session :session uri :uri headers :headers}]
	(let [body
	(str
	"<html><body>"
	session "<br>\n"
	$ cat src/login_app/core.clj
	(ns login-app.core)

	(defn handler [request]
	{:status 200
	:headers {"Content-Type" "text/html"}
	:body "Hello World"})


	[ snip ]
	sub frobnicate {
	my( $self, $data ) = @_;
	...
	$self->twiddle( $data );
	...
	}

	sub twiddle {
	my( $self, $data ) = @_;
	...
	while ( my $data = $self->next ) {
	my $featues = Text::FeatureCount->new->analyze($data->{content});
	sub frobnicate {
	#...
	my $dbh = MyApp->dbh;
	#...
	}

	# or

	sub frobnicate {
	#...