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--Top Ranking URLs Report for Nozzle.io
--Data Studio Template can be found here: https://datastudio.google.com/u/1/reporting/359d4414-0cd6-4da1-8df5-2c6908e0ddec/page/pyxcB

WITH
-- find the latest versioned keyword data
-- this can also be used to pin a query to an older version, good for static reports
latest_keyword_source_versions AS (
 SELECT keyword_source_id, MAX(keyword_source_version_id) AS keyword_source_version_id
 FROM nozzledata.nozzle_nozzleofficial.keywords
 WHERE keyword_source_id=930701976723823

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# Based on younesbelkada/finetune_llama_v2.py
# Install the following libraries:
# pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy

from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from transformers import (

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import { NextResponse } from "next/server";

import { getLowestPrice, getHighestPrice, getAveragePrice, getEmailNotifType } from "@/lib/utils";
import { connectToDB } from "@/lib/mongoose";
import Product from "@/lib/models/product.model";
import { scrapeAmazonProduct } from "@/lib/scraper";
import { generateEmailBody, sendEmail } from "@/lib/nodemailer";

export const maxDuration = 300; // This function can run for a maximum of 300 seconds
export const dynamic = "force-dynamic";

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# Must have conda installed
# It costs approximately $0.2 (in GPT-4 API fees) to generate one example with analysis and design, and around $2.0 for a full project.

conda create -n metagpt python=3.11.4
conda activate metagpt
npm --version # to check you have npm installed
# optional: install node if you don't have it
npm install -g @mermaid-js/mermaid-cli
git clone https://github.com/geekan/metagpt
cd metagpt

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{
    "basics": {
        "name": "Pavlos Hatziapostolou",
        "label": "Architectural Engineer",
        "email": "[email protected]",
        "phone": "6946507797",
        "summary": "Creative and results-driven Architectural Engineer with over 20 years of comprehensive experience in designing, supervising, and executing a wide range of architectural projects from residential renovations to healthcare facilities construction. ",
        "location": {
            "address": "Filikis Etairias 30",
            "postalCode": "54621",

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Chat GPT "DAN" (and other "Jailbreaks")

• https://chat.openai.com/
• Is ChatGPT "DAN" Real? Gonna find out [Part 1]
  (https://www.youtube.com/watch?v=-q8woRG9FrI)
• Part 2: I thought ChatGPT DAN was a hoax, but...
  (https://www.youtube.com/watch?v=rHZRrDu3A2U&lc=UgxfrxX8aK7gnCzkend4AaABAg)

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var Horseman = require('node-horseman');
var horseman = new Horseman();

horseman
	.open('http://httpbin.org/ip')
	.setProxy('http://api-key:@proxy.crawlera.com:8010') //change the proxy before use.
	.html('body')
  	.then(function(body) {
    	console.log(body);
	});

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// require libs
// run npm install @google-cloud/bigquery
const { BigQuery } = require('@google-cloud/bigquery');
const fs = require('fs');
// BigQuery Config - see https://cloud.google.com/docs/authentication/production#create_service_account
const options = {
  keyFilename: '{path_to_key_file}',
  projectId: '{project_id}',
};

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-- Author: Krisjan Oldekamp
-- https://stacktonic.com/article/google-analytics-4-and-big-query-create-custom-channel-groupings-in-a-reusable-sql-function

create or replace function `<your-project>.<your-dataset>.channel_grouping`(tsource string, medium string, campaign string) as (
    case
        when (tsource = 'direct' or tsource is null) 
            and (regexp_contains(medium, r'^(\(not set\)|\(none\))$') or medium is null) 
            then 'direct'
        when regexp_contains(campaign, r'^(.*shop.*)$') 
            and regexp_contains(medium, r'^(.*cp.*|ppc|paid.*)$') 

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###################################################
# Author    Krisjan Oldekamp / Stacktonic.com
# Email     [email protected]
# Article   https://stacktonic.com/article/build-a-data-driven-attribution-model-using-google-analytics-4-big-query-and-python
####################################################

#pip install marketing_attribution_models
#pip install --upgrade 'google-cloud-bigquery[bqstorage,pandas]'
#pip install pyarrow -> newest version!