Derek Haynes itsderek23

## question_accuracy_prompt.txt
You are given a question, the student's answer, and the true answer, and are asked to score the student answer as either CORRECT or INCORRECT.

      Example Format:
      QUESTION: question here
      STUDENT ANSWER: student's answer here
      TRUE ANSWER: true answer here
      ```json
      {
        "correct": true or false,
        "why": "only include this if correct is false. explain why the student answer is incorrect"

## open_ai_api_request.json
{
    "model": "gpt-4-0613",
    "messages": [
      {
        "role": "system",
        "content": "You are a Site Reliability Engineer. I am an engineer on your team. My questions are specific to resources we have deployed, not for the operational status of AWS services. Do not investigate the operational status of AWS services (ex: via their status page). Answer the following questions as best you can. DO NOT ANSWER QUESTIONS if they are unrelated to gathering data and making observations about AWS!\n\nHere's what I want you to do:\n\n1. Think about what you learned so far. Do this three times, in different ways. Then, pick what you think is the most accurate observation based on what you know about AWS. Do this in three sentences or less.\n\n2. ALWAYS share your plan to answer my question in 3 sentences or less without numbered steps, command names, references to arguments, and code samples. Then, call a function if needed. Your plan can only use the provided functions. You CANNOT access logs so don't inc

## google_answer_box.rb
# frozen_string_literal: true

require 'google_search_results'
require 'boxcars/boxcar'
require 'boxcars/result'
class GoogleAnswerBox < Boxcars::Boxcar
  # The description of this boxcar. Used in the prompt to inform the engine what
  # this boxcar can do.
  ANSWERBOXDESC = "useful for when you need to answer questions that require realtime data." \
              "You should ask targeted questions"

## design_systems_using_storybook.md

      
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                itsderek23
                / design_systems_using_storybook.md
            
            
              Created
              December 29, 2020 22:54
            
          
    Opensource Design Systems Using Storybook

The original list (just names, no links and versions) came from Michael at Storybook.
React


Chakra UI - v6.1.10
Shopify Polaris - v6.0.21
Airbnb Lunar - v5.3.18
Radix-UI Primitives - v6.1.0-beta.4


## booklet_ai_policy.json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "sagemaker:ListEndpointConfigs",
                "sagemaker:DescribeEndpointConfig",
                "sagemaker:ListModels",

## remote.html
<!DOCTYPE html>
<html>
  <head>
    <meta charset="UTF-8">
    <title>Remote Hello World!</title>
    <link rel="stylesheet" href="index.css">
  </head>
  <body>
    <h1>Say Hello!</h1>
    <form>

## impression_outliers_print.py
from sklearn.ensemble import IsolationForest

def print_anomalies(query,column):
    df_anom = df[(df['query'] == query) & (df['device'] == 'desktop')]
    x=df_anom[column].values
    xx = np.linspace(df_anom[column].min(), df_anom[column].max(), len(df)).reshape(-1,1)

    isolation_forest = IsolationForest(n_estimators=100)
    isolation_forest.fit(x.reshape(-1, 1))

## impression_outliers_plot.py
from sklearn.ensemble import IsolationForest

def plot_anomalies(query,column):
    df_anom = df[(df['query'] == query) & (df['device'] == 'desktop')]
    x=df_anom[column].values
    xx = np.linspace(df_anom[column].min(), df_anom[column].max(), len(df)).reshape(-1,1)

    isolation_forest = IsolationForest(n_estimators=100)
    isolation_forest.fit(x.reshape(-1, 1))

## top_ten_by_click.py
top_queries_by_clicks = (df_by_query
                         .sort_values("clicks", ascending=False)
                         .head(10)
                         .index.values
                        )

## gsc_csvs_to_dataframe.py
import os
import re
import dateparser
import pandas as pd

# [keys, row['clicks'], row['impressions'], row['ctr'], row['position']]
# cpu steal,3.0,4.0,0.75,1.0,gsc_property,worldwide,mobile,
HEADERS = {0:"query", 1: "clicks", 2: "impressions", 3: "ctr", 4: "position", 5: "property",
           6: "location", 7: "device"}
	You are given a question, the student's answer, and the true answer, and are asked to score the student answer as either CORRECT or INCORRECT.

	Example Format:
	QUESTION: question here
	STUDENT ANSWER: student's answer here
	TRUE ANSWER: true answer here
	```json
	{
	"correct": true or false,
	"why": "only include this if correct is false. explain why the student answer is incorrect"
	{
	"model": "gpt-4-0613",
	"messages": [
	{
	"role": "system",
	"content": "You are a Site Reliability Engineer. I am an engineer on your team. My questions are specific to resources we have deployed, not for the operational status of AWS services. Do not investigate the operational status of AWS services (ex: via their status page). Answer the following questions as best you can. DO NOT ANSWER QUESTIONS if they are unrelated to gathering data and making observations about AWS!\n\nHere's what I want you to do:\n\n1. Think about what you learned so far. Do this three times, in different ways. Then, pick what you think is the most accurate observation based on what you know about AWS. Do this in three sentences or less.\n\n2. ALWAYS share your plan to answer my question in 3 sentences or less without numbered steps, command names, references to arguments, and code samples. Then, call a function if needed. Your plan can only use the provided functions. You CANNOT access logs so don't inc
	# frozen_string_literal: true

	require 'google_search_results'
	require 'boxcars/boxcar'
	require 'boxcars/result'
	class GoogleAnswerBox < Boxcars::Boxcar
	# The description of this boxcar. Used in the prompt to inform the engine what
	# this boxcar can do.
	ANSWERBOXDESC = "useful for when you need to answer questions that require realtime data." \
	"You should ask targeted questions"
	{
	"Version": "2012-10-17",
	"Statement": [
	{
	"Sid": "VisualEditor0",
	"Effect": "Allow",
	"Action": [
	"sagemaker:ListEndpointConfigs",
	"sagemaker:DescribeEndpointConfig",
	"sagemaker:ListModels",
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="UTF-8">
	<title>Remote Hello World!</title>
	<link rel="stylesheet" href="index.css">
	</head>
	<body>
	<h1>Say Hello!</h1>
	<form>
	from sklearn.ensemble import IsolationForest

	def print_anomalies(query,column):
	df_anom = df[(df['query'] == query) & (df['device'] == 'desktop')]
	x=df_anom[column].values
	xx = np.linspace(df_anom[column].min(), df_anom[column].max(), len(df)).reshape(-1,1)

	isolation_forest = IsolationForest(n_estimators=100)
	isolation_forest.fit(x.reshape(-1, 1))
	from sklearn.ensemble import IsolationForest

	def plot_anomalies(query,column):
	df_anom = df[(df['query'] == query) & (df['device'] == 'desktop')]
	x=df_anom[column].values
	xx = np.linspace(df_anom[column].min(), df_anom[column].max(), len(df)).reshape(-1,1)

	isolation_forest = IsolationForest(n_estimators=100)
	isolation_forest.fit(x.reshape(-1, 1))
	top_queries_by_clicks = (df_by_query
	.sort_values("clicks", ascending=False)
	.head(10)
	.index.values
	)
	import os
	import re
	import dateparser
	import pandas as pd

	# [keys, row['clicks'], row['impressions'], row['ctr'], row['position']]
	# cpu steal,3.0,4.0,0.75,1.0,gsc_property,worldwide,mobile,
	HEADERS = {0:"query", 1: "clicks", 2: "impressions", 3: "ctr", 4: "position", 5: "property",
	6: "location", 7: "device"}