ai-generated-examples

// This model estimates the number of real monthly active users across all blockchains
// by creating a table of the top 10 blockchains and their estimated user bases

import "hub:ozziegooen/sTest" as sTest

@name("Top 10 Blockchains and Other")
@doc("Estimated user bases and real user percentages for major blockchains")
blockchains = [
  { name: "Bitcoin", users: 2M to 6M, real_percentage: 85% to 95% },
  { name: "Ethereum", users: 1.5M to 4M, real_percentage: 75% to 90% },

/*
This calculator estimates how many ice cream scoops it would take to scoop out the entire moon.
The model considers different scoop sizes and accounts for uncertainties in measurements.

Results:
- For a medium-sized scoop, it would take approximately 2.3e25 scoops to empty the moon.
- This number is surprisingly large, highlighting the vast difference in scale between everyday objects and celestial bodies.
- The calculation demonstrates the importance of using distributions to account for uncertainties in both scoop and moon volumes.
*/

import "hub:ozziegooen/sTest" as sTest
// This code models and projects the percentages of different AI chip market segments in 2030.
// It includes estimates for data center inference, optical accelerators, and memristor-based in-memory computing.
// The code creates a calculator with key inputs and displays function plots from now until 2030.

@doc("Calculates market share percentage over time")
marketShareProjection(initialShare, growthRate, maxShare, year) = {
  share = initialShare * (1 + growthRate) ^ (year - 2023)
  min([share, maxShare])
}

// This model estimates the number of people looking at themselves in the mirror at any given moment globally.
// It considers global population, time of day, and typical mirror-viewing habits.

import "hub:ozziegooen/sTest" as sTest

// ===== Inputs =====

@name("Global Population (2023)")
@doc("Estimate of the global population in 2023")
global_population = 7.8B to 8B

import "hub:ozziegooen/sTest" as sTest
// Risk assessment model for children playing unsupervised in NYC public parks
// ==== Inputs ====

@name("Key Inputs")
inputs = {
  @name("Total NYC Park Visitors per Year")
  @doc("Based on NYC Parks Department data")
  visitors_per_year = 90M to 110M

/*
Bitcoin Price Projection Model

This code models the projected value of Bitcoin over the next year, considering key factors such as adoption rate, market volatility, and regulatory impact. It provides a calculator for users to input their assumptions and view the projected price distribution.

Key Components:
1. Current Bitcoin Price: Set at $63,126.50 USD
2. Projection Model: Calculates future price based on time and input factors
3. Interactive Calculator: Allows users to adjust key parameters
4. Test Suite: Ensures basic model functionality

// This model calculates HSV transmission probabilities for various sexual activities,
// considering different transmission directions and test accuracies.

import "hub:ozziegooen/sTest" as sTest

// == Inputs ==

@name("Transmission Rates (per 1000 acts)")
@doc(
  "Base transmission rates for different sexual activities and transmission directions"

import "hub:ozziegooen/sTest" as sTest
// Fermi estimate for how much wood a woodchuck could chuck in a year
// ===== Inputs =====

@name("Key Inputs")
inputs = {
  @name("Woodchuck Size (kg)")
  @doc("Estimated weight range of an adult woodchuck")
  woodchuck_size = 3 to 4

import "hub:ozziegooen/sTest" as sTest
/*
This model estimates the number of people eating Shin Ramen globally at any given time
using a Fermi estimation approach, considering factors like global sales, population, and consumption patterns.
*/

// ===== Inputs =====

@name("Key Inputs")
inputs = {

// This model estimates HSV transmission risks for various sexual acts, including a Fermi estimate breakdown and visualizations.
// This model had a very small amount of human editing. I (Ozzie) think it's pretty mediocre. 

import "hub:ozziegooen/sTest" as sTest

@name("Sexual Acts and Their HSV Transmission Risks")
@doc("List of sexual acts with their base transmission risks and gender ratios")
sexual_acts = [
  { name: "Vaginal Intercourse", risk: 0.065, gender_ratio: 6 },
  { name: "Anal Intercourse", risk: 0.3, gender_ratio: 2 },

// Cost-benefit analysis of 10 different AI safety strategies.
// Note: This was manually edited to incorperate the plotOfResults() function.

import "hub:ozziegooen/sTest" as sTest

@name("AI Safety Strategies")
@doc("List of 10 AI safety strategies with their costs and benefits")
strategies = [
  { n: "AI Ethics", c: 1M to 5M, b: 5M to 20M },
  { n: "Alignment Research", c: 2M to 10M, b: 10M to 50M },

// I'm interested in the upcoming presidential election. How likely is one vote, in each of the top 10 key swing states, worth, in terms of how likely is it to change the result of the election? You'll need to take into account the electoral vote, the chance each state will be deciding, and the chance that each state will be decided by 1-2 votes.
// Note: One o1 call was used for this.

/*
Enhanced Model to Estimate the Value of One Vote in Key Swing States for the Upcoming Presidential Election

This model estimates the value of a single vote in pivotal swing states by considering electoral votes, the probability of the state being decisive, the likelihood of the state being decided by a narrow margin, voter turnout variations, demographic influences, and the overall electoral college threshold.
*/

import "hub:ozziegooen/sTest" as sTest

//Simple finance model, 50 lines

import "hub:ozziegooen/sTest" as sTest

test = sTest.test
expect = sTest.expect

// Input Variables
income = normal(50000, 10000)
expenses = normal(40000, 8000)

// 50-line model on simulation of predator/prey populations

import "hub:ozziegooen/sTest" as sTest

run(init, steps, dt, p) = {
  s = [init]
  List.reduceWhile(
    List.upTo(0, steps),
    s,
    {

// Cost-benefit analysis of several longtermist interventions
// Used One o1-mini call, then some user-fixing

import "hub:ozziegooen/sTest" as sTest
expect = sTest.expect

// ------------------------------
// Parameter Definitions
// ------------------------------

// Model the health costs, in terms of expected minutes of being sick, that you would lose by using a towel that has been used multiple times before without being cleaned.
// Note: This model went through one pass of o1-mini.

import "hub:ozziegooen/sTest" as sTest

/* 
  Model to estimate health costs of using an unclean towel.
  Parameters:
    - towelUseCount: Number of times the towel has been used before cleaning.
*/

// When should I eat food in the day? Make some interesting math models that would find the optimal times. 200 lines

import "hub:ozziegooen/sTest" as sTest

energy(t) = 100 * Math.sin(t * Math.pi / 12) + 100

hunger(t, lastMeal) = min([100, max([0, (t - lastMeal) * 10])])

mealQuality(t, hunger) = energy(t) * hunger / 100

// Make a projection of the economic impacts of self-driving cars, over the next 100 years. 100+ lines.

import "hub:ozziegooen/sTest" as sTest

y0 = 2023
yrs = 100
t = [0, yrs]

@name("Adoption rate of self-driving cars over time")
adoption_rate(t) = 1 / (1 + exp(-normal(0.1, 0.02) * (t - 30)))

import "hub:ozziegooen/sTest" as sTest
import "hub:ozziegooen/helpers" as h

/* Event data structure:
   [value_mean, value_std], [cost_min, cost_max], [attendance_alpha, attendance_beta] */
@name("Event Data")
@doc("Contains data for various events: value, cost, and attendance parameters")
eventData = {
  "Movie": [[6, 1], [10, 20], [5, 15]],
  "Hackathon": [[8, 2], [50, 150], [3, 17]],

// Complex financial import "hub:ozziegooen/sTest" as sTest
import "hub:ozziegooen/sTest" as sTest

@name("Initial Revenue")
@doc("Projected initial annual revenue range")
initial_revenue = 1M to 5M

@name("Cost of Goods Sold Percentage")
cogs_percentage = uniform(0.3, 0.5)