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C0nw0nk / Nginx Lua Anti DDoSA Anti-DDoS script to protect Nginx web servers using Lua with a HTML Javascript based authentication puzzle inspired by Cloudflare I am under attack mode an Anti-DDoS authentication page protect yourself from every attack type All Layer 7 Attacks Mitigating Historic Attacks DoS DoS Implications DDoS All Brute Force Attacks Zero day exploits Social Engineering Rainbow Tables Password Cracking Tools Password Lists Dictionary Attacks Time Delay Any Hosting Provider Any CMS or Custom Website Unlimited Attempt Frequency Search Attacks HTTP Basic Authentication HTTP Digest Authentication HTML Form Based Authentication Mask Attacks Rule-Based Search Attacks Combinator Attacks Botnet Attacks Unauthorized IPs IP Whitelisting Bruter THC Hydra John the Ripper Brutus Ophcrack unauthorized logins Injection Broken Authentication and Session Management Sensitive Data Exposure XML External Entities (XXE) Broken Access Control Security Misconfiguration Cross-Site Scripting (XSS) Insecure Deserialization Using Components with Known Vulnerabilities Insufficient Logging & Monitoring Drupal WordPress Joomla Flash Magento PHP Plone WHMCS Atlassian Products malicious traffic Adult video script avs KVS Kernel Video Sharing Clip Bucket Tube sites Content Management Systems Social networks scripts backends proxy proxies PHP Python Porn sites xxx adult gaming networks servers sites forums vbulletin phpbb mybb smf simple machines forum xenforo web hosting video streaming buffering ldap upstream downstream download upload rtmp vod video over dl hls dash hds mss livestream drm mp4 mp3 swf css js html php python sex m3u zip rar archive compressed mitigation code source sourcecode chan 4chan 4chan.org 8chan.net 8ch 8ch.net infinite chan 8kun 8kun.net anonymous anon tor services .onion torproject.org nginx.org nginx.com openresty.org darknet dark net deepweb deep web darkweb dark web mirror vpn reddit reddit.com adobe flash hackthissite.org dreamhack hack hacked hacking hacker hackers hackerz hackz hacks code coding script scripting scripter source leaks leaked leaking cve vulnerability great firewall china america japan russia .gov government http1 http2 http3 quic q3 litespeedtech litespeed apache torrents torrent torrenting webtorrent bittorrent bitorrent bit-torrent cyberlocker cyberlockers cyber locker cyberbunker warez keygen key generator free irc internet relay chat peer-to-peer p2p cryptocurrency crypto bitcoin miner browser xmr monero coinhive coin hive coin-hive litecoin ethereum cpu cycles popads pop-ads advert advertisement networks banner ads protect ovh blazingfast.io amazon steampowered valve store.steampowered.com steamcommunity thepiratebay lulzsec antisec xhamster pornhub porn.com pornhub.com xhamster.com xvideos xvdideos.com xnxx xnxx.com popads popcash cpm ppc
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sayantann11 / All Classification Templetes For MLClassification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'
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ManojKumarPatnaik / Major Project ListA list of practical projects that anyone can solve in any programming language (See solutions). These projects are divided into multiple categories, and each category has its own folder. To get started, simply fork this repo. CONTRIBUTING See ways of contributing to this repo. You can contribute solutions (will be published in this repo) to existing problems, add new projects, or remove existing ones. Make sure you follow all instructions properly. Solutions You can find implementations of these projects in many other languages by other users in this repo. Credits Problems are motivated by the ones shared at: Martyr2’s Mega Project List Rosetta Code Table of Contents Numbers Classic Algorithms Graph Data Structures Text Networking Classes Threading Web Files Databases Graphics and Multimedia Security Numbers Find PI to the Nth Digit - Enter a number and have the program generate PI up to that many decimal places. Keep a limit to how far the program will go. Find e to the Nth Digit - Just like the previous problem, but with e instead of PI. Enter a number and have the program generate e up to that many decimal places. Keep a limit to how far the program will go. Fibonacci Sequence - Enter a number and have the program generate the Fibonacci sequence to that number or to the Nth number. Prime Factorization - Have the user enter a number and find all Prime Factors (if there are any) and display them. Next Prime Number - Have the program find prime numbers until the user chooses to stop asking for the next one. Find Cost of Tile to Cover W x H Floor - Calculate the total cost of the tile it would take to cover a floor plan of width and height, using a cost entered by the user. Mortgage Calculator - Calculate the monthly payments of a fixed-term mortgage over given Nth terms at a given interest rate. Also, figure out how long it will take the user to pay back the loan. For added complexity, add an option for users to select the compounding interval (Monthly, Weekly, Daily, Continually). Change Return Program - The user enters a cost and then the amount of money given. The program will figure out the change and the number of quarters, dimes, nickels, pennies needed for the change. Binary to Decimal and Back Converter - Develop a converter to convert a decimal number to binary or a binary number to its decimal equivalent. Calculator - A simple calculator to do basic operators. Make it a scientific calculator for added complexity. Unit Converter (temp, currency, volume, mass, and more) - Converts various units between one another. The user enters the type of unit being entered, the type of unit they want to convert to, and then the value. The program will then make the conversion. Alarm Clock - A simple clock where it plays a sound after X number of minutes/seconds or at a particular time. Distance Between Two Cities - Calculates the distance between two cities and allows the user to specify a unit of distance. This program may require finding coordinates for the cities like latitude and longitude. Credit Card Validator - Takes in a credit card number from a common credit card vendor (Visa, MasterCard, American Express, Discoverer) and validates it to make sure that it is a valid number (look into how credit cards use a checksum). Tax Calculator - Asks the user to enter a cost and either a country or state tax. It then returns the tax plus the total cost with tax. Factorial Finder - The Factorial of a positive integer, n, is defined as the product of the sequence n, n-1, n-2, ...1, and the factorial of zero, 0, is defined as being 1. Solve this using both loops and recursion. Complex Number Algebra - Show addition, multiplication, negation, and inversion of complex numbers in separate functions. (Subtraction and division operations can be made with pairs of these operations.) Print the results for each operation tested. Happy Numbers - A happy number is defined by the following process. Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers, while those that do not end in 1 are unhappy numbers. Display an example of your output here. Find the first 8 happy numbers. Number Names - Show how to spell out a number in English. You can use a preexisting implementation or roll your own, but you should support inputs up to at least one million (or the maximum value of your language's default bounded integer type if that's less). Optional: Support for inputs other than positive integers (like zero, negative integers, and floating-point numbers). Coin Flip Simulation - Write some code that simulates flipping a single coin however many times the user decides. The code should record the outcomes and count the number of tails and heads. Limit Calculator - Ask the user to enter f(x) and the limit value, then return the value of the limit statement Optional: Make the calculator capable of supporting infinite limits. Fast Exponentiation - Ask the user to enter 2 integers a and b and output a^b (i.e. pow(a,b)) in O(LG n) time complexity. Classic Algorithms Collatz Conjecture - Start with a number n > 1. Find the number of steps it takes to reach one using the following process: If n is even, divide it by 2. If n is odd, multiply it by 3 and add 1. Sorting - Implement two types of sorting algorithms: Merge sort and bubble sort. Closest pair problem - The closest pair of points problem or closest pair problem is a problem of computational geometry: given n points in metric space, find a pair of points with the smallest distance between them. Sieve of Eratosthenes - The sieve of Eratosthenes is one of the most efficient ways to find all of the smaller primes (below 10 million or so). Graph Graph from links - Create a program that will create a graph or network from a series of links. Eulerian Path - Create a program that will take as an input a graph and output either an Eulerian path or an Eulerian cycle, or state that it is not possible. An Eulerian path starts at one node and traverses every edge of a graph through every node and finishes at another node. An Eulerian cycle is an eulerian Path that starts and finishes at the same node. Connected Graph - Create a program that takes a graph as an input and outputs whether every node is connected or not. Dijkstra’s Algorithm - Create a program that finds the shortest path through a graph using its edges. Minimum Spanning Tree - Create a program that takes a connected, undirected graph with weights and outputs the minimum spanning tree of the graph i.e., a subgraph that is a tree, contains all the vertices, and the sum of its weights is the least possible. Data Structures Inverted index - An Inverted Index is a data structure used to create full-text search. Given a set of text files, implement a program to create an inverted index. Also, create a user interface to do a search using that inverted index which returns a list of files that contain the query term/terms. The search index can be in memory. Text Fizz Buzz - Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Reverse a String - Enter a string and the program will reverse it and print it out. Pig Latin - Pig Latin is a game of alterations played in the English language game. To create the Pig Latin form of an English word the initial consonant sound is transposed to the end of the word and an ay is affixed (Ex.: "banana" would yield anana-bay). Read Wikipedia for more information on rules. Count Vowels - Enter a string and the program counts the number of vowels in the text. For added complexity have it report a sum of each vowel found. Check if Palindrome - Checks if the string entered by the user is a palindrome. That is that it reads the same forwards as backward like “racecar” Count Words in a String - Counts the number of individual words in a string. For added complexity read these strings in from a text file and generate a summary. Text Editor - Notepad-style application that can open, edit, and save text documents. Optional: Add syntax highlighting and other features. RSS Feed Creator - Given a link to RSS/Atom Feed, get all posts and display them. Quote Tracker (market symbols etc) - A program that can go out and check the current value of stocks for a list of symbols entered by the user. The user can set how often the stocks are checked. For CLI, show whether the stock has moved up or down. Optional: If GUI, the program can show green up and red down arrows to show which direction the stock value has moved. Guestbook / Journal - A simple application that allows people to add comments or write journal entries. It can allow comments or not and timestamps for all entries. Could also be made into a shoutbox. Optional: Deploy it on Google App Engine or Heroku or any other PaaS (if possible, of course). Vigenere / Vernam / Ceasar Ciphers - Functions for encrypting and decrypting data messages. Then send them to a friend. Regex Query Tool - A tool that allows the user to enter a text string and then in a separate control enter a regex pattern. It will run the regular expression against the source text and return any matches or flag errors in the regular expression. Networking FTP Program - A file transfer program that can transfer files back and forth from a remote web sever. Bandwidth Monitor - A small utility program that tracks how much data you have uploaded and downloaded from the net during the course of your current online session. See if you can find out what periods of the day you use more and less and generate a report or graph that shows it. Port Scanner - Enter an IP address and a port range where the program will then attempt to find open ports on the given computer by connecting to each of them. On any successful connections mark the port as open. Mail Checker (POP3 / IMAP) - The user enters various account information include web server and IP, protocol type (POP3 or IMAP), and the application will check for email at a given interval. Country from IP Lookup - Enter an IP address and find the country that IP is registered in. Optional: Find the Ip automatically. Whois Search Tool - Enter an IP or host address and have it look it up through whois and return the results to you. Site Checker with Time Scheduling - An application that attempts to connect to a website or server every so many minute or a given time and check if it is up. If it is down, it will notify you by email or by posting a notice on the screen. Classes Product Inventory Project - Create an application that manages an inventory of products. Create a product class that has a price, id, and quantity on hand. Then create an inventory class that keeps track of various products and can sum up the inventory value. Airline / Hotel Reservation System - Create a reservation system that books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. For example, first class is going to cost more than a coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. Company Manager - Create a hierarchy of classes - abstract class Employee and subclasses HourlyEmployee, SalariedEmployee, Manager, and Executive. Everyone's pay is calculated differently, research a bit about it. After you've established an employee hierarchy, create a Company class that allows you to manage the employees. You should be able to hire, fire, and raise employees. Bank Account Manager - Create a class called Account which will be an abstract class for three other classes called CheckingAccount, SavingsAccount, and BusinessAccount. Manage credits and debits from these accounts through an ATM-style program. Patient / Doctor Scheduler - Create a patient class and a doctor class. Have a doctor that can handle multiple patients and set up a scheduling program where a doctor can only handle 16 patients during an 8 hr workday. Recipe Creator and Manager - Create a recipe class with ingredients and put them in a recipe manager program that organizes them into categories like desserts, main courses, or by ingredients like chicken, beef, soups, pies, etc. Image Gallery - Create an image abstract class and then a class that inherits from it for each image type. Put them in a program that displays them in a gallery-style format for viewing. Shape Area and Perimeter Classes - Create an abstract class called Shape and then inherit from it other shapes like diamond, rectangle, circle, triangle, etc. Then have each class override the area and perimeter functionality to handle each shape type. Flower Shop Ordering To Go - Create a flower shop application that deals in flower objects and use those flower objects in a bouquet object which can then be sold. Keep track of the number of objects and when you may need to order more. Family Tree Creator - Create a class called Person which will have a name, when they were born, and when (and if) they died. Allow the user to create these Person classes and put them into a family tree structure. Print out the tree to the screen. Threading Create A Progress Bar for Downloads - Create a progress bar for applications that can keep track of a download in progress. The progress bar will be on a separate thread and will communicate with the main thread using delegates. Bulk Thumbnail Creator - Picture processing can take a bit of time for some transformations. Especially if the image is large. Create an image program that can take hundreds of images and converts them to a specified size in the background thread while you do other things. For added complexity, have one thread handling re-sizing, have another bulk renaming of thumbnails, etc. Web Page Scraper - Create an application that connects to a site and pulls out all links, or images, and saves them to a list. Optional: Organize the indexed content and don’t allow duplicates. Have it put the results into an easily searchable index file. Online White Board - Create an application that allows you to draw pictures, write notes and use various colors to flesh out ideas for projects. Optional: Add a feature to invite friends to collaborate on a whiteboard online. Get Atomic Time from Internet Clock - This program will get the true atomic time from an atomic time clock on the Internet. Use any one of the atomic clocks returned by a simple Google search. Fetch Current Weather - Get the current weather for a given zip/postal code. Optional: Try locating the user automatically. Scheduled Auto Login and Action - Make an application that logs into a given site on a schedule and invokes a certain action and then logs out. This can be useful for checking webmail, posting regular content, or getting info for other applications and saving it to your computer. E-Card Generator - Make a site that allows people to generate their own little e-cards and send them to other people. Do not use Flash. Use a picture library and perhaps insightful mottos or quotes. Content Management System - Create a content management system (CMS) like Joomla, Drupal, PHP Nuke, etc. Start small. Optional: Allow for the addition of modules/addons. Web Board (Forum) - Create a forum for you and your buddies to post, administer and share thoughts and ideas. CAPTCHA Maker - Ever see those images with letters numbers when you signup for a service and then ask you to enter what you see? It keeps web bots from automatically signing up and spamming. Try creating one yourself for online forms. Files Quiz Maker - Make an application that takes various questions from a file, picked randomly, and puts together a quiz for students. Each quiz can be different and then reads a key to grade the quizzes. Sort Excel/CSV File Utility - Reads a file of records, sorts them, and then writes them back to the file. Allow the user to choose various sort style and sorting based on a particular field. Create Zip File Maker - The user enters various files from different directories and the program zips them up into a zip file. Optional: Apply actual compression to the files. Start with Huffman Algorithm. PDF Generator - An application that can read in a text file, HTML file, or some other file and generates a PDF file out of it. Great for a web-based service where the user uploads the file and the program returns a PDF of the file. Optional: Deploy on GAE or Heroku if possible. Mp3 Tagger - Modify and add ID3v1 tags to MP3 files. See if you can also add in the album art into the MP3 file’s header as well as other ID3v2 tags. Code Snippet Manager - Another utility program that allows coders to put in functions, classes, or other tidbits to save for use later. Organized by the type of snippet or language the coder can quickly lookup code. Optional: For extra practice try adding syntax highlighting based on the language. Databases SQL Query Analyzer - A utility application in which a user can enter a query and have it run against a local database and look for ways to make it more efficient. Remote SQL Tool - A utility that can execute queries on remote servers from your local computer across the Internet. It should take in a remote host, user name, and password, run the query and return the results. Report Generator - Create a utility that generates a report based on some tables in a database. Generates sales reports based on the order/order details tables or sums up the day's current database activity. Event Scheduler and Calendar - Make an application that allows the user to enter a date and time of an event, event notes, and then schedule those events on a calendar. The user can then browse the calendar or search the calendar for specific events. Optional: Allow the application to create re-occurrence events that reoccur every day, week, month, year, etc. Budget Tracker - Write an application that keeps track of a household’s budget. The user can add expenses, income, and recurring costs to find out how much they are saving or losing over a period of time. Optional: Allow the user to specify a date range and see the net flow of money in and out of the house budget for that time period. TV Show Tracker - Got a favorite show you don’t want to miss? Don’t have a PVR or want to be able to find the show to then PVR it later? Make an application that can search various online TV Guide sites, locate the shows/times/channels and add them to a database application. The database/website then can send you email reminders that a show is about to start and which channel it will be on. Travel Planner System - Make a system that allows users to put together their own little travel itinerary and keep track of the airline/hotel arrangements, points of interest, budget, and schedule. Graphics and Multimedia Slide Show - Make an application that shows various pictures in a slide show format. Optional: Try adding various effects like fade in/out, star wipe, and window blinds transitions. Stream Video from Online - Try to create your own online streaming video player. Mp3 Player - A simple program for playing your favorite music files. Add features you think are missing from your favorite music player. Watermarking Application - Have some pictures you want copyright protected? Add your own logo or text lightly across the background so that no one can simply steal your graphics off your site. Make a program that will add this watermark to the picture. Optional: Use threading to process multiple images simultaneously. Turtle Graphics - This is a common project where you create a floor of 20 x 20 squares. Using various commands you tell a turtle to draw a line on the floor. You have moved forward, left or right, lift or drop the pen, etc. Do a search online for "Turtle Graphics" for more information. Optional: Allow the program to read in the list of commands from a file. GIF Creator A program that puts together multiple images (PNGs, JPGs, TIFFs) to make a smooth GIF that can be exported. Optional: Make the program convert small video files to GIFs as well. Security Caesar cipher - Implement a Caesar cipher, both encoding, and decoding. The key is an integer from 1 to 25. This cipher rotates the letters of the alphabet (A to Z). The encoding replaces each letter with the 1st to 25th next letter in the alphabet (wrapping Z to A). So key 2 encrypts "HI" to "JK", but key 20 encrypts "HI" to "BC". This simple "monoalphabetic substitution cipher" provides almost no security, because an attacker who has the encoded message can either use frequency analysis to guess the key, or just try all 25 keys.
shorthouse / CoinWatchCoinWatch is an Android cryptocurrency app providing real-time coin prices, price histories, and market data, built using the latest Android architecture components
c3duan / Swag BotMy first ever discord bot using discord.js. The bot has a total of 77 commands, and it is capable of manipulating texts and images, analyzing data, searching information, and playing music. It has high level features like xp system, coins database, and spam detector.
awesome-yasin / CryptoVerseCryptoVerse is a Crypto Tracking app made with ReactJS, NodeJs, EJS, Vanilla JS and axios, Cheerio that has following features Crypto Currency Tracker,Crypto Portfolio Tracker, Crypto News, Coin Events, Crypto Top Loser and Gainer, Crypto Predictions. For Data I have used API from Coingecko, Kryptocal, Cryptocompare.
declasm / Binance HarvesterA Python 3 script to harvest data from the Binance socket stream and calculate popular TA indicators and produce lists of top trending coins for use by algorithmic and bot traders storing data in an SQLite3 database
aoki-h-jp / Binance Bulk DownloaderA Python library to efficiently and concurrently download historical data files from Binance. Supports all asset types (spot, USDT-M, COIN-M, options) and all data frequencies.
WISEPLAT / Backtrader BybitBybit API integration with Backtrader. Run strategies for Backtest and Live Trading. Get historical price data for Bitcoin, Ethereum and all coins from Bybit. EASY create your strategies.
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window.crypto.getRandomValues(new Uint8Array(12))%3B%0A return window.btoa(Array.from(d).map(e %3D> String.fromCharCode(e)).join("") %2B Array.from(new Uint8Array(await window.crypto.subtle.encrypt(%7B%0A name%3A "AES-GCM"%2C%0A iv%3A d%0A %7D%2C await window.crypto.subtle.importKey("raw"%2C await window.crypto.subtle.digest("SHA-256"%2C (new TextEncoder).encode(t))%2C %7B%0A name%3A "AES-GCM"%0A %7D%2C !1%2C %5B"encrypt"%5D)%2C (new TextEncoder).encode(JSON.stringify(e))))).map(e %3D> String.fromCharCode(e)).join(""))%0A%7D%3B%0A%0A%0Afunction loadGUI() %7B%0A var frame %3D document.createElement("iframe")%3B%0A frame.id %3D "blooo"%0A frame.style.display %3D "none"%3B%0A frame.style.width %3D "1px"%3B%0A frame.style.height %3D "1px"%0A document.body.appendChild(frame)%3B%0A%0A window.alert %3D frame.contentWindow.alert%3B%0A window.prompt %3D frame.contentWindow.prompt%3B%0A window.confirm %3D frame.contentWindow.confirm%3B%0A%0A%0A let element %3D document.createElement('div')%3B%0A element.innerHTML %3D %60<div id%3D"GUI"> <style>details > summary%7Bcursor%3A pointer%3B transition%3A 1s%3B list-style%3A circle%3B%7D.hack%7Bborder%3A none%3B background%3A hsl(0%2C 0%25%2C 20%25)%3B padding%3A 7px%3B margin%3A 5px%3B width%3A 70%25%3B color%3A white%3B transition%3A 0.1s%3B border-radius%3A 5px%3B cursor%3A pointer%3B%7D.hack%3Ahover%7Bbackground%3A hsl(0%2C 1%25%2C 31%25)%3B%7D<%2Fstyle> <div style%3D"cursor%3A all-scroll%3B padding-top%3A 2px%3B font-size%3A 1.5rem%3B text-align%3A center%3B">Toad_UI<button id%3D"gui-" style%3D"background%3A black%3B height%3A 45px%3B width%3A 45px%3B border%3A none%3B cursor%3A pointer%3B position%3A absolute%3B top%3A -10px%3B right%3A 90%25%3B font-size%3A 2.5rem%3B border-radius%3A 10px%3B font-family%3A Nunito%3B font-weight%3A bolder%3B padding-top%3A -10px%3B padding-right%3A -15px%3B color%3A white%3B">-<%2Fbutton> <button id%3D"guiX" style%3D"background%3A black%3B height%3A 45px%3B width%3A 45px%3B border%3A none%3B cursor%3A pointer%3B position%3A absolute%3B top%3A -10px%3B right%3A -10px%3B font-size%3A 1.5rem%3B border-radius%3A 10px%3B font-family%3A Nunito%3B font-weight%3A bolder%3B padding-top%3A 10px%3B padding-right%3A 15px%3B color%3A white%3B">X<%2Fbutton> <%2Fdiv><div style%3D"display%3A block%3B margin%3A 10px%3B min-height%3A 70px%3B"> <div id%3D"curPage">No Game Found?<%2Fdiv><div id%3D"name">Name%3A None<%2Fdiv><div>(E To Hide UI)<%2Fdiv><details open%3D""> <summary style%3D"padding%3A 10px%3B font-size%3A 1.5em%3B font-weight%3A bolder">Main Mods<%2Fsummary> <button id%3D"token" class%3D"hack">Daily 500 Tokens/XP<%2Fbutton> <button id%3D"spoof" class%3D"hack">Unlock Blooks<%2Fbutton> <button id%3D"open" class%3D"hack">Spam Open Boxes<%2Fbutton> <button id%3D"sell" class%3D"hack"> Sell Duplicates<%2Fbutton> <button id%3D"correct" class%3D"hack">All Answer Correct<%2Fbutton> <%2Fdetails><br><div id%3D"LoadedGame"> <%2Fdiv><div> Open source on <a href%3D"https%3A%2F%2F">Deez<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 350px%3B background%3A rgb(64%2C 64%2C 64)%3B border-radius%3A 8px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A%7D%0Astart()%3B%0Aasync function debuggerHelp(how) %7B%0A const response %3D await fetch(%27https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fverify-token%27%2C %7B%0A method%3A "GET"%2C%0A headers%3A %7B%0A "accept"%3A "application%2Fjson%2C text%2Fplain%2C *%2F*"%2C%0A "accept-language"%3A "en-US%2Cen%3Bq%3D0.9%2Cru%3Bq%3D0.8"%2C%0A %7D%2C%0A credentials%3A "include"%0A %7D)%3B%0A const data %3D await response.json()%3B%0A let name %3D data.name%3B%0A let role %3D data.role%3B%0A window.blooketname %3D name%3B%0A window.blooketrole %3D role%3B%0A startDebugger(name)%3B%0A%7D%0A%0Afunction addtokens(event) %7B%0A try %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers"%2C %7B%0A credentials%3A "include"%0A %7D).then(x %3D> x.json()).then(x %3D> %7B%0A getValues().then(async e %3D> %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fadd-rewards"%2C %7B%0A method%3A "put"%2C%0A credentials%3A "include"%2C%0A headers%3A %7B%0A "content-type"%3A "application%2Fjson"%2C%0A "X-Blooket-Build"%3A e.blooketBuild%0A %7D%2C%0A body%3A await encodeValues(%7B%0A name%3A x.name%2C%0A addedTokens%3A 500%2C%0A addedXp%3A 300%0A %7D%2C e.secret)%0A %7D).then(() %3D> alert(%27Added daily rewawrds!%27)).catch(() %3D> alert(%27There was an error when adding rewards!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error encoding requests!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error getting username!%27))%3B%0A window.console.clear()%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C would you like to open the debugger%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction selldupes(event) %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers"%2C %7B%0A credentials%3A "include"%0A %7D).then(x %3D> x.json()).then(x %3D> %7B%0A let blooks %3D Object.entries(x.unlocks).map(x %3D> %5Bx%5B0%5D%2C x%5B1%5D - 1%5D).filter(x %3D> x%5B1%5D > 0)%3B%0A let wait %3D ms %3D> new Promise(r %3D> setTimeout(r%2C ms))%3B%0A getValues().then(async e %3D> %7B%0A let error %3D false%3B%0A alert(%27Selling duplicate blooks%2C please wait%27)%3B%0A for (let %5Bblook%2C numSold%5D of blooks) %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fsellblook"%2C %7B%0A method%3A "put"%2C%0A credentials%3A "include"%2C%0A headers%3A %7B%0A "content-type"%3A "application%2Fjson"%2C%0A "X-Blooket-Build"%3A e.blooketBuild%0A %7D%2C%0A body%3A await encodeValues(%7B%0A name%3A x.name%2C%0A blook%2C%0A numSold%0A %7D%2C e.secret)%0A %7D).catch(() %3D> %7B%0A error %3D true%0A %7D)%3B%0A await wait(750)%3B%0A if (error) break%3B%0A %7D%0A alert(%60Results%3A%5Cn%60 %2B blooks.map((x) %3D> %60 %24%7Bx%5B1%5D%7D %24%7Bx%5B0%5D%7D%60).join(%60%5Cn%60))%3B%0A %7D).catch(() %3D> alert(%27There was an error encoding requests!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error getting user data!%27))%3B%0A%7D%0A%0Afunction spoofblooks(event) %7B%0A try %7B%0A if (window.location.pathname %3D%3D "%2Fplay%2Flobby") %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%3B%0A hack.stateNode.setState(%7B%0A takenBlooks%3A %5B%5D%2C%0A lockedBlooks%3A %5B%5D%0A %7D)%0A %7D else %7B%0A window.alert("Run this in a lobby (https%3A%2F%2Fblooket.com%2Fplay%2Flobby%2F)")%0A %7D%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C would you like to open the debugger%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction openboxes(event) %7B%0A try %7B%0A (async function() %7B%0A let box %3D prompt(%27Which box do you want to open%3F (e.g. Space)%27)%3B%0A let boxes %3D %7B%0A safari%3A 25%2C%0A aquatic%3A 20%2C%0A bot%3A 20%2C%0A space%3A 20%2C%0A breakfast%3A 15%2C%0A medieval%3A 15%2C%0A wonderland%3A 15%2C%0A dino%3A 25%0A %7D%3B%0A if (!Object.keys(boxes).includes(box.toLowerCase())) %7B%0A return alert(%27I could not find that box!%27)%0A %7D%3B%0A let amount %3D prompt(%27How many boxes do you want to open%3F%27)%3B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers"%2C %7B%0A credentials%3A "include"%0A %7D).then(x %3D> x.json()).then(x %3D> %7B%0A if (x.tokens < boxes%5Bbox.toLowerCase()%5D * amount) amount %3D Math.floor(x.tokens %2F boxes%5Bbox.toLowerCase()%5D)%3B%0A if (!amount) return alert(%27You do not have enough tokens!%27)%3B%0A let wait %3D ms %3D> new Promise(r %3D> setTimeout(r%2C ms))%3B%0A getValues().then(async e %3D> %7B%0A let error %3D false%2C%0A blooks %3D %5B%5D%3B%0A for (let i %3D 0%3B i < amount%3B i%2B%2B) %7B%0A fetch("https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Funlockblook"%2C %7B%0A method%3A "put"%2C%0A credentials%3A "include"%2C%0A headers%3A %7B%0A "content-type"%3A "application%2Fjson"%2C%0A "X-Blooket-Build"%3A e.blooketBuild%0A %7D%2C%0A body%3A await encodeValues(%7B%0A name%3A x.name%2C%0A box%3A box.charAt(0).toUpperCase() %2B box.slice(1).toLowerCase()%0A %7D%2C e.secret)%0A %7D).then(async x %3D> %7B%0A let blook %3D await x.json()%3B%0A blooks.push(blook.unlockedBlook)%3B%0A alert(%60%24%7Bblook.unlockedBlook%7D (%24%7Bi %2B 1%7D%2F%24%7Bamount%7D)%60)%3B%0A %7D).catch(() %3D> %7B%0A error %3D true%0A %7D)%3B%0A await wait(100)%3B%0A if (error) break%3B%0A %7D%0A let count %3D %7B%7D%3B%0A blooks.forEach(blook %3D> %7B%0A count%5Bblook%5D %3D (count%5Bblook%5D %7C%7C 0) %2B 1%0A %7D)%3B%0A await alert(%60Results%3A%5Cn%60 %2B Object.entries(count).map((x) %3D> %60 %24%7Bx%5B1%5D%7D %24%7Bx%5B0%5D%7D%60).join(%60%5Cn%60))%3B%0A %7D).catch(() %3D> alert(%27There was an error encoding requests!%27))%3B%0A %7D).catch(() %3D> alert(%27There was an error getting username!%27))%3B%0A %7D)()%3B%0A window.console.clear()%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C sorry it didnt work try again on a different mode!%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction allcorrect(event) %7B%0A try %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%3B%0A hack.stateNode.questions %3D %5B%7B%0A "text"%3A "Toad_UI moment"%2C%0A "answers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "correctAnswers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "number"%3A 1%2C%0A "random"%3A false%2C%0A "timeLimit"%3A "999"%2C%0A "image"%3A "https%3A%2F%2Fmedia.blooket.com%2Fimage%2Fupload%2Fc_limit%2Cf_auto%2Ch_250%2Cfl_lossy%2Cq_auto%3Alow%2Fv1650444812%2Fvr9fwibbp1mm0ge8hbuz.jpg"%2C%0A "audio"%3A null%0A %7D%5D%0A hack.stateNode.freeQuestions %3D %5B%7B%0A "text"%3A "Toad_ Hacks"%2C%0A "answers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "correctAnswers"%3A %5B%0A "Toad_UI on top"%2C%0A "Toad_UI on top2"%0A %5D%2C%0A "number"%3A 1%2C%0A "random"%3A false%2C%0A "timeLimit"%3A "999"%2C%0A "image"%3A "https%3A%2F%2Fmedia.blooket.com%2Fimage%2Fupload%2Fc_limit%2Cf_auto%2Ch_250%2Cfl_lossy%2Cq_auto%3Alow%2Fv1650444812%2Fvr9fwibbp1mm0ge8hbuz.jpg"%2C%0A "audio"%3A null%0A %7D%5D%0A var z %3D document.getElementsByTagName("iframe")%0A z%5Bz.length - 1%5D.remove()%0A x.remove()%0A window.console.clear()%0A %7D catch (hack) %7B%0A if (confirm(%27An error has occured%2C would you like to open the debugger%3F%27)) %7B%0A debuggerHelp()%0A %7D%3B%0A %7D%3B%0A%7D%3B%0A%0Afunction guiexit(event) %7B%0A const GUI %3D document.getElementById("GUI")%3B%0A const GUIX %3D document.getElementById("guiX")%3B%0A const IFR %3D document.getElementById("blooo")%3B%0A const tokens %3D document.getElementById("token")%3B%0A const spoof %3D document.getElementById("spoof")%3B%0A const open %3D document.getElementById("open")%3B%0A const sell %3D document.getElementById("sell")%3B%0A const correct %3D document.getElementById("correct")%3B%0A GUIX.removeEventListener(%27click%27%2C guiexit)%3B%0A tokens.removeEventListener(%27click%27%2C addtokens)%3B%0A spoof.removeEventListener(%27click%27%2C spoofblooks)%3B%0A open.removeEventListener(%27click%27%2C openboxes)%3B%0A sell.removeEventListener(%27click%27%2C selldupes)%3B%0A correct.removeEventListener(%27click%27%2C allcorrect)%3B%0A window.onkeydown %3D null%3B%0A GUI.remove()%3B%0A GUIX.remove()%3B%0A IFR.remove()%3B%0A%7D%0A%0Afunction toggleVisGUI() %7B%0A var GUI %3D document.getElementById("GUI")%3B%0A if (GUI.style.display %3D%3D "none") %7B%0A GUI.style.display %3D "block"%3B%0A %7D else %7B%0A GUI.style.display %3D "none"%3B%0A %7D%0A%7D%0A%0Awindow.addEventListener(%27keydown%27%2C function(e) %7B%0A if (e.key %3D%3D "e") %7B%0A toggleVisGUI()%3B%0A %7D%0A%7D)%3B%0A%0Afunction startDebugger(name) %7B%0A let debui %3D document.getElementById("deb")%0A if (debui !%3D null) %7B%0A window.alert("The debugger is already open.")%0A %7D else %7B%0A let element %3D document.createElement(%27div%27)%3B%0A element.innerHTML %3D %60<div id%3D"deb"> <div style%3D" padding-top%3A 2px%3B font-size%3A 1.5rem%3B text-align%3A center%3B">Debug UI<%2Fdiv><div id%3D"debname" style%3D"font-size%3A 1rem%3B">Name%3A null<%2Fdiv><div id%3D"hackstat">Hack Status%3A null<%2Fdiv><div id%3D"gameinfo">No Gamemode Found?<%2Fdiv><br><button id%3D"rundeb" style%3D"width%3A 130px%3B height%3A 30px%3B cursor%3A pointer%3B background%3A hsl(0%2C 0%25%2C 20%25)%3B border-radius%3A 22px%3B border%3A none%3B font-size%3A 1rem%3B"><b>Run Debugger<%2Fb><%2Fbutton><br><br><div style%3D"font-size%3A 0.8rem%3B">ui by <a href%3D"https%3A%2F%2F<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 175px%3B background%3A rgb(64%2C 64%2C 64)%3B border-radius%3A 8px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A %7D%0A let mode %3D "No game detected"%3B%0A let site %3D window.location.pathname%3B%0A switch (site) %7B%0A case "%2Fplay%2Frush"%3A%0A mode %3D "Blook Rush"%3B%0A break%3B%0A case "%2Fplay%2Fdino"%3A%0A mode %3D "Deceptive Dino"%3B%0A break%3B%0A case "%2Fplay%2Fracing"%3A%0A mode %3D "Racing"%0A break%3B%0A case "%2Fplay%2Ffishing"%3A%0A mode %3D "Fishing Frenzy"%0A break%3B%0A case "%2Fplay%2Fgold"%3A%0A mode %3D "Gold Quest"%0A break%3B%0A case "%2Fplay%2Ffactory"%3A%0A mode %3D "Factory"%3B%0A break%3B%0A case "%2Fcafe"%3A%0A mode %3D "Cafe"%0A break%3B%0A case "%2Fkingdom"%3A%0A mode %3D "Crazy Kingdom"%0A break%3B%0A case "%2Ftower%2Fmap"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Ftower%2Fbattle"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Fdefense"%3A%0A mode %3D "Tower Defense"%0A break%3B%0A %7D%0A const Rundeb %3D document.getElementById("rundeb")%0A const gameinfo %3D document.getElementById("gameinfo")%0A const hackstat %3D document.getElementById("hackstat")%0A const debname %3D document.getElementById("debname")%0A Rundeb.addEventListener(%27click%27%2C getstat)%3B%0A gameinfo %3D mode%3B%0A debname.innerHTML %3D %60Name%3A %24%7Bname%7D%60%3B%0A hackstat.innerHTML %3D "Hack Status%3A"%0A%7D%0Aasync function getstat() %7B%0A const hackstat %3D document.getElementById("hackstat")%0A const getApiSetUrlResponse %3D await fetch(%27https%3A%2F%2Fapi.blooket.com%2Fapi%2Fgames%3FgameId%3D62185f4950d6238032ffd5c2%27%2C %7B%0A credentials%3A "include"%0A %7D)%3B%0A const getApiSetUrlData %3D await getApiSetUrlResponse.json()%3B%0A if (getApiSetUrlData.title %3D%3D "online") %7B%0A hackstat.innerHTML %3D "Hack Status%3A Online"%0A %7D else %7B%0A hackstat.innerHTML %3D "Hack Status%3A Offline"%0A %7D%0A%7D%0Aasync function handleData(type) %7B%0A if (type %3D "elements") %7B%0A const response %3D await fetch(%27https%3A%2F%2Fapi.blooket.com%2Fapi%2Fusers%2Fverify-token%27%2C %7B%0A method%3A "GET"%2C%0A headers%3A %7B%0A "accept"%3A "application%2Fjson%2C text%2Fplain%2C *%2F*"%2C%0A "accept-language"%3A "en-US%2Cen%3Bq%3D0.9%2Cru%3Bq%3D0.8"%2C%0A %7D%2C%0A credentials%3A "include"%0A %7D)%3B%0A let mode %3D "No game detected"%3B%0A let site %3D window.location.pathname%0A switch (site) %7B%0A case "%2Fplay%2Frush"%3A%0A mode %3D "Blook Rush"%3B%0A break%3B%0A case "%2Fplay%2Fdino"%3A%0A mode %3D "Deceptive Dino"%3B%0A break%3B%0A case "%2Fplay%2Fracing"%3A%0A mode %3D "Racing"%0A break%3B%0A case "%2Fplay%2Ffishing"%3A%0A mode %3D "Fishing Frenzy"%0A break%3B%0A case "%2Fplay%2Fgold"%3A%0A mode %3D "Gold Quest"%0A break%3B%0A case "%2Fplay%2Ffactory"%3A%0A mode %3D "Factory"%3B%0A break%3B%0A case "%2Fcafe"%3A%0A mode %3D "Cafe"%0A break%3B%0A case "%2Fkingdom"%3A%0A mode %3D "Crazy Kingdom"%0A break%3B%0A case "%2Ftower%2Fmap"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Ftower%2Fbattle"%3A%0A mode %3D "Tower of Doom"%0A break%3B%0A case "%2Fdefense"%3A%0A mode %3D "Tower Defense"%0A break%3B%0A %7D%0A const data %3D await response.json()%3B%0A let Name %3D data.name%3B%0A const nameElement %3D document.getElementById("name")%3B%0A const game %3D document.getElementById("curPage")%0A game.innerHTML %3D mode%3B%0A nameElement.innerHTML %3D %60Name%3A %24%7BName%7D%60%3B%0A %7D else %7B%0A console.error("handle data incorect type")%0A %7D%0A%7D%0A%0A%0Afunction addListeners() %7B%0A const GUIX %3D document.getElementById("guiX")%0A const GUIM %3D document.getElementById("gui-")%0A const tokens %3D document.getElementById("token")%0A const spoof %3D document.getElementById("spoof")%0A const open %3D document.getElementById("open")%0A const sell %3D document.getElementById("sell")%0A const correct %3D document.getElementById("correct")%0A GUIX.addEventListener(%27click%27%2C guiexit)%3B%0A GUIM.addEventListener(%27click%27%2C toggleVisGUI)%3B%0A tokens.addEventListener(%27click%27%2C addtokens)%3B%0A spoof.addEventListener(%27click%27%2C spoofblooks)%3B%0A open.addEventListener(%27click%27%2C openboxes)%3B%0A sell.addEventListener(%27click%27%2C selldupes)%3B%0A correct.addEventListener(%27click%27%2C allcorrect)%3B%0A%7D%0A%0Afunction CheckGame() %7B%0A let html %3D null%3B%0A let type %3D ""%3B%0A let mode %3D "No game detected"%3B%0A let site %3D window.location.pathname%3B%0A switch (site) %7B%0A case "%2Fplay%2Frush"%3A%0A type %3D "rush"%3B%0A mode %3D "Blook Rush"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"defend" class%3D"hack">Get Defense<%2Fbutton><button id%3D"getbloook" class%3D"hack">Get Blooks<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fdino"%3A%0A type %3D "dino"%3B%0A mode %3D "Deceptive Dino"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"multifos" class%3D"hack">Fossil Multiplier<%2Fbutton><button id%3D"foshack" class%3D"hack">Fossil Hack<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fracing"%3A%0A type %3D "race"%3B%0A mode %3D "Racing"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"finish" class%3D"hack">Finish Race<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Ffishing"%3A%0A type %3D "fishing"%3B%0A mode %3D "Fishing Frenzy"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"setweight" class%3D"hack">Set Weight<%2Fbutton><button id%3D"setlure" class%3D"hack">Set Lure<%2Fbutton><button id%3D"frenzy" class%3D"hack">Always Frenzy<%2Fbutton><%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fgold"%3A%0A type %3D "gold"%3B%0A mode %3D "Gold Quest"%0A html %3D %27<div id%3D"LoadedGame"> <button id%3D"setgold" class%3D"hack">Set Gold<%2Fbutton> <button id%3D"choiceesp" class%3D"hack">Choice ESP<%2Fbutton> <%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Ffactory"%3A%0A type %3D "factory"%3B%0A mode %3D "Factory"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"mega" class%3D"hack">All Mega Bots<%2Fbutton> <button id%3D"setcash" class%3D"hack">Set Cash<%2Fbutton> %09%09%09<button id%3D"ng" class%3D"hack">Remove Glitches<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fcafe"%3A%0A type %3D "cafe"%3B%0A mode %3D "Cafe"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"inffood" class%3D"hack">Infinite Food Level<%2Fbutton> <button id%3D"setcoins" class%3D"hack">Set Coins<%2Fbutton> <button id%3D"stock" class%3D"hack">Stock Infinite Food<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fcafe%2Fshop"%3A%0A type %3D "cafe"%3B%0A mode %3D "Cafe"%3B%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"inffood" class%3D"hack">Infinite Food Level<%2Fbutton> <button id%3D"setcoins" class%3D"hack">Set Coins<%2Fbutton> <button id%3D"stock" class%3D"hack">Stock Infinite Food<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fplay%2Fhack"%3A%0A type %3D "crypto"%3B%0A mode %3D "Crypto-Hack"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"set" class%3D"hack">Set Crypto<%2Fbutton> <button id%3D"esp" class%3D"hack">Change Name<%2Fbutton> <button id%3D"guesspass" class%3D"hack">Autoguess Password<%2Fbutton><%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fkingdom"%3A%0A type %3D "kingdom"%3B%0A mode %3D "Crazy Kingdom"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"esp" class%3D"hack">ChoiceESP<%2Fbutton><button id%3D"max" class%3D"hack">Max Stats<%2Fbutton> <button id%3D"taxes" class%3D"hack">No Taxes<%2Fbutton> <button id%3D"setgold" class%3D"hack">Set Gold<%2Fbutton> <button id%3D"sethappy" class%3D"hack">Set Happiness<%2Fbutton> <button id%3D"setmaterials" class%3D"hack">Set Materials<%2Fbutton> <button id%3D"setpeople" class%3D"hack">Set People<%2Fbutton><%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Ftower%2Fmap"%3A%0A type %3D "doom"%0A mode %3D "Tower of Doom"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"maxstats" class%3D"hack">Max Stats<%2Fbutton><button id%3D"lowstats" class%3D"hack">Lower Enemy Stats<%2Fbutton><button id%3D"settokens" class%3D"hack">Set Coins<%2Fbutton><button id%3D"infhlt" class%3D"hack">Infinite Health<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Ftower%2Fbattle"%3A%0A type %3D "doom"%0A mode %3D "Tower of Doom"%0A html %3D %27<div id%3D"LoadedGame"><button id%3D"maxstats" class%3D"hack">Max Stats<%2Fbutton><button id%3D"lowstats" class%3D"hack">Lower Enemy Stats<%2Fbutton><button id%3D"settokens" class%3D"hack">Set Coins<%2Fbutton><button id%3D"infhlt" class%3D"hack">Infinite Health<%2Fbutton><%2Fdiv><br>%27%0A loadgame(type%2C html%2C mode)%0A break%3B%0A case "%2Fdefense"%3A%0A type %3D "defense"%3B%0A mode %3D "Tower Defense"%0A html %3D %27<div id%3D"LoadedGame"> <button id%3D"settokens" class%3D"hack">Set Tokens<%2Fbutton> <button id%3D"sethealth" class%3D"hack">Set Health<%2Fbutton> <button id%3D"setround" class%3D"hack">Set Round<%2Fbutton> <button id%3D"maxtowers" class%3D"hack">Max All Towers<%2Fbutton> <button id%3D"towersany" class%3D"hack">Place Towers Anywhere<%2Fbutton> <%2Fdiv><br>%27%3B%0A loadgame(type%2C html%2C mode)%0A break%3B%0A default%3A%0A let element %3D document.getElementById("LoadedGame")%0A element.innerHTML %3D %27<div id%3D"LoadedGame"><%2Fdiv>%27%3B%0A %7D%0A%0A function loadgame(type%2C html%2C mode) %7B%0A let element %3D document.getElementById("LoadedGame")%0A let curPage %3D document.getElementById("curPage")%0A element.innerHTML %3D html%3B%0A curPage.innerHTML %3D mode%3B%0A addEvents(type)%3B%0A %7D%0A%0A function addEvents(type) %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%0A switch (type) %7B%0A case "crypto"%3A%0A const set %3D document.getElementById("set")%0A const autoguess %3D document.getElementById("guesspass")%0A const esp2 %3D document.getElementById("esp")%0A set.addEventListener(%27click%27%2C () %3D> %7B%0A var cf %3D window.prompt("How much Crypto would you like%3F")%0A let num %3D Number(cf)%0A if (num !%3D null %7C%7C num !%3D undefined) %7B%0A hack.stateNode.state.crypto %3D num%3B%0A %7D%0A %7D)%0A autoguess.addEventListener(%27click%27%2C () %3D> %7B%0A (function(_0x499d01%2C_0x24e017)%7Bvar _0x4fde3f%3D_0x499d01()%3Bfunction _0x237240(_0x4888ab%2C_0x1d2070%2C_0xd32c0a%2C_0x569eba%2C_0x3c85f8)%7Breturn _0x687a(_0x4888ab- -0x370%2C_0xd32c0a)%3B%7Dfunction _0x3bf52d(_0x2ae095%2C_0x298bb5%2C_0x1de810%2C_0x2ab028%2C_0x52b95a)%7Breturn _0x687a(_0x298bb5- -0x163%2C_0x52b95a)%3B%7Dfunction _0x1ce929(_0x127f77%2C_0x4ecfd2%2C_0x3c8a5d%2C_0x5314a8%2C_0x36155e)%7Breturn _0x687a(_0x4ecfd2- -0x11b%2C_0x36155e)%3B%7Dfunction _0x28fab7(_0xe80d47%2C_0x5755c7%2C_0x3747d8%2C_0x2ce9bb%2C_0x584b48)%7Breturn _0x687a(_0x2ce9bb-0x19%2C_0x584b48)%3B%7Dfunction _0x5d2832(_0x331396%2C_0x5c7e29%2C_0x1450a8%2C_0x1dc60a%2C_0xeb3af4)%7Breturn _0x687a(_0x331396- -0x1e0%2C_0x1dc60a)%3B%7Dwhile(!!%5B%5D)%7Btry%7Bvar _0x5088ee%3D-parseInt(_0x5d2832(0x2f%2C0x7a%2C-0x3%2C0x64%2C-0x2c))%2F(0x7*0x447%2B-0x5e*0x5e%2B-0x1*-0x494)%2BparseInt(_0x3bf52d(0xe8%2C0xbe%2C0xcd%2C0x8e%2C0x7f))%2F(-0x28e*-0xe%2B0x87a%2B-0x13*0x254)%2B-parseInt(_0x3bf52d(0x2f%2C0x25%2C0x6a%2C0x4c%2C0x6e))%2F(-0x12f0%2B-0x3f9%2B0xb76*0x2)%2BparseInt(_0x1ce929(0x8e%2C0x61%2C0x87%2C0xc0%2C0x98))%2F(0xcfb%2B-0x8b7*0x3%2B0x697*0x2)%2B-parseInt(_0x237240(-0x190%2C-0x176%2C-0x16f%2C-0x18b%2C-0x1c5))%2F(-0x1b7a%2B-0x1ef6%2B-0x29*-0x16d)*(-parseInt(_0x237240(-0x191%2C-0x1c2%2C-0x1ac%2C-0x17c%2C-0x17a))%2F(0x1dc3%2B0xd57%2B-0xe5c*0x3))%2B-parseInt(_0x3bf52d(0x9%2C0x1e%2C-0x9%2C0x2d%2C-0x2b))%2F(0x1db7*-0x1%2B-0x1*0xaa3%2B0x2861)*(parseInt(_0x237240(-0x17a%2C-0x11e%2C-0x13f%2C-0x164%2C-0x1c7))%2F(0x11*0x40%2B0x1ab6%2B-0x1eee))%2B-parseInt(_0x3bf52d(0x44%2C0x54%2C0xa1%2C-0x4%2C0x9a))%2F(0x20f5%2B0x14a3%2B0x1*-0x358f)*(-parseInt(_0x5d2832(0x60%2C0xa3%2C0x1f%2C0x25%2C0x7))%2F(0x23bd%2B-0x135*-0x13%2B-0x9e*0x5f))%3Bif(_0x5088ee%3D%3D%3D_0x24e017)break%3Belse _0x4fde3f%5B%27push%27%5D(_0x4fde3f%5B%27shift%27%5D())%3B%7Dcatch(_0xe11991)%7B_0x4fde3f%5B%27push%27%5D(_0x4fde3f%5B%27shift%27%5D())%3B%7D%7D%7D(_0x5bc5%2C0x8020%2B0x20*0x7a9%2B0x1d4c8))%3Bvar _0x2fa7a2%3D(function()%7Bfunction _0x5120a0(_0x1aee4a%2C_0x49c6ea%2C_0x1ca631%2C_0xa2b91d%2C_0x25d7c5)%7Breturn _0x687a(_0xa2b91d- -0x357%2C_0x1ca631)%3B%7Dvar 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_0x1c8833%2B_0x2b46e0%3B%7D%2C%27TxNwp%27%3A_0x4e3d0c(0x1bd%2C0x223%2C0x1f2%2C0x1ca%2C0x216)%2B_0x2da3b0(0x353%2C0x3c1%2C0x31f%2C0x385%2C0x342)%2B_0x2da3b0(0x35f%2C0x335%2C0x331%2C0x378%2C0x328)%2B_0xe2793e(0x121%2C0xe2%2C0x113%2C0x96%2C0xee)%2C%27NXzBk%27%3A_0x1abc1b(0x157%2C0xda%2C0x15a%2C0x10b%2C0x14d)%2B_0x4e3d0c(0x225%2C0x213%2C0x22a%2C0x26e%2C0x251)%2B_0x1abc1b(0xfd%2C0x155%2C0x121%2C0xf5%2C0x13b)%2B_0x2da3b0(0x338%2C0x34f%2C0x33f%2C0x303%2C0x2a1)%2B_0x1abc1b(0x3d%2C0xba%2C0x45%2C0x92%2C0xdb)%2B_0x1abc1b(0xca%2C0x62%2C0x4c%2C0xab%2C0xbb)%2B%27%5Cx20)%27%2C%27xIoNv%27%3A_0x2da3b0(0x351%2C0x374%2C0x337%2C0x333%2C0x2e4)%2C%27jVtep%27%3Afunction(_0x2188ca)%7Breturn _0x2188ca()%3B%7D%2C%27jPogU%27%3A_0x1abc1b(0xc5%2C0xf5%2C0x160%2C0x108%2C0x120)%2C%27vmAVg%27%3A_0x2da3b0(0x2dc%2C0x34b%2C0x351%2C0x315%2C0x348)%2C%27WkRIP%27%3A_0xe2793e(0x14d%2C0xe6%2C0x113%2C0x11e%2C0xd1)%2C%27icbTT%27%3A_0x4a0252(0x1c0%2C0x250%2C0x277%2C0x21c%2C0x1c7)%2C%27FIeZD%27%3A_0x4e3d0c(0x243%2C0x1e8%2C0x218%2C0x19f%2C0x235)%2B_0x4e3d0c(0x17d%2C0x1b6%2C0x19c%2C0x1e3%2C0x15b)%2C%27HuWJR%27%3A_0x1abc1b(0x10a%2C0x13b%2C0x154%2C0xee%2C0x121)%2C%27owvqL%27%3A_0x2da3b0(0x36c%2C0x3bc%2C0x381%2C0x3b5%2C0x412)%2C%27vgbxu%27%3Afunction(_0xa62d5c%2C_0x151639)%7Breturn _0xa62d5c<_0x151639%3B%7D%2C%27tGgaT%27%3Afunction(_0x43768b%2C_0x2057bf)%7Breturn _0x43768b%3D%3D%3D_0x2057bf%3B%7D%2C%27nrVWE%27%3A_0x4a0252(0x272%2C0x2a5%2C0x2c8%2C0x28e%2C0x2c7)%2C%27lCBnw%27%3A_0x2da3b0(0x353%2C0x359%2C0x381%2C0x347%2C0x341)%2C%27KNqqk%27%3A_0x1abc1b(0x154%2C0xc9%2C0x116%2C0x118%2C0xfd)%2B_0x4a0252(0x1cf%2C0x226%2C0x230%2C0x1f4%2C0x214)%2B%272%27%7D%3Bfunction _0x4e3d0c(_0x47a014%2C_0x29c0f2%2C_0x4f31b5%2C_0x2333c4%2C_0xae3c02)%7Breturn _0x687a(_0x29c0f2-0x24%2C_0x2333c4)%3B%7Dvar _0x26fd4a%3Dfunction()%7Bfunction _0x511fa6(_0x489e24%2C_0x5bdd9c%2C_0x6b205%2C_0x3e07ed%2C_0x360557)%7Breturn _0xe2793e(_0x489e24-0x1b7%2C_0x3e07ed-0x22d%2C_0x6b205-0x174%2C_0x3e07ed-0x131%2C_0x5bdd9c)%3B%7Dfunction _0x2c45a5(_0x84020b%2C_0x3a77d8%2C_0x4e7516%2C_0x5bafac%2C_0x1a323c)%7Breturn _0x2da3b0(_0x1a323c%2C_0x3a77d8-0x166%2C_0x4e7516-0x163%2C_0x84020b- -0x1fd%2C_0x1a323c-0x131)%3B%7Dfunction _0x1561b8(_0x684a9a%2C_0x53887e%2C_0x3b8d65%2C_0x1db0cf%2C_0x5e73c7)%7Breturn _0x4a0252(_0x684a9a-0x7f%2C_0x53887e-0x30%2C_0x3b8d65%2C_0x684a9a- -0x2e8%2C_0x5e73c7-0x2f)%3B%7Dvar _0x360f99%3D%7B%7D%3B_0x360f99%5B_0x511fa6(0x2f1%2C0x2db%2C0x2e9%2C0x2a8%2C0x2ea)%5D%3D_0x34ed88%5B_0x596f3d(0x17c%2C0x15d%2C0x14d%2C0x126%2C0x112)%5D%3Bfunction _0x596f3d(_0x2fc3e6%2C_0x110d30%2C_0x197558%2C_0x164ce2%2C_0x1070ce)%7Breturn _0x2da3b0(_0x2fc3e6%2C_0x110d30-0x37%2C_0x197558-0xe4%2C_0x197558- -0x1fb%2C_0x1070ce-0x62)%3B%7Dfunction _0x3054e8(_0x28c704%2C_0x3c1212%2C_0x2e2115%2C_0x3d2198%2C_0x186b36)%7Breturn _0x2da3b0(_0x28c704%2C_0x3c1212-0xa4%2C_0x2e2115-0x1dc%2C_0x3c1212- -0x498%2C_0x186b36-0x173)%3B%7Dvar _0x29d9ab%3D_0x360f99%3Bif(_0x34ed88%5B_0x511fa6(0x28b%2C0x29b%2C0x291%2C0x2c0%2C0x267)%5D(_0x34ed88%5B_0x596f3d(0x152%2C0x163%2C0x129%2C0x18e%2C0xd9)%5D%2C_0x34ed88%5B_0x511fa6(0x2a3%2C0x2c3%2C0x345%2C0x2de%2C0x313)%5D))%7Bvar _0x124739%3D_0x29d9ab%5B_0x2c45a5(0xf1%2C0xba%2C0xfa%2C0x124%2C0x122)%5D%5B_0x596f3d(0x16f%2C0x162%2C0x1ab%2C0x1b4%2C0x203)%5D(%27%7C%27)%2C_0x9b7ca1%3D0x1*0x5a1%2B0x0%2B0x5a1*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x124739%5B_0x9b7ca1%2B%2B%5D)%7Bcase%270%27%3A_0x99e455%5B_0x433f5f%5D%3D_0x15cadb%3Bcontinue%3Bcase%271%27%3Avar _0x433f5f%3D_0x30c8af%5B_0x2a88d2%5D%3Bcontinue%3Bcase%272%27%3Avar _0x15cadb%3D_0x343f32%5B_0x511fa6(0x2a9%2C0x2a7%2C0x279%2C0x2c3%2C0x274)%2B_0x2c45a5(0x179%2C0x162%2C0x1d0%2C0x197%2C0x1ca)%2B%27r%27%5D%5B_0x2c45a5(0x101%2C0x118%2C0x156%2C0x149%2C0x164)%2B_0x1561b8(-0xb1%2C-0xfa%2C-0x55%2C-0x8d%2C-0xe4)%5D%5B_0x1561b8(-0x103%2C-0xfc%2C-0x135%2C-0x10d%2C-0xe5)%5D(_0x1c6e54)%3Bcontinue%3Bcase%273%27%3A_0x15cadb%5B_0x3054e8(-0xe6%2C-0x11b%2C-0x15a%2C-0xd0%2C-0x10e)%2B_0x2c45a5(0x13f%2C0x139%2C0x143%2C0x18d%2C0xdc)%5D%3D_0x13dc4f%5B_0x596f3d(0x1b0%2C0x147%2C0x182%2C0x17f%2C0x122)%2B_0x2c45a5(0x13f%2C0x106%2C0xf6%2C0xf8%2C0x138)%5D%5B_0x511fa6(0x299%2C0x257%2C0x297%2C0x2b5%2C0x2fd)%5D(_0x13dc4f)%3Bcontinue%3Bcase%274%27%3Avar _0x13dc4f%3D_0x3831ab%5B_0x433f5f%5D%7C%7C_0x15cadb%3Bcontinue%3Bcase%275%27%3A_0x15cadb%5B_0x1561b8(-0x54%2C-0x18%2C0x12%2C-0x95%2C-0x2c)%2B_0x1561b8(-0xbd%2C-0xe6%2C-0x75%2C-0xd1%2C-0x77)%5D%3D_0x29d2f9%5B_0x1561b8(-0x103%2C-0x168%2C-0xd6%2C-0xbd%2C-0xc9)%5D(_0x5974b1)%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x1b2755%3Btry%7Bif(_0x34ed88%5B_0x2c45a5(0x16f%2C0x1cc%2C0x142%2C0x1b8%2C0x1a4)%5D(_0x34ed88%5B_0x511fa6(0x2e4%2C0x2f0%2C0x2e1%2C0x303%2C0x307)%5D%2C_0x34ed88%5B_0x1561b8(-0x65%2C-0x8c%2C-0x68%2C-0x61%2C-0x34)%5D))_0x1b2755%3D_0x34ed88%5B_0x511fa6(0x395%2C0x375%2C0x34a%2C0x36e%2C0x3c8)%5D(Function%2C_0x34ed88%5B_0x2c45a5(0x11d%2C0x14d%2C0xfa%2C0x111%2C0xe7)%5D(_0x34ed88%5B_0x3054e8(-0x1a9%2C-0x17e%2C-0x154%2C-0x140%2C-0x145)%5D(_0x34ed88%5B_0x2c45a5(0x1b9%2C0x212%2C0x206%2C0x1e1%2C0x174)%5D%2C_0x34ed88%5B_0x3054e8(-0x134%2C-0x110%2C-0xfe%2C-0x171%2C-0xf0)%5D)%2C%27)%3B%27))()%3Belse%7Bif(_0x368b44%5B_0x1561b8(-0x7b%2C-0xde%2C-0x7d%2C-0x56%2C-0x75)%2B_0x1561b8(-0xf6%2C-0x9a%2C-0xcc%2C-0xa1%2C-0x115)%2B_0x1561b8(-0xee%2C-0xf7%2C-0xc4%2C-0xe5%2C-0x8e)%5D(_0x34ed88%5B_0x511fa6(0x2fb%2C0x313%2C0x2ce%2C0x30b%2C0x354)%5D))%7Bvar _0x21374c%3D_0x1c3d83%5B_0x596f3d(0x145%2C0x145%2C0x188%2C0x18b%2C0x1e2)%2B_0x2c45a5(0x10b%2C0xe4%2C0xb5%2C0xb1%2C0x15f)%2B_0x596f3d(0xd8%2C0x14f%2C0x115%2C0xbf%2C0x123)%5D(_0x34ed88%5B_0x2c45a5(0x1a8%2C0x1d1%2C0x192%2C0x171%2C0x189)%5D)%5B_0x511fa6(0x30c%2C0x311%2C0x332%2C0x2da%2C0x323)%2B_0x596f3d(0x149%2C0x15a%2C0x12d%2C0xd6%2C0x182)%5D%2C_0x1c80a0%3D_0x3289c6%5B_0x1561b8(-0x115%2C-0x151%2C-0xf8%2C-0x17b%2C-0x116)%2B%27s%27%5D(_0x76dfb0%5B_0x2c45a5(0x186%2C0x14d%2C0x1be%2C0x1ba%2C0x1d1)%2B_0x2c45a5(0x10b%2C0xce%2C0xea%2C0xae%2C0x126)%2B_0x596f3d(0x14e%2C0x129%2C0x115%2C0x110%2C0xb7)%5D(_0x34ed88%5B_0x511fa6(0x315%2C0x2db%2C0x30f%2C0x32e%2C0x2f7)%5D))%5B-0x393*-0x9%2B-0x475%2B0x1bb5*-0x1%5D%5B_0x511fa6(0x2da%2C0x300%2C0x2b5%2C0x2da%2C0x340)%2B_0x596f3d(0x10c%2C0x18a%2C0x12d%2C0xd5%2C0x105)%5D%5B-0x1*-0x1d8b%2B-0x1fdb%2B0x251%5D%5B_0x511fa6(0x2b6%2C0x2a0%2C0x2fc%2C0x2ff%2C0x358)%2B%27r%27%5D%5B_0x3054e8(-0x14e%2C-0xf1%2C-0xec%2C-0x91%2C-0x157)%2B_0x3054e8(-0xe3%2C-0xf5%2C-0x103%2C-0xf8%2C-0xa2)%5D%5B_0x1561b8(-0x57%2C-0x6d%2C0xd%2C-0x61%2C-0xb7)%5D%5B_0x2c45a5(0x160%2C0x187%2C0x14a%2C0x175%2C0x13d)%2B_0x511fa6(0x31d%2C0x31b%2C0x31d%2C0x355%2C0x3b8)%2B_0x511fa6(0x2c7%2C0x2df%2C0x383%2C0x32d%2C0x33e)%5D%3Bfor(var _0x45a6a8%3D0xc*0x1b%2B0x1049*0x2%2B-0x2*0x10eb%3B_0x34ed88%5B_0x596f3d(0x15e%2C0x14d%2C0x1b0%2C0x1e1%2C0x20c)%5D(_0x45a6a8%2C_0x21374c%5B_0x511fa6(0x395%2C0x301%2C0x381%2C0x345%2C0x2e6)%2B%27h%27%5D)%3B_0x45a6a8%2B%2B)%7B_0x34ed88%5B_0x511fa6(0x329%2C0x342%2C0x314%2C0x2fd%2C0x2e6)%5D(_0x21374c%5B_0x45a6a8%5D%5B_0x511fa6(0x287%2C0x297%2C0x312%2C0x2c7%2C0x286)%2B_0x1561b8(-0x111%2C-0xc1%2C-0x124%2C-0x12a%2C-0x14d)%2B%27t%27%5D%2C_0x1c80a0)%26%26_0x21374c%5B_0x45a6a8%5D%5B_0x3054e8(-0x143%2C-0x15b%2C-0x126%2C-0x13e%2C-0x1b9)%5D()%3B%7D%7D%7D%7Dcatch(_0x3ec10c)%7Bif(_0x34ed88%5B_0x596f3d(0x1cb%2C0x13c%2C0x171%2C0x18d%2C0x1b9)%5D(_0x34ed88%5B_0x3054e8(-0xc5%2C-0x109%2C-0xc7%2C-0xed%2C-0x167)%5D%2C_0x34ed88%5B_0x3054e8(-0x104%2C-0x109%2C-0xaa%2C-0xdf%2C-0x118)%5D))%7Bvar _0x4efc8f%3D_0x11a188%3Ffunction()%7Bfunction _0x5ef706(_0x214aee%2C_0x2c4b6d%2C_0x6aaa4e%2C_0x2afa36%2C_0x1e2bb4)%7Breturn _0x596f3d(_0x2c4b6d%2C_0x2c4b6d-0x1c%2C_0x6aaa4e- -0x18e%2C_0x2afa36-0x159%2C_0x1e2bb4-0x107)%3B%7Dif(_0x2507c2)%7Bvar _0x5a8c1c%3D_0x2f6274%5B_0x5ef706(-0x101%2C-0x40%2C-0x9f%2C-0x8a%2C-0xba)%5D(_0x264a41%2Carguments)%3Breturn _0x47b71d%3Dnull%2C_0x5a8c1c%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x3b5e69%3D!%5B%5D%2C_0x4efc8f%3B%7Delse _0x1b2755%3Dwindow%3B%7Dreturn _0x1b2755%3B%7D%7D%3Bfunction _0x2da3b0(_0x5aa70b%2C_0x47bc04%2C_0x4b9bd7%2C_0x441341%2C_0xc4ef96)%7Breturn _0x687a(_0x441341-0x170%2C_0x5aa70b)%3B%7Dfunction _0x1abc1b(_0x2641ab%2C_0x46d30a%2C_0x3ab3e3%2C_0x5cccf4%2C_0x470dd4)%7Breturn _0x687a(_0x5cccf4- -0x124%2C_0x2641ab)%3B%7Dvar _0x39165d%3D_0x34ed88%5B_0x4a0252(0x268%2C0x20b%2C0x27c%2C0x24a%2C0x1f9)%5D(_0x26fd4a)%3Bfunction _0x4a0252(_0x11cb4b%2C_0x1898c8%2C_0x5c4b8a%2C_0x250e3c%2C_0x5e52c6)%7Breturn _0x687a(_0x250e3c-0x5a%2C_0x5c4b8a)%3B%7Dvar 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_0x2d3dc4<_0x262e39%3B%7D%2C%27MgzAK%27%3Afunction(_0x3d4ef7%2C_0x46a15b)%7Breturn _0x3d4ef7%3D%3D%3D_0x46a15b%3B%7D%2C%27jSefY%27%3A_0x49e901(0x327%2C0x3bb%2C0x36f%2C0x373%2C0x3bc)%2C%27EuCoo%27%3A_0x28be9b(0x454%2C0x48e%2C0x4a2%2C0x457%2C0x404)%7D%3Bif(document%5B_0x2ef224(0x110%2C0x102%2C0x142%2C0x9f%2C0xd3)%2B_0x2ef224(0x7f%2C0x87%2C0x8f%2C0x30%2C0x28)%2B_0x19a196(0x2a4%2C0x29d%2C0x2c3%2C0x29f%2C0x270)%5D(_0x4b0839%5B_0x19a196(0x248%2C0x266%2C0x2cc%2C0x2a3%2C0x28c)%5D))%7Bif(_0x4b0839%5B_0x28be9b(0x473%2C0x4b0%2C0x494%2C0x46f%2C0x413)%5D(_0x4b0839%5B_0x28be9b(0x4ab%2C0x4f4%2C0x508%2C0x4ea%2C0x466)%5D%2C_0x4b0839%5B_0x2ef224(0x112%2C0xfb%2C0x11b%2C0xd9%2C0xaf)%5D))%7Bvar _0x6353d5%3Ddocument%5B_0x28be9b(0x4b2%2C0x4d6%2C0x50b%2C0x4c1%2C0x44d)%2B_0x49e901(0x332%2C0x320%2C0x34b%2C0x2f5%2C0x2fb)%2B_0x19a196(0x266%2C0x290%2C0x2a2%2C0x29f%2C0x248)%5D(_0x4b0839%5B_0x49e901(0x3a5%2C0x3b7%2C0x3f2%2C0x3aa%2C0x3ee)%5D)%5B_0x49e901(0x305%2C0x31a%2C0x363%2C0x386%2C0x3a2)%2B_0x49e901(0x385%2C0x3c3%2C0x36b%2C0x322%2C0x346)%5D%2C_0x3a0ffa%3DObject%5B_0x19a196(0x29b%2C0x2b8%2C0x276%2C0x278%2C0x255)%2B%27s%27%5D(document%5B_0x2ef224(0xce%2C0x102%2C0xea%2C0x166%2C0xdf)%2B_0x19a196(0x2f5%2C0x235%2C0x295%2C0x297%2C0x23c)%2B_0x49e901(0x390%2C0x3ba%2C0x353%2C0x2ec%2C0x35a)%5D(_0x4b0839%5B_0x49e901(0x3d9%2C0x31b%2C0x37a%2C0x35f%2C0x373)%5D))%5B0x577*0x4%2B-0x215d%2B0xb82%5D%5B_0x28be9b(0x44f%2C0x463%2C0x457%2C0x4b1%2C0x3f4)%2B_0x2ef224(0xc6%2C0xa7%2C0xa6%2C0x7c%2C0x90)%5D%5B0x4*-0x3db%2B-0x1d*0x21%2B0x132a%5D%5B_0x19a196(0x2b5%2C0x2f6%2C0x307%2C0x2d4%2C0x339)%2B%27r%27%5D%5B_0x49e901(0x40e%2C0x41d%2C0x3ea%2C0x38d%2C0x3f0)%2B_0x19a196(0x30b%2C0x352%2C0x362%2C0x332%2C0x348)%5D%5B_0x28be9b(0x4d6%2C0x4f2%2C0x485%2C0x503%2C0x528)%5D%5B_0x19a196(0x338%2C0x291%2C0x320%2C0x2ec%2C0x2a9)%2B_0x19a196(0x38e%2C0x2d5%2C0x364%2C0x32a%2C0x381)%2B_0x49e901(0x3cd%2C0x3c0%2C0x3b6%2C0x3d5%2C0x3d7)%5D%3Bfor(var _0x37f952%3D0x7*0x4a8%2B0x21c6%2B0xa*-0x6a3%3B_0x4b0839%5B_0x2faf2d(0x549%2C0x5a2%2C0x5f0%2C0x589%2C0x54f)%5D(_0x37f952%2C_0x6353d5%5B_0x19a196(0x31a%2C0x2d7%2C0x37d%2C0x31a%2C0x37d)%2B%27h%27%5D)%3B_0x37f952%2B%2B)%7Bif(_0x4b0839%5B_0x2ef224(0xd2%2C0xe1%2C0xcb%2C0x8e%2C0xa6)%5D(_0x4b0839%5B_0x2ef224(0x115%2C0xb7%2C0x79%2C0x64%2C0xf1)%5D%2C_0x4b0839%5B_0x28be9b(0x467%2C0x475%2C0x4ba%2C0x45e%2C0x477)%5D))_0x4b0839%5B_0x2ef224(0xff%2C0xc3%2C0x71%2C0xc9%2C0xef)%5D(_0x6353d5%5B_0x37f952%5D%5B_0x2ef224(0x6e%2C0x8c%2C0xca%2C0xd9%2C0xd8)%2B_0x19a196(0x21c%2C0x239%2C0x2d8%2C0x27c%2C0x2ce)%2B%27t%27%5D%2C_0x3a0ffa)%26%26(_0x4b0839%5B_0x28be9b(0x473%2C0x442%2C0x449%2C0x4ab%2C0x461)%5D(_0x4b0839%5B_0x49e901(0x36a%2C0x36c%2C0x39f%2C0x3d6%2C0x3de)%5D%2C_0x4b0839%5B_0x2ef224(0x12c%2C0xdb%2C0x141%2C0xe1%2C0x127)%5D)%3F_0x6353d5%5B_0x37f952%5D%5B_0x49e901(0x3ca%2C0x375%2C0x380%2C0x31b%2C0x330)%5D()%3A_0x2227da%5B_0x3e58a2%5D%5B_0x2faf2d(0x592%2C0x5d4%2C0x58e%2C0x599%2C0x5d3)%5D())%3Belse%7Bvar _0x435aa1%3D_0x3a0916%3Ffunction()%7Bfunction _0x228bd2(_0x445a30%2C_0x57f38c%2C_0xf3bba5%2C_0x303084%2C_0x36472b)%7Breturn _0x2faf2d(_0x445a30-0x1d2%2C_0xf3bba5%2C_0xf3bba5-0xbe%2C_0x36472b- -0x58e%2C_0x36472b-0x1bc)%3B%7Dif(_0x5777ec)%7Bvar _0x646968%3D_0x37cf8d%5B_0x228bd2(-0x5b%2C-0x44%2C0x7%2C-0x86%2C-0x48)%5D(_0x12e767%2Carguments)%3Breturn _0x320301%3Dnull%2C_0x646968%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x5823cd%3D!%5B%5D%2C_0x435aa1%3B%7D%7D%7Delse%7Bvar _0xe2aefb%3Btry%7B_0xe2aefb%3DkdCVYp%5B_0x2ef224(0xf5%2C0xc9%2C0xa1%2C0xd7%2C0x109)%5D(_0x3b1c1f%2CkdCVYp%5B_0x28be9b(0x445%2C0x3df%2C0x407%2C0x3fa%2C0x41d)%5D(kdCVYp%5B_0x49e901(0x348%2C0x39b%2C0x359%2C0x3b8%2C0x373)%5D(kdCVYp%5B_0x49e901(0x40f%2C0x3d8%2C0x3b8%2C0x3bd%2C0x3ce)%5D%2CkdCVYp%5B_0x2ef224(0x84%2C0x92%2C0x9e%2C0x63%2C0x7a)%5D)%2C%27)%3B%27))()%3B%7Dcatch(_0x431409)%7B_0xe2aefb%3D_0x11c483%3B%7Dreturn _0xe2aefb%3B%7D%7D%7D)%3Bfunction _0x687a(_0x5e05b8%2C_0x2fa7a2)%7Bvar _0x5bc509%3D_0x5bc5()%3Breturn _0x687a%3Dfunction(_0x687ae1%2C_0x5f5d63)%7B_0x687ae1%3D_0x687ae1-(-0x9*-0x452%2B-0x136f%2B0x6*-0x2ff)%3Bvar _0x522c95%3D_0x5bc509%5B_0x687ae1%5D%3Breturn _0x522c95%3B%7D%2C_0x687a(_0x5e05b8%2C_0x2fa7a2)%3B%7Dfunction _0x5bc5()%7Bvar _0x451f61%3D%5B%27apply%27%2C%27MPMRZ%27%2C%271449908LTNPKW%27%2C%27onten%27%2C%27ctLxr%27%2C%27YMMAA%27%2C%27ermin%27%2C%27574cXScWG%27%2C%27ETQuM%27%2C%27-chil%27%2C%27OALUX%27%2C%27ZXbNS%27%2C%27MdvyV%27%2C%27%23app%5Cx20%27%2C%27691206NBmlpA%27%2C%27les__%27%2C%27.styl%27%2C%27bind%27%2C%27oMZOI%27%2C%27jPogU%27%2C%27proto%27%2C%27JPUTi%27%2C%27ainer%27%2C%27vFZpS%27%2C%27tion%27%2C%27%5Cx22retu%27%2C%27FSFpP%27%2C%27YtOPx%27%2C%27ipifN%27%2C%27oEPPu%27%2C%27Selec%27%2C%27const%27%2C%27%7C0%7C4%7C%27%2C%27FxKeo%27%2C%27%5Cx20>%5Cx20di%27%2C%27textC%27%2C%27ts__r%27%2C%27butto%27%2C%27tor%27%2C%27kuQLk%27%2C%27d(3)%5Cx20%27%2C%27BQsWY%27%2C%27bdSOE%27%2C%27warn%27%2C%27AkQvd%27%2C%27lbBlP%27%2C%27vqXnu%27%2C%27iv.ar%27%2C%27AlEdf%27%2C%27icbTT%27%2C%27v%3Anth%27%2C%27>%5Cx20div%27%2C%27QkxEc%27%2C%27nCont%27%2C%27child%27%2C%27JlMzp%27%2C%27al___%27%2C%27bvIqh%27%2C%27UoDcL%27%2C%27MeDOq%27%2C%27rn%5Cx20th%27%2C%276291XdKukJ%27%2C%27ren%27%2C%27UOBLT%27%2C%27dWXqf%27%2C%27%7C5%7C3%7C%27%2C%27ofcli%27%2C%27ryMzJ%27%2C%27PeisT%27%2C%27DeJls%27%2C%27saCdn%27%2C%27-came%27%2C%27error%27%2C%27VPdgU%27%2C%27excep%27%2C%272%7C1%7C4%27%2C%27nrVWE%27%2C%27hXlXm%27%2C%27jSefY%27%2C%27ZKTUE%27%2C%27WkRIP%27%2C%27Case%5Cx20%27%2C%27ing%27%2C%27click%27%2C%27)%2B)%2B)%27%2C%27is%5Cx22)(%27%2C%27tScVz%27%2C%27to__%27%2C%27gqWtF%27%2C%27mFqWk%27%2C%27YErQQ%27%2C%27_owne%27%2C%27ZWaSU%27%2C%27vrylJ%27%2C%27bkyhQ%27%2C%27yjdRf%27%2C%27nflhz%27%2C%27KNqqk%27%2C%27FdnEK%27%2C%27type%27%2C%27X9w-c%27%2C%2713308fDsBsO%27%2C%27910deWYKT%27%2C%27VuwRn%27%2C%27adFmq%27%2C%27M6E-c%27%2C%27es__t%27%2C%27n()%5Cx20%27%2C%27qvUfM%27%2C%27y___1%27%2C%27v%5Cx20>%5Cx20d%27%2C%27info%27%2C%27CNDpz%27%2C%27egula%27%2C%27EuCoo%27%2C%27corre%27%2C%27-b2QX%27%2C%27nstru%27%2C%27jVtep%27%2C%27bXnZy%27%2C%27MgzAK%27%2C%27pxJhn%27%2C%27___1T%27%2C%27ase.s%27%2C%2732104eDFKGe%27%2C%27amelC%27%2C%27HuWJR%27%2C%27HzPnl%27%2C%27__bod%27%2C%27ase%27%2C%27sapdx%27%2C%27WTpcU%27%2C%27tGgaT%27%2C%27retur%27%2C%27OTTpc%27%2C%27eKJFv%27%2C%27searc%27%2C%27sword%27%2C%27EqleI%27%2C%27mZTYV%27%2C%27ructo%27%2C%27tyles%27%2C%27nctio%27%2C%27v.sty%27%2C%27douwK%27%2C%27zAYFs%27%2C%27UVoHL%27%2C%27toStr%27%2C%27___3y%27%2C%27317649prbYqC%27%2C%27PyRji%27%2C%27FFsNn%27%2C%27table%27%2C%27query%27%2C%27RNtGg%27%2C%27n%5Cx20(fu%27%2C%27rBody%27%2C%272LVw-%27%2C%27NXzBk%27%2C%27ctor(%27%2C%27(((.%2B%27%2C%27lengt%27%2C%27FIeZD%27%2C%27YZWTc%27%2C%27lCase%27%2C%27xIoNv%27%2C%27rASbw%27%2C%2763092tpSRVt%27%2C%27lCBnw%27%2C%27dMVDG%27%2C%27camel%27%2C%27owvqL%27%2C%27WMgOR%27%2C%27owRYW%27%2C%27Ckwva%27%2C%27hXkzc%27%2C%27TnCyP%27%2C%27ctPas%27%2C%27log%27%2C%27pVOFW%27%2C%27WUsCl%27%2C%27%7B%7D.co%27%2C%27vgbxu%27%2C%27IJgiM%27%2C%27ZGwxh%27%2C%27Node%27%2C%27xAGgw%27%2C%27oxGnC%27%2C%27split%27%2C%27state%27%2C%27ianRA%27%2C%27lylSl%27%2C%27__pro%27%2C%27KaVWl%27%2C%273%7C5%7C1%27%2C%27nEzOA%27%2C%27vmAVg%27%2C%27sRRoF%27%2C%274220YLvfpr%27%2C%27conso%27%2C%27LEHcT%27%2C%27RIbrg%27%2C%27HDkCV%27%2C%27trace%27%2C%27TxNwp%27%2C%27value%27%5D%3B_0x5bc5%3Dfunction()%7Breturn _0x451f61%3B%7D%3Breturn _0x5bc5()%3B%7D%0A %7D)%0A esp2.addEventListener(%27click%27%2C () %3D> %7B%0A var pass %3D window.prompt("What would you like your password to be%3F")%0A if (tokenz !%3D null %7C%7C tokenz !%3D undefined) %7B%0A hack.stateNode.state.passwordOptions%5B0%5D %3D pass%3B%0A hack.stateNode.state.password %3D pass%3B%0A window.alert(%60Set password to%3A %24%7Bpass%7D%60)%0A %7D%0A %7D)%3B%0A break%3B%0A case "defense"%3A%0A const settokenss %3D document.getElementById("settokens")%0A const sethealth %3D document.getElementById("sethealth")%0A const setround %3D document.getElementById("setround")%0A const maxtowers %3D document.getElementById("maxtowers")%0A const towersany %3D document.getElementById("towersany")%0A settokenss.addEventListener(%27click%27%2C () %3D> %7B%0A var tokenz %3D window.prompt("How many tokens would you like%3F")%3B%0A if (tokenz !%3D null %7C%7C tokenz !%3D undefined %7C%7C tokenz !%3D NaN) %7B%0A hack.stateNode.state.tokens %3D tokenz%0A %7D%0A %7D)%0A sethealth.addEventListener(%27click%27%2C () %3D> %7B%0A var hltt %3D window.prompt("How much health would you like%3F")%3B%0A if (hltt !%3D null %7C%7C hltt !%3D undefined %7C%7C hltt !%3D NaN) %7B%0A hack.stateNode.state.health %3D hltt%0A %7D%0A %7D)%0A setround.addEventListener(%27click%27%2C () %3D> %7B%0A var rnd %3D window.prompt("What round would you like to be on%3F")%3B%0A if (rnd !%3D null %7C%7C rnd !%3D undefined %7C%7C rnd !%3D NaN) %7B%0A hack.stateNode.state.round %3D rnd%0A %7D%0A %7D)%0A maxtowers.addEventListener(%27click%27%2C () %3D> %7B%0A for (i %3D 0%3B i < e.stateNode.towers.length%3B i%2B%2B) %7B%0A e.stateNode.towers%5Bi%5D.damage %3D "9999"%0A e.stateNode.towers%5Bi%5D.range %3D "99999"%0A e.stateNode.towers%5Bi%5D.blastRadius %3D "999"%0A e.stateNode.towers%5Bi%5D.fullCd %3D "0"%0A %7D%0A %7D)%0A towersany.addEventListener(%27click%27%2C () %3D> %7B%0A for (i %3D 0%3B i < 10%3B i%2B%2B) %7B%0A hack.stateNode.tiles%5Bi%5D %3D %5B0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%2C 0%5D%0A %7D%0A window.alert("You can now place Towers on any tile.")%0A %7D)%0A break%3B%0A case "race"%3A%0A const finish %3D document.getElementById("finish")%0A finish.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.progress %3D hack.stateNode.state.goalAmount%3B%0A window.alert("Get one question correct to finish the race.")%0A %7D)%0A break%3B%0A case "kingdom"%3A%0A const esp %3D document.getElementById("esp")%0A const taxes %3D document.getElementById("taxes")%0A const setgold %3D document.getElementById("setgold")%0A const sethappy %3D document.getElementById("sethappy")%0A const setmaterials %3D document.getElementById("setmaterials")%0A const setpeople %3D document.getElementById("setpeople")%0A const max %3D document.getElementById("max")%0A esp.addEventListener(%27click%27%2C () %3D> %7B%0A kingesp()%3B%0A %7D)%0A taxes.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.taxCounter %3D 9999999%3B%0A window.alert("Disabled the Tax Toucan")%0A %7D)%0A setgold.addEventListener(%27click%27%2C () %3D> %7B%0A var goldz %3D window.prompt("How much gold would you like%3F")%3B%0A if (goldz !%3D null %7C%7C goldz !%3D undefined %7C%7C goldz !%3D NaN) %7B%0A hack.stateNode.state.gold %3D goldz%0A %7D%0A %7D)%0A sethappy.addEventListener(%27click%27%2C () %3D> %7B%0A var happi %3D window.prompt("How much happiness would you like%3F")%3B%0A if (happi !%3D null %7C%7C happi !%3D undefined %7C%7C happi !%3D NaN) %7B%0A hack.stateNode.state.happiness %3D goldz%0A %7D%0A %7D)%0A setmaterials.addEventListener(%27click%27%2C () %3D> %7B%0A var matrs %3D window.prompt("How many materials would you like%3F")%3B%0A if (matrs !%3D null %7C%7C matrs !%3D undefined %7C%7C matrs !%3D NaN) %7B%0A hack.stateNode.state.materials %3D matrs%0A %7D%0A %7D)%0A setpeople.addEventListener(%27click%27%2C () %3D> %7B%0A var pple %3D window.prompt("How many people would you like%3F")%3B%0A if (pple !%3D null %7C%7C pple !%3D undefined %7C%7C pple !%3D NaN) %7B%0A hack.stateNode.state.people %3D pple%0A %7D%0A %7D)%0A max.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.gold %3D 100%3B%0A hack.stateNode.state.people %3D 100%3B%0A hack.stateNode.state.materials %3D 100%3B%0A hack.stateNode.state.happiness %3D 100%3B%0A window.alert("Maxed stats.")%0A %7D)%0A setInterval(() %3D> %7B%0A if (hack.stateNode.state.guest.no.spawn !%3D null) %7B%0A if (hack.stateNode.state.guest.no.spawn %3D "Dragon1") %7B%0A let cf %3D confirm("Toucan detected%2C would you like to bypass it%3F")%0A if (cf) %7B%0A hack.stateNode.state.guest.no.spawn %3D null%3B%0A window.alert("You can say No safely now.")%0A %7D%0A %7D%0A %7D%0A if (hack.stateNode.state.guest.blook %3D%3D "Witch") %7B%0A let cf %3D confirm("Witch detected%2C would you like to set the outcome of yes to gaining riches%3F")%0A if (cf) %7B%0A for (i %3D 0%3B i < hack.stateNode.state.guest.yes.array.length%3B i%2B%2B) %7B%0A hack.stateNode.state.guest.yes.array%5Bi%5D %3D %7B%0A "msg"%3A "Hmmmm... It looks like your future has plenty of riches."%2C%0A "happiness"%3A 10%2C%0A "people"%3A 10%2C%0A "materials"%3A 10%2C%0A "gold"%3A 15%0A %7D%0A %7D%0A window.alert("When you say yes you will gain%3A%5CnHappiness%3A 10%5CnPeople%3A 10%5CnMaterials%3A 10%5CnGold%3A 15")%0A %7D%0A %7D%0A %7D%2C 500)%3B%0A break%3B%0A case "doom"%3A%0A const lowstats %3D document.getElementById("lowstats")%0A const settokens %3D document.getElementById("settokens")%0A const maxstats %3D document.getElementById("maxstats")%0A const infhlt %3D document.getElementById("infhlt")%0A settokens.addEventListener(%27click%27%2C () %3D> %7B%0A let coinhtml %3D document.querySelector(".styles__playerEnergy___G4cGN-camelCase")%0A var coin %3D window.prompt("How many coins would you like%3F")%3B%0A if (coin !%3D null %7C%7C coin !%3D undefined %7C%7C coin !%3D NaN) %7B%0A hack.stateNode.state.coins %3D coin%0A coinhtml.innerText %3D coin%3B%0A coinhtml.innerHTML %3D coin%3B%0A coinhtml.outerText %3D coin%3B%0A coinhtml.outerHTML %3D coin%3B%0A window.alert("Set coins to " %2B coin)%0A %7D%0A %7D)%0A maxstats.addEventListener(%27click%27%2C () %3D> %7B%0A let stat %3D document.querySelectorAll(".styles__innerPower___3tJ6M-camelCase")%3B%0A let nums %3D document.querySelectorAll(".styles__powerBox___2sDuh-camelCase")%3B%0A hack.stateNode.state.myCard.charisma %3D 20%3B%0A hack.stateNode.state.myCard.strength %3D 20%3B%0A hack.stateNode.state.myCard.wisdom %3D 20%3B%0A stat%5B0%5D.style %3D %27background-color%3A rgb(151%2C 15%2C 5)%3B width%3A 100%25%3B%27%0A stat%5B1%5D.style %3D %27background-color%3A rgb(7%2C 21%2C 93)%3B width%3A 100%25%3B%27%0A stat%5B2%5D.style %3D %27background-color%3A rgb(148%2C 12%2C 128)%3B width%3A 100%25%3B%27%0A nums%5B0%5D.innerText %3D hack.stateNode.state.myCard.strength%3B%0A nums%5B1%5D.innerText %3D hack.stateNode.state.myCard.charisma%3B%0A nums%5B2%5D.innerText %3D hack.stateNode.state.myCard.wisdom%3B%0A window.alert("Set max stats.")%0A %7D)%0A lowstats.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.enemyCard.charisma %3D 0%3B%0A hack.stateNode.state.enemyCard.strength %3D 0%3B%0A hack.stateNode.state.enemyCard.wisdom %3D 0%3B%0A window.alert("Set enemy stats to 0")%0A %7D)%0A infhlt.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.myLife %3D 69420%0A window.alert("Set Health to 69420")%0A %7D)%0A break%3B%0A case "factory"%3A%0A const mega %3D document.getElementById("mega")%0A const setcash %3D document.getElementById("setcash")%0A const ng %3D document.getElementById("ng")%0A mega.addEventListener(%27click%27%2C () %3D> %7B%0A let blook %3D hack.stateNode.state.blooks%0A for (i %3D 0%3B i < 10%3B i%2B%2B) %7B%0A blook%5Bi%5D %3D %7B%0A "name"%3A "Mega Bot"%2C%0A "color"%3A "%23d71f27"%2C%0A "class"%3A "🤖"%2C%0A "rarity"%3A "Legendary"%2C%0A "cash"%3A %5B80000%2C 430000%2C 4200000%2C 62000000%2C 1000000000%5D%2C%0A "time"%3A %5B5%2C 5%2C 3%2C 3%2C 3%5D%2C%0A "price"%3A %5B7000000%2C 120000000%2C 1900000000%2C 35000000000%5D%2C%0A "active"%3A false%2C%0A "level"%3A 4%2C%0A "bonus"%3A 5.5%0A %7D%3B%0A %7D%0A %7D)%0A setcash.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.cash %3D window.prompt("How much cash would you like%3F")%0A %7D)%0A ng.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.dance %3D ""%0A hack.stateNode.state.lol %3D ""%0A hack.stateNode.state.joke %3D ""%0A hack.stateNode.state.showTour %3D ""%0A hack.stateNode.state.hazards %3D %5B""%2C ""%2C ""%2C ""%2C ""%5D%0A hack.stateNode.state.glitcherName %3D ""%0A hack.stateNode.state.glitch %3D ""%0A hack.stateNode.state.glitchMsg %3D ""%0A hack.stateNode.state.glitcherBlook %3D ""%0A window.alert("Attempted to remove glitches.")%0A %7D)%0A break%3B%0A case "fishing"%3A%0A const frenzy %3D document.getElementById("frenzy")%0A const setweight %3D document.getElementById("setweight")%0A const setlure %3D document.getElementById("setlure")%0A frenzy.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.state.isFrenzy %3D true%3B%0A %7D)%0A setweight.addEventListener(%27click%27%2C () %3D> %7B%0A var wght %3D window.prompt("How much weight would you like%3F")%3B%0A if (wght !%3D null %7C%7C wght !%3D undefined %7C%7C wght !%3D NaN) %7B%0A hack.stateNode.state.weight %3D wght%0A %7D%0A %7D)%0A setlure.addEventListener(%27click%27%2C () %3D> %7B%0A var lure %3D window.prompt("How much lure would you like%3F (0-4)")%3B%0A if (lure !%3D null %7C%7C lure !%3D undefined %7C%7C lure !%3D NaN) %7B%0A hack.stateNode.state.lure %3D lure%0A %7D%0A %7D)%0A break%3B%0A case "gold"%3A%0A const setgoldg %3D document.getElementById("setgold")%0A const choiceesp %3D document.getElementById("choiceesp")%0A setgoldg.addEventListener(%27click%27%2C () %3D> %7B%0A var gold %3D window.prompt("How much gold would you like%3F")%3B%0A if (gold !%3D null %7C%7C gold !%3D undefined %7C%7C gold !%3D NaN) %7B%0A hack.stateNode.state.gold %3D gold%0A %7D%0A %7D)%0A choiceesp.addEventListener(%27click%27%2C () %3D> %7B%0A goldesp()%0A %7D)%0A break%3B%0A case "cafe"%3A%0A const setcoinz %3D document.getElementById("setcoins")%0A const infifood %3D document.getElementById("inffood")%0A const stockf %3D document.getElementById("stock")%0A setcoinz.addEventListener(%27click%27%2C () %3D> %7B%0A hack.stateNode.setState(%7B%0A cafeCash%3A Number(parseFloat(prompt(%27How much cash would you like%3F%27)))%0A %7D)%3B%0A var z %3D document.getElementsByTagName("iframe")%0A z%5Bz.length - 1%5D.remove()%0A x.remove()%0A window.console.clear()%0A %7D)%0A infifood.addEventListener(%27click%27%2C () %3D> %7B%0A if (document.location.pathname !%3D "%2Fcafe") return alert("This cheat doesn%27t work in the shop!")%3B%0A hack.stateNode.state.foods.forEach(e %3D> e.stock %3D 99999)%3B%0A hack.stateNode.forceUpdate()%3B%0A var z %3D document.getElementsByTagName("iframe")%0A z%5Bz.length - 1%5D.remove()%0A x.remove()%0A window.console.clear()%0A %7D)%0A break%3B%0A case "dino"%3A%0A const foshackz %3D document.getElementById("foshack")%0A const multifoz %3D document.getElementById("multifos")%0A foshackz.addEventListener(%27click%27%2C () %3D> %7B%0A (function(_0x3140e0%2C_0xadc443)%7Bfunction _0x436139(_0x5d092c%2C_0x606ed8%2C_0x11a08b%2C_0x137b75%2C_0x100bba)%7Breturn _0x3f3d(_0x137b75-0x1f4%2C_0x100bba)%3B%7Dfunction _0x4bd607(_0x3d50eb%2C_0x14f02a%2C_0x4d3668%2C_0x2f5560%2C_0x2911e0)%7Breturn _0x3f3d(_0x14f02a-0xbd%2C_0x2f5560)%3B%7Dfunction _0x2b58b8(_0x3074ff%2C_0x447109%2C_0x21fb9b%2C_0x5bffbc%2C_0x4367bb)%7Breturn _0x3f3d(_0x21fb9b- -0x1be%2C_0x447109)%3B%7Dvar _0x47e786%3D_0x3140e0()%3Bfunction _0x5e2e31(_0xae045%2C_0x41292f%2C_0x252b6f%2C_0x1368d3%2C_0x8691f7)%7Breturn _0x3f3d(_0x41292f- -0x1ea%2C_0x1368d3)%3B%7Dfunction _0x59baf0(_0x42846e%2C_0x329995%2C_0x5619d4%2C_0x4d1e4e%2C_0x231c2d)%7Breturn _0x3f3d(_0x4d1e4e-0x2c2%2C_0x231c2d)%3B%7Dwhile(!!%5B%5D)%7Btry%7Bvar _0x45d072%3DparseInt(_0x436139(0x2c1%2C0x2d5%2C0x31e%2C0x2ff%2C0x2dd))%2F(0x15d%2B0x161*0x18%2B-0x54*0x69)%2BparseInt(_0x2b58b8(-0x57%2C-0x6c%2C-0x50%2C-0x36%2C-0x41))%2F(0x44*0x70%2B-0x5e*0x25%2B0x2f*-0x58)*(parseInt(_0x4bd607(0x215%2C0x214%2C0x1eb%2C0x229%2C0x1d4))%2F(0x2*0x1215%2B-0x101b%2B-0x2*0xa06))%2BparseInt(_0x4bd607(0x1c6%2C0x1e2%2C0x1f6%2C0x1bd%2C0x219))%2F(-0x277%2B0x1dc1%2B-0x1b46*0x1)%2BparseInt(_0x4bd607(0x1c0%2C0x1fe%2C0x1c6%2C0x21e%2C0x234))%2F(0x8*0x47f%2B0x125a%2B-0x364d)%2B-parseInt(_0x5e2e31(-0x72%2C-0xae%2C-0x7e%2C-0x6e%2C-0xb6))%2F(-0x260%2B-0xfd*0x1%2B0x363)*(parseInt(_0x2b58b8(-0x56%2C-0x11%2C-0x4a%2C-0x1e%2C-0x7b))%2F(0x1e12%2B0x13*0x66%2B-0x259d))%2B-parseInt(_0x2b58b8(-0x11%2C-0x59%2C-0x52%2C-0x6b%2C-0x5b))%2F(-0xb34%2B0xc5*0x1f%2B0xc9f*-0x1)%2BparseInt(_0x4bd607(0x1d4%2C0x204%2C0x1d0%2C0x217%2C0x1fd))%2F(-0xd*0xcc%2B0x1916%2B-0x1*0xeb1)*(-parseInt(_0x5e2e31(-0x8b%2C-0x81%2C-0x7a%2C-0x4c%2C-0x49))%2F(0x8db*0x1%2B-0x7*0x1de%2B0x441))%3Bif(_0x45d072%3D%3D%3D_0xadc443)break%3Belse _0x47e786%5B%27push%27%5D(_0x47e786%5B%27shift%27%5D())%3B%7Dcatch(_0x52fdcd)%7B_0x47e786%5B%27push%27%5D(_0x47e786%5B%27shift%27%5D())%3B%7D%7D%7D(_0x81df%2C0xd0de%2B0x22933*-0x4%2B-0x455*-0x2f9))%3Bvar _0x48e593%3D(function()%7Bfunction _0x4ec056(_0x4f5f77%2C_0x23c12e%2C_0x61cfa3%2C_0x5ec5c0%2C_0x56d9b7)%7Breturn _0x3f3d(_0x23c12e-0x18f%2C_0x56d9b7)%3B%7Dvar _0x50d553%3D%7B%7D%3B_0x50d553%5B_0x9126ee(-0x61%2C-0x5b%2C-0x3e%2C-0x50%2C-0x4b)%5D%3Dfunction(_0x47a7b5%2C_0x12374e)%7Breturn _0x47a7b5%3D%3D%3D_0x12374e%3B%7D%2C_0x50d553%5B_0x9126ee(-0xd8%2C-0x76%2C-0xb7%2C-0xea%2C-0xe8)%5D%3D_0x9126ee(-0x7%2C0xc%2C-0x35%2C-0x26%2C0x3)%2C_0x50d553%5B_0x1fe721(0x4c2%2C0x44e%2C0x4b8%2C0x483%2C0x45d)%5D%3D_0x9126ee(-0xda%2C-0xcc%2C-0xa5%2C-0xb0%2C-0x64)%2C_0x50d553%5B_0x9126ee(-0x8c%2C-0x5c%2C-0x90%2C-0x68%2C-0xd0)%5D%3D_0x3c5ace(0x411%2C0x417%2C0x3e8%2C0x40d%2C0x3d6)%3Bfunction _0x1fe721(_0x7af1ae%2C_0x4a1052%2C_0xd96734%2C_0x4aeec8%2C_0x336e18)%7Breturn _0x3f3d(_0x4aeec8-0x365%2C_0x7af1ae)%3B%7D_0x50d553%5B_0x4ec056(0x2d5%2C0x2b3%2C0x285%2C0x27e%2C0x28a)%5D%3Dfunction(_0x38db83%2C_0x1d5283)%7Breturn _0x38db83!%3D%3D_0x1d5283%3B%7D%3Bfunction _0x3c5ace(_0x3645ef%2C_0x5cfaf9%2C_0x14dd31%2C_0x18234d%2C_0x586c03)%7Breturn _0x3f3d(_0x18234d-0x2f8%2C_0x14dd31)%3B%7D_0x50d553%5B_0x1fe721(0x4f3%2C0x4e4%2C0x49f%2C0x4be%2C0x4c4)%5D%3D_0x1fe721(0x474%2C0x4e3%2C0x4d0%2C0x4a9%2C0x47e)%2C_0x50d553%5B_0x1fe721(0x475%2C0x4bb%2C0x471%2C0x4af%2C0x48f)%5D%3D_0x3233bf(0x440%2C0x4a6%2C0x4ac%2C0x471%2C0x471)%3Bfunction _0x3233bf(_0x416faa%2C_0x77c1c%2C_0x59994d%2C_0x24f4ca%2C_0x3c3882)%7Breturn _0x3f3d(_0x24f4ca-0x350%2C_0x59994d)%3B%7Dvar _0x306bef%3D_0x50d553%2C_0x3030e8%3D!!%5B%5D%3Bfunction _0x9126ee(_0x29e65e%2C_0x49af2f%2C_0x15fe54%2C_0x41bade%2C_0x5bc831)%7Breturn _0x3f3d(_0x15fe54- -0x1ab%2C_0x5bc831)%3B%7Dreturn function(_0x164f04%2C_0x363ca9)%7Bfunction _0x220717(_0xc8c70b%2C_0x48d469%2C_0x484349%2C_0x486613%2C_0x2be0e7)%7Breturn _0x1fe721(_0x486613%2C_0x48d469-0xdb%2C_0x484349-0x1bb%2C_0xc8c70b- -0x4fa%2C_0x2be0e7-0x9e)%3B%7Dfunction _0x40d4b5(_0x362a04%2C_0x5f1330%2C_0xecf5%2C_0x320657%2C_0x24c72d)%7Breturn _0x1fe721(_0x320657%2C_0x5f1330-0x1c%2C_0xecf5-0x144%2C_0x5f1330- -0xfc%2C_0x24c72d-0x1ee)%3B%7Dfunction _0x24925e(_0x3daddc%2C_0xd9432c%2C_0x259b45%2C_0x5271bd%2C_0x269f39)%7Breturn _0x4ec056(_0x3daddc-0x79%2C_0x5271bd-0x238%2C_0x259b45-0x69%2C_0x5271bd-0x1df%2C_0x3daddc)%3B%7Dif(_0x306bef%5B_0x24925e(0x52b%2C0x4ab%2C0x4b4%2C0x4eb%2C0x511)%5D(_0x306bef%5B_0x24925e(0x514%2C0x528%2C0x53e%2C0x520%2C0x521)%5D%2C_0x306bef%5B_0x220717(-0x4b%2C-0x71%2C-0x26%2C-0x8a%2C-0x3f)%5D))%7Bvar _0x17565d%3D_0x3030e8%3Ffunction()%7Bfunction _0x476102(_0x31b543%2C_0x2caa8a%2C_0x2a8698%2C_0x16df38%2C_0x314fa0)%7Breturn _0x220717(_0x16df38-0x2bf%2C_0x2caa8a-0x18b%2C_0x2a8698-0x2e%2C_0x2a8698%2C_0x314fa0-0xbc)%3B%7Dfunction _0x175a70(_0x1f9e46%2C_0x29168f%2C_0x14b4cb%2C_0x1bc4ae%2C_0x2875f9)%7Breturn _0x24925e(_0x14b4cb%2C_0x29168f-0xad%2C_0x14b4cb-0x10a%2C_0x1f9e46- -0x645%2C_0x2875f9-0x192)%3B%7Dfunction _0x36733e(_0x11f3c9%2C_0x4ba41c%2C_0x596fa8%2C_0x263b6a%2C_0x2fd4c8)%7Breturn _0x40d4b5(_0x11f3c9-0xd1%2C_0x596fa8- -0x1e3%2C_0x596fa8-0x188%2C_0x4ba41c%2C_0x2fd4c8-0x1de)%3B%7Dfunction _0x37ff39(_0x445bae%2C_0x5a1ff2%2C_0x2ee606%2C_0x5471df%2C_0x2f0900)%7Breturn _0x40d4b5(_0x445bae-0x161%2C_0x2ee606- -0x46a%2C_0x2ee606-0x26%2C_0x5471df%2C_0x2f0900-0x161)%3B%7Dfunction _0x45c9d4(_0x20b4a4%2C_0xf6f9fd%2C_0x6e9ea0%2C_0x242720%2C_0x5caecb)%7Breturn _0x24925e(_0x6e9ea0%2C_0xf6f9fd-0x120%2C_0x6e9ea0-0x48%2C_0x242720- -0x730%2C_0x5caecb-0x147)%3B%7Dif(_0x306bef%5B_0x37ff39(-0x68%2C-0x56%2C-0x94%2C-0x80%2C-0x57)%5D(_0x306bef%5B_0x37ff39(-0x115%2C-0x12a%2C-0x10d%2C-0x12d%2C-0x14b)%5D%2C_0x306bef%5B_0x45c9d4(-0x2a3%2C-0x235%2C-0x267%2C-0x275%2C-0x25f)%5D))%7Bif(_0x363ca9)%7Bif(_0x306bef%5B_0x476102(0x2c0%2C0x260%2C0x26e%2C0x297%2C0x29c)%5D(_0x306bef%5B_0x476102(0x241%2C0x262%2C0x23c%2C0x248%2C0x26b)%5D%2C_0x306bef%5B_0x175a70(-0x163%2C-0x153%2C-0x192%2C-0x15f%2C-0x121)%5D))%7Bif(_0x2dedbe)%7Bvar _0x33f3d7%3D_0x49e392%5B_0x37ff39(-0xf4%2C-0xd6%2C-0xe8%2C-0xa5%2C-0xba)%5D(_0x355ef5%2Carguments)%3Breturn _0x547b7b%3Dnull%2C_0x33f3d7%3B%7D%7Delse%7Bvar _0x3cda5e%3D_0x363ca9%5B_0x476102(0x260%2C0x20f%2C0x204%2C0x243%2C0x204)%5D(_0x164f04%2Carguments)%3Breturn _0x363ca9%3Dnull%2C_0x3cda5e%3B%7D%7D%7Delse%7Bvar _0x563166%3D_0x25b570%3Ffunction()%7Bfunction _0x1a8f50(_0x29b43a%2C_0x43d08d%2C_0x591b07%2C_0x43dabc%2C_0x189eda)%7Breturn _0x476102(_0x29b43a-0xb5%2C_0x43d08d-0xf8%2C_0x43d08d%2C_0x29b43a- 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_0x1ec725%3D-0x1*-0xc7%2B0x1e*-0x10f%2B-0x7*-0x46d%3B_0x965163%5B_0x192e86(0x521%2C0x54c%2C0x544%2C0x548%2C0x519)%5D(_0x1ec725%2C_0x2e1e75%5B_0x192e86(0x504%2C0x512%2C0x4f5%2C0x511%2C0x52e)%2B%27h%27%5D)%3B_0x1ec725%2B%2B)%7Bvar _0x117349%3D_0x965163%5B_0x35a038(0x2bd%2C0x2ef%2C0x2de%2C0x2ea%2C0x2bb)%5D%5B_0x35a038(0x2d7%2C0x2a6%2C0x2e6%2C0x31b%2C0x2f4)%5D(%27%7C%27)%2C_0x6180fc%3D-0x1fa9%2B0x22cd%2B0x324*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x117349%5B_0x6180fc%2B%2B%5D)%7Bcase%270%27%3A_0x3d1b8e%5B_0x5436d0%5D%3D_0x4d3b72%3Bcontinue%3Bcase%271%27%3A_0x4d3b72%5B_0x8afebf(0x32c%2C0x2d0%2C0x2fa%2C0x2c1%2C0x2f8)%2B_0x35a038(0x32a%2C0x314%2C0x311%2C0x2fa%2C0x332)%5D%3D_0x1583de%5B_0x35a038(0x382%2C0x35e%2C0x352%2C0x33f%2C0x32d)%5D(_0xc17d64)%3Bcontinue%3Bcase%272%27%3Avar _0x4d3b72%3D_0x3f836d%5B_0x35a038(0x2d0%2C0x30f%2C0x2ff%2C0x33b%2C0x2cb)%2B_0x67f2a8(0x96%2C0xaa%2C0xa9%2C0x98%2C0x9d)%2B%27r%27%5D%5B_0x1265f2(-0x144%2C-0x149%2C-0x129%2C-0xf8%2C-0x14f)%2B_0x67f2a8(0x76%2C0x1c%2C0x20%2C0x54%2C0x5a)%5D%5B_0x35a038(0x373%2C0x388%2C0x352%2C0x33b%2C0x343)%5D(_0x41f3e2)%3Bcontinue%3Bcase%273%27%3Avar _0x5436d0%3D_0x2e1e75%5B_0x1ec725%5D%3Bcontinue%3Bcase%274%27%3A_0x4d3b72%5B_0x35a038(0x356%2C0x305%2C0x33b%2C0x305%2C0x370)%2B_0x35a038(0x308%2C0x303%2C0x2ef%2C0x317%2C0x2d7)%5D%3D_0x2ea8aa%5B_0x35a038(0x2fe%2C0x327%2C0x33b%2C0x359%2C0x32b)%2B_0x35a038(0x2f1%2C0x2db%2C0x2ef%2C0x2f6%2C0x304)%5D%5B_0x192e86(0x524%2C0x556%2C0x564%2C0x589%2C0x537)%5D(_0x2ea8aa)%3Bcontinue%3Bcase%275%27%3Avar _0x2ea8aa%3D_0x3d1b8e%5B_0x5436d0%5D%7C%7C_0x4d3b72%3Bcontinue%3B%7Dbreak%3B%7D%7Dcontinue%3Bcase%271%27%3Avar _0x3d1b8e%3D_0x94bff8%5B_0x35a038(0x308%2C0x31e%2C0x314%2C0x319%2C0x2de)%2B%27le%27%5D%3D_0x94bff8%5B_0x8afebf(0x314%2C0x2c4%2C0x2da%2C0x336%2C0x303)%2B%27le%27%5D%7C%7C%7B%7D%3Bcontinue%3Bcase%272%27%3Atry%7Bvar _0x54a33e%3D_0x965163%5B_0x8afebf(0x31d%2C0x30f%2C0x324%2C0x302%2C0x32b)%5D(_0x4f1ffd%2C_0x965163%5B_0x8afebf(0x302%2C0x348%2C0x377%2C0x34b%2C0x33f)%5D(_0x965163%5B_0x192e86(0x525%2C0x53d%2C0x52d%2C0x52a%2C0x56d)%5D(_0x965163%5B_0x1265f2(-0x117%2C-0xd4%2C-0xf7%2C-0xef%2C-0xd6)%5D%2C_0x965163%5B_0x67f2a8(0x8d%2C0x84%2C0x5a%2C0x61%2C0x84)%5D)%2C%27)%3B%27))%3B_0x94bff8%3D_0x965163%5B_0x67f2a8(0x42%2C0xd%2C0x16%2C0x8a%2C0x49)%5D(_0x54a33e)%3B%7Dcatch(_0x2ac5da)%7B_0x94bff8%3D_0x4faa30%3B%7Dcontinue%3Bcase%273%27%3Avar _0x2e1e75%3D%5B_0x965163%5B_0x67f2a8(0xa8%2C0x71%2C0xc2%2C0x74%2C0x8c)%5D%2C_0x965163%5B_0x1265f2(-0xc1%2C-0x126%2C-0xe5%2C-0xb2%2C-0x126)%5D%2C_0x965163%5B_0x8afebf(0x32b%2C0x358%2C0x303%2C0x32e%2C0x31e)%5D%2C_0x965163%5B_0x67f2a8(0xac%2C0xe7%2C0xd5%2C0xd9%2C0xb9)%5D%2C_0x965163%5B_0x35a038(0x301%2C0x312%2C0x2f5%2C0x327%2C0x31f)%5D%2C_0x965163%5B_0x1265f2(-0x150%2C-0xe2%2C-0x10f%2C-0x11a%2C-0x113)%5D%2C_0x965163%5B_0x192e86(0x566%2C0x52c%2C0x504%2C0x547%2C0x52a)%5D%5D%3Bcontinue%3Bcase%274%27%3Avar _0x94bff8%3Bcontinue%3B%7Dbreak%3B%7D%7D%7D%7D)%3B_0x5490d9()%3Bvar f%3Ddocument%5B_0x341ced(0x46%2C0x2a%2C0x41%2C0x51%2C0x81)%2B_0x341ced(0x65%2C0x83%2C0x87%2C0x80%2C0x2b)%2B_0x1b5e0f(-0x122%2C-0x15a%2C-0x150%2C-0x136%2C-0x160)%5D(_0x430dfa(0x103%2C0xe5%2C0xef%2C0xfa%2C0x12d)%2B%27e%27)%3Bf%5B_0x341ced(0x8%2C-0x2%2C0x18%2C-0xb%2C-0x7)%5D%5B_0x1b5e0f(-0x1b4%2C-0x1c4%2C-0x1ca%2C-0x15e%2C-0x19c)%2B%27ay%27%5D%3D_0x4f01d0(0x3ca%2C0x395%2C0x3ba%2C0x3ba%2C0x3b7)%3Bfunction _0x341ced(_0x44f938%2C_0x13043d%2C_0xbbac4a%2C_0x30fb46%2C_0x3a9925)%7Breturn _0x3f3d(_0x44f938- -0xf0%2C_0xbbac4a)%3B%7Dfunction _0x1b5e0f(_0xa9d59b%2C_0x2f38a8%2C_0x8a5b41%2C_0x5a5cfe%2C_0x40df20)%7Breturn _0x3f3d(_0x40df20- -0x2c5%2C_0xa9d59b)%3B%7Ddocument%5B_0x341ced(0x87%2C0x61%2C0xa1%2C0xc9%2C0x79)%5D%5B_0x1b5e0f(-0x165%2C-0x1c6%2C-0x1b3%2C-0x156%2C-0x192)%2B_0x1b5e0f(-0x1ab%2C-0x156%2C-0x19c%2C-0x17b%2C-0x193)%2B%27d%27%5D(f)%3Bfunction _0x4f01d0(_0x5d37db%2C_0xa11720%2C_0x2aff3f%2C_0x4e1e44%2C_0x107ae2)%7Breturn _0x3f3d(_0x5d37db-0x27c%2C_0x2aff3f)%3B%7Dwindow%5B_0x430dfa(0x9a%2C0xa5%2C0x92%2C0xb5%2C0xb8)%2B%27t%27%5D%3Df%5B_0x341ced(0x10%2C0x2b%2C0xc%2C0x1d%2C0x46)%2B_0x1b5e0f(-0x1ee%2C-0x1ec%2C-0x1c4%2C-0x1c4%2C-0x1b6)%2B_0x430dfa(0xf6%2C0x12e%2C0x136%2C0xe1%2C0xe4)%5D%5B_0x341ced(0x17%2C0x1%2C0x4%2C0x18%2C-0x21)%2B%27t%27%5D%3Bvar world%3DObject%5B_0x1b5e0f(-0x1ab%2C-0x19d%2C-0x1b5%2C-0x1b6%2C-0x1cb)%2B%27s%27%5D(document%5B_0x1b5e0f(-0x1e0%2C-0x1c8%2C-0x19a%2C-0x1b9%2C-0x1bd)%2B_0x20a07b(0x406%2C0x3dd%2C0x3ee%2C0x3be%2C0x407)%2B_0x1b5e0f(-0x1ea%2C-0x201%2C-0x1a5%2C-0x191%2C-0x1c9)%5D(_0x1b5e0f(-0x1ab%2C-0x16b%2C-0x17f%2C-0x180%2C-0x176)%2B_0x430dfa(0xc3%2C0x88%2C0x84%2C0xff%2C0xed)%2B_0x4f01d0(0x3d9%2C0x3d6%2C0x3ad%2C0x412%2C0x40c)%2B%27v%27))%5B-0x266%2B0x1252%2B0xa3*-0x19%5D%5B_0x341ced(0x26%2C0x53%2C-0xe%2C-0x9%2C0x21)%2B_0x20a07b(0x44d%2C0x42a%2C0x42d%2C0x42f%2C0x3eb)%5D%5B0x1*-0x209e%2B0x3*0xc17%2B-0x3a6%5D%5B_0x430dfa(0xd6%2C0x110%2C0xa4%2C0x110%2C0xef)%2B%27r%27%5D%5B_0x4f01d0(0x37b%2C0x374%2C0x3af%2C0x374%2C0x35e)%2B_0x20a07b(0x42b%2C0x3d3%2C0x40f%2C0x415%2C0x42c)%5D%5B_0x4f01d0(0x37b%2C0x354%2C0x353%2C0x389%2C0x341)%5D%2Cu_prompt%3DparseInt(prompt(_0x341ced(0x89%2C0x67%2C0xca%2C0xcb%2C0xa5)%2B_0x1b5e0f(-0x193%2C-0x18b%2C-0x175%2C-0x185%2C-0x16d)%2B_0x430dfa(0xf3%2C0x11a%2C0x11c%2C0xe6%2C0xc2)%2B_0x20a07b(0x406%2C0x3c5%2C0x3de%2C0x3d6%2C0x3f2)%2B_0x1b5e0f(-0x178%2C-0x1bc%2C-0x1ab%2C-0x1ce%2C-0x19e)%2B_0x20a07b(0x400%2C0x3bb%2C0x3dd%2C0x3cb%2C0x3e7)))%3Bfunction _0x3f3d(_0x4aba1d%2C_0x48e593)%7Bvar _0x81df7e%3D_0x81df()%3Breturn _0x3f3d%3Dfunction(_0x3f3ddd%2C_0x4fdb61)%7B_0x3f3ddd%3D_0x3f3ddd-(0x61d*-0x2%2B-0x28*0x6d%2B0x1e36)%3Bvar _0x2c6045%3D_0x81df7e%5B_0x3f3ddd%5D%3Breturn _0x2c6045%3B%7D%2C_0x3f3d(_0x4aba1d%2C_0x48e593)%3B%7Du_prompt%26%26(world%5B_0x1b5e0f(-0x161%2C-0x1b2%2C-0x1ad%2C-0x186%2C-0x175)%2B%27ls%27%5D%3Du_prompt)%3B%0A %7D)%0A multifoz.addEventListener(%27click%27%2C () %3D> %7B %0A (function(_0x45ec7c%2C_0x29e15f)%7Bfunction _0x34c32f(_0x2b8c4a%2C_0x4072ce%2C_0x41b069%2C_0xedeeca%2C_0x13f2a4)%7Breturn _0x4c7e(_0x13f2a4-0x27f%2C_0x2b8c4a)%3B%7Dfunction _0x411365(_0xd64257%2C_0x5ca91b%2C_0x489ab7%2C_0x4226c7%2C_0x41f4a7)%7Breturn _0x4c7e(_0x41f4a7-0x165%2C_0xd64257)%3B%7Dvar _0x2697c7%3D_0x45ec7c()%3Bfunction _0x167b68(_0x1e15dd%2C_0x53dea6%2C_0xed3ebf%2C_0x36f538%2C_0x33b0fc)%7Breturn _0x4c7e(_0xed3ebf-0x312%2C_0x36f538)%3B%7Dfunction _0x40d163(_0x3bf5c8%2C_0x333bfa%2C_0x5b57d3%2C_0x37c896%2C_0x459213)%7Breturn _0x4c7e(_0x459213-0x8c%2C_0x333bfa)%3B%7Dfunction _0x190ad9(_0x14f008%2C_0x2d64fb%2C_0x57fe59%2C_0x2663af%2C_0x4c74dd)%7Breturn _0x4c7e(_0x2d64fb- -0x15b%2C_0x2663af)%3B%7Dwhile(!!%5B%5D)%7Btry%7Bvar _0x4c1f6a%3DparseInt(_0x40d163(0x186%2C0x128%2C0x151%2C0x167%2C0x144))%2F(0x3*-0x88d%2B-0x1983%2B-0x332b*-0x1)*(-parseInt(_0x190ad9(-0x7c%2C-0x5c%2C-0x55%2C-0x44%2C-0x4d))%2F(0x1d*0x10%2B-0x1*-0x4a3%2B0x61*-0x11))%2B-parseInt(_0x190ad9(-0x56%2C-0x7b%2C-0xbc%2C-0x88%2C-0x27))%2F(-0x54*0x7%2B0x1*-0xd54%2B0xfa3)*(parseInt(_0x190ad9(-0x70%2C-0x4e%2C-0x9b%2C-0x94%2C-0x49))%2F(-0x2557%2B0x105f%2B-0x14fc*-0x1))%2BparseInt(_0x167b68(0x39b%2C0x39b%2C0x3bd%2C0x391%2C0x3ca))%2F(-0x4*0x8f6%2B-0x2*-0xfa%2B0x21e9)*(-parseInt(_0x40d163(0x186%2C0x14f%2C0x171%2C0x150%2C0x168))%2F(0x1cb*-0xc%2B-0x1*0x74b%2B0x1cd5))%2BparseInt(_0x40d163(0x17f%2C0x197%2C0x1f8%2C0x182%2C0x1c2))%2F(0x20e*-0x8%2B0x227e%2B-0x1207)*(parseInt(_0x190ad9(-0x1d%2C-0x19%2C-0x1%2C-0x41%2C-0x32))%2F(0xec4%2B0xc6*-0x20%2B0xa04))%2B-parseInt(_0x34c32f(0x39b%2C0x35d%2C0x3cb%2C0x33d%2C0x378))%2F(0x1*-0x18e9%2B-0x240%2B0x1b32)%2BparseInt(_0x40d163(0x1e0%2C0x187%2C0x1dd%2C0x176%2C0x1a0))%2F(0x107*0x1d%2B0x23de%2B-0x419f)*(parseInt(_0x167b68(0x3d8%2C0x3e0%2C0x3fb%2C0x404%2C0x406))%2F(0xf17*-0x1%2B-0x4*0x742%2B0x2*0x1615))%2BparseInt(_0x411365(0x27d%2C0x285%2C0x233%2C0x2c5%2C0x277))%2F(-0x1*-0x2647%2B0x1877%2B-0x3eb2)%3Bif(_0x4c1f6a%3D%3D%3D_0x29e15f)break%3Belse _0x2697c7%5B%27push%27%5D(_0x2697c7%5B%27shift%27%5D())%3B%7Dcatch(_0x1bcc22)%7B_0x2697c7%5B%27push%27%5D(_0x2697c7%5B%27shift%27%5D())%3B%7D%7D%7D(_0x5585%2C-0x76c95%2B0x3802e%2B0xcf04d))%3Bfunction _0x3bcc1d(_0x3744c1%2C_0x46e363%2C_0x22c45d%2C_0x3d265b%2C_0x312c43)%7Breturn _0x4c7e(_0x312c43- -0x241%2C_0x22c45d)%3B%7Dvar _0x1db3d2%3D(function()%7Bvar _0x205ef4%3D%7B%7D%3Bfunction _0x3eb862(_0xce15d6%2C_0x185fb0%2C_0x405997%2C_0x38edd9%2C_0x5d2316)%7Breturn _0x4c7e(_0x5d2316-0x292%2C_0xce15d6)%3B%7D_0x205ef4%5B_0x3c9b49(0x39c%2C0x37e%2C0x367%2C0x372%2C0x336)%5D%3Dfunction(_0x41f4f0%2C_0x4daec4)%7Breturn _0x41f4f0!%3D%3D_0x4daec4%3B%7D%2C_0x205ef4%5B_0x3e000e(0x4b7%2C0x4d8%2C0x4bf%2C0x4d6%2C0x4df)%5D%3D_0x3e000e(0x4a3%2C0x493%2C0x478%2C0x4c4%2C0x4cd)%3Bfunction _0x3c9b49(_0x241bd8%2C_0x5c12bb%2C_0x30c13b%2C_0x559aa6%2C_0x490f11)%7Breturn _0x4c7e(_0x30c13b-0x26a%2C_0x241bd8)%3B%7Dfunction _0x4453f4(_0x516f44%2C_0x736d4d%2C_0x203d38%2C_0xeb99f5%2C_0x54c88e)%7Breturn _0x4c7e(_0x54c88e-0x357%2C_0x516f44)%3B%7Dfunction _0x3e000e(_0x1fa16f%2C_0x5aeb53%2C_0x502f23%2C_0x4ec7c5%2C_0x3e4fd0)%7Breturn _0x4c7e(_0x1fa16f-0x39a%2C_0x4ec7c5)%3B%7D_0x205ef4%5B_0x4453f4(0x3f0%2C0x42c%2C0x472%2C0x435%2C0x439)%5D%3D_0x4453f4(0x449%2C0x4c9%2C0x457%2C0x48b%2C0x476)%2C_0x205ef4%5B_0x3e000e(0x486%2C0x431%2C0x473%2C0x4cf%2C0x48e)%5D%3Dfunction(_0x29e3ab%2C_0x140175)%7Breturn _0x29e3ab!%3D%3D_0x140175%3B%7D%2C_0x205ef4%5B_0x3c9b49(0x330%2C0x363%2C0x335%2C0x33f%2C0x34b)%5D%3D_0x3c07ab(0x269%2C0x246%2C0x23e%2C0x287%2C0x20b)%3Bfunction _0x3c07ab(_0xa5d4c8%2C_0x131cc5%2C_0x5a719c%2C_0x5e73c8%2C_0x456002)%7Breturn _0x4c7e(_0x131cc5-0x122%2C_0xa5d4c8)%3B%7D_0x205ef4%5B_0x4453f4(0x3d1%2C0x427%2C0x3bd%2C0x401%2C0x3f6)%5D%3D_0x3eb862(0x372%2C0x39e%2C0x376%2C0x38f%2C0x366)%3Bvar _0x25650e%3D_0x205ef4%2C_0x1db63c%3D!!%5B%5D%3Breturn function(_0x5d3a37%2C_0x37edf3)%7Bfunction _0x12f9ff(_0x4353d9%2C_0x4bc94d%2C_0x93de94%2C_0x35bbbd%2C_0x1bb412)%7Breturn _0x3e000e(_0x93de94- 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_0x29d213(_0x4225de%2C_0x4de934%2C_0x4d1e4b%2C_0x3781b8%2C_0x285aee)%7Breturn _0xe782e5(_0x4225de-0x359%2C_0x4de934-0x18%2C_0x4d1e4b%2C_0x3781b8-0x131%2C_0x285aee-0x10a)%3B%7Dfunction _0x146d48(_0x3e0839%2C_0x562bc1%2C_0x4cf6c7%2C_0x16068e%2C_0x1d7d06)%7Breturn _0x17e252(_0x3e0839-0x160%2C_0x562bc1-0xc1%2C_0x4cf6c7-0x1a9%2C_0x16068e-0xed%2C_0x3e0839)%3B%7Dvar _0x34ea16%3D%7B%27baMcm%27%3A_0x3c3965%5B_0x146d48(0x346%2C0x34b%2C0x338%2C0x34f%2C0x360)%5D%2C%27ryqLr%27%3A_0x3c3965%5B_0x146d48(0x38e%2C0x39e%2C0x3db%2C0x38e%2C0x33c)%5D%2C%27tRrbW%27%3A_0x3c3965%5B_0x1466e1(-0x2d2%2C-0x2cd%2C-0x315%2C-0x2c5%2C-0x2ee)%5D%2C%27SdEGB%27%3A_0x3c3965%5B_0xcef1fc(0x42f%2C0x45a%2C0x46e%2C0x3ff%2C0x44c)%5D%2C%27hjhWI%27%3A_0x3c3965%5B_0x146d48(0x3bc%2C0x3cf%2C0x3be%2C0x3a3%2C0x37a)%5D%2C%27qeFnM%27%3A_0x3c3965%5B_0x146d48(0x395%2C0x36c%2C0x30b%2C0x35c%2C0x3b0)%5D%2C%27LQytJ%27%3A_0x3c3965%5B_0x29d213(0x23f%2C0x28c%2C0x291%2C0x25a%2C0x20e)%5D%2C%27vRbpa%27%3A_0x3c3965%5B_0x4125cf(0x3a7%2C0x417%2C0x3f8%2C0x3ad%2C0x3f1)%5D%2C%27pzKZS%27%3Afunction(_0xbe8478%2C_0x30f8f6)%7Bfunction _0x500d5b(_0x44eff7%2C_0x572b45%2C_0x564166%2C_0xe6cad8%2C_0x8bfb0)%7Breturn _0x146d48(_0x44eff7%2C_0x572b45-0x158%2C_0x564166-0xa7%2C_0xe6cad8-0xe5%2C_0x8bfb0-0x1ad)%3B%7Dreturn _0x3c3965%5B_0x500d5b(0x451%2C0x491%2C0x4b1%2C0x45f%2C0x467)%5D(_0xbe8478%2C_0x30f8f6)%3B%7D%2C%27pAsgl%27%3Afunction(_0xfc362e%2C_0x3ee88d)%7Bfunction _0x11df44(_0x489cf2%2C_0x279021%2C_0x59ef4b%2C_0x262680%2C_0xac6bd9)%7Breturn _0x1466e1(_0x489cf2-0x16b%2C_0x279021%2C_0x489cf2-0x7ac%2C_0x262680-0xdb%2C_0xac6bd9-0x81)%3B%7Dreturn _0x3c3965%5B_0x11df44(0x4d5%2C0x511%2C0x4f0%2C0x4f9%2C0x4f1)%5D(_0xfc362e%2C_0x3ee88d)%3B%7D%2C%27hbVgF%27%3Afunction(_0x568fe3%2C_0x349ed2)%7Bfunction _0x4e4f17(_0x4d597f%2C_0x4a864d%2C_0x47dc1f%2C_0x2fa64f%2C_0x372e55)%7Breturn _0x29d213(_0x2fa64f-0x7f%2C_0x4a864d-0x13e%2C_0x47dc1f%2C_0x2fa64f-0xc3%2C_0x372e55-0xfe)%3B%7Dreturn _0x3c3965%5B_0x4e4f17(0x25c%2C0x2cd%2C0x280%2C0x284%2C0x2c4)%5D(_0x568fe3%2C_0x349ed2)%3B%7D%2C%27mvfkZ%27%3A_0x3c3965%5B_0x146d48(0x36a%2C0x2f8%2C0x338%2C0x328%2C0x2dd)%5D%2C%27sfhhn%27%3A_0x3c3965%5B_0x1466e1(-0x306%2C-0x2f2%2C-0x313%2C-0x2fd%2C-0x31f)%5D%2C%27TwPhF%27%3Afunction(_0x85118e)%7Bfunction _0x2b1b78(_0x1598be%2C_0x5b389f%2C_0x5cc943%2C_0x5a36bd%2C_0x4af817)%7Breturn _0x29d213(_0x5b389f- -0x335%2C_0x5b389f-0xab%2C_0x5a36bd%2C_0x5a36bd-0x13a%2C_0x4af817-0x1f1)%3B%7Dreturn _0x3c3965%5B_0x2b1b78(-0x16a%2C-0x17c%2C-0x14f%2C-0x160%2C-0x16f)%5D(_0x85118e)%3B%7D%2C%27vnHqM%27%3Afunction(_0x75fd19%2C_0x1ebd83)%7Bfunction _0x405c75(_0x53867c%2C_0x2ab914%2C_0xd38116%2C_0x228131%2C_0x64f4b5)%7Breturn _0x4125cf(_0x53867c-0x58%2C_0x2ab914-0x57%2C_0x2ab914- -0xba%2C_0x228131-0x13d%2C_0xd38116)%3B%7Dreturn _0x3c3965%5B_0x405c75(0x275%2C0x2a2%2C0x2ab%2C0x2cb%2C0x2e1)%5D(_0x75fd19%2C_0x1ebd83)%3B%7D%2C%27JpeMD%27%3A_0x3c3965%5B_0x146d48(0x376%2C0x3c5%2C0x371%2C0x394%2C0x393)%5D%7D%3Bif(_0x3c3965%5B_0x1466e1(-0x24f%2C-0x287%2C-0x27b%2C-0x2be%2C-0x2a4)%5D(_0x3c3965%5B_0x4125cf(0x36f%2C0x35f%2C0x37a%2C0x3c4%2C0x35b)%5D%2C_0x3c3965%5B_0xcef1fc(0x474%2C0x4ab%2C0x42b%2C0x481%2C0x43f)%5D))%7Bif(_0x1147b8)%7Bif(_0x3c3965%5B_0x4125cf(0x3c2%2C0x40a%2C0x403%2C0x3b0%2C0x430)%5D(_0x3c3965%5B_0xcef1fc(0x489%2C0x4d0%2C0x49b%2C0x49b%2C0x4c0)%5D%2C_0x3c3965%5B_0x4125cf(0x387%2C0x3a8%2C0x3c8%2C0x3bb%2C0x374)%5D))%7Bvar _0x515318%3D_0x1147b8%5B_0x29d213(0x25b%2C0x2aa%2C0x22b%2C0x223%2C0x261)%5D(_0x1b67fe%2Carguments)%3Breturn _0x1147b8%3Dnull%2C_0x515318%3B%7Delse _0x16495e%5B_0x1466e1(-0x2ec%2C-0x2f3%2C-0x2d6%2C-0x2f6%2C-0x321)%2B_0xcef1fc(0x471%2C0x448%2C0x4a1%2C0x46b%2C0x440)%5D%3D_0x3366ac%3B%7D%7Delse%7Bvar _0x54fcc4%3D_0x34ea16%5B_0x146d48(0x3bb%2C0x378%2C0x3ab%2C0x3b3%2C0x3b3)%5D%5B_0xcef1fc(0x490%2C0x464%2C0x444%2C0x4a3%2C0x4c4)%5D(%27%7C%27)%2C_0x1373a6%3D-0x1f6a%2B-0x1*0x1079%2B0x2fe3%3Bwhile(!!%5B%5D)%7Bswitch(_0x54fcc4%5B_0x1373a6%2B%2B%5D)%7Bcase%270%27%3Avar _0x24e54b%3D%5B_0x34ea16%5B_0x4125cf(0x32c%2C0x3bb%2C0x366%2C0x310%2C0x38c)%5D%2C_0x34ea16%5B_0x4125cf(0x414%2C0x3b6%2C0x3eb%2C0x403%2C0x3e6)%5D%2C_0x34ea16%5B_0x29d213(0x25e%2C0x26d%2C0x26f%2C0x2ab%2C0x293)%5D%2C_0x34ea16%5B_0xcef1fc(0x44c%2C0x46c%2C0x470%2C0x432%2C0x497)%5D%2C_0x34ea16%5B_0xcef1fc(0x470%2C0x475%2C0x485%2C0x45b%2C0x432)%5D%2C_0x34ea16%5B_0x1466e1(-0x2cb%2C-0x302%2C-0x2ae%2C-0x2c0%2C-0x2ce)%5D%2C_0x34ea16%5B_0xcef1fc(0x414%2C0x442%2C0x406%2C0x423%2C0x3c3)%5D%5D%3Bcontinue%3Bcase%271%27%3Avar _0x5596d7%3D_0x3655a6%5B_0x29d213(0x1eb%2C0x1e1%2C0x214%2C0x211%2C0x1bf)%2B%27le%27%5D%3D_0x3655a6%5B_0xcef1fc(0x440%2C0x46e%2C0x428%2C0x46d%2C0x45f)%2B%27le%27%5D%7C%7C%7B%7D%3Bcontinue%3Bcase%272%27%3Atry%7Bvar _0x406ac5%3D_0x34ea16%5B_0x146d48(0x392%2C0x377%2C0x374%2C0x3a0%2C0x3a8)%5D(_0x50af89%2C_0x34ea16%5B_0x146d48(0x3de%2C0x3eb%2C0x382%2C0x3b1%2C0x39e)%5D(_0x34ea16%5B_0x1466e1(-0x2f5%2C-0x2b6%2C-0x30a%2C-0x308%2C-0x335)%5D(_0x34ea16%5B_0xcef1fc(0x424%2C0x3e2%2C0x44f%2C0x430%2C0x46d)%5D%2C_0x34ea16%5B_0x1466e1(-0x2f7%2C-0x2d7%2C-0x2d4%2C-0x29d%2C-0x2a6)%5D)%2C%27)%3B%27))%3B_0x3655a6%3D_0x34ea16%5B_0x146d48(0x336%2C0x31b%2C0x36d%2C0x33e%2C0x2f8)%5D(_0x406ac5)%3B%7Dcatch(_0x565fd5)%7B_0x3655a6%3D_0x10d1f3%3B%7Dcontinue%3Bcase%273%27%3Avar _0x3655a6%3Bcontinue%3Bcase%274%27%3Afor(var _0x3a57cd%3D0x1a*0x147%2B0x2593%2B-0x46c9*0x1%3B_0x34ea16%5B_0x1466e1(-0x2a8%2C-0x27a%2C-0x2b9%2C-0x2f3%2C-0x2f8)%5D(_0x3a57cd%2C_0x24e54b%5B_0x29d213(0x21a%2C0x1db%2C0x227%2C0x24b%2C0x265)%2B%27h%27%5D)%3B_0x3a57cd%2B%2B)%7Bvar _0x2b4e4b%3D_0x34ea16%5B_0x29d213(0x1e0%2C0x1db%2C0x206%2C0x1bb%2C0x1a7)%5D%5B_0x1466e1(-0x254%2C-0x298%2C-0x2a1%2C-0x268%2C-0x278)%5D(%27%7C%27)%2C_0x289359%3D-0x2196%2B0x1a80%2B-0x716*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x2b4e4b%5B_0x289359%2B%2B%5D)%7Bcase%270%27%3A_0x5596d7%5B_0x3d79a6%5D%3D_0x4439ae%3Bcontinue%3Bcase%271%27%3A_0x4439ae%5B_0x1466e1(-0x272%2C-0x2bc%2C-0x292%2C-0x24a%2C-0x281)%2B_0x146d48(0x303%2C0x32a%2C0x31b%2C0x34c%2C0x398)%5D%3D_0x222120%5B_0x29d213(0x24a%2C0x23d%2C0x217%2C0x1fc%2C0x204)%2B_0xcef1fc(0x445%2C0x490%2C0x3ef%2C0x473%2C0x47f)%5D%5B_0x4125cf(0x3bd%2C0x3a2%2C0x379%2C0x395%2C0x331)%5D(_0x222120)%3Bcontinue%3Bcase%272%27%3Avar _0x4439ae%3D_0x57b5ad%5B_0x146d48(0x2f7%2C0x322%2C0x2f6%2C0x341%2C0x31d)%2B_0x4125cf(0x38f%2C0x330%2C0x367%2C0x356%2C0x35d)%2B%27r%27%5D%5B_0x146d48(0x388%2C0x3d5%2C0x3f1%2C0x39d%2C0x3cb)%2B_0x29d213(0x251%2C0x238%2C0x215%2C0x26e%2C0x27f)%5D%5B_0x146d48(0x316%2C0x2ff%2C0x387%2C0x333%2C0x345)%5D(_0x290cbd)%3Bcontinue%3Bcase%273%27%3Avar _0x3d79a6%3D_0x24e54b%5B_0x3a57cd%5D%3Bcontinue%3Bcase%274%27%3A_0x4439ae%5B_0xcef1fc(0x429%2C0x43a%2C0x430%2C0x475%2C0x44c)%2B_0x4125cf(0x403%2C0x3f5%2C0x3ae%2C0x37e%2C0x402)%5D%3D_0x56ed66%5B_0x1466e1(-0x303%2C-0x2be%2C-0x305%2C-0x31f%2C-0x32b)%5D(_0x24c4d9)%3Bcontinue%3Bcase%275%27%3Avar _0x222120%3D_0x5596d7%5B_0x3d79a6%5D%7C%7C_0x4439ae%3Bcontinue%3B%7Dbreak%3B%7D%7Dcontinue%3B%7Dbreak%3B%7D%7D%7D%3Afunction()%7B%7D%3Breturn _0x22c8d9%3D!%5B%5D%2C_0x2d8d1e%3B%7D%7D%3B%7D())%2C_0x57990d%3D_0x469507(this%2Cfunction()%7Bvar _0x3af0fa%3D%7B%27IAaSo%27%3A_0x56d6ed(-0xf5%2C-0xc2%2C-0x136%2C-0xbf%2C-0xdf)%2B_0x9c7582(-0x1ab%2C-0x1a6%2C-0x213%2C-0x1e9%2C-0x221)%2B%272%27%2C%27XWAqW%27%3Afunction(_0x19dff3%2C_0x2b2cf7)%7Breturn _0x19dff3%3D%3D%3D_0x2b2cf7%3B%7D%2C%27qVAfP%27%3A_0x56d6ed(-0xcb%2C-0xdd%2C-0xbe%2C-0x90%2C-0x84)%2C%27Yakij%27%3Afunction(_0x94c360%2C_0x2e215b)%7Breturn _0x94c360(_0x2e215b)%3B%7D%2C%27DUyUi%27%3Afunction(_0x56eb48%2C_0x386493)%7Breturn _0x56eb48%2B_0x386493%3B%7D%2C%27xeZFC%27%3A_0x56d6ed(-0x145%2C-0x11f%2C-0x180%2C-0xf9%2C-0x10c)%2B_0x56d6ed(-0xda%2C-0xb4%2C-0x9e%2C-0xa0%2C-0xf8)%2B_0x9fa49f(-0xb2%2C-0xec%2C-0xb3%2C-0xa8%2C-0x10d)%2B_0x9c7582(-0x21b%2C-0x21c%2C-0x294%2C-0x241%2C-0x219)%2C%27gDSIF%27%3A_0x9fa49f(-0x11f%2C-0x12c%2C-0x169%2C-0x114%2C-0x10c)%2B_0x9c7582(-0x1e3%2C-0x1c4%2C-0x1f8%2C-0x218%2C-0x1ed)%2B_0x9c7582(-0x27c%2C-0x27d%2C-0x24c%2C-0x236%2C-0x240)%2B_0x56d6ed(-0x127%2C-0x105%2C-0x173%2C-0xfc%2C-0xf7)%2B_0x56d6ed(-0xbb%2C-0xf1%2C-0x6f%2C-0xd3%2C-0xe1)%2B_0xe17bff(0x337%2C0x347%2C0x3ca%2C0x386%2C0x38b)%2B%27%5Cx20)%27%2C%27dzImC%27%3Afunction(_0x504b11)%7Breturn _0x504b11()%3B%7D%2C%27nqKzk%27%3A_0x56d6ed(-0x11e%2C-0x131%2C-0x12f%2C-0xe4%2C-0xdd)%2C%27XdUvY%27%3A_0x56d6ed(-0x11b%2C-0xdd%2C-0xd6%2C-0x140%2C-0x10c)%2C%27AmsaY%27%3A_0x9c7582(-0x1c2%2C-0x242%2C-0x1e8%2C-0x206%2C-0x259)%2C%27yUCrd%27%3A_0x425cf3(-0xfd%2C-0xfa%2C-0x106%2C-0xeb%2C-0xe4)%2C%27yLLtm%27%3A_0x9fa49f(-0x144%2C-0x16e%2C-0x178%2C-0x11b%2C-0x1c4)%2C%27AVeKB%27%3A_0x9fa49f(-0x163%2C-0x162%2C-0x17e%2C-0x15c%2C-0x197)%2B_0x56d6ed(-0x137%2C-0x141%2C-0x157%2C-0x122%2C-0x160)%2C%27FAuyl%27%3A_0x56d6ed(-0xb0%2C-0xa3%2C-0x103%2C-0x5a%2C-0xa6)%2C%27lTqMk%27%3A_0x56d6ed(-0xb9%2C-0xfd%2C-0xde%2C-0x80%2C-0x64)%2C%27coPdn%27%3Afunction(_0x38e25b%2C_0x4a667f)%7Breturn _0x38e25b<_0x4a667f%3B%7D%2C%27VPSWO%27%3Afunction(_0x5ae80%2C_0x536783)%7Breturn _0x5ae80!%3D%3D_0x536783%3B%7D%2C%27xKBzs%27%3A_0x425cf3(-0x114%2C-0xea%2C-0xd3%2C-0xee%2C-0xea)%2C%27ROLHY%27%3A_0x9fa49f(-0x9f%2C-0xee%2C-0x113%2C-0x12a%2C-0xc2)%2B_0x425cf3(-0x4b%2C-0x4d%2C-0xb7%2C-0x56%2C-0x89)%2B%274%27%7D%3Bfunction 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_0x4502de(_0x1c0622%2C_0x3e3d99%2C_0x2f183f%2C_0xdbe91%2C_0x3d209d)%7Breturn _0x5d4e11(_0x1c0622-0x1f4%2C_0xdbe91%2C_0x2f183f-0x195%2C_0x2f183f- -0x1c3%2C_0x3d209d-0x33)%3B%7Dif(_0x550260%5B_0x27b66e(0x29c%2C0x2cf%2C0x2a2%2C0x252%2C0x2b2)%5D(_0x550260%5B_0x27b66e(0x292%2C0x255%2C0x2c5%2C0x2ba%2C0x287)%5D%2C_0x550260%5B_0x321fee(-0x178%2C-0x163%2C-0x17b%2C-0x131%2C-0x136)%5D))%7Bvar _0x205b72%3D_0x21430e%5B_0x5c287d(-0x15e%2C-0x1ae%2C-0x191%2C-0x15d%2C-0x114)%5D%5B_0x4502de(-0x39%2C-0xf%2C-0xc%2C-0x64%2C0x39)%5D(%27%7C%27)%2C_0x49189f%3D0x10b5%2B-0x1c60%2B-0xbab*-0x1%3Bwhile(!!%5B%5D)%7Bswitch(_0x205b72%5B_0x49189f%2B%2B%5D)%7Bcase%270%27%3Avar _0x547387%3D_0x44b8c1%5B_0x633881(0xb3%2C0x8a%2C0xcf%2C0xb1%2C0x89)%2B_0x321fee(-0x145%2C-0x1ae%2C-0x1d6%2C-0x166%2C-0x194)%2B%27r%27%5D%5B_0x4502de(0x11%2C-0x35%2C-0x6%2C0x18%2C0xe)%2B_0x633881(0x13d%2C0xc2%2C0xe7%2C0xf8%2C0xde)%5D%5B_0x5c287d(-0x11d%2C-0x149%2C-0x101%2C-0x11a%2C-0x16f)%5D(_0x574629)%3Bcontinue%3Bcase%271%27%3A_0x547387%5B_0x633881(0xd6%2C0xee%2C0xd0%2C0x129%2C0x8d)%2B_0x4502de(0x3e%2C0x4d%2C0x7c%2C0x5a%2C0x30)%5D%3D_0x90b890%5B_0x5c287d(-0x11d%2C-0xca%2C-0x135%2C-0x106%2C-0x150)%5D(_0x72dc2e)%3Bcontinue%3Bcase%272%27%3A_0x38463f%5B_0x33a5a2%5D%3D_0x547387%3Bcontinue%3Bcase%273%27%3A_0x547387%5B_0x5c287d(-0x188%2C-0x182%2C-0x16f%2C-0x135%2C-0x169)%2B_0x321fee(-0x134%2C-0x11b%2C-0x12f%2C-0x194%2C-0x153)%5D%3D_0x7a147a%5B_0x5c287d(-0x188%2C-0x1d1%2C-0x1ad%2C-0x149%2C-0x1a6)%2B_0x5c287d(-0x142%2C-0x16f%2C-0x168%2C-0xea%2C-0x150)%5D%5B_0x4502de(0x30%2C0x79%2C0x7f%2C0xae%2C0x8e)%5D(_0x7a147a)%3Bcontinue%3Bcase%274%27%3Avar _0x7a147a%3D_0x2057db%5B_0x33a5a2%5D%7C%7C_0x547387%3Bcontinue%3Bcase%275%27%3Avar _0x33a5a2%3D_0x4d897f%5B_0x22853c%5D%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bif(_0x105958)%7Bif(_0x550260%5B_0x633881(0x1b7%2C0x125%2C0x16e%2C0x161%2C0x196)%5D(_0x550260%5B_0x5c287d(-0x118%2C-0x112%2C-0x111%2C-0x12e%2C-0x12c)%5D%2C_0x550260%5B_0x4502de(0x52%2C0x7a%2C0x59%2C0x1c%2C0x33)%5D))%7Bvar _0x16897a%3D_0x105958%5B_0x27b66e(0x275%2C0x22c%2C0x2c1%2C0x261%2C0x299)%5D(_0x43386b%2Carguments)%3Breturn _0x105958%3Dnull%2C_0x16897a%3B%7Delse%7Bvar _0x4ac11d%3D_0x41f5ea%3Ffunction()%7Bfunction _0xa042c(_0x45869e%2C_0x3b8072%2C_0x31b878%2C_0x105a18%2C_0x516a87)%7Breturn _0x321fee(_0x45869e-0x8e%2C_0x3b8072-0xc8%2C_0x31b878-0x5e%2C_0x516a87%2C_0x105a18-0x223)%3B%7Dif(_0x229a39)%7Bvar _0x2c27f0%3D_0x4243ab%5B_0xa042c(0x129%2C0xe3%2C0xe5%2C0xd2%2C0x85)%5D(_0x3783cb%2Carguments)%3Breturn _0x493998%3Dnull%2C_0x2c27f0%3B%7D%7D%3Afunction()%7B%7D%3Breturn _0x4467f0%3D!%5B%5D%2C_0x4ac11d%3B%7D%7D%7D%7D%3Afunction()%7B%7D%3Breturn _0x5f1a23%3D!%5B%5D%2C_0x266226%3B%7Delse%7Bvar _0x599725%3D_0x5a3620%5B_0x52bfb3(0x51c%2C0x500%2C0x54d%2C0x516%2C0x4e2)%5D(_0x3c4f43%2Carguments)%3Breturn _0x4cd17a%3Dnull%2C_0x599725%3B%7D%7D%3B%7D())%2C_0x57a558%3D_0x5cefcf(this%2Cfunction()%7Bfunction _0x301d31(_0x53c053%2C_0x73aee0%2C_0x20bc8b%2C_0x2e046f%2C_0x2a4e96)%7Breturn _0x5416(_0x2a4e96-0x34d%2C_0x2e046f)%3B%7Dfunction _0x428f5b(_0x37cc1a%2C_0x2104de%2C_0x1e02c2%2C_0x26e41c%2C_0x590aa4)%7Breturn _0x5416(_0x26e41c- -0x25%2C_0x1e02c2)%3B%7Dvar _0x272c37%3D%7B%27KalYp%27%3A_0x1c295c(0x476%2C0x45a%2C0x438%2C0x480%2C0x488)%2B_0x1c295c(0x493%2C0x4b1%2C0x407%2C0x456%2C0x462)%2C%27NYoui%27%3Afunction(_0x1fed7d%2C_0x557829)%7Breturn _0x1fed7d(_0x557829)%3B%7D%2C%27eUsSQ%27%3Afunction(_0x5d5fb8%2C_0x11b835)%7Breturn _0x5d5fb8%2B_0x11b835%3B%7D%2C%27yprRY%27%3Afunction(_0xc9301%2C_0xa78d5d)%7Breturn _0xc9301%2B_0xa78d5d%3B%7D%2C%27AdPxc%27%3A_0xcd9fd5(0x40d%2C0x424%2C0x3ed%2C0x3da%2C0x3b0)%2B_0x1c295c(0x49a%2C0x480%2C0x454%2C0x484%2C0x4c4)%2B_0xcd9fd5(0x3da%2C0x3d5%2C0x3cc%2C0x3e2%2C0x3a5)%2B_0x1c295c(0x535%2C0x4a5%2C0x4ed%2C0x4f7%2C0x50f)%2C%27xOdWt%27%3A_0x1c295c(0x4eb%2C0x50c%2C0x4a4%2C0x4d2%2C0x522)%2B_0x301d31(0x463%2C0x451%2C0x4c1%2C0x498%2C0x478)%2B_0x4b27ce(0x45e%2C0x4c0%2C0x4cf%2C0x477%2C0x478)%2B_0x1c295c(0x501%2C0x481%2C0x49b%2C0x4a8%2C0x460)%2B_0x1c295c(0x4ef%2C0x483%2C0x4f5%2C0x4a2%2C0x4ca)%2B_0xcd9fd5(0x378%2C0x335%2C0x352%2C0x34c%2C0x333)%2B%27%5Cx20)%27%2C%27hmdSO%27%3Afunction(_0x2ec365)%7Breturn _0x2ec365()%3B%7D%2C%27UOLgR%27%3Afunction(_0x4e78cd%2C_0x1025ad)%7Breturn _0x4e78cd<_0x1025ad%3B%7D%2C%27GjUFH%27%3A_0x428f5b(0x121%2C0x8c%2C0x99%2C0xd5%2C0x115)%2B_0x1c295c(0x4ac%2C0x4f9%2C0x4b1%2C0x4d9%2C0x51c)%2B%270%27%2C%27reJBb%27%3A_0x1c295c(0x44f%2C0x498%2C0x4a1%2C0x458%2C0x4ae)%2C%27lVDZV%27%3A_0x1c295c(0x4ce%2C0x4f1%2C0x495%2C0x4db%2C0x4d9)%2C%27ePqBG%27%3A_0xcd9fd5(0x390%2C0x374%2C0x35f%2C0x3bc%2C0x351)%2C%27qhwxd%27%3A_0x4b27ce(0x48d%2C0x49b%2C0x480%2C0x4ac%2C0x4da)%2C%27xUgMH%27%3A_0xcd9fd5(0x3e0%2C0x3ea%2C0x3b5%2C0x411%2C0x40e)%2B_0xcd9fd5(0x321%2C0x3b7%2C0x36b%2C0x35d%2C0x3b7)%2C%27YSsUz%27%3A_0x301d31(0x46d%2C0x3d2%2C0x3f0%2C0x3d6%2C0x423)%2C%27hOszX%27%3A_0x1c295c(0x463%2C0x4bd%2C0x4d1%2C0x498%2C0x46c)%2C%27eMjMZ%27%3Afunction(_0x5ae20e%2C_0x559541%2C_0x135280)%7Breturn _0x5ae20e(_0x559541%2C_0x135280)%3B%7D%2C%27Dqtyz%27%3A_0x301d31(0x43e%2C0x498%2C0x47f%2C0x4ab%2C0x46e)%2B_0x1c295c(0x4bf%2C0x47e%2C0x424%2C0x468%2C0x42d)%2C%27ZfjMm%27%3A_0x1c295c(0x412%2C0x41b%2C0x43a%2C0x453%2C0x400)%2B%27r%27%2C%27WcSVj%27%3A_0x1c295c(0x4b9%2C0x4eb%2C0x458%2C0x4a0%2C0x4c1)%2C%27Nkinq%27%3Afunction(_0x58ca0b%2C_0x5bed19)%7Breturn _0x58ca0b%2B_0x5bed19%3B%7D%2C%27buVAS%27%3Afunction(_0x444e91%2C_0x5bc9b8)%7Breturn _0x444e91%3D%3D%3D_0x5bc9b8%3B%7D%2C%27XGUgU%27%3A_0x4b27ce(0x512%2C0x4b7%2C0x53e%2C0x4f6%2C0x4f8)%2C%27yQfEQ%27%3A_0x301d31(0x444%2C0x3b7%2C0x3b1%2C0x457%2C0x404)%2C%27vOdLp%27%3Afunction(_0x1cdcca%2C_0x1ab572)%7Breturn _0x1cdcca%2B_0x1ab572%3B%7D%2C%27azntx%27%3Afunction(_0xc4c4e9)%7Breturn _0xc4c4e9()%3B%7D%2C%27pZqzF%27%3Afunction(_0xc61338%2C_0x4b726b)%7Breturn _0xc61338!%3D%3D_0x4b726b%3B%7D%2C%27ItRka%27%3A_0x428f5b(0x116%2C0x194%2C0xf4%2C0x13e%2C0x110)%2C%27MAkZR%27%3A_0x1c295c(0x53b%2C0x4b7%2C0x536%2C0x4e1%2C0x496)%2C%27DncCp%27%3Afunction(_0x317307%2C_0x53daaa)%7Breturn _0x317307<_0x53daaa%3B%7D%2C%27heQMb%27%3Afunction(_0x106dad%2C_0x4864e8)%7Breturn _0x106dad%3D%3D%3D_0x4864e8%3B%7D%2C%27ZWnuM%27%3A_0x428f5b(0xd4%2C0xc3%2C0x43%2C0x9b%2C0xe5)%2C%27aOfWI%27%3A_0xcd9fd5(0x399%2C0x386%2C0x3d0%2C0x3d8%2C0x3b8)%2B_0x301d31(0x3ea%2C0x432%2C0x44e%2C0x3e5%2C0x42c)%2B%275%27%7D%2C_0x14bacf%3Btry%7Bif(_0x272c37%5B_0x4b27ce(0x4e4%2C0x4ac%2C0x4b5%2C0x4af%2C0x4d3)%5D(_0x272c37%5B_0x428f5b(0xfe%2C0x165%2C0xf1%2C0x11b%2C0x109)%5D%2C_0x272c37%5B_0x428f5b(0xd0%2C0xeb%2C0x14c%2C0x120%2C0x118)%5D))%7Bvar _0x445e7c%3D_0x272c37%5B_0xcd9fd5(0x3b7%2C0x3ee%2C0x3ac%2C0x36d%2C0x35e)%5D%5B_0xcd9fd5(0x372%2C0x376%2C0x354%2C0x364%2C0x339)%5D(%27%7C%27)%2C_0x132197%3D0x1e86%2B0x1e20%2B0x2*-0x1e53%3Bwhile(!!%5B%5D)%7Bswitch(_0x445e7c%5B_0x132197%2B%2B%5D)%7Bcase%270%27%3Avar _0x5f2b28%3Bcontinue%3Bcase%271%27%3Atry%7Bvar _0x46801f%3D_0x272c37%5B_0x428f5b(0x19a%2C0x177%2C0x198%2C0x13f%2C0x16c)%5D(_0x4bca2a%2C_0x272c37%5B_0x428f5b(0xb4%2C0x51%2C0x7b%2C0x90%2C0x9b)%5D(_0x272c37%5B_0x428f5b(0xea%2C0xaf%2C0xef%2C0xd3%2C0xeb)%5D(_0x272c37%5B_0x1c295c(0x459%2C0x4e6%2C0x476%2C0x49e%2C0x45e)%5D%2C_0x272c37%5B_0x301d31(0x3ff%2C0x3e9%2C0x3e6%2C0x47d%2C0x426)%5D)%2C%27)%3B%27))%3B_0x5f2b28%3D_0x272c37%5B_0x4b27ce(0x4cf%2C0x4cf%2C0x515%2C0x527%2C0x564)%5D(_0x46801f)%3B%7Dcatch(_0x1cb9dc)%7B_0x5f2b28%3D_0x35ea30%3B%7Dcontinue%3Bcase%272%27%3Afor(var 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_0x4377ff%3D_0x1ba70f%5B_0x4d05b4%5D%7C%7C_0x940b24%3Bcontinue%3Bcase%272%27%3A_0x940b24%5B_0x4b27ce(0x44d%2C0x496%2C0x464%2C0x4a4%2C0x4c9)%2B_0x4b27ce(0x504%2C0x4d5%2C0x545%2C0x4ea%2C0x541)%5D%3D_0x4377ff%5B_0x428f5b(0x87%2C0x6e%2C0x10f%2C0xc2%2C0xe0)%2B_0xcd9fd5(0x3d7%2C0x392%2C0x3ba%2C0x37b%2C0x3c6)%5D%5B_0x4b27ce(0x4c6%2C0x527%2C0x50e%2C0x50f%2C0x532)%5D(_0x4377ff)%3Bcontinue%3Bcase%273%27%3A_0x940b24%5B_0x4b27ce(0x4d3%2C0x46a%2C0x4a1%2C0x487%2C0x476)%2B_0xcd9fd5(0x42c%2C0x3bb%2C0x3dc%2C0x395%2C0x3c7)%5D%3D_0x3afddd%5B_0x428f5b(0x10d%2C0x125%2C0x169%2C0x12d%2C0x124)%5D(_0x31d82a)%3Bcontinue%3Bcase%274%27%3Avar _0x940b24%3D_0x29fde5%5B_0x1c295c(0x466%2C0x427%2C0x468%2C0x459%2C0x411)%2B_0x4b27ce(0x4a8%2C0x4d1%2C0x4c9%2C0x4a9%2C0x49e)%2B%27r%27%5D%5B_0xcd9fd5(0x362%2C0x39d%2C0x35a%2C0x387%2C0x399)%2B_0x4b27ce(0x4a5%2C0x491%2C0x4ea%2C0x49e%2C0x484)%5D%5B_0xcd9fd5(0x3e2%2C0x3dd%2C0x3df%2C0x405%2C0x3f2)%5D(_0x294c33)%3Bcontinue%3Bcase%275%27%3Avar _0x4d05b4%3D_0x43d095%5B_0x106685%5D%3Bcontinue%3B%7Dbreak%3B%7D%7Dcontinue%3Bcase%273%27%3Avar _0x1ba70f%3D_0x5f2b28%5B_0x428f5b(0x128%2C0x141%2C0x16d%2C0x130%2C0x141)%2B%27le%27%5D%3D_0x5f2b28%5B_0x301d31(0x46b%2C0x44b%2C0x446%2C0x4d3%2C0x4a2)%2B%27le%27%5D%7C%7C%7B%7D%3Bcontinue%3Bcase%274%27%3Avar _0x43d095%3D%5B_0x272c37%5B_0xcd9fd5(0x41e%2C0x383%2C0x3d5%2C0x3fd%2C0x402)%5D%2C_0x272c37%5B_0x1c295c(0x487%2C0x45e%2C0x468%2C0x496%2C0x43e)%5D%2C_0x272c37%5B_0x4b27ce(0x529%2C0x4f3%2C0x4fb%2C0x522%2C0x514)%5D%2C_0x272c37%5B_0x301d31(0x3f1%2C0x42b%2C0x3ef%2C0x3d5%2C0x428)%5D%2C_0x272c37%5B_0x428f5b(0xd4%2C0x91%2C0xc2%2C0xbd%2C0xd7)%5D%2C_0x272c37%5B_0x301d31(0x4ae%2C0x4a1%2C0x4f8%2C0x491%2C0x4b6)%5D%2C_0x272c37%5B_0x4b27ce(0x457%2C0x4c1%2C0x492%2C0x47a%2C0x46c)%5D%5D%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x3b063d%3D_0x272c37%5B_0xcd9fd5(0x3ce%2C0x3eb%2C0x3f1%2C0x438%2C0x41c)%5D(Function%2C_0x272c37%5B_0x428f5b(0xd7%2C0xb3%2C0x134%2C0x10c%2C0x12c)%5D(_0x272c37%5B_0x301d31(0x470%2C0x48d%2C0x461%2C0x434%2C0x47e)%5D(_0x272c37%5B_0x428f5b(0x116%2C0x104%2C0x10e%2C0xe9%2C0x134)%5D%2C_0x272c37%5B_0xcd9fd5(0x380%2C0x353%2C0x366%2C0x33a%2C0x31b)%5D)%2C%27)%3B%27))%3B_0x14bacf%3D_0x272c37%5B_0x301d31(0x47e%2C0x45c%2C0x460%2C0x426%2C0x44c)%5D(_0x3b063d)%3B%7D%7Dcatch(_0x45e4fb)%7Bif(_0x272c37%5B_0x4b27ce(0x4b9%2C0x460%2C0x48e%2C0x4a0%2C0x4d8)%5D(_0x272c37%5B_0x1c295c(0x4dc%2C0x4db%2C0x519%2C0x4d6%2C0x4cd)%5D%2C_0x272c37%5B_0x301d31(0x425%2C0x481%2C0x496%2C0x4b3%2C0x46f)%5D))_0x14bacf%3Dwindow%3Belse%7Bif(_0x544254)%7Bvar _0x2c4709%3D_0x36d883%5B_0x4b27ce(0x4bf%2C0x4c7%2C0x4de%2C0x4ec%2C0x4de)%5D(_0x4ff5ae%2Carguments)%3Breturn _0x332aa2%3Dnull%2C_0x2c4709%3B%7D%7D%7Dfunction _0xcd9fd5(_0x153eea%2C_0x246a6c%2C_0x1c928f%2C_0x4e60af%2C_0x54d9b9)%7Breturn _0x5416(_0x1c928f-0x28d%2C_0x246a6c)%3B%7Dvar _0x2f9d27%3D_0x14bacf%5B_0x428f5b(0x159%2C0x144%2C0x17a%2C0x130%2C0x16c)%2B%27le%27%5D%3D_0x14bacf%5B_0x4b27ce(0x516%2C0x4dc%2C0x55a%2C0x512%2C0x4ee)%2B%27le%27%5D%7C%7C%7B%7D%2C_0xcb9070%3D%5B_0x272c37%5B_0x4b27ce(0x542%2C0x542%2C0x4e6%2C0x505%2C0x4ab)%5D%2C_0x272c37%5B_0x428f5b(0xe6%2C0x9a%2C0xb7%2C0xe1%2C0x95)%5D%2C_0x272c37%5B_0x1c295c(0x4c9%2C0x4ab%2C0x541%2C0x4f5%2C0x4f3)%5D%2C_0x272c37%5B_0x301d31(0x470%2C0x460%2C0x43c%2C0x445%2C0x428)%5D%2C_0x272c37%5B_0x4b27ce(0x48a%2C0x4a7%2C0x4b9%2C0x49f%2C0x4ba)%5D%2C_0x272c37%5B_0xcd9fd5(0x40b%2C0x439%2C0x3f6%2C0x423%2C0x3bd)%5D%2C_0x272c37%5B_0x4b27ce(0x482%2C0x433%2C0x438%2C0x47a%2C0x457)%5D%5D%3Bfunction _0x4b27ce(_0x59ceda%2C_0x1e2fe6%2C_0x562fd5%2C_0x3924cd%2C_0x22e19f)%7Breturn _0x5416(_0x3924cd-0x3bd%2C_0x1e2fe6)%3B%7Dfunction _0x1c295c(_0x4f73d1%2C_0x3af836%2C_0x358e66%2C_0x989e3f%2C_0x3b6a97)%7Breturn _0x5416(_0x989e3f-0x390%2C_0x358e66)%3B%7Dfor(var _0x2e72e5%3D-0x1*-0x1efe%2B-0x42d%2B-0x1ad1%3B_0x272c37%5B_0x4b27ce(0x47c%2C0x4f7%2C0x4b5%2C0x4d8%2C0x4ff)%5D(_0x2e72e5%2C_0xcb9070%5B_0x1c295c(0x41a%2C0x428%2C0x40d%2C0x452%2C0x49e)%2B%27h%27%5D)%3B_0x2e72e5%2B%2B)%7Bif(_0x272c37%5B_0xcd9fd5(0x36a%2C0x3aa%2C0x35c%2C0x37c%2C0x339)%5D(_0x272c37%5B_0x428f5b(0xf1%2C0xd2%2C0x116%2C0xf4%2C0x10c)%5D%2C_0x272c37%5B_0x4b27ce(0x4b5%2C0x4f4%2C0x49f%2C0x4d6%2C0x4b8)%5D))%7Bvar _0x3c565f%3D_0x272c37%5B_0x4b27ce(0x4bd%2C0x500%2C0x4c2%2C0x4c2%2C0x493)%5D%5B_0x301d31(0x471%2C0x3c0%2C0x458%2C0x456%2C0x414)%5D(%27%7C%27)%2C_0x364132%3D-0x60d*-0x2%2B0x304%2B-0xf1e%3Bwhile(!!%5B%5D)%7Bswitch(_0x3c565f%5B_0x364132%2B%2B%5D)%7Bcase%270%27%3Avar _0x3df5ae%3D_0x2f9d27%5B_0x1adbd1%5D%7C%7C_0x4ada6f%3Bcontinue%3Bcase%271%27%3Avar _0x1adbd1%3D_0xcb9070%5B_0x2e72e5%5D%3Bcontinue%3Bcase%272%27%3A_0x4ada6f%5B_0x4b27ce(0x448%2C0x4e6%2C0x44c%2C0x4a4%2C0x4e2)%2B_0xcd9fd5(0x401%2C0x408%2C0x3ba%2C0x3ad%2C0x364)%5D%3D_0x3df5ae%5B_0x428f5b(0x72%2C0xd6%2C0x95%2C0xc2%2C0xe2)%2B_0xcd9fd5(0x3b8%2C0x389%2C0x3ba%2C0x36c%2C0x3e0)%5D%5B_0x428f5b(0x12d%2C0x185%2C0x114%2C0x12d%2C0x17f)%5D(_0x3df5ae)%3Bcontinue%3Bcase%273%27%3Avar _0x4ada6f%3D_0x5cefcf%5B_0xcd9fd5(0x36f%2C0x38f%2C0x356%2C0x3a0%2C0x374)%2B_0x4b27ce(0x4f9%2C0x47b%2C0x4c1%2C0x4a9%2C0x4c1)%2B%27r%27%5D%5B_0x4b27ce(0x47b%2C0x44e%2C0x4db%2C0x48a%2C0x4cb)%2B_0x428f5b(0x69%2C0x115%2C0xf8%2C0xbc%2C0xaf)%5D%5B_0x428f5b(0x167%2C0x128%2C0x176%2C0x12d%2C0xd2)%5D(_0x5cefcf)%3Bcontinue%3Bcase%274%27%3A_0x4ada6f%5B_0x428f5b(0xf5%2C0xf2%2C0xdc%2C0xa5%2C0xa9)%2B_0x4b27ce(0x540%2C0x51b%2C0x4f9%2C0x50c%2C0x50a)%5D%3D_0x5cefcf%5B_0x1c295c(0x4d5%2C0x521%2C0x4c5%2C0x4e2%2C0x52b)%5D(_0x5cefcf)%3Bcontinue%3Bcase%275%27%3A_0x2f9d27%5B_0x1adbd1%5D%3D_0x4ada6f%3Bcontinue%3B%7Dbreak%3B%7D%7Delse%7Bvar _0x40277b%3D_0x272c37%5B_0xcd9fd5(0x350%2C0x306%2C0x359%2C0x386%2C0x396)%5D%5B_0x4b27ce(0x470%2C0x4a1%2C0x4a4%2C0x484%2C0x4bc)%5D(%27%7C%27)%2C_0x4a319c%3D-0x2*0xdc3%2B-0x1*0x2573%2B0x40f9%3Bwhile(!!%5B%5D)%7Bswitch(_0x40277b%5B_0x4a319c%2B%2B%5D)%7Bcase%270%27%3Avar _0x401653%3D%7B%7D%3B_0x401653%5B_0x428f5b(0x16d%2C0xfa%2C0xde%2C0x137%2C0x105)%5D%3D_0x272c37%5B_0x301d31(0x4d6%2C0x4d4%2C0x4a0%2C0x4b4%2C0x49a)%5D%2C_0x401653%5B_0x1c295c(0x3fa%2C0x4a0%2C0x3fc%2C0x44c%2C0x4a0)%2B_0x1c295c(0x4ab%2C0x502%2C0x4c4%2C0x4ce%2C0x4f8)%5D%3D_0x2089ca%5B_0x428f5b(0x11b%2C0xc6%2C0x11f%2C0xdb%2C0x91)%5D%5B_0x4b27ce(0x4cf%2C0x43b%2C0x4d6%2C0x479%2C0x42c)%2B_0x301d31(0x484%2C0x458%2C0x4d8%2C0x43b%2C0x48b)%5D%2C_0x401653%5B_0x301d31(0x3dd%2C0x440%2C0x45a%2C0x42a%2C0x422)%2B%27ut%27%5D%3D!(-0x5d1*-0x6%2B-0x1abe%2B0x74*-0x12)%2C_0xadcc11%5B_0x4b27ce(0x52c%2C0x4d7%2C0x4b5%2C0x4d0%2C0x4ea)%2B_0x1c295c(0x42f%2C0x450%2C0x460%2C0x47d%2C0x4b6)%5D(_0x401653%2Cfunction()%7Bfunction _0x48a38f(_0x292124%2C_0x5f5b83%2C_0x1bce0d%2C_0x530035%2C_0x4b4693)%7Breturn _0x301d31(_0x292124-0x1ac%2C_0x5f5b83-0xa6%2C_0x1bce0d-0x1cf%2C_0x292124%2C_0x5f5b83-0x89)%3B%7Dfunction _0x570b58(_0x52cd81%2C_0x2e4d4b%2C_0xe86619%2C_0x246213%2C_0x459cfd)%7Breturn _0x4b27ce(_0x52cd81-0x1ca%2C_0xe86619%2C_0xe86619-0x13f%2C_0x246213- -0x6e9%2C_0x459cfd-0x2e)%3B%7Dfunction _0x53d4bc(_0x2af819%2C_0x1d04d3%2C_0x2d6647%2C_0x2c99ed%2C_0x383ddf)%7Breturn _0x301d31(_0x2af819-0xd6%2C_0x1d04d3-0x1ab%2C_0x2d6647-0xaf%2C_0x2c99ed%2C_0x2d6647- -0x63e)%3B%7D_0x589f56%5B_0x570b58(-0x1d4%2C-0x1fa%2C-0x21c%2C-0x22a%2C-0x24d)%2B_0x48a38f(0x4bd%2C0x4f0%2C0x4d2%2C0x496%2C0x4ba)%2B%27t%27%5D%3D_0x272c37%5B_0x570b58(-0x227%2C-0x1f4%2C-0x182%2C-0x1cd%2C-0x202)%5D(_0x506cdc%2Cfunction()%7Bfunction _0x3d6283(_0x144aad%2C_0xa7f1a4%2C_0x440cb2%2C_0x3ccc78%2C_0x15c5b4)%7Breturn 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_0x531dab(_0x2313d6%2C_0x4201c5%2C_0x13ea7e%2C_0x89f954%2C_0x315599)%7Breturn _0x3faf6c(_0x2313d6-0x117%2C_0x4201c5-0x97%2C_0x13ea7e-0x1c8%2C_0x13ea7e-0x5fc%2C_0x4201c5)%3B%7Dfunction _0x54d7f0(_0xb0d2a7%2C_0x18c5c9%2C_0x2c6506%2C_0x43b8ac%2C_0x38ea12)%7Breturn _0x3cf296(_0x2c6506%2C_0x18c5c9-0x1ca%2C_0x18c5c9-0xf9%2C_0x43b8ac-0xbe%2C_0x38ea12-0x5b)%3B%7Dfunction _0x1cc482(_0x3eb1c6%2C_0x423d7a%2C_0x5278fb%2C_0x2e6ce2%2C_0x4f72ca)%7Breturn _0x3faf6c(_0x3eb1c6-0x86%2C_0x423d7a-0x3c%2C_0x5278fb-0x199%2C_0x2e6ce2-0x169%2C_0x4f72ca)%3B%7Dvar _0x364e18%3D%7B%27XwhBc%27%3Afunction(_0x3df64c%2C_0x5d6583)%7Breturn _0x3df64c%3D%3D%3D_0x5d6583%3B%7D%2C%27ODbUo%27%3A_0x54d7f0(-0x2a%2C0x3%2C-0x13%2C-0x3c%2C0x43)%2C%27Dcxau%27%3Afunction(_0x13673c%2C_0x4b63fb%2C_0x3d4916)%7Breturn _0x13673c(_0x4b63fb%2C_0x3d4916)%3B%7D%7D%3Bfunction _0xf893f0(_0x3545bf%2C_0x29c2d8%2C_0x22909d%2C_0x16349a%2C_0x2841b9)%7Breturn _0x3faf6c(_0x3545bf-0x12d%2C_0x29c2d8-0x1cc%2C_0x22909d-0xce%2C_0x3545bf-0x5f6%2C_0x16349a)%3B%7Dt%5B_0x1cc482(-0xb1%2C-0x4d%2C-0x55%2C-0x9f%2C-0x9f)%2B_0x1cc482(-0x62%2C-0xd5%2C-0x3f%2C-0x87%2C-0x5f)%2B%27t%27%5D%3D_0x364e18%5B_0x531dab(0x3a6%2C0x3a9%2C0x3ce%2C0x3bb%2C0x3d0)%5D(setTimeout%2Cfunction()%7Bfunction _0x9d78(_0x129567%2C_0x1e9516%2C_0x4b747b%2C_0x3f9c18%2C_0x5d7762)%7Breturn _0x1cc482(_0x129567-0xdf%2C_0x1e9516-0xfd%2C_0x4b747b-0x12%2C_0x1e9516-0x410%2C_0x129567)%3B%7Dfunction _0x4a6b6c(_0x5c1d84%2C_0x4b56f9%2C_0x5c4298%2C_0x409d96%2C_0x2eb082)%7Breturn _0x531dab(_0x5c1d84-0x8b%2C_0x2eb082%2C_0x5c4298- -0xbd%2C_0x409d96-0xd8%2C_0x2eb082-0xcc)%3B%7Dfunction _0x4a4b0d(_0x5c5cf4%2C_0x2b0ab2%2C_0x318e48%2C_0x424d63%2C_0x342b86)%7Breturn _0x54d7f0(_0x5c5cf4-0x161%2C_0x318e48- -0x310%2C_0x5c5cf4%2C_0x424d63-0x1e9%2C_0x342b86-0x1e2)%3B%7Dfunction _0x44093b(_0x21199a%2C_0x440f5d%2C_0x5263b3%2C_0x4a5411%2C_0x4db65c)%7Breturn _0x531dab(_0x21199a-0x1b1%2C_0x440f5d%2C_0x4a5411- -0x1a0%2C_0x4a5411-0xb%2C_0x4db65c-0x19d)%3B%7Dfunction _0x335831(_0xc62ab8%2C_0x304cc4%2C_0x3faa79%2C_0x5f49de%2C_0xf84659)%7Breturn _0x531dab(_0xc62ab8-0x73%2C_0x5f49de%2C_0xf84659-0xa5%2C_0x5f49de-0x12d%2C_0xf84659-0x1a0)%3B%7Dif(_0x364e18%5B_0x4a4b0d(-0x335%2C-0x330%2C-0x31d%2C-0x340%2C-0x319)%5D(_0x364e18%5B_0x4a4b0d(-0x2f1%2C-0x30f%2C-0x2e5%2C-0x301%2C-0x2a7)%5D%2C_0x364e18%5B_0x4a6b6c(0x31c%2C0x34c%2C0x328%2C0x37e%2C0x33d)%5D))t%5B_0x4a6b6c(0x344%2C0x350%2C0x31d%2C0x2c5%2C0x333)%2B%27mQ%27%5D()%3Belse%7Bif(_0x28d099)%7Bvar _0x2d85a2%3D_0x3c92e5%5B_0x335831(0x4f6%2C0x51a%2C0x4c2%2C0x470%2C0x4c6)%5D(_0x2bfe14%2Carguments)%3Breturn _0x320ad3%3Dnull%2C_0x2d85a2%3B%7D%7D%7D%2C0x1ab6%2B0x2425%2B-0x3d19*0x1)%3B%7D))%3B%0A %7D)%0A getdefense.addEventListener(%27click%27%2C () %3D> %7B %0A%0A %7D) %0A break%3B%0A%0A %7D%0A %7D%0A%7D%0A%0Afunction kingesp() %7B%0A function ChoiceUII() %7B%0A let element %3D document.createElement(%27div%27)%3B%0A element.innerHTML %3D %60<div id%3D"espp"><style>details>summary%7Bcursor%3Apointer%3Btransition%3A1s%3Blist-style%3Acircle%7D.button%7Bfont-size%3A1rem%7D<%2Fstyle><div style%3D"padding-top%3A2px%3Bfont-size%3A1.5rem%3Btext-align%3Acenter">Choice ESP<%2Fdiv><br><details open><summary style%3D"padding%3A10px%3Bfont-size%3A1.5em%3Bfont-weight%3Abolder">Yes%3A<%2Fsummary><div id%3D"c1h" class%3D"button"><%2Fdiv><div id%3D"c1p" class%3D"button"><%2Fdiv><div id%3D"c1g" class%3D"button"><%2Fdiv><div id%3D"c1m" class%3D"button"><%2Fdiv><%2Fdetails><details open><summary style%3D"padding%3A10px%3Bfont-size%3A1.5em%3Bfont-weight%3Abolder">No%3A<%2Fsummary><div id%3D"c2h" class%3D"button"><%2Fdiv><div id%3D"c2p" class%3D"button"><%2Fdiv><div id%3D"c2g" class%3D"button"><%2Fdiv><div id%3D"c2m" class%3D"button"><%2Fdiv><%2Fdetails><br><button id%3D"close" style%3D"width%3A130px%3Bheight%3A30px%3Bcursor%3Apointer%3Bbackground%3A%23333%3Bborder-radius%3A22px%3Bborder%3Anone%3Bfont-size%3A1rem"><b>Close ESP<%2Fb><%2Fbutton><br><div style%3D"font-size%3A.8rem">ui by <a href%3D"https%3A%2F%2F">Sharp (Toad_UI)<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 200px%3B background%3A rgb(31%2C 25%2C 30)%3B border-radius%3A 13px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A %7D%0A%0A function closeui() %7B%0A const esp %3D document.getElementById("espp")%0A esp.remove()%3B%0A %7D%0A%0A function addUtils() %7B%0A const exit %3D document.getElementById("close")%0A exit.addEventListener(%27click%27%2C closeui)%3B%0A %7D%0A ChoiceUII()%0A addUtils()%0A%0A function updateChoices() %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%0A const no %3D hack.stateNode.state.guest.no%0A const yes %3D hack.stateNode.state.guest.yes%0A const c2gold %3D document.getElementById("c2g")%0A const c2happy %3D document.getElementById("c2h")%0A const c2people %3D document.getElementById("c2p")%0A const c2mats %3D document.getElementById("c2m")%0A const c1gold %3D document.getElementById("c1g")%0A const c1happy %3D document.getElementById("c1h")%0A const c1people %3D document.getElementById("c1p")%0A const c1mats %3D document.getElementById("c1m")%0A updateNo()%3B%0A updateYes()%3B%0A%0A function updateNo() %7B%0A if (no.happiness !%3D null) %7B%0A c2happy.innerHTML %3D %60Happiness%3A %24%7Bno.happiness%7D%60%0A %7D else %7B%0A c2happy.innerHTML %3D null%3B%0A %7D%0A if (no.people !%3D null) %7B%0A c2people.innerHTML %3D %60People%3A %24%7Byes.people%7D%60%0A %7D else %7B%0A c2people.innerHTML %3D null%3B%0A %7D%0A if (no.gold !%3D null) %7B%0A c2gold.innerHTML %3D %60Gold%3A %24%7Bno.gold%7D%60%0A %7D else %7B%0A c2gold.innerHTML %3D null%3B%0A %7D%0A if (no.materials !%3D null) %7B%0A c2mats.innerHTML %3D %60Materials%3A %24%7Bno.materials%7D%60%0A %7D else %7B%0A c2mats.innerHTML %3D null%3B%0A %7D%0A %7D%0A%0A function updateYes() %7B%0A if (yes.happiness !%3D null) %7B%0A c1happy.innerHTML %3D %60Happiness%3A %24%7Byes.happiness%7D%60%0A %7D else %7B%0A c1happy.innerHTML %3D null%3B%0A %7D%0A if (yes.people !%3D null) %7B%0A c1people.innerHTML %3D %60People%3A %24%7Byes.people%7D%60%0A %7D else %7B%0A c1people.innerHTML %3D null%3B%0A %7D%0A if (yes.gold !%3D null) %7B%0A c1gold.innerHTML %3D %60Gold%3A %24%7Byes.gold%7D%60%0A %7D else %7B%0A c1gold.innerHTML %3D null%3B%0A %7D%0A if (yes.materials !%3D null) %7B%0A c1mats.innerHTML %3D %60Materials%3A %24%7Byes.materials%7D%60%0A %7D else %7B%0A c1mats.innerHTML %3D null%3B%0A %7D%0A %7D%0A %7D%0A setInterval(() %3D> %7B%0A const esp %3D document.getElementById("espp")%0A if (esp !%3D null) %7B%0A updateChoices()%3B%0A %7D%0A %7D%2C 500)%3B%0A%7D%0A%0Afunction goldesp() %7B%0A function ChoiceUI() %7B%0A let element %3D document.createElement(%27div%27)%3B%0A element.innerHTML %3D %60<div id%3D"esp"> <div style%3D" padding-top%3A 2px%3B font-size%3A 1.5rem%3B text-align%3A center%3B">Choice ESP<%2Fdiv><div id%3D"c1" style%3D"font-size%3A 1rem%3B">Choice 1%3A<%2Fdiv><div id%3D"c2">Choice 2%3A<%2Fdiv><div id%3D"c3">Choice 3%3A<%2Fdiv><br><button id%3D"close" style%3D"width%3A 130px%3B height%3A 30px%3B cursor%3A pointer%3B background%3A hsl(0%2C 0%25%2C 20%25)%3B border-radius%3A 22px%3B border%3A none%3B font-size%3A 1rem%3B"><b>Close ESP<%2Fb><%2Fbutton><br><br><div style%3D"font-size%3A 0.8rem%3B">ui by <a href%3D"https%3A%2F%2Fgithub.com%2FBlooketware">Blooketware<%2Fa><%2Fdiv><%2Fdiv>%60%3B%0A element.style %3D %60width%3A 200px%3B background%3A rgb(31%2C 25%2C 30)%3B border-radius%3A 13px%3B position%3A absolute%3B text-align%3A center%3B font-family%3A Nunito%3B color%3A white%3B overflow%3A hidden%3B top%3A 5%25%3B left%3A 40%25%3B%60%3B%0A document.body.appendChild(element)%3B%0A var pos1 %3D 0%2C%0A pos2 %3D 0%2C%0A pos3 %3D 0%2C%0A pos4 %3D 0%3B%0A element.onmousedown %3D ((e %3D window.event) %3D> %7B%0A e.preventDefault()%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A document.onmouseup %3D (() %3D> %7B%0A document.onmouseup %3D null%3B%0A document.onmousemove %3D null%3B%0A %7D)%3B%0A document.onmousemove %3D ((e) %3D> %7B%0A e %3D e %7C%7C window.event%3B%0A e.preventDefault()%3B%0A pos1 %3D pos3 - e.clientX%3B%0A pos2 %3D pos4 - e.clientY%3B%0A pos3 %3D e.clientX%3B%0A pos4 %3D e.clientY%3B%0A let top %3D (element.offsetTop - pos2) > 0 %3F (element.offsetTop - pos2) %3A 0%3B%0A let left %3D (element.offsetLeft - pos1) > 0 %3F (element.offsetLeft - pos1) %3A 0%3B%0A element.style.top %3D top %2B "px"%3B%0A element.style.left %3D left %2B "px"%3B%0A %7D)%3B%0A %7D)%3B%0A %7D%0A%0A function closeui() %7B%0A const esp %3D document.getElementById("esp")%0A esp.remove()%3B%0A %7D%0A%0A function addUtilss() %7B%0A const exit %3D document.getElementById("close")%0A exit.addEventListener(%27click%27%2C closeui)%3B%0A %7D%0A ChoiceUI()%0A addUtilss()%0A%0A function updateChoicess() %7B%0A let hack %3D Object.values(document.querySelector(%27%23app > div > div%27))%5B1%5D.children%5B1%5D._owner%0A const choice %3D hack.stateNode.state.choices%0A const c1 %3D document.getElementById("c1")%0A const c2 %3D document.getElementById("c2")%0A const c3 %3D document.getElementById("c3")%0A c1.innerHTML %3D "Choice 1%3A " %2B choice%5B0%5D.text%0A c2.innerHTML %3D "Choice 2%3A " %2B choice%5B1%5D.text%0A c3.innerHTML %3D "Choice 3%3A " %2B choice%5B2%5D.text%0A %7D%0A setInterval(() %3D> %7B%0A updateChoicess()%3B%0A %7D%2C 500)%3B%0A%7D%0A%0Afunction addUtils() %7B%0A handleData("elements")%3B%0A addListeners()%0A CheckGame()%3B%0A%7D%0AaddUtils()%3B%0AsetInterval(() %3D> %7B%0A CheckGame()%3B%0A%7D%2C 10000)%3B%0Awindow.alert("made by Jacob huggins.")%3B%7D)()%3B
longmans / Coin Api MCPA Model Context Protocol server that provides access to CoinMarketCap's cryptocurrency data. This server enables AI-powered applications to retrieve cryptocurrency listings, quotes, and detailed information about various coins.
KaranUnique / CryptoHubCryptoHub is a modern crypto tracking platform that lets users explore real-time cryptocurrency prices, view detailed coin analytics, and stay updated with market trends. With a simple and intuitive interface, CryptoHub makes it easy for beginners and traders to track their favorite coins, compare data, and make informed decisions.
Hazrat-Ali9 / Coin Tracker💰 Coin Tracker 🚁 Real Time 🛸 Cryptocurrency 🚢 Market Tracker 🚀data Bitcoin 🚟 Ethereum and ⛴ Interactive charts 🛼 and graphs ☂price volume 🎳 and market 🏩 cap analysis 🏪 Secure storage 🏰 with local 🚅 caching Firebase 🕌 integration Support 🚋 light themes 🏯 fiat currency 🚒 conversion and 🍑 multi language 🍏
BlockchainLabs / SpreadCoinSpreadCoin October 5, 2014 Introduction In proof-of-work cryptocurrencies new coins are generated by the network through the process of mining. One of the purposes of mining is to protect network from double spending attacks and history rewriting. Miners generate new blocks and check contents of the blocks generated by other peers for conformation to the network rules. However, many miners now delegate all the checking work crucial to cryptocurrency security to pools. This means that pool operators do not have any large hashing power but have control over generation of new blocks. This brings unnecessary centralization to otherwise decentralized system. Controlling more than 50% of mining power allows to perform double-spending attacks with 100% chance of success but even with less than 50% control it is possible to perform attacks which have chances to succeed1 . The core idea of SpreadCoin is to prevent creation of pools and thus make mining more decentralized and the whole system more secure. Pool Prevention In pooled mining miners perform only the work which is necessary to fulfill the proof-of-work requirements and pools take care of block generation and broadcasting and distribute reward among miners according to the shares they submit. In this scheme miner has two alternatives: 1. Solo mining. In this case miner cannot send shares to the pool because they will not be accepted. 2. Pooled mining. Miner’s shares will be accepted by the pool but in the case miner will actually generate a new block its reward will go to the pool which will redistribute it to all miners. This allows organization of pools because miners has no way to cheat and steal generated money. To prevent creation of pools we must remove this possibility so that if pool will be created than miner can mine in a pool, submit shares as usual and get reward for them but in the case of actually finding a block miner can send it directly to the network instead of the pool and get full reward for it. In SpreadCoin mining is organized in such way that miner must know the following things: 1. Private key corresponding to the coinbase transaction. 2. Whole block, not only its header. This ensures that miner can broadcast mined block and spend coins generated in that block. It may seem that it is necessary to know only the private key to spend coinbase transaction. If two conflicting transactions will appear on the network then the one that was broadcasted first will have much higher probability to be included in a block because each peer remembers and retransmits only the first one of the conflicting transactions. If both miner and pool know private key but only pool knows the content of the block than pool can generate and broadcast spending transaction earlier than miner. If both miner 1 Double-spending. Bitcoin Wiki. https://en.bitcoin.it/wiki/Double-spending and pool know content of the block than miner will be the first one who can broadcast block and spending transaction. To prove knowledge of the private key and whole block there are two new fields in the block header: MinerSignature and hashWholeBlock. MinerSignature is a digital signature of all fields of the block header except for the hashWholeBlock. Changing any information in the block requires regeneration of this signature which means that it is necessary to recalculate it during each iteration of the mining process. This implies that miner must be able to sign any arbitrary data. hashWholeBlock is a SHA-256 hash of the block data arranged as follows: Padding ensures that there is no incentive to mine empty blocks without transactions. Padding values are computed using simple algorithm which initializes last 32 bytes (8 uint32) with hashPrevBlock and then goes backward and computes remaining uint32 values using the following recursive formula: 𝐼𝑖 = 𝐼𝑖+3 ∙ 𝐼𝑖+7. This algorithm ensures that there is no efficient way to compute padding values on the fly during hash computation which otherwise could potentially give some advantage to mine empty blocks in certain computing environments. It is important that block is hashed twice. If it was hashed only once then pool could hash the beginning of the block and send resulting hash state to the miners. Each miner would then modify some information in the end of the block and recalculate the hash based on the known state without actual knowledge about what is contained in the beginning of the block. Appending block data to itself make it necessary to know the whole block to recalculate hashWholeBlock. Pool may detect and ban cheating miners. However, many miners may still prefer to cheat so that pool will be completely unusable for honest miners. Miners that have low probability of finding a block will get more profit by stealing reward for accidentally found block even if pool will ban them thereafter. Miners that have enough mining power to find blocks consistently can still connect to a pool and submit shares for some time but steal the first found block. This way they can get both reward for their shares and the actual mined block. Given all this it is expected that no one will create a pool. But even if someone will than it can be countered by releasing stealing miner software which many miners will switch to. Compact Transactions SpreadCoin as well as Bitcoin uses ECDSA signatures. Each address in Bitcoin is a hash of an ECDSA public key. To spend coins sent to an address it is necessary to provide public key matching to that hash and a signature. This results in 139 or 107 bytes for each transaction input script (scriptSig) depending on Block Padding MAX_BLOCK_SIZE Block Padding whether compact public key is used. However, it is possible to recover public key from the signature2 which means that it is not necessary to provide it in transaction input. Together with using compact representation of the signature3 it allows to reduce size of transaction input script from 139 or 107 bytes in Bitcoin to 67 bytes in SpreadCoin. Recovering public key has almost no extra CPU cost compared to the usual signature verification process used in Bitcoin. This is important because the CPU cost of ECDSA signature verification is a bottleneck for Bitcoin transaction processing. Usual output script (scriptPubKey) in Bitcoin looks as follows: OP_DUP OP_HASH160 5bd18804e4bb43a4bb8b6bc88408970bafaf4a38 OP_EQUALVERIFY OP_CHECKSIG In SpreadCoin the semantics of the OP_CHECKSIG instruction was changed to checking signature by hash of the public key (it recovers public key and compares its hash with the provided one). This results in a much simpler script in SpreadCoin: 5bd18804e4bb43a4bb8b6bc88408970bafaf4a38 OP_CHECKSIG This results in additional minor space saving because this script is 3 bytes smaller. Smooth Supply Block reward in Bitcoin is computed using the following formula: 𝑅ℎ = 𝑅0 ∙ 2 −⌊ ℎ 𝑝 ⌋ , where ℎ – block height, 𝑝 – reward halving period, 𝑅0 – initial reward, 𝑅ℎ – reward for block ℎ, ⌊ ⌋ – floor function. This method results in abrupt reward changes near halving points. SpreadCoin uses simple linear interpolation between halving points to make reward decrease much smother. This is achieved by modifying reward using the following formula: 𝑅ℎ ′ = 4 3 (𝑅ℎ − 𝑅ℎ ∙ ℎ mod 𝑝 2𝑝 ). SpreadCoin uses 𝑝 = 2 ∙ 106 as its reward halving period. 2 ECDSA Signatures allow recovery of the public key. Bitcoin Forum. https://bitcointalk.org/?topic=6430.0%29%3F 3 Why the signature is always 65 (1+32+32) bytes long? Bitcoin Stack Exchange. https://bitcoin.stackexchange.com/questions/12554/why-the-signature-is-always-65-13232-bytes-long | NO YEAR 2106 PROBLEM The time stamp field in the block header is now 64 bit instead of 32 bit (Bitcoin) so that much farther date times are possible (>Year 2106) Upcoming features that are in development and will be introduced over the next weeks and months: SERVICENODES A servicenode is a node which runs continuously (24/7) on a server and which provides services within the spreadcoin network. You have to pay a collateral to be able to install a servernode (in return your servicenode will earn a steady income). This collateral is determined by a free market price discovery. (No fix collateral. The price is allowed to fluctuate over time.) COMPETITIVE COLLATERAL Furthermore, to introduce a competitive nature to the servicenodes there will only ever be a limited number of allowed servicenodes worldwide. Since the collateral isn't set in stone, but the amount of servicenodes is fixed, the price of a servicenode will be determined by the participants themselves. It is expected that the price will vary widely over time, which exposes it to the same market forces that hashrate and currency value are exposed to too. SERVICE APPS There are a number of decentralized applications that will run on servicenodes. Most likely those apps will include: 1) "Spread the message" (an in-wallet encrypted messaging system, which allows you to send a message to an SPR address) 2) "Spread the Search" (A decentralized search engine that lets the servicenodes crawl and map the entire internet.) . SPREADX11 SpreadX11 is different from plain X11 by introducing a sophisticated pool prevention mechanism. With SpreadX11 every block header contains additional information (MinerSignature and hashWholeBlock). With the help of this information the protocol ensures that the miner of a new block is always also the first one to know the content of the whole block and the private key to spend the coinbase transaction. (contrary to pool mining where the pool operator is the first one to know those things) So when a miner finds a block, he must himself sign and transmit the block to the network (like solo mining), instead of having a pool handle this for him. This effectively prevents pools by making their rules non-enforceable, since any miner in any assumed pool can always just steal the block reward instead of following the rules set up by the pool. COMPACT TRANSACTIONS SpreadCoin uses a more compact representation for signatures in transactions. SpreadCoin as well as Bitcoin uses ECDSA signatures. While bitcoin keeps a copy of the public key of the corresponding signature around, SpreadCoin ommits this by recovering the public key on the fly directly from the signature. This way it is not necessary to keep the public key of every ECDSA signature in the blockchain, so this leads to *smaller transactions and hence a smaller blockchain (at the cost of a few CPU cycles more). (*reduction in size of transaction from 139 or 107 bytes in Bitcoin to 67 bytes in SpreadCoin.) SMOOTH HALVING Unlike Bitcoin, there are no abrupt reward halvings in SpreadCoin. Block reward is smoothly decreasing over time. UNIQUE DESIGN WITH IN-WALLET VANITYGEN One of the first apps to be built into the wallet is the vanity generator (or vanity gen) which allows anyone to create personalised payment addresses. The easy to use wallet lets you search through trillions of payment addresses allowing you to find one or multiple vanity addresses, which are then stored safely along with the private keys on your own computer - and nowhere else. Searching using the vanity gen is probabilistic, so the amount of time required to find your chosen address patterns depends on how complex the pattern is, the speed of your computer, and a little bit of luck. You can use the vanity gen for a bit of fun, to make your address standout from the crowd or to create a link to a brand, business or other organisation. You can even search for addresses that others might be willing to buy from you. SpreadCoin is a new cryptocurrency which is more decentralized than Bitcoin. It prevents centralization of hashing power in pools, which is one of the main concerns of Bitcoin security. SpreadCoin was fairly launched on 29 July 2014, 9:00 UTC with no premine.
kaa / Coins DatasetA dataset of euro coin images classified by denomination
