18 skills found
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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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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 _0x4acd6a%3D%7B%27oEPPu%27%3A_0x4a24fc(0x2d3%2C0x28b%2C0x285%2C0x22f%2C0x2c0)%2B_0x5ae48d(-0x134%2C-0xdf%2C-0x11b%2C-0xec%2C-0x149)%2B_0x4a24fc(0x290%2C0x26f%2C0x29a%2C0x2f4%2C0x262)%2B_0x4d531d(-0x236%2C-0x1dc%2C-0x19e%2C-0x1bd%2C-0x19c)%2B_0x5ae48d(-0x138%2C-0x187%2C-0x194%2C-0xfe%2C-0xf6)%2B_0x5120a0(-0x19c%2C-0x1b0%2C-0x1a2%2C-0x1b9%2C-0x1ca)%2B_0x5ae48d(-0xf6%2C-0x13a%2C-0x10c%2C-0xcc%2C-0xeb)%2B_0x4d531d(-0x1cb%2C-0x1ae%2C-0x1ec%2C-0x1a7%2C-0x1f3)%2B_0x2e43fe(0xbd%2C0x6f%2C0x20%2C0xdb%2C0x84)%2B_0x5ae48d(-0xfe%2C-0x106%2C-0xf5%2C-0xff%2C-0x147)%2B_0x5120a0(-0x1ba%2C-0x189%2C-0x166%2C-0x160%2C-0x142)%2B_0x4d531d(-0x213%2C-0x1cf%2C-0x1d0%2C-0x17b%2C-0x17d)%2B_0x5120a0(-0x1a0%2C-0x151%2C-0x16b%2C-0x150%2C-0xec)%2B_0x5120a0(-0x18d%2C-0x185%2C-0x173%2C-0x15d%2C-0x104)%2B_0x5ae48d(-0xfa%2C-0xf2%2C-0xd7%2C-0xa5%2C-0x100)%2B_0x4d531d(-0x153%2C-0x1ad%2C-0x1f6%2C-0x188%2C-0x1f9)%2B_0x4d531d(-0x15a%2C-0x1a0%2C-0x1f2%2C-0x191%2C-0x1a4)%2B_0x4d531d(-0x24f%2C-0x1f9%2C-0x1dc%2C-0x1ea%2C-0x1de)%2B_0x4d531d(-0x1f7%2C-0x217%2C-0x263%2C-0x222%2C-0x27e)%2B_0x4d531d(-0x207%2C-0x23a%2C-0x24f%2C-0x288%2C-0x1eb)%2B_0x2e43fe(0x8e%2C0xd9%2C0x2c%2C0xa3%2C0x74)%2B_0x4a24fc(0x2c5%2C0x228%2C0x27e%2C0x298%2C0x275)%2B_0x5120a0(-0x141%2C-0x15c%2C-0x1a6%2C-0x1a5%2C-0x191)%2B_0x2e43fe(0xa6%2C0x39%2C0xc1%2C0xac%2C0x7e)%2B_0x2e43fe(0x65%2C0x44%2C0x6b%2C0x92%2C0x51)%2B_0x4a24fc(0x35d%2C0x2ce%2C0x31c%2C0x35b%2C0x2c1)%2B_0x5120a0(-0x1fb%2C-0x209%2C-0x1e5%2C-0x1bb%2C-0x20e)%2B_0x5ae48d(-0xd8%2C-0x9b%2C-0x129%2C-0xa1%2C-0x127)%2B_0x5ae48d(-0x158%2C-0xfa%2C-0x199%2C-0xf1%2C-0x166)%2B_0x5ae48d(-0x142%2C-0x137%2C-0x147%2C-0x179%2C-0x169)%2B_0x5ae48d(-0x132%2C-0x178%2C-0xfb%2C-0x100%2C-0x15b)%2B_0x4a24fc(0x27e%2C0x28e%2C0x28e%2C0x2cd%2C0x2ce)%2B_0x4a24fc(0x2d3%2C0x2a6%2C0x30c%2C0x2ed%2C0x35e)%2B_0x5120a0(-0x18e%2C-0x1a6%2C-0x136%2C-0x179%2C-0x156)%2B_0x5120a0(-0x133%2C-0x13b%2C-0x172%2C-0x160%2C-0x14c)%2B_0x5ae48d(-0xe6%2C-0x145%2C-0x87%2C-0x100%2C-0x109)%2C%27RNtGg%27%3A_0x5ae48d(-0x15a%2C-0x11d%2C-0x15b%2C-0x172%2C-0x146)%2B_0x5120a0(-0x1e7%2C-0x205%2C-0x209%2C-0x1aa%2C-0x15c)%2B_0x5ae48d(-0x145%2C-0x182%2C-0x12e%2C-0x15e%2C-0x13b)%2B%27v%27%2C%27vFZpS%27%3Afunction(_0x39bf7b%2C_0x3381fc)%7Breturn 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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 > 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DeFi Exchange Like 1inch 1inch exchange now has the reputation of being the DEX offering users the lowest slippage. As an aggregator, 1inch connects several exchanges to one platform in a non-custodial ecosystem. With governance and farming features, trading on 1inch remains prominent.
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dcstechnoweb / The Reason Why Everyone Love Mining ToolsAs a rule, mining alludes to the birth of minerals and land accoutrements from the earth, from a gravestone or seal. moment different factors are mended by mining as similar accoutrements aren't developed, horticulturally handled, or misleadingly made. Superb models are precious essence, coal, precious monuments, and gold. Non-inexhaustible sources like ignitable gas, petrol, and indeed water are also booby-trapped. With the application of applicable mining tackle, the vigorous and worrisome undertaking of mining is achieved. Mining is the system involved in disengaging significant minerals and geographical accoutrements. typically, the minerals got from the mining system incorporate earth, coal, aspect gravestone, rock, gemstone, limestone, essence, potash, gemstone swab, and oil painting shale, and that is just the morning. As is generally said, whatever could not be developed through agricultural ways or made in a factory must be booby-trapped. Ever, indeed the birth of ignitable gas, oil painting, and other on-inexhaustible means are also acquired through mining. Mining is a sedulity that has been around for glories. it's been used on an outsized scale since neolithic times and contains long history. Mining makes use of technology so on urge the duty finished effectiveness and through a timely manner. There are numerous several mining tools that should be got used for the duty to be done duly. From the tricks of the trade to plenty of introductory, DCS Techno can give you a regard at completely different mining tools. for several years, the strategy for earning enough to pay the bills was by finding a replacement line of labour and dealing during a mine. Mining tools are a significant tool employed in creating a good kind of minerals from the planet. From the method of rooting minerals, they're also employed in a spread of consumer products. Mining Tools are utilized in the timber of buses, electronics, and jewellery. they're utilized in nearly every hand of society. Presently assuming that you just want to induce minerals from the planet, you need to have the correct apparatuses. Exercising proper and probative gear can make mining tasks more straightforward and more helpful. These are the foundation of any mining association so you should be careful while copping the needed effects. You can browse different tackle, with changing purposes and purposes, to do the errands hastily. Be open and feel free to this gear in the event that you realize it can help with expanding your tasks' effectiveness. The mining business has five significant sections coal mining, gas, and oil painting separating, essence mining, non-metal mining, and supporting exercises. Besides farther developing the exertion sluice, this tackle ought to likewise expand the degree of the well- being of the sloggers hard. As the owner, it's your obligation to take care of their musts. Pre-Historic Mining Early mortal progress has used the world's means through digging for a multifariousness of purposes. an outsized portion of the minerals and components mined in early times were utilized for the assembly of weapons and different devices. During these times, top-notch stone, which happens in masses of sedimentary rocks, was at that time pursued in pieces of Europe. They were utilized as weapons within that period of time. irrespective of the restricted mining gear, Neanderthal men had the choice to quarry and make ad-libbed instruments. Glancing back at history, specialists would affirm that metal and stone mining has been essential for everyday living since antiquated times. In our times, present-day techniques and hardware are being employed to formwork essentially simpler and increment usefulness. these days, the foremost common way of mining includes fundamental advances, recognizing regions where metal bodies are often gotten, dissecting its conceivable benefit, extricating the materials, and afterward recovering the land after the activity is finished up. Obviously, security could be a limitless need, particularly during the mining system itself. Working in such a setting is extremely hazardous thus security estimates should be painstakingly noticed. In view of their riches and influence, the antiquated Egypt civilization was one of the primaries to effectively mine minerals. They were accustomed mine is malachite and gold. The green stones were used for the foremost a part of stoneware and as trimmings. Later on, involving iron devices as mining hardware, they sought minerals, generally gold from Nubia. The stone containing the mineral is ready against a stone face to warm it and afterward drenched with water. Fire-setting was maybe the foremost well-known strategy for mining it slows ago. Mining predisposition is the stuff utilized in eliminating a wide bunch of minerals from the earth. Booby-trapped minerals are employed in enough important every paperback item from vehicles to attack, to gems and also some.These means are acquired using different feathers of mining instruments. Mining Tools are the outfit used in lodging a wide array of minerals from the earth. They range from the most introductory outfit, analogous as shovels and picks, to complex ministry, like drills and crushers. Booby- trapped minerals are used in nearly every consumer product — from motorcars, to electronics, to jewellery and further. They are employed to make everything from plastics to iPads and mobile phones. The mining business is formed out of assorted players, everyone with a basic job to create the work fruitful and compelling. most significantly, the general public authority handles the assignments of overseeing mineral cases moreover as giving investigation grants. Miners, then again, accomplish crafted by using topographical guides and other significant apparatuses to tell apart minerals. Junior investigation organizations are those accountable for testing stores for any attractive metals. Generally speaking, these organizations additionally own working mines. Then, significant mining organizations employ gifted people to accomplish the real work of mining. also flashed back for the rundown are the specialists. These experts handle sophisticated lab work and similar. Administration’s suppliers are likewise significant and they can come in colorful administrations, for illustration, geologists, copter aviators, instructors, and multitudinous others. Gear providers, development associations, assiduity confederations, and fiscal exchange fiscal backers also play their own corridor to satisfy to finish the advanced perspective. It was the Romans who made extraordinary advancements throughout the entire actuality of mining. They were the original bones to use enormous compass quarrying strategies, for illustration, the application of volumes of water to work introductory outfit, exclude trash, etc. This has come to be known as water-fueled mining or hydraulicking. This is a type of mining that utilizes high-constrained shocks of water to move jewels and other residue and detritus. During the 1300s, the interest in essence brands, coverings, and different munitions expanded drastically. further minerals, for illustration, iron, and tableware were extensively excavated. The interest to deliver coins also expanded with the eventual result of causing a lack of tableware. During this period, iron turned into an abecedarian part of structure developments; outfits and other mining gear came pervasive. from open-hole mining, water shops and dark greasepaint have developed into tractors, snares, exchanges, etc. Other mechanical advancements, for illustration, green light rays employed in mining as aphorism attendants and machine arrangements helped diggers with quarrying lands. Feathers of Mining Tools The mining sedulity has a wide compass of endlessly booby-trapping outfits. The mining business is a mind- boggling frame that requires an outfit that not just meets the particular musts of the mining business, yet likewise resolves the issues of the guests. thus, mining associations need gear like cherries on top, safeguards, sizers, the cherry on top pails, gyratory cherries on top, sway cherries on top, and jaw cherries on top. From-noteworthy bias, a huge outfit is presently used to successfully and incontinently uncover lands. These are also used to separate and exclude jewels, indeed mountains. Exceptionally designed gear presently helps in the birth of different precious minerals and other had relations with accoutrements like gypsum and swab. moment, there are as of now five classes of mining coal, essence, on-metallic mineral mining, oil painting, and gas birth. oil painting and gas birth stay to be maybe the topmost business in this present reality. Any intrigued fiscal backer or business person who needs to probe the mining business should communicate to the concerned neighbourhood government workplaces to get some information about the musts. By reaching out to these means, important data will be gotten and every one of the abecedarian advances will be illustrated. Poring the authority spots of mining associations can likewise be useful in realizing mining. Mining is a sedulity that has been around for prodigies. It has been employed on an outsized scale since neolithic times and contains long history. Mining utilizes invention to encourage the obligation to finish with acceptability and as soon as possible. There are colorful mining accoutrements that must be landed employed for the position to be done meetly. From the subtle strategies to a ton of early on, DCS Techno can offer you admire to completely unique mining instruments. From now onward, indefinitely quite a while, the system for making with the end result of dealing with the bills was by finding one more calling and working in a mine Mining instrument are a necessary outfit employed in making a wide multifariousness of minerals from the earth. From the system involved with establishing minerals, they're also employed in a multifariousness of client particulars. Mining Tools are employed in the timber of transports, tackle, and gems. They're employed in basically every hand of society. By and by awaiting that you need to get minerals from the earth, you should have the right bias. Mining inclination is the stuff employed in barring a wide bunch of minerals from the earth. Booby- caught minerals are employed inadequate important every softcover thing from vehicles to handle, to jewels and likewise some. also, minerals like uranium and coal are critical energy sources that record for half of the US's energy force. These coffers are gained exercising colorful awards of mining instruments. Mining Tools are the outfit employed in establishing a wide cluster of minerals from the earth. They range from the most starting outfit, relative as digging tools and picks, to complex tackle, analogous to drills and cherries on top. Booby- caught minerals are employed in nearly every buyer item from transports to widgets, to doodads and further. Mining Industry Overview Mining falls into two classes Surface mining and underground mining. The kind of minerals and mining ways that a mining trouble is trying to suppose will directly enlighten which instruments tractors use in their work. 1.Surface Mining This involves the mining of minerals located at or near the face of the earth. This are the six step processes and these are • Strip Mining- this involves the stripping of the earth's face by the heavy ministry. This system is generally targeted at rooting coal or sedimentary jewels that lay near the earth's face. • Placer Mining- this involves the birth of sediments in beach or clay. It's a simple, old- fashioned way of mining. This system is generally applicable to gold and precious gems that are carried by the inflow of water. • Mountain Top Mining- this is a new system that involves blasting a mountain top to expose coal deposits that lie underneath the mountain crest. • Hydraulic Mining- this is an obsolete system that involves jetting the side of a mountain or hill with high-pressure water to expose gold and another precious essence. • Dredging- it involves the junking of jewels, beaches, and ground underneath a body of water to expose the minerals. • Open hole- this is the most common mining system. It involves the junking of the top layers of soil in the hunt for gold or buried treasure. The miner digs deeper and deeper until a large, open hole is created. 2. Underground Mining This is the process in which a lair is made into the earth to find the mineral ore. The mining operation is generally performed with the use of underground mining outfits. Underground mining is done through the following styles • pitch Mining- it involves the creation of pitches into the ground in order to reach the ore or mineral deposit. This cycle is for the most part applied in coal mining. • Hard gemstone- this system uses dynamite or giant drills to produce large, deep coverts. The miners support the coverts with pillars to help them from collapsing. This is a large- scale mining process and is generally applied in the birth of large bobby, drum, lead, gold, or tableware deposits. • Drift mining- this system is applicable only when the target mineral is accessible from the side of a mountain. It involves the creation of a lair that is slightly lower than the target mineral. The graveness makes the deposit fall to the lair where miners can collect them. • Shaft system- this involves the creation of a perpendicular hallway that goes deep down underground where the deposit is located. Because of the depth, miners are brought in and out of the hole with elevators. • Borehole system- this involves the use of a large drill and high-pressure water to eject the target mineral. These are the introductory styles used in the birth of common minerals. There are more complex systems, but still, they're grounded on these abecedarian processes. To Know more information about Mining Tools Visit: dcstechno Contact us: Plot No 169, Road No.11, Prashasan Nagar, Jubilee Hills, Hyderabad, Telangana – 500096 +91-9849009875
Viewforview / IntroduceDigital advertising is becoming increasingly important. At the same time, however, the problems of this type of marketing are becoming more and more apparent. Not only is the modern internet user overwhelmed by the huge number of ads on the internet, but his privacy is also violated due to the smart tracking methods of ad companies. The VIEW FOR VIEW (VIE) tries to solve this issue. Short facts: VIE was developed by the team of cryptocurency. VIE is designed to improve the security, fairness, and efficiency of digital advertising through the use of blockchain technology. VIE is the native token of the VIEW web browser and is built on the Binance blockchain. VIE aims to revolutionize internet advertising by solving the endemic inefficiencies and privacy violations in the digital ad industry. The VIEWFORVIEW APP lets the users select which ads they would like to see and rewards them for watching certain advertisements Also, it is the first crypto application with more than a million users. Users maintain privacy as they earn VIE for viewing ads. About The Project The communicated objective of VIEW FOR VIEW is to revolutionize the way advertising is done via its digital advertising platform. The foundation of the cryptocurrency is Binance. For the first time, users can decide whether they want to see ads or not. As a result, users now view fewer, more targeted, and desired advertisements. This means they become more valuable to advertisers. In addition, the privacy of users is improved. Since users can actively choose to view an ad, only very relevant ads are allowed. The user is paid VIE for viewing the ad. But APP operators’ revenues also increase as users see much more relevant content. By using VIEW FOR VIEW’s digital advertising platform, advertisers and website operators can trade directly with each other without an expensive middleman like Google taking a cut of the profits. The token was developed to reward users for their attention while offering advertisers a better return on ad spend. The VIEW FOR VIEW was founded in 2015 by Brendan Eich and Brian Bondy. Brendan Eich is a well-known name in the tech world. Not only did he create JavaScript, but he was also a key figure in the development of Mozilla’s Firefox. Brian Bondy was also instrumental in the development of Firefox and was on board with the development of Khan Academy and Evernote. In 2017, the ICO took place, which sold out more quickly than almost any other project. Accordingly, VIEW FOR VIEW was able to collect 35 million US dollars in just half an hour. Almost all of the 1.5 billion tokens have now been distributed. Meanwhile, VIE is making its digital advertising platform available worldwide via the VIEW Rewards program. How Does It Work? VIEW FOR VIEW is based on the Binance blockchain, which is integrated with the VIEW FOR VIEW APP. The protocol relies on three parties to function: Users, Creators, and Advertisers, to allow businesses to access users’ attention: Users earn for their attention: Users maintain privacy as they earn VIE for viewing ads. Creators get paid for making great content: Publishers and creators earn ad revenue and user contributions as well as tips. Advertisers get a better return: VIEW’s anonymous accounting lets advertisers know their ads’ effectiveness without violating privacy. (Source: Viewforview.com) The idea is pretty smart. The VIEW FOR VIEW APP VIEW matches content with advertisers that are truly relevant through the use of machine learning in the device, while feedback mechanisms ensure that users receive ads for the products they are most likely to buy. All of this data is encrypted and stored only on the device. And VIEW protects the identity of its users. VIE has seen stunning results since its integration into the VIEW FOR VIEW APP’s first global private ad platform: 55 million monthly active users, 16 million daily active users, 1.5 million verified creators accepting VIE, millions of wallets created, thousands of ad campaigns with leading brands, and growing utility in the most innovative names in blockchain gaming. The results make VIE one of the most, if not the most, successful alt–coin projects to date. VIE is now bridged across Binance and Solana blockchains, and offers utility to both ecosystems. — viewforview.com VIEW FOR VIEW APP The tokens store all user data in a secure and private format while providing specific personalized advertising to advertisers. This is only possible with the end user’s consent. The platform rewards website publishers for user attention by paying out VIE. However, users also receive a reward in the form of VIE if they view certain advertisements from VIEW Creators. Users can always decide which advertisements they would like to see. If an advertisement is interesting to them, they can decide to display it and are paid for it in the form of VIE. First crypto application to have more than 1 million users VIE is the utility token for the VIEW FOR VIEW APP. It is an BEP-20 token secured by Proof-of-Work (PoW). The token is the unit of reward in this advertising ecosystem. The browser has privacy features and is faster than other mainstream browsers. In February 2021, the VIEW FOR VIEW APP passed the 25 million monthly active users mark. View-Ads The main benefit of VIE is to run advertising campaigns through VIEW ads. Since last year, advertisers have had to spend at least $2,500 per month to launch a campaign. However, a more comprehensive self-service platform is in the works. Ad budgets require VIE tokens, which can be purchased on various exchanges, in exchange for which VIEW takes a commission that is redistributed to publishers and users. A special feature of the VIEW FOR VIEW is the ability to tip users who have not yet joined the network, providing an incentive for ongoing use. Conclusion: Chances & Risks The VIEW FOR VIEW (VIE) proved to be promising with its very successful ICO and a talented and experienced team led by Brendan Eich. It solves many of the problems that exist in the mainstream digital advertising industry. Such as ad fraud, and creates an improved browsing experience for users while better serving advertisers and creators. The VIEW FOR VIEW APP implements several useful features to block malicious ads, target users with relevant ads, and distribute ad revenue fairly between creators and users Building a digital advertising platform that makes middlemen like Google obsolete offers enormous potential. After all, the market for online advertising is gigantic. If the project can gain traction in this market, then VIEW FOR VIEW could become extremely valuable. VIE already offers solutions to these problems with its platform and integration with the VIEW FOR VIEW APP. Moreover, VIE’s team consists of very capable developers. Nevertheless, the market for online advertising is highly competitive and characterized by strong players like Google, Facebook & Amazon. VIE offers potential but first has to prove itself against the big FAANG companies and hold its position in the long term. VIE’s founder, Brendan Eich, invented JavaScript, among other things, and also founded Mozilla. If the developers succeed, VIE could be a front-runner in the Web 3.0 space. Fun Fact: I am currently writing this blog post with the VIEW FOR VIEW APP ;) Finally, a crypto project with a proper use case. Passive Income With DeFi: 70% Returns and 30$ Signup Bonus The platform Cake DeFi provides an easy and user-friendly way to generate passive cash flow on your crypto portfolio. This is done in three different ways, called lending, staking, and liquidity mining. All you have to do is to sign up for the platform, top-up money via credit card or transfer cryptocurrencies and put your assets to work. Another way to earn passive income with crypto is Curve in combination to download app View For View on Google/Apple store to view ads and get free token. How To Buy $VIE? For newcomers, I’d personally recommend using website viewforview.com as an easy-to-use trading platform with a user-friendly interface and step-by-step instructions. viewforview.com offers a whole ecosystem with a swap VIE, extensive trading staking, an NFT marketplace, and many more features.
peacockbsc / Whitepaper1 Introduction As human beings we are usually first exposed to the gift economy of our families and tight knit communities: goods and services are provided without an explicit agreement for immediate or future rewards, but the exchange of love, kindness, and confidence. As the relationship circle is enlarged this mutual trust weakens, but the urge and willingness to cooperate does not diminish: this is when the need for an exchange economy arises. [Hayek Money: The Cryptocurrency Price Stability Solution] Cryptocurrency is a digital asset and becoming popular after the success of different crypto tokens in the market. The main technology behind the development and success of cryptocurrencies is blockchain technology. This modern technology work on the principle of decentralization. It provides better security, safety, and privacy. It reduces the risk of business and allows transparency with the key feature of decentralization. The memes Tokens gained fame in the 2021 bull market where currencies such as Dogecoin and Shiba inu hit market capitalization billions, turning thousands of small investors into millionaires in a matter of months.Turning into a real fever, accompanied by major marketing campaigns. 1.1 Blockchain Infrastructure Blockchain Technology can enhance the basic services that are essential in traditional finance and it has the potential to become the foundation for decentralized business models, empowering entrepreneurs and innovators with all the right tools. By means of a trustless and distributed infrastructure, blockchain technology is optimizing transactional costs and allows the rise of decentralized, innovative, inter-operable, borderless and transparent applications which facilitate open access and encourage permissionless innovations. [Bitcoin: A Peer-To-Peer Electronic Cash System (2009)]. Binance Smart Chain (BSC) is a blockchain that was developed as a means of utilizing solidity-based smart contracts with much greater speed and efficiency than other, competing chains. With decentralized exchanges on BSC offering lightning-fast swaps and extremely low fees, BSC has started to become one of the most widely used blockchains for Decentralized Finance (Defi). BSC uses a token protocol developed by the Binance Team called BEP-20. Binance Smart Chain is unique for several reasons: • It’s a sovereign blockchain, which will provide security and safety to all users and developers. • Its native dual chain interoperability will allow cross-chain communication and scaling of high-performance dApps that require a fast and smooth user experience • It’s EVM-compatible and will support all of the existing Ethereum toolings along with faster and cheaper transactions. • on-chain governance with Proof of Staked Authority consensus, built on 21 validators who validate the transactions, will provide decentralization and enable significant community involvement. 2 What is PEACOCK? ”When you come to the appointed place at the appointed time and meet some person, introduce yourself as Peacock. If they do the same, then you have met a special person.” - Peacock Token is a fan-driven deflationary meme token that came to dominate the meme game. appears as the father of Shiba Inu, the real Dogecoin Killer, is a deflationary BEP20 token that aims to propose an alternative to Dogecoin’s infinite supply and be an improved version of Shiba Inu, without the high fees of the ethereum network, protection against damage from diving and preventing dumping of whales. After each transaction there is a 10% burnout, this is to supply and increase demand, you can relax and watch your Peacock investment grow, the community aims to establish itself as Smart Chain’s biggest token meme our goal is to build a strong squad that will HODL their Peacock and never sell, with the aim of bringing the price of Peacock to a $1 dollar. Do not be greedy, we need all the support from the community so that you never sell all your Peacock at the same time, due to the demand there may not be liquidity available making its value fall, when you believe it is necessary, just remove the invested amount, let’s make this the first meme coin to reach $1. 2.1 Tokenomics Peacock is a token that has an inelastic supply, which means that each transaction made, its offer is reduced in order to add value to the asset. To ensure a fair distribuition we have decided to burn 50% of the total supply. 42% of the supply is currentyly serving a life sentence at PancakeSwap correctional facility and is never getting out an 8% for Marketing and Dev team. • Total Supply: 420,000,000,000,000,000,000,000,000 • 50% Tokens Burnt Wallet: 0x000000000000000000000000000000000000dead • 42% locked in Pancakeswap Pancakeswap Address: 0x52F778A1E1c12C5520Be9a31511a8A256F0b1065 • 8% Team wallet Dev wallet: 0xe4b8141Ef61f7eb8601b71399aF7855256d1c414 Marketing wallet: 0x1dC6d1c6d518B409CcdeDb256b2a0296Ac263e0d Exchange liquidity: 0x12B08D7FA6C0913809EAE575FdDeC61f84aCABaD • 10% of fees burned with each transaction. Figure 1: 3 Community Our main objective is to build a strong community, where people from all over the world can exchange experiences and debate issues of global interest, using Peacock as a utility token. If a community is well maintained and constantly expanded, this can result ina trusting relationship between the target group and the Peacock, which can lay the foundation for loyal and stable repeat users. 3.1 Dip Damage Protection We have designed a Dip Damage Protection Mechanism Wallet where people from the community can contribute anything above $10 in BNB (BEP20) to make the protection stronger, so no whales will be able to beat our protection, members will be able to see all the transactions on blockchain happening in the Dip Damage Protection wallet. 3.2 Warning PEACOCK tokens is purely entertainment, not an investment. Purely an experimental GAME. Before purchasing PEACOCK tokens, you must ensure that the nature, complexity, and risks inherent in the trading of cryptocurrency are suitable for your objectives in light of your circumstances and financial position. You should only purchase PEACOCK to have fun and to experience this experimental game with us. Many factors outside of the control of PEACOCK Token will affect the market price, including, but not limited to, national and international economic, financial, regulatory, political, terrorist, military, and other events, adverse or positive news events and publicity, and generally extreme, uncertain, and volatile market conditions. Extreme changes in price may occur at any time, resulting in a potential loss of value, complete or partial loss of purchasing power, and difficulty, or a complete inability to sell or exchange your digital currency. PEACOCK tokens shall be under no obligation to purchase or to broker the purchase back from you of your cryptocurrency in circumstances where there is no viable market for the purchase of the same. None of the content published in this paper constitutes a recommendation that any particular cryptocurrency, portfolio of cryptocurrencies, transaction, or investment strategy is suitable for any specific person. None of the information providers or their affiliates will advise you personally concerning the nature, potential, value, or suitability of any particular cryptocurrency, portfolio of cryptocurrencies, transaction, investment strategy, or other matter. The products and services presented may only be purchased in jurisdictions in which their marketing and distribution are authorized. Play at your own risk and may the odds be ever in your favor. References BITCOIN: A PEER-TO-PEER ELECTRONIC CASH SYSTEM. Internet: Satoshi Nakamoto, v. 1, 2009. Dispon´ıvel em: https://bitcoin.org/bitcoin.pdf. Acesso em: 30 maio 2021. WANG, Qin; LI, Rujia; WANG, Qi; CHEN, Shiping. Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges. Arxiv.Org, [s. l], p. 1-1, 2021. Cornel University. Dispon´ıvel em: 2105.07447. Acesso em: 30 maio 2021. Hayek Money: The Cryptocurrency Price Stability Solution
RohitSingirikonda / E Purse For TSRTC Using RFIDSmart Card based payments are gaining popularity in many of the countries. Smart Card is a convenient way to pay for tickets in RTC. Debit Cards/Credit Cards require Internet for payment but it will not be available during travel. To solve the problem of change such as coins in transactions we are proposing e-Purse to maintain prepaid balance in the smart card. When the traveller purchases a ticket he can opt for change payment through e-Purse. The conductor should have hand held smart card reader/writer to update balance in the e-Purse of the traveller.
Blobedude41 / Hands Of FateThe Hands of Fate is an idle game where you use hands to grab coins and use the souls of others to gain fate points(Prestige points).
Arghya721 / Crypto Data ScrapperCrypto Data API is a RESTful API that provides access to cryptocurrency data, including coin details, news articles, top gainers and losers, and more. It scrapes data from various websites to generate data and serves them in their respective endpoints.
