Sunday, November 17, 2019

WHAT ARTIFICIAL INTELLIGENCE CANNOT DO , a grim note to the top 100 intellectuals of this planet , Part 4 - Capt Ajit Vadakayil



THIS POST IS CONTINUED FROM PART 3, BELOW--


















In machine learning, bias is a mathematical property of an algorithm. 

The counterpart to bias in this context is variance.

Algorithms with high bias tend to be rigid.  As a result they can miss underlying complexities in the data they consume.  In the fields of science and engineering, bias referred to as precision.

Bias is a term data scientists use to describe a particular mathematical property of the algorithm that influences its prediction performance.

Bias is generally coupled with variance, another algorithm property.  Bias and variance interact, and data scientists typically seek a balance between the two.


Bias arises when we generalize relationships using a function, while variance arises when there are multiple samples or input.


Bias is the algorithm’s tendency to consistently learn the wrong thing by not taking into account all the information in the data - this is underfitting.

When a mode is built using so many predictors that it captures noise along with the underlying pattern then it tries to fit the model too closely to the training data leaving very less scope for generalizability.  This phenomenon is known as overfitting.


Bias of an estimator is the “expected” difference between its estimates and the true values in the data. Intuitively, it is a measure of how “close”(or far) is the estimator to the actual data points which the estimator is trying to estimate. A model that generalizes well is a model that is neither underfit nor overfit.

Bias and variance are two errors in the total error in the learning algorithm, if you try to reduce one error, the other error might go up.  When the learning algorithm has the high bias problem, working on reducing the bias will cause the variance to go up, causing over-fitting problem. And, when the learning algorithm is suffering from the high variance problem, working on reducing the variance will cause the bias to go up, causing under-fitting problem.







Bias leads to a phenomenon called underfitting.   This is caused by the introduction of error due to the oversimplification of the model.   On the contrary, variance occurs due to complexity in the machine learning algorithm. ... This is known as bias-variance tradeoff.


Reducing just the bias, will not improve the model, and vice versa. The ‘sweet spot’ is to land the data points at a place where there is optimum bias and optimum variance. Basically, find a pattern by not taking any of the extremes such that it tampers accuracy







We have to avoid overfitting because it gives too much predictive power to even noise elements in our training data. But in our attempt to reduce overfitting we can also begin to underfit, ignoring important features in our training data.


Bias is an error in the learning algorithm, when the learning algorithm is weak to learn from the data. In case of high bias, the learning algorithm is unable to learn relevant details in the data. Hence, it performs poor on the training data as well as on the test dataset.  

Bias is the accuracy of our predictions.  A high bias means the prediction will be inaccurate. A model is said to have high bias when its structure does not describe the data model. 

A linear model will always have low performance if it is used in a non-linear data set, no matter how much data is used, the model will always have low performance. Bias is the error that occurs when trying to approximate the behavior of a problem’s data.

The goal of any supervised machine learning algorithm is to achieve low bias and low variance. In turn the algorithm should achieve good prediction performance.

Bias is used for making the learning of the target function of any model easier. This is done by making simplifying assumptions.   These simplifying assumptions are termed bias. It is basically the difference between the model’s average prediction and the actual correct value which is being attempted to be predicted 

When there are fewer assumptions made about the target function form, it indicates low bias. When there are more assumptions made about the target function form, it indicates a higher bias.

Algorithm bias is associated with rigidity. High bias, can cause an algorithm to adhere so strongly to rules that it misses complexities in the data.. 

A model has a low bias if predicts well on the training data..

When a model has a high bias it means that it is very simple and that adding more features should improve it.   Bias is the contribution to total error from the simplifying assumptions built into the method

Bias error is the difference between the predicted data points and the actual data points which was caused because our model was oversimplified.  A model with high bias is too simple and has low number of predictors.

 It is missing some other important predictors due to which it is unable to capture the underlying pattern of data. It misses how the features in the training data set relate to the expected output. It pays very little attention to the training data and oversimplifies the model. This leads to high error on training and test data.

On the other hand, models with high bias are more rigid, less sensitive to variations in data and noise, and prone to missing complexities. Importantly, data scientists are trained to arrive at an appropriate balance between these two properties.

The bias is an error from erroneous assumptions in the learning algorithm.

When a machine is biased, it is unable or less able to adapt to various training models, preferring one route as a primary mechanism. This means the developed AI algorithm is rigid and inflexible, unable to adjust when a variation is created in the data at hand. It is also unable to pick up on discreet complexities that define a particular data set.

Sources of bias-- There are two key ways bias can be introduced and amplified during the machine learning process: by using non-representative data and while fitting and training models.

Machine learning algorithms themselves may amplify bias if they make predictions that are more skewed than the training data. Such amplification often occurs through two mechanisms: 1) incentives to predict observations as belonging to the majority group and 2) runaway feedback loops.

Bias is used to allow the Machine Learning Model to learn in a simplified manner. Ideally, the simplest model that is able to learn the entire dataset and predict correctly on it is the best model. Hence, bias is introduced into the model in the view of achieving the simplest model possible.

In model building, it is imperative to have the knowledge to detect if the model is suffering from high bias or high variance. The methods to detect high bias and variance is given below:

Detection of High Bias:--
The model suffers from a very High Training Error.
The Validation error is similar in magnitude to the training error.
The model is underfitting.

Detection of High Variance:--
The model suffers from a very Low Training Error.
The Validation error is very high when compared to the training error.
The model is overfitting.





Bias is used for making the learning of the target function of any model easier. This is done by making simplifying assumptions.

These simplifying assumptions are termed bias. It is basically the difference between the model’s average prediction and the actual correct value which is being attempted to be predicted by us. 

Models with high bias are more rigid, less sensitive to variations in data and noise, and prone to missing complexities..

A model with high bias is too simple and has low number of predictors. It is missing some other important predictors due to which it is unable to capture the underlying pattern of data. It misses how the features in the training data set relate to the expected output.

 It pays very little attention to the training data and oversimplifies the model. This leads to high error on training and test data.
When the parametric algorithms are taken into consideration, they have a high bias which makes them very easy and fast to learn and understand.

But, these are less flexible as they fail to give a high predictive performance when complex problems are being considered.

When there are fewer assumptions made about the target function form, it indicates low bias. When there are more assumptions made about the target function form, it indicates a higher bias.
Examples of low-bias machine learning algorithms include: Decision Trees, k-Nearest Neighbors and Support Vector Machines.

Examples of high-bias machine learning algorithms include: Linear Regression, Linear Discriminant Analysis and Logistic Regression.

Linear machine learning algorithms often have a high bias but a low variance.
Nonlinear machine learning algorithms often have a low bias but a high variance.

Bias are the simplifying assumptions made by a model to make the target function easier to learn.

Generally, linear algorithms have a high bias making them fast to learn and easier to understand but generally less flexible. In turn, they have lower predictive performance on complex problems that fail to meet the simplifying assumptions of the algorithms bias.

Increasing the bias will decrease the variance.
Increasing the variance will decrease the bias.


When we are talking about errors, we can find reducible and irreducible errors.

Irreducible errors are errors that cannot be reduced no matter what algorithm you apply. They are usually known as noise and, the can appear in our models due to multiple factors like an unknown variable, incomplete characteristics or a wrongly defined problem. It is important to mention that, no matter how good is our model, our data will always have some noise component or irreducible errors we can never remove.



The irreducible error cannot be reduced regardless of what algorithm is used. It is the error introduced from the chosen framing of the problem and may be caused by factors like unknown variables that influence the mapping of the input variables to the output variable.


The prediction error for any machine learning algorithm can be broken down into three parts:--

Bias Error
Variance Error
Irreducible Error

Total Error = Bias + Variance + Irreducible Error

Irreducible error cannot be avoided.

The parameterization of machine learning algorithms is often a battle to balance out bias and variance.

Even for an ideal model, it is impossible to get rid of all the types of errors. The “irreducible” error rate is caused by the presence of noise in the data and hence is not removable. However, the Bias and Variance errors can be reduced to a minimum and hence, the total error can also be reduced significantly.

Reducible errors have two components – bias and variance. This kind of errors derivate from the algorithm selection and the presence of bias or variance causes overfitting or underfitting of data.
Low bias offers more flexibility. As examples, we have decision trees, k-nearest neighbour (KNN) and vector support machines.

High Bias can be identified when we have:-- 
High training error.
Validation error or test error is the same as training error.

High Variance can be identified when:-- 
Low training error.
High validation error or high test error.

High bias is due to a simple model and we also see a high training error. To fix that we can do the following things:--

Add more input features.
Add more complexity by introducing polynomial features.
Decrease Regularization term.

One way to reduce the error is to reduce the bias and the variance terms. However, we cannot reduce both terms simultaneously, since reducing one term leads to increase in the other term. This is the idea of bias variance trade/off.

Ideally, you must find a model at the sweet spot between overfitting and underfitting. In other words, the model with a complexity where the curves of variance and bias intersect

In general, if we want to increase the complexity of a function we need to add more parameters to this function. In the case of neural networks, our parameters are the weights and biases. To add additional weights and biases we just need to increase the number of layers and the number of neurons in the neural network. 

When the parametric algorithms are taken into consideration, they have a high bias which makes them very easy and fast to learn and understand.

But, these are less flexible as they fail to give a high predictive performance when complex problems are being considered.

In the case of the predictive model, the main focus is not on reducing the bias to the maximum extent. A model with slightly more bias error is acceptable as long as the test set error is minimized considerably.


Boosting is a meta-learning algorithm that reduces both bias and variance. ... The model based on boosting tries to reduce the error in predictions by, for example, focusing on poor predictions and trying to model them better in the next iteration, and hence reduces bias





A model has a high variance if it predicts very well on the training data but performs poorly on the test data


When the model performs well on the Training Set and fails to perform on the Testing Set, the model is said to have Variance.

Variance is an error in the learning algorithm, when the learning algorithm tries to over-learn from the dataset or tries to fit the training data as closely as possible. In case of high variance, the algorithm performs poor on the test dataset, but performs pretty well on the training dataset.

High variance error of a model implies that it is highly sensitive to small fluctuations. This model flounders outside of its comfort zone (training data)..

As a general rule, the more flexible a model is, the higher its variance and the lower its bias. The less flexible a model is, the lower its variance and the higher its bias.

High variance, can cause an algorithm to pay too much attention to data points that might actually be noise.

A model with high variance is likely to produce quite different hypothesis functions given different training sets with the same underlying structure just due to different noise in two datasets.

Models with high variance can easily fit into training data and welcome complexity but are sensitive to noise.

Low Variance: Suggests small changes to the estimate of the target function with changes to the training dataset.
High Variance: Suggests large changes to the estimate of the target function with changes to the training dataset.

Examples of low-variance machine learning algorithms include: Linear Regression, Linear Discriminant Analysis and Logistic Regression.

Examples of high-variance machine learning algorithms include: Decision Trees, k-Nearest Neighbors and Support Vector Machines.

Variance is the amount that the estimate of the target function will change if different training data was used

Variance is the contribution to the total error due to sensitivity to noise in the data.

The variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs .. A model has a low variance if it generalizes well on the test data

High variance can cause an algorithm to base estimates on the random noise found in a training data set, as opposed to the true relationship between variables

Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but has high error rates on test data.

When a model  has high variance it describes very well the training data but at the moment of training with a different dataset it produces a very different model from the previous one and therefore a bad result at the moment of predicting. The variance is the amount by which the model will change with a different training set.

High variance means that the algorithms have become too specific.

The variance error can be easily eliminated by taking many samples from different models and averaging it out which is not possible in the case of bias.

Variance is high for a more complex model and is low for simpler models

Some variance is expected when training a model with different subsets of data. However, the hope is that the machine learning algorithm will be able to distinguish between noise and the true relationship between variables. 

Small training data sets often lead to high variance models. A model with low variance will be relatively stable when the training data is altered (e.g., if you add or remove a point of training data).

High variance is often caused due to a lack of training data. The model complexity and quantity of training data need to be balanced. A model of higher complexity requires a larger quantity of training data. Hence, if the model is suffering from high variance, more datasets can reduce the variance.

If we want to reduce the amount of variance in a prediction, we must add bias.

Models with low variance tend to be less complex with a simple underlying structure. They also tend to be more robust (stable) to different training data (i.e., consistent, but inaccurate). Models that fall in this category generally include parametric algorithms, such as regression models. 

Depending on the data, algorithms with low variance may not be complex or flexible enough to learn the true pattern of a data set, resulting in underfitting.

The opposite of a high bias model is a high variance one, which is fluid and better able to expand, morph and accommodate fluctuations in training data. While this approach is preferred over a biased one, developers should keep in mind that an easily changeable algorithm is also more noise-sensitive and might pose difficulties with data generalization

Variance is neither good nor bad for investors in and of itself. However, high variance in a stock is associated with higher risk, along with a higher return. Low variance is associated with lower risk and a lower return.

The higher the variance of the model, the more complex the model is and it is able to learn more complex functions. However, if the model is too complex for the given dataset, where a simpler solution is possible, a model with high Variance causes the model to overfit.

If the dataset consists of too many features for each data-point, the model often starts to suffer from high variance and starts to overfit. Hence, decreasing the number of features is recommended.

Machine learning algorithms that have a high variance are strongly influenced by the specifics of the training data. A high variance machine learning algorithm is extremely perceptive to the data which may lead to the overfitting of training data.

A model with high Variance will have the following characteristics:--

Overfitting: A model with high Variance will have a tendency to be overly complex. This causes the overfitting of the model.

Low Testing Accuracy: A model with high Variance will have very high training accuracy (or very low training loss), but it will have a low testing accuracy (or a low testing loss).

Overcomplicating simpler problems: A model with high variance tends to be overly complex and ends up fitting a much more complex curve to a relatively simpler data. The model is thus capable of solving complex problems but incapable of solving simple problems efficiently

Any model which has very large number of predictors will end up being a very complex model which will deliver very accurate predictions for the training data that it has seen already but this complexity makes the generalization of this model to unseen data very difficult i.e a high variance model. Thus, this model will perform very poorly on test data.

Variance  helps us to understand the spread of the data.

When the changes in the training dataset suggest a small change in the estimate of the target function, there is a low variance. When the changes in the training dataset suggest a large change in the estimate of the target function, there is high variance.

Variance of an estimator is the “expected” value of the squared difference between the estimate of a model and the “expected” value of the estimate(over all the models in the estimator).

Variance can be described as the error caused by sensitivity to small variances in the training data set, or how much an estimate for a given data point will change if a different training data set is used.
Models with high variance can easily fit into training data and welcome complexity but are sensitive to noise.

Variance is the amount which indicates the variability of any model prediction.. Variance is the variability of model prediction for a given data point or a value which tells us spread of data.

When the changes in the training dataset suggest a small change in the estimate of the target function, there is a low variance. When the changes in the training dataset suggest a large change in the estimate of the target function, there is high variance.

The variance is an error from sensitivity to small fluctuations in the training set   

For high variance models an alternative is feature reduction, -- including more training data is also a viable option..

Machine learning algorithms that have a high variance are strongly influenced by the specifics of the training data   Low variance is associated with lower risk and a lower return.

High variance stocks tend to be good for aggressive investors who are less risk-averse, while low variance stocks tend to be good for conservative investors who have less risk tolerance. Variance is a measurement of the degree of risk in an investment.

Variance of an estimator, does not depend on the parameter being estimated. It is a measure of how far values can the estimate take, away from its expected value

Variance in data is the variability of the model in a case where different Training Data is used. This would significantly change the estimation of the target function. Statistically, for a given random variable, Variance is the expectation of squared deviation from its mean.

Regularization is a process to decrease model complexity. Hence, if the model is suffering from high variance (which is caused by a complex model), then an increase in regularization can decrease the complexity and help to generalize the model better.

Machine learning algorithms that have a high variance are strongly influenced by the specifics of the training data. This means that the specifics of the training have influences the number and types of parameters used to characterize the mapping function.

High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting).

On the one hand, we want algorithms to model the training data very closely, otherwise we’ll miss relevant features and interesting trends.  However, on the other hand we don’t want our model to fit too closely, and risk over-interpreting every outlier and irregularity.

Generally, nonlinear machine learning algorithms that have a lot of flexibility have a high variance. For example, decision trees have a high variance, that is even higher if the trees are not pruned before use.

Again, variance occurs when the model performs well on the trained dataset but does not do well on a dataset that it is not trained on. Ideally, the result should not change too much from one set of data to another.



Underfitting occurs for a model with Low Variance and High Bias.   The best way to understand the problem of underfitting and overfitting is to express it in terms of bias and variance.

A model has a high bias if it makes a lot of mistakes on the training data. We also say that the model underfits.

A lot of bias may lead to the underfitting of the data because if we have a high bias it will make assumptions without even caring about the data. Bias can cause an algorithm to incorrectly identify or miss important relationships.  Underfitting describes when a model is unable to capture the "true" underlying pattern of the data set (i.e., the model fits the data poorly).

Underfitting means that the model does not fit well even to the data it is trained with.

Underfitting is often a result of an excessively simple model.. When a model is unable to capture the essence of the training data properly because of low number of parameters then this phenomenon is known as Underfitting.

When the complexity of the model is too less for it to learn the data that is given as input, the model is said to “Underfit”.  As examples, we have linear regression algorithms, logistic regression or linear discriminant analysis.

Where the model is not complex enough it misses out  on the important features of the data..  Since underfitting is a result of a low complexity of a model all we need to do is to increase the complexity.

In other words, the excessively simple model fails to “Learn” the intricate patterns and underlying trends of the given dataset.   To avid underfit provide better predictor variables (feature engineering) or reduce the constraints applied to the model (regularization).

Underfitting happens when the statistical model cannot adequately capture the structure of the underlying data. The hypothesis function is too simple.  

Underfitting destroys the accuracy of our machine learning model. Its occurrence simply means that our model or the algorithm does not fit the data well enough. It usually happens when we have less data to build an accurate model and also when we try to build a linear model with a non-linear data. 

In such cases the rules of the machine learning model are too easy and flexible to be applied on such a minimal data and therefore the model will probably make a lot of wrong predictions. Underfitting can be avoided by using more data and also reducing the features by feature selection.

Underfitting will cause poor predictions because the fundamental relationship generated by the model does not match how the data behaves. No matter how many observations you gather for your data, the algorithm won’t be able to model the true shape of the data (e.g., a linear regression on an exponential data set).

A model with high bias is simpler than it should be and hence tends to underfit the data. In other words, the model fails to learn and acquire the intricate patterns of the dataset.

In case of underfitting, the bias is an error from a faulty assumption in the learning algorithm. This is such that when the bias is too large, the algorithm would be able to correctly model the relationship between the features and the target outputs.

When a model is underfit, it does not perform well on the training sets, and will not so on the testing sets, which means it fails to capture the underlying trend / pattern of the data.

 Underfitting may occur if we are not using enough data  to train the model, just like we will fail the exam if we did not review enough material; it may also happen if we are trying to fit a wrong model to the data, just like we will score low in any exercises or exams if we take the wrong approach and learn it the wrong way. 

We call any of these situations high bias in machine learning, although its variance is low as performance in training and test sets are pretty consistent, in a bad way.

Bias is the algorithm’s tendency to consistently learn the wrong thing by not taking into account all the information in the data .   When a model is unable to capture the essence of the training data properly because of low number of parameters then this phenomenon is known as Underfitting.

Underfitting happens when we have very less amount of data to build an accurate model or when we try to build a linear model with a nonlinear data.  These kind of models are very simple to capture the complex patterns in data like Linear and logistic regression.

Linear regression has one problem, is that it tends to underfit the data. It gives us the lowest mean-squared error for unbiased estimators. Hence with underfitting, we aren't getting the best predictions. One way to reduce the mean-squared error is a technique known as LWLR.






It’s entirely possible to have state-of-the-art algorithms, the fastest computers, and the most recent GPUs, but if your model overfits or underfits to the training data, its predictive powers are going to be terrible no matter how much money or technology you throw at it.



If the model has a bias problem (underfitting), then both the testing and training error curves will plateau quickly and remain high. This implies that getting more data will not help! We can improve model performance by reducing regularization and/or by using an algorithm capable of learning more complex hypothesis functions.

Since underfitting means less model complexity, training longer can help in learning more complex patterns. This is especially true in terms of Deep Learning.

During training and validation, it is important to check the loss that is generated by the model. If the model is underfitting, the loss for both training and validation will be significantly high. In terms of Deep Learning, the loss will not decrease at the rate that it is supposed to if the model has reached saturation or is underfitting.

If a graph is plotted showing the data points and the fitted curve, and the curve is over-simplistic , then the model is suffering from underfitting. A more complex model can be tried out.

High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).

Parameter based learning algorithms usually have high bias and hence are faster to train and easier to understand. However, too much bias causes the model to be oversimplified and hence underfits the data. Hence these models are less flexible and often fail when they are applied on complex problems.




A model with high Bias means the model is Underfitting the given data--   and a model with High Variance means the model is Overfitting the given data.

Overfitting means that the model has memorized the training data and can’t generalize to things it hasn’t seen.


A low bias and high variance problem is overfitting.

When a mode is built using so many predictors that it captures noise along with the underlying pattern then it tries to fit the model too closely to the training data leaving very less scope for generalizability. This phenomenon is known as Overfitting.

Overfitting will cause poor predictions because the model is overmatching the training data (in some extreme cases, memorizing the training data), and not making any inductive leaps about the true relationship. 

Overfitting is associated with high variance, and therefore the models produced in an overfitting scenario will differ wildly depending on what training data is used. Overfit models handle their training data perfectly, but fail to generalize to new data sets.

Overfitting refers to a model that models the training data too well. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.

The variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs..

The problem where the model chosen is too complex, and becomes specific to the training data set is called overfitting.


Overfitting means that the neural network performs very well on training data, but fails as soon as it sees some new data from the problem domain. Underfitting, on the other hand, means, that the model performs poorly on both datasets.

Both overfitting and underfitting are not desirable phenomenons. However, by far the most common problem in deep learning and machine learning is overfitting.


Overfitting is a much bigger problem because the evaluation of deep learning/machine learning models on training data is quite different from the evaluation that is actually most important for us, which is the evaluation of the model on unseen data (validation set).

When a model gets trained with so much of data, it starts learning from the noise and inaccurate data entries in our data set. Then the model does not categorize the data correctly, because of too much of details and noise.

The causes of overfitting are the non-parametric and non-linear methods because these types of machine learning algorithms have more freedom in building the model based on the dataset and therefore they can really build unrealistic models.

A solution to avoid overfitting is using a linear algorithm if we have linear data or using the parameters like the maximal depth if we are using decision trees.

The commonly used methodologies used to prevent overfitting are :-- 
Cross- Validation: A standard way to find out-of-sample prediction error is to use 5-fold cross validation.
Early Stopping: Its rules provide us the guidance as to how many iterations can be run before learner begins to over-fit.
Pruning: Pruning is extensively used while building related models. It simply removes the nodes which add little predictive power for the problem in hand.

Regularization: It introduces a cost term for bringing in more features with the objective function. Hence it tries to push the coefficients for many variables to zero and hence reduce cost term.


Overfitting is the case where the generalization of the model is unreliable. This is due to the model learning “too much” from the training data set.

The phenomenon of memorization can cause overfitting. We are over extracting too much information from the training sets and making our model just work well with them, which is called low bias in machine learning.


However, at the same time, it will not help us generalize with data and derive patterns from them. The model as a result will perform poorly on datasets that were not seen before. We call this situation high variance in machine learning.
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Overfitting occurs when we try to describe the learning rules based on a relatively small number of observations, instead of the underlying relationship.. Overfitting also takes place when we make the model excessively complex so that it fits every training sample, such as memorizing the answers for all questions

Decision tress are prone to Overfitting especially when a tree is particularly deep. This is due to the amount of specificity we look at leading to smaller sample of events that meet the previous assumptions.

A decision tree is a lot like a flowchart. To utilize a flowchart you start at the starting point, or root, of the chart and then based on how you answer the filtering criteria of that starting node you move to one of the next possible nodes. This process is repeated until an ending is reached.

Pruning can help increase the performance of a decision tree by stripping out branches containing features that have little predictive power/little importance for the model

Pruning is the process of removing the unnecessary structure from a decision tree, effectively reducing the complexity to combat overfitting with the added bonus of making it even easier to interpret.

Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood.

Models with low bias algorithms tend to be more complex, with a more flexible underlying structure. The higher level of flexibility in the models can allow for more complex relationships between data but can also cause overfitting because the model is free to memorize the training data, instead of generalizing a pattern found in the data.

Models with low bias also tend to be less stable between training data sets. Non-parametric models (e.g., decision trees) typically have low bias and high variability.

Any high complexity model (Decision trees) will be prone to overfitting due to low bias and high variance .  The best we can do is try to settle somewhere in the middle of the spectrum.  An ideal model will exist in the happy place between overfitting and underfitting, where the “true” relationship between the data is captured, but the random noise of the data set is not.

Pruning is the process of removing the unnecessary structure from a decision tree, effectively reducing the complexity to combat overfitting with the added bonus of making it even easier to interpret.

The parameters to look out for to determine if the model is overfitting or not is similar to those of underfitting ones.  These are listed below:--
Training and Validation Loss: it is important to measure the loss of the model during training and validation. A very low training loss but a high validation loss would signify that the model is overfitting. Additionally, in Deep Learning, if the training loss keeps on decreasing but the validation loss remains stagnant or starts to increase, it also signifies that the model is overfitting.
Too Complex Prediction Graph: If a graph is plotted showing the data points and the fitted curve, and the curve is too complex to be the simplest solution which fits the data points appropriately, then the model is overfitting.
Classification: If every single class is properly classified on the training set by forming a very complex decision boundary, then there is a good chance that the model is overfitting.
Regression: If the final “Best Fit” line crosses over every single data point by forming an unnecessarily complex curve, then the model is likely overfitting.

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Random forest are also known as Ensemble Technique. 

These techniques are divide and conquer approach. It uses small number of weak learner to generate a strong learner. In random Forest, small learners are small trees. Together with the power of majority voting they make strong learner.

Pre-pruning stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning  allows the tree to perfectly classify the training set

Solutions for overfitting include simplifying your model (e.g., selecting a model with fewer parameters or reducing the number of attributes), gathering more training data, or reducing noise in the training data (e.g., finding and correcting errors, removing outliers)..

Overfitting can  be showcased in two forms of supervised learning: Classification and Regression.

Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data

Overfitting a model is a condition where a statistical model begins to describe the random error in the data rather than the relationships between variables. This problem occurs when the model is too complex. ... Thus, overfitting a regression model reduces its generalizability outside the original dataset.

Again, Bias denotes the simplicity of the model. A high biased model will have a simpler architecture than that of a model with a lower bias. Similarly, complementing Bias, Variance denotes how complex the model is and how well it can fit the data with a high degree of diversity. An ideal model should have Low Bias and Low Variance. 

However, when it comes to practical datasets and models, it is nearly impossible to achieve a “zero” Bias and Variance. These two are complementary of each other, if one decreases beyond a certain limit, then the other starts increasing. This is known as the Bias-Variance Tradeoff. Under such circumstances, there is a “sweet spot” as shown in the figure, where both bias and variance are at their optimal values.

If the model is overfitting, the developer can take the following steps to recover from the overfitting state:--

Early Stopping during Training: This is especially prevalent in Deep Learning. Allowing the model to train for a high number of epochs (iterations) may lead to overfitting. Hence it is necessary to stop the model from training when the model has started to overfit. This is done by monitoring the validation loss and stopping the model when the loss stops decreasing over a given number of epochs (or iterations).

Train with more data: Often, the data available for training is less when compared to the model complexity. Hence, in order to get the model to fit appropriately, it is often advisable to increase the training dataset size.

Train a less complex model: As mentioned earlier, the main reason behind overfitting is excessive model complexity for a relatively less complex dataset. Hence it is advisable to reduce the model complexity in order to avoid overfitting. For Deep Learning, the model complexity can be reduced by reducing the number of layers and neurons.

Regularization: Regularization is the process of simplification of the model artificially, without losing the flexibility that it gains from having a higher complexity. With the increase in regularization, the effective model complexity decreases and hence prevents overfitting.

Handling overfitting-- Reduce the network's capacity by removing layers or reducing the number of elements in the hidden layers.

Apply regularization, which comes down to adding a cost to the loss function for large weights.
Use Dropout layers, which will randomly remove certain features by setting them to zero.

In case of overfitting, variance is an error resulting from fluctuations in the training dataset. A high value for variance would cause  the algorithm may capture the most data points put would not be generalized enough to capture new data points.

Overfitting means that the neural network models the training data too well. Overfitting suggests that the neural network has a good performance. But it fact the model fails when it faces new and yet unseen data from the problem domain.

Again, Overfitting is happening for two main reasons:--
The data samples in the training data have noise and fluctuations.
The model has very high complexity

When the statistical model contains more parameters than justified by the data. This means that it will tend to fit noise in the data and so may not generalize well to new examples. The hypothesis function is too complex.

If the model has a variance problem (overfitting), the training error curve will remain well below the testing error and may not plateau. If the training curve does not plateau, this suggests that collecting more data will improve model performance.  To prevent overfitting and bring the curves closer to one another, one should increase the severity of regularization, reduce the number of features and/or use an algorithm that can only fit simpler hypothesis functions

With the passage of time, the model will keep on learning and thus the error for the model on the training and testing data will keep on decreasing. If it will learn for too long, the model will become more prone to overfitting due to presence of noise and less useful details. Hence the performance of our model will decrease. In order to get a good fit, we will stop at a point just before where the error starts increasing. At this point the model is said to have good skills on training dataset as well our unseen testing dataset.

Again, it’s entirely possible to have state-of-the-art algorithms, the fastest computers, and the most recent GPUs, but if your model overfits or underfits to the training data, its predictive powers are going to be terrible no matter how much money or technology you throw at it.




In statistics and machine learning,  bias–variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa.


The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set

In reality, we want something in the middle with some amount of bias and variance called Bias-Variance tradeoff. The optimal machine learning model should have some authority to generalize but at the same time, it should be very open to listening to data.

This tradeoff applies to all forms of supervised learning: classification, regression (function fitting), and structured output learning. It has also been invoked to explain the effectiveness of heuristics in human learning.

High Bias High Variance – Models are inaccurate and also inconsistent on average.
Low Bias Low Variance: This is the unicorn.

It is obvious that  it is nearly impossible to have a model with no bias or no variance since decreasing one increases the other. This phenomenon is known as the Bias-Variance Trade off

If hypothetically, infinite data is available, it is possible to tune the model to reduce the bias and variance terms to zero but is not possible to do so practically. Hence, there is always a tradeoff between the minimization of bias and variance.

High bias means that the algorithm have  failed to understand the pattern in the input data.
It’s generally not possible to minimize both errors simultaneously, since high bias would always means low variance, whereas low bias would always mean high variance.

Finding a trade-off between the two extremes is known as Bias/Variance Tradeoff.

We have to avoid overfitting because it gives too much predictive power to even noise elements in our training data. But in our attempt to reduce overfitting we can also begin to underfit, ignoring important features in our training data.

Supervised machine learning algorithms can best be understood through the lens of the bias-variance trade-off.

The objective of any machine learning algorithm is to achieve low bias and low variance, achieving at the same time a good performance predicting results.

The Bias-Variance Tradeoff is relevant for supervised machine learning - specifically for predictive modeling. It's a way to diagnose the performance of an algorithm by breaking down its prediction error.

The bias-variance dilemma or bias-variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set.

The bias opposite to the variance refers to the precision opposite to consistency of the trained models.

In the machine learning world, precision is everything. When we try to develop a model, we try to make it as much accurate as possible playing with the different parameters. But, the hard truth is that we cannot build a one-hundred per cent accurate model due to we cannot build a free of errors model.

What we can do, it is trying to understand the possible sources of errors and this will help us to obtain a more precise model.

High Bias Low Variance: Models are consistent but inaccurate on average. Tend to be less complex with a simple or rigid structure like linear regression or Bayesian linear regression.
Low Bias High variance: Models are somewhat accurate but inconsistent on averages. Tend to be more complex with a flexible structure like decision trees or k-nearest neighbour (KNN).

We want to avoid both overfitting and underfitting.  Bias is the error stemming from incorrect assumptions in the learning algorithm; high bias results in underfitting, and variance measures how sensitive the model prediction is to variations in the datasets. Hence, we need to avoid cases where any of bias or variance is getting high.


So, does it mean we should always make both bias and variance as low as possible? The answer is yes, if we can. But in practice, there is an explicit trade-off between themselves, where decreasing one increases the other. This is the so-called bias–variance tradeoff.

The name bias-variance dilemma comes from two terms in statistics: bias, which corresponds to underfitting, and variance, which corresponds to overfitting

If the model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data.

From the understanding of bias and variance individually , it can be concluded that the two are complementary to each other. In other words, if the bias of a model is decreased, the variance of the model automatically increases. The vice-versa is also true, that is if the variance of a model decreases, bias starts to increase.





Bias and variance are actually side effects of one factor: the complexity of the model.

This tradeoff is somewhat like the electron and the proton balance in an atom, both are equally important and their harmony is important for the universe as a whole. 

Similarly, bias and variance are two kinds of errors to be minimised during the model building. Any low complexity model- will be prone to underfitting because of high bias and low variance

The bias-variance tradeoff refers to the fact that models with high bias will have low variance and vice versa, so it it important to choose a model that has an optimal contribution from both such that total error is minimized

Bias-variance tradeoff is a serious problem in machine learning. It is a situation when you can’t have both low bias and low variance. But you have to have a tradeoff by training a model which captures the regularities in the data enough to be reasonably accurate and generalizable to a different set of points from the same source, by having optimum bias and optimium variance.

Bias-variance tradeoff forms an essential entity of machine and statistical learning. All the learning algorithms involve a significant measure of error.

These errors can be reducible and non-reducible. We cannot do anything about non-reducible errors. The reducible errors which are the bias and variance can be made use off.

These reducible errors can be effectively minimized and the efficiency of a working system can be maximized. The main goal of a learning algorithm is to reduce these bias and variance errors to the minimum and make the most feasible model.

Achieving such a goal is not very easy that is when a tradeoff is made to reduce the possible sources of errors when a certain model is being selected based on their varied flexibility and complexity.


We have to avoid overfitting because it gives too much predictive power to even noise elements in our training data. But in our attempt to reduce overfitting we can also begin to underfit, ignoring important features in our training data. We need a balance.

The bias–variance decomposition is a way of analyzing a learning algorithm's expected generalization error with respect to a particular problem as a sum of three terms, the bias, variance, and a quantity called the irreducible error, resulting from noise in the problem itself.


This tradeoff applies to all forms of supervised learning: classification, regression (function fitting), and structured output learning. It has also been invoked to explain the effectiveness of heuristics in human learning.

This bias-variance tradeoff equation works well with both predictive as well as explanatory models. When we take the explanatory model into consideration, the main goal would be to reduce the bias to the maximum extent to get the underlying theory’s most accurate representation.






On the one hand, we want our algorithm to model the training data very closely, otherwise we’ll miss relevant features and interesting trends.

However, on the other hand we don’t want our model to fit too closely, and risk over-interpreting every outlier and irregularity.

Bias-Variance Dichotomy-- The concept here is that while adding complexity to the machine learning model might improve the fit to the training data, it need not improve the prediction accuracy on the training data (i.e new data).

The optimal region of the Bias-Variance tradeoff, the model is neither underfitting nor overfitting.  Hence, since there is neither underfitting nor overfitting, it can also be said that the model is most Generalized, as under these conditions the model is expected to perform equally well on Training and Validation Data.

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance

Bias Variance tradeoff as the name suggests is a tradeoff between bias and variance. An algorithm can’t be more complex and less complex at the same time. . To build a good model, we need to find a good balance between bias and variance such that it minimizes the total error.

Big difference in errors between the Training and Testing Set clearly signifies that something is wrong with the Polynomial model. This drastic change in error is due to a phenomenon called Bias-Variance Tradeoff

Models with higher bias tend to be relatively simple (low-order or even linear regression polynomials) but may produce lower variance predictions when applied beyond the training set

Machine Learning is a scientific field of study which involves the use of algorithms and statistics to perform a given task by relying on inference from data instead of explicit instructions. 

In Machine Learning, only a part of the data is available to the user at the time of training (fitting) the model, and then the model has to perform equally well on data that it has never encountered before. Which is, in other words, the generalization of the model over a given data, such that it is able to correctly predict when it is deployed.

The bias–variance decomposition is a way of analyzing a learning algorithm's expected generalization error with respect to a particular problem as a sum of three terms, the bias, variance, and a quantity called the irreducible error, resulting from noise in the problem itself.

Again, models with high bias tend to under-fit, while models with high variance tend to overfit.

A good model should do one of two things:--
Capture the patterns in the given training data set
Correctly compute the output for a new instance

The model should be complete enough to represent the data, but the more complex the model, the better it represents the training data. However, there is a limit to how complex the model can get.
If the model is too complex, then it will pick up specific random features (noise or example)  in the training data set.

If the model is not complex enough, then it might miss out on important dynamics of the data given.

It is generally impossible to minimize the two errors  at the same time and this trade-off is what is known as bias/variance trade-off.


Spear phishing accounts for 92% of cyberattacks.

Spear phishing is the fraudulent practice of sending emails ostensibly from a known or trusted sender in order to induce targeted individuals to reveal confidential information.


Phishing is a broader term for any attempt to trick victims into sharing sensitive information such as passwords, usernames, and credit card details for malicious reasons. ... 



Unlike spear-phishing attacks, phishing attacks are not personalized to their victims, and are usually sent to masses of people at the same time.

Spear phishing attempts have been used to swindle individuals and companies out of millions of dollars.  They can also do damage in other areas, such as stealing secret information from businesses or causing emotional stress to individuals..

Last year, in 2018, airbnb customers were targeted by a spear phishing attack where cyber attackers used social engineering methods to pick victims. they then sent out a fake email stating the implications of the general data protection regulation (gdpr). the fake email prompted its recipients not to accept further bookings until they comply with gdpr (sent by attacks via airbnb). The attached link took the customers to a spoof site, which then collected the personal details of the victims.

Spear phishing attackers plan their attacks by first identifying their target. there are a few elements common to spear phishing attacks –

Source looks like a legit one – scammers send emails to these targets in such a way that it seems to be from a legitimate source. these sources closely resemble a genuine email id.

For instance, mnop@gmail.com becomes rnop@gmail.com, where ‘m’ is replaced by ‘rn,’ making it look like an ‘m.’ ( most long sighted people don’t wear reading glasses )

Spear phishing can lead to the compromise of sensitive data. if the required security measures are not put in place properly, the targeted attack may lead to a destructive security breach..


Filter your inbox – Configure your email application so that it blocks spam-emails efficiently. It must separate emails generated from trusted sources from those outside the system.
Encryption – If an email is sent after cryptographically signing it, then only the person with the private key can access the content of the mail. It makes it difficult for an imposter to pass off as a legit source.
Anti-spam software and devices – It has been noticed that spear phishing messages target systems that are already compromised. In this scenario, anti-spam software and devices can identify a compromised mail server.
Update all software – Updating installed applications and software is a crucial step. If not done regularly, cybercriminals can misuse this lag, to exploit the known-unknown vulnerabilities.
Keep an eye on your online activities – Have you been sharing your personal information on social media accounts? If yes, then a potential scammer can use the same details to frame a personalized message, which might lead you to a spear phishing attack.
Use smart passwords – If you are someone who uses the same password or variations of it on different platforms, then change your password to a random phrase or combination of numbers, letters, and special symbols.
Implement a data protection program – Every mid to large size corporation should have a data protection program. Install a data loss prevention software so that it can efficiently protect sensitive data.
Awareness – Make sure that your employees are well-aware of spear phishing attacks. They should know how to detect such attacks. Train them on good email practices –
Not to reveal personal information on emails unless sent from a trusted source (after cross-verifying the source from a legit database)
Never click on links sent through emails, especially the ones asking for your financial or banking details
Always report suspicious emails
As emails are the most common entry points of targeted attacks, it is vital to protect organizations from anticipated attacks

Spear-phishing is defined as cyberattacks targeting specific users within an organization to distribute malware or extract sensitive data. Hackers typically use two different methods to gain access: emails sent with file attachments and files without attachments (emails containing malicious links). 

Employees should be instructed to forward emails to a dedicated person within the organization to verify its authenticity. Organizations also need clear processes to help employees both identify attacks and report them to a designated point person.

The better users become at detecting spear phishing, the less likely the organization is to be compromised by an attacker.


Machine learning – the heart of what we call artificial intelligence today – gets “smart” by observing patterns in data, and making assumptions about what it means, whether on an individual computer or a large neural network.

So, if a specific action in computer processors takes place when specific processes are running, and the action is repeated on the neural network and/or the specific computer, the system learns that the action means that a cyber-attack has occurred, and that appropriate action needs to be taken.

But here is where it gets tricky. AI-savvy malware could inject false data that the security system would read – the objective being to disrupt the patterns the machine learning algorithms use to make their decisions. Thus, phony data could be inserted into a database to make it seem as if a process that is copying personal information is just part of the regular routine of the IT system, and can safely be ignored. 

Instead of trying to outfox intelligent machine-learning security systems, hackers simply “make friends” with them – using their own capabilities against them, and helping themselves to whatever they want on a server.

There are all sorts of other ways hackers could fool AI-based security systems. It’s already been shown for example, that an AI-based image recognition system could be fooled by changing just a few pixels in an image. In one famous experiment at Kyushu University in Japan, scientists were able to fool AI-based image recognition systems nearly three quarters of the time, “convincing” them that they were looking not at a cat, but a dog or even a stealth fighter.

Another tactic involves “bobbing and weaving,” where hackers insert signals and processes that have no effect on the IT system at all – except to train the AI system to see these as normal. Once it does, hackers can use those routines to carry out an attack that the security system will miss – because it’s been trained to “believe” that the behavior is irrelevant, or even normal.

Yet another way hackers could compromise an AI-based cybersecurity system is by changing or replacing log files – or even just changing their timestamps or other metadata, to further confuse the machine-learning algorithms.

Thus, the great strength of AI has the potential to be its downfall.

Companies that install advanced AI security systems tend to become complacent about cybersecurity, believing that the system will protect them, and that by installing it they’ve assured their safety.
Keeping a human eye on the AI that is ostensibly protecting organizations is the first step in ensuring that they are getting their money’s worth out of their cybersecurity systems.

Hardening the AI: One tactic hackers use to attack is inundating an AI system with low-quality data in order to confuse it. To protect against this, security systems need to account for the possibility of encountering low-quality data.

Stricter controls on how data is evaluated – for example, examining the timestamps on log files more closely to determine if they have been tampered with – could take from hackers a weapon that they are currently successfully using.

More attention to basic security: Hackers most often infiltrate organizations using their tried and true tactics – APT or run of the mill malware. By shoring up their defenses against basic tactics, organizations will be able to prevent attacks of any kind – including those using advanced AI – by keeping malware and exploits off their networks altogether.

Educating employees on the dangers of responding to phishing pitches – including rewarding those who avoid them and/or penalizing those who don’t – along with stronger basic defenses like sandboxes and anti-malware systems, and more intelligent AI defense systems can go a long way to protect organizations. AI has the potential to keep our digital future safer; with a little help from us, it will be able to avoid manipulation by hackers, and do its job properly

Hackers are using AI to build customized programs capable of getting past a company’s defenses. State-of-the-art defenses generally rely on examining what the attack software is doing, rather than the more commonplace technique of analyzing software code for danger signs. But the new generation of AI-driven programs can be trained to stay dormant until they reach a very specific target, making them exceptionally hard to stop

Data is at the heart of any AI implementation and IT security is no different. To be effective, AI algorithms must be driven by the right data systems. The data must not just exist but should be current. 

After all, AI seeks to mimic human intelligence and should, therefore (ideally), be designed to continuously improve itself based on new knowledge. So, identifying the required data sets must be the first thing the business does in their quest to operationalize the new AI-driven cybersecurity algorithms.

Security orchestration, automation, and response (SOAR) are tools that help organizations collect security information from multiple sources. SOAR enables incident triage and analysis by combining human and machine capabilities. This allows the defining, prioritizing, and driving of incident response through a standard workflow connecting data sources and data platforms. 

SOAR is an essential component in optimizing the output of AI-based cybersecurity tools. It improves alert quality, reduces the time needed for onboarding cyber analysts, and improves security management.

SOAR  is a solution stack of compatible software programs that allow an organization to collect data about security threats from multiple sources and respond to low-level security events without human assistance.









Autonomic Intelligent Cyber Sensor (AICS), developed with funding from the Department of Energy, employs artificial intelligence to detect intruders, isolate them and retaliate against them..

AICS uses a proprietary cluster algorithm to learn and map the business and operational systems so it can recognize anomalies.

It constantly monitors not only network traffic across industrial control systems, but its sensors keep tabs on voltages and amperages in connected systems to look for irregularities indicating an intruder is present

AICS uses machine learning to add to its knowledge base of threats, making it better able to identify threats as time goes on

AICS also employs honeypots, monitored networks that appear to be part of the production system but that isolate and quarantine intruders. AI is used to update these virtual decoys in ways that mimic a live network so prevent intruders do not realize they are being observed.  Once in the honeypot, and intruder can be tracked, analyzed, diverted from targeted systems and potentially hacked back

Honeypots, take the bait and trap approach. A honeypot is an isolated computer or network site that is set up to attract hackers. Cyber security analysts use honeypots to research evolving tactics, prevent attacks and catch intruders




DIGRESSION:  A HONEYPOT JEWESS NAMED HELENA CREATED THE JEWISH DEEP STATE .

AMONG CRIMINAL MINDS SHE IS NO 1  , SINCE HISTORY OF MAN BEGAN 65 MILLION YEARS AGO..



http://ajitvadakayil.blogspot.com/2018/12/helena-mother-of-roman-emperor.html


Deception technologies play an important defensive strategy. By setting an irresistible honeypot, attackers are fooled into thinking they have gained access to the real system or target. Once tricked, the methods and tactics of the attackers can be safely monitored to gain critical intelligence and identify where defence systems needed to be ramped up.

Late in 1988, a man named Robert Morris had an idea: he wanted to gauge the size of the internet. To do this, he wrote a program designed to propagate across networks, infiltrate Unix terminals using a known bug, and then copy itself. This last instruction proved to be a mistake. The Morris worm replicated so aggressively that the early internet slowed to a crawl, causing untold damage.

The worm had effects that lasted beyond an internet slowdown. For one thing, Robert Morris became the first person successfully charged under the Computer Fraud and Abuse Act (although this ended happily for him—he’s currently a tenured professor at MIT). More importantly, this act also led to the formation of the Computer Emergency Response Team (the precursor to US-CERT), which functions as a nonprofit research center for systemic issues that might affect the internet as a whole.

The Morris worm appears to have been the start of something. After the Morris worm, viruses started getting deadlier and deadlier, affecting more and more systems. It seems as though the worm presaged the era of massive internet outages in which we live. You also began to see the rise of antivirus as a commodity—1987 saw the release of the first dedicated antivirus company


https://en.wikipedia.org/wiki/Robert_Tappan_Morris

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Network breaches and malware did exist and were used for malicious ends during the early history of computers, however. The Russians, for example, quickly began to deploy cyberpower as a weapon. In 1986, the German computer hacker Marcus Hess hacked an internet gateway in Berkeley, and used that connection to piggyback on the Arpanet. He hacked 400 military computers, including mainframes at the Pentagon, with the intent of selling their secrets to the KGB. He was only caught when an astronomer named Clifford Stoll detected the intrusion and deployed a honeypot technique.



EternalBlue was leaked by the Shadow Brokers hacker group on April 14, 2017, and was used as part of the worldwide WannaCry ransomware attack on May 12, 2017. The exploit was also used to help carry out the 2017 NotPetya cyberattack on June 27, 2017 and reportedly is used as part of the Retefe banking trojan since at least September 5, 2017. No Anti-Virus or even next generation EPP can effectively prevent exploitation using EternalBlue.


To a large extent, cybersecurity relies on file signatures to detect malware, and rules-based systems for detecting network abnormalities. Protection often stems from an actual virus outbreak – as security experts isolate the malicious files and identify unique signatures that help other systems become alert and immune. 

The same is true for the rules-based system: Rules are set based on experience of potential malicious activity, or systems are locked down to restrict any access to stay on the safe side. The only problem with these approaches is their reactive nature. Hackers always find innovative ways to bypass the known rules. Before a security expert discovers the breach, it’s often too late.

Traditional malware is designed to perform its damaging functions on every device they find their way into. One example is the NotPetya ransomware outbreaks, in which hundreds of thousands of computers were infected in a short period of time. This method works when the attacker’s goal is to inflict maximum damage. It’s not as effective if an attacker has a specific target in mind.

But the advent of disruptive technologies like Artificial Intelligence means our devices and applications are understanding us better. For example, an iPhone X uses AI to automatically recognize faces. While it’s a great feature, it creates an intricate puzzle where the chances of sensitive data going in wrong hands are high. Today, hackers are seen using the same technology to develop smart malware that can prey on targets by pinpointing them from millions of users.

What makes AI cybersecurity unique is its adaptability. Intelligent cybersecurity doesn’t need to follow specific rules. Rather, it can watch patterns and learn. Even better, AI can be directly integrated into everyday protection tools – such as spam filters, network intrusion and fraud detection, multi-factor authentication, and incident response.



Malicious actors on the web closely monitor cyber security trends and react to them by reshaping viruses, exploits and other attack methods to subvert safety nets. Thus, there always arise instances in which attackers seize the advantage and their opponents appear to have brought a knife to a gunfight, figuratively speaking.

1 in every 20 malware attacks around the globe involves the use of ransomware. Moreover, most of its victims pay about $300 to escape the system interference involved in each hack, though some criminals will charge thousands more.


Ransomware is a type of malicious software, or malware, designed to deny access to a computer system or data until a ransom is paid. Ransomware typically spreads through phishing emails or by unknowingly visiting an infected website. Ransomware can be devastating to an individual or an organization



WannaCry is a ransomware worm that spread rapidly through across a number of computer networks in May of 2017. After infecting a Windows computers, it encrypts files on the PC's hard drive, making them impossible for users to access, then demands a ransom payment in bitcoin in order to decrypt them.


WannaCry spread like wildfire, encrypting hundreds of thousands of computers in more than 150 countries in a matter of hours.  WannaCry caused panic. Systems were down, data was lost and money had to be spent. It was a wake-up call that society needed to do better at basic cybersecurity.

Nearly all ransomware perpetrators demand payment in Bitcoin, which uses blockchain encryption to prevent intrusion and tracking, as Europol identified in its 2016 Internet Organized Crime Threat Assessment report. The more items in a person’s household that are remotely controlled using IoT methods – security, vehicles, range stoves, and thermostats, to name a few – the greater the risk.  


MAJOR CITIES CAN BE GUTTED BY FIRES


Businesses have even more to lose from IoT-centric malware attacks than individuals, especially if they use these solutions for functions like building security or any number of back-end processes. But the opposite approach – using legacy systems to manage business operations or utilities, for example – can be just as flawed and unsafe.

The Mirai botnet-assisted malware that battered Amazon, PayPal, Reddit and Dyn – the latter a firm providing server backup for massive swaths of the world’s internet – with service outages possessed sophisticated coding that allowed for easy updates as hackers passed it among themselves. This function effectively circumvented many malware countermeasures.

DDoS attackers once focused largely on governments and financial institutions, but Total Retail reported that in light of the Mirai hack, businesses throughout the entire private sector should consider the potential for this threat. Upon putting DDoS in place, black-hat hackers can plunder an organization’s databases with aplomb and sell corporate secrets or employee information on the black market, or simply hold the network hostage like a ransomware attack.

DDoS is short for Distributed Denial of Service. DDoS is a type of DOS attack where multiple compromised systems, which are often infected with a Trojan, are used to target a single system causing a Denial of Service (DoS) attack

A distributed denial-of-service (DDoS) attack occurs when multiple systems flood the bandwidth or resources of a targeted system, usually one or more web servers. Such an attack is often the result of multiple compromised systems (for example, a botnet) flooding the targeted system with traffic



AI-powered cyberattacks are not a hypothetical future concept. All the required building blocks for the use of offensive AI already exist: highly sophisticated malware, financially motivated – and ruthless – criminals willing to use any means possible to increase their return on investment, and open-source AI research projects which make highly valuable information available in the public domain.

One of the most notorious pieces of contemporary malware – the Emotet trojan – is a prime example of a prototype-AI attack. Emotet’s main distribution mechanism is spam-phishing, usually via invoice scams that trick users into clicking on malicious email attachments. The Emotet authors have recently added another module to their trojan, which steals email data from infected victims.

It can automatically insert itself into pre-existing email threads, advising the victim to click on a malicious attachment, which then appears in the final, malicious email. This insertion of the malware into pre-existing emails gives the phishing email more context, thereby making it appear more legitimate.

AI-powered Emotet trojan could create and insert entirely customized, more believable phishing emails. Crucially, it would be able to send these out at scale, which would allow criminals to increase the yield of their operations enormously.

The consequences of these developing attack methods could be highly destructive, and even life-threatening. By undermining data integrity, these stealthy attacks cause trust in organizations to falter, and may even cause systemic failures to occur. Imagine an oil rig using faulty geo-prospection data to drill for oil in the wrong place, or a physician making a diagnosis using compromised medical records. As the AI arms race continues, we can only expect this circle of innovation to escalate.

Sophisticated threat actors can often maintain a long-term presence in their target environments for months at a time, without being detected. They move slowly and with caution, to evade traditional security controls and are often targeted to specific individuals and organizations. 

AI will also be able to learn the dominant communication channels and the best ports and protocols to use to move around a system, discretely blending in with routine activity. This ability to disguise itself amid the noise will mean that it is able to expertly spread within a digital environment, and stealthily compromise more devices than ever before.

 AI malware will also be able to analyse vast volumes of data at machine speed, rapidly identifying which data sets are valuable and which are not. This will save the (human) attacker a great deal of time and effort.

Not only will AI-driven attacks be much more tailored and consequently more effective, their ability to understand context means they will be even harder to detect. Traditional security controls will be impotent against this new threat, as they can only spot predictable, pre-modelled activity. AI is constantly evolving and will become ever-more resistant to the categorization of threats that remains fundamental to the modus operandi of legacy security approaches.



Attackers won’t just target AI systems, they will enlist AI techniques themselves to supercharge their own criminal activities. Automated systems powered by AI could probe networks and systems searching for undiscovered vulnerabilities that could be exploited. 

AI could also be used to make phishing and other social engineering attacks even more sophisticated by creating extremely realistic video and audio or well-crafted emails designed to fool targeted individuals.  

AI could also be used to launch realistic disinformation campaigns.  For example, imagine a fake AI-created, realistic video of a company CEO announcing a large financial loss, a major security breach, or other major news.  Widespread release of such a fake video could have a significant impact on the company before the true facts are understood.



Data scientists spend more than  80% of their time in data preparation..  Feature engineering is a vital part of this. Without this step, the accuracy of your machine learning algorithm reduces significantly.

Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work.   It is a process of transforming the given data into a form which is easier to interpret

It’s often said that “data is the fuel of machine learning.” This isn’t quite true: data is like the crude oil of machine learning which means it has to be refined into features — predictor variables — to be useful for training a model. 

Without relevant features, you can’t train an accurate model, no matter how complex the machine learning algorithm. The process of extracting features from a raw dataset is called feature engineering.

The machine learning algorithm learns to predict using the features. Thus, to solve a problem using machine learning we need to feed in features that allow machine learning algorithm to predict more accurately. This may look like a simple concept, but in real-life applications, it can take a lot of trial and error to figure out which features are most important for solving the problem. 

Feature engineering is a process of selecting important variables or creating new variables from the existing ones, allowing our model to learn better and provide better accuracy.

Feature engineering means building features for each label while filtering the data used for the feature based on the label’s cutoff time to make valid features. These features and labels are then passed to modeling where they will be used for training a machine learning algorithm. 

Traditionally, feature engineering is done by hand, building features one at a time using domain knowledge. However, this manual process is error-prone, tedious, must be started from scratch for each dataset, and ultimately is limited by constraints on human creativity and time. 

Furthermore, in time-dependent problems where we have to filter every feature based on a cutoff time, it’s hard to avoid errors that can invalidate an entire machine learning solution.

Automated feature engineering overcomes these problems through a reusable approach to automatically building hundreds of relevant features from a relational dataset. Moreover, this method filters the features for each label based on the cutoff time, creating a rich set of valid features. 

In short, automated feature engineering enables data scientists to build better predictive models in a fraction of the time.

Not only does feature engineering prepare the dataset to be compatible with the algorithm, but it also improves the performance of the machine learning models.

A typical machine learning starts with data collection and exploratory analysis. Data cleaning comes next. This step removes duplicate values and correcting mislabelled classes and features.

Feature engineering is the next step. The output from feature engineering is fed to the predictive models, and the results are cross-validated.     

An algorithm that is fed the raw data is unaware of the importance of the features. It is making predictions in the dark.

You can think of feature engineering as the guiding light in this scenario.  

When you have relevant features, the complexity of the algorithms reduces. Even if you use an algorithm that is not ideal for the situation, the results will still be accurate.


Simpler models are often easier to understand, code, and maintain.




FRAUD FLOURISHES IN INDIA BECAUSE THERE IS NOT PUNISHMENT..

SALMAN KHAN WAS BRAND AMBASSADOR FOR YATRA DOT COM WHO WAS CHEATING PEOPLE..

http://ajitvadakayil.blogspot.com/2011/09/credit-card-fraud-by-yatra-dot-com-capt.html

CHECK THIS OUT BELOW- WITHOUT HELP FROM POLICE / JUDGES/ POLITICIANS -- CAN THIS HAPPEN ?

https://www.tripadvisor.in/ShowTopic-g293860-i511-k6070520-Yatra_com_the_biggest_fraud_company-India.html

capt ajit vadakayil
..


TILL I WROTE A 33 PART POST ON SHELL COMPANIES, NOBODY CARED.. 

KHAINI MUNCHING POT BELLIED PANDU HAWALDARS WHO DID NOT KNOW ENGLISH WERE EXPECTED TO ACT AGAINST SHELL COPANIE.. RBI GOVERNOR AND FINANCE MINISTRY PLAYED BALL.

EVEN TODAY  IPL OWNERSARE MERRILY USING SHELL COMPANIES..  ARUN JAITLY ( PART OF BCCI ) FOUND NOTHING WRONG..

I ASK—WHY IS A BLOGGER EXPECTED TO EXPOSE SHELL COMPANIES ? 

WHAT IS OUR MAIN STREAM MEDIA WORTH?     POT HOLE JOURNALISTS LIKE FAYE SORPOTEL DSOUZA GET AWARDS..

OUR THINK TANKS ARE FILLED WITH DESH DROHI CUNTS WHO HAVE SHIT FOR BRAINS..

PM MODI IS MORE INTERESTED IN GIVING US HAJAAAAR  JAANKAARI ABOUT GANDHI AND BR AMBEDKAR THAN DOING HIS FUCKIN’ JOB..   

MODI HAS IGNORED MORE THAN 300 CRITICAL SUGGESTION I GAVE .. CAPT AJIT VADAKAYIL WILL WRITE HIS LEGACY..  HIS NOBEL PRIZE WILL STINK..

HAVE YOU NOTICED THAT DEEP STATE DARLING SUBRAMANIAN SWAMY DOES NOT SAY A WORD AGAINST SHELL COMPANIES ?    HE PRETENDS TO BE A GREAT FINANCIAL GENIUS , RIGHT?     A MOSSAD SPONSORED GENIUS WHO MADE CHANDRASHEKHAR SELL 47 TONNES OF GOLD TO ROTHSCHILD.

GAMBLING CASINOS WERE STARTED IN GOA TO LAUNDER DRUG MONEY.. ALL ARE INVOLVED..



PABLO ESCOBAR INVESTED HIS DRUG MONEY IN PAINTINGS.   HE LEARNT THIS FROM ROTHSCHILD..


PENNING 33 POSTS BELOW, WAS MOST PAINFUL FOR ME..  IT CHANGED MY PERSONALITY FOR THE WORSE..


http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_21.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_22.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_28.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_24.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_27.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_65.html

http://ajitvadakayil.blogspot.com/2017/02/shell-companies-for-money-laundering_96.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_2.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_4.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_6.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_7.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_8.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_9.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_11.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_16.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_17.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_18.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_19.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_20.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_21.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_26.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_22.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_24.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_12.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_50.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_27.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_28.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_29.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_33.html

http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_30.html


http://ajitvadakayil.blogspot.com/2017/03/shell-companies-for-money-laundering_31.html







BELOW:  MALAYALI FATHER HELPING OUT IN HINDI HOMEWORK






THIS POST IS NOW CONTINUED TO PART 5 BELOW--


https://ajitvadakayil.blogspot.com/2019/11/what-artificial-intelligence-cannot-do_4.html



CAPT AJIT VADAKAYIL
..

200 comments:


  1. SOMEBODY ASKED ME --

    CAPTAIN, ARE THE GERMANS IN SOUTH AMERICA , CHRISTIANS?

    SORRY--

    GERMAN IN SOUTH AMERICA ARE ALL JEWS-- OR GERMAN CHRISTIANS WITH JEWISH WIVES..

    AFTER WW2 JEW HITLER AND HIS JEWISH THUGS ESCAPED TO SOUTH AMERICA BY SUBMARINES..

    http://ajitvadakayil.blogspot.com/2015/10/if-zionist-jews-created-isis-who.html

    THEY WERE WARNED BY THE ZIONISTS -- PRETEND TO BE CHRISTIANS AND CHANGE YOUR NAMES.. KOSHER WORSHIP MUST BE DONE ONLYBIN SECRET AT HOME .. NEVER ATTEND ANY PUBLIC SYNAGOGUES..

    ONE JEW ADOLF EICHMANN IGNORED THIS STRICT INSTRUCTION AND MOSSAD MADE AN EXAMPLE OUT OF HIM..

    THE ENTIRE MEDIA OF SOUTH AND CENTRAL AMERICA IS JEWISH..( LIKE THE REST OF THE PLANET )..

    ENEMY OF JEWS IS ENEMY OF THE NATION..

    THESE JEWISH MEDIA BARONS HAVE KILLER ORGS TO CREATE NEWS, PLANT BOMBS IN PUBLIC SPACES, ASSASSINATE POLICE..

    THESE JEWISH MEDIA ARE CONTROLLED BY JEWISH OLIGARCHS WHOSE TENTACLES ARE IN EVERY GOVT INSTITUTIONS..

    WHEN IT CAME TO PABLO ESCOBAR, THE JEWISH DEEP STATE CONTROLLED THE LEFT AND RIGHT WING GUERRILLAS..

    THIS IS BASED ON ROTHSCHILDs DIKTAT- " IF YOU WANT TO CONTROL THE OPPOSITION LEAD THEM YOURSELF"..

    GANDHI AND INDIAN NATIONAL CONGRESS WAS CONTROLLED BY JEW ROTHSCHILD..

    TILL TRUMP CAME ON SCENE NOBODY BELIEVED ME..

    ROTHSCHILDs CANDIDATE HILLARY CLINTON WAS GIVEN PRESIDENTIAL DEBATE QUESTIONS IN ADVANCE..

    HAVE YOU SEEN HOW THE JEWISH MEDIA HAS LAID SIEGE ON TRUMP TODAY? PEOPLE NOW SMELL A RAT..

    THE US JUDICIARY WAS CONTROLLED BY THE JEWISH DEEP STATE STILL TRUMP MANAGED TO TILT IT A WEE BIT AWAY FROM JEWS..

    http://ajitvadakayil.blogspot.com/2018/10/second-defeat-for-deep-state-capt-ajit.html

    THE RULING FAMILIES OF AMERICA ARE ALL JEWISH DESCENDANTS OF ROTHSCHILDs DRUG RUNNERS..

    http://ajitvadakayil.blogspot.com/2010/12/dirty-secrets-of-boston-tea-party-capt.html

    MODI / AJIT DOVAL / RAW KNOW SHIT ABOUT WORLD INTRIGUE..

    BHARATMATA IS RACING TO BE THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS.. WITH PEOPLE LIKE JEW DARLING MODI IN CHARGE WHO KNOWS?

    HESS ( NO 2 RANK IN GERMANY ) PARACHUTED TO ENGLAND ONE DAY BEFORE START OF WW2 TO TELL THE BRITISH KING THAT HITLER IS A JEW..

    HE GOT A SHOCK OF HIS LIFE WHEN HE CAME TO KNOW THE HARD WAY, THAT THE BRITISH ROYALTY ARE OF GERMAN JEW BLOOD-- THAT CHURCHILL/ ROOSEVELT/ STALIN/ EISENHOWER ARE ALL JEWS.

    http://ajitvadakayil.blogspot.com/2011/11/rudolf-hess-honourable-man-who-was.html

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. had posted this on the previous comments section but i guess because of the limit it didn't get published. Was escobar himself initially a cia asset in the cocaine for guns scheme they implemented to accrue funds for intelligence related activities who later went rogue? the turn of events seems to suggest this based on what we get to see in Narcos. have to say though as an aside that while i have no intention to support or feel that a criminal drug peddler deserves any support from us, the scene in Narcos mexico where felix approaches the real el padrino is one that can give goosebumps. The real el padrino in front of the fake pretender talking beside a pool with a pet hippopotamus in it.

      Delete
  2. THE CONSPIRACY OF JEWISH DEEP STATE DARLINGS MODI/ AMIT SHAH/ PRASAD/ JAVEDEKAR/ CHANDRACHUD/ NARIMAN / BJP/ RSS -- TO BRING DOWN LOFTY SANATANA DHARMA TO THE LOW LEVEL OF INVADER RELIGIONS WILL NOT WORK...

    SABARIMALA IS A PILGRIMAGE , THIS PLANETs LARGEST -- NOT A WEE BOHRA/ PARSI TEMPLE..

    http://ajitvadakayil.blogspot.com/2018/11/5900-year-old-sabarimala-this-planets.html

    HINDUS HAVE WOKEN UP..

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENTS IN WEBSITES OF--

      PM MODI
      PMO
      AJIT DOVAL
      RAW
      NIA
      ED
      IB
      CBI
      AMIT SHAH
      HOME MINISTRY
      NEW CJI
      GOGOI
      ALL SUPREME COURT JUDGES
      ATTORNEY GENERAL
      ALL HIGH COURT CHIEF JUSTICES
      ALL SUPREME COURT LAWYERS
      CMs OF ALL INDIAN STATES
      DGPs OF ALL STATES
      GOVERNORS OF ALL STATES
      PRESIDENT OF INDIA
      VP OF INDIA
      SPEAKER LOK SABHA
      SPEAKER RAJYA SABHA
      DAVID FRAWLEY
      STEPHEN KNAPP
      WILLIAM DALRYMPLE
      KONRAED ELST
      FRANCOIS GAUTIER
      DEFENCE MINISTER - MINISTRY
      ALL THREE ARMED FORCE CHIEFS.
      RAJEEV CHANDRASHEKHAR
      MOHANDAS PAI
      SURESH GOPI
      MOHANLAL
      ALL CONGRESS SPOKESMEN
      RAHUL GANDHI
      SONIA GANDHI
      PRIYANKA VADRA
      SHASHI THAROOR
      ARUNDHATI ROY
      NITI AYOG
      AMITABH KANT
      ROMILA THAPAR
      RAM MADHAV
      RAJ THACKREY
      UDDHAV THACKREY
      VIVEK OBEROI
      GAUTAM GAMBHIR
      ASHOK PANDIT
      ANUPAM KHER
      KANGANA RANAUT
      VIVEK AGNIHOTRI
      KIRON KHER
      MEENAKSHI LEKHI
      SMRITI IRANI
      PRASOON JOSHI
      MADHUR BHANDARKAR
      SWAPAN DASGUPTA
      SONAL MANSINGH
      MADHU KISHWAR
      SUDHIR CHAUDHARY
      GEN GD BAKSHI
      SAMBIT PATRA
      RSN SINGH
      SWAMY
      RAJIV MALHOTRA
      SADGURU JAGGI VASUDEV
      SRI SRI RAVISHANKAR
      BABA RAMDEV
      RSS
      VHP
      AVBP
      THE QUINT
      THE SCROLL
      THE WIRE
      THE PRINT
      MK VENU
      MADHU TREHAN
      CLOSET COMMIE ARNAB GOSWMI
      RAJDEEP SARDESAI
      PAAGALIKA GHOSE
      NAVIKA KUMAR
      ANAND NARASIMHAN
      SRINIVASAN JAIN
      SONAL MEHROTRA KAPOOR
      VIKRAM CHANDRA
      NIDHI RAZDAN
      FAYE DSOUZA
      RAVISH KUMAR
      PRANNOY JAMES ROY
      AROON PURIE
      VINEET JAIN
      RAGHAV BAHL
      SEEMA CHISTI
      DILEEP PADGOANKAR
      VIR SANGHVI
      KARAN THAPAR
      PRITISH NANDI
      SHEKHAR GUPTA
      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      NCW
      REKHA SHARMA
      SWATI MALLIWAL
      CHETAN BHAGAT
      DEVDUTT PATTANAIK
      AMISH TRIPATI
      KUNHALIKUTTY
      ASHISH NANDI
      PAVAN VARMA
      RAMACHANDRA GUHA
      JOHN DAYAL
      KANCHA ILIAH
      RAHUL EASHWAR
      JANAM TV
      MARUNADAN TV
      CM PINARAYI VIJAYAN
      ALL KERALA MINISTERS
      ALL KERALA MLAs
      ALL KERALA COLLECTORS
      DGP BEHERA
      INDU MALHOTRA
      ROHINGTON NARIMAN
      CHANDRACHUD
      KHANWILKAR
      TOM VADAKKAN
      GOVERNOR OF KERALA
      SRIDHARAN PILLAI
      PARASARAN
      SAI DEEPAK
      VIDYASAGAR GURUMURTHY
      I&B DEPT/ MINISTER
      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. dear captain, have deleted my old twitter account due to twitter blocking messages, have instead created a new account to spread the messages, below a few of verified tweets, need to click on show more replies for some tweets

      https://twitter.com/SwarajyaMag/status/1194965536465010688

      https://twitter.com/upword_/status/1195005875879997440

      for readers who's messages are being blocked, use screenshots, do not type vadakayil in the tweet, also use bitly to shorten the blogsite's url so that it cannot be picked up by twitter's algorithms

      Delete
    3. https://twitter.com/shree1082002/status/1196107634828431361

      Delete
    4. Your Registration Number is : PMOPG/E/2019/0662837

      Delete
    5. https://mobile.twitter.com/punithdg619/status/1196263704485982210

      Delete
    6. pranam captain,

      tweets with screenshots spaced over time to avoid account block (twitter aggressive tweeting algorithm probably)

      https://twitter.com/prashantjani777/status/1196332084371099648
      https://twitter.com/prashantjani777/status/1196408726724448261
      https://twitter.com/prashantjani777/status/1196413077845090305
      https://twitter.com/prashantjani777/status/1196414552776347648

      Delete
  3. Sirrr I posted in ur previous blog, but my comment didn't go through as I didn't realize it had already hit 200 post comments.

    It's really nice to see u back..really thought something happened to u.. Feel relieved now. Good to see back, Back stronger, hope u had a good rest sir.. Was feeling empty this past one month without ur presence albeit if its only hearing from u through ur blogs. Good to hear u speak to the world again and guide us again. May God always keep u in best shape and take care of ur health.

    I didn't get to wish all Hindu readers, Happy Deepavali everyone, belatedly. May this 2019 festive celebration and coming of Sir after a long break helps all the readers ease their issues/problems/sufferings and make the world a better place. Wish everyone peace and happiness always.

    ReplyDelete
  4. Dear Captain, suddenly the SC has passed direction to explore the option of hydrogen fuel cell tech for all vehicles. Seems they are not happy with gasoline or CNG and want a big change from imminent flood of Electric vehicles in India.

    What is your take on this Hydrogen fuel cell tech for all vehicles? Is it good for all or big good for nothing.

    Glad you heard the prayers of all Vadakayil readers and are back with a bang.

    ReplyDelete
    Replies
    1. https://www.youtube.com/watch?v=gmtF-yPVUls

      Instead of storing electricity in a battery, you make it as you go. In a fuel cell, hydrogen (stored as compressed gas) is stripped of its electrons, then combined with oxygen. The resulting electricity powers a motor (and in turn the wheels), and water, the only byproduct, drips out a tail pipe

      Delete
  5. RAZOR CLARIFICATION: EVERY INDIAN HERE NOTE THIS, DUE TO BIG FIGHT THAT GOT ERUPTED AND HOW EUROPEANS DECEIVE, AND ARE PRETENDERS. I WILL CLARIFY HOW ABCINDIAGOGO WAS TRICKED AS WELL.

    Please allow this comment because of big fight that happened, I want people to know what I have been through honestly.

    There was a big fight with this European guy who is somewhere from Eastern Europe, and went on for weeks. So what happened?

    I am an Indian guy, and have been here for the past 5 years, until for about 4 weeks my account got hacked. How did it start? He talked about Vimanas and obsession, I will tell you why.

    I am an Indian guy, and always was until for 4 weeks, where my account was hacked? How did it start?

    I am the only Indian who fights for India on Facebook. I was on a page called Occult Science Page on FB. One guy made a article about how Vimana Scripts are false. I said, that is European propaganda. Fight started escalating.

    I posted a German Library Stolen Scriptures pic found here:

    https://defence.pk/pdf/threads/help-recover-the-rare-text-on-vedic-phonology-from-germany.236826/

    For a while, there was pin drop silence after I put those photos. See how European mind works? It is lowest level mind. After a while, one guy decided to take a stab and said "designated shitting streets", I replied Europeans have "designated animal fucking streets" and Euroepans started gaming up. Things got really heated up.

    NO EUROPEAN CAN FIGHT ALONE, HE WILL RUN AWAY WHEN CONFRONTED. But will take the enemy by ganging up and write "ONE EUROPEAN GUY DID IT ALL". Pretentious, fake lie and deceitful mind.

    Because I am developing my game and I post pictures of my game related stuff on FB, my email address was also open. Somebody decided to have a go, and diverted my email address, and I got locked out.

    Remember, how I had asked abc email address, he got his address. Now for about three weeks, After having gone through security check where I had to wait 3 weeks for my security change info, I got my account back.

    So the deal with parents and all that is the same, I am the same Indian guy, now. For the fight and abuse was not started by me.

    You were exactly away, security info change to take place took 3 weeks. By the way, my game has substantially changed. I will showcase it, I want Indians to know how history is manipulated. Europeans are all pretenders, they pretend and lie without any shame.

    ReplyDelete
  6. Also, one last thing. Lots of people know me, but I don't know my enemies, because of being too open on FB and I am the only Indian who writes about how advanced great Inida was. it doesn't get much likes at all.

    But if someone writes, on how shit India is, it will get 40-50 likes. This is called pretentious inferiority. Euroepans will pretend as if they have not seen any great Indian status, but will quickly take notice of shit Indian news and spread it like wildfire.

    ReplyDelete
    Replies
    1. NOBODY BELIEVES YOU..

      WHAT IS YOUR REAL NAME ?

      Delete
    2. Ask abcindiagogo, as I posted him screenshots. Why would I lie? Yes, I put a lot of stuff open for my game, lots of people know me because I am the only developer who does 3D Modelling, programming, technical art, design, video editing, etc and I just graduated out of Uni 2 years ago. People are shocked as I have experience of 50 year olds, being 25.

      Captain if you don't believe me, then find out yourself, because you can. I have given you more than enough information about myself. I can't name myself openly sorry, because, too many people know me, but I do not know who knows me, and I do not know with what intentions they can come.





      Delete
    3. And secondly, abcindiagogo knows me but not my name. Neither has he nor me ever have exposed our names because he wants it that way for the good.

      But if you wish, I can post screenshots. But I cannot reveal my name, and I will not reveal my name because I develop my game for over 16 hrs/day.

      Everything has calmed down right now, I don't want more noise as my game's graphics, animations, everything has taken a significant leap comparable to multi million modern day games.

      Delete
    4. I have realized again and again, it is all about THOUGHT PROCESS, "HOW YOU THINK" executes two things:

      -> The time it requires to get the job done
      -> The quality of the work produced

      It is definitely possible to produce high quality work simply by changing the thought process. Example to build a building for just person may seem a tremendous task, but the power of freedom and execution of thought makes it way easier for one person to achieve it than a team.

      How can one person do so much, the reason is because, everything is a possibility of one thing which is what makes the thing unified.

      Like say you like apple, but I don't These are not two different things, these are simply two possibilities. I have understood Vedanta through your blogs, specially Erwin Schrodinger "consciousness is singular"

      Delete
    5. RAZOR

      TIME FOR YOUR BULLSHIT IS OVER

      WHAT IS YOUR REAL NAME?

      WHICH PLACE IN INDIA ARE YOUR FROM?

      THIS BLOGSITE PLACES BOTH YOU AND ABCINDIAGOGO IN THE SAME BOAT..

      LAST WARNING!

      Delete
    6. Stop, I am Indian but I stay in Oceania.
      Why would I bullshit you? I am your loyalest follower, when you were gone, I was afraid and thought you were gone or stopped blogging, but found you on Times Of India.

      I am not bull-shiting, you are not in Oceania shithole, you were in probably higher evolved countries like USA/ Singapore. I am the lowest level cerebal in the white world aka white niggers.

      Delete
    7. Razor bro is saying lot of people knows him because he is 3D modeller, pyaz, pakoda,lahsan etc. But not telling name , city etc and st same time is very desperate to comment in this blog. He has clearly exposed himself but I cannot find a word for these type of creature in any dictionary. Kuch fitt na baith ra ispe . Ye h kya cheez

      Delete
    8. RAZOR IS SPAMMED

      HE STAYS IN OCEANA - TEE HEEEEEE

      Delete
  7. Mega machines

    https://youtu.be/GN6n-JSbfBo

    ReplyDelete
  8. Your Registration Number is : PMOPG/E/2019/0662185

    ReplyDelete
  9. North Indian channel India TV of foolish rajat Sharma is calling ayyapa born of Shiva and Vishnu.

    ReplyDelete
    Replies
    1. AYYAPPA IS HUMAN

      VISHNU AND SHIVA ARE COSMIC ALLEGORIES..

      SAMUDRA MANTHAN EPISODE HAPPENED DOZENS OF MILLENNIUMS BEFORE BIRTH OF AYYAPPA 5900 YEARS AGO..

      Delete
    2. I will put this in my game, with a quote that is found in textbook that clearly says something similar like this "Indian gods are personification of forces of nature to make understanding of nature simpler"

      Delete
    3. https://twitter.com/kannanlp/status/1196124932201037824?s=19

      Delete
  10. Of the track Sir...

    Of all the places you visited,which coffee did like you like the most...

    I tried so many flavors recently ranging from Nicaragua / Colombia / Equador / Maimi etc...But found "Monsoon Malabar" to be the tastiest...Its remarkable how every thing which is Indian turns out to be naturally balanced...
    Please share your thoughts.
    Regards
    Rakesh

    ReplyDelete
  11. Hello Everyone,

    I have downloaded all of Captain's blog (including all "Load-More" comments as well) in PDF-format and segregated them into 2 types of folders.
    ----------------------

    1) CAV_All_Blogs :---
    This folder has all the blogs stored individually in the monthly-format of all years like how see on the blog-now. This is done for easy record-keeping of which blog was published when.

    2) CAV_MERGED :---
    This folder has all the blogs Merged together yearwise. For example all blogs of 2010 are merged into just one PDF-file. In some cases this was not-possible hence there are part-1, part-2, etc. The reason for this is because it makes it easier to search for any topic because you only have to go through say 10 files, whereas in regular-format we would have to go through 100s of individual blogs which is too difficult & time consuming.
    ----------------------


    WARNING:---

    -> Both the folders have a size of approx. 4.1GB (four-point-one GB) EACH, so download ONLY if you have more than enough monthly-data-balance remaining.
    -> This means you will use 4.1 GB (four-point-one GB) data for downloading ANY ONE of the above folders.
    -> If you want to download BOTH then you will end up using approx. 8.2GB (eight-point-two GB) data.
    ----------------------


    NOTE:---

    -> In the CAV_Merged folder, the "2019_CAV_Blogs_Part-2" PDF is incomplete. It does NOT have the last-3 blogs from October-2019 and the current blogs of November-2019. This is because comments-section is not fully utilized so I will only be downloading the individual blogs which can be found in CAV_All_Blogs. Once the comments sections are fully used-up, then I will merge it with the Part-2 PDF later on.

    -> There are two-PDFs of Captain's profile views in "CAV_All_Blogs" one from October-30, one from November-14.

    -> I have no selfish/evil/hidden-intention/agenda/etc. for downloading & hosting the copy of these blogs. I am doing this because I wanted to have a full copy of Captain's blogs so that there is a backup in case it is deleted/blocked by Google or Indian-Govt in future. I hope everyone downloads these folders so that there are multiple copies with readers too.
    ----------------------


    INSTRUCTIONS:---

    1. Place mouse-cursor on top of folder-icon
    2. Click on the Arrow-icon to begin download
    3. OR, on top-right-side of page, you can click on "Download-All" button.
    ----------------------


    LINK:---

    -> "CAV_All_Blogs"
    https://drive.google.com/open?id=1vHUqMQEIdvaI39_iItl_XL20H897t1VU

    -> "CAV_Merged"
    https://drive.google.com/open?id=1N6QW-Km1BL5HJcELbKeAtfz7i4UJHND-
    ----------------------

    ReplyDelete
    Replies
    1. Thank god you are here, note what I wrote above and please explain it to him. Perfect timing. You can prove right here that we are separate.

      Delete
    2. And see, abc is patriotic just as how I am. While he writes tonnes of stuff about how to improve stuff he has no control over it, I have control over what I do (crating a game about India's suppressed past with tremendous amount of effort put in by only 1 person) but don't have control over people.

      How sad.

      Delete
    3. Razor,
      We don't need to know about anyone or anything about you or abc. We come here for captain and his blogs and revelations and try to do our bit.
      Everyone who is transparent in their identity respects,loves,admires captain and is motivated by this blog.
      We are here for strength and honor.... If you want make a statement make it through your games... Don't beat your drum here about how superior you are to people around you. Stop flashing your wee torch before the sun himself. Prove your worth through your game development rather than proving who you are why we or captain should even listen to your yapping.....

      Delete
    4. RAZOR IS SPAMMED..

      WE DONT WANT HIM HERE

      HE IS IN OCEANA, RIGHT?

      BRAAAYYYYYYYYYYYYYYYYYYYY

      Delete
  12. The All India Muslim Personal Law Board will be filing a review petition against the Supreme Court verdict on the Ayodhya dispute.

    ReplyDelete
  13. JNU is a country within a country with its own laws.

    Who will bell this cat? Certainly not our JNU alumnus finance minister...

    ReplyDelete
    Replies
    1. JNU IS CONTROLLED BY THE JEWISH DEEP STATE

      AL JAZEERA MAY APPEAR TO BE A ISLAMIC TV CHANNEL- BUT IS IT CONTROLLED BY JEWS..

      Delete
    2. Just fire a missile on that JNU. at jadavpur type unis also put some emphasis on studies. here in jnu we have 40 year ols students enrolled for undergrad BA courses

      Delete
  14. https://www.google.com/amp/s/m.timesofindia.com/india/in-a-first-maoists-use-drones-over-crpf-camp-in-bastar-shoot-at-sight-orders-issued/amp_articleshow/72094839.cms

    ReplyDelete
  15. captain,

    have you watched the movie Gemini.Man directed by Ang Lee ? an interesting movie in which a human fights against his clone.

    ReplyDelete
  16. Lucknow's real name was Lakshmanapura.

    Original city was centered at Lakshman Tila.

    According to Skanda Purana, it was built by Rama's brother Lakshmana himself.

    Aurangzeb destroyed the settlement/Vishnu temple and built Tilewali Masjid on its ruins

    https://twitter.com/TIinExile/status/1195992802988974080?s=20

    ReplyDelete
  17. Captain what do you think of Uniform Civil Code and Citizenship amendment bill which are supposedly the main agendas for the upcoming parliament session?

    ReplyDelete
  18. Captain, RSS must be rightly called as Rothschild Sewak Sangh. Now they are planning to sell BPCL to Rothschild company.

    ReplyDelete
  19. Dear Captain,

    How do you prevent Dysentery when you're in such trip?
    and also post treatment please

    My one such holiday got screwed up although I was on mineral water bottles.

    ReplyDelete
  20. Dear captain,

    The suicide note of IIT student Fathima circulating in social media looks tampered to me. Its like someone added a death note to a simple diary entry of a homesick girl . I have underlined the girly writing in following tweet.

    https://twitter.com/sangeethikaraj/status/1196242642830422016

    "I never realised that I would miss my home so much. I abhor this place. How I yearn for my home, to be suspended in that delicious inertia like an interminable sleep from which I can never be roused"

    This is simple homesickness of a girl staying in hostel for first time. I used to write this sort of stuff in my hostel days. After this "In case of my death, blame will be on" does not follow logically. It is a disconnected sentence.
    The sentences mentioning death at the beginning and end are different in style from middle portion on homesickness . Who uses words like "nuncupative" ?

    Fathima's notes should be analyzed by forensic linguists. Anyone could edit her phone after death if she had not locked it. Someone might have tried to divert the investigation towards teachers knowing that the girl was upset about teachers.


    ReplyDelete
    Replies
    1. https://www.theweek.in/news/india/2019/11/13/iit-madras-students-suicide-family-members-blame-faculty-kerala-cm-offers-to-help.html

      HER FATHER IS A LAIR

      ################## LATHEEF SAID HIS DAUGHTER HAD ACQUIRED FIRST RANK IN THE ALL INDIA IIT ENTRANCE EXAMINATION.

      I HEARD THIS FELLOW LYING ON MALAYALAM TV THAT SHE WAS A TOPPER IN HSE..

      WHAT IS THIS HSE ???

      MANY FOREIGN FUNDED NGOs ARE ACTIVE IN IIT MADRAS FOR THE PAST SIX YEARS, EVER SINCE MODI BECAME PM.. THEY HAVE THE SUPPORT OF DMK.

      THESE NGOs DO PROPAGANDA THAT DALIT AND QUOTA STUDENTS ARE HARASSED BY HINDU PROFESSORS..

      THESE QUOTA STUDENTS PLAIGIARISE THEIR PROJECTS ( STEAL FROM INTERNET ) .. AND WHEN CAUGHT CLAIM HARASSMENT..

      IIT ALL INDIA TOPPER WANTS TO STUDY SOME BULLSHIT SOCIAL SCIENCES ?

      CHAKKAR KYA HAI?

      BOTHER HER FATHER AND MOTHER ARE LIARS..

      WE ACCEPT THAT THEY HAVE EVERY RIGHT TO LASH OUT IN ALL DIRECTIONS.. BEING EXTREMELY DISTRAUGHT..

      Delete
  21. https://www.zerohedge.com/energy/fukushima-will-be-reincarnated-27-billion-wind-and-solar-energy-hub

    Radioactive farmland to harvest wind n sun.
    There must be a way to neutralise this radiation using a small amount of radioactive material to react and both turn to inert material? Wishful thinking on my part.

    ReplyDelete
  22. https://www.thehindu.com/news/international/un-calls-for-talks-to-end-bolivia-crisis-as-death-toll-rises/article30004163.ece

    Protests by pro-Morales supporters rock bolivia

    ReplyDelete
    Replies
    1. TRUMP HAS SUPPORTED THE COUP IN BOLIVIA..

      LOOKS LIKE TRUMP IS PART OF THE SWAMP ( DEEP STATE )..

      WIDELY REGARDED AS THE COUNTRY'S FIRST PRESIDENT TO COME FROM THE INDIGENOUS POPULATION, EVO MORLESs ADMINISTRATION HAS FOCUSED ON THE IMPLEMENTATION OF PATRIOTIC POLICIES, POVERTY REDUCTION, AND COMBATING THE INFLUENCE OF THE JEWISH DEEP STATE AND THE JEWISH OLIGARCHS .

      DURING THE 1938-1940 IMMIGRATION WAVE, JEWISH REFUGEES RECEIVED HELP FROM THE GERMAN JEWISH BUSINESSMAN MORITZ HOCHSCHILD WHO WAS A FRONT FOR ROTHSCHILD’S INVESTMENTS IN BOLIVIA.

      UNDER EVO MORALE THE BOLIVIA’S ECONOMY BOOMED.. EVEN AS OTHER RESOURCE-DRIVEN ECONOMIES LIKE VENEZUELA AND BRAZIL WITH MAJOR SEA PORTS FALTERED. MORALES’ GOVERNMENT RAN BUDGET SURPLUSES DURING THE GOOD YEARS — 2006 TO 2014 — AND USED THE CASH FLOW TO PAY DOWN PUBLIC SECTOR DEBT AND BUILD UP ITS INTERNATIONAL RESERVES, PROVIDING IT A GOOD BUFFER SINCE THEN.

      GDP PER CAPITA GREW 4 FOLD MORALES’S TENURE, REACHING A RECORD $4030 IN 2017. HE JUST PREVENTED THE JEWS FROM STEALING

      ROTHSCHILD’S AGENTS IN BOLIVIA FOR STEALING MINERALS INCLUDED JEW SIMÓN ITURRI PATIÑO AND JEW CARLOS VÍCTOR ARAMAYO,

      METALLGESELLSCHAFT IN GERMANY WAS OWNED BY JEW ROTHSCHILD.

      JEW GERMÁN BUSCH PRESIDENT OF BOLIVIA BETWEEN 1937 AND 1939—HE ENSURED THAT THE JEWSIH TENTACLES OF THE JEWSIH OLIGARCHS TOOK A VICE GRIP OVER BOLIVIA.

      JEW ALBERTO NATUSCH BUSCH ( NEPHEW ) WAS A BOLIVIAN GENERAL AND DICTATOR IN 1979.

      EVO MORALES SUPPORTED MADURO, WHO HAS PREVENTED JEWS FROM STEALING.. FOR THIS HE WAS REMOVED BY A USA/ ISRAEL SPONSORED COUP AND REPLACED BY A DEEP STATE PUPPET JEWESS

      EVO MORAES CHUCKED OUT JEWISH NGOs ..

      “WE DON’T NEED NGOS USING SOCIAL AND ENVIRONMENTAL MOVEMENTS TO CREATE OPPOSITION AND CONSPIRE,” EVO MORALES TOLD REPORTERS IN 2015.

      MORALES WAS BORN TO A POOR FAMILY OF LLAMA HERDERS IN 1959, AT A TIME WHEN INDIGENOUS PEOPLE WERE DOUSED WITH PESTICIDES WHEN ENTERING GOVERNMENT BUILDINGS. SUCH WAS THE ATROCITIES OF THE JEWISH OLIGARCHS.

      EVO MORALES PROMISED NOTHING LESS THAN A COSMIC REBALANCING. “WE WILL END THE COLONIAL STATE AND THE NEOLIBERAL MODEL,” HE VOWED. “FIVE HUNDRED YEARS OF RESISTANCE BY THE INDIGENOUS PEOPLES OF AMERICA ARE OVER.”

      MORE THAN HALF OF THE NATIONAL ASSEMBLY WERE WOMEN, MANY OF THEM INDIGENOUS, WHO WORE JAGUAR SKINS AND FLOWING POLLERA SKIRTS WITH NEWFOUND PRIDE.

      WE ASK AJIT DOVAL..

      READ THE POST BELOW-- UNDERSTAND WORLD INTRIGUE AND ADVISE GULLIBLE FELLOW MODI..

      https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

      AJIT DOVAL-- YOUR JOB IS NOT TO PROTECT INDIA FROM PAKISTAN-- BUT ALSO FROM JEWISH DEEP STATE TO WHOSE TUNE NAIVE MODI IS DANCING TODAY.

      BY THE WAY--AJIT DOVAL I CAN WRITE A BOOK ABOUT YOUR STAY AS AN AGENT DEEP INSIDE PAKISTAN.. I CAN EVEN DESCRIBE YOUR CIRCUMCISED WEE WILY.

      capt ajit vadakayil
      ..


      Delete
    2. Trump was part of the swamp ..... he mentioned it in one of his campaigning ..... but said that he has changed his ways ..... the lady in the audio which i had commented earlier, said this.

      Delete
  23. https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

    SPREAD THE LINK ABOVE BY WHATS-APP...

    WE ASK AJIT DOVAL..DO YOUR JOB ...

    UNDERSTAND WORLD INTRIGUE AND ADVISE GULLIBLE FELLOW MODI..

    AJIT DOVAL-- YOUR JOB IS NOT TO PROTECT INDIA FROM PAKISTAN-- BUT ALSO FROM JEWISH DEEP STATE TO WHOSE TUNE NAIVE MODI IS DANCING TODAY.

    BY THE WAY--AJIT DOVAL I CAN WRITE A BOOK ABOUT YOUR STAY AS AN AGENT DEEP INSIDE PAKISTAN.. I CAN EVEN DESCRIBE YOUR CIRCUMCISED WEE WILY.

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      AJIT DOVAL
      RAW
      CBI
      IB
      NIA
      ED
      PMO
      PM MODI
      EXTERNAL AFFAIRS MINISTER/ MINISTRY
      AMBASSADORS TO FROM BOLIVIA- VENEZUELA/ INDIA..
      AMIT SHAH
      HOME MINISTRY
      ALL DGPs OF INDIA
      DEFENCE MINISTER / MINISTRY
      RBI
      RBI GOVERNOR
      INDIAN AMBASSADORS TO ALL SOUTH AMERICAN AND CENTRAL AMERICAN NATIONS
      NEW CJI BOBDE
      ALL SUPREME COURT JUDGES
      ATTORNEY GENERAL
      PRESIDENT OF INDIA
      VP OF INDIA
      CMs OF ALL INDIAN STATES
      GOVERNORS OF ALL STATES
      DAVID FRAWLEY
      STEPHEN KNAPP
      WILLIAM DALRYMPLE
      KONRAED ELST
      FRANCOIS GAUTIER
      DEFENCE MINISTER - MINISTRY
      ALL THREE ARMED FORCE CHIEFS.
      NITI AYOG
      AMITABH KANT
      SADGURU JAGGI VASUDEV
      SRI SRI RAVISHANKAR
      BABA RAMDEV
      THE QUINT
      THE SCROLL
      THE WIRE
      THE PRINT
      MK VENU
      MADHU TREHAN
      CLOSET COMMIE ARNAB GOSWMI
      RAJDEEP SARDESAI
      PAAGALIKA GHOSE
      NAVIKA KUMAR
      ANAND NARASIMHAN
      SRINIVASAN JAIN
      SONAL MEHROTRA KAPOOR
      VIKRAM CHANDRA
      NIDHI RAZDAN
      FAYE DSOUZA
      RAVISH KUMAR
      PRANNOY JAMES ROY
      AROON PURIE
      VINEET JAIN
      RAGHAV BAHL
      SEEMA CHISTI
      DILEEP PADGOANKAR
      VIR SANGHVI
      KARAN THAPAR
      PRITISH NANDI
      SHEKHAR GUPTA
      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      NCW
      REKHA SHARMA
      SWATI MALLIWAL
      CHETAN BHAGAT
      DEVDUTT PATTANAIK
      AMISH TRIPATI
      PAVAN VARMA
      RAMACHANDRA GUHA
      I&B DEPT/ MINISTER
      LAW MINISTER/ MINISTRY
      SHASHI THAROOR
      ARUNDHATI ROY
      VIVEK OBEROI
      GAUTAM GAMBHIR
      ASHOK PANDIT
      ANUPAM KHER
      KANGANA RANAUT
      VIVEK AGNIHOTRI
      KIRON KHER
      MEENAKSHI LEKHI
      SMRITI IRANI
      PRASOON JOSHI
      MADHUR BHANDARKAR
      SWAPAN DASGUPTA
      SONAL MANSINGH
      MADHU KISHWAR
      SUDHIR CHAUDHARY
      GEN GD BAKSHI
      SAMBIT PATRA
      RSN SINGH
      SWAMY
      RAJIV MALHOTRA
      RSS
      VHP
      AVBP

      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. https://mobile.twitter.com/Chn_123/status/1196322321948016640
      Sent direct messages to IB and amit shah from their websites

      Delete
    3. Sir, sent to --

      https://twitter.com/tuki00108/status/1196366119591800832

      @narendramodi @HMOIndia @PMOIndia @Ajit_Doval @dir_ed @RAWHeadOffice @NIA_India @nib_india @Vikram_Sood @rawnksood @SpokespersonMoD @DefenceMinIndia @rajnathsingh @AmitShah

      @easterncomd @NorthernComd_IA @SpokespersonMoD @DefenceMinIndia @adgpi @IAF_MCC @indiannavy @HQ_IDS_India @crpfindia @BSF_India @Whiteknight_IA @ChinarcorpsIA

      @CPMumbaiPolice @CPDelhi @CPBlr @DgpPradesh @D_Roopa_IPS @dgpup @dgpcidkarnataka @dgp_ap @DGP_MP @TelanganaDGP @DGPPunjabPolice @IPS_Association @AddlCPTraffic @AddlCPWest @smittal_ips @TheKeralaPolice @PoliceTamilnadu @DGP_FIRE @DGPOdisha @dgpgujarat @DGPMaharashtra @bihar_police @assampolice @knagarajips @CG_Police @DelhiPolice @DGP_Goa @JmuKmrPolice @cpkarimnagar @rama_rajeswari @DevenBhartiIPS @RSPraveenSwaero @IGWomenSafety @adgzonekanpur @adgzonelucknow @adgzonevaranasi

      Posted blog link to embassy from /to Brazil,Bolivia,Columbia,Canada,Peru,Argentina and Mexico on Facebook and twitter.

      Delete
    4. https://twitter.com/prashantjani777/status/1197023525296574464
      https://twitter.com/prashantjani777/status/1196800552572792838
      https://twitter.com/prashantjani777/status/1196786563503579136

      twitter blocked my account again and had to wait for a day inspite of using screenshots/tweeting over time instead of at once. not sure how they do it. possibly usage of words "deep state" or some other trigger.

      Delete
  24. Dear Sir, you stated as below
    "AMONG CRIMINAL MINDS SHE IS NO 1 , SINCE HISTORY OF MAN BEGAN 65 MILLION YEARS AGO.."

    65 million, that's a smoking gun, really would like to put some more light on this.

    Regards sachin

    ReplyDelete
    Replies
    1. http://ajitvadakayil.blogspot.com/2019/07/shiva-lingam-meterorite-hit-65-million.html

      Delete
  25. https://timesofindia.indiatimes.com/city/varanasi/bhu-professor-i-am-a-muslim-why-cant-i-teach-sanskrit/articleshow/72092216.cms

    SANSKRIT IS STEEPED IN HINDU SPIRITUALITY... IT IS BEYOND THE WHEREWITHAL OF A MUSLIM...

    WE ALL KNOW WHAT JEWESS LEELA SAMSON DID TO INDIAN CLASSICAL DANCE WHEN SHE WAS MADE CHAIRMAN OF SANGEET NATAK AKADEMI ....SHE MURDERED KATHAKALI...

    http://ajitvadakayil.blogspot.com/2018/10/sanatana-dharma-hinduism-exhumed-and.html

    ReplyDelete
    Replies
    1. Captain, a most foul thing was done by Toilet-Paper yesterday in their Sunday-edition (17-11-2019). On Page-8 which was the left-side-page(IIRC), they had two-articles placed in the same-section (right-side of paper). The two-articles were (IIRC):---

      1) "In this Sanskrit school in Rajasthan, 80% of the students are muslim"
      2) "In India's only Urdu-university, 40% of the staff do not even know the language"

      The articles were placed in exact order as above. This was deliberately done to invoke communal-hatred by attempting to show that in India the muslims are "good-hearted people" who will learn Sanskrit but hindus are "intolerant" because they do not even allow the proper administration of the sole urdu-university.

      To be honest, Ministry-of-I&B should have jailed the owners-&-editors of Toilet-paper and filed multiple-cases against them just for yesterday's disgusting attempt to create communal-tensions-&-hatred.

      Delete
  26. Captain,

    Last week I went on a tour of Madurai, rameswaram, Thanjavur, Vellore, Tirupati

    Life is so simple and peaceful in South Indian states compare to North

    People are more discipline and everything is cheaper than north

    Temples gives great peace of mind compare to North Indian marble temples

    Also, I found Jagan Reddy promoting Christianity at great speed.... Found some staff members of Tirupati board eating nonveg in tirumala where it is strictly prohibited, many Christians are working in tirumala temple

    ReplyDelete
  27. https://twitter.com/realDonaldTrump/status/1196080086686011398?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Etweet

    SLEEPY JOE WITH A TENT AT THE CROTCH TO POKE SMALL GIRLS.. HE DOES NOT WEAR UNDIES WHEN HE GETS THIS CHANCE

    https://www.youtube.com/watch?v=b3ir0GY5Mr0

    ReplyDelete
  28. Respected Ajit Sir and Suchitra Madam,
    I knew well that by Gods Grace and our prayers ,,,everything would have been good for you and your family.S
    Welcome back to your blog mission.
    I am myself M Tech from IIT and I know and agree whatever Ajit sir had said that Mtechs from IIt are worst so should be discarded.
    Have seen through myself that most of the Mtech students come from Reservation Quota,
    Will do needless , below medicore studies.
    But still want to rectify that not all are dumbass.
    I can understand the above blogpost, with What ever technicalities you have written
    But do you think Sir that even 99 % of your followers will understand even an iota of it.
    For you I will just say that you are way ahead of your time...And alas as it happens with all of the prophets...No one is ready to Listen, Leave alone assimilate or Follow Your messages.
    Still Hoping for the Best.
    Yours Sincerely,
    Samvid

    ReplyDelete
  29. Captain, why does India have so many Government-Services ?

    Please take a look at the sheer number of services. It is mind-boggling ! The sheer amount of bureacracy & effort into simply administering these services must be huge !

    https://en.wikipedia.org/wiki/Civil_Services_of_India#Construction
    https://en.wikipedia.org/wiki/Civil_Services_Examination_(India)#List_of_Services
    https://en.wikipedia.org/wiki/Central_Civil_Services#Central_Services_(Group_A)

    ReplyDelete
    Replies
    1. Captain, you have complained many times about Iodized-Salt. Guess what ? India even has a civil-service called "Indian-Salt-Service" which is "is tasked with several functions including monitoring and quality updation of salt....". Please look at this. It comes under Central-Services-(Group-B).

      https://en.wikipedia.org/wiki/Indian_Salt_Service
      https://en.wikipedia.org/wiki/Central_Civil_Services#Central_Services_(Group_B)

      Delete
    2. IODISED SALT DUMBS DOWN THE POPULATION AND MAKES FEMALES LESS FERTILE..

      http://ajitvadakayil.blogspot.com/2015/10/iodized-salt-infertility-and.html

      Delete
    3. It is shocking that inspite of there being an entire civil-service dedicated to Salt (even though it is a small-service), India is allowing iodized-salt. The Central-Govt must either shut down this service or should punish serving-&-retired personnel of this service for not doing their jobs properly by raising awareness about & banning iodized-salt.

      Delete
    4. is himalayan salt better than sea salt ?

      one ayurvedic shopkeeper gave an argument that due to oceanic pollution and animal remains in the sea, the sea salt is not good, making it non veg(due to living organism dead remains).

      Delete
    5. A SAILOR WITH 40 YEARS EXPERIENCE MUST LEARN FROM A SHOPKEEPER ?

      Delete
    6. Himalayan sea salt is nothing but the sea salt beds that rose upwards into land mass upon collision that happened millions of years ago.

      Common Sea salt/rock salt is also good. Do not pay heed to illiterate shopkeeper.

      Delete
    7. Sorry Sir, did not mean that !!!!!

      Delete
  30. https://timesofindia.indiatimes.com/city/chennai/fans-toil-for-rajinis-blockbuster-entry/articleshow/72100668.cms

    INDIAN FILM STARS PAY A MONTHY SALARY TO THEIR FAN CLUB MEMBERS..

    THAT IS WHY WHEN A KANNADA SUPERSTAR DIED -- MONEY DRIED UP -- AND FANS RAN AMOK..

    ReplyDelete
  31. Dear Capt Ajit sir,
    This woman has helped 13.5 million refugees to have hope by helping to receive rain water and provide basic sanitation,.. https://egyptianstreets.com/2018/12/27/female-architect-invents-refugee-tents-that-collect-rainwater-and-store-solar-energy/

    ReplyDelete
  32. Protests in Iran instigated by Jewish Deep State to remove Khomenei/Rouhani?

    ReplyDelete
  33. Captain, see how foul YouTube deleted this pro-Hindu, pro-India song by listening to complaints from anti-India, anti-Hindu types ! Below is the same music-video uploaded by another person, but YouTube deleted the Original which had 7-million views and counting when I last saw it ! The first-URL is of the copy and the second-URL is of the original-video which no longer exists because youtube deleted it saying it "has been removed for violating YouTube's policy on hate speech" ! You can see youtube's message when you open the second-URL !

    "Hinduon ka Hindustan, Dallo/Mullo jao Pakistan"
    https://www.youtube.com/watch?v=y41rikYwbCg
    https://www.youtube.com/watch?v=lI7qi8jcTpE
    ----------------------

    Captain next time please use these songs for all anti-National anti-Hindu types !
    (Warning: Strong-Language is used!)

    "Desh Drohi ki m ka sda"
    https://www.youtube.com/watch?v=5bNHf_ZYmac

    "Godhra karadenge hum yaad agar bkc karega" ( "We will remind you of Godhra if you do nonsense", directed against anti-Hindu types)
    https://www.youtube.com/watch?v=QKbIRYOeDEY

    ReplyDelete
    Replies
    1. I THINK there is no room for Shit songs like this in santana dharma.

      Delete
    2. The issue is that the pro-India, pro-Hindu people are being penalized/sanctioned/silenced all in the name of being "against hate-speech". Agreed those music-videos are corny, but the message in all of them was clear ---- anti-India, anti-Hindu people must not be in India and create problems for those who are pro-India and pro-Hindu since India is the homeland of Sanatana-Dharma.

      Where in any of these videos were the lyrics threatening anybody ? Did they instigate the Hindus to perform some unprovoked jihad/riots/etc. against non-Hindus ? NO, all they said is that --- don't mess with us patriots & hindus or there will be retribution --- just like how everything in Gujurat was ok until the jihadi-crowd burnt a train-compartment leading to Hindus retaliating causing the Godhra-riots.

      Also all those type of videos are more popular, sophisticated music never is popular. Even in white-nations shitty pop/rock/rap music with meaningless lyrics and debauched videos are more popular than meaningful music-videos.

      Delete
  34. Captain the USA has a Latin motto "E-Pluribus-Unum" which translates to "Of-many, One" in English. Is this a subtle-message by the global-elite headed by Rothschild-family, that the USA is just another superpower created by them to implement their agendas just like how British-empire was created by them ?

    https://en.wikipedia.org/wiki/E_pluribus_unum
    https://en.wikipedia.org/wiki/Great_Seal_of_the_United_States

    ReplyDelete
  35. Captain, see this interesting way of leftist anti-India agenda. This guy has created a character called "bhakt-bannerjee" and behaves as a stereotypical "bhakt" interviewing the usual leftist types (Ravish, Nobel-Abhijit, Swara, etc.).

    The thing is that through this indirect method, they attempt to portray all pro-India, pro-Hindu, anti-psuedo-Intellectual-&-psuedo-Liberal people as obnoxius, ignorant, hateful people !!! This is most deceitful backhanded method of propaganda using some stereotypes as a front to have a veneer of legitimacy !!!

    https://www.youtube.com/watch?v=FdNYEY3uEiY
    https://www.youtube.com/watch?v=JBA4fWsCj88
    https://www.youtube.com/watch?v=_A9_RgJrrFE

    ReplyDelete
  36. Dear Capt Ajit sir,

    As Netflix ramps up its repertoire of original series, a new show about the Mayan Civilization was announced. It will arrive in 2021.
    https://culturacolectiva.com/movies/netflix-series-inspired-by-ancient-mayan-civilization-releases?fbclid=IwAR2yL_xNCSnawghJQABtPgLicPuLWdOaF9_HpXHfIazJFPFgz8dRp_Qvzg8

    ReplyDelete
    Replies

    1. NETFLIX HAS TURNED OUT TO BE A JEWISH DEEP STATE PROPAGANDA MACHINE LIKE HISTORY CHANNEL TV OR WIKIPEDIA..

      PABLO EXCOBAR WAS IMPRISONED IN THE "CATHEDRAL" BY THE COLOMBIAN GOVT.. THE ENTIRE WORLD WENT HOOO HAAAA -- HOW A CRIMIMAL WAS PUT IN A LUXURY JAIL..

      SORRY-- IT WAS NOT A LUXURY JAIL, BUT AN ISOLATED SANATORIUM HIGH UP THE MOUNTAINS , BOUGHT BY PABLO AND DONATED TO THE COLOMBIAN GOVT TO BE USED AS A PRISON FOR HIM..

      https://www.youtube.com/watch?v=bXUcnFuvqcE&feature=emb_logo

      WHY POINT FINGERS AT PABLO? AT LEAST HE REMAINED INSIDE THE LUXURY PRISON..

      I CAN WRITE SEPARATE POSTS ABOUT DOZENS OF ROTHSCHILDs AGENTS WHO WERE PUT IN SUCH "LUXURY JAILS"-- AND SOME WHO SPEND THEIR PRISON TERMS TRAVELLING ALL OVER THE WORLD IN LUXURY ON ROTHSCHILDs EXPENSE..

      TAKE THE CASE OF INDIAs NO 1 PATRIOT AS PER MY NCERT HISTORY BOOK ( SIC) LOK MANYA BAL GANGADHARA TILAK..

      TILAK WAS A CHITPAVN JEW AND AN AGENT OF ROTHSCHILD.

      TILAK WAS THROWN INTO PRISON-- BURMAs DEADLY MANDALAY JAIL FROM WHERE NOBODY COMES OUT ALIVE ( TOXIC WATER ).. HIS JAILING FOR 6 YEARS GOT GREAT ROTHSCHILD MEDIA COVERAGE ..

      AFTER 18 MONTHS TILAK RESURFACED BACK IN INDIA..

      ALL WENT RUNNING TO SEE HIM, EXPECTING TO SEE A GHOST TYPE SKELETON..

      INSTEAD WHAT THEY SAW WAS TILAK WITH INCREASED BODY WEIGHT , CHUBBY ROSY CHEEKS.

      TILAK HAS WRITTEN A BOOK IN JAIL ( SIC) NAMED " THE ARCTIC HOME IN THE VEDAS"..

      THIS TRAITOR HAD GIVEN AWAY OUR SANSKRIT AND VEDAS TO THE WHITE SKINNED , BLUE EYED, BLONDE HAIRED MAN..

      TILAK WAS WHISKED AWAY THE SAME NIGHT FROM MANDALAY JAIL TO EUROPE BY ROTHSCHILDs SHIP WHERE HE LIVED IN PALACES WHERE ROTHSCHILDs AGENT MAX MULLER ADVISED HIM ON WHAT TO WRITE...

      WHAT ABOUT KATHIAWARI JEW GANDHI?

      EVERY TIME GANDHI WAS THROWN INTO JAIL, THIS EVENT WOULD BE COVERED WITH GREAT FANFARE BY ROTHSCHILDs MEDIA..

      THE SAME NIGHT , GANDHI WOULD BE WHISKED OFF IN SECRET TO ONE OF JEW AGA KHANs NEAREST PALACES -- OR MARWARI JEW BIRLAs ( OPIUM DRUG RUNNING AGENT OF ROTHSCHILD) LUXURY BUNGALOWS.

      TWO NAKED TEENAGE GIRLS WOULD BE WAITING FOR GANDHI WITH ENEMA KIT , VASELINE AND BLANKET..

      http://ajitvadakayil.blogspot.com/2017/01/mahatma-gandhi-and-his-endless.html

      SHOULD I TALK ABOUT NEHRU WHOSE JEW GRANDFATHER WAS THE LAST POLICE CHIEF OF THE LAST MOGHUL EMPEROR? FROM WHERE HE WROTE "GLIMPSES OF WORLD HISTORY" REDUCED INDIAs HISTORY TO A 6000 YEAR OLD CIVILIZATION?

      capt ajit vadakayil
      ..

      Delete
    2. Even Amazon prime in same bracket. The latest series FAMILY MAN in disguise of showing an officer from intelligence dept catching terrorists has shown everythin in the basket to spoil Indian image showing spoilt kids, weak understanding between husband wife, unnecessary adultery and gay scenes, extra marital affair.
      Only deep state can fund such evel of screenplays and scenes right from mumbai to kashmir.

      The producers themselves dont seem to have proper family and appear gay like.

      Even Person of interest is smartly poisoned with adultery scene between two ladies in the last episode.
      the deep state is in continuous hunt for popular programs and posion them.

      Delete
    3. JEW NAPOLEONs LUXURY PRISON IN ST HELENA ISLAND WAS A ROTHSCHILD OWNED PALACE.. NAPOLEON DIED HERE IN 5TH MAY 1821
      .
      http://ajitvadakayil.blogspot.com/2011/02/napoleon-unknown-side-capt-ajit.html

      NAPOLEON TRAINED AND MENTORED JEW SIMON BOLIVAR

      IN THIS ISLAND WATERS YOU CAN SWIM WITH GREAT SHARKS, THEY WONT BITE YOU.

      ALL THE LUXURY ITEMS IN NAPOLEONs LUXURY JAIL, INCLUDING MARBLE FOUNTAINS IN THE GARDEN, WERE STRIPPED OFF BY JEW ROTHSCHILD AFTER HIS DEATH.

      https://www.youtube.com/watch?v=UF-0TnN0yzw

      capt ajit vadakayil
      ..

      Delete
  37. https://timesofindia.indiatimes.com/business/india-business/india-sent-over-two-lakh-students-to-us-in-2018-19-second-largest-after-china-report/articleshow/72104784.cms

    ALMOST ALL THESE STUDENTS GET INTO USELESS US UNIVERSITIES..

    LATER THEY GET A JOB IN USA.. THE SALARY IS TOO LESS..

    THEY LIVE HAND TO MOUTH, PAYING BACK THE BANK LOANS THEY TOOK, LIVING IN RAT HOLES ALONG WITH HOBOS..

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. Example below:-

      https://www.youtube.com/watch?v=1KTnosEbpw4

      https://www.youtube.com/watch?v=gc1DVpLP97M

      Delete
    2. IN USA YU CAN TAKE A BRAND NEW FRIDGE OR WASHING MACHINE ..

      AFTER 3 MONTHS IF YOU ARE NOT SATISFIED YOU CAN RETURN IT.. FOR ZERO PAYMENT..

      THIS IS HOW YOU SEE FANCY STUFF IN THESE RATHOLES..

      Delete
    3. captain why are patels and telugus desperate to work in USA?i have seen many patels working in warehouses and their woman cleaning undies and doing nanny jobs

      Delete
  38. Captain,

    Was German jew Rothschild extremely strong minded phyco like the guys in the video? While i was watching these two guys(eater and been eaten) my mind compared them with Rothschild mindset. An interesting thing is that both had very thin upper lip.

    I suspect that most of the serial killers in the west were descendants of europeans migrated after the world wars.

    https://www.youtube.com/watch?v=O41pcfHtb48.

    The world leaders of large countries still do not understand the type of enemies they are dealing with. If Rothschild rules one more century then the whole world will be doomed.

    Regards,
    Muthu Swamynathan.

    ReplyDelete
    Replies
    1. Hello Mr.Muthu,

      But don't you think that it is the ancestors of our country who shooed away the ancestors to cold europe (like the A111T albino that captain mentioned before)...and it is "karma" that their descendants are troubling us now in return...not taking sides here but just an observation...hope captain agrees..

      Delete
    2. Hi Shivoham,

      Indians who sent them away had very less bad karma because indians knew that albino skin did not suit for the hot tropical climate. Going by the world events, Rothshild caused huge pain and suffering to europeans in the World wars. Even today, Europeans are facing violence and living in fear due to the massive migration(mostly uneducated) of people from the middle east. So, None of the Indians(inc Captain) can agree with you.

      Delete
  39. Pranams captain
    SOS from me
    One of my distant cousins on a visit to the US was sucked in a huge tide of wave when along with her friends she was walking on a beach in California yesterday. She is yet to be traced. When contacted the authorities tell that they are unable to do anything and hope that the tidal wave washes her back to shore. Captain you being an expert in this, pl tell if any hope of her survival?
    With tears and pranams Bala

    ReplyDelete
    Replies
    1. Capt. Ajit VadakayilJune 19, 2018 at 7:36 AM

      https://www.business-standard.com/article/current-affairs/selfie-turns-fatal-as-two-tn-tourists-drown-in-separate-incidents-in-goa-118061800252_1.html

      DIED WHILE TAKING SELFIES IS A "FALSE NARRATIVE" AND MUST BE STOPPED..

      OHH--HE WAS CLICKING SELFIES ? THEN HE DESERVES TO DIE !

      BALLS !

      http://ajitvadakayil.blogspot.com/2017/05/false-narrative-capt-ajit-vadakayil.html

      PEOPLE DOWN AT SEA BECAUSE OF RIP CURRENTS

      WAVES COME TO THE SHORE

      BUT IN CERTAIN PARTS OF THE BEACH THE RECEDING WAVE ( SHORE TO SEA ) STRENGTH IS STRONGER --CALLED RIP CURRENT

      IF YOU FEEL THAT YOU ARE BEING DRAGGED OFF THE SHORE DESPITE BEING A GOOD SWIMMER-- THE SOLUTION IS TO SWIM 40 METRES PARALLEL TO THE SHORE ( TO AVOID THE CRACK OF THE UNDERWATER SAND BAR ) AND THEN APPROACH SHORE AGAIN..

      YOU DONT EVEN NEED TO SWIM, THE WAVES WILL PUSH YOU ASHORE..

      https://www.youtube.com/watch?v=PuAlDTC_gIQ

      https://www.youtube.com/watch?v=RJ4hcaJ91TY

      WE ASK THE MODI GOVT TO EDUCATE PORT OFFICERS OF INDIA ...

      HOW DO I KNOW?

      I DID IT AT MANGALORE PORT ENTRANCE -- WE WENT SWIMMING IN ROUGHS SEAS OF MONSOON WHEN THE SHIP WAS AT THE TANKER BERTH-- IT HAPPENED 38 YEARS AGO

      I HAS BLACK CRUDE OIL SLUDGE ( TAR ) STICKING ALL OVER MY BODY..

      capt ajit vadakayil
      ..

      Delete
    2. "When contacted the authorities tell that they are unable to do anything and hope that the tidal wave washes her back to shore"

      For the tidal wave to wash someone back to shore you don't need authorities. I hope everything is fine and the persons dealing with this are competent. God Speed.

      Delete
    3. famous Ganpatipule Beach in ratnagiri maharashtra is one such beach ..... it has claimed many lives till now ..... i myself as a kid remember being sucked in ..... out of panic i clawed my fingers in the sand and held through. It scared the hell out of me, I was speechless.

      Delete
    4. My sincere apologies if this may sound insensitive, but do ask the local-authorities or any people/professionals online if they can use radar/sonar/sensors to detect the person's body. I found these two links that says it is a difficult but somewhat plausible idea.

      https://www.researchgate.net/post/How_to_find_a_human_body_from_water_using_Sensor

      http://theconversation.com/how-science-is-helping-the-police-search-for-bodies-in-water-73931

      Delete
  40. https://timesofindia.indiatimes.com/blogs/cogito-ergo-sum/aatish-taseer-symbolizes-the-mothering-evil-of-the-left/

    ReplyDelete
  41. http://thedailyswitch.com/culture/these-are-jnu-research-studies-that-the-indian-taxpayer-is-paying-for

    ReplyDelete
  42. https://timesofindia.indiatimes.com/city/delhi/injured-monkey-gets-maneka-gandhis-attention/articleshow/72110644.cms

    HAVE YOU EVER EXPERIENCED MONKEY MENACE?

    A MONKEY IS USELESS WHEN ALIVE -- OR WHEN DEAD..

    ReplyDelete
  43. Capt is it ok for the mother to go for choroonu to guruvayur when it's the 7th day of her period, but there is no discharge?

    ReplyDelete
  44. https://swarajyamag.com/insta/heritage-lost-original-manuscript-of-kautilyas-arthashastra-set-to-perish-due-to-poor-storage-conditions

    ReplyDelete
  45. Dear Captain

    Gates foundation India taken inroad into improving health, nutrition and ensuring safe sanitation practices in India

    https://twitter.com/BMGFIndia/status/1196435801338339328?s=20

    https://twitter.com/BillGates/status/1196492955210506241?s=20

    https://twitter.com/RajivKumar1/status/1196427572583624704?s=20

    ReplyDelete
  46. https://timesofindia.indiatimes.com/india/farmers-need-info-in-climate-fight-bill-gates/articleshow/72117761.cms

    WHY IS THIS DESH DROHI AND DEEP STATE AGENT BILL GATES BEING ALLOWED TO ADVISE INDIA ?

    WHAT IS MODIs COMPULSION ??

    ReplyDelete
  47. ABOLISH SOCIAL SCIENCES AND LANGUAGE ( URDU/ ARABIC/ PERSIAN ) DEPTS IN JNU..

    DONT NOT FEED THE BLACK MAMBA..

    ReplyDelete
  48. IF ALTAF HUSSAIN WANTS ASYLUM, INDIA MUST AFFORD IT..

    FIRST SET TERMS..

    MUHAJIR ARE IMMGRANTS FROM INDIA TO PAKISTAN IN 1947.. THEY ARE THIRS CLASS CIIZENS IN PAKISTAN..

    DO YOU KNOW WHO THE FIRST CLASS CITIZENS ARE ? THEY ARE THE PASHTUN KHAN JEWISH OLIGARCHS AND CRYPTO JEW PUNJABIS..

    I HAD A PAKISTANI CHIEF OFFICER WHOSE GRANDPARENTS WERE FROM LUCKNOW..

    HE TOLD ME NAKED TRUTHS.. THEY ARE TREATED LIKE SHIT IN PAKISTAN.. THEY CANT ENTER MOSQUES OF THE SECOND GRADE PAKISTANIS..

    BEFORE HIS GRANDPARENTS DIED THEY WERE OF THE OPINION THAT THEY MADE A BIG MISTAKE..

    MUSLIMS IN INDIA DID NOT WANT TO TO TO PAKISTAN IN 1947.. HINDUS IN PAKISTAN CAME RUNNING TO INDIA..

    ROTHSCHILDs MEDIA GAVE FAKE NEWS OF MUSLIMS GETTING SLAUGHTERED ALL OVER INDIA.. STILL NOTHING HAPPENED..

    OVERNIGHT SLAUGHTERED PIGS WERE FOUND IN MOSQUES AND COW HEADS WERE FOUND IN TEMPLES ( PLANTED BY ROTHSCHILDs AGENTS) ALL OVER INDIA..

    HINDU / MUSLIM RIOTS WERE DELIBERATELY IGNITED BY ROTHSCHILD..BY KILLING HINDUS IN TRAINS COMING FROM PAKISTAN TO INDIA.. THE PATIENT HINDUs KSHATRIYA BLOOD BOILED..

    THERE ARE MORE MUSLIMS IN INDIA THAN IN PAKISTAN..

    IF OWAISI CONTINUES DRIVING WEDGES-- HE MUST BE JAILED AS AN EXEMPLARY MEASURE..

    COW AND PIG THINGY IS A ROTHSCHILD SIGNATURE..

    http://ajitvadakayil.blogspot.com/2011/02/murky-truths-of-sepoys-mutiny-1857.html

    ReplyDelete
  49. FRAUD FLOURISHES IN INDIA BECAUSE THERE IS NOT PUNISHMENT..

    SALMAN KHAN WAS BRAND AMBASSADOR FOR YATRA DOT COM WHO WAS CHEATING PEOPLE..

    http://ajitvadakayil.blogspot.com/2011/09/credit-card-fraud-by-yatra-dot-com-capt.html

    CHECK THIS OUT BELOW- WITHOUT HELP FROM POLICE / JUDGES/ POLITICIANS -- CAN THIS HAPPEN ?

    https://www.tripadvisor.in/ShowTopic-g293860-i511-k6070520-Yatra_com_the_biggest_fraud_company-India.html

    capt ajit vadakayil
    ..

    ReplyDelete
  50. Delhi sure has interesting showdowns like pot boilers
    Lawyers vs Police both went on dharna.

    Now so called students exceeding their right to education .

    ReplyDelete
  51. Right to protest ,right to education won over a citizens ability to travel and go to work in Delhi yesterday.

    Peaceful protest via disruption of economy has become the norm.

    ReplyDelete
  52. https://www.thehindu.com/news/international/us-says-israeli-settlements-no-longer-considered-illegal-angers-palestinians/article30013631.ece

    The Trump administration on November 18 said it no longer considers Israeli settlements in the West Bank to be a violation of international law, reversing four decades of American policy and further undermining the Palestinians’ effort to gain statehood.

    Captain,
    As you said,trump seems to be playing a double game(I guess may be to attract jewish community votes in USA)

    ReplyDelete
    Replies
    1. He's a crypto jew. RW=LW. Both paths lead to Rothschild.

      Delete
  53. Respected Sir,
    Can a woman meditate during her periods.
    Thank you

    ReplyDelete
    Replies
    1. Respected Sir,
      Pardon me sir but could you give an idea as to what is meant by swadhyaya.
      Is it just observing one's thought without analysing and coming to a conclusion about it or is it critically analysing it thread bare as to why/how/what the thought means and killing the errant thought.
      What is the goal of swadhyaya.
      Thank you

      Delete
    2. Hi Trinity,

      All are basic questions. You will get answers for all these questions when you read the swadhayaya post.

      Delete
    3. Thank you Muthuji,i read Sanatana Dharma part-103 on swadhyaya and have got an understanding about it.
      Respected Sir,
      During the gurukul stage is the sishya guided to self-realization.my query is can a self-realized person be able to lead a normal grihastha life sir.
      Thank you

      Delete
    4. http://ajitvadakayil.blogspot.com/2018/08/sanatana-dharma-hinduism-exhumed-and.html?m=1

      Delete
    5. All ancient great sages were married with children .. They were 12 strand dna without junk. This fact is emphasised all over the blog

      Delete
  54. PMC bank fraud HDIL properties to be auctioned off to recover debt and help pay back account holders.

    Over inflated property collateral when realised may not be same as inflated value against which loans were given.

    Using shell groups they can buy back for a low price.

    ReplyDelete
  55. Dear Capt Ajit sir,

    The Maha debacle of NCP+CONG+SS has now drifted to WB between TMC-Mamata and AIMIM-Owaisi striking the roots....asking why Mamata let BJP win 18 seats !!!

    ReplyDelete
    Replies
    1. What has Owaisi got to offer other than instigating violence by his cleverly crafted speech.
      Owaisi's win in Malegaon and Kishanganj has given him extra wings to expand to every opportunity he finds throughout India.
      This Tu Tu Main Main of Owaisi-Mamata is to polarize the Muslim voters of Bengal to vote for AIMIM & dump TMC.
      Why did Mamata allow?
      Did she really allow?
      Minimum 48 lives has been lost in 2019 Parliamentary election only in Bengal.
      Brutal case of hacking the Skull, butchering the neck, hanging on trees etc etc.
      For a moment it was like Bengal is no more under control, even out of control from Jihadi Mamata.
      Modi did nothing for the families of deceased.
      Modi is doing nothing.
      Faaltu aadmi nikla.

      What was the reason for Modi to praise BJD & NCP?

      Everyone is playing extremely dirty politics.

      Hardly anyone cares about people.

      Owaisi has done more mischief than Kanhaiya Kumar.

      Openly instigating Communal Violence, Amit Shah as HMO is allowing this.

      Modi-Shah duo, their hands are soaked in the blood of Martyrs.

      Meanwhile on Twitter a battle of Rightwing is going on if Owaisi is Shia or Sunni.
      Bcoz of Owaisi the Right wings got divided supporting either camps, the one supporting his Sunni title, the supporting his Shia title.

      Delete
  56. Captain, are mentally ill people aware and responsible for their own wrongdoings? Is karma applicable for them too?

    ReplyDelete
  57. Dear Capt Ajit sir,

    Mystery!!! Nine unnatural deaths and its link with Sonia Gandhi...starting with Sanjay Gandhi, Indira, Rajiv, 3 of Vadra's family,from INC - Madhavrao Scindia, Jitender Prasadah and Rajesh pilot all died on a sunday....;-)
    ttps://m.dailyhunt.in/news/india/english/post+card+news+english-epaper-pcardeng/mystery+nine+unnatural+deaths+and+its+link+with+sonia+gandhi-newsid-83596321?fbclid=IwAR0VLv4-3TPPLQzsq7kAc5ADv_ekE9-IznjCcwLDd8B4673Uz-4VehToD-s

    ReplyDelete
  58. Namaskar Captain ,

    Welcome Back!!
    I always believed you will be back soon. I thought you are just taking the time off to concentrate on more urgent personal matters.
    Hope all's been taken care off.
    Good to see you back.
    Thank you :)

    ReplyDelete
  59. Dear Capt Ajit sir,

    This is open admission of Jagan's affiliation to boost Christianity conversions....and another way attack Hindus by looting Tirupati temple funds...why are we mute to elect such govts ?

    ReplyDelete
  60. Dear Capt Ajit sir,
    The same Pinari Vijayan govt is doing the right thing now....based on your advise dharma is restored, police is doing right checks not to allow women of ages 10-50 officially as per ID proof...grand salute to them.
    https://www.news18.com/news/india/family-forced-to-leave-behind-12-year-old-after-kerala-cops-stop-her-from-trekking-to-sabarimala-2392377.html

    ReplyDelete
  61. PROFILE ALL JUDGES PAST AND PRESENT WHO DO NOT ALLOW THE ELECTED EXECUTIVE TO TAP THE PHONES AND COMPUTERS OF FOREIGNPYROLL DESH DROHIS TO THE WATAN...

    COLLEGIUM JUDGES CREATED THE RED CORRIDOR IN INDIA...WHERE 20% OF INDIAN LANDMASS IS IN THE HANDS OF NAXALS ..

    http://ajitvadakayil.blogspot.com/2017/08/right-to-privacy-in-india-is-not.html

    http://ajitvadakayil.blogspot.com/2012/09/bauxite-mining-naxalite-menace-joshua.html

    capt jit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
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      TOM VADAKKAN
      GOVERNOR OF KERALA
      SRIDHARAN PILLAI
      PARASARAN
      SAI DEEPAK
      VIDYASAGAR GURUMURTHY
      I&B DEPT/ MINISTER
      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. Screenshots sent to -

      @rashtrapatibhvn @MVenkaiahNaidu @VPSecretariat @PMOIndia @narendramodi @AmitShah @rajnathsingh @DefenceMinIndia @LokSabhaSectt @NITIAayog @RajyaSabha @piyushgoyal @amitabhk87 @MIB_India @PrakashJavdekar @OfficeOfRSP @rsprasad @nsitharaman @FinMinIndia @nstomar @OfficeOfNG @nitin_gadkari @naqvimukhtar @narendramodi_in @mygovindia @RailMinIndia @AgriGoI @manojsinhabjp @Rao_InderjitS @DVSBJP @shripadynaik @RadhamohanBJP @dpradhanbjp @Gen_VKSingh @iramvilaspaswan @umasribharti @harsimrat_badal @DrJitendraSingh @sureshpprabhu @smritiirani @santoshgangwar @drharshvardhan @Kiren_Rijiju @JPNadda @irvpaswan @M_Lekhi @SuPriyoBabul @LtGovDelhi @VajubhaiValaBJP @vpsbadnore @jandkgovernor @MEAIndia @DrSJaishankar@indiandiplomats @IndianDiplomacy

      Delete
    3. https://twitter.com/kkarthikeyan09/status/1196836779086663680

      Delete
    4. https://twitter.com/shree1082002/status/1196845300477857792

      Delete
    5. Verified posted under Union Minister of State for Home Affairs

      https://twitter.com/kishanreddybjp/status/1196833326310408192?s=20

      Delete
  62. Dear Capt Ajit sir,
    You were right again....many if the Rothschild agents at the helm of govts as PM/Prez have no morality to fight fir drugs smuggling..when they do it !!!! https://www.independent.co.uk/news/world/americas/jair-bolsonaro-cocaine-g20-plane-drugs-entourage-brazil-silva-rodrigues-a8977141.html?fbclid=IwAR19VdzymOZ-lq6MsKgudFlFAChU9wtYDZq4brY8v4G48rL0qJiKex6lGNY

    ReplyDelete
    Replies
    1. PABLO ESCOBAR DID ONLY COCAINE SMUGGLING... HIS SON TOLD THAT PABLO NEVER EVEN TRIED COCAINE IN HIS ENTIRE LIFE AND CASTIGATED ( REDUCED SALARY ) HIS GANG MEMBERS WHO USED COCAINE..

      WHO WAS RABINDRANATH TAGOREs GRANDFATHER?

      THIS PIR ALI MUSLIM, WAS A DRUG RUNNER AS WELL AS A WHOREHOUSE OWNER ( THE PLANETs LARGEST ) SUPPLYING SMALL GIRL AND BOYS TO HIS WHITE JEWISH MASTERS.

      http://ajitvadakayil.blogspot.com/2011/08/opium-drug-running-tagore-family-capt.html

      WHEN I FIRST REVEALED ABOUT TAGORE-- ANGRY BONGS SWARMED ALL OVER ME..

      TODAY THEY ACCEPT IT.. EVEN WIKIPEDIA HAS ACCEPTED IT..

      http://ajitvadakayil.blogspot.com/2010/12/nobel-prize-and-knighthood-for-tagore.html

      INDIA IS THE ONLY NATION ON THIS PLANET WHERE ALL OUR SYMBOLS AND HEROS ARE FAKE GIVEN TO US BY JEW ROTHSCHILD.

      ANTHEM
      FLAG CHAKRA
      ASHOKA SYMBOL
      JEW GANDHI

      PARSIS PROMISED THE GUJARAT KING THAT THEY WILL INTEGRATE INTO INDIAN SOCIETY LIKE SUGAR IN MILK.. THEY HAVE STILL NOT INTEGRATED..

      AT LEAST CHITPAVAN JEWS MARRIED INTO INDIAN SOCIETY AND PRODUCED SOME GREAT PATRIOTS..

      http://ajitvadakayil.blogspot.com/2012/09/ganesh-babarao-savarkar-unsung-hero-of.html

      capt ajit vadakayil
      ..

      Delete
    2. https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

      shall i put above link into pablo escobar son's website with your permission ?

      Delete
    3. BE MY GUEST..

      TELL HIM CAPT AJIT VADAKAYIL ASKS HIM TO BE PROUD OF HIS PATRIOT FATHER PABLO..

      Delete
    4. THIS CAPTAIN! This blogsite has enough weight to heal the wounds of his son.

      In India, Narcos was THE most popular show on Netflix. Now people shall what a patriot Pablo was.

      Delete
  63. namaste Guruji

    welcome back sir

    pranam Guruji

    ReplyDelete
  64. DURING SABARIMALA MANDLAKALAM ( 17TH NOV TO 14TH JAN ), MY WIFE LIGHTS GINGELI OIL ( SESAME ) LAMP IN HER PUJA ROOM, WHERE AYYAPPA TAKES POLE POSITION..

    IN THE TOP PANTHEON ARE --

    1) DANAVA GOD MUTHAPPAN ( SHIVA IN BHAIRAVA FORM WITH WOLF AS VAHANA )

    2) MOOKAMBIKA DEVI ( SHIVAs WIFE IN SHAKTII FORM )..

    http://ajitvadakayil.blogspot.com/2012/10/muthappan-deity-of-north-kerala-capt.html

    http://ajitvadakayil.blogspot.com/2013/03/kudajadri-mountain-chitramoola-cave-adi.html

    THE 12,000 YEAR OLD BAALBEK TEMPLE WAS A MUTHAPPAN TEPLE..

    http://ajitvadakayil.blogspot.com/2019/08/secrets-of-12000-year-old-baalbek.html

    ReplyDelete
  65. How to check magnetic field ? is there a website that shows magnetic field variation for next month?

    ReplyDelete
    Replies
    1. Let me clarify...I would love to see magnetic field strength around Sabarimala varies from now till few days after makaravilakku....it would be ideal to see for an entire year...trying to understand the physical implications of concepts explained in the blogsite. appreciate any advice from techs here

      Delete
    2. THE MAGNETOSPHERE SHIELDS OUR HOME PLANET FROM SOLAR AND COSMIC PARTICLE RADIATION, AS WELL AS EROSION OF THE ATMOSPHERE BY THE SOLAR WIND - THE CONSTANT FLOW OF CHARGED PARTICLES STREAMING OFF THE SUN

      MAGNETIC FIELDS BEND PARTICLES. IF YOU HAVE DANGEROUS HIGH-SPEED PARTICLES FROM SPACE, THE MAGNETOSPHERE DEFLECTS THEM LIKE A SHIELD TO COSMIC RADIATION. THIS IS WHY WE STILL HAVE AN ATMOSPHERE SURROUNDED BY AN OZONE LAYER. ...

      LIKE A BUBBLE THAT SURROUNDS EARTH, THE MAGNETOSPHERE PROTECTS US FROM THE FURY OF THE SUN

      https://www.youtube.com/watch?v=xTHngFzi8mY

      MORE THAN 3.5 BILLION YEARS AGO, MARS HAD A MUCH THICKER ATMOSPHERE THAT KEPT THE SURFACE TEMPERATURES MODERATE ENOUGH TO ALLOW FOR SUBSTANTIAL AMOUNTS OF SURFACE WATER TO FLOW, POOL, AND PERHAPS EVEN FORM AN OCEAN. ( FOR LIFE ) BUT SINCE THE MAGNETIC FIELD OF MARS FELL APART AFTER ITS IRON INNER CORE WAS SOMEHOW UNDONE, ABOUT 90 PERCENT OF THE MARTIAN ATMOSPHERE WAS STRIPPED AWAY BY CHARGED PARTICLES IN THAT SOLAR WIND, WHICH CAN REACH SPEEDS OF 250 TO 750 KILOMETERS PER SECOND.

      Delete
    3. Dear Capt Ajit sir,

      This is a huge revelation again....hope ISRO looks into this aspect and uses it for their research on Mars !!
      Also, like reader Manju above, I am also interested to exactly measure Magnetic field now and getting closer to 15 Jan - Makara Sankranti, how much would it be in Sabarimalai using simple instruments. Can you pls clarify whether this magnetic changes are limited to Sabarimalai or all over the world on that day ?
      Also, can we distinguish by percentage what actually makes that huge magnetic impact on that day by way of : 1. Polarity reversals 2. Rotation axis of earth changing 3. Any other cause.
      Which is the best Magnetometers and Gaussmeters you recommend to do experiments on this unique revelation from you ?

      Delete

  66. IN THE NETFLIX SERIAL PABLO ESCOBAR, THEY SHOW A TRUTH

    A RESPECTED OLD CATHOLIC PRIEST WAS ARM TWISTED BY THE DEEP STATE TO TELL A LIE LIVE ON TV -- THAT PABLO ESCOBAR WAS A PEDOPHILE..

    THIS IS JUST ONE OF THE FOULD PROPAGANDA WHICH DESTROYED PABLOs ROBIN HOOD IMAGE ..

    THIS IS AS PER ROTHSCHILDs MOTTO-- TO KILL A DOG FIRST GIVE IT A BAD NAME..

    THIS WORKED WELL WITH GADDAFI, WHERE THE DEEP STATE MEDIA GAVE LYING PROPAGANDA THAT GADDAFI PROCURED THOUSANDS OF VIAGRA TABLETS WHICH HE DISTRIBUTED TO HIS ARMY TO RAPE WOMEN OF LIBYA..

    MIND YOU PATRIOT GADDAFI TOOK OVER HIS NATION FROM JEW KING IDRIS, WHO ALLOWED JEWS TO STEAL..

    http://ajitvadakayil.blogspot.com/2011/10/ethnic-cleansing-of-blackskinned-people.html

    WHEN GADDAFI WAS IN POWER JEWISH FUNDED NGOs-- SPROUTED ALL OVER LIBYA , AS GOOD SAMARITANS.. HUNDREDS OF THEM..

    ANTI-GADDAFI INSURGENTS WERE ADEPT AT USING PROPAGANDA. HERE IS A SPECIFIC EXAMPLE:

    "ONE STORY, TO WHICH CREDENCE WAS GIVEN BY THE FOREIGN MEDIA EARLY ON IN BENGHAZI, WAS THAT EIGHT TO 10 GOVERNMENT TROOPS WHO REFUSED TO SHOOT PROTESTERS WERE EXECUTED BY THEIR OWN SIDE. THEIR BODIES WERE SHOWN ON TV.

    BUT DONATELLA ROVERA, SENIOR CRISIS RESPONSE ADVISER FOR AMNESTY INTERNATIONAL, SAYS THERE IS STRONG EVIDENCE FOR A DIFFERENT EXPLANATION. SHE SAYS AMATEUR VIDEO SHOWS THEM ALIVE AFTER THEY HAD BEEN CAPTURED, SUGGESTING IT WAS THE REBELS WHO KILLED THEM.

    THERE IS A FOUL CONNECTION BETWEEN QATAR’S JEWISH AL-THANI ROYAL FAMILY AND HILLARY CLINTON’S CLINTON FOUNDATION, WHICH ACCEPTED MILLIONS DURING THIS PERIOD (REF. JUDICIAL WATCH, WIKILEAKS), AS WELL AS THE EXILED LIBYANS WITH CONNECTIONS TO SUNNI EXTREMISM ACTING AS WESTERN SUPPORTERS. AL JAZEERA IS A JEWISH TV CHANNEL MASQUERADING AS A ISLAMIC CHANNEL.

    IN THE EARLY DAYS OF THE INVASION, GADDAFI SAID THAT THOSE WHO WERE BEHIND IT WERE NOT LIBYANS, BUT RATHER AL-QAEDA AND FOREIGN FIGHTERS. QATAR HAS SINCE PROVEN TO PLAY A CENTRAL ROLE. THE COUNTRY’S LEADER, JEW SHEIKH TAMIM BIN HAMAD AL-THANI, WAS ONE OF HILLARY CLINTON’S CLOSEST PARTNERS AND AMONG THE BIGGEST CONTRIBUTORS TO THE CLINTON FOUNDATION AT THE TIME.

    ALMOST ALL MASS DEMONSTRATIONS IN LIBYA WERE PRO-GADDAFI—NOT ANTI-GADDAFI AS PER FOUL LYING PROPAGANDA BY JEWSIH DEEP STATE MEDIA.. SAME HAPPENED IN VENEZUELA.. PRO-MADURAO RALLIES WERE PHOTO-SHOOPED AS ANTI- MADURO/ PRO- JEW GUAIDO RALLIES.

    SINCE 1995, ATTEMPTS HAD BEEN MADE TO MURDER GADDAFI BY AL-QAEDA ( CREATED / FUNDED/ ARMED BY JEWS LIKE ISIS ), WHICH HAS HAD ONE OF ITS STRONGEST BASES IN THE BENGHAZI AREA FOR YEARS

    GADDAFI WAS THE FIRST MUSLIM LEADER WHO CALLED FOR OSAMA BIN LADEN’S ARREST.

    THE FACT THAT LIBYAN ISLAMIC FIGHTING GROUP LEADER, ABDELHAKIM BELHADJ—ALSO COMMANDER OF THE AL-QAEDA FACTION IN LIBYA—ENDED UP AS THE MILITARY GOVERNOR OF TRIPOLI IN COOPERATION WITH THE WEST AT THE END OF NATO’S WAR—A MAN WHO FOUGHT WITH OSAMA BIN LADEN IN AFGHANISTAN ONLY YEARS BEFORE—IS HARDLY A COINCIDENCE.

    BELHADJ WAS LATER PHOTOGRAPHED TOGETHER WITH AND HONOURED BY ROTHSCHILD AGENT , REPUBLICAN SENATOR JOHN MCCAIN AT A CEREMONY WHERE HE WAS REFERRED TO AS LIBYA’S “HEROIC FREEDOM FIGHTER”. BELHADJ HAS SINCE BECOME AFRICA’S RICHEST MAN, NOW WITH A NET WORTH OF AROUND 21 BILLION DOLLARS.

    IT WAS ALSO A KNOWN FACT THAT GADDAFI HAD LONG CHALLENGED THE JEWISH SAUDI ROYAL DYNASTY —IN THE HOMELAND OF WAHHABISM AND SUNNI EXTREMISM.

    IT WAS AT THIS TIME THAT GADDAFI ALSO WARNED THAT MILLIONS OF AFRICANS WOULD FLEE FROM LIBYA AND HEAD FOR EUROPE IF LIBYA WAS DESTABILISED. MILLIONS HAVE SINCE UNDERTAKEN THE VOYAGE TO EUROPE VIA ITALY SPONSORED BY THE JEWISH DEEP STATE.

    CONTINUED TO 2--

    ReplyDelete
    Replies
    1. CONTINUED FROM 1--

      BY 25 FEBRUARY, ALL AMERICAN DIPLOMATS HAD ALREADY LEFT LIBYA. THE FOLLOWING DAY, THE UN SECURITY COUNCIL IMPOSED SANCTIONS ON THE COUNTRY. THE REBELS’ INTERIM GOVERNMENT WAS PUT IN PLACE, WITH ITS BASE IN BENGHAZI, AND IN CLOSE CONTACT WITH THE WEST AND A NUMBER OF WELL-KNOWN ISLAMISTS. COUNTRIES LIKE QATAR WERE ALREADY CONTRIBUTING MASSIVE FINANCIAL SUPPORT; PRISONERS WERE ALSO REPORTEDLY FREED FROM JAILS IN EGYPT SO THAT THEY COULD PARTICIPATE, AS WELL AS FROM A NUMBER OF WESTERN COUNTRIES.

      THE TUMULT IN BENGHAZI RECEIVED IMMEDIATE MEDIA ATTENTION, WITH AL JAZEERA LEADING THE WAY. JUST DAYS LATER, LIBYA’S JUSTICE MINISTER, MUSTAFA ABDUL-JALIL, SUDDENLY PULLED OUT OF GADDAFI’S JAMAHIRIYA GOVERNMENT AND BECAME THE REBELS’ LEADER. HE SET UP AN INTERIM GOVERNMENT RIGHT AWAY WITH HIMSELF AS THE LEADER IN BENGHAZI.

      ON 14 MARCH 2011, HILLARY CLINTON MET WITH THE MAN WHO HAS SINCE BECOME THE INTERIM PRIME MINISTER, JEW MAHMOUD JIBRIL, IN PARIS, WHERE THE FRENCH-JEWISH PHILOSOPHER BERNARD HENRI-LÉVY HELPED HIS FRIEND AND BUSINESS PARTNER, THE U.S.-EDUCATED, MUSLIM BROTHERHOOD MEMBER MAHMOUD JIBRIL, TO PULL OFF THE MEETING.

      JIBRIL WAS THE FORMER CEO OF JTRACK, WHICH WAS RESPONSIBLE FOR CHANGING AL JAZEERA’S OPPOSITION TO THE AMERICAN NARRATIVE AFTER NEW OWNERS TOOK OVER THE CHANNEL IN THE WAKE OF THE CHANNEL HAVING BEEN HIGHLY CRITICAL OF THE U.S. DURING THE 2003 INVASION OF IRAQ.

      UNDER JIBRIL, JEWSIH DEEP STATE MOUTHPIECE AL JAZEERA BECAME AN INSTRUMENTAL VOICE IN THE FORMATION OF THE MYTH THAT “THE ARAB SPRING” WAS ABOUT OPPRESSED PEOPLE IN THE MIDDLE EAST WANTING “WESTERN DEMOCRACY, ECONOMIC LIBERALISM AND A WESTERN LIFESTYLE”

      THE INTERIM GOVERNMENT LEADER, JEW MUSTAFA ABDUL JALIL, ALSO QUICKLY CALLED FOR AN IMMEDIATE NO-FLY ZONE BECAUSE “GADDAFI IS ATTACKING THE PEOPLE FROM THE AIR”. HE ASSERTED THAT GADDAFI HIMSELF WAS RESPONSIBLE FOR ALL OF THE ATTACKS PERPETRATED IN LIBYA. AT THE TIME HE ALSO SAID THAT HE KNEW THAT GADDAFI HIMSELF HAD ORCHESTRATED THE LOCKERBIE BOMBING, WHERE AMERICAN PAN AM FLIGHT 103 EXPLODED OVER SCOTLAND, KILLING 270 PEOPLE.

      PRESIDENT JEW BARACK OBAMA COMMENTED AT THE TIME THAT “MILITARY FIGHTER JETS AND HELICOPTERS BOMBED INNOCENT PEOPLE WHO HAD NO CHANCE TO DEFEND THEMSELVES AGAINST THE AIRSTRIKES”. HE LIED THAT “WATER SUPPLIES WERE CUT OFF IN MISURATA, WHICH AFFECTED HUNDREDS OF THOUSANDS OF PEOPLE”.

      GADDAFI NEVER THREATENED CIVILIAN MASSACRE IN BENGHAZI, AS OBAMA ALLEGED.. GADDAFI OFFERED AMNESTY TO ANY REBEL THAT “PUT DOWN THEIR WEAPONS”. GADDAFI EVEN PROMISED THEM A CHANCE TO FLEE THE COUNTRY VIA EGYPT IN ORDER TO PREVENT CONTINUED BATTLES.

      THE LIBYAN WAR IS THE RESULT OF JEW SARKOZY’S PERSONAL VENDETTA. GADDAFI’S DAUGHTER WAS MURDERED BY A BOMB.. MURDERING THE CHILDREN AND FAMILIES OF HEADS OF STATE GOES THOROUGHLY BEYOND THE UN MANDATE, WHICH WAS ABOUT PROTECTING CIVILIANS—NOT KILLING THE FAMILIES OF LEADERS WHO DO NOT ALLOW JEWS TO STEAL FROM THEIR MOTHERLAND

      Capt ajit vadakayil
      ..

      Delete
    2. Captain, After going through the above comment i understand saudi-qatar masterplan. when saudi threatened qatar and qatar based al-jazeera, Iran foolishly took the side with Qatar. Deep State plan worked well. Qatar will gain iran trust only to screw Iran.

      Delete
    3. Captain,

      Many news like the below ones are published to fool Iran that Qatar has sided with Iran.

      https://www.thenational.ae/world/mena/qatar-s-silence-on-iran-s-ship-attacks-devastating-at-all-levels-1.939252

      Delete
  67. Captain, what do you think about Shia Islam? Please tell if you consider it appropriate. I read some hadiths recently and was intrigued by the neutrality of their scripture.

    This one, for example -

    “Once I asked Imam abu ‘Abd Allah, recipient of divine
    supreme covenant, ‘May Allah keep my soul in service for your
    cause, I have a neighbor who prays a great deal, generously
    gives charity and very often visits Makka and he seems to be
    acceptable.’ The Imam, recipient of divine supreme covenant,
    asked, ‘How is his Intelligence, O ibn Ishaq?’ I then said, ‘May
    Allah keep my soul in service to your cause, he does not have
    much Intelligence.’ ‘Nothing from what he does will be raised
    up (to heaven),’ replied the Imam.”

    Please reveal if there is some truth in Shia Islam and how it compares with Sanatan Dharm.

    ReplyDelete
  68. https://ajitvadakayil.blogspot.com/2019/11/paradox-redemption-victory-in-defeat.html

    WE ASK THE NATIONs SECURITY AGENCIES TO ADVISE JEW DARLING PM MODI, WHAT IS WORLD INTRIGUE.. FIRST UNDERSTAND IT..

    BHARATMATA IS RACING TO BE THIS PLANETs NO 1 SUPERPOWER IN 14 YEARS...

    IN HIS PENCHANT TO WRANGLE A NOBEL PRIZE MODI ( WHO WORE A SIKH TURBAN IN 1976 ) IS DANCING TO THE TUNE OF THE DEEP STATE ...

    WE THE PEOPLE FEAR THAT BHARATMATA WILL BE CUT DOWN BEFORE THE FINISH LINE..

    JNU STUDENT JEW ALI ZEIDAN BECAME PM OF LIBYA AFTER GADDAFIS OUSTER BY THE JEWISH DEEP STATE ..

    JNU STUDENT COMMIE BABURAM BHATTARAI BECOME PM OF NEPAL AFTER THE MASSACRE OF THE NEPAL ROYAL FAMILY..

    IT IS BETTER THAT WE SHUT DOWN THE SOCIAL SCIENCES DEPT OF JNU

    capt ajit vadakayil
    ..

    ReplyDelete
    Replies
    1. PUT ABOVE COMMENT IN WEBSITES OF--
      PM MODI
      PMO
      AJIT DOVAL
      RAW
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      ALL THREE ARMED FORCE CHIEFS.
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      MOHANLAL
      NITI AYOG
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      RAM MADHAV
      RAJ THACKREY
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      SWAMY
      RAJIV MALHOTRA
      SADGURU JAGGI VASUDEV
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      BABA RAMDEV
      RSS
      VHP
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      THE QUINT
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      THE WIRE
      THE PRINT
      MK VENU
      MADHU TREHAN
      CLOSET COMMIE ARNAB GOSWMI
      RAJDEEP SARDESAI
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      VIKRAM CHANDRA
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      DILEEP PADGOANKAR
      VIR SANGHVI
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      PRITISH NANDI
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      SIDHARTH VARADARAJAN
      ARUN SHOURIE
      N RAM
      NCW
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      AMISH TRIPATI
      I&B DEPT/ MINISTER
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      WEBSITES OF DESH BHAKT LEADERS
      SPREAD OF SOCIAL MEDIA

      Delete
    2. https://twitter.com/kkarthikeyan09/status/1196839023441895424
      https://twitter.com/kkarthikeyan09/status/1196841918325501952

      Delete
    3. Sir, sent emails.

      mcitoffice@gov.in,
      narendramodi1234 ,
      contact@amitshah.co.in,
      prakash.j@sansad.nic.in,
      info.nia@gov.in,
      information@cbi.gov.in,
      Rajnath Singh <38ashokroad@gmail.com>,
      mos-defence@gov.in,
      alokmittal.nia@gov.in,
      kg.thang@nic.in,
      secylaw-dla@nic.in,
      as-niti@gov.in,
      vch-niti@nic.in,
      amitabh.kant@nic.in,
      connect@mygov.nic.in,
      jscpg-mha@nic.in,
      rmo@mod.nic.in,
      17akbarroad@gmail.com" <17akbarroad@gmail.com>,
      webmaster.indianarmy@nic.in,
      proiaf.dprmod@nic.in,
      pronavy.dprmod@nic.in

      mohan.pai@manipalglobal.com,
      fayedsouza@gmail.com,
      editor@thequint.com,
      letters@scroll.in,
      editorial@thewire.in,
      feedback@theprint.in,
      contact@newslaundry.com,
      contact@republicworld.com,
      mail@rajdeepsardesai.net,
      navikakumar@timesnow.tv,
      vikramchandra@berkeley.edu,
      ravish@ndtv.com,
      aroonpurie@aajtak.com,
      vineet.jain@timesgroup.com,
      raghav.bahl@thequint.com,
      seema.chishti@expressindia.com,
      connect@virsanghvi.com,
      karanthapar@itvindia.net,
      barkha.dutt@gmail.com,
      svaradarajan@gmail.com,
      sv@thewire.in,
      nram.thehindu@gmail.com,
      ramachandraguha@yahoo.in,
      connect@mygov.nic.in,
      as-niti@gov.in,
      amitabh.kant@nic.in

      Aroonpurie Aajtak and Navika kumar times now blocked the message.

      Delete
  69. https://timesofindia.indiatimes.com/world/uk/sweden-discontinues-assange-rape-investigation/articleshow/72128597.cms

    FALSE RAPE CHARGES BY THE JEWISH DEEP STATE

    ReplyDelete
  70. MN SHAMSHEER WHO LOOKED LIKE AK GOPALANs TWIN BROTHER AT THE AGE OF 18, IS THE FUTURE KING OF KERALA COMMUNISM WITH JEWISH DEEP STATE HELP..

    LIKE PINARAYI VIJAYAN , MN SHAMSHEER WAS EDUCATED AT BRENNAN COLLEGE KANNUR , WHICH IS WORSE THAN JNU...

    HARDCORE NAXALS AND ISLAMIC EXTREMISTS ( MALAPPURAM ) ARE SLOWLY MERGING IN KERALA..

    DEEP STATE AGENT, AND IMF CHIEF COMMIE GITA GOPINATH IS AK GOPALANs BLOOD.

    capt ajit vadakayil
    ..

    PUT ABOVE COMMENT IN WEBSITES OF--
    CM PINARAYI VIJAYAN
    KODIYERI BALAKRISHNAN
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    AJIT DOVAL
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    MK VENU
    MADHU TREHAN
    CLOSET COMMIE ARNAB GOSWMI
    RAJDEEP SARDESAI
    PAAGALIKA GHOSE
    NAVIKA KUMAR
    ANAND NARASIMHAN
    SRINIVASAN JAIN
    SONAL MEHROTRA KAPOOR
    VIKRAM CHANDRA
    NIDHI RAZDAN
    FAYE DSOUZA
    RAVISH KUMAR
    PRANNOY JAMES ROY
    AROON PURIE
    VINEET JAIN
    RAGHAV BAHL
    SEEMA CHISTI
    DILEEP PADGOANKAR
    VIR SANGHVI
    KARAN THAPAR
    PRITISH NANDI
    SHEKHAR GUPTA
    SIDHARTH VARADARAJAN
    ARUN SHOURIE
    N RAM
    NCW
    REKHA SHARMA
    SWATI MALLIWAL
    CHETAN BHAGAT
    DEVDUTT PATTANAIK
    AMISH TRIPATI
    I&B DEPT/ MINISTER
    LAW MINISTER/ MINISTRY
    WEBSITES OF DESH BHAKT LEADERS
    SPREAD OF SOCIAL MEDIA

    ReplyDelete
    Replies
    1. Sir, Screenshots sent to --

      @VishnuNDTV @sagarikaghose @Shehla_Rashid @RanaAyyub @ReallySwara @TeestaSetalvad @kavita_krishnan @JohnDayal @Kancha_ilaiah @prakashraaj @SudheenKulkarni @annavetticad @SitaramYechury @brindacpim @Ram_Guha @PavanK_Varma @virsanghvi @KaranThapar_TTP @UmarKhalidJNU @seemay @ayesha_kidwai @DineshVarsh @kramdas @awardreturner
      @Ram_Guha @svaradarajan @Arundatiroy @Nidhi @DeShobhaa
      @Koenraad_Elst @chetan_bhagat @authoramish @devduttmyth
      @mammukka @dulQuer @PKKunhalikutty @ikamalhaasan @VinodDua7 @AnnieRaja92 @PavanK_Varma @madhutrehan @supriya_sule @nramind @suhelseth @ShashiTharoor @irfhabib @laramdas @khanumarfa
      @PJkanojia

      @HMOIndia @AmitShah @NIA_India @AmitShahOffice @amitmalviya @abhijitmajumder @easterncomd @NorthernComd_IA @SpokespersonMoD @DefenceMinIndia @adgpi @IAF_MCC @indiannavy @HQ_IDS_India @crpfindia @BSF_India @Whiteknight_IA @ChinarcorpsIA

      @KeralaNews24x7 @PIBTvpm @THKerala @TOIKochiNews @kairalionline @manoramanews @manoramaonline @ManoramaTopNews @mathrubhumi @mathrubhumieng @mathrubhuminews @mahaabvp @asianet @asianetnewstv @DDNewsMalayalam @IeMalayalam @livenewskerala @marunadannews @Metrom_com_au @ML_Express @Nri_malayalee @thatsMalayalam @ZeeMalayalam @ddmalayalam @advprathibha @Forumkeralam1 @kerala_kaumudi @thenewsminute


      Delete
  71. Respected Captain,

    Namaskaram, There's a discussion on Twitter on Ayyappa not an avatar of Vishnu.
    They want 2 stick to Balarama and quote BHOOTHANATHA UPAKYANAM utter nothing about Ayyappa being avatar of Vishnu.

    https://mobile.twitter.com/surnell/status/1196801983018987520

    https://mobile.twitter.com/Kali_DarkEnergy/status/1196792962866245632

    Requesting you to enlighten us pls.

    ReplyDelete
    Replies
    1. TWO AVATARS AT THE SAME TIME -- BROTHERS ?

      BALLS !

      Delete
    2. Captain,

      Is it true that there were two avatars present at the same place - Parshurama & Rama? Is it true that Rama broke Shiv Dhanush?

      Delete
    3. PARASHURAMA AS A CHIRANJEEVI IS AN EXCEPTION..

      ALL SOULS OF ALL VISHNU AVATARS AFTER THEIR TENURE ON EARTH MERGED BACK WITH THE MOTHER FIELD OF BRAHMAN..

      THE SOUL OF PARASHURAMA WILL BE PRESENT OF PLANET EARTH TILL KALKI IS READY FOR ACTION..

      Delete
  72. IsR0 to launch catro satellite 3 on nov 25th, it will carry 13 nano satellites for US, why is it making the same mistake again? When will gujju chaiwala stop licking?

    ReplyDelete
  73. A MUSLIM PROFESSOR CANNOT DO JUSTICE TO SANSKRIT

    SANSKRIT IS STEEPED IN SPIRITUALITY... A SINGLE MESSIAH SINGLE HOLY BOOK MUSLIM CAN NEVER UNDERSTAND THE TRUE ESSENCE OF SANSKRIT..

    WE DONT WANT MUSLIMS TO TEACH SANSKRIT IN OUR UNIVERSITIES..

    ReplyDelete
    Replies
    1. Unless they understand karma and believe in rebirth all such panditry is useless to teach.

      Delete
  74. https://timesofindia.indiatimes.com/india/kamal-haasan-rajinikanth-hint-at-joining-hands-politically-if-need-arises/articleshow/72129792.cms

    BOTH ARE ATHEISTS

    BOTH DONT HAVE LEADERSHIP SPIRIT

    http://ajitvadakayil.blogspot.com/2017/08/rajnikanth-entry-into-politics-babaji.html

    RAJNIKANTH CANT EVEN TAKE A DECISION..

    ReplyDelete
  75. Dear captain,

    When I listen to RSN Singh, he sound very genuine. When I listen to Pushpendra Kulshreshtra, kuch gadbad hai daya..

    They were fighting for kashmir Issue.

    Your opinion sir?

    ReplyDelete
  76. https://timesofindia.indiatimes.com/india/to-parliament-question-govt-doesnt-spell-out-if-it-used-pegasus/articleshow/72131871.cms

    THE ELECTED EXECUTIVE HAS THE POWERS TO TAP INTO ANY PHONE FOR NATIONAL SECURITY..

    OUR COLLEGIUM JUDICIARY CREATED THE RED CORRIDOR WHERE 20% OF OUR LANDMASS IS CONTROLLED BY NAXALS...

    PUNISH DEEP STATE PAYROLL JUDGE RETIRED AND PRESENT , WHO TAMPERED WITH NATIONAL SECURITY...

    http://ajitvadakayil.blogspot.com/2017/08/right-to-privacy-in-india-is-not.html

    ReplyDelete
  77. https://twitter.com/tavleen_singh/status/1196614959909629953

    I AGREE

    MY ELDER SON WITNESSES THE ENORMOUS PLASTIC GARBAGE ON EITHER SIDE OF THE RAILWAY TRACK FROM DELHI TO AGRA..

    IT IS A NATIONAL SHAME.. SHIT ON EITHER SIDE OF THE RAIL TRACKS, BEING VIDEO GRAPHED BY WESTERN TOURISTS..

    BALLS TO MODIs SWATCH BHARAT..

    TO GET MODI TO DO THIS I HAD TO BOLLOCK HIM BADLY ( QUICK WIN AFTER BECOMING PM ) ..

    NOW THIS FELLOW SANS CHARACTER MODI CLAIMS THAT IT IS HIS OWN BRAINCHILD..

    ReplyDelete
  78. Hello Captain,

    Here is a video, an activist mentioning your name at 11:25

    https://www.youtube.com/watch?v=wuuRfo44TXg

    Thanks!
    Vinod

    ReplyDelete
    Replies
    1. "MUSLIM KILLED " CERTIFICATE IS TO MONOPOLIZE THE MEAT BUSINESS.

      ON SHIPS OF SCI-- MK CRTIFICATE WAS REQUIRED.. NOT ON SHIPS I COMMANDED

      Delete
    2. @Vinod, Veeresh Malik is Captain's friend.

      Veeresh sir speaks very fluidly Captain.

      Delete
    3. https://youtu.be/wuuRfo44TXg?t=922

      Captain, Mr.Malik claims that "Pork (Indian-Wild-Boar) was an important part of India's diet". He also claims Buddha's last meal was pork.

      Delete
  79. YOU MUST WATCH THE NETFLIX SERIES EL CHAPO. IT IS SINISTER AND SLICK..

    UNLIKE COLOMBIA , THE JEWISH OLIGARCHY DECIDES WHICH DRUG LORD WILL GET WHICH AREA.. THIS COMMUNICATION IS GIVEN IN A MEETINGS OF ALL DRUG LORDS ..

    IT IS A MEXICAN GOVT ORDER , DO IT OR ELSE..

    THE DEEP STATE SPONORS THE DRUGS FLOWING IN FROM MEXICO TO USA… THIS IS WHY ROTHSCHILDs MEDIA DOES NOT WANT A WALL.

    https://en.wikipedia.org/wiki/Guadalajara_Cartel

    EL CHAPO WAS A MERE DRIVER IN THE CARTEL OF JEW DRUG LORD MIGUEL ÁNGEL FÉLIX GALLARDO

    HE WAS MADE THE BOSS OF BOSSES BY THE MEXICAN GOVT.

    EL CHAPO WAS KING OF TUNNELS AT THE MEXICAN BORDER..

    https://www.youtube.com/watch?v=m7AUT0TB2Wq

    https://www.youtube.com/watch?v=_h1v7uEvFpE


    https://en.wikipedia.org/wiki/Joaqu%C3%ADn_%22El_Chapo%22_Guzm%C3%A1n

    EL CHAPO ESCAPED FROM JAIL BY A TUNNEL..

    https://www.youtube.com/watch?v=ZLW11qfuY4I

    GUADALAJARA AND MEXICO CITY IS INFESTED WITH JEWISH OLIGARCHS .. THEY WORK FOR THE DEEP STATE AND THEIR TENTACLES SPREAD EVERYWHERE CONTROLLING EVERYTHING IN MEXICO..

    JEWS CONTROLLED THE BLACK SLAVE TRADE OF MEXICO.. THE JEWISH VAEZ ACEVEDO FAMILY WERE THE KINGPINS OF THE SLAVE TRADE..
    .
    http://ajitvadakayil.blogspot.com/2012/05/black-african-slave-trade-and-jews-capt.html

    OVER THE APPROXIMATELY THREE HUNDRED YEARS IT LASTED, THE SLAVE TRADE BROUGHT ABOUT 320,000 AFRICANS TO THE COLONY.

    MANY BLACKS WERE BORN IN MEXICO AND FOLLOWED THEIR PARENTS INTO SLAVERY. NOT UNTIL 1829 WAS THE INSTITUTION ABOLISHED BY THE LEADERS OF THE NEWLY INDEPENDENT NATION.

    AFRICAN SLAVES LABORED IN THE SILVER MINES OF ZACATECAS, TAXCO, GUANAJUATO, AND PACHUCA IN THE NORTHERN AND CENTRAL REGIONS; ON THE SUGAR PLANTATIONS OF THE VALLE DE ORIZABA AND MORELOS IN THE SOUTH; IN THE TEXTILE FACTORIES ("OBRAJES") OF PUEBLA AND OAXACA ON THE WEST COAST AND IN MEXICO CITY; AND IN HOUSEHOLDS EVERYWHERE.

    INDIANS REPLACED THESE BLACK SLAVES..

    OUR NCERT SCHOOL HISTORY BOOKS MUST BE BURNT, AS IT GIVES ROTHSCHILD APPROVED HISTORY.. MODI AND HIS MINION KAYASTHA MINISTERS JAVEDEKAR/ PRASAD ENSURE WE ARE TAUGHT DEEP STATE SPONSORED FALSE HISTORY…

    http://ajitvadakayil.blogspot.com/2010/04/indentured-coolie-slavery-reinvented.html

    ROTHSCHILD SCREWED THE SPAINISH CATHOLIC CROWN..

    http://ajitvadakayil.blogspot.com/2012/10/explosion-on-ss-maine-grooming-of.html

    ALL MAJOR DRUG LORDS ( CAPOS ) IN MEXICO WERE JEWS …

    THE BASIC STRUCTURE OF A DRUG CARTEL IS AS FOLLOWS:

    FALCONS (SPANISH: HALCONES): CONSIDERED AS THE "EYES AND EARS" OF THE STREETS, THE "FALCONS" ARE THE LOWEST RANK IN ANY DRUG CARTEL. THEY ARE RESPONSIBLE FOR SUPERVISING AND REPORTING THE ACTIVITIES OF THE POLICE, THE MILITARY, AND RIVAL GROUPS.

    HITMEN (SPANISH: SICARIOS): THE ARMED GROUP WITHIN THE DRUG CARTEL, RESPONSIBLE FOR CARRYING OUT ASSASSINATIONS, KIDNAPPINGS, THEFTS, AND EXTORTIONS, OPERATING PROTECTION RACKETS, AND DEFENDING THEIR PLAZA (TURF) FROM RIVAL GROUPS AND THE MILITARY.

    LIEUTENANTS (SPANISH: TENIENTES): THE SECOND HIGHEST POSITION IN THE DRUG CARTEL ORGANIZATION, RESPONSIBLE FOR SUPERVISING THE HITMEN AND FALCONS WITHIN THEIR OWN TERRITORY. THEY ARE ALLOWED TO CARRY OUT LOW-PROFILE MURDERS WITHOUT PERMISSION FROM THEIR BOSSES.

    DRUG LORDS (SPANISH: CAPOS): THE HIGHEST POSITION IN ANY DRUG CARTEL, RESPONSIBLE FOR SUPERVISING THE ENTIRE DRUG INDUSTRY, APPOINTING TERRITORIAL LEADERS, MAKING ALLIANCES, AND PLANNING HIGH-PROFILE MURDERS.

    GUADALAJARA CARTEL WAS THE FIRST FULL-FLEDGED MEXICAN DRUG CARTEL, FROM WHICH MOST OF THE BIG CARTELS SPAWNED) .. SINALOA CARTEL SPAWNED FROM THE GUADALAJARA CARTEL

    https://en.wikipedia.org/wiki/Sinaloa_Cartel

    TIJUANA CARTEL SPAWNED FROM THE GUADALAJARA CARTEL

    https://en.wikipedia.org/wiki/Tijuana_Cartel

    CONTINUED TO 2--

    ReplyDelete
    Replies
    1. CONTINUED FROM 1-

      IN 2002 THE SINALOA CARTEL SAW THAT METH WAS THE NEXT BIG THING, AND STARTED TO BE ACTIVE IN CREATING THESE SUPER LABS AND MAKING THE METH IN MEXICO..

      PRECURSOR EPHEDRINE FOR METH WENT FROM INDIA TO MEXICO IN HUGE QUANTITIES .. INDIAN POLITICIANS/ JUDGES/ POLICE/ WERE ALL INOLVED .

      http://ajitvadakayil.blogspot.com/2017/02/breaking-bad-tv-serial-review-where.html

      CHEMICALLY SPEAKING, METHYLAMINE IS JUST AMMONIA WITH ONE HYDROGEN ATOM SWAPPED OUT FOR A METHYL GROUP—A CARBON ATOM AND THREE HYDROGEN ATOMS.

      JEW KING MAXIMILIAN I OF MEXICO ( YOUNGER BROTHER OF AUSTRIAN EMPEROR FRANZ JOSEPH I OF AUSTRIA ) IMPORTED RICH EUROPEAN JEWS TO MEXICO.

      THE MEXICAN REVOLUTION ( 1910 ) DROVE AWAY THE BLOOD SUCKING JEWS .. BETWEEN 1918 AND 1920 THE JEWS SRATED COMING INTO MEXICO IN THOUSANDS FROM RUSSIA, POLAND, LITHUANIA, THE BALKANS AND THE MIDDLE EAST. TEN THOUSAND ARRIVED FROM EASTERN EUROPE TO THE PORT OF VERACRUZ AT THE INVITATION OF PRESIDENT JEW PLUTARCO ELÍAS CALLES.

      THE MEXICAN INQUISITION SUCCEEDED IN ELIMINATING ALL VESTIGES OF OPEN JUDAISM IN MEXICO BUT THERE ARE AN ESTIMATED 24,000 MEXICANS WITH JEWISH ANCESTRY

      THE OFFICIAL JEWISH POPULATION IN MEXICO WAS ESTIMATED AT 23,000 IN 1930..

      THE CURRENT JEWISH POPULATION IS MORE THAN 65000, ABOUT 78% OF WHOM ARE IN MEXICO CITY , WHICH HAS MORE THAN 31 OPEN SYNAGOGUES. THERE ARE DOZENS OF HIDDEN SECRET ORTHODOX SYNAGOGUES ( MOSTLY HIDDEN INSIDE HOUSES ON AMSTERDAM AVENUE ) FOR THE CRYPTO JEWS .

      .. THESE JEWS ARE ALL VERY RICH..

      Capt ajit vadakayil
      ..

      Delete
  80. https://timesofindia.indiatimes.com/india/india-witnessing-average-sea-level-rise-of-1-7mm/year/articleshow/72134279.cms

    NONSENSE !

    ReplyDelete
  81. ALTAF HUSSAIN CRIED ON REPUBLIC TV, WHILE NARRATING ATROCITIES ON MUHAJIRS ( MUSLIMS WHO WERE TRICKED BY ROTHSCHILD , INTO MIGRATING TO PAKISTAN FROM INDIA IN 1947 )

    INDIA HAS MORE MUSLIMS THAN PAKISTAN..

    I KNOW FROM MY PAKISTANI OFFICERS THAT MUHAJIRS ARE THIRD CLASS CITIZENS OF PAKISTAN..

    THE MALAYALI FATHER OF THE IIT MADRAS MUSLIM GIRL WHO COMMITTED SUICIDE MUST BE WARNED-- NOT TO PLAY POLITICS ..

    ReplyDelete
    Replies
    1. No more refugees, asylum-seekers, etc. ! Enough is enough ! The glass of milk is full, there is too much sugar, too much salt, too much spice ! Only high-value expats must be allowed for business-&-training purpose that too on visas like how UAE does, no PR no citizenship !

      Delete
  82. Toilet paper busy stirring pot with a 12 year old being turned away.
    Were her parents retarded? They didn't know the rules?

    ReplyDelete
  83. WE ASK MODI.. WHY HAVE YOU TAKEN JNU WOMAN NIRMALA SITARAMAN AS YOUR FINANCE MINISTER?

    WILL SHE PAY OFF YOUR EXTRAVAGANT KOSHER PURCHASES ( LIKE RAFALE ) WITHOUT DEMUR? LIKE HOW ARUN JATLEY PAID OFF THE KOSHER BARAK MISSILE PURCHASES..

    SENIOR CITIZENS ( NON-PENSION ) WHO HAVE WORKED THEIR ASSES OFF THEIR WHOLE LIVES ARE NOW IN SHIT STREET..

    THEY HAVE PAID TAX-- DEDUCTED AT SOURCE BY THE COMPANY, AND THEN WHATEVER THEY SAVED FOR THEIR OLD AGE , THE INTEREST IS BEING TAXED AGAIN THIS TIME BY BANKS AT SOURCE..

    WHY THIS DOUBLE TAXATION ?

    IN KERALA OLD SENIOR CITIZENS HAVE TO PAY 900 RUPEES A DAY FOR UNSKILLED LABOUR --SAY TO CLEAN THE YARD..

    CAPT AJIT VADAKAYIL HAS LOST HIS TRUST IN INDIAN BANKS AFTER THE PMC SCAM ..

    I NOW PLAN TO SHIFT MY MONEY EARNED BY THE SWEAT OF MY BALLS AT SEA, INTO A FOREIGN BANK.. THIS IS WHAT YOU WANTED, RIGHT, NARENDRA DAMODARDAS MODI ? YOU HAVE GIVEN ROTHSCHILD A DRIVERS SEAT IN BANKING / INSURANCE ALL OVER AGAIN..

    YOUR JEWISH MASTERS WILL SOON GIVE YOU A NOBEL PRIZE..AFTER ALL YOU WORE A SIKH TURBAN FOR ROTHSCHILD IN 1976..

    capt ajit vadakayil
    ..
    PUT ABOVE COMMENT IN WEBSITES OF--
    PM MODI
    PMO
    AJIT DOVAL
    RAW
    NIA
    ED
    IB
    CBI
    AMIT SHAH
    HOME MINISTRY
    NEW CJI
    GOGOI
    ALL SUPREME COURT JUDGES
    ATTORNEY GENERAL
    ALL HIGH COURT CHIEF JUSTICES
    CMs OF ALL INDIAN STATES
    DGPs OF ALL STATES
    GOVERNORS OF ALL STATES
    PRESIDENT OF INDIA
    VP OF INDIA
    SPEAKER LOK SABHA
    SPEAKER RAJYA SABHA
    DEFENCE MINISTER - MINISTRY
    ALL THREE ARMED FORCE CHIEFS.
    RBI
    RBI GOVERNOR
    FINANCE MINISTER/ MINISTRY
    RAJEEV CHANDRASHEKHAR
    MOHANDAS PAI
    NITI AYOG
    AMITABH KANT
    RAM MADHAV
    RAJ THACKREY
    UDDHAV THACKREY
    VIVEK OBEROI
    GAUTAM GAMBHIR
    ASHOK PANDIT
    ANUPAM KHER
    KANGANA RANAUT
    VIVEK AGNIHOTRI
    KIRON KHER
    MEENAKSHI LEKHI
    SMRITI IRANI
    PRASOON JOSHI
    MADHUR BHANDARKAR
    SWAPAN DASGUPTA
    SONAL MANSINGH
    MADHU KISHWAR
    SUDHIR CHAUDHARY
    GEN GD BAKSHI
    SAMBIT PATRA
    RSN SINGH
    SWAMY
    RAJIV MALHOTRA
    THE QUINT
    THE SCROLL
    THE WIRE
    THE PRINT
    MK VENU
    MADHU TREHAN
    CLOSET COMMIE ARNAB GOSWMI
    RAJDEEP SARDESAI
    PAAGALIKA GHOSE
    NAVIKA KUMAR
    ANAND NARASIMHAN
    SRINIVASAN JAIN
    SONAL MEHROTRA KAPOOR
    VIKRAM CHANDRA
    NIDHI RAZDAN
    FAYE DSOUZA
    RAVISH KUMAR
    PRANNOY JAMES ROY
    AROON PURIE
    VINEET JAIN
    RAGHAV BAHL
    SEEMA CHISTI
    DILEEP PADGOANKAR
    VIR SANGHVI
    KARAN THAPAR
    PRITISH NANDI
    SHEKHAR GUPTA
    SIDHARTH VARADARAJAN
    ARUN SHOURIE
    N RAM
    NCW
    REKHA SHARMA
    SWATI MALLIWAL
    CHETAN BHAGAT
    I&B DEPT/ MINISTER
    LAW MINISTER/ MINISTRY
    WEBSITES OF DESH BHAKT LEADERS
    SPREAD OF SOCIAL MEDIA

    ReplyDelete
    Replies
    1. Sent emails to-

      nsitharaman@gmail.com
      mosfinance@nic.in,
      appointment.fm@gov.in,
      secyexp@nic.in,
      pramod.das@gov.in,
      mathewag@nic.in,

      & many others..

      Sent DM on N.Sitaraman's Facebook page -- https://www.facebook.com/nirmala.sitharaman/

      Delete
  84. https://www.facebook.com/100004403417133/posts/1435743223249111?d=n&sfns=mo


    Video about JNU from an insider doing rounds.

    ReplyDelete
  85. Dear Capt Ajit sir,
    I saw it live few days back when Baba Ramdev used choicest words for Owaisi, EVR Periyar followers...something made him say it...not normal for him...something is brewing...Kalki effect is ON. https://www.news18.com/news/buzz/twitter-trends-boycottpatanjali-after-ramdev-calls-ambedkar-and-periyar-supporters-intellectual-terrorists-2390835.html

    ReplyDelete
  86. dear captain,
    despite reading a few vedantic texts(includind those by AdiShankara), it was not easy to understand the concept of TAT TWAM ASI. Every body tells tha paramatma resides in you. Nobody mentions that there is a frequency change in the Atma and Paramatma. Your explanation that paramatma shears a part of himself and sends it to earth makes things crystal clear. The text says it is very difficult to comprehend paramatma. with your explanation i am able to make progress in the spiritual path. My salutations to you.

    ReplyDelete
  87. *Bankruptcy Proceedings initiated against Aviva Life Insurance*

    Aviva Life once offered the Lowest Rate for Term Insurance.Now what happens to those Policy Holders and all those pending death claims?
    The claims will never be settled.And the Policy documents can be thrown into the Trash Can.

    *This is a nice wake up call for those who talk about Pvt Life & Health Insurance Companies offering Lower Premium Rate on Term & Health Insurance Policies.*

    Aviva Life Insurance offered one of the lowest Term Insurance Premium Rates.But no Company can run a business at a loss for a longer period of time by merely offering discounted rates.

    Wait and watch.Many more Pvt Insurance Companies to follow.

    *Reliance Health Insurance has been banned for failure to maintain required solvency margins since June 2019.*

    Doing rounds

    ReplyDelete
  88. R's Kaurava Sena (bjp) has done more harm (sabrimala, homosexuality) than R's pandavas sena (congress) to bharata

    ReplyDelete
  89. https://in.news.yahoo.com/kerala-govt-sabarimala-authority-contempt-155550121.html

    LAST YEAR HUNDREDS OF PILGRIMS WERE THROWN INTO JAIL .. MOST OF THEM LOST THEIR JOBS

    BJP/ AMIT SHAH/ RSS/ MODI / CHANDRACHUD/ NARIMAN -- WANTED AN ENCORE, HOPING THAT PINARAI VIJAYANs VOTES WILL BE TRANSFERRED TO BJP..

    SUPREME COURT DELIBERATELY KEPT THE REVIEW PETITION IN SUSPENDED ANIMATION..

    PINARAYI VIJAYAN IS NOT AN IDIOT.. HE HAS PEOPLEs SUPPORT..

    JUST SHOWS WHAT A CHARACTERLESS ORG BJP/ RSS IS.. MODI /AMIT SHAH LACK INTEGRITY..

    PEOPLE IN KERALA WILL NEVER FORGIVE THESE BJP / RSS GOONS OCCUPYING PM/ HOME MINISTERs CHAIRS....

    ReplyDelete
  90. https://www.oneindia.com/india/make-new-law-exclusively-for-sabarimala-in-4-weeks-sc-tells-kerala-govt-2981201.html

    WE THE PEOPLE DONT CARE FOR SUPREME COURT DEAD LINES..

    IMAGINE UNDERTRIALS HAVE BEEN ROTTING IN JAIL FOR 30 YEARS..

    http://ajitvadakayil.blogspot.com/2014/09/undertrial-prisoners-in-indian-remand.html

    IT IS HIGH TIME WE CREATE MILITARY COURTS TO PUNISH DESH DROHI JUDGES IN FOREIGN PAYROLL.

    ReplyDelete
  91. Guruji Pranam
    In such a situation when banks are not trustworthy will it be wise to invest in gold???
    Regards

    ReplyDelete
  92. "PUTIN: From Agent to President. VERY RARE Videos and Photos. Alpha Male Walk. #PutinStyle"

    https://www.youtube.com/watch?v=qlRLXmvN3tY

    ReplyDelete
  93. Dear Capt Ajit sir,

    In order to make India #1, we should be fit and healthy and nowadays people want instant results rather than traditional, which they don't believe and so to go with the trend...is this Green coffee thingy good for reducing obesity ??
    https://greencoffeebeans.co.in/216ca.fit/desk/?d7s3numgtlllqotqhaevbv0q
    Why Does It Have Scientists And Media Buzzing?

    The most talked about natural weight loss is finally here! Beans that grows in Vietnam,Brazil,Cuba and India, Green Coffee is a revolutionary breakthrough!

    Green Coffee contains a key ingredient CGA (Chlorogenic Acid), which is extracted from the rind of the beans. This is mother nature's answer to weight loss!

    ReplyDelete
  94. jews are a consequence of parashurama's mistake so he needs to purge the plague from earth just like he did the last time

    ReplyDelete
    Replies
    1. Yes. Let's hope soon. Crypto jew lineage in many countries is lessening. Let's pray.

      Delete
    2. However i feel that pir ali, kayastha and crypto jew population in india is much bigger than assumed.

      Delete
  95. Dear Capt Ajit sir,

    You were right, the Breaking of the Berlin wall was a slip of the tongue...
    1. The fall of the Berlin Wall happened by mistake.
    At a press conference on the evening of November 9, 1989, East German politburo member Günter Schabowski prematurely announced that restrictions on travel visas would be lifted. When asked when the new policy would begin, he said, “Immediately, without delay.” In actuality, the policy was to be announced the following day and would still have required East Germans to go through a lengthy visa application process. Schabowski’s confused answers and erroneous media reports that border crossings had opened spurred thousands of East Berliners to the Berlin Wall.

    At the Bornholmer Street checkpoint, Harald Jäger, the chief officer on duty, faced a mob growing in size and frustration. Receiving insults, rather than instructions, from his superiors and nervously expecting results of his cancer diagnostic tests the next day, the overwhelmed Jäger opened the border crossing on his own, and the other gates soon followed.
    https://www.history.com/news/10-things-you-may-not-know-about-the-berlin-wall


    https://www.boredpanda.com/berlin-wall-anniversary-120000-ribbons/?fbclid=IwAR1_bxSs_kRxhcGqamcLWsh0QVWRtP-e8Q1dbnUZ9tqrBIsXKQh5len1AZM

    ReplyDelete
  96. Dear Ajit Sir,

    Saw a pic of M K Gandhi.
    Someone has clearly highlighted the 4spots where Gandhi was shot.

    It's altogether 4 bullets but FIR is made for 3 bullets only.
    4 bullet spots are clearly visible.
    The FIR is also in public domain for everyone.

    Why?
    Plz do clarify.

    Gandhi ko pehle kisne mara?

    Last time when you put about Gandhi you kept it a suspense that it was 3 or was it 4 《- in this manner

    I wish I could upload the picture & the FIR for you to see.

    But what teases the brain is that who shot the first bullet?

    Kaun tha?

    Kindly plz break this mystery, atleast the mystery will flush down the gutter forever.

    Regards

    ReplyDelete
    Replies
    1. Ajit Sir,

      Kept searching in that Champaran movement series regarding Gandhi, still couldn't get it.

      Gandhi ko kisne maara

      Sashtri ka aapne reveal kar diya tha, Gandhi kyun mysterious hai ab tak

      Kaun ho sakta hai?

      Who shot the first bullet?

      Plz reveal kar dijiye sab k liye

      Kindly plzzz

      I'm not getting it

      Searched a lot

      Delete
  97. Dear Capt Ajit sir,
    Why is Vatican investing on these kind of things...

    Some comments from the post :
    1.The Lucifer telescope is more powerful then the so called Hubble telescope.
    2. The Vatican has everything and anything Terrestrial or from the past from the Giants or technology revealed to man. They have so much stuff. The Vatican is super wealthy.

    https://www.youtube.com/watch?v=_b4o_zUtBDs&feature=youtu.be&fbclid=IwAR0xAAUUXqjaTNX3FKdDiTDiKrQCiPhssb5ru_pr8OHGGmax2nqx8DhYDTc

    ReplyDelete
  98. Dear Readers,
    Malayali Father helping out in Hindi Homework.

    ROFL

    Many words I didn't understand but there are this expressions, Lol

    The father says, don't question & complains that this generation kids are different while during his time no one questioned back to the tutor.
    Pakka Malayali all 3 of them, by face & expressions

    Even a region has got it's identity in terms of expression.

    India is a beautiful country indeed.

    ReplyDelete
  99. https://timesofindia.indiatimes.com/india/will-not-allow-nrc-in-bengal-there-will-be-no-division-on-the-basis-of-religion-mamata-banerjee/articleshow/72142017.cms

    KICK OUT ALL ILLEGAL BANGLADESI MUSLIMS FROM BENGAL

    ReplyDelete
  100. https://aninews.in/news/world/asia/india-singapore-reaffirm-partnership-to-promote-regional-stability20191120174730/

    India to allow Integrated Test Range to foreign countries.

    ReplyDelete