Trending December 2023 # The Amd Ryzen Xt Series Skimps On Bundled Coolers And Clock Speeds # Suggested January 2024 # Top 19 Popular

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With its new Ryzen XT-series CPUs, AMD may have stolen a page from Intel’s playbook by offering a moderate clock speed bump while taking away the bundled cooler.

Ryzen 9 3900XT with 12 cores, a boost clock of 4.7GHz, and 105-watt TDP for $499

Ryzen 7 3800XT with 8 cores, a boost clock of 3.9GHz, and 105W TDP for $399

Ryzen 5 3600XT with 6 cores, a boost clock of 4.5GHz, and 95W TDP for $249

On paper, they offer little change compared to the original Ryzen 3000-series launch. The only spec difference is a 100MHz higher clock for the Ryzen 9 3900XT over the original Ryzen 3900X, a 200MHz boost for the Ryzen 7 3800XT over the Ryzen 7 3800X, and a 100MHz bump for the Ryzen 5 3600XT over the Ryzen 5 3600X.

Coming almost a year after the original mind-blowing Ryzen launch, the XT-series’ mere 4-percent clock boost, is likely one big letdown to many hardware fans. Intel took flak for a slow drip of single-digit clock bumps over many generations of new CPUs.


The Ryzen 5 3600XT will come with a cooler, while the Ryzen 9 XT and Ryzen 7 XT won’t.

Farewell to the free cooler

That’s not the only Intel-like move hardware junkies are likely to decry. For years, Intel has not included any cooler with its K-series of overclockable CPUs. Intel’s rationale was that most enthusiasts would want to choose their own after-market cooler. 

AMD flipped that stance on its head when it decided to offer fairly decent coolers with most of its X-series of performance CPUs. With the XT, AMD is shifting its position. The company will bundle a Wraith cooler with its Ryzen 5 3600XT, but the Ryzen 9 3900XT and the Ryzen 7 3800XT will no longer do so.

AMD’s explanation echoes Intel’s. “The AMD Ryzen 9 3900XT, AMD Ryzen 7 3800XT and Ryzen 5 3600XT processors feature tailored specifications engineered for enthusiasts who regularly choose aftermarket cooling for the highest possible performance,” AMD officials said in a press release this morning. “As a result, AMD is recommending the use of an AIO solution with a minimum 280mm radiator or equivalent air cooling to experience these products at their best.” There’s a good chance AMD will get the same blowback Intel has for this move.


That Ryzen 7 XT box won’t look likke this one since it won’t come with a bundled cooler.

Why did AMD take away the cooler?

(We should point out the original Ryzen 3000X parts (with cooler!) are considerably lower in price now after a year on the street. A Ryzen 3900X on Amazon, for example, goes for $416.)

New A520 chipset 

Besides the new CPUs, AMD also confirmed it has a budget A520 chipset on tap too. The company described the chipset as a product to offer “a  streamlined, trusted platform to satisfy everyday PC users.”

One last bit of news from AMD concerned its StoreMI technology, which improves storage performance through tiering of data from an SSD to a hard drive. AMD said StoreMI has been reimagined for 2023 with a new user interface and features, as well as improved performance from newer algorithms. The company did not release details on when StoreMI 2.0 would be released, nor what its compatibility would be with previous motherboards.

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Microsoft Surface Laptop 4 Launched With Amd Ryzen And Intel Cpu Options

After launching the new Surface Laptop Go in the global market last year, Microsoft has now unveiled its new Surface Laptop 4 lineup. The Redmond giant is offering a choice between Intel-powered or AMD’s Ryzen-powered Surface Laptop 4 this year. The CPU options are available for both the 13.5-inch and the 15-inch models.

Surface Laptop 4 Launched: Specifications

The Surface Laptop 4 features the same design as the Surface Laptop 3, which was unveiled back in 2023. Both the 13.5-inch and 15-inch variants pack a PixelSense touchscreen display with a 3:2 aspect ratio and 2256 x 1504-pixel resolution (201 PPI). The bezels are fairly slim and you have an HD camera, along with a studio microphone array, onboard.

The Surface Laptop 4 now comes with either the latest 11th-gen Intel Core processor or AMD Ryzen 4000-series CPU. Now, it is worth mentioning that Microsoft is not using the latest Ryzen 5000-series processors like other laptop makers such as Acer or Asus. Instead, the company is sticking with the older Ryzen 4000-series CPUs with Zen 2 architecture. As for the Intel variants, you have Intel’s latest Iris Xe graphics onboard as well.

As for the ports selection, Microsoft Surface Laptop 4 includes 1x USB Type-C port, 1x USB-A port, a 3.5mm headphone jack, and the Surface Connect port. Much like the Surface Pro 7+ from earlier this year, this device also includes a removable SSD storage slot on the rear.

Price and Availability Surface Laptop 4 (13.5-inch Models)

Now, the base model of the 13.5-inch Surface Laptop 4 comes with either the AMD Ryzen 5 4680U CPU or the Intel Core i5 1135G7 processor. Both the variants pack 8GB of RAM and 256GB of SSD storage and start at $999 (~Rs. 75,132).

However, the Intel CPU-powered model can go up to $2,299 (~Rs 1,72,902) for the top model that comes with an Intel Core i7 CPU paired with 32GB of RAM and 1TB of SSD storage. Moreover, Microsoft plans to bring a higher-end AMD model with 16GB of RAM and 256GB of SSD storage later this year. It will be priced starting at $1,199 (~Rs 90,173).

The 13.5-inch Surface Laptop 4 in two finishes and four color variants. The new Ice Blue and Platinum variants have tone-on-tone Alcantara finish, whereas the Sandstone and Matte Black variants boast an all-metal finish.

Surface Laptop 4 (15-inch Models)

The 15-inch AMD-powered Surface Laptop 4 variants, on the other hand, start at $1,299 for the base model. It packs the Ryzen 7 4980U CPU, paired with 8GB of RAM and 256GB of SSD storage. The higher-end variant comes with 16GB of RAM, 512GB of SSD storage, and is priced at $1,699 (~Rs 1,27,799).

As for the color variants, you can choose between Platinum and Matte Black metal finishes for the 15-inch Surface Laptop 4. There is no Alcantara finish available for this variant.

The Surface Laptop 4 will go on sale in the US, Japan, and Canada from April 15 (i.e tomorrow). There is currently no word for when Microsoft will bring its latest Surface Laptop to other markets.

Time Series Analysis And Forecasting


Time Series Data Analysis is a way of studying the characteristics of the response variable with respect to time as the independent variable. To estimate the target variable in the name of predicting or forecasting, use the time variable as the point of reference. A Time-Series represents a series of time-based orders. It would be Years, Months, Weeks, Days, Horus, Minutes, and Seconds. It is an observation from the sequence of discrete time of successive intervals.

The time variable/feature is the independent variable and supports the target variable to predict the results. Time Series Analysis (TSA) is used in different fields for time-based predictions – like Weather Forecasting models, Stock market predictions, Signal processing, Engineering domain – Control Systems, and Communications Systems. Since TSA involves producing the set of information in a particular sequence, this makes it distinct from spatial and other analyses. We could predict the future using AR, MA, ARMA, and ARIMA models.

Learning Objectives

We will discuss in detail TSA Objectives, Assumptions, and Components (stationary and non-stationary).

We will look at the TSA algorithms.

Finally, we will look at specific use cases in Python.

This article was published as a part of the Data Science Blogathon.

Table of Contents What Is Time Series Analysis?

Definition: If you see, there are many more definitions for TSA. But let’s keep it simple.

A time series is nothing but a sequence of various data points that occurred in a successive order for a given period of time

Objectives of Time Series Analysis:

To understand how time series works and what factors affect a certain variable(s) at different points in time.

Time series analysis will provide the consequences and insights of the given dataset’s features that change over time.

Supporting to derive the predicting the future values of the time series variable.

Assumptions: There is only one assumption in TSA, which is “stationary,” which means that the origin of time does not affect the properties of the process under the statistical factor.

How to Analyze Time Series?

To perform the time series analysis, we have to follow the following steps:

Collecting the data and cleaning it

Preparing Visualization with respect to time vs key feature

Observing the stationarity of the series

Developing charts to understand its nature.

Model building – AR, MA, ARMA and ARIMA

Extracting insights from prediction

Significance of Time Series

TSA is the backbone for prediction and forecasting analysis, specific to time-based problem statements.

Analyzing the historical dataset and its patterns

Understanding and matching the current situation with patterns derived from the previous stage.

Understanding the factor or factors influencing certain variable(s) in different periods.

With the help of “Time Series,” we can prepare numerous time-based analyses and results.

Forecasting: Predicting any value for the future.

Segmentation: Grouping similar items together.

Classification: Classifying a set of items into given classes.

Descriptive analysis: Analysis of a given dataset to find out what is there in it.

Intervention analysis: Effect of changing a given variable on the outcome.

Components of Time Series Analysis

Let’s look at the various components of Time Series Analysis-

Trend: In which there is no fixed interval and any divergence within the given dataset is a continuous timeline. The trend would be Negative or Positive or Null Trend

Seasonality: In which regular or fixed interval shifts within the dataset in a continuous timeline. Would be bell curve or saw tooth

Cyclical: In which there is no fixed interval, uncertainty in movement and its pattern

Irregularity: Unexpected situations/events/scenarios and spikes in a short time span.

What Are the limitations of Time Series Analysis?

Time series has the below-mentioned limitations; we have to take care of those during our data analysis.

Similar to other models, the missing values are not supported by TSA

The data points must be linear in their relationship.

Data transformations are mandatory, so they are a little expensive.

Models mostly work on Uni-variate data.

Data Types of Time Series

Let’s discuss the time series’ data types and their influence. While discussing TS data types, there are two major types – stationary and non-stationary.

Stationary: A dataset should follow the below thumb rules without having Trend, Seasonality, Cyclical, and Irregularity components of the time series.

The mean value of them should be completely constant in the data during the analysis.

The variance should be constant with respect to the time-frame

Covariance measures the relationship between two variables.

Non- Stationary: If either the mean-variance or covariance is changing with respect to time, the dataset is called non-stationary.

Methods to Check Stationarity

During the TSA model preparation workflow, we must assess whether the dataset is stationary or not. This is done using Statistical Tests. There are two tests available to test if the dataset is stationary:

Augmented Dickey-Fuller (ADF) Test

Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Test

Augmented Dickey-Fuller (ADF) Test or Unit Root Test

The ADF test is the most popular statistical test. It is done with the following assumptions:

Null Hypothesis (H0): Series is non-stationary

Alternate Hypothesis (HA): Series is stationary

p-value <= 0.05 Accept (H1)

Kwiatkowski–Phillips–Schmidt–Shin (KPSS) Test

These tests are used for testing a NULL Hypothesis (HO) that will perceive the time series as stationary around a deterministic trend against the alternative of a unit root. Since TSA is looking for Stationary Data for its further analysis, we have to ensure that the dataset is stationary.

Converting Non-Stationary Into Stationary

Let’s discuss quickly how to convert non-stationary to stationary for effective time series modeling. There are three methods available for this conversion – detrending, differencing, and transformation.


It involves removing the trend effects from the given dataset and showing only the differences in values from the trend. It always allows cyclical patterns to be identified.


This is a simple transformation of the series into a new time series, which we use to remove the series dependence on time and stabilize the mean of the time series, so trend and seasonality are reduced during this transformation.

Yt= Yt – Yt-1

Yt=Value with time


This includes three different methods they are Power Transform, Square Root, and Log Transfer. The most commonly used one is Log Transfer.

Moving Average Methodology

The Moving Average (MA) (or) Rolling Mean: The value of MA is calculated by taking average data of the time-series within k periods.

Let’s see the types of moving averages:

Simple Moving Average (SMA),

Cumulative Moving Average (CMA)

Exponential Moving Average (EMA)

Simple Moving Average (SMA)

The SMA is the unweighted mean of the previous M or N points. The selection of sliding window data points, depending on the amount of smoothing, is preferred since increasing the value of M or N improves the smoothing at the expense of accuracy.

To understand better, I will use the air temperature dataset.

import pandas as pd from matplotlib import pyplot as plt from import plot_acf df_temperature = pd.read_csv('temperature_TSA.csv', encoding='utf-8') df_temperature.head() # set index for year column df_temperature.set_index('Any', inplace=True) = 'year' # Yearly average air temperature - calculation df_temperature['average_temperature'] = df_temperature.mean(axis=1) # drop unwanted columns and resetting the datafreame df_temperature = df_temperature[['average_temperature']] df_temperature.head() # SMA over a period of 10 and 20 years  df_temperature['SMA_10'] = df_temperature.average_temperature.rolling(10, min_periods=1).mean() df_temperature['SMA_20'] = df_temperature.average_temperature.rolling(20, min_periods=1).mean() # Grean = Avg Air Temp, RED = 10 yrs, ORANG colors for the line plot colors = ['green', 'red', 'orange'] # Line plot df_temperature.plot(color=colors, linewidth=3, figsize=(12,6)) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.legend(labels =['Average air temperature', '10-years SMA', '20-years SMA'], fontsize=14) plt.title('The yearly average air temperature in city', fontsize=20) plt.xlabel('Year', fontsize=16) plt.ylabel('Temperature [°C]', fontsize=16) Cumulative Moving Average (CMA)

The CMA is the unweighted mean of past values till the current time.

# CMA Air temperature df_temperature['CMA'] = df_temperature.average_temperature.expanding().mean() # green -Avg Air Temp and Orange -CMA colors = ['green', 'orange'] # line plot df_temperature[['average_temperature', 'CMA']].plot(color=colors, linewidth=3, figsize=(12,6)) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.legend(labels =['Average Air Temperature', 'CMA'], fontsize=14) plt.title('The yearly average air temperature in city', fontsize=20) plt.xlabel('Year', fontsize=16) plt.ylabel('Temperature [°C]', fontsize=16) Exponential Moving Average (EMA)

EMA is mainly used to identify trends and filter out noise. The weight of elements is decreased gradually over time. This means It gives weight to recent data points, not historical ones. Compared with SMA, the EMA is faster to change and more sensitive.

It has a value between 0,1.

Represents the weighting applied to the very recent period.

Let’s apply the exponential moving averages with a smoothing factor of 0.1 and 0.3 in the given dataset.

# EMA Air Temperature # Let's smoothing factor - 0.1 df_temperature['EMA_0.1'] = df_temperature.average_temperature.ewm(alpha=0.1, adjust=False).mean() # Let's smoothing factor - 0.3 df_temperature['EMA_0.3'] = df_temperature.average_temperature.ewm(alpha=0.3, adjust=False).mean() # green - Avg Air Temp, red- smoothing factor - 0.1, yellow - smoothing factor - 0.3 colors = ['green', 'red', 'yellow'] df_temperature[['average_temperature', 'EMA_0.1', 'EMA_0.3']].plot(color=colors, linewidth=3, figsize=(12,6), alpha=0.8) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.legend(labels=['Average air temperature', 'EMA - alpha=0.1', 'EMA - alpha=0.3'], fontsize=14) plt.title('The yearly average air temperature in city', fontsize=20) plt.xlabel('Year', fontsize=16) plt.ylabel('Temperature [°C]', fontsize=16) Time Series Analysis in Data Science and Machine Learning

When dealing with TSA in Data Science and Machine Learning, there are multiple model options are available. In which the Autoregressive–Moving-Average (ARMA) models with [p, d, and q].

q== moving average lags

Before we get to know about Arima, first, you should understand the below terms better.

Auto-Correlation Function (ACF)

Partial Auto-Correlation Function (PACF)

Auto-Correlation Function (ACF)

ACF is used to indicate how similar a value is within a given time series and the previous value. (OR) It measures the degree of the similarity between a given time series and the lagged version of that time series at the various intervals we observed.

Python Statsmodels library calculates autocorrelation. This is used to identify a set of trends in the given dataset and the influence of former observed values on the currently observed values.

Partial Auto-Correlation (PACF)

PACF is similar to Auto-Correlation Function and is a little challenging to understand. It always shows the correlation of the sequence with itself with some number of time units per sequence order in which only the direct effect has been shown, and all other intermediary effects are removed from the given time series.

Auto-Correlation and Partial Auto-Correlation plot_acf(df_temperature) plot_acf(df_temperature, lags=30)

Observation: The previous temperature influences the current temperature, but the significance of that influence decreases and slightly increases from the above visualization along with the temperature with regular time intervals.

Types of Auto-Correlation

Interpret ACF and PACF plots

ACFPACFPerfect ML -ModelPlot declines graduallyPlot drops instantlyAuto Regressive chúng tôi drops instantlyPlot declines graduallyMoving Average modelPlot decline graduallyPlot Decline graduallyARMAPlot drop instantlyPlot drop instantlyYou wouldn’t perform any model

Remember that both ACF and PACF require stationary time series for analysis.

Now, we will learn about the Auto-Regressive model.

What Is an Auto-Regressive Model?

An auto-regressive model is a simple model that predicts future performance based on past performance. It is mainly used for forecasting when there is some correlation between values in a given time series and the values that precede and succeed (back and forth).

An AR model is a Linear Regression model that uses lagged variables as input. The Linear Regression model can be easily built using the scikit-learn library by indicating the input. Statsmodels library is used to provide autoregression model-specific functions where you have to specify an appropriate lag value and train the model. It is provided in the AutoTeg class to get the results using simple steps.

Creating the model AutoReg()

Call fit() to train it on our dataset.

Returns an AutoRegResults object.

Once fit, make a prediction by calling the predict () function

The equation for the AR model (Let’s compare Y=mX+c)

Yt =C+b1 Yt-1+ b2 Yt-2+……+ bp Yt-p+ Ert

Key Parameters

p=past values

Yt=Function of different past values

Ert=errors in time


Lets’s check whether the given data set or time series is random or not

from matplotlib import pyplot from pandas.plotting import lag_plot lag_plot(df_temperature)

Observation: Yes, it looks random and scattered.

Implementation of Auto-Regressive Model #import libraries from matplotlib import pyplot from statsmodels.tsa.ar_model import AutoReg from sklearn.metrics import mean_squared_error from math import sqrt # load csv as dataset #series = read_csv('daily-min-temperatures.csv', header=0, index_col=0, parse_dates=True, squeeze=True) # split dataset for test and training X = df_temperature.values train, test = X[1:len(X)-7], X[len(X)-7:] # train autoregression model = AutoReg(train, lags=20) model_fit = print('Coefficients: %s' % model_fit.params) # Predictions predictions = model_fit.predict(start=len(train), end=len(train)+len(test)-1, dynamic=False) for i in range(len(predictions)): print('predicted=%f, expected=%f' % (predictions[i], test[i])) rmse = sqrt(mean_squared_error(test, predictions)) print('Test RMSE: %.3f' % rmse) # plot results pyplot.plot(test) pyplot.plot(predictions, color='red')


predicted=15.893972, expected=16.275000 predicted=15.917959, expected=16.600000 predicted=15.812741, expected=16.475000 predicted=15.787555, expected=16.375000 predicted=16.023780, expected=16.283333 predicted=15.940271, expected=16.525000 predicted=15.831538, expected=16.758333 Test RMSE: 0.617

Observation: Expected (blue) Against Predicted (red). The forecast looks good on the 4th and the deviation on the 6th day.

Implementation of Moving Average (Weights – Simple Moving Average) import numpy as np alpha= 0.3 n = 10 w_sma = np.repeat(1/n, n) colors = ['green', 'yellow'] # weights - exponential moving average alpha=0.3 adjust=False w_ema = [(1-ALPHA)**i if i==N-1 else alpha*(1-alpha)**i for i in range(n)] pd.DataFrame({'w_sma': w_sma, 'w_ema': w_ema}).plot(color=colors, kind='bar', figsize=(8,5)) plt.xticks([]) plt.yticks(fontsize=10) plt.legend(labels=['Simple moving average', 'Exponential moving average (α=0.3)'], fontsize=10) # title and labels plt.title('Moving Average Weights', fontsize=10) plt.ylabel('Weights', fontsize=10) Understanding ARMA and ARIMA

ARMA is a combination of the Auto-Regressive and Moving Average models for forecasting. This model provides a weakly stationary stochastic process in terms of two polynomials, one for the Auto-Regressive and the second for the Moving Average.

ARMA is best for predicting stationary series. ARIMA was thus developed to support both stationary as well as non-stationary series.


Understand the signature of ARIMA

Implementation Steps for ARIMA

Step 1: Plot a time series format

Step 2: Difference to make stationary on mean by removing the trend

Step 3: Make stationary by applying log transform.

Step 4: Difference log transform to make as stationary on both statistic mean and variance

Step 5: Plot ACF & PACF, and identify the potential AR and MA model

Step 6: Discovery of best fit ARIMA model

Step 7: Forecast/Predict the value using the best fit ARIMA model

Step 8: Plot ACF & PACF for residuals of the ARIMA model, and ensure no more information is left.

Implementation of ARIMA in Python

We have already discussed steps 1-5 which will remain the same; let’s focus on the rest here.

from statsmodels.tsa.arima_model import ARIMA model = ARIMA(df_temperature, order=(0, 1, 1)) results_ARIMA = results_ARIMA.summary() results_ARIMA.forecast(3)[0] Output: array([16.47648941, 16.48621826, 16.49594711]) results_ARIMA.plot_predict(start=200) Process Flow (Re-Gap)

In recent years, the use of Deep Learning for Time Series Analysis and Forecasting has increased to resolve problem statements that couldn’t be handled using Machine Learning techniques. Let’s discuss this briefly.

Recurrent Neural Networks (RNN) is the most traditional and accepted architecture fitment for Time-Series forecasting-based problems.

RNN is organized into successive layers and divided into




Each layer has equal weight, and every neuron has to be assigned to fixed time steps. Do remember that every one of them is fully connected with a hidden layer (Input and Output) with the same time steps, and the hidden layers are forwarded and time-dependent in direction.

Components of RNN

Input: The function vector of x(t)​ is the input at time step t.


The function vector h(t)​ is the hidden state at time t,

This is a kind of memory of the established network;

This has been calculated based on the current input x(t) and the previous-time step’s hidden-state h(t-1):

Output: The function vector y(t) ​is the output at time step t.

Weights : Weights: In the RNNs, the input vector connected to the hidden layer neurons at time t is by a weight matrix of U (Please refer to the above picture),

Internally weight matrix W is formed by the hidden layer neurons of time t-1 and t+1. Following this, the hidden layer with to the output vector y(t) of time t by a V (weight matrix); all the weight matrices U, W, and V are constant for each time step.


A time series is constructed by data that is measured over time at evenly spaced intervals. I hope this comprehensive guide has helped you all understand the time series, its flow, and how it works. Although the TSA is widely used to handle data science problems, it has certain limitations, such as not supporting missing values. Note that the data points must be linear in their relationship for Time Series Analysis to be done.

Key Takeaways

Time series is a sequence of various data points that occurred in a successive order for a given period of time.

Trend, Seasonality, Cyclical, and Irregularity are components of TSA.

Frequently Asked Questions Related

Amd Barcelona Arrives, The Processor War Heats Up

AMD Barcelona arrives, the processor war heats up

This moment was a long time coming. We’ve heard about the Barcelona line of processors from AMD for some time now, but the release date kept getting pushed further and further back due to unnamed “complications.” In a world where dual core has become the norm, AMD is pushing the envelope by providing us with four cores of processing. The new quad-core Barcelona Opteron processors are supposed to be faster, more efficient, and more powerful than anything that AMD has offered in a consumer-level desktop.

As you may already know, Intel released their version of the quad core processor back in November 2006, placing AMD nearly a full generation behind its primary competitor. Talk to any AMD representative, however, and they’ll tell you that they’re actually ahead of the game, not behind it. This is because the Intel quad-core processor really just pulls two dual core processors and melds them into a single package. By contrast, the AMD solution is that of a “native” quad-core design. They say that this design will outdo Intel not only in terms of performance, but also power efficiency.

John Fruehe, worldwide business development manager for AMD’s server and workstation division, said that “the fact that it has four cores is probably the most boring part.” He goes on to describe such features like the “new 2MB level 3 cache that all four cores can share, [and] each core continues to have its own independent level 2 cache, so that you get better performance.” This is all a part of the three-stage cache architecture. The L1, L2, and L3 cache are 64KB, 512KB, and 2MB respectively with the first two caches being core-specific. AMD feels that this design “is better suited for the coming age of virtualization.”

In many ways, Barcelona is not a wholly new architecture as much as it is an improvement over current designs. AMD took what they already had and made it better, rather than creating something completely new altogether. It will be interesting to see actual systems in action, comparing AMD’s quad-core solution against those offered by Intel, the company that still outsells AMD by a fairly significant margin.

In fact, on the same day that AMD finally announced the availability of the Barcelona microprocessors (today), Intel decided to rain on their parade by issuing a statement telling the world that Intel processors are selling better than ever and are doing much better than expected. Normally, this wouldn’t be a cause for alarm for AMD, but given that Intel wasn’t scheduled to make an earnings announcement until October 16th, it is clear that today’s statement was pure strategy. In it, Intel exclaims that demand for its products was “brisker than originally thought” and the margins would be higher than expected.

And the processor war continues. I’ve seen a lot more AMD-powered computers than I have in the past, so just based on my personal experience, I’d say that AMD is slowly taking away some market share from the giant Intel. Where do you stand? Are you an AMD aficionado, an Intel loyalist, or do you just grab whatever’s best at the time?

The Doomsday Clock Is Now Closer To Midnight Than Ever Before

The Doomsday Clock is now closer to midnight than ever before—so close that, instead of minutes, we’re now counting down to metaphorical oblivion in seconds. According to the Bulletin of the Atomic Scientists, we have just 100 seconds to midnight.

Here’s what that actually means.

What is the Doomsday Clock?

The Doomsday Clock is neither a meaningless art project nor a precise scientific measurement; it’s somewhere in the middle. The graphic was introduced in 1947, when artist Martyl Langsdorf designed a clockface at seven minutes to midnight for the cover of the Bulletin of Atomic Scientists. She picked the time of 11:53 p.m. somewhat arbitrarily, but the idea was to make people think about how much more dangerous life was becoming in the nuclear age. That was rather the point of the Bulletin itself, which was put out by a group of scientists who’d participated in the Manhattan Project as a response to the bombings of Hiroshima and Nagasaki.

What happens when the Doomsday Clock hits midnight?

We’re not 100 actual seconds from an apocalyptic disaster at this moment, or at any other moment, based on the position of the Doomsday Clock. In fact, during the Cuban Missile Crisis, when the world was arguably perched most precariously on the precipice of nuclear war, the infamous minute hand didn’t move a bit. The clock (which, by the way, does not exist physically—you can’t go visit it, which would probably be the most existentially distressing long weekend one could plan) is meant to remind us that global catastrophe has been just around the proverbial corner from the moment our species entered its nuclear age. It’s not keeping tabs on day-to-day threats; a board of scientists and policy experts convene just twice a year to decide whether it’s time for a tick, and they only make announcements on clock hand movement (or lack thereof) periodically.

What makes the Doomsday Clock tick?

In its original iteration, the clock’s ticking rested on the opinion of just one man: Eugene Rabinowitch, a scientist who edited the Bulletin’s print publication in its infancy. He used his own scientific expertise and that of fellow researchers to determine whether or not it was time to move the clock forward (or back). Now the Bulletin’s Science and Security Board makes the call, along with their Board of Sponsors (which includes 13 Nobel Laureates). The group tries to determine whether the world is objectively more or less safe than it was the year before, and pushes the minute hand forward or back accordingly. Though it was designed to broadcast the threat of nuclear proliferation, the clock-setters started to factor in climate change starting in 2007.

How bad is it that the Doomsday Clock is at 100 seconds to midnight?

Again, this is just a metaphor—no one has crunched the numbers on the statistical likelihood of our obliteration in comparison to its likelihood a year ago. But as a group of experts with a long history of evaluating how our species is doing, the folks behind the Doomsday Clock are worth paying attention to.

In 2023, the Bulletin of the Atomic Scientists ticked the clock forward to land on two minutes to midnight—as close as it had ever gotten, and at the same level as it was the Cold War. In 2023, the clock stayed in that position. Now we’ve moved forward by 20 seconds more, pushing us into new territory.

“We are now expressing how close the world is to catastrophe in seconds—not hours, or even minutes,” Rachel Bronson, president and CEO of the Bulletin of the Atomic Scientists, said at a press conference on Thursday. “It is the closest to Doomsday we have ever been in the history of the Doomsday Clock. We now face a true emergency—an absolutely unacceptable state of world affairs that has eliminated any margin for error or further delay.”

The group highlighted the same three factors that influenced their decision in 2023: The threat of nuclear war, the growing catastrophes of unchecked climate change, and the proliferation of disinformation online.

“To say the world is nearer to doomsday today than during the Cold War—when the United States and Soviet Union had tens of thousands more nuclear weapons than they now possess—is to make a profound assertion that demands serious explanation,” the group said in a statement.

Volt: A Customizable Clock And Battery Indicator Screen For Your Charging Iphone

It was only a few days ago that we showed you a tweak called Vettr, which displayed a nice and simplistic time and battery interface on your display whenever you had your iPhone plugged into a power source.

But now, a new jailbreak tweak called Volt is challenging Vettr head-on with a number of features that you can customize to your liking. We’ll show you what Volt is all about in this review.

A step up from Vettr?

For what it’s worth, Vettr is still a great free jailbreak tweak that provides a similar battery level and time indicator on the Lock screen as long as you’re connected to a power source. On the other hand, Volt takes a lot of what Vettr users wanted and implements it into a preferences pane in the Settings app so you can actually customize the tweak’s look and function and tailor it to your own needs.

One thing I want to mention right off the bat is that Volt and Vettr are not copies of one another, and they are certainly not rip-offs of one another. This kind of jailbreak tweak was requested on Reddit, and two developers went out on a limb to try and create it. It just turns out that the free version of this tweak has fewer options and was completed sooner, and the paid one has a lot of options and was completed later, which is to be expected.

Out of the box, it looks almost exactly like Vettr. In fact, the only difference I could really tell was the font for the time was smaller, but after seeing the options it had to offer, I knew it was a step up from the freebie.

Apart from toggling the tweak on or off on demand, the tweak also comes with the following configuration options:

Using 24 or 12-hour time

Choosing the text color for the time

Configuring the size of the time text

Changing the colors of the battery dots

Changing the background color or image

Using a blur for the background instead

Disabling screen dim so the iPhone shows Volt for as long as it’s charging

Checking for updates automatically

When you’re done configuring the tweak, you are able to apply your settings with the Apply button at the top of the preferences pane. You can also reset your settings to the defaults if need be.


For most, it makes sense to use this kind of a tweak with a dock, as we have, so you can see the information as your phone is standing upright, but it works even if you’re just using a Lightning cable and putting the iPhone on its back on your desk too (which in my opinion defeats the purpose, but to each their own). This is very much like an Apple Watch-esque Nightstand mode for iPhone.

By default, the tweak will allow the Lock screen to auto-dim. This is a smart idea because if the screen is allowed to stay on for long periods of time, pixels may eventually wear out. For those who only have their iPhones on for maybe an hour at a time to get a quick charge while they sit at their desk, this won’t be much of a problem; it mostly arises for people who charge overnight. On the other hand, it can be configured not to auto-dim, and this can turn your iPhone into a very nice desk clock while it charges.

If you ever want your display to shut off, even when you have auto-dim turned off, all you have to do is press the Lock button. You can also double-tap on the display and your Lock screen will appear instead of Volt.

The next time you push the Lock button to wake your device, the display will remain on with the Volt interface as long as it’s connected to a power source. As soon as you disconnect the power source, Volt will stop appearing on the Lock screen until it is connected again.

Wrapping up

If you’re interested in giving it a try on your jailbroken iOS 8 or iOS 9 device, you can download it from Cydia’s BigBoss repository right now for just $0.99.

Remember not to leave your iPhone’s display sitting on the same screen for long periods at a time to prevent screen pixel damage.

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