Trending December 2023 # Fix: ”This Application Requires Directx Version 8.1 Or Greater To Run” # Suggested January 2024 # Top 15 Popular

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Fix: ”This application requires DirectX version 8.1 or greater to run”






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aDirectX issues in Windows 10 are the common ache for a plentitude of users that reside in the gaming world.

One of those errors affects a lot of users that are keen to play some older, legacy titles. Allegedly, they keep seeing the ”This application requires DirectX version 8.1 or greater to run‘ prompt.

This basically means that, even though you have DirectX 11 or 12 installed, it just won’t cut it for older applications. In order to help you resolve this peculiar and rather complex problem, we provided some solutions below. If you’re stuck with this error every time you start the game, make sure to check the list below.

How to fix ”This application requires Directx version 8.1 or greater’ error in Windows 10

Install DirectX Runtime June 2010

Run the application in a compatibility mode

Reinstall the troubled program

Enable DirectPlay

Solution 1 – Install DirectX Runtime June 2010

For some peculiar reason, the older games or applications that are dependant on DirectX need older DirectX versions in order to run. Now, even though you’re positive that you have DirectX 11 or 12 installed, our guess is that you’ll need to obtain and install older DirectX version and resolve the issues.

For that matter, most of the games come with the matching DirectX installer package and additional redistributables. On the other hand, if you’re unable to locate them within the game installation folder, they can be easily found online and downloaded.

You can download DirectX Runtime installer here.

Solution 2 – Run the application in a compatibility mode

While we’re at it with older games played on Windows 10, let’s try to use compatibility mode to overcome this issue. Compatibility issues are quite frequent with older game titles, like GTA Vice City or I.G.I.-2: Covert Strike played on Windows 10 platform.

Expert tip:

Open the Compatibility tab.

Check the ”Run this program in compatibility mode for” box.

From the drop-down menu, select Windows XP or Windows 7.

Now, check the ”Run this program as an administrator” box.

Save changes and run the application.

On the other hand, if you’re still prompted with ”This application requires Directx version 8.1 or greater to run” error, make sure to continue with the steps below.

ALSO READ: How to fix Age of Mythology Extended Edition bugs on Windows 10

Solution 3 – Reinstall the troubling program

Some users managed to resolve the issue by simply reinstalling the application (most of the time, game). Integration problems are also quite common, again, especially with older game titles. So, without further ado, follow the steps below to uninstall the troubling game and install it again:

In the Windows Search bar, type Control and open Control Panel.

Select Category View.

Restart your PC.

Select Compatibility tab.

Check the ”Run this program in compatibility mode for” box.

From the drop-down menu, select Windows XP or Windows 7.

Now, check the ”Run this program as an administrator” box.

Confirm changes and run the installer.

Furthermore, if you’re a Steam user, you can do so within the client, as it has a better success rate.

Solution 4 – Enable DirectPlay

DirectPlay is a legacy component that was excluded from a few latest Windows iterations. But, as we already determined that this problem plagues older games, it’s safe to say that it’s vital to enable this option. Follow the steps below to enable DirectPlay and, hopefully, resolve this issue:

In the Windows Search bar, type Turn Windows and open Turn Windows features on or off.

Scroll down until your reach Legacy Components.

Expand Legacy Components and check the ”DirectPlay” box.

With DirectPlay enabled, you should be able to run all games from the past decade without issues whatsoever.


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You're reading Fix: ”This Application Requires Directx Version 8.1 Or Greater To Run”

This Version Of Drawing File Is Not Supported

This version of drawing file is not supported [SIMPLE FIX]






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Download Outbyte Driver Updater.

Launch it on your PC to find all the problematic drivers.

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Users of CAD software keep seeing this error message This version of drawing file is not supported. They have reported that the issue arises when trying to export or import files from/to a .DWG format into their preferred Auto CAD software. This issue seems to be present in a variety of situations and with a wide range of CAD software options.

Getting this error can be extremely annoying, as it doesn’t allow you access to the information stored inside the files, and no specific error code is shown other than the one previously mentioned.

This issue is varied across different platforms and also happens in different CAD software. It is extremely hard to pinpoint the issue for this reason. Our team has researched the issue intensively, and we came up with a troubleshooting guide to try and fix this issue.

It is worth mentioning that this troubleshooting guide will only apply to only a set of users. Solving the issue at its base requires personalized troubleshooting methods for each case in part.

How to fix Drawing file is not valid AutoCAD error? 1. Make sure the files you’re trying to open are compatible with your CAD software

When it comes to CAD software format compatibility issues, it is recommended that you use the same version of the software in which the files were originally created. In order to find out in which CAD software and which version they were created, follow these steps:

Inside the text editor, search for a value code at the start of the ‘text’.

That will be the version of the software that was used to create that file.

Check online to see if that version of the software is still available for download.

Note: Files with the format .DWG, and .DXF can only be worked with if your version of the software isn’t older than the version of the software in which they were created in.

2. Use another software to open the DWG file

After opening the file in the CAD software of your choosing, export it as a new file with either .DWG or .DXF format.

Re-try to open the file in your initial CAD software.

In case you don’t have another compatible software on your PC, follow these steps:

Choose an application from Windows Store and repeat the steps found in Method 2.


Still experiencing troubles? Fix them with this tool:


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Bear Run Or Bull Run, Can Reinforcement Learning Help In Automated Trading?

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


Every human being wants to earn to their maximum potential in the stock market. It is very important to design a balanced and low-risk strategy that can benefit most people. One such approach talks about using reinforcement learning agents to provide us with automated trading strategies based on the basis of historical data.

Reinforcement Learning

Reinforcement learning is a type of machine learning where there are environments and agents. These agents take actions to maximize rewards. Reinforcement learning has a very huge potential when it is used for simulations for training an AI model. There is no label associated with any data, reinforcement learning can learn better with very few data points. All decisions, in this case, are taken sequentially. The best example would be found in Robotics and Gaming.

Q – Learning

Q-learning is a model-free reinforcement learning algorithm. It informs the agent what action to undertake according to the circumstances. It is a value-based method that is used to supply information to an agent for the impending action.  It is regarded as an off-policy algorithm as the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn’t needed.

Q here stands for Quality. Quality refers to the action quality as to how beneficial that reward will be in accordance with the action taken. A Q-table is created with dimensions [state,action].An agent interacts with the environment in either of the two ways – exploit and explore. An exploit option suggests that all actions are considered and the one that gives maximum value to the environment is taken. An explore option is one where a random action is considered without considering the maximum future reward.

Q of st and at is represented by a formula that calculates the maximum discounted future reward when an action is performed in a state s.

The defined function will provide us with the maximum reward at the end of the n number of training cycles or iterations.

Trading can have the following calls – Buy, Sell or Hold

Q-learning will rate each and every action and the one with the maximum value will be selected further. Q-Learning is based on learning the values from the Q-table. It functions well without the reward functions and state transition probabilities.

Reinforcement Learning in Stock Trading

Reinforcement learning can solve various types of problems. Trading is a continuous task without any endpoint. Trading is also a partially observable Markov Decision Process as we do not have complete information about the traders in the market. Since we don’t know the reward function and transition probability, we use model-free reinforcement learning which is Q-Learning.

Steps to run an RL agent:

Install Libraries

Fetch the Data

Define the Q-Learning Agent

Train the Agent

Test the Agent

Plot the Signals

Install Libraries

Install and import the required NumPy, pandas, matplotlib, seaborn, and yahoo finance libraries.

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set() !pip install yfinance --upgrade --no-cache-dir from pandas_datareader import data as pdr import fix_yahoo_finance as yf from collections import deque import random Fetch the Data

yf.pdr_override() df_full = pdr.get_data_yahoo("INFY", start="2023-01-01").reset_index() df_full.to_csv(‘INFY.csv',index=False) df_full.head()

This code will create a data frame called df_full that will contain the stock prices of INFY over the course of 2 years.

Define the Q-Learning Agent

The first function is the Agent class defines the state size, window size, batch size, deque which is the memory used, inventory as a list. It also defines some static variables like epsilon, decay, gamma, etc. Two neural network layers are defined for the buy, hold, and sell call. The GradientDescentOptimizer is also used.

The Agent has functions defined for buy and sell options. The get_state and act function makes use of the Neural network for generating the next state of the neural network. The rewards are subsequently calculated by adding or subtracting the value generated by executing the call option. The action taken at the next state is influenced by the action taken on the previous state. 1 refers to a Buy call while 2 refers to a Sell call. In every iteration, the state is determined on the basis of which an action is taken which will either buy or sell some stocks. The overall rewards are stored in the total profit variable.

df= df_full.copy() name = 'Q-learning agent' class Agent: def __init__(self, state_size, window_size, trend, skip, batch_size): self.state_size = state_size self.window_size = window_size self.half_window = window_size self.trend = trend chúng tôi = skip self.action_size = 3 self.batch_size = batch_size self.memory = deque(maxlen = 1000) self.inventory = [] self.gamma = 0.95 self.epsilon = 0.5 self.epsilon_min = 0.01 self.epsilon_decay = 0.999 tf.reset_default_graph() chúng tôi = tf.InteractiveSession() self.X = tf.placeholder(tf.float32, [None, self.state_size]) self.Y = tf.placeholder(tf.float32, [None, self.action_size]) feed = tf.layers.dense(self.X, 256, activation = tf.nn.relu) self.logits = tf.layers.dense(feed, self.action_size) chúng tôi = tf.reduce_mean(tf.square(self.Y - self.logits)) self.optimizer = tf.train.GradientDescentOptimizer(1e-5).minimize( self.cost ) def act(self, state): if random.random() <= self.epsilon: return random.randrange(self.action_size) return np.argmax(, feed_dict = {self.X: state})[0] ) def get_state(self, t): window_size = self.window_size + 1 d = t - window_size + 1 res = [] for i in range(window_size - 1): res.append(block[i + 1] - block[i]) return np.array([res]) def replay(self, batch_size): mini_batch = [] l = len(self.memory) for i in range(l - batch_size, l): mini_batch.append(self.memory[i]) replay_size = len(mini_batch) X = np.empty((replay_size, self.state_size)) Y = np.empty((replay_size, self.action_size)) states = np.array([a[0][0] for a in mini_batch]) new_states = np.array([a[3][0] for a in mini_batch]) Q =, feed_dict = {self.X: states}) Q_new =, feed_dict = {self.X: new_states}) for i in range(len(mini_batch)): state, action, reward, next_state, done = mini_batch[i] target = Q[i] target[action] = reward if not done: target[action] += self.gamma * np.amax(Q_new[i]) X[i] = state Y[i] = target cost, _ = [self.cost, self.optimizer], feed_dict = {self.X: X, self.Y: Y} ) self.epsilon *= self.epsilon_decay return cost def buy(self, initial_money): starting_money = initial_money states_sell = [] states_buy = [] inventory = [] state = self.get_state(0) for t in range(0, len(self.trend) - 1, self.skip): action = self.act(state) next_state = self.get_state(t + 1) inventory.append(self.trend[t]) initial_money -= self.trend[t] states_buy.append(t) print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money)) elif action == 2 and len(inventory): bought_price = inventory.pop(0) initial_money += self.trend[t] states_sell.append(t) try: invest = ((close[t] - bought_price) / bought_price) * 100 except: invest = 0 print( 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,' % (t, close[t], invest, initial_money) ) state = next_state invest = ((initial_money - starting_money) / starting_money) * 100 total_gains = initial_money - starting_money return states_buy, states_sell, total_gains, invest def train(self, iterations, checkpoint, initial_money): for i in range(iterations): total_profit = 0 inventory = [] state = self.get_state(0) starting_money = initial_money for t in range(0, len(self.trend) - 1, self.skip): action = self.act(state) next_state = self.get_state(t + 1) inventory.append(self.trend[t]) starting_money -= self.trend[t] bought_price = inventory.pop(0) total_profit += self.trend[t] - bought_price starting_money += self.trend[t] invest = ((starting_money - initial_money) / initial_money) self.memory.append((state, action, invest, next_state, starting_money < initial_money)) state = next_state batch_size = min(self.batch_size, len(self.memory)) cost = self.replay(batch_size) if (i+1) % checkpoint == 0: print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost, starting_money)) Train the Agent close = df.Close.values.tolist() initial_money = 10000 window_size = 30 skip = 1 batch_size = 32 agent = Agent(state_size = window_size, window_size = window_size, trend = close, skip = skip, batch_size = batch_size) agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)

Output –

Test the Agent

The buy function will return the buy, sell, profit, and investment figures.

states_buy, states_sell, total_gains, invest = = initial_money) Plot the calls

Plot the total gains vs the invested figures. All buy and sell calls have been appropriately marked according to the buy/sell options as suggested by the neural network.

fig = plt.figure(figsize = (15,5)) plt.plot(close, color='r', lw=2.) plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy) plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell) plt.title('total gains %f, total investment %f%%'%(total_gains, invest)) plt.legend() plt.savefig(name+'.png')

Output –

End Notes

Q-Learning is such a technique that helps you develop an automated trading strategy. It can be used to experiment with the buy or sell options. There are a lot more Reinforcement Learning trading agents that can be experimented with. Try playing around with the different kinds of RL agents with different stocks.

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Application Support: A Necessity Or A Waste Of Money?

Until lately, both customers and developers focused only on application development. Today, the competitive situation dictates different rules.

An application cannot be left unchanged after its release. After the publication of a mobile application, work with it is just starting. 

Let’s look at three stages of the life cycle of a mobile application:

Plan. Idea, research of competitors and users, checking the viability of the idea, the first prototypes.

Design and development. The most expensive and important part.

Testing and QA. As a rule, software testing is carried out in parallel with the development stage, but some companies may put this stage in a separate task.


Support and maintain. After the release, the life of the product is just beginning.

The product must live and change with the environment, respond to threats, adapt to changes in devices and operating systems, react to user feedback about its convenience and functionality.

There is a need to add new features during the performance, change existing features, fix bugs and vulnerabilities. That is why developers and business analysts need technical support for the mobile application. 

In the sphere of mobile technologies, operating systems are updated every year, and new devices appear once in two-three months.

The main tasks to be solved after the publication of the application are:

getting feedback from the end-users and solve their problems;

improving stability and adding new functionality;

adaptation of the application for new devices and OS versions;

tracking the degree of satisfaction of the business needs of the customer company;

adjusting the product development plan.

But, remember – warranty and technical support are different things. The guarantee is set out in the main development contract. Warranty works mean bug fixes or functionality enhancements to the specification.

Support goes beyond the scope of the terms of reference and requires a separate document with prescribed tasks, conditions, response time.

In fact, technical support is an urgent solution to problems that interfere with a digital product’s quality functioning. Let’s divide these problems into three categories:

Level 1. These are force majeure situations, which are solved first of all. For example, users can not send an order, register, or login.

Level 2. For example, interface errors that do not critically affect the ability to send an order or other important action. Such problems are usually solved during the working day if there is no emergency.

Level 3. For example, minor bugs in features and screens that do not relate to any critical service functionality.

Now let’s talk about one of the most important points – testing, especially pre-release testing.  Oleh Sadykow (Co-Founder at DeviQA – leading automation testing company) says, “Before releasing an app, you need to make sure there are no bugs (at least big ones) for certain.”

Regression testing must be done first. Ideally, the development process should be designed so that there are only small features left for the test before the release, the bugs of which do not take much time to fix.

It is also important to consider that possible fixes cannot affect other parts of the product and its behavior in principle.

After all, there is simply no time to fix everything before the release. After the regression, the tester should check if there are any negative consequences from fixing bugs found with the regression test or not. And also whether the developers even fixed these bugs.

The next step is a unit test. If an application’s reaction is not the same as planned, the test is considered not successful.

But the developers understand what part of the code the bug is in and fix it. This is not the whole benefit of unit tests. They can be very helpful in striving against bugs after updates. The last step is the final testing of an app.

It involves checking all application functions against the specification or backlog that the team has agreed with the customer.

In application development, problems of this sphere, competitive features are constantly being identified, new ideas and feedback from users appear, which require a prompt response.

The right solution in such a situation is the timely organization of technical support for the professional development team’s mobile application.

Steam Launch Options Windowed: 3 Easy Ways To Run This Option

Steam Launch Options Windowed: 3 Easy Ways to Run This Option Easily switch to Windowed Mode with our solutions




Because they need to run dual displays without any trouble, some users are wondering how to launch Steam games in Windowed Mode.

Checking the game settings is the first thing you should try.

Many users also suggest changing Steam launch parameters to open the game in Windowed Mode.



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This software will simplify the process by both searching and updating your drivers to prevent various malfunctions and enhance your PC stability. Check all your drivers now in 3 easy steps:

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Launch it on your PC to find all the problematic drivers.

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The Windowed Mode feature in Steam allows users to run some legacy games on their computer, which otherwise won’t launch. It is also helpful in playing certain games with high system requirements on a low-end PC.

Besides, it helps eliminate a range of errors. But not all Steam games can be opened as easily in Windowed Mode. Each may require a unique method, so you must know them all. Let’s find out!

Why should I run Steam games in Windowed Mode?

Here are a few reasons why many prefer playing games in Windowed Mode:

Seamlessly switch between apps: The primary benefit of Windowed Mode is the ability to switch between apps or check notifications and other elements while gaming.

Eliminates problems with resolution: Some older games tend to run better when in Windowed Mode. Otherwise, the display quality is affected.

Best for multi-monitor setup: Gamers with a multi-monitor setup prefer Windowed Mode for games since it allows them to check things easily on the secondary monitor.

How do I launch Steam games in Windowed Mode?


Before making changes to the game settings, try using the Alt + Enter keyboard shortcut. Though not a universal thing and applicable in every game, it might work for some. Also, some other solutions, too, won’t work for every game. So make sure to try them all and identify the one that does.

1. Modify game settings

If the Steam game doesn’t switch to Windowed Mode immediately, restart it for the changes to come into effect. Remember, not every game offers a built-in option to switch between display modes, so you may have to try the next method.

2. Change Steam launch parameters

If the Steam game doesn’t have a built-in setting to enable Windowed Mode, you can modify the launch options. Though, this, too, has been found to fail at times.

3. Change shortcut target

If the previous methods didn’t work, another way to open Steam games in Windowed Mode is to change the Target field for the shortcut.

And while you are at it, do not forget to check the best ways to boost gaming performance in Windows.

Tell us which method worked for you and the game you were trying to play in Windowed Mode. In case none worked, it’s likely that the developers didn’t add the functionality, and the game can’t run that way.

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Understanding The Main Triggers Of This Bitcoin Bull Run

Bitcoin has hit new all-time highs quite a few times in the last week. At the time of writing, bitcoin has struck a high of $34,830. Clearly, bitcoin is in a bull run, especially with Coinbase striking, what could be, potential OTC deals pushing BTC out of exchanges.

With more BTC being bought up by hungry institutions or high-net-worth individuals, the scenario for bitcoin is getting more bullish by the second. While the retail FOMO plays a part in this rally, I think it’s time to take a step back and look at what’s happening in the market.

The Bigger Picture

Phase 1

In hindsight, these two events among others are what sparked the bull run that we see today.

Let’s look at what has happened since August.

MicroStrategy invested ~half of $1 billion in cash reserves in Bitcoin without moving the price of BTC.

Since this was the first major investment by a traditional finance company in bitcoin, it was paraded all over the news for bringing more credibility to bitcoin among retail.

CashApp and many companies invest in bitcoin to prevent their cash reserves from debasing due to inflation by the Fed.

Even with billions of dollars moving into bitcoin, the price seemed to stay put as it hovered around the previous all-time high. After two failed attempts, the price went above the 2023-high at $19,666.

Phase 2

Michael Saylor invested the other half of $1 billion in bitcoin despite what the critics had to say.

Major Bitcoin outflows from major exchanges such as Coinbase Pro, Binance, etc.

Drying up of the exchange reserves as a result of point 2 and retail pulling out their BTC from exchanges, signifying the strength of the rally.

Unlike 2023, this bull run showed that retail is more matured. Hence, the bull run this time around isn’t as volatile as it was in 2023.

More companies/institutions are actively looking to buy more BTC or are already buying it.

Point of inflection

Since 2023, things have been difficult, for both the front end of the bitcoin ecosystem which includes investors, exchanges, companies built around bitcoin, and the backend, which includes miners and related companies.

Let’s take a look at miners and what’s happening with them, especially since they are the major source of selling pressure in the entire bitcoin ecosystem.

After the March crash, the worst was behind for miners, and by the start of the 3rd quarter, things were already looking up for them. This is when bitcoin crossed $8,000 and eventually hit $10,000.

At this point miners were not profitable enough, hence, selling pressure was present. Considering the price now, miners will only have to sell a portion of their mined bitcoins to cover all expenses incurred due to mining.

This selling pressure has now reduced, which is the third reason why bitcoin is heading higher without stopping.


Together. these events in whatever order, have caused bitcoin to surge. As for what the future holds, bitcoin will keep surging, as more people keep depositing stablecoins to exchanges.

Perhaps, the best point for a local top would be at $40,200. From this point, we can expect bitcoin to start its retracement, but then again, the further one tries to predict the future, the more uncertain the conclusions are going to be.

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