Watch Online Exploitation Grindhouse and Softcore Streaming Movies

Why are you selling this site?
I have another different project and need money to start it !

How is it monetized?
Exoclick , Backlink Sales, Banner Ads

Does this site come with any social media accounts?
No

How much time does this site take to run?
1 Hour / Day

What challenges are there with running this site?
It's wordpress and there is any difficulty to run site
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Watch Online Exploitation Grindhouse and Softcore Streaming Movies

Tv Series Streaming Website – With Earnings

Why are you selling this site?
I do not have time to update the site because of my work and I want to free up some of my server's space

How is it monetized?
It can be monetized using Adsense, Infolinks, CpaLead and Amazon.

Does this site come with any social media accounts?
No it doesn't

How much time does this site take to run?
You will only need to spend 30-60 minutes on the site once a new episode of a TV series has been released. I will tell you where to…

Tv Series Streaming Website – With Earnings

NFL streaming through Amazon Prime Video

I’ve been trying to stream the games online through various platforms but it has been nothing more than a big headache so far. I decided that I should invest in a subscription so I could peacefully watch the games and so far I’ve been thinking about trying out Amazon Prime. I found this link with other streaming options (https://medium.com/@sherivargas185xd/how-to-watch-amazon-prime-video-geographical-restrictions-and-how-to-bypass-them-ac2fddb85015), but I was wondering if anyone else has…

NFL streaming through Amazon Prime Video

ERROR TwitterReceiver Spark Streaming Scala

Estoy intentando con IntelliJ y Spark Streaming conectar con la API de Twitter para poder acceder a los tweets. Por el momento lo único que quiero que haga es que imprima cada tweet por la consola.

Parece que viendo los errores, el problema viene antes del SparkContext, ya que veo que el tiempo de 4 segundos que le he puesto si que lo está haciendo. No sé si el problema viene en el “ConfigurationBuilder” que hay algo que estoy haciendo mal.

El código que tengo actualmente es el siguiente:

import twitter4j.conf.ConfigurationBuilder import twitter4j.auth.OAuthAuthorization import org.apache.spark.streaming.twitter._  import org.apache.spark._ import org.apache.spark.streaming._  object Main {    def main(args: Array[String]): Unit = {      val apiKey = "XXXX"     val apiKeySecret = "XXXX"     val accessToken = "XXXX"     val accessTokenSecret = "XXXX"      val cb = new ConfigurationBuilder     cb.setDebugEnabled(true)       .setOAuthConsumerKey(apiKey)       .setOAuthConsumerSecret(apiKeySecret)       .setOAuthAccessToken(accessToken)       .setOAuthAccessTokenSecret(accessTokenSecret)        val conf = new SparkConf().setAppName("twitter_spark_streaming").setMaster("local[*]")     val ssc = new StreamingContext(conf, Seconds(4))      val auth = new OAuthAuthorization(cb.build)     //val tweets = TwitterUtils.createStream(ssc,Some(auth))     val tweets = TwitterUtils.createStream(ssc, Some(auth), null, StorageLevel.MEMORY_AND_DISK_2)      val statuses = tweets.map(status => status.getText())     statuses.print()          ssc.start()     ssc.awaitTermination()   }  } 

Me está dando el siguiente error y no consigo encontrar la solución.

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 19/08/09 13:33:20 INFO SparkContext: Running Spark version 2.3.0 19/08/09 13:33:20 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 19/08/09 13:33:20 INFO SparkContext: Submitted application: twitter_spark_streaming 19/08/09 13:33:20 INFO SecurityManager: Changing view acls to: aresa 19/08/09 13:33:20 INFO SecurityManager: Changing modify acls to: aresa 19/08/09 13:33:20 INFO SecurityManager: Changing view acls groups to:  19/08/09 13:33:20 INFO SecurityManager: Changing modify acls groups to:  19/08/09 13:33:20 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(aresa); groups with view permissions: Set(); users  with modify permissions: Set(aresa); groups with modify permissions: Set() 19/08/09 13:33:21 INFO Utils: Successfully started service 'sparkDriver' on port 54295. 19/08/09 13:33:21 INFO SparkEnv: Registering MapOutputTracker 19/08/09 13:33:21 INFO SparkEnv: Registering BlockManagerMaster 19/08/09 13:33:21 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information 19/08/09 13:33:21 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up 19/08/09 13:33:21 INFO DiskBlockManager: Created local directory at C:\Users\aresa\AppData\Local\Temp\blockmgr-89c10385-b401-4be9-bb71-c2868b0cb6f5 19/08/09 13:33:21 INFO MemoryStore: MemoryStore started with capacity 1988.7 MB 19/08/09 13:33:21 INFO SparkEnv: Registering OutputCommitCoordinator 19/08/09 13:33:21 INFO Utils: Successfully started service 'SparkUI' on port 4040. 19/08/09 13:33:21 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://MSI:4040 19/08/09 13:33:21 INFO Executor: Starting executor ID driver on host localhost 19/08/09 13:33:21 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 54336. 19/08/09 13:33:21 INFO NettyBlockTransferService: Server created on MSI:54336 19/08/09 13:33:21 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy 19/08/09 13:33:21 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, MSI, 54336, None) 19/08/09 13:33:21 INFO BlockManagerMasterEndpoint: Registering block manager MSI:54336 with 1988.7 MB RAM, BlockManagerId(driver, MSI, 54336, None) 19/08/09 13:33:21 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, MSI, 54336, None) 19/08/09 13:33:21 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, MSI, 54336, None) 19/08/09 13:33:22 INFO ReceiverTracker: Starting 1 receivers 19/08/09 13:33:22 INFO ReceiverTracker: ReceiverTracker started 19/08/09 13:33:22 INFO TwitterInputDStream: Slide time = 4000 ms 19/08/09 13:33:22 INFO TwitterInputDStream: Storage level = Serialized 1x Replicated 19/08/09 13:33:22 INFO TwitterInputDStream: Checkpoint interval = null 19/08/09 13:33:22 INFO TwitterInputDStream: Remember interval = 4000 ms 19/08/09 13:33:22 INFO TwitterInputDStream: Initialized and validated org.apache.spark.streaming.twitter.TwitterInputDStream@575b0ffe 19/08/09 13:33:22 INFO MappedDStream: Slide time = 4000 ms 19/08/09 13:33:22 INFO MappedDStream: Storage level = Serialized 1x Replicated 19/08/09 13:33:22 INFO MappedDStream: Checkpoint interval = null 19/08/09 13:33:22 INFO MappedDStream: Remember interval = 4000 ms 19/08/09 13:33:22 INFO MappedDStream: Initialized and validated org.apache.spark.streaming.dstream.MappedDStream@45d09d8b 19/08/09 13:33:22 INFO ForEachDStream: Slide time = 4000 ms 19/08/09 13:33:22 INFO ForEachDStream: Storage level = Serialized 1x Replicated 19/08/09 13:33:22 INFO ForEachDStream: Checkpoint interval = null 19/08/09 13:33:22 INFO ForEachDStream: Remember interval = 4000 ms 19/08/09 13:33:22 INFO ForEachDStream: Initialized and validated org.apache.spark.streaming.dstream.ForEachDStream@1fbf4d6a 19/08/09 13:33:22 INFO ReceiverTracker: Receiver 0 started 19/08/09 13:33:22 INFO RecurringTimer: Started timer for JobGenerator at time 1565350404000 19/08/09 13:33:22 INFO JobGenerator: Started JobGenerator at 1565350404000 ms 19/08/09 13:33:22 INFO JobScheduler: Started JobScheduler 19/08/09 13:33:22 INFO DAGScheduler: Got job 0 (start at Main.scala:50) with 1 output partitions 19/08/09 13:33:22 INFO DAGScheduler: Final stage: ResultStage 0 (start at Main.scala:50) 19/08/09 13:33:22 INFO DAGScheduler: Parents of final stage: List() 19/08/09 13:33:22 INFO DAGScheduler: Missing parents: List() 19/08/09 13:33:22 INFO StreamingContext: StreamingContext started 19/08/09 13:33:22 INFO DAGScheduler: Submitting ResultStage 0 (Receiver 0 ParallelCollectionRDD[0] at makeRDD at ReceiverTracker.scala:613), which has no missing parents 19/08/09 13:33:22 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 46.5 KB, free 1988.7 MB) 19/08/09 13:33:22 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 15.8 KB, free 1988.6 MB) 19/08/09 13:33:22 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on MSI:54336 (size: 15.8 KB, free: 1988.7 MB) 19/08/09 13:33:22 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1039 19/08/09 13:33:22 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 0 (Receiver 0 ParallelCollectionRDD[0] at makeRDD at ReceiverTracker.scala:613) (first 15 tasks are for partitions Vector(0)) 19/08/09 13:33:22 INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks 19/08/09 13:33:22 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, executor driver, partition 0, PROCESS_LOCAL, 10840 bytes) 19/08/09 13:33:22 INFO Executor: Running task 0.0 in stage 0.0 (TID 0) 19/08/09 13:33:23 INFO RecurringTimer: Started timer for BlockGenerator at time 1565350403200 19/08/09 13:33:23 INFO BlockGenerator: Started BlockGenerator 19/08/09 13:33:23 INFO BlockGenerator: Started block pushing thread 19/08/09 13:33:23 INFO ReceiverTracker: Registered receiver for stream 0 from MSI:54295 19/08/09 13:33:23 INFO ReceiverSupervisorImpl: Starting receiver 0 19/08/09 13:33:23 INFO ReceiverSupervisorImpl: Called receiver 0 onStart 19/08/09 13:33:23 INFO ReceiverSupervisorImpl: Waiting for receiver to be stopped 19/08/09 13:33:23 WARN ReceiverSupervisorImpl: Restarting receiver with delay 2000 ms: Error starting Twitter stream java.lang.NullPointerException     at org.apache.spark.streaming.twitter.TwitterReceiver.onStart(TwitterInputDStream.scala:89)     at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:149)     at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:131)     at org.apache.spark.streaming.scheduler.ReceiverTracker$  ReceiverTrackerEndpoint$  $  anonfun$  9.apply(ReceiverTracker.scala:600)     at org.apache.spark.streaming.scheduler.ReceiverTracker$  ReceiverTrackerEndpoint$  $  anonfun$  9.apply(ReceiverTracker.scala:590)     at org.apache.spark.SparkContext$  $  anonfun$  34.apply(SparkContext.scala:2178)     at org.apache.spark.SparkContext$  $  anonfun$  34.apply(SparkContext.scala:2178)     at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)     at org.apache.spark.scheduler.Task.run(Task.scala:109)     at org.apache.spark.executor.Executor$  TaskRunner.run(Executor.scala:345)     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)     at java.util.concurrent.ThreadPoolExecutor$  Worker.run(ThreadPoolExecutor.java:624)     at java.lang.Thread.run(Thread.java:748) 19/08/09 13:33:23 INFO ReceiverSupervisorImpl: Stopping receiver with message: Restarting receiver with delay 2000ms: Error starting Twitter stream: java.lang.NullPointerException Exception in thread "receiver-supervisor-future-0" java.lang.AbstractMethodError     at org.apache.spark.internal.Logging$  class.initializeLogIfNecessary(Logging.scala:99)     at org.apache.spark.streaming.twitter.TwitterReceiver.initializeLogIfNecessary(TwitterInputDStream.scala:60)     at org.apache.spark.internal.Logging$  class.log(Logging.scala:46)     at org.apache.spark.streaming.twitter.TwitterReceiver.log(TwitterInputDStream.scala:60)     at org.apache.spark.internal.Logging$  class.logInfo(Logging.scala:54)     at org.apache.spark.streaming.twitter.TwitterReceiver.logInfo(TwitterInputDStream.scala:60)     at org.apache.spark.streaming.twitter.TwitterReceiver.onStop(TwitterInputDStream.scala:106)     at org.apache.spark.streaming.receiver.ReceiverSupervisor.stopReceiver(ReceiverSupervisor.scala:170)     at org.apache.spark.streaming.receiver.ReceiverSupervisor$  $  anonfun$  restartReceiver$  1.apply$  mcV$  sp(ReceiverSupervisor.scala:194)     at org.apache.spark.streaming.receiver.ReceiverSupervisor$  $  anonfun$  restartReceiver$  1.apply(ReceiverSupervisor.scala:189)     at org.apache.spark.streaming.receiver.ReceiverSupervisor$  $  anonfun$  restartReceiver$  1.apply(ReceiverSupervisor.scala:189)     at scala.concurrent.impl.Future$  PromiseCompletingRunnable.liftedTree1$  1(Future.scala:24)     at scala.concurrent.impl.Future$  PromiseCompletingRunnable.run(Future.scala:24)     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)     at java.util.concurrent.ThreadPoolExecutor$  Worker.run(ThreadPoolExecutor.java:624)     at java.lang.Thread.run(Thread.java:748) 19/08/09 13:33:24 INFO JobScheduler: Added jobs for time 1565350404000 ms 19/08/09 13:33:24 INFO JobScheduler: Starting job streaming job 1565350404000 ms.0 from job set of time 1565350404000 ms  19/08/09 13:33:24 INFO JobScheduler: Added jobs for time 1565350404000 ms 19/08/09 13:33:24 INFO JobScheduler: Starting job streaming job 1565350404000 ms.0 from job set of time 1565350404000 ms ------------------------------------------- Time: 1565350404000 ms -------------------------------------------  19/08/09 13:33:24 INFO JobScheduler: Finished job streaming job 1565350404000 ms.0 from job set of time 1565350404000 ms 19/08/09 13:33:24 INFO JobScheduler: Total delay: 0,043 s for time 1565350404000 ms (execution: 0,005 s) 19/08/09 13:33:24 INFO ReceivedBlockTracker: Deleting batches:  19/08/09 13:33:24 INFO InputInfoTracker: remove old batch metadata:   Process finished with exit code -1 

No sé si el problema puede venir por incompatibilidades de estas dependencias.

Muchas gracias!

Cloud storage for streaming audio files

I’m working on a WordPress site for a client who has about 200 audio files or songs and wants them included on the site as posts with the ability to play the songs using a media player on the site. Now I’d rather not store the files on the local site. So I’m wondering if people have suggestions on free storage for audio files that need to be streamed from a wordpress site? Also interested in suggestions for media players that can pay remote audio files? Thanks

what is a smart way to fill up white space on a profile page (audio streaming platform)

I have a pretty simple profile page on my website which already contains some elements: the banner, profile picture, about section, timeline, suggested users panel and some various other little things. However, there is a fairly large unwanted white space (roughly 200 pixels) on the left side of the timeline.

Unfortunately, I don’t know how to fill up this space as most of what’s available (as far as posts and user info go) is already placed on the page. Someone suggested adding a user gallery, and although it’s somewhat a good idea, the website is an audio streaming platform – not an image sharing one.

Image of profile page

What are some suggestions? Every idea is welcome 🙂

Ubuntu 16.04 streaming video has no soud

This is what I have installed: Ubuntu 16.04 dual boot with Windows 8.1, FF 60.8.0esr, My sound for streaming video does not work, tried with Youtube, no sound, but my local files (video & audio files play fine). I had previously problem with missing items in my desktop, had to run this command to get back

sudo apt-get install ubuntu-desktop 

how can I fix this problem? Already tried to remove/install ubuntu-resctricted-extras and pavucontrol.

Architecting dynamic audio streaming application

I’m planning to develop a simple application that works as a radio station, streaming some random music on a React Native app. Every hour or so, after a track ends, a custom audio track should be played on the stream based on the user location.

I’d like to keep most of the logic within the server-side, and have the app only to stream this station, and send data to the server so it can change the content being streamed. It also should support multiple users at the same time. So, besides the geographical coordinates, we should have the user id (all users must be logged in to access the streaming) sent as well.

Also, it’s necessary to track the state of the audio stream to keep a log of how many minutes the user actually played the stream and also to log if the specific tracks were delivered to the user. It means every time the user start or stop the streaming, the server must be aware of that.

I never worked with media streaming before, much less collecting data from it. That’s the reason why I’m requesting the help of more experienced developers on this area to make good decisions in terms of architecture to keep if as efficient as possible.

Here are one idea that I got to make this work. However, I’m quite sure there is a better way to do it:

1- The Youtube Style:

My first idea would have the app to send a request to an API endpoint like /playMusic, containing the user_id, timestamp and location_data and have its response a URL of the audio stream of a single track. On the mobile app, we detect when it the track has ended, and we do another request to get another song. In the server side, we check if it’s the proper time and location to play the custom audio track, if so, we have it send the URL for this specific track, otherwise, just send the URL of the next song. If the player state changes to PAUSE or STOP, we can can send a request to the server with to have the state logged and calculate the total playing time and other metrics by doing calculations with timestamp. There is a very serious problem with this: if the device runs out of battery, or the user closes the app forcefully, the state won’t be updated in the server, and it will count as it was playing forever. One workaround would check the absence of request to get another song.

I’ll probably use S3 and CloudFront to deliver the content.

Any feedback would be appreciated, it would be awesome if you guys could provide some insights on the best way to do it and/or foresee some issues with the implementation.