Tag: google

How to Disable an Adblocker-blocker or Create an Anti-Adblock Killer!

How to Disable an Adblocker-blocker or Create an Anti-Adblock Killer!

History & Theory:

Digital Advertisement:

I get it. Ads are a necessary evil in content delivery game. Hell, I have been in the engineering side of content delivery for 10 yrs myself.  So, back in the days of #dotcom #bubble, we endured Banner ADs. When the #BigBrother, oops #Google came up, they swept the market clean with their (initially, atleast) non-intrusive text ADs. And people even appreciated the contextual advertisement, just when you were searching for a suspension for your car, you see 4 different ADs for OEM grade replacement suspension, grease monkeys to install them and so on.

Fast forward 10 yrs and Google is the global powerhouse of advertisement. Google knows what your mothers’ cousin` once removed does like and runs ads tailored to it in no less than 50 websites run by Google and countless other affiliates. The convenience transformed itself into a mild hindrance and a major nuisance in no time.  In its core, Google, Microsoft and Yahoo ADs were all based on a relevance relevance engine. I.E. based on the content that is currently served by the publisher (website you’re visiting) they search for the relevant ADs from their database and one that matches and has the target profile matching yours (this is where privacy advocates go crazy) they serve this AD. In its simplest form the process look something like the below diagram.

ADsense process diagram
Contextual Advertisement – Process Flow (ADSense/ADWords)

For the inquisitive lot, who want to know the technicality, it looks a lot more complex than this and it is presented below.

Tentative Process flow of How ADWords and ADSense content advertisement happens.

Enter ADBlockers:

And soon, people found a way to block the ADs. As seen above, All of these Adverts are programmed to run using great stores of data from the backend. So, when a user visits site a lot happens in the backend and a script is used get the resultant AD piece. Technically inclined people started writing custom scripts that would stop this script which renders the ADs. In no time all the bells and whistles like #blacklist #whitelist #regular-expression support all came in. Once the modern browser came with support for content filtering built-in, it was easy to supplement them with custom lists and scripts to block these ads. And ADBlockers for every device, OS, browsers became available and public knowledge of the same exploded their use in around 2013-2015 period. (see graph below) . So, All seems rosy from here.


Publishers and their representative trade bodies, on the other hand, argue that Internet ads provide revenue to website owners, which enable the website owners to create or otherwise purchase content for the website. Publishers claim that the prevalent use of ad blocking software and devices could adversely affect website owner revenue and thus in turn lower the availability of free content on websites. So, there is no wonder that publishers have begun to block or evict users found to be using #ADBlockers. (A page from my personal experience, I do not remember a time when I did not use ADblock, before Mozilla, I used MyIE (Maxthon) which had this configurable filters). But, off-lately the publishers have become more aggressive and have rolled out a slew of their own warriors. AKA ADBlocker-Blocker. Which are nifty little utilities you can embed in your site and traffic from ADB enabled users will be blocked until they disable or whitelist you.  Some majors like Economist, Wired and others have announced a novel approach, either you can disable ADB on their site or pay a small fee to see their site without the clutter of advertisements. For the sites that do not offer this feature or If you wish to  simply override them, read on.

Practice & Implementation

So, enter Anti-AdBlocker Killer — https://github.com/reek/anti-adblock-killer

It’s simple, really: it tricks sites that use #anti-adblocker technology into thinking you aren’t using an adblocker. The #adblocker-blocker lets you keep your adblocker on when you visit a page that would usually disable it by using a JavaScript file and filter list. This means you can work around bans on adblockers from common news companies, like Forbes, which lock you out when you’re detected.

It works against a number of different technologies used to detect #adblock users, and is likely to be a part of the next #armsrace as publishers work out how to block the #adblockers using #adblocker-blockers. If you’re still reading, I will conclude my narration and give step-by-step instruction on how to enable it and activate.

Step-by-step Instruction to Activate Anti-Adblock Killer

  1. Step 1 – Get a Script Manager:
    1.  Greasemonkey or Scriptish
    2.  Tampermonkey or Native
    3.  Tampermonkey or Violentmonkey
    4.  Tampermonkey or NinjaKit
    5.  Tampermonkey
        • (* After installation, depending on your browser, may require a browser restart for it to effect)
  2. Step 2 – Subscribe to a FilterList
    1. Subscribe from github.com (I prefer this)
    2. Subscribe from reeksite.com 
      • At this point, if you chose Github list, you’ll be prompted with a list of Extension and you can chose to Manualy install AAKiller. (representative screenshot is shown below) 
  3. Step 3 – Get User Scripts
    1. Install from greasyfork.org
    2. Install from openuserjs.org
    3. Install from github.com
    4. Install from reeksite.com

Once this is done, you’re on your way to enjoy AD-Blocker pop-up free browsing.

Google launches new TensorFlow Object Detection API

Google launches new TensorFlow Object Detection API

Object Detect API

Google has finally launched its new TensorFlow object detection API. This new feature will give access to researchers and developers to the same technology Google uses for its own personal operations like image search and street number identification in street view.

The company was planning to release this new feature for quite a few time and finally, it is available to open source community. The system which the tech company has released won a Microsoft’s Common Objects in Context object detection challenge last year. The company won the challenge by beating 23 teams participating in the challenge.

According to the company, it released this new system to bring general public close to AI, and also get help from developers and AI scientist to collaborate with the company and make new and innovative things using Google’s technology.

Google is not the first company offering AI technology to the general public, user and developers. Microsoft, Facebook, and Amazon have also given access to people to use their respective AI technology. Moreover, Apple in its recent WWDC has also rolled out AI technology named as CoreML for its users.

One of the main benefits which the company is offering with this new release is giving users to use this new technology on mobile phones through its object detection system. The system is based on MobileNets image recognition models which can handle and do tasks like object detection, facial recognition, and landmark recognition.

Google makes its TensorFlow artificial intelligence platform available on iOS

Google makes its TensorFlow artificial intelligence platform available on iOS

Logo of Tensor Flow

Google this week has published a new version of its TensorFlow machine learning software that adds support for iOS. Google initially teased that it was working on iOS support for TensorFlow last November, but said it was unable to give a timeline. An early version of TensorFlow version 0.9 was released yesterday on GitHub, however, and it brings iOS support.

For those unfamiliar, TensorFlow is Google’s incredibly powerful artificial intelligence software that powers many of Google’s services and initiatives, including AlphaGo. Google describes TensorFlow as “neural network” software that processes data in a way that’s similar how our brain cells process data (via CNET).

With Google adding iOS support to TensorFlow, apps will be able to integrate the smarter neural network capabilities into their apps, ultimately making them considerably smarter and capable.

At this point, it’s unclear when the final version of TensorFlow 0.9 will be released, but the early pre-release version is available now on GitHub. In the release notes, Google points out that because TensorFlow is now open source, 46 people from outside the company contributed to TensorFlow version 0.9.

In addition to adding support for iOS, TensorFlow 0.9 adds a handful of other new features and improvements, as well as plenty of smaller bug fixes and performance enhancements. You can read the full change log below and access TensorFlow on GitHub.


Major Features and Improvements

  • Python 3.5 support and binaries
  • Added iOS support
  • Added support for processing on GPUs on MacOS
  • Added makefile for better cross-platform build support (C API only)
  • fp16 support for many ops
  • Higher level functionality in contrib.{layers,losses,metrics,learn}
  • More features to Tensorboard
  • Improved support for string embedding and sparse features
  • TensorBoard now has an Audio Dashboard, with associated audio summaries.

Big Fixes and Other Changes

  • Turned on CuDNN Autotune.
  • Added support for using third-party Python optimization algorithms (contrib.opt).
  • Google Cloud Storage filesystem support.
  • HDF5 support
  • Add support for 3d convolutions and pooling.
  • Update gRPC release to 0.14.
  • Eigen version upgrade.
  • Switch to eigen thread pool
  • tf.nn.moments() now accepts a shift argument. Shifting by a good estimate of the mean improves numerical stability. Also changes the behavior of the shift argument to tf.nn.sufficient_statistics().
  • Performance improvements
  • Many bugfixes
  • Many documentation fixes
  • TensorBoard fixes: graphs with only one data point, Nan values, reload button and auto-reload, tooltips in scalar charts, run filtering, stable colors
  • Tensorboard graph visualizer now supports run metadata. Clicking on nodes while viewing a stats for a particular run will show runtime statistics, such as memory or compute usage. Unused nodes will be faded out.