softdev12

TLDR: This is digital identity obfuscation for web privacy. Advertising and marketing networks have gotten exceptionally good at tracking people across the web. For example, if you have a Google account, you can go into your Settings for Google Ads to see your Age/Gender inference.[1]

[1] https://www.google.com/ads/preferences

Two weeks ago my clickbait blocker was posted on Hacker News and people were kind and thought it was okay. So now I'm posting an enhancement to google and bing searches that I just built. It allows you to do things like sort your results by fewest ads or to see what frameworks the website behind the result uses, etc. The code is open source so if you have any issues or feature requests, you can submit them on my github repo.

This was definitely a consideration. It was why there is a blacklist feature and why only links of a certain length and certain type are analyzed. Any suggestions can be made on github via a pull request.

Good point. There is some consideration for building an option to limit the number of links analyzed. So if you set it to 5 or 10, then the tool would stop at that limit. Feel free to open an issue on github.

Thanks for the comment. There is a new version under development that will use a different approach. Most likely a neural network.

Thanks. This was built because someone posted an ASK HN a few weeks ago. They wanted to know if there was a way to stop clickbait. The text frequency approach seemed like the simplest and cleanest approach to build this in a short timeframe.

Firefox addon is in the works. If you want to contribute code, go to github and make a pull request.

Thanks for the comment. There have been a lot of studies done using the general TF-IDF approach. You can read about it on https://en.wikipedia.org/wiki/Tf%E2%80%93idf

There is also a new version planned using a different approach.

Thanks for the suggestion. The code is open sourced on github. Feel free to do a pull request with your approach and I'll take a look at it.

This is an experiment in React Native to see how far its performance could be stretched versus pure native code. Don't expect super fine grained motion control. It just hooks the accelerometer and gyroscope into an 8 octave sax pad. There are 4 different types of sax. Feedback is welcome.

React Native experience was really quite nice. The android and ios versions have almost the exact identical logic and about 50 percent of the same codebase. I imagine as the android framework evolves, the shared code will increase.

interesting. i was under the impression you could just create a snapshot (before destroying the environment) and then restore as needed. am i missing something?

Edit: http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_R...