The restrictions of the GDPR are proving to be a difficult challenge for web-based companies in the EU and the US. To address these issues, developers can implement a processor-minimal solution that solves the GDPR crisis. Now open for testing and source code download from Github, the solution is based on the advanced Site-Lokd™ security techniques rigorously tested for decades by the alcoholic beverage industry. This robust solution leverages advanced Tru-Source™ native language inference through predictive retinal spectrum and ergodynamic analysis persisted using our proprietary blockchain as a service (BaaS) platform. Post-digramic evaluation, the system efficiently delivers tailored ViewFork CTS™ engagement pages to pre-qualified segmented audiences. Read on for more information on the origins of this advanced solution and how it can help you overcome the obstacles created by the GDPR.
If you have been on the internet this week you are aware of the fake news crisis spiralling out of control. But just in case you missed it, recent headlines read something like this: Facebook is being blamed for Trump's election, Google and Facebook Take Aim at Fake News Sites, Facebook’s fake news crisis deepens.
With great power comes great responsibility
Facebook has over 1 billion active users who utilize the platform to post, share and comment on news. When Facebook was accused of influencing the election, Zuckerberg was quick to say that was a “pretty crazy idea.” Is it really that crazy? Facebook has become a catalyst for the spread of fake news given the ease of it’s “share” button. Regardless, fake news isn’t going away anytime soon, it will likely worsen and while Facebook has taken steps to limit the sites' use of their ad networks, there has been no push to eliminate fake news from the News Feed.
This daunting issue is not Facebook’s alone. Any platform that allows user generated content would be wise to get out ahead of this growing problem in order to prevent this spam and protect their brand.
The App Store is a developer's best friend, until your app is rejected. (Are you suffering from App Store Rejection? You aren't alone - watch this humorous video.)
App Store Guidelines
"We will reject Apps for any content or behavior that we believe is over the line. What line, you ask? Well, as a Supreme Court Justice once said, “I’ll know it when I see it”. And we think that you will also know it when you cross it."
(App Store Review Guidelines)
At Inversoft, we like open source and we like Java.
When we built out our platform to support our new cloud product offerings we started using Chef to help us manage our deployment strategy.
When we began working on some new backend features for our cloud product offerings, I set out to find a Chef Client written in Java in order to simplify our integration.
As luck wouldn’t have it (yes you read that correctly), I was unable to find a Java library that really made my life easier. There are other Chef libraries out there, but all of them were very lightweight wrappers around HTTP calls. Some went so far as to return the JSON response from the Chef server as a String rather than right POJO.
Rather than limping along with a library that was essentially a glorified URLConnection, I did what any software engineer would do, I wrote it myself.
Behold Barista! A native binding for Chef that provides rich domain objects and REST bindings to work with a Chef server.
Building a properly authenticated HTTP request to Chef is not great fun, so I don’t suggest you do it yourself unless you enjoy the pain. We’ve done the heavy lifting for you and we did this without using any third party encryption libraries. This means you can pick up this library without dragging along any unnecessary dependencies like Bouncycastle.
CleanSpeak can filter many types of user-generated content (e.g., chat messages, forum posts and reviews). Running this material through CleanSpeak on a “per message” basis ensures each piece of content is acceptable before allowing it to be seen in your community. Filtering by message makes sense for these specific use cases. But what if you have big data that you want to filter as a whole?
According to Wikipedia, Batch processing is the execution of a series of jobs in a program on a computer without manual intervention (non-interactive). Strictly speaking, it is a processing mode: the execution of a series of programs each on a set or "batch" of inputs, rather than a single input (which would instead be a custom job).
So when might you consider batch processing?
Maybe you purchased a list of names & addresses and want to make sure they don’t contain any vulgar language before including them in your marketing campaign?
Perhaps you allow users to upload files and want to make sure they don’t contain inappropriate content?
Or you gather a list of reviews and want to check them all at once to ensure the language is acceptable before posting to your site?