Tech Doodling

Photo by  Franck V.  on  Unsplash Machine learning has seen enormous growth in recent years. It is actively transforming every industry all around us. From autonomous vehicles to chess players, a simple path following robots to Sophia and from recognizing horrible handwritten text to extremely intelligent new Google Assistant, Machine Learning is everywhere. Thanks to Big Data, high power CPUs, giant GPUs and of course to researchers who brought ML from something to everything. For raising reality straight out of a fantasy (sounds filmic. Doesn't it?). Thanks for saving us from the thing that most of the real human being hate. I'm talking about the holy MATHS. Ok, enough. The blessed and the noble human (researchers and innovators) are working hard to come up with more accurate and efficient techniques which work in a constrained environment. One of the major efforts of ML & JS communities is to let us implement ML in JavaScript using JavaScript machine learning libr

Photo by  Greg Rakozy  on  Unsplash JavaScript has been a favorite language to developers throughout recent few years. It was initially supported by web browsers but soon after its release, server side implementations of the language were introduced by different organizations like Netscape, Microsoft and the open-source Node.js. It is not only used for designing webpages but for databases, server scripting and for mobile and desktop applications too. One of the most prominent reasons to learn JavaScript is its enormous community and its products. There are a number of cool JavaScript libraries which makes the life of a developer very easy. ECMA International has introduced the ECMA2017 standards which are quite effective and saves enough amount of time. With all this, the community has made large efforts to bring Machine Learning and JavaScript together. Taking all these into consideration, JavaScript seems worth our time. If you are a beginner or an intermediate JavaScript dev

Photo by  Nicolas Hirajeta  on  Unsplash After creating a fake master fingerprint to unlock most fingerprint protected devices, Thomas Brewster at Forbes made an attempt to use a fake 3D printed version of his real head to unlock a bunch of phones. The reporter printed a 3D model of his head at Backface in Birmingham, U.K. An image of his head was taken using 50 cameras. After some preprocessing, the model was printed using a 3D printer. The final product was ready after some post processing (coloring and other final touches) in a few days. The entire process cost just over £300. (I tried to be super quick at describing the process. Tell me how I did.) For the tests, he used 4 Android devices (LG G7 ThinQ, Samsung S9, Samsung Note8, OnePlus 6) and iPhoneX. He used his real head, I mean the head he is born with, to activate face lock on all devices and then tried to unlock them with his fake 3D printed head. According to his report, only the iPhone X was successful to

Last year we read that Facebook stole the well-known, widely used story feature from SnapChat. The theft was well received by Facebook users. According to TechCrunch , about 150M FB users are using the story feature daily. WhatsApp stories get even more users, up to 450M per day. We can say that the big fishes are always there keeping an eye on the trends by either spying on their users or using some cool techs or whatever. The same happened in the last couple of days but this time the big fish is SnapChat. SnapChat launches its “ new feature ” called Lens Challenge where its users can participate in different challenges like they do in TikTok’s lip syncing challenges. Users can select to participate in challenges by creating a snap with a Lens that is themed to a particular song or a melody etc. The company offers official challenges where the users can select any Lens from the Lens Explorer section of the App, select a song and lip sync with it and send it to one of the offic