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What Are Some of the Best AI Software Development Tools? Top 8 Software to Boost Your ML Project With
Which AI software development tools, frameworks, libraries, and other technologies should you add to your toolbox? And the number of emerging AI tech these days sure is... overwhelming! Which one(s) the perfect fit for your own machine learning project/model/problem? Which one's equipped with precisely those features that you need for a fully functioning AI algorithm? To lend you a hand, we've made a “drastic” sorting out and narrowed the high amount of AI software available to a shortlist of... 8. The 8 best AI technologies to consider “turbocharging” your ML project with: 1. Infosys Nia A knowledge-based AI platform to go with if your AI-powered project's goal is to:   gain in-depth insights into customer behavior forecast revenues reduce financial transaction frauds optimize asset efficiency streamline how your team manages customer inquiries    "And how does it work?"  "What does it do, more precisely?" It collects organizational data on the legacy systems, the people and the processes involved and “piles it up” into a self-learning knowledge base. One that developers and data analysts in your team can leverage to create high-performing, scalable ML models. And all that even if they don't have data science expertise, thanks to the platform's easy-to-use ML workbench. Key features:   extensibility: for data preparation, machine learning methods, visualizations self-service provisioning: elastic cloud deployments GUI-based features: enabling your AI software development team to build accurate ML models integrated enterprise framework: for data preparation, reports, deployment, and modeling streaming fast predictions: Infosys Nia Prediction Server   2. Deeplearning4j The second — yet not “the second best” —  AI software development tool in our list is an:   open-source distributed  customizable at scale   … deep-learning library written for Scala and Java. One that Clojure programmers, too, using Hadoop and other file systems can use for building their deep neural networks.  A library designed as a plug-and-play AI solution for fast prototyping. Key features:   it can be used in business environments on distributed CPUs and GPUs tailored to perfectly fit a micro-service architecture GPU support for scaling on AWS  Python, Java, and ScalaAPIs it scales on Hadoop it can import neural net models from other frameworks — Caffe, TensorFlow, Theano —  via Keras it comes with a cross-team toolkit for DevOps, data scientists, data engineers   3. Torch  An open source machine learning library & a Lua-based script language & a scientific computing framework. Why/how has it “earned” its place on our shortlist here?   first of all, it provides a “heavy load” of algorithms of deep machine learning the Facebook AI Research Group, the Idiap Research Institute, IBM and Yandex are just some of the heavy-weighting names using it it's built to “fuel” machine learning projects with both speed and flexibility, without adding an unnecessary overhead   Key features:   linear algebra routines; and it supports plenty of them: for indexing, type-casting, cloning, slicing, sharing storage etc. N-dimensional arrays efficient GPU support  numeric optimization routines it's embeddable, with ports for Android and iOS backends great interface to C (via LuaJIT)   4. Tensorflow, One of the Most Popular AI Software Development Tools A Google-powered open-source software library for machine learning projects. One that's conveniently easy to use across a wide range of platforms. You get to use it with:   Java  Python Go C++ Rust JavaScript   As a new user, you'd be joining the high league of all those big names that are currently using this AI software development technology in their ML-enabled projects: Uber, Intel, Twitter, eBay... “And how does it work?” Basically, what it does is that it provides you with a library storing numerical computation that uses data flowgraphs.  In short: you'd be building your neural networks using flowgraphs:   the nodes in the graphs stand for the math operations the graph edges represent the tensors (multidimensional arrays of data) communicating between them   It's this flowgraphs-based structure that enables developers to deploy deep learning frameworks over several central processing units (CPUs) on tablet devices, mobile, and desktop. But probably one of TensorFlow's biggest strengths and the reason for its wide adoption is its documentation: It provides plenty of support for newcomers (those new to Python here included: from tutorials to detailed documentation, to online resources... Another interesting feature is given by its multiple APIs:   the lowest level API: gives your complete programming control the higher level API: makes repetitive tasks more consistent and easier to carry out for different users   Top TensorFlow-powered Apps:   RankBrain: deployment of deep neural nets on a large-scale basis for search ranking on Google.com Massively Multitask for Drug Discovery: a deep neural network model for detecting favorable drug candidates On-Device Computer Vision for OCR: computer vision model that performs optical character recognition for real-time translations   5. OpeNN The library that you should go with if your AI software development team is made of devs with rich experience in implementing neural networks. OpenNN (Open Neural Networks Library) is a C++ programming library designed to learn from both:   mathematical models and datasets   Note: Neural Designer, a predictive analytics software that creates visual content enhancing the interpretation of data entries —  e.g. tables and graphs —  is OpenNN-powered. Key features:   it provides plenty of support —  documentation, tutorials —  for helping users get into neural networks, even if it's built for developers with a solid AI background it implements data mining methods by bundling multiple functions bundles of functions that can get embedded into other software tools via API (thus enabling and streamlining the interaction between these software tools and the predictive analytics tasks) it's a high performant neural network library: high processing speed, great memory management (since it's built in C++) and CPU parallelization    Datasets:   time series prediction pattern recognition function regression   Mathematical Models:   optimal shape design optimal control    Datasets and Mathematical Models:   inverse problems   6. Apache SystemML  An IBM-powered machine learning technology. Or, if we are to detail this short definition a bit: It's a scalable, flexible in-memory data processing framework providing a huge database of algotihms focused on: clustering, classification, regression, collaborative filtering.   Key Features:   automatic optimization based on both cluster and data characteristics (scalability & efficiency) algorithm customization via Python-like and R-like languages it can be run on top of Apache Spark, due to its great scalability capabilities multiple execution modes: Standalone, Spark MLContext, Hadoop Batch, JMLC (Java Machine Learning Connector) 7. Caffe A deep learning framework written in C++, with a Python interface built around 3 main features:   speed modularity expressiveness   Speaking of the latter, this is an AI software development tool that provides developers with an automatic inspection tool based on imaging.  If your machine learning project includes computer vision-related tasks, Caffe (Convolutional Architecture for Fast Feature Embedding) makes a great, robust choice.  Key features:   high performance extensible code, that enables active development expressive architecture  an active community constantly improving it   8. Apache Mahout How important is scalability for your machine learning app project? If “critical” is the word you'd use, then Apache Mahout is the AI software development tool for your project. It's designed with scalability in mind and as a tool empowering data scientists, mathematicians, statisticians to implement their own algorithms quick and easy. Key features:   provides pre-built algorithms for Apache Flink, Apache Spark, H20 support for various distributed back-ends (Apache Spark here included) comes packed with modular native solvers for GPU, CPU, CUDA acceleration Matrix and vector libraries   The END!  These are the top 8 AI software development tools to narrow down your options to. To evaluate first, putting them against:    your project's goals your team's experience with machine learning algorithms ... and to determine whether they're the perfect fit. ... Read more
Adriana Cacoveanu / Nov 21'2018
How Do I Get into AI Development? Where Do I Start? A Complete Beginner Guide to Learning AI
How does a complete beginner get into AI development? What learning resources does he/she use along the journey to learn about artificial neural networks, the basic AI algorithms, the simplest machine learning models and all that?    “How important is a solid math background?” "And what programming language should I learn/deepen my knowledge of?” Here's a step-by-step guide for a complete beginner to AI, that should put you on the right track, so can you get started with AI software development... the right way:   1. A Solid Background in Mathematics Is Just... Crucial Just think about it:   machine learning comes down to... linear algebra you need at least some basic knowledge of calculus for training neural networks   And there are a few more topics that you should add to the list:   probability and statistics various algorithms   Learn as much math as you can before you jump into the best courses and other learning resources on AI that you can find.  It will greatly help you...   2. Narrow Your Focus: What Do You Want to Build? Clearly articulate your goal, make it fit into one simple sentence:  "To develop an algorithm that predicts a person's blood pressure", for instance. It's only then that you'll be able to:   break your task/problem down into smaller parts narrow your focus (for AI is a discouragingly broad term) identify the specific resources that you'll need    3. Learn By Doing: Try to Solve a Simple Problem for a Start In other words: try writing a simple neural net first, then gradually focus on more complex ones. As a start, tackle an easy problem. Experiment with multiple approaches for harnessing algorithmic decision-making while trying to solve it. Get into AI software development by finding the quickest solution to a given problem: Train a simple machine learning algorithm and evaluate its performance. Next, level up your knowledge by optimizing your basic solution. Experiment with upgrading various components and monitor the resulting change. Try your hand at:   building your own simulator writing the AI code for games like Sudoku or Tic Tac Toe developing code for pattern recognition    4. Get Started with Deep Learning: Learn About Artificial Neural Networks As a newcomer, you must be particularly interested in deep learning, am I right? Now, if you want to explore this machine learning method, you'll need to get familiar with the basics of artificial neural networks. In this respect, you might find this online resource here on Deep Learning enlightening enough.  As for the open-source framework to use for testing the newly acquired skills you have:   Google-powered TensorFlow, by far one of the most popular ones; a Python-based one Theano, Scikit-learn, Keras, all  Python-based frameworks, as well Deeplearning4j, a Java framework  5. Choose Your Programming Language: Consider Performance and Libraries Availability “What programming language should I learn to get started with AI development?” Actually, choosing the language is not that important.  Go for a mainstream language (although you can still do ML/AI with lesser popular languages, as well). One that:   provides you with lots of tools and high-quality libraries stands out in terms of performance    So, it could be either Python or C++, either Java or C or Octave. Each one has its own strengths and limitations when it comes to performance and libraries availability.   6. Learn Computational Learning Theory to Get into AI Development And this is particularly important when you delve deep into the field of Natural Language Processing.   7. Build a Powerful Computing HardWare or Use a Cloud-Based One Expect some significant hardware requirements for running artificial intelligence and implementing machine learning. A powerful hardware system, using a bundle of CPUs and high-performing GPUs is a must if you're thinking:   considerably big models; you'll be testing lots of alternative models before you decide on the final one more and more complex experiments that involve harnessing the power of AI   And here, you have 2 options:   you either put together your own powerful enough supermachine you go with a cloud-based alternative    Speaking of the latter, here are 2 cloud computing platforms to consider:   Cloud TPU: a Google-powered hardware custom designed specifically for carrying out tensor operations in a more efficient way than a GPU or CPU Google CoLab: a Jupyter notebook environment that doesn't need any setup; you get quick access to the cloud-based GPU for running your scripts to   8. Get Familiar with Most Machine Learning Algorithms If you're determined to get into AI development you should be/get comfortable with:   support vector machines (SVM) recurrent neural networks (RNN) deep learning (DL) a whole lot of other decision trees and random forests   There's no shortcut here!   9. Enter a Kaggle Competition Put your newly acquired skills to the test! Commit to solving the problems that other AI developers are working on by participating in a Kaggle competition. Test out multiple approaches and go with the most effective solution. Not only that you will get to test your skills in AI software development but your collaboration skills, as well: You'd be joining a large community, asking questions on an AI-focused forum whenever you get stuck while learning artificial intelligence, you'd be sharing your groundbreaking ideas and so on.   10. 2 Free Online Courses to Try Your Hand At One of the questions at the beginning of this post has been: “ What learning resources does he/she use along the journey to learn...” So, here I am now, ready to give you 2 recommendations:   Stanford University – Machine Learning: Google Brain's founder, Andre NG, is teaching this course; it's loaded with real-time examples of AI-driven technologies, with valuable information that will help you gain a better understanding of how neural networks learn...   Learn with Google AI: a Google-powered project including a machine learning course for newcomers (incorporating the TensorFlow library as well)   The END! Sure hope these 10 tips will help you grow more confident and eager to get into AI development.  ... Read more
Adriana Cacoveanu / Nov 19'2018
How to Build a Machine Learning App: Choosing the Best Image Recognition API
You're ready to turn your idea of a machine learning app using image recognition into “the next best thing”! It's going to revolutionize mobile advertising, the education sector, the automobile industry, the world of finance... you name it. But then, reality strikes you: "How do I implement image recognition functionality into my application the... easy way?" And the “easy-to-use” factor becomes particularly important if you have no machine learning background. How do you incorporate such a service/API into your app? An app that should analyze, organize, alter different images? Now, here's what you need to keep in mind when you build a machine learning-powered app, plus a selection of the best image recognition APIs. So you can compare and experiment with in order to select the one that perfectly suits your goals and your machine learning background... 1. 4 Things to Keep in Mind When Building a Machine Learning App Before you jump into enabling machine learning capabilities into your web or mobile app, make sure:   that you've gained in-depth knowledge of that specific market that you're targeting  that you've properly prepared your data: make sure you've selected the best data sources and data collecting techniques you've chosen the best algorithm for your app (run it, tune it, test it) you're using the right method: for on-device machine learning you'll need to pay attention to your model's size (make sure it's not oversized; otherwise, you'll need to rely on cloud services for machine learning)   Note: organize your dataset ensuring that your images are of different lengths, that they feature plenty of particularities, thus helping your custom model to identify the target objects/emotions/scenes more accurately.   2. Implement Image Recognition to Your App: Choosing the Best API “What are the best image recognition APIs in the market?” you must be asking yourself right now. “What's the best solution for me to incorporate image recognition into my machine learning app if: I have little to no machine learning background I'm looking for an image analysis software that's straightforward to implement, easy to use, yet powerful … one that should enable me to quickly train a custom model"   2.1. Mobile Vision API A Google-powered framework equipped with the capability to detect objects in images and videos. For this, it uses 3 types of detectors:   a face detector a text detector a barcode detector   Image source: Google Developers. The “face detector” is “loaded” with some great features such as: providing information about the state of the human faces in the analyzed images/videos: eyes open/closed, smiling, crying etc. identifying parts of a face: mouth, nose, eyes analyzing multiple faces on a single image identifying human faces on recorded videos, on mobile camera and still images Note: do keep in mind that this API does not provide face recognition capabilities; it cannot tell whether 2 images, presenting human faces, are identical or not.   2.2. Google Vision API Looking for something a bit more complex, more... refined that an "object detection” service? For an image recognition software that does more than just:   provide similar images “detect” faces and visual objects   … and detects “details” about the uploaded images instead? One that identifies whether:   the being in the picture is a human or a dog the characters are sad or happy (sentiment analysis) they're racy or engaged activities marked as “not OK” in the Google Safe Search   … and labels the given images (“weather”, “autumn”, “dog walking”, “male”)? Then, the Google Vision API (or “Cloud Vision API”) is what you're looking for. Unlike other leading image recognition solutions available, it “spoils” you with:   a simple REST API landmark detection functionality   How does it do it? The API connects the code of your machine learning app to Google's image recognition capabilities. Now, here's how you set it up:     Sign Up for a Google Compute Engine Account Select a Project (if you're a newly registered user, then the “My First Project” is selected by default) “Grab” an API key from the menu on the left side of the screen (save it to a text file) and run it in your project (just enable the API at this URL) Select your app project  You're now ready to roll with your new image recognition API integrated to your app project; just save the text in a google_vision.json file: It's this JSON request that will point out to Google Vision API the specific image to parse and the detection capabilities to trigger. Note: remember that you should use this API in personal applications only!    2.3. Clarifai    Here's a custom image recognition software in our list to start experimenting with if:   you're looking for a visual search tool with a video-analysis functionality added to, as well you need an easy to implement and to use API for tagging images; for recognizing and understanding the content features in your images/videos you're looking for an API with a strong concept modeling you're planning to incorporate an image recognition functionality that enables you to create and to train your own custom models to test against   “But how do I use Clarifai's Custom Training API to set up my own model?” It's pretty straightforward:   for declaring a positive you use: clarifai.positive('https://goo.gl/1Q8W8S 'dog', callback); for predicting an image you use: clarifai.predict('https://goo.gl/xNNRJg 'dog', callback); for declaring a negative you use: clarifai.predict('https://goo.gl/xNNRJg 'lion', callback);   2.4. Einstein Vision Looking to get in on a little AI action? To build an image recognition app leveraging AI and deep learning models trained to recognize images at scale?  Consider Einstein Vision then! Integrate it into your machine learning app and start to explore its two APIs:   Einstein Object Detection: empowers you to train models that should recognize several distinct objects in an image (providing information such as the location and the size of each item) Einstein Image Classification: enables you to create and to train models to detect and classify images at scale   “Where would I use such an AI-enabled app?” Here's one of its most common image-recognition use cases: You can use all those contextual clues stored in your images (your customers' preferences, your products/services' level of quality, your inventory levels etc.) to empower your marketing, sales and/or service teams. This way, they'll gain more accurate insights about your customers.   2.5. Amazon Rekognition What if you're not looking for the best tool for sentiment analysis, object and scene detection, but for one that rocks at facial recognition instead?  Then you go with Amazon Rekognition. It's designed to:   provide detailed information (e.g. beard recognition) run facial comparisons and assess the likelihood that 2 faces are of the very same person   2.6. Google Tensorflow Object Detection API A non-complicated way to integrate image recognition functionality into your machine learning app. Tensorflow Object Detection API is an open source framework designed around the idea that: Building, training and deploying of object detection models should be quick and easy. In this respect, the available guide supports the whole idea. Image source: Github Here's how to use the API:   Download the frozen model (.pb — protobuf) and run it into memory Load categories, labels, visualization tools and so on using the built-in helper code Launch a new session and run the resulting model on one of your images    2 tips for incorporating and using this API in your machine learning app:   figure out how you can speed up the API so you can use it for real-time object detection on mobile devices experiment with the more accurate models to see the difference   The END!  Have I managed to (at least partially) answer your questions:     “What do I need to know for building a machine learning app?” “How do I build custom image recognition functionality into my web/mobile app?” “What are the best image recognition APIs in the market right now?” Photo by Antoine Beauvillain on Unsplash.  ... Read more
RADU SIMILEANU / Nov 16'2018
AI vs Machine Learning: Is AI Different from Machine Learning? Or Are They the Same Thing?
AI, AR, VR, ML, DL... AR vs Machine Learning: is there a difference between these 2 technologies? Which one(s)? Or do these 2 acronyms refer to the very same tech? Keeping up with which tech does what, with parsing the differences between all the fancy 2-letter acronyms emerging these days becomes increasingly challenging. Especially when the terms are often used interchangeably, like artificial intelligence and machine learning. Now that's frustrating: how could you possibly distinguish a clear-cut demarcation line between such a broad concept and “catch-all” term as AI (or “machine intelligence”) and machine learning? Time to shed some light here:   1. What Is Artificial Intelligence? A more than succinct, yet descriptive enough definition would go something like this: The capability of a machine to perform tasks that require human intelligence. And here I'm referring to tasks such as:   recognizing images/voices understanding languages, translating planning problem-solving learning   In short: once a computer system reaches a level where it understands, analyzes, tells the difference between objects and makes decisions all by itself — based on understood criteria —  then we can already talk about artificial (or machine) intelligence.  Now, a more detailed definition of artificial intelligence would be: The theory and development of machines that mimic intelligent human behavior. That carry out tasks requiring human intelligence, in a more human-like way: they can reflect, make decisions, interact with humans and perform different complex tasks.   2. AI: Types and Applications We couldn't talk about a complete and accurate “AI vs machine learning” analysis without focusing on the artificial intelligence typology and its specific applications. Therefore, you should know that AI comes in two different “flavors”:   2.1. General AI It involves broader applications: A machine that learns to perform a wide range of complex tasks (that require human intelligence) and gains the ability to solve various problems in a human-like way. Therefore, being broader in scope, general AI is harder to achieve than the “applied AI” alternative: In fact, we don't yet have systems or devices capable to successfully handle any task that a human being can. That type of machine capable to mimic the human brain, to understand, interpret, respond to various stimuli...   2.2. Applied AI (or “Vertical” or “Weak” or “Narrow”) Defining the applied or “weak” AI is crucial for properly identifying the clear-cut differences between AI and machine learning: It's that type of artificial intelligence — of “smart” system — that addresses a specific need. That focuses on handling one single predefined task (e.g. personalizing ads or trading stocks). But maybe a few examples would be more appropriate for you to grasp the full meaning of applied AI:   LinkedIn messaging Netflix recommendations Spotify discovery mode Siri   3. AI vs Machine Learning: What Is Machine Learning More Precisely? First of all, we should make it clear that: Machine learning is a subset of artificial intelligence. And if we are to detail this statement a bit: Machine learning is that subcategory of AI that uses statistical techniques to identify patterns of repetition in databases. Once properly trained, it can analyze loads and loads of data sets, predict accurate outputs and sort new inputs all by itself (e.g. voice search). For instance, after going through huge volumes of customer data, it can recommend the most appropriate products, based on online shoppers' past choices and search history. Or it can trigger certain functionalities of a software based on a particular user's voice.  “But what do you mean by “training” a machine learning?” Here, I'm referring to “neural networks”. Basically, for each machine learning there's a neuronal network programmer (or a team of them) who builds these networks for training and learning. And what he does more precisely is choose specific factors of importance to determine the outcome of a given situation. And they keep “polishing” and further adjusting these factors (or “weighs”) in the outcome until the network reaches the proper result according to the given input. Once the machine learning reaches that level where it's capable to understand and to adjust the factors of importance on its own, to deliver accurate results (in real-time), it will keep improving itself. It will keep “learning” how to deliver more and more accurate results without any human intervention. In short: you “feed” the algorithm with huge volumes of data and it will then learn, adjust itself and continuously evolve when it comes to determining the most accurate outcome of a situation. Just think:   image recognition voice recognition   Now, in an AI vs machine learning debate, one where we're trying to identify the differences between the two concepts, we can say that: Artificial intelligence is the broad concept, whereas machine learning is the technology powering much of the development in the AI field. That machine learning is a type of AI that learns — while getting fed huge amounts of data  — and improves all by itself.  With no human intervention to keep “telling” it which is the matching rule between new inputs and the most probable outputs.   4. In Conclusion... What better way of ending this comparative analysis of the two terms/techs then by pinpointing the main differences between AI and machine learning in a shortlist?  Therefore, here it goes:   while machine learning can exist without AI, the latter can not exist without ML (the main reason behind the confusion when using these terms and why their definitions are often interchanged) once a machine can make a choice or any decision on its own, once it can spot the difference between 2 items, it grows into AI; then, there's more than machine learning technology that's being leveraged there   The END!  Is it clearer for you now which is the key difference between the two concepts? Where precisely you should draw the demarcation line between these 2 technologies? ... Read more
Adriana Cacoveanu / Nov 12'2018
Can LastPass Just Block Your Account and Withhold Your Passwords? Yes! Here Is What They Have Put Us Through
What if you lose your LastPass master password? Then you're doomed... You'll lose your password vault for good. But hey, you can still to lose all your sensitive data even if you don't forget that crucial password! I mean, if it has already happened to us... Apparently, there's no guarantee that one day, for no reason, LastPass won't:   lock you out of your account and block your access for... mere fun keep advising you to use their recovery password form… one that doesn't work and that you had already tried, several times, with no success keep suggesting that you're some sort of a "liar", insisting that you had, in fact, changed your master password and that's why you can't log in now keep giving you a "suicidal" advice: delete your account and open a new one, even if this means losing all your data refuse to allow you to retrieve the data that you stored in your "old" account to export it to that new account they keep insisting to create refuse to refund you the money you had paid, in advance, for a service that apparently doesn't serve your needs: it keeps you blocked out and puts a garnishment on your passwords   So, just beware of which company you choose to trust with your sensitive data! Their "The last password you'll ever need" slogan might just turn into: "The last password you'll ever have". For once they block you, you'll be left with... none.  But let's rewind and go back to the day when it all started. Little did we expect for it to turn into our worst-ever scenario, considering that we had been happy LastPass users since... 2009.   1. It Started Like Just Another Ordinary Log In to Our LastPass Account... But —  surprise, surprise — we couldn't sign in. And our master password was the same old one: we did NOT forget it! I mean, we had been LastPass users for almost 10 years': We were fully aware of what would happen if we ever lost that priceless password! So, we jumped straight to their “Recover Account” form, which brutally served us the following message: And it was about that time that things started to go wrong. When the “ordinary” slowly turned into... extraordinary: An extraordinarily bad experience with the LastPass support team.   2. When in Trouble, Contact LastPass Support and... Start a Deaf Dialogue This is where our deaf dialogue with lovely Michelle from LastPass's support team started. And it was such a nice and fruitful chat that we had there! I let her know that, by some mysterious reasons, that day, from all the other days in the previous 9 years, I couldn't access our account. Nor could I use their recovery account system for... it didn't work. Lovely Michelle either:   suggested that I was lying when I told her about my attempts to use their recovery account form understood everything just too well, but she had a script to follow, so she decided to ignore parts of my message thought she was dealing with some a retarded person    … and told me that, in fact, I had managed, somehow, to change my master password. Then she kindly advised me to... go through their recovery account steps. Even though I had told her I already had done that. But, who was I to come in between her and the script she had to follow blindly? And then came her somehow “suicidal” advice for us, the OPTASY team, one of the loyal LastPass customers: To delete my current account (for which I had already paid in advance) and create a new one! Just like that!   3. News Alert! LastPass Can Block You Out and Withhold Your Stored Data For that's what happens when they advise you to delete your account and “start over”: Your password vault goes... down the drain or gets stuck in their cloud, no matter how you want to look at it. Here's a tricky question for you: What would be a worse scenario for you?    To lose all the passwords that you've trusted LastPass with? To lose all your passwords with no guarantee that no one else can access them later on?   And here's charming Michelle's brutally honest answer to my legitimate question(s): “What's gonna happen with all the records in our OLD account? How can we import them into the new one?” And that reply just... sent cold shivers down our spines...   4. Being Punished Without Fault: No Refund and No Chance to Export Our Data Now, you do guess that it was about then that we reached the climax of our conversation with the LastPass support team (aka Michelle). And so, masochistically enough, we dared to pop up another question: “If you're not able to help me reset the password, please let me know how can I export all the data from my old account and refund the money I paid in advance.” The answer was a... slap in the face, like the previous ones: To sum up now:   locked out from our LastPass account, after several years left to somehow make their not-working “recovery account system”... work for us forced to keep trying it over and over again given just one option: to create a new account and lose all our passwords (and the money paid in advance, as well)   Can you imagine that we trusted LastPass for years?  And that we ended up getting treated like this? With no fault.   5. Their Invariable Response? To Start Over and Knowingly Lose All Passwords Needless to add that I kept on explaining to the LastPass support that in vain did they point out to the recovery steps to take: I had already taken them, even before I had even contacted them in the first place. With zero success... I claimed back the money we had paid in advance for their password manager service, as well as the possibility to export our password to that new account that they insisted that we should set up. Michelle's answer: The END! No happy ending, though, to this story of our terrible experience with LastPass. Who would have thought that all these years we were trusting them with our most valuable data! And that one day they'd just... kick us out and withhold precisely that sensitive data with:   no fault from our side no clear explanation on their side So, just beware, be informed, be skeptical about trusting LastPass... ... Read more
Adriana Cacoveanu / Oct 12'2018
Can I Trust LastPass with My Passwords? No! Our Unexpectedly Bad Experience with Them
“Trust LastPass at your own risk!” would be our answer. One based both on:   this password manager's own “beefy” record of critical security vulnerabilities, cross-site scripting bugs, breaches and major architectural issues our bad experience with LastPass, as a client   And before we dig into the heavy load of evidence that we base our “case” on, allow us to expose some of their former clients' testimonials:   “I lost my entire LastPass passwords in March 2017. It was a disaster for me. I have had LastPass since the beginning, can you imagine all the passwords saved over the years? I think you should do some research on LastPass and the changes, the bad changes that have happened with LastPass” (Barbara's comment, 5 Best LastPass Alternatives to Manage Your Passwords)   “About a month ago when I tried to log in to LastPass I got the message that I had entered the wrong vault password - but I can assure you that nor I, nor my cat has changed it... When I contacted LastPass, they in a rude manner "taught" me that what I hadn't experienced what I had in fact had experienced, since it is "impossible", and their "help" consisted in giving me the clue to the main password to LastPass - i.e. the password, which I explained to them isn't valid anymore... “ (Robert's comment, You Should Probably Stop Using LastPass Temporarily) “Around a month ago I switched from LastPass to Bitwarden as my password manager. To make sure my passwords were protected I deleted my LastPass account, now I get an email asking me to renew my subscription for my DELETED LastPass account. I wonder what else they stored about me... “ (user/dumah310, LastPass storing email from deleted account) 1. But First: How Does LastPass Work? In plain language: LastPass stores your encrypted passwords (and secure notes) in the cloud and secures them via a master password. And the “master password” is both the strength and the main vulnerability of this password management service. Now before I back up the above statement with our own experience with LastPass, here's an excerpt of an “enlightening” HackerNews post: “Users must also devise a “master password” to retrieve the encrypted passwords stored by the password management software. This “master password” is a weak point. If the “master password” is exposed, or there is a slight possibility of potential exposure, confidence in the passwords are lost.“ 2. 5 Security Vulnerabilities Over the Last 7 Years... and Still Counting “How secure is LastPass from being hacked?” I'll leave it to you to evaluate it while going through its “impressive” record of security flaws and vulnerabilities reached over the last years:   2.1. In 2011 a Cross-Site Scripting Vulnerability Was Detected   In February 2011 Mike Cardwell, a security researcher, tracked down an XSS bug on the company's website. Once “exploited”, this vulnerability could basically enable attackers to steal:   hashed passwords the list of websites that users log into (along with the IP addresses, time and dates of their logins) their email addresses underlying cryptographic salts   LastPass fixed that bug within hours.   2.2. That Same Year A Second “Likely” Security Breach Was Identified Later on that year, in May, the company's team spotted a new “anomaly” in both their incoming and outgoing network traffic. Therefore, suspicions arose that a hacker might have accessed their servers. What kind of risks did this “abnormal activity” entail? Well, the attacker could check thousands of passwords in a short period of time, using a combination of user emails, guesses on their master password and the salt. As LastPass CEO confirmed it himself back then, in an interview for PCWorld.com: “ You can combine the user's e-mail, a guess on their master password, and the salt and do various rounds of one-way mathematics against it. When you do all of that, what you're potentially left with is the ability to see from that data whether a guess on a master password is correct without having to hit our servers directly through the website.”    2.3. In 2015 A Hacker Attack Compromised the Company's Servers   Here's another answer to your “Can we trust LastPass?” question: In June 2015 a post on the company's blog announced that their team had detected suspicious behavior on their network. The result? LastPass servers got hacked and the cryptographically protected content compromised. And we're talking here about:   user passwords password reminders cryptographic salts email addresses   2.4. In 2016 A Vulnerability that Enabled Reading Plaintext Passwords Was Exposed Within a year, in July 2016, a new security vulnerability in the autofill functionality was identified and then detailed by the representative of DETECTIFY, an independent online security firm. Basically, the article raised new suspicions about whether one could trust LastPass with their passwords: The URL-parsing code of the LastPass browser extension — the HTML piece of code that was added to every page that the “victim” would visit —  was poorly written. Sloppy enough to enable a potential attacker to read plaintext passwords once the user landed on a malicious website.   2.5. In 2017 a “Major Architectural Problem” Was Discovered  In June 2017 Google's security researcher Tavis Ormandy made a new discovery: A security vulnerability in the LastPass Chrome extension (that applied to Firefox and Edge, as well), which, once exploited, could enable a hacker to steal passwords or engage in remote code execution. He described it as a “major architectural problem” to point out that this time we weren't facing some... signs of carelessness, but a hole in LastPass' security shield instead. “How safe is LastPass?” Users started to ask themselves again and many even started looking for alternatives.   3. About Our Own Unexpectedly Bad Experience as a LastPass Client  Let us share with you some glimpses of our rough experience as LastPass users.  I would start by saying that: Yes, the worst-possible scenario did happen to us. We've apparently lost all the passwords “safely” stored in our LastPass account. There are zero chances to retrieve them, to export them to another password manager or/and to get a refund, considering that we had paid for one year in advance. How did it all begin? With us trying to log into our account, as usual. But, we got this “welcome” message instead: “Invalid password” We next tried to reset our master password, using their reset password form. With no success, though: “LastPass account recovery failed for... Your current web browser did not save account recovery data on this computer. Please try account recovery again with every browser and on every computer you...” And then the “dialogue of the deaf” began, with: Us stating that we did NOT reset our password, for it was not possible and the LastPass support team claiming that we did restart it. And telling us that there's no option but to:   create a whole new account say goodbye to all our passwords "safely" stored there for good; there's no chance to export that user sensitive data to another password manager service lose all hope of getting a refund for the money we had paid in advance, due to their “No refund policy”   In short: if for some mysterious reasons, one day LastPass doesn't recognize your current master password anymore and you're not allowed to reset it either... you're doomed. Now, can you guess what's our answer to this question: “Can we trust LastPass?”   4. Bottom Line: Should You Trust LastPass? “Trust this service at your own risk!” For one day, no matter whether you've:   disabled the auto-fill functionality enabled a two-factor authentication (for both LastPass and your other critical accounts) chosen an "invincible” master password for your LastPass account kept both your software and your machine “spotless clean” and up-to-date used one different password per account   … you still run the risk to find yourself locked out!   Just talking from experience...   ... Read more
Adriana Cacoveanu / Oct 09'2018
What Makes Magento 2 the Best Choice for Mobile Commerce? 6 Obvious Reasons to Consider It
Why Magento 2 and not Shopify, WooCommerce, Joomla, BigCommerce, Volusion and the list of popular e-commerce platforms could go on? Why is Magento 2 the best choice for mobile commerce? After all, they all provide responsive product pages design, right?  Yes, but that's just the “tip of the iceberg”.There are lots of other factors to consider, as well, when striving to ensure your e-store's success on mobile:   the shopping cart the checkout experience the page load times (considering the high level of unpredictability specific to mobile connectivity) the admin UI the “manage your store on the go” functionality   … and so on. Magento 2's built to meet all your mobile commerce-specific expectations, plus a few more. Now, out of all the most obvious reasons why you should consider it as your platform of choice for your mobile e-store, we've selected the 7 most compelling ones:   1. Intuitive and Easier to Use Admin UI A huge “leap” forward from Magento's discouragingly complex and confusing former admin panel. How is it better?   it's cleaner it's more (non-technical) user-friendly it's easier to use    Practically, in Magento 2 store admins are no longer dependent on developers for every little change they need to make in their online stores. From finding precisely the tools they need to adding new product listings, admins can now perform all the common tasks in their dashboards much quicker.   2. A Simple Checkout Process: It Makes Magento 2 the Best Choice for Mobile Commerce And this is that part of your mobile e-store that can make or break its reputation for good.  A cumbersome, overly complex, lengthy checkout experience will only make your customers “run for the hills” and never come back to... pick up their abandoned carts (and maybe even spread the news about the frustrating checkout experience they had in your store). Do you see my point here, right? This platform's simple, frictionless checkout process is directly responsible for the success of any e-commerce website using Magento 2.   3. All Magento 2 Themes and Templates Are Responsive by Default Magento 2 comes jam-packed with free, responsive themes for you to just scan through, select from and use to deliver mobile-friendly shopping experiences. That, of course, in addition to the always available options of:   going with a third-party theme having a Magento 2 developer build a custom theme for you, from scratch, and to tailor it to your store's specific needs   4. Easy to Manage Your Magento 2 Store Right On Your Smartphone Another enhancement that makes Magento 2 the best choice for mobile commerce.  Just imagine that as a store admin you'll get to manage all its features:   catalog management features CMS SEO and marketing features order management   … on the go, right from your mobile phone, right from your admin panel. And it's this type of convenience that turns Magento 2 into the most popular platform among e-commerce business owners.   5. Caching Capabilities And you need to consider how unpredictable a mobile connectivity can get.  Luckily, Magento 2's got your back: its catching capabilities are the “safety net” you need when your online store's visitors are facing issues of limited connectivity. It supports Varnish Full Cache, which makes it easy for developers in your team to boost your Magento store's performance despite the internet connectivity's limitations.   6. Powerful Built-In Marketing Features  Speaking of conveniences, Magento 2 provides you with a heavy load of robust marketing features right out of the box. I'm talking here about:   visual merchandising optimized product category pages  sharing an email drag and drop functionality wishlist creation feature customer segmentation   In short: all the modern features you could possibly think of for “fueling” your mobile marketing strategy with. The END!  What do you think, can these 6 reasons here stand for 6 clear answers to your question:   “What makes Magento 2 the best choice for mobile commerce?” ... Read more
Adriana Cacoveanu / Sep 20'2018
How Can You, As a Client, Prevent Missed Deadlines on Your Web Projects? 6 Best Practices
Lots of helpful tips and tricks, tons of best practices, plenty of great advice on how to prevent missed deadlines on your web projects. And yet: all these “how to's” are targeting project managers, team leaders and, overall, web development teams. But what about you, the client? What can you do to help the teams working on your web projects avoid missing deadlines? What best practices should you adopt in order to streamline the development process? And what bad client habits should you break to avoid scope creep and, implicitly, delaying your own project? Now that we've gone through all your possible questions and dilemmas as a client regarding the “deadline issue”, let's dig for some answers, too. In this respect, here are the 6 best practices that you should stick to when working with a web development team, to ensure that they'll meet their deadline:   1. Clearly Articulate all Your Project Requirements — Ideas, Vision, Expectations Do speak now or forever hold your peace! In other words: share your detailed specifications, your requirements, even just your glimpses of ideas in a very early phase of your project's development life-cycle. This way, you'll empower your contacted team to come up with an accurate project estimate. And thus, to ensure that they'll meet the deadline you will have agreed upon. What's your vision for the project? What do you expect your software product to do? What features should it incorporate? What are your predictions in terms of website traffic? Be sure to express all your requirements as accurately as possible, whether under the form of:   drawings on a sheet of paper detailed specifications verbal explanations screenshots   2. Over the Budget? Discuss Prioritization of the Key Features Another best practice to prevent missed deadlines on your web projects, as a client, is to prioritize specific tasks included in the project.  And this practice gets particularly helpful when you find yourself budget-constrained. What are the essential features and functionalities that your website/application should have? Identify them, then discuss prioritizing those specific implementations with the development team.  This way you:   stay on budget (still) meet the deadline set some realistic expectations draft an updated roadmap for your development team to follow   Tip: are you familiar with the MVP (minimum viable product) philosophy?   3. Give Them Timely Access to Materials They Need to Move Forward More often than not, it's clients' failure to carry out their own parts of the projects (on time) that lead to significant delays in the development process. And forgetting/overlooking/refusing/being out of reach to give your development team timely access to those materials that are crucial for their work is one such example. I'm talking here about materials such as:   project-specific content data brand fonts   … and other resources they might need to advance in their work.  Which leads us to another best practice that clients (too) often fail to follow:   4. Be Reachable: Stay Active on Communication Channels  It's crucial that you be available on (all) communication channels. The team of web developers working on your project might need:    your approval on certain tasks that they will have completed before they can focus on the next development phases your input to the next-in-line deliverable your decision regarding a multi-solution challenge they're facing   So, you do get the point: the more difficult it'll be for them to reach you, the higher are the chances that they miss their deadline.   5. Ask Your Questions to Prevent Missing Deadlines on Your Web Projects Do dare to ask the project manager, the customer service manager or team lead all your questions. Whether technical or not. For you do not need to be a Drupal, Magento, WordPress, React, Laravel, Angular or any other technology expert. Yet, you must ask your development team any inquiries that you might have regarding the used tools and platforms. ... regarding their specific procedures, internal processes and, overall, their particular approach to project management. Ask your questions and allow them to shed light on any “blurriness” that you might be facing. Otherwise, confusions will only lead to last minute changes of scope and missed deadlines.   6. Set Realistic Deadlines to Accommodate Your Last-Minute Requirements, Too Are there any last minute changes, unplanned requirements or off-the-plan tasks that you need to integrate into your project's development cycle?  Talk about them with the project manager and maybe you'll reach an agreement to add a few more developers to your project.  And also, keep in mind to set a realistic deadline to accommodate all these emergencies, as well.    What's a Scope Creep More Precisely? Something that many clients are guilty of, I must say.  It comes down to: Changing the scope of a project.  And there are multiple causes for this:   urgent, last-minute requests coming from the client, that imply high volumes of extra work poor scheduling poor budgeting lack of cooperation   The END! These are the 5 most effective best practices to adopt, as a client, in order to prevent missed deadlines on your web projects.  ... Read more
Adriana Cacoveanu / Sep 18'2018
What Is the Best WordPress Plugin for Posting to Social Media? Top 5
Repetitive (not to say boring), time-consuming... And it all gets even more cumbersome when you add more authors to the equation and you increase frequency to more than just 2 posts a week. Yes, I am talking about regularly sharing your blog posts on your social media. But what if you could just schedule them and create your own calendar? And automate the whole process? Then, the question that arises is:  “What is the best WordPress plugin for posting to social media?” One perfectly equipped to:   cope with an above-the-average posting frequency  work in the context of a multi-author WordPress blog re-post old posts, as well track the overall success of your posts   And there sure are lots and lots of “luring” WordPress plugins to schedule posts, so: Which one best meets all your particular requirements? Let me give you some clues here. 5, to be more specific: WordPress' Default Feature for Scheduling Posts: Main Limitations Let's not jump straight to the WordPress plugins to schedule posts for social media before we've evaluated what the platform's built-in feature offers us, in this respect. For, you can very easily schedule your blog posts to social media in WordPress without the need of a plug-in solution. Here's how this out-of-the-box functionality works:   you go to Settings > General, in your admin panel you set your Timezone (since WordPress uses Universal Time by default) next, once you're ready to hit the “Publish” button and launch your blog post “out into the wild”, just hit the “Edit” button next to it … and set the time and date that you'd like your post to be shared on social media then, you click the “Schedule” button   And that's it! Your post will get automatically published according to your preferences of time and date. But what if:   you'd need to go through this process several times... a day? there are several authors posting on the same blog and, implicitly, sharing content on the same social media accounts?   For more complex expectations about your scheduler, you go with a plugin. With the best WordPress plugin for posting to social media, that should meet all your requirements.   1. Blog2Social A hands-off solution to rely on for both:   automating your posting on social media; from your WordPress blog straight to your social media networks scheduling their publishing; you get to set a specific time for your posts to go live   It will automatically share your content on LinkedIn, Pinterest, Medium, Twitter... Screenshot: WordPress.org  Features you'll love:   automated posting to several social media networks setting the time and date that you want your posts to be published tracking and monitoring your posts' success on each network tailoring your posts' templates so that they fit each network's specifications you're allowed to select the right images for your posts automated re-publishing of your scheduled old posts   2. Social Auto Poster     If this is not the best WordPress plugin for posting to social media, then it's definitely the most flexible one: You get to configure it to the slightest detail; to fine-tune it till it meets even your “overly” specific needs. Moreover, it enables you to auto-share both new and older blog posts. Screenshot: Codecanyon.net Top features:   choosing the post type to be shared auto-sharing new blog posts to those specific social networks that you will have selected custom scheduling: set the most suitable days and hours for sharing content on social media auto-posting to all your linked Facebook accounts supporting any kind of format: post, eCommerce products, page, custom post type   A social auto-poster WordPress plugin that's conveniently compatible with a whole “plethora” of networks: Tumblr, Facebook, Twitter, Pinterest, Instagram...    3. NextScripts   Here's another “must-check” WordPress plugin that's loaded with social media integrations. Screenshot: WordPress.org Let us look over some of its “hard-to-resist-to” features:   automatically sharing both new and older posts you're free to configure which posts should and which ones shouldn't get published … and also the time and date for publishing them you're also free to delay your scheduled posts auto-importing mentions/comments from your social media accounts as WordPress comments   4. Revive Old Post   Is keeping the same level of consistency — through regular posting at... regular times of the day/week — getting a bit challenging? Maybe, at times, you have no fresh content to share with your visitors... Screenshot: Revive.Social Then how about bringing some of your old articles back into the spotlight? Especially since you have Revive Old Post at hand, probably the best WordPress plugin for posting to social media. Once you've set everything up, the plugin turns into a 100% hands-off solution. You'll be putting the whole process of re-posting old content on... autopilot. But, let us go through some of this plugin's key features. Basically, it empowers you to set:   the age of the posts to be re-published the number of posts to be posted per day the posting frequency how many times the same old post can be posted the format of the posts to be shared on social media   Note: once plugged in, the Revive Old Post enables you to track the traffic that re-sharing these old posts will bring on your blog, right in Google Analytics.   5. Auto Post Scheduler ​​​​​​ It makes the best WordPress plugin for posting to social media especially in the case of a multi-author blog. One with a high volume of content drafted for being published on a daily basis. Screenshot: WordPress.org Basically, it's one of those few WordPress plugins to schedule posts that takes the full process, with all its particularities, off your back: From sharing scheduled posts, to recycling old posts to be published, it will automate all posting to social media-related operations. The END! These are the top 5 WordPress plugins to schedule posts for social media that you should evaluate first when looking for the best social auto poster for your own website.   ... Read more
Adriana Cacoveanu / Sep 14'2018