Adriana Cacoveanu

Adriana Cacoveanu

ADRIANA CACOVEANU, Content Writer

Adriana is the OPTASY team's digital content creator and copywriter. Her “mission” within our team is to masterfully blend the 2 main ingredients' listed in any valuable blog post's recipe (valuable information + the reader-friendly writing), as well as to craft informative and engaging content promoting our work: study cases on Drupal.org, fresh content for various pages on our company website, e-book content etc.
 

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The Drupal Quality Initiative: How Do You Know When Your Contributed Project Is Ready to Be Released? How Do You Assess Its Quality?
Let's say you've been working on this contributed project for a few months now. It has gone from Beta 1 to Beta 2 to Beta... Now, how long till its final release? How do you know when it's ready for the Drupal community to see and use? And this is precisely why the Drupal quality initiative was launched in the first place. So that can we finally have some sort of a checklist at hand to use whenever we need to assess our code's level of quality:   the standards that we should evaluate our contributed projects by  the specific elements that go into the quality of our projects, such as contributed Drupal modules a certain hierarchy of quality that we could rate our own projects by   And so on... For, let's admit it now: Except for our own personal methodologies for self-assessment, there's no standardized benchmark that could help us evaluate our contributed Drupal projects. There's no way of knowing for sure when our projects are 100% ready to go from beta to... full release. Now, here are the legitimate questions that this initiative brings forward, along with some of the suggested paths to take:   1. What Drupal-Specific Quality Metrics Should We Use to Evaluate Our Code? How do you know when your contributed project is efficient enough to... be used by other members of the Drupal community? You need some sort of criteria for measuring its level of quality, right?    2. The Drupal Quality Initiative: A Checklist for Project Quality Assessment And this is how the “Big Checklist” for Drupal modules has been put together. One outlining all those areas of a contributed Drupal project that you should carefully evaluate when assessing its quality. Areas such as:   team management documentation testing code design requirements DevOps   All those factors and Drupal-specific elements that go into the quality of a contributed project. 3. Introducing the Idea of a Multi-Leveled Quality Hierarchy What if we had multiple levels of quality to rate our Drupal projects? Imagine some sort of hierarchy of quality that would challenge us to keep improving the way we write code for Drupal. To keep growing as teams working with Drupal. Your project might be rated “level 1”, from a quality standpoint, on its first release. But it would still stand stand the chance to get a higher score for if you strove to meet all the other criteria on the checklist. 4. You'll Be Particularly Interested in The Drupal Quality Initiative If You're A...   Site builder, scanning through the pile of contributed Drupal modules in search of the ones that perfectly suit your project's specific needs Drupal contributor in need of some sort of checklist that would include all those standards of quality and best practices to help you assess your own code's value   5. What About Non-Drupal Software Projects? How Is Their Quality Assessed? In other words: how do other communities assess their projects' levels of quality? What metrics do they use? And here, the Drupal quality initiative's... initiator gives the “The Capability Maturity Level”, set up by the Software Engineering Institute, as an example. The process model highlights 5 levels of “maturity” that a project can reach throughout its different development phases.They range from:   the“initial chaos” to planning and collecting project requirements … all the way to continuous process improvement   Now, just imagine a similar multi-level evolutionary benchmark that we could use to assess our own Drupal projects' levels of... maturity.   6. A Few Quality Indicators and Suggested Tools And the whole Drupal Quality Initiative comes down to identifying the key endpoints for assessing a project's quality, right? Here are just some of the suggested questions to use during this evaluation process:   Is it easy to use? Does it perform the intended functions? Is it efficient enough? How many detected bugs are there per 1000 lines of code How secure is it?   Now, for giving the most accurate answers to these quality assessing questions, you'll need the right toolbox, right? All those powerful tools to help you:   check whether your code is spell checked monitor the status of specific operations check whether all strings use translation see whether your code has been properly formatted   The END! And this is just a brief overview of the Drupal Quality Initiative. What do you think now, does the suggested checklist stand the chance to turn into a standardized Drupal benchmark for assessing quality? How do you currently determine your contributed projects' value? ... Read more
Adriana Cacoveanu / Jan 25'2019
How Do You Deal with Duplicate Content in Drupal? 4 Modules to Get this Issue Fixed
Accidentally creating duplicate content in Drupal is like... a cold:  Catching it is as easy as falling off a log. All it takes is to:   further submit your valuable content on other websites, as well, and thus challenging Google with 2 or more identical pieces of content move your website from HTTP to HTTPs, but skip some key steps in the process, so that the HTTP version of your Drupal is still there, “lurking in the dark” have printer-friendly versions of your Drupal site and thus dare Google to face another duplicate content “dilemma”   So, what are the “lifebelts” or prevention tools that Drupal “arms” you with for handling this thorny issue? Here are the 4 modules to use for boosting your site's immunity system against duplicate content. And for getting it fixed, once the harm has already been made:   1. But How Does It Crawl into Your Website? Main Sources of Duplicate Content  Let's get down to the nitty-gritty of how Drupal 8 duplicate content “infiltrates” into your website. But first, here are the 2 major categories that these sources fall into:   malicious non-malicious   The first ones include all those scenarios where spammers post content from your website without your consent. The non-malicious duplicate content can come from:   discussion forums that create both standard and stripped-down pages (for mobile devices) printer-only web page versions, as already mentioned items displayed on multiple pages of the same e-commerce site   Also, duplicate content in Drupal can be either:   identical or similar And since it comes in “many stripes and colors”, here are the 7 most common types of duplicate content:   1.1. Scraped Content Has someone copied content from your website and further published it? Do not expect Google to distinguish the copy from its source. That said, it's your job and yours only to stay diligent and protect the content on your Drupal site from scrapers.   1.2. WWW and non-WWW Versions of Your Website Are there 2 identical version of your Drupal website available? A www and a non-www one? Now, that's enough to ring Google's “duplicate content in Drupal” alarm.   1.3. Widely Syndicated Content  So, you've painstakingly put together a list of article submission sites to give your valuable content (blog post, video, article etc.) more exposure. And now what? Should you just cancel promoting it? Not at all! Widely syndicated content risks to get on Google's “Drupal 8 duplicate content” radar only if you set no guidelines for those third-party websites. That is when these publishers don't place any canonical tags in your submitted content pointing out to its original source. What happens when you overlook such a content syndication agreement? You leave it entirely to Google to track down the source. To scan through all those websites and blogs that your piece of content gets republished on. And often times it fails to tell the original from its copy.   1.4. Printed-Friendly Versions This is probably one of the sources of duplicate content in Drupal that seems most... harmless to you, right? And yet, for search engines multiple printer-friendly versions of the same content translates as: duplicate pages.   1.5. HTTP and HTTPs Pages Have you made the switch from HTTP to HTTPs? Entirely? Or are there:   backlinks from other websites still leading to the HTTP version of your website? internal links on your current HTTPs website still carrying the old protocol?   Make sure you detect all these less obvious sources of identical URLs on your Drupal website.   1.6. Appreciably Similar Content  Your site's vulnerable to this type of duplicate content “threat” particularly if it's an e-commerce one. Just think of all those too common scenarios where you display highly similar product descriptions on several different pages on your eStore.    1.7. User Session IDs  Users themselves can non-deliberately generate duplicate content on your Drupal site.  How? They might have different session IDs that generate new and new URLs. 2. 4 Modules at Hand to Identify and Fix Duplicate Content in Drupal What are the tools that Drupal puts at your disposal to detect and eliminate all duplicate content?   2.1. Redirect Module Imagine all the functionality of the former Global Redirect module (Drupal 7) “injected” into this Drupal 8 module! In fact, you can still define your Global Redirect features by just:   accessing the Redirect module's configuration page clicking on “URL redirects”    Image Source: WEBWASH.net What this SEO-friendly module does is provide you with a user-friendly interface for managing your URL path redirects:   create new redirects identify broken URL paths (you'll need to enable the “Redirect 4040” sub-module for that) set up domain level redirects (use the “Redirect Domain” sub-module) import redirects   Summing up: when it comes to handling duplicate content in Drupal, this module helps you redirect all your URLs to the new paths that you will have set up. This way, you avoid the risk of having the very same content displayed on multiple URL paths.   2.2. Taxonomy Unique Module   How about “fighting” duplicate content on your website at a vocabulary level? In this respect, this Drupal 8 module:   prevents you from saving a taxonomy term that already exists in that vocabulary is configurable for every vocabulary on your Drupal site allows you to set custom error messages that would pop up whenever a duplicate taxonomy term is detected in the same vocabulary   2.3. PathAuto Module   Just admit it now: How much do you hate the /node125 type of URL path aliases? They're anything but user-friendly. And this is precisely the role that Pathauto's been invested with: To automatically generate content friendly path aliases (e.g. /blog/my-node-title) for a whole variety of content. Let's say that you want to modify the current “path scheme” on your website with no impact on the URLs (you don't want the change to affect user's bookmarks or to “intrigue” the search engines). The Pathauto module will automatically redirect those URLs to the new paths using any HTTP redirect status.   2.4. Intelligent Content Tools       Personalization is key when you strive to prevent duplicate content in Drupal, right?  And this is precisely what this module here does: it helps you personalize content on your website. How? Through its 3 main functionalities delivered to you as sub-modules:   auto tagging text summarizing  detecting plagiarized content    Leveraging Natural Language Processing, this last sub-module scans content on your website and alerts you of any signs of duplicity detected. Word of caution: keep in mind that the module is not yet covered by Drupal's security advisory policy!   3. To Sum Up Setting a goal to ensure 100% unique content on your website is as realistic as... learning a new language in a week.  Instead, you should consider setting up a solid strategy ”fueled” by (at least) these 4 modules “exposed” here. One that would help you avoid specific scenarios where entire pages or clusters of pages get duplicated. Now, that's a far less utopian goal to set, don't you think? ... Read more
Adriana Cacoveanu / Jan 16'2019
10 Reasons Why AI Projects Fail or How You Can Easily Sabotage Your Own AI Project- Part 2
And I'm back, as promised, with a handful of reasons — ranging from “the usual suspects” to more nuanced ones — why AI projects fail. From:   getting too absorbed in keeping your technological assets up to date to the point of skipping to put together a solid business strategy for your AI implementation to getting overly excited about AI and trying to “force integrate” it into... everything  to skipping to further maintain it, once launched   … there are quite a few mistakes that you can easily avoid and thus foolproof your AI project. So, let me expose these hidden “traps” to you:   Mistake #6: You Put Technology Before a Solid Business Strategy AI-powered or not, it's still a... business that you're running, right? So, getting focused on technology only, turning it into the unique driver of “business” value is simply... non-realistic. First, you need to build your solid business strategy. One to include:   thorough research of your target markets all the technological assets needed to reach your AI project's goal ... along with all the resources to be invested, of course   Technology, no matter how advanced, never comes before business strategy.  That, of course, if it's business value that you try to achieve and not just... building AI for AI's sake.   Mistake #7: You Cut Down on Testing Time Probably one of the most common reasons why AI projects fail: You get all too eager (and over-confident) to release your AI-powered software out into the wild and you deliberately skip some major debugging phases. To avoid this trap, make sure to include, while setting up your business strategy, the due resources for properly testing your AI project before “setting it free”. For, placing it into the spotlight prematurely, faulted and vulnerable to future bugs, will “doom” your AI solution to years of... public distrust.   Mistake #8: You Get Stuck in a Never-Ending Development Cycle Now, mind you don't avoid a pitfall only to... fall into the next one: A never-ending design-develop-design-develop... process. For, yes, one of the worst AI mistakes is to release a buggy, poorly tested AI-enabled software product.  But it's equally risky to keep postponing its launch and get tangled up in this loop of continuously polishing it and testing it. You just risk having your competition leverage all the AI opportunities out there while you're constantly updating your software. Instead, consider launching the best possible version of your AI software. Then, collect the relevant data and the message coming from your target market to iterate and release an updated version.   Mistake #9: Baking AI into Everything- Why AI Projects Fail Trying to turn AI into an “all-purpose” tool is yet another frequent mistake behind many AI fails. I know you must be infatuated with AI (we, too, are infinitely excited with the still unexplored opportunities of artificial intelligence). Yet, do keep in mind that the right sequence is the following: You first identify the specific need/problem in the market and then come up with the suitable AI solution for it! You don't just jump on the latest AI technology and... force-fit it into any software product. For, let's face it: There are tasks where AI rocks and tasks where human staff's emotional intelligence is needed (take certain customer service scenarios, for instance). So, don't try to bake AI into... everything or your project will only swell the ranks of failed AI projects.   Mistake #10: You Skip Further Maintaining Your AI Solution A “launch and run” strategy won't propel your AI project too far ahead... That is: Just like any other product, an AI-powered software product needs periodical maintenance. Regular “infusions” of new methods, new models and training data.   The more complex it gets, the more crucial a solid maintenance strategy becomes. So, mind you do not underrate this phase. It's another too common reason why AI projects fail.   The END! These are the 10 most “popular” AI project mistakes. Which one(s) had you been more “vulnerable” to before seeing them all exposed in this post here? Photo by Rock'n Roll Monkey on Unsplash.  ... Read more
Adriana Cacoveanu / Jan 14'2019
10 Reasons Why AI Projects Fail or How You Can Easily Sabotage Your Own AI Project- Part 1
Running an AI startup? Or just planning to implement AI technology into your next software product? Then you sure don't run short on AI advice, right? Everyone's telling you why you should adopt AI, how to successfully incorporate AI into your business processes... But no one tells you why AI projects fail. What mistakes you should avoid to foolproof your AI implementation. What are those gotchas —  going from obvious pitfalls to more subtle traps —  that can easily change your AI project's results from success to major failure? It might not be as resounding as Amazon Echo's nasty blunder: The Alexa-powered device decided to throw a “surprise party”, with loud music and all that jazz, once it got a house in Berlin all for itself... Yet it would still mean flushing all your high hopes for AI and the invested resources down the drain... Now, back to the most common reasons why so many fail with AI. Or, better said: The 10 AI project mistakes to avoid.   Mistake #1: You Start Big and Spread Yourself Too Thin In other words: Don't bite off more than you can chew! I know you might be overexcited about the incredible AI potential right now. But jumping on a too complex AI project, with long time horizons, is the perfect “recipe” for failure. For, let's face it: Expecting AI to instantly transform all your business processes, to go from no value to 100% value for your AI project is as realistic as... checking off all your New Year's resolutions on the 1st of January. Instead, start small and grow big. Take your time to learn more about the technology you're implementing. To gradually gain all the needed expertise, to fail fast and organically grow your AI project. Rather than artificially pumping it up.   Mistake #2: You Keep Your R&D Expenses to a Minimum Failing to see research & development spending as an investment is one of the most common AI project mistakes. In short, getting stingy when it comes to investing in:   training programs for your employees research on advanced algorithms heavy experimentation with those cutting-edge AI technologies that you expect your team to develop computing infrastructure   … is not a way to save money. It's the shortest path to AI project failure, actually.   Mistake #3: Vague Goals, The Key Reason Why AI Projects Fail What's your vision?  What short-term goals have you set for your specific application of AI, in your... specific industry (be it health care or finance or...)?  Make sure you articulate those goals crystal clear and share them with your team. Oh, you don't have a vision? Not just yet? Only high hopes and expectations about how AI will completely transform your business? Or is it just a few ambiguous, fragmented goals and vague objectives that you have at hand? Then I'm sorry to break it to you: no clear vision means no great value that you could “reap” from your AI project.   Mistake #4: Your AI-Powered Software Doesn't Meet Any Real Need Your new AI technology needs to be usable. And that says it all: Building AI for AI's sake is as profitable as... designing bathing suits for Eskimos. Above all things, your AI project has to meet real business needs. Therefore, make sure you don't fall into the “technology bubble” trap. Do your research, identify the current needs in the finance, health care, disease research or any other field that you target and adapt your new tech accordingly. The main reason why AI projects fail? They're built to awe, not to serve. They put outstanding, revolutionary technology before real people's needs. Instead of aligning it to them.   Mistake #5: You Rely on AI Newbies Only If you ran a 5 star Michelin restaurant would you ask your cashier to cook that exquisite dish recently added to your menu?  See my point? One of the most common reasons why AI projects fail is because startups hand their projects to AI enthusiasts with great potential and zero work experience. Be better than that! Aim for AI expertise when you're recruiting for the team that will be working on your new AI project. Don't just expect AI newbies to... turn into AI experts overnight or your internal staff to jump on a totally new technology and turn your project into the... next new AI companion robot!   Enthusiasm won't compensate for all those imminent mistakes and fails to execute your AI strategy. And these are just 5 of the most common reasons behind AI fails. Stay tuned, for we have 5 more to expose to you in all their “glory” in our next post... Photo by Rock'n Roll Monkey on Unsplash.  ... Read more
Adriana Cacoveanu / Jan 11'2019
CES 2019 Future-Shaping Tech Trends and Hot Gadgets: From Predictable to Surprising
8k TVs, companion and laundry folding robots, 5G smartphones, voice assistant for cars, foldable phones... This is CES 2019 — the world's largest electronics show taking place these days in Las Vegas — in just a few words... Or, simply put: A sneak peek into the future. And there are more than 4000 CES 2019 exhibitors there, each one competing for its own sphere of influence over the future. For its right to set the new tech trends. So, you can just imagine that:   sorting through all the announcements that companies leading the change will make about their future product releases staying on top of all those critical “teasers” regarding the innovative technologies and exciting features that their devices will feature “playing with” all the gadgets sprawled over the +2 million square feet exhibition hall   … can get overwhelming.  And finding a straight answer to your legitimate question: What is the hottest consumer technology device for 2019? …  turns into a “mission impossible” sort of challenge. Not to worry, though. We've done our homework, scored the heavy-weighing offer and put together a selection of the most influential tech trends and the gadgets at CES 2019. Of those gadgets with the highest potential to grow from far-out concepts to production-ready devices.   1. AR-VR-MR: The “Sideshow” Performers AR/VR-enabled devices are present at the show, but they're far from taking center stage... Especially as VR technology is concerned, CES 2019 doesn't seem to be that long-time awaited chance for it to “incarnate” into a truly remarkable consumer product. Or at least into an extravagant, daring concept. But let's not get too frustrated (again) and, instead, let's analyze 2 contestants for the title of “the best VR/AR-fueled device” at the 2019 international CES:   1.1. HTC Vive Pro More of an upgraded version of the Vive VR headset, HTC's Vivre Pro comes with some significant features geared to provide a more realistic VR experience:   improved 3D audio a separate wireless transmitter 78% increase in screen resolution noise-canceling microphones improved design to make them sit more comfortably   1.2. Deepframe AR Window  Augmented reality at scale.  Just imagine this technology brought to a 65-inch 4k OLED! That's Deepframe. The not-yet-for-consumers kind of display uses unique opticals reflecting life-sized digital elements over a physical space. With no VR eyewear on, the user emerges into a mixed reality world.   2. 5G-Enabled Phones: From Hype to Reality  Have you been starting to lose your patience on the 5G networks “issue”? For, let's face it: It's been a while since all this 5G hype turned into... hope... next into even higher hopes, then... nothing. We had already started fantasizing about:   the extreme speed at which we would download the latest episodes of our top favorite TV series on our phones this networking technology turbocharging all our future VR experiences being free to go see a particular doctor... anywhere on the globe basically 5G enabling our cars to interact with our PCs, with our smart homes, with...   Luckily, at CES 2019 there's been clear evidence that companies are (finally) ready to take the charge and implement 5G into their devices.   2.1. Samsung's 5G Smartphone: Samsung Galaxy 10 The big absent from CES 2019 Las Vegas has been Samsung Galaxy 10. Yet, from the company's CEO's press release we now know that:   it will be released this year “equipped with” 5G technology … and 7nm chipsets, among other features Verizon, Sprint, and AT&T will be its US carriers running 5G programs FCC approved Samsung's intention to roll out 5G network this year   Note: another great news is that Motorola and OnePlus, too, are some 2 other giants planning to release 5G-enabled phones this year. We'll see which South Korean company will first reach the “finish line” with its own 5G-powered device.   3. Voice-Controlled Technology... for Cars 3.1. Speak's Music Muse: Alexa-Powered Voice Assistant for Cars How about that! Bringing voice-controlled technology to your car... And here's what this Oreo-sized device would enable you to do once you plug it into your car's USD port and connect it to your smartphone:   you get to use voice commands and ask for a weather report, play music ... control your Alexa-powered devices at home (e.g. you could close your garage doors or turn off your front door light) ... add items to your to-do list   And, most of all: It comes with hands-free calling functionality.   4. AI Technology Is... Everywhere at CES 2019  Artificial intelligence steals the spotlight at the annual consumer electronics show in Las Vegas. Here are just 2 of the AI marvels that stole our attention:   4.1. IBM 50-Qubit Quantum Computer No other machine at the show 2019 International CES could rival this “beast” when it comes to calculations. The uses that IBM had in mind for it? All those AI and machine learning-powered scenarios where huge volumes of data need to be crunched at super speed.   4.2. “Nervana Neural Network Processor for Inference” (NNP-I) Intel and Facebook joined their forces to create a more affordable AI chip addressing companies with high workload demands. When will it go from concept to product? Sometime this year those “target” companies will be able to use NNP-I to accelerate inference and thus better cope with their workload challenge. The END! Back to you now: What do you think about the tech trends and devices highlighted here? Do you predict that they'll turn into actual products this year? What have been your expectations from CES 2019? And, most importantly: how does your wishlist of next-generation gadgets look like? Photo by Owen Beard on Unsplash  ... Read more
Adriana Cacoveanu / Jan 09'2019
What Is the Best Membership Plugin for WordPress? A Top 5 Feature-Rich Options at Hand
Say you've invested so much time and effort to come up with this valuable content for your website. And now you don't want to be all altruistic and share it with everybody. Instead, you plan to grant access to it to logged in members (or paying subscribers) only. So, you start looking for a plugin for access control. But what is the best membership plugin for WordPress? The most suitable one for your specific feature needs as a membership website owner... For your own scenario, which might require that:   members be enabled to administer their profiles straight from your website's front-end page admins be allowed to easily track down members using specific meta information (e.g. “category”) your plugin support multilevel membership functionality (logged-in member, paying logging-in user, admin, editor etc.) your WordPress plugin support dripping so that your content gets “served” according to a strict time schedule  you don't need to get tangled up in code writing and customization work members be enabled to CRUD their custom post type content (galleries, events, posts, reviews)   Oh, and yes: You might also expect this WordPress membership plugin to accommodate a decent number of members (approx. 800-100?), be easy to set up and to get updates, too. Now, here are the 5 most powerful free WordPress membership plugins and WordPress paid membership plugins that you should weigh first: 1. MemberPress It's the equivalent of “vanilla ice cream” among membership plugins for WordPress. I mean, it easily checks all the checkboxes on your “must-have features” list:   easy to set up easy to use feature-packed rich documentation and stellar support   And speaking of its load of features, let me outline some of the most powerful ones: Easy setup From setting up pricing and login to adding membership plans and putting together “Thank you” pages for your members, setting everything up gets ridiculously easy with this plugin. Subscription management The plugin empowers your members to create and update their own membership subscriptions. Content dripping It enables you to “disclose” content to your members according to your time schedule.  This feature is particularly valuable if it's learning material or online courses that you provide on your WordPress website. Content Access Control And restricting access to certain content on your website is the essential feature of any WordPress paid membership plugin, right? In this respect, MemberPress provides you with flexible access control: You get to limit (or “condition”, if you prefer) access to specific tags, blog posts, files, pages, categories on your website.  Payment Gateways The plugin comes equipped with built-in support for Stripe and Paypal payment gateways.  Need Authorize.net integration, as well? Then you'll need to first upgrade to Developer edition. Integrations Here are some of the third-party services that it easily connects with:   Amazon Web Services MailChimp Aweber BluBurry GetResponse   2. Restrict Content Pro Why does this plugin stand high chances to be the best membership plugin for WordPress? We can cut down all the reasons down to... 2 heavy-weighting ones:   it's conveniently simple: easy to set up and easy to use it's ideally flexible: it comes with built-in interrogations, add-ons and pro add-ons; you're free to add exclusively those features that you need   And now, let's detail some of its key features: Easy to use I'll only say one thing: It seamlessly integrates with WordPress UI...  Built-in integrations Some of the most tempting ones are:   PayPal MailChimp Stripe CSV export email manager MailPoet   … that other plugins would provide you with as paid add-ons. Add-ons You get to extend its functionality, free of charge, with specific extra features that you might need. And I'm referring here to:   force strong passwords download monitor multiple email marketing integrations   Pro add-ons If your feature needs are higher, you can always go for the professional or ultimate plan and “indulge” in some pro add-ons. Here's just a sneak peek into the “pro add-ons menu”:   WooCommerce member discounts restrict past content drip content restriction timeouts   Payment gateways Is it Braintree, Stripe or maybe Paypal that you need to integrate with your website? Stay assured: they all come as free add-ons... Should I also add that Restrict Content Pro has won the reputation of a developer-friendly WordPress plugin for membership websites? 3. Ultimate Member, The Best Membership Plugin for WordPress Community-Like Websites Disclosure: Ultimate Member is the perfect fit for community-like websites, where you'd focus more on the social aspect of membership rather than on selling paid memberships. For instance: it doesn't come with a built-in paid membership functionality that you could leverage. It's perfectly true that you could go for an add-on to hook it up with WooCommerce (e.g. Um-Switcher). But wouldn't it be more straightforward to opt for another WordPress paid membership plugin then? One geared specifically at selling paid memberships? So, back to the major strengths of this free WordPress membership plugin: Is is a social community that you plan to build? One depending on multi-tiered membership functionality? Where each member would administer its own account? Then Ultimate Member will make your perfect ally. Here are some of the features that makes it perfectly suitable for “social community building” scenarios: Content restriction  From:   enabling you to restrict the entire WordPress site to limiting access to specific blog posts, pages or categories, to controlling which menus to be served to each user roles   ... this plugin “spoils” you with unmatched flexibility. Still, do keep in mind that large scale content restriction, although powerful, is not its defining feature. Building communities is... 4. Paid Memberships Pro   Looking for the best membership plugin for WordPress focused on the subscription selling aspect? Here's your winner! Unlike Ultimate Member, Paid Membership Pro is designed to power subscription-based websites. It makes your conveniently powerful tool for... collecting payments from your members. And, branching out from this particularity, the are features such as:   accommodating thousands of members enabling you to put together a whole “ecosystem” of membership levels (weekly, monthly... payments) granting access to discounts, e-learning materials, private communities on your websites to members only supporting updates   Content restriction  Is it particular pages or posts that you want to limit access to? It's as easy as checking... the due checkboxes. Is it entire content categories that you want to restrict? Paid Membership Pro has got you covered. If, let's say, you need to restrict a certain widget or an individual video on a web page, feel free to use shortcode or PHP functions to narrow down your focus. Payment gateways Authorize.net, Paypal, CyberSource, Stripe, 2Checkout, CyberSource... What payment option do you need to integrate into your website? Note: you can always process the membership checkout through WooCommerce instead. Third-party Tools integration From AffilliateWP to MailChimp, to Kissmetricts... you have a whole list of third-party services to choose from and integrate with your membership website. Content dripping Not in your interest to serve all your restricted content all at once to your subscribers? Then... don't! This plugin enables you to “drip” your valuable content based on a time schedule.  You could set up a “Series” gradually unlocking content depending on the number of days passed since a member registered on your website. Paid Add-Ons A bit more... needy when it comes to the specific functionality that you'd want to “inject” into your membership website? Then, go for the paid version and unlock the 60+ add-ons available. Let me point out just a few of:   Zapier integration for automation  different email marketing services integrations Slack notifications email notifications selling access to particular pages/posts as an add-on package affiliate program integrations   5. WooCommerce Memberships   How many of the here-below checkboxes would you check?   you already have an e-commerce website it's a feature-rich plugin that you're looking for … one to reward your subscribers with various discounts you're looking for a plugin that easily and seamlessly integrates with WooCommerce   If you've ticked them all, then you might want to weigh the standout features of this WordPress membership plugin for WooCommerce: Unique options  Unlocking special discounts, granting content access upon a product purchase, limiting product viewing to members only... This plugin comes with quite a few “surprise” features that you won't find in any other of its “rivals” on this list here. Different membership levels You get “spoiled” with lots and lots of membership options that keep on... further multiplying if you decide to integrate with WooCommerce Subscriptions, too. This way, you'd gain even more control over free trials, drip content, recurring payments, for instance. Access Control  You're free to limit access to specific blog posts, pages and other content types on your website. The END!  By now you must have realized that: It's not “the best membership plugin for WordPress” that you need, but the most relevant one for your own membership website. For your specific requirements and feature needs. ... Read more
Adriana Cacoveanu / Jan 08'2019
What Will Be the Most Influential Mobile UI Design Trends in 2019? Top 4
A few more weeks and... “Chin-Chin: Happy New Year!”. Meanwhile, while you were making your final edits to your wish list for Santa and adding a few more lines to your New Year's resolutions list, we've been doing our homework, too. We've run our investigations and come up with our own list: one including the most influential mobile UI design trends in 2019. Both those trends:   that have timidly stepped on the mobile app design scene this year and will just grow more powerful next year and those that will emerge in 2019 and quickly take over the “scene”   So, here they are: the 4 major trends to look into and rush to capitalize on next year.   1. Buttonless Screens: From Niche to Norm Mobile UI designs without buttons have been around for some time, but we somehow didn't consider this would become... mainstream, right? Just think:   Instagram and its buttonless design that kind of forces you to rely on gestures for swiping through different stories on the page, for moving backward and/or forward Apple, Samsung, and Google, that are advocating for edgeless, clean screens; they implicitly “force us”, mobile app designers, to drastically trim down our in-app button collections. To remove buttons completely... all those e-commerce apps that have simplified their checkout processes by... removing the cart button; customers can just drag and drop items into their shopping carts.   Conveniently intuitive, right? The “buttonless UI” will be one of most prominent mobile UX trends in 2019: After the “power button & volume button & homepage single button" trio, we'll be witnessing the growth of the “buttonless screen” trend in 2019. In other words: it's time to rethink your mobile UI/UX designs; to make them more gestures-focused. Which might be as simple as... adding animations to show the gestures that end users need to perform for carrying out specific tasks.   2. Visual and Voice Interfaces Will Work Together “Will graphical user interfaces ever be taken over by voice user interfaces?” “ is a question on Quora. On the contrary: Not only that voice-assisted interfaces (VUI) won't “annihilate” the visual ones, but the 2 of them will... happily coexist starting next year.  Get ready to witness a seamless integration of the two types of UI in 2019's mobile apps! Or, even better:  Instead of being one of the passive witnesses, how about leveraging this trend, one of the most influential mobile UI design trends in 2019? How would these apps, supporting a cohabitation of voice and visual interfaces, look like from a user's perspective?   the mic button will become... optional; mobile app users can just utter their questions/commands and the apps will interpret them. he/she (the user) will be able to speak commands like “Show me the cheapest option and book me...” or “Pick it up!” or “Narrate these 3 chapters to me!” and have the app read a book, book a flight or answer a call    3. Bottom Navigation: One of the Dominant Mobile UI Design Trends in 2019 Which side are you on? Are you a top navigation or a bottom navigation “fan”? Well, you can call yourself a “visionary” if the bottom navigation has been your top choice for some time now. It looks like it's going to be one of the prominent mobile app UX design trends in 2019. Why? Because:   devices will have even larger screens the single homepage button “rocks supreme” and app users got so used to the swiping gesture “extreme” convenience is key: all the major buttons should be displayed within reach on the app's screen   And this type of navigation comes down to:   bottom sheets swipe-up gestures    3.1. Bottom Sheets Why will bottom sheets become app developers' top choice when it comes to displaying sub-flows? Because they're highly flexible! Users get to scroll both vertically, for unlocking more content, and horizontally (carousel), for swiping through similar content with no need to skip screens. Pop-up dialogues, overflow drop-downs, hamburger side-drawers will start to fade compared to bottom sheets' “all within a swipe's reach” type of convenience.   3.2. Swipe Up Gestures  You'd swipe up to open an app drawer, then swipe up again to go back or to close the app... It's been a while since in-app swipe up gestures have started to “outshine” buttons/bottom tabs. And they're perfectly fit for bottom navigation. They enable you, the mobile app developer, to... keep everything“minimalistic” in your app:   the top area (content) the bottom area (navigation)   Simple, intuitive, convenient. No wonder that this will be one of the most influential mobile UI design trends in 2019.   4. Mobile App Design for Larger Screens You'll need to swiftly adapt your mobile UI designs to devices with increasingly large screens. They'll be the ones “dictating” how you'll design your mobile apps' interfaces in 2019. And there are few challenges to expect, consider, and properly prepare yourself for before this trend becomes... a norm:   How will you seamlessly integrate in-app gestures into your mobile apps? “Fully” integrate them, I mean... Losing buttons/bottom tabs will automatically enlarge the screens and leave you with more screen real estate; how will you fully leverage those enlarged screens? How will you optimize your apps' UX and UI so that the user can rely on his/her thumb and thumb only to navigate through and perform actions within the app?   The END! Let's recap, now! Here's your New Year's resolutions list:   "I'll design buttonless user interfaces in all my mobile apps in 2019" "I'll somehow make voice and visual interfaces work together" "I'll design “bottom navigation” and swipe-up gestures navigation in my next year's apps" "I'll adjust and properly adapt my mobile app designs to fit devices with larger screens" Photo by Gilles Lambert on Unsplash  ... Read more
Adriana Cacoveanu / Dec 13'2018
Must-Have Skills in the Age of AI: Stay Relevant and Competitive as a Developer
“AI will replace software developers by 20XX...” Does this kind of alarming forecast sound (too) familiar to you? How do you stay relevant in the workforce of the future? What are the essential skills in the age of AI to hone or to develop? That is the question... Now, what we do know is that:   you definitely need to (re)adapt to remain competitive in the context of automation processes, a highly automated workplace … and software-driven machines that can now process unstructured data meaningfully there are skills that can't be automated: soft skills become increasingly valuable more and more businesses will be interested in bringing “fusion skills”, a mix of human and machine talents, into their workplace   In other words: as we teach machines to learn, we, too, need to start learning from them in order to remain relevant and competitive in the workforce of the future. It's definitely not a “one-way street”.   1. Understanding AI and Its Disruptive Power What do you think of when you say “artificial intelligence”?   shopping recommendation engines? chatbots? voice/image recognition engines?   These are the most common applications of AI, right?  And they're all powered by... data: There are massive amounts of raw data all around us, waiting to be processed... meaningfully. Data that's powering any organizational decision these days. Now, here's how AI turns all this data into actionable knowledge: first, it's the big data techniques that unlock the power of the unprocessed data next, there are the machine learning algorithms that enable computers to assimilate all these huge volumes of data and finally, there are the deep learning and neural network patterns that add “meaning” to the process up to the point of... predicting human behavior “And how precisely does AI impact my job?” The immediate impacts of Robotics Process Automation on your job as a software developer will be:   disrupting all those repetitive, mundane processes and operations (and thus enhancing your job) providing you with more... time   “Time” that you could invest in honing all those skills that can't be automated: soft skills (or people skills), the ability to engage in creative researches etc.   2. Thriving in the Age of Automation: Upskill Yourself There's no denying it: In order to stay relevant and competitive in the age of AI you need to skill up. Constantly... You need to make learning an integral part of your daily work as a software developer. And thank God, there's a whole plethora of:   platforms for open online courses AI incubators and university labs (Carnegie Mellon or MIT) competency-based training tools open educational resources (EdTech)   … for you to choose from. Go for bite-sized upskilling sessions and turn it into a continuous process!   3. Skills in the Age of AI that Can't Be Automated And this is your trump card in this “human meets machine” or “AI threatening to replace developers” type of debate: Soft skills become extremely valuable in the age of automation because... they can't be automated.  What skills I'm referring to here?   creativity critical and innovative thinking collaborating skills social skills empathy adaptability   In short: those type of skills that are outside the purview of rigid algorithms. Hone your “people skills” to thrive in a workplace based more and more on collaboration. And on crafting authentic human experiences. Perfect your creativity and critical thinking to generate out-of-the-box solutions to common problems.   4. Develop These 5 “Fusion” Skills "Fusion skills” are the must-have skills in the age of AI. I'm talking here about the kind of abilities that result from human & machine interactions turned into a continuous collaboration, such as:   4.1. Intelligent Interrogation  Develop the skill of asking machines the “right” questions.  It will become a crucial one since, as human, you can't predict interactions between complex layers of data working independently. You cannot probe overly complex systems...   4.2. Bot-Based Empowerment  Instead of constantly fearing it and fighting it back: Embrace the power of AI bots. Let them empower you to:   boost your career as a software developer become more productive (they're taking the mundane operations off your back, remember) extend your competencies   4.3. Judgment Integration One of the truly powerful skills in the age of AI is anticipating when a machine can't make a decision. Due to lack of ethical context, let's say. It's then that you can intervene and provide the needed input.   4.4. Reciprocal apprenticing As already stated here: The learning process is no longer a “one-way street”. You, too, need to learn from the AI agents integrated into your workplace, not just the other way around. It means that you'll need to:   develop all those new skills needed for performing tasks in collaboration with machines learn how to successfully carry out AI-enhanced processes   4.5. Relentless Re-Imagining Keep honing — or developing it if you have none — the skills responsible for reimagining the status quo. Dare to re-think how AI can improve business models, organizational processes and overall your entire work as a software developer. Once you've managed that, you'll be ready to... adapt yourself to all those imminent changes. To start perfecting all those relevant skills in the age of AI.   5. Aspire to Be an Expert-Generalist Software Developer  Organizations will be “hunting” expert-generalists, so make sure you're prepared to... stand out.  Not just as a specialist, but as a well-rounded software developer:   with expertise in multiple areas who's an excellent communicator  who's always the first to volunteer for challenging projects who's curious, highly trainable and extremely adaptable   In other words: to stay competitive you'll need to go from having in-depth expertise in one area, to... having a broad breadth of knowledge and multiple expertise areas.   6. The Workplace of the Future: AI + HI (Human Intelligence) In the end, perfecting/developing the crucial skills in the age of AI comes down to this common sense criterion: Invest in honing those skills that enable you, as a software developer, to collaborate with different AI agents. The workforce of the future will not be focused exclusively on human intelligence (not anymore) or on AI, but on... collaborative intelligence.  In other words: let AI empower you in your work, perfect those skills that machines can't automate and... learn to collaborate with AI systems. Photo by Franck V. on Unsplash.  ... Read more
Adriana Cacoveanu / Dec 05'2018
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