YesPlz AI

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What is traditional eCommerce search? eCommerce search is an essential part of the shopping experience, particularly when it comes to text search. Users type in their search queries and expect to receive relevant search results. In a traditional eCommerce site search experience (without any AI technology), a user’s keyword search is matched against a manually configured glossary of keywords. Unfortunately, due to ever-changing fashion vocabulary and the complexity of search queries, traditional eCommerce search still falls short in reaching its full potential, leading to poor matching results and frustrated shoppers. The Current State of Search Technology in 2023 In 2023, the fashion eCommerce industry has evolved to use algorithms like NLP (Natural Language Processing) to further interpret shoppers’ search language, attempting to define intent and meaning behind search queries more effectively. But, the hypothetical value is often not the same as the reality, because of: 1-high prices 2-large data sets needed to train the algorithm 3- the sheer complexity of search queries The two types of algorithm-powered text search models are: Contextual: : Matches user search intent using context clues relevant to the individual user. Search results are specific to a user. In theory Difficult to provide relevant search results, and expensive to run Reality: Semantic: Uses NLP to identify meaning and logic behind search. For example, a “work dress” search is synonymous with “wear-to-work” or “office dress” In theory: Depends on quality of product tags, which may be mismatched.
Too slow to capture up-to-date, modern fashion keywords.
Requires manually updating glossaries. Reality: In 2023, the majority of search technology still requires manual configuration, is expensive, and is still prone to errors. And, it’s not that these search models are bad–but there’s still untapped potential in making text search even better, more optimized, and more relevant. But, how? By using fashion tagging at its core, we’ve created text search that combines different types of AI, unlocking a new, revolutionary approach to traditional text search. How YesPlz Is Solving Common Text Problems in eCommerce Search The most common problems in “traditional” eCommerce search are: irrelevant search results; poor understanding of search intent; and 3) low-quality (or missing) product tags. At YesPlz, we’ve built a comprehensive text search system to solve these problems. Our formula to resolve the problems includes: Laser-accurate AI image tagging, to better create matching, accurate search results, without the need for product descriptions Machine learning that analyzes past user inputs and interactions to predict best search intentions Fresh, updated keywords to keep search keywords relevant and modern Thanks to machine learning, we can customize search results for each of our clients’ specific needs. Here’s the YesPlz antidote to inaccurate, one-dimensional eCommerce text search: Outdated Solution YesPlz AI Solution aka updated keywords with most popular trends and suggestions Fresh, popular keywords Lacks up-to-date, popular keywords. Product discovery isn’t quick or effective. Automatically update with latest keywords based on past user input Autocomplete suggestions No keywords to nudge or guide shoppers. Mismatched search results. Suggested autocomplete keywords, based on popularity or similarity. Instant search results with product preview Search results only show after typing in complete search keywords. High error rates for typos, and unmatched keywords. Updated search results displayed with each keystroke. Additional Filter Paired with Text Search Hundreds of low-accurate matching search results, leading to higher bounce rate. Fully integrated filter alongside search results. Shoppers filter down further, increasing possibility for add-to-cart. Enhanced image tagging Search results limited to matching text descriptions.Huge missed potential for matching results. Key product attributes extracted from image, not product descriptions. Increases percentage of higher matching results. Brand recognition No brand recognition from catalog. Missed opportunity to help shopper discover favorite products from brands. Brand recognition from search, leading to search results that match brands. YesPlz Enhanced eCommerce Site Search Solutions vs. Top Competitors YesPlz’s eCommerce site search solutions are designed to stand out to both retailers and shoppers, from its underlying technology to its customizable features. But, what exactly is the difference between YesPlz eCommerce search and top competitors? Here’s a quick overview of all the features your eCommerce could be unlocking: The impact of added features for text search: Accurate product tags when using enhanced image tagging, building the foundation for personalization Higher conversion rates– Customers who land on an autocomplete page suggestion are than those who don’t. six times more likely to convert Increased average cart size and volume with additional integrated product filter Lower bounce rate with intuitive UX design Fresh updated keywords keep eCommerce relevant and on top of the latest trends More relevant, precise search results when powered by both visual and text AI The power of the above features, when combined, means outstanding text search that always provides relevant, updated results. Before/After: Creating Enhanced eCommerce Search Solutions for W Concept W Concept’s story: A trendsetting online retailer for luxury fashion, W Concept has captivated the attention of shoppers worldwide with a vast selection of clothing, shoes, handbags, and beauty products. But, W Concept had a problem. Its current text search technology didn’t have instant search, lacked autocomplete to make search intuitive and accurate, and had low matching search results. Here’s the before/after snapshot of how YesPlz transformed W Concept’s text search: 1. No automated product tagging, leading to inaccurate search results Before: Product tagging for entire catalog, completely automated by fashion-trained AI, that isn’t reliant on vendor information, increasing the accuracy of matching search results After: W Concept’s robust product catalog also required robust product tags to set the foundation for enhanced text search. No product tags? No problem. With thousands of vendors, W Concept was missing product information, and had some inaccurate information from third-party vendors. YesPlz easily generated these product tags using visual AI. 2. Plain text match searching Before: Rich search suggestions based on both keyword and similar search After: By training the data to understand what other styles resemble a search term, we made W Concept’s search more accurate and robust. For example, if a shopper searched for “tank top,” behind the scenes, search results are based on data that understands the number of similar items to a tank top (including products that don’t have the term “tank top” in their description). A sleeveless shirt, ruched tank top, or square neck tank top still fall under the search query–but wouldn’t be picked up by traditional search unless the terminology was an exact match. In addition, autocomplete offers the best of both visual and text based search, with updated keywords on the most popular, modern trends. No product preview images, not sparking the visual nature of shopping Before: Product previews combined with instant search After: All it takes is a single keyword to show matching results with a product preview, resulting in a much more intuitive and better quality discovery experience. Shoppers needed to click on a search term to filter further Before: Integrated side filter helps shoppers quickly filter down as they search After: W Concept shoppers needed to click on a search term, load a new page, and then were able to filter based on their preferred product attributes. This not only wasted time, but wasn’t providing the most optimized product filtering experience. With an added integrated side filter, shoppers can filter based on their text search (for example, a white tank top text search would lead to further filter options related to brand, silhouette, fit)--with white tank top as a preselected first filter. YesPlz filters are designed to be visual, and UX-friendly, unlike text-heavy filters. By creating a custom, enhanced eCommerce search that solved all of the above problems, YesPlz transformed W Concept’s text search–resulting in always relevant, effective eCommerce site search that converts. Can your eCommerce text search do all of this? When powered by visual and text AI, YesPlz text search is revolutionizing the way shoppers search. If your text search provider isn't doing enough, contact us for a free demo. is next-gen, all-in-one product discovered, powered by artifical intelligence. YesPlz AI Also published . here

Traditional vs. Enhanced eCommerce Search: Unlocking New Text Features

A shopper lands on your website and navigates to the search bar. She wants a red skirt, mid-length, with a fun, playful print. When she types in her search query for “red midi skirt,” the shopper receives dozens of relevant search results. The building blocks of these search results (and even product recommendations) is image tagging (also known as fashion tagging). Behind the scenes, a human tagged each of the product attributes by hand, a painful task when considering the hundreds or thousands of products uploaded on an eCommerce website per day. Unfortunately, manual image tagging can take hours to complete and is prone to errors. For example, one human tagger might label the red skirt as “mid length skirt” and another human tagger might consider the skirt to be a “midi-skirt,” leading to inaccurate product data. But, is there an easier way to tag that saves time, energy, and is more accurate? The answer: building an artificial intelligence that understands fashion product attributes to correctly tag information. In this blog post, we’ll walk you through: Image tagging 101 What is good image tagging? How YesPlz image tagging works The impact of good fashion tagging Image Tagging 101 Image tagging is the process of labeling product attributes on an image. A product attribute can include all different elements of a product, from a design style to color and material type. A single clothing category can have between 20 to 60 different attributes, which can take significant time to tag the attributes of a single product image. If we look at the dress below, it would have about 9 different attributes including dress category, neck design, material, and mood or occasion. Traditionally, a member of the merchandising/eCommerce team is responsible for tagging. Designers and third-party vendors provide tagging information, and the in-house team is responsible for cleaning up and organizing the tags. But, when information comes in from third parties, it can be inconsistent. Depending on the vendor, they may have a different word for "lace"--and when you take into account the sheer volume of products that need to be tagged, this inconsistency creates a mess for fashion eCommerce. Alternatively, the in-house merchandising experts may make tagging mistakes (we're all human, after all). Because of the complications of fashion tagging, some fashion eCommerce brands may skip it altogether to save time. While it may be tempting to skip, without your eCommerce brand can't: accurate image tagging, provide a superior search experience to shoppers categorize products create product descriptions improve search results offer more filtering options generate recommendations utilize powerful personalization tools Image tagging contains crucial information that enriches a shopper’s online experience. If your eCommerce is looking to implement the next great personalization tool, image tags provide the building blocks to do so. Image tagging creates the foundation for a superior shopping experience When asked to describe a superior online shopping experience, shoppers will consistently answer that search and recommendations are key differentiators in the crowded fashion eCommerce space. Without fashion tagging, shoppers need to rely on very limited information to find what they’re looking for–and they have difficulty finding search results that meet their needs. Bad image tagging creates a bad online shopping experience. While shoppers may not understand the mechanics of why their experience is bad, they will interpret a poor online shopping experience as a cue to not return. Image tagging doesn’t need to be a “necessary evil” for retailers–but can be an enjoyable experience. For retailers, fashion tagging is a necessary but tedious task. When manually tagging, a single batch of products requires days of work–including tagging and double-checking fashion terminology. And we know that retailers are tired of doing work–and receiving little in return. Unfortunately, it’s not an option to skip image tagging–without it, retailer product catalogs would be disorganized. Without fashion tagging, you can’t have powerful and accurate site search, fashion-forward recommendations, or well-organized data to personalize user experiences. But, poor automated tagging tools can still cost retailers hours of time, correcting mistakes and re-sorting taxonomies. What makes a good image tagging solution? There are hundreds of image tagging tools on the market, but not all of them are created equal. Even if they’re powered by artificial intelligence, it’s important to remember that artificial intelligence is only as good as the input data that trained the algorithm. And, when artificial intelligence makes mistakes, fashion experts with a deep understanding of fashion should be the ones to review those mistakes. Other considerations when choosing a fashion tagging tool are the quality of the tags (are they accurate?) and speed (slow tagging means more work for retailers). Lastly, we recommend that you review the types of tags you’d like to see (specific styles, details, moods, or occasions) and make sure that the image tagging tool can quickly and accurately tag for your criteria. Image Tagging In Action: Searching For A Shirtdress A shopper wants to buy a shirtdress, defined as a women's dress with a collar and buttons. She types in her search query and receives two sets of results: Result #1: Search Results Without Image Tagging The retailer relied on text tagging to create product tags, leading to mixed search results that include t-shirt dresses and men’s shirts–unrelated to the shopper’s original search query. These search results highlight the importance of image tagging–a good image tagging tool would be able to identify the product type and product attributes. Result #2: Search Results With Image Tagging Next, the shopper searches for a shirtdress with YesPlz’s Style Filter. She receives the following search results, which are clear and accurate: The difference between the two search results? Image tagging. In the second set of search results, YesPlz AI image tagging has correctly identified and tagged the product attributes, leading to accurate, robust search results. Are you curious to know more about how works? Let’s go behind the scenes and explore the YesPlz process and why we’re different from other solutions. YesPlz image tagging How Our Image Tagging Process Works Here is the YesPlz process for ensuring accurate and fast image tagging: Step 1: Before building our tool, we defined key fashion attributes that matter to shoppers through user interviews. We started with user interviews and sat down with shoppers to define what key fashion attributes they care about the most. Our interviews took hours of probing each individual to find out more about their most important attributes. And, as a result, we’re happy to say that our image tagging tool reflects the real needs of shoppers. Because of the preparation we’ve put into our image tagging tool, retailers can be assured that our fashion tagging tool tags product attributes that shoppers are searching for, such as fit, style, and occasion. We have a clear understanding of the criteria for image tagging, leading to more accurate tagging from the start. Step 2: We train the AI using image and text–then fashion experts correct the AI. Through a combination of computer vision and Natural Language Process (NLP) technology, YesPlz AI is able to do deep image tagging which is accurate, fast, and seamless. AI Training: In addition, our technology can identify product attributes even in all sorts of image types whether the clothing image is on or off a model, even if the image has a noisy background or is user generated content. Step 3: Fashion expert intervention: AI will inevitably make incorrect inferences–but who is catching those mistakes? At YesPlz, our fashion data annotators are from Parsons School of Design and Fashion Institute of Technology. They understand the details of fashion, and are up-to-date on the latest fashion trends and terminology. We have fashion data annotators in-house that speak the language of fashion, leading to highly accurate tagging. The result of AI training with computer vision and NLP, combined with our data annotators from fashion backgrounds is fast, accurate image tagging for retailers. Step 4: We provide data enrichment and image tagging output for retailers. The last step in the YesPlz image tagging process is output which helps retailers make better decisions through data. We provide retailers with data enrichment using an API that allows retailers to easily receive image tagging information in an easy-to-use format that fits their preferences. In addition, our data output is available in English, Korean, and Arabic. The impact of good fashion tagging Good, quality fashion tagging is necessary to build out dynamic search and recommendations and hyper-personalization. While image tagging is very much “behind the scenes,” your customers are affected by poor image tagging when they receive mismatched search results or off-target recommendations. Image tagging, when accurate and quick, can provide better quality data for your fashion eCommerce, which you can then use for personalization. But, the common objections we’ve seen to image tagging are: it’s inaccurate it takes too long YesPlz AI is solving these problems for retailers by providing fast, accurate tagging. Our AI is trained to recognize product attributes in all types of pictures, even low-quality images, and is then reinforced with the input of fashion experts. Are you interested in keeping up-to-date with the latest retail and eCommerce trends? Follow YesPlz AI on LinkedIn for updates. This story was published by next-gen fashion search. YesPlz AI,

What Makes "Good" Fashion Image Tagging

Since Amazon announced the ability to offer in-skill purchases for developers creating Alexa skills, I’ve added four pieces of premium content to two different skills. I recently published an article about , and in this article, I follow up with step-by-step code samples to show you exactly how you can do that via the ASK SDK. why you should consider adding premium content Types of In-Skill Products Before getting into the code, I want to review the types of in-skill products (or ISPs) that Amazon has made available for Alexa, the different ways you can surface them, and some considerations to keep in mind about when and how often you prompt your customer with reminders about your content. There are three types of ISPs that your skill can offer: : These are products that are basically an on/off switch. They typically gate a feature within your skill — once the customer has purchased this product, they have on-going access to the feature. These are typically features like expansion packs, additional levels, or premium sound effects. Entitlements : A subscription provides access to a feature or content but unlike an entitlement, the customer pays for the feature on a monthly or annual basis. Once cancelled, the user loses access to the premium feature. You would typically offer a subscription for a feature that has ongoing, evolving content such as access to a library of songs or videos. You can also offer free trial periods with subscriptions. Subscription : A consumable is a product that can be purchased, used, and purchased again. Examples would include extra lives or a set number of hints in a game. Consumables Once you’ve decided which type of product you want to offer, you should think about how you are going to message that product to your customers. You can do this on the home page description for your skill, but since many customers don’t read that content thoroughly, you’re going to need to make them aware of the products you offer via your voice content. The best way to make users aware of this content and to increase your conversion rate is to do it during a natural part of the conversation, where the premium content might be useful: If you’re offering an expansion pack with extra levels to a game, you may want to offer it when a returning user who has completed all built-in levels launches your skill. If you are offering a different game as part of a multi-game skill, you may want to offer it when a user attempts to switch from one game to another. If you are offering extra lives or hints, you may want to offer them at the point that a user has used up all of their current lives or hints as a way to be able to continue playing. It’s also a best practice to have an intent that allows the customer to explore what you have to offer — responding to phrases like “What do you have available to buy?” If a customer has refused an offer to purchase premium content, you should go into an exponential backoff mode — offering the content less and less frequently on subsequent uses of the skill. I recommend you don’t completely stop offering your premium content however. I have seen customers who refused an upsell ultimately accept the offer. Selecting a new machine in Slot Machine For the coding sample, I am going to be referring to one of the premium games that I offer for purchase in my Slot Machine skill. Slot Machine allows customers to play multiple machines, and they can say “choose a different game” to switch from one machine to another. My premium content can be presented to the user in one of three places — when they first launch the skill, when they switch to a different machine, and after continuously playing the same machine for a given amount of time. Using the CLI to Add Your Product In order to add an in-skill product to your Alexa skill, you have to use the ASK CLI. If you haven’t used the CLI, to set it up. Once you have it set up, you’ll want to clone the skill to which you want to add a premium product. Do this with the clone command follow steps 1–3 from these instructions ask clone This will provide you with a list of your Alexa skills. Use the arrow keys to select the skill you want to clone and hit enter. Cloning will copy the skill manifest, metadata, and code if you are using a Lambda function to your machine. After you have this local copy, change to the root directory of the newly cloned skill and use this command to set up your in-skill product. ask add isp This will present you with a list of product types you can add List of in-skill product types you can choose (Use arrow keys)> ConsumableEntitlementSubscription Select the one that you’d like to use, and accept the default template that you’re presented with. After doing this, you’ll be prompted to type in the name of your new in-skill product. Suppose you call it . This will create a new file called in an isp folder in your skill. extragame extragame.json Open this file, and you’ll see several details that you need to fill out to describe your skill. Specifically, you’ll need to provide a name, description, icons (in 108x108 and 512x512 format), and some release details that describe the pricing and release date for your product offering. It’s also worth noting that you will need to provide a link to a privacy policy for your skill. Not to worry, you can find several examples of policies you can leverage by searching online for “Alexa skill privacy policy.” Once you’ve filled in this information, you can deploy your skill using which will upload the new product settings for your skill. ask deploy Checking for Product Availability Now that you’ve uploaded a product setting, your need to add support in your code. The first thing you should do is use the to check which products are available when the user launches your skill. The following code shows how you can do this and save the availability of each product within your session attributes: MonetizationServiceClient const ms = handlerInput.serviceClientFactory.getMonetizationServiceClient(); return ms.getInSkillProducts(event.request.locale).then((inSkillProductInfo) => {let state;attributes.paid = {}; if (inSkillProductInfo) {inSkillProductInfo.inSkillProducts.forEach((product) => {if (product.entitled === 'ENTITLED') {state = 'PURCHASED';} else if (product.purchasable == 'PURCHASABLE') {state = 'AVAILABLE';} if (state) {  
    attributes.paid\[product.referenceName\] = {  
      productId: product.productId,  
      state: state,  
    };  
  }  
} });}).catch((error) => {// Ignore errors}); Now within your object, you have a list of all products, with a state set to either or . You can check against this object when you attempt to sell a product offer to see whether they have already purchased it, or whether it is available for them to purchase. attributes.paid PURCHASED AVAILABLE Setting an Upsell Response The code to offer a product for purchase can be a little tricky. Basically, you are going to hand-off the purchase request to Alexa, exiting your user session in the process. Alexa will handle the interaction and payment flow with the customer, including offering any discounts that Amazon may be running at the time. Your code will then be called back with a new session and the result of whether the customer purchased your product or not. The response to Alexa kicks off this process. Fortunately, you can pass a token as part of this response which will be returned to you as part of the new session request. I’ve found this to be handy and use this to let me know both which product I was offering (remember, I support multiple products), and which handler sent me the request (since I can make this upsell offer in multiple locations). This allows me to seamlessly drop the user back into the interaction flow. If they declined a purchase response, I know what they were attempting to do before I interrupted them with an offer, so I can drop them right back where they were. If they accepted the purchase, I also have the context so can drop them in the appropriate part of the code. For example, if the user is being sold a holiday-themed slot machine during the process of selecting a new game, I'd use a token named Connections.SendRequest holiday.select Here’s what that code looks like: // Create an upsell directiveconst directive = {'type': ' Connections.SendRequest’,‘name’: ‘Upsell’,‘payload’: {‘InSkillProduct’: {productId: attributes.paid[upsellProduct].productId,},‘upsellMessage’: 'We have a holiday themed game available for purchase, want to hear more?',},‘token’: 'holiday.select',}; return handlerInput.responseBuilder.addDirective(directive).withShouldEndSession(true).getResponse(); Note that the field needs to be in plaintext. Any SSML tags that you set for different voices or speech variation will be ignored when read by Alexa. From a customer interaction standpoint, what they will hear is something like this — note that after the user says yes to the upsellMessage set by your code, the interaction is handled directly by Alexa: upsellMessage Alexa, select a new machineWe have a holiday themed game available for purchase, want to hear more?YesThis will add the Holiday Game to the list of slot machines you can play. Prime members save $0.19. Without Prime, your price is $0.99 plus tax. Would you like to buy it?YesPlease say your 4-digit code to confirm the purchase. If you are handling an explicit purchase as opposed to an upsell (i.e. responding to a request like “Alexa, buy the holiday game”), then you set in the directive to ‘Buy’ and remove the field. You’ll still want to set the token so you can handle the response when they return to your skill. name upsellMessage And don’t forget the cancellation message! Again, like the purchase case, you’ll need to support an utterance that allows the customer to refund their purchase (“Alexa, refund the holiday game”). For this message, you set in the directive to ‘Cancel’ and again remove the field. name upsellMessage Processing the User Response Whether the user says yes or no to your upsell request, or if there’s some other error during the purchasing flow, your skill will be invoked with a new session with a request type of You can then take the user to whatever the natural continuation point for them is. If they hear this message while selecting a new machine and make the purchase, I drop them into that machine selected and ready to play. If they decline the purchase, I tell them what other machines we have available so they can continue the process of selecting a new machine. If there was some sort of error during purchase, I put them into the same flow as if they declined the purchase. Alexa will handle telling them there was an error, so no need for our code to repeat that. The handler for the response looks like this: Connections.Response canHandle: function(handlerInput) {const request = handlerInput.requestEnvelope.request;return (request.type === 'Connections.Response');},handle: function(handlerInput) {const event = handlerInput.requestEnvelope;const attributes = handlerInput.attributesManager.getSessionAttributes(); // options[0] will be the name of the game we were selling// options[1] will be the intent they were sold inconst options = event.request.token.split('.');const accepted = (event.request.payload &&((event.request.payload.purchaseResult == 'ACCEPTED') ||(event.request.payload.purchaseResult == 'ALREADY_PURCHASED')));let nextAction = options[1]; if ((event.request.name === 'Upsell') && !accepted) {// Don't upsell them again on the next roundattributes.noUpsell = true;} // Did they accept?if (accepted) {// If this is was a cancellation remove itif (event.request.name === 'Cancel') {attributes[options[0]] = undefined;if (attributes.paid && attributes.paid[options[0]]) {attributes.paid[options[0]].state = 'AVAILABLE';}} else {// We'll auto-select// Make sure we put it into the list of paid products as purchasedif (attributes.paid && attributes.paid[options[0]]) {attributes.paid[options[0]].state = 'PURCHASED';}nextAction = 'autoselect';}} // And go to the appropriate next step// Select, SelectYes, Spin, and Launch are intent handlersif (nextAction === 'select') {return Select.handle(handlerInput);} else if (nextAction === 'autoselect') {attributes.gameToSelect = options[0];return SelectYes.handle(handlerInput);} else if (nextAction === 'spin') {return Spin.handle(handlerInput);} else {// Just drop them directly into a gamereturn Launch.handle(handlerInput);} You’ll notice that in addition to setting up the new machine if selected and handing off to the appropriate next intent for processing, there is code that sets a flag if the user was given an upsell message and declined it. This is to avoid an infinite loop, since I drop the user back into the same path they were previously in. Before presenting an upsell message, my code checks whether this flag is set to avoid an infinite loop. This also helps ensure that customers are only heard an upsell message once during their session, giving them a better customer experience. noUpsell I hope you found this guide useful, and good luck adding premium content to your own Alexa skills!

How to add In-Skill Products to your Alexa Skill

Nikola Tesla was a brilliant inventor and visionary who died largely forgotten. His contributions to the world were enormous, but people still remember Edison and Franklin and da Vinci and Bell. For decades, attempts to honour Tesla with a New York museum at Wardenclyffe failed repeatedly. Only recently has this borne fruit. Here are some of Tesla’s contributions: invented the first alternating current (AC) motor developed AC generation and transmission technology created the Tesla coil, a high-voltage transformer which came to be the genesis for the cathode ray tube, radio transmitter, radar, and many other technologies invented X-ray technology invented dynamos and the induction motor invented the first working radio invented the fluorescent light bulb invented the remote control invented wireless transmission of electricity designed the first hydroelectric generating plant at Niagara Falls In 1887 and 1888, he was granted more than 30 patents for his inventions. When Tesla died in 1943, he died penniless and forgotten. Smalltalk was the brainchild of Alan Kay, a true visionary who led a brilliant team at Xerox PARC. Today, Smalltalk is greatly underestimated. Although Kay never thought of Smalltalk as the central focus of his vision, it remains a powerful force for the advancement of programming technology. More than four decades later, no programming language has yet to catch up to Smalltalk in terms of simplicity and elegance, minimal cognitive friction, object-oriented purity, elegant live coding and debugging, enormous programmer productivity, and professional respect. At Slant and StackOverflow, Smalltalk is revered: read . The Wisdom of the Crowd Capers Jones of Namcook Analytics has shown . Smalltalk’s tremendous advantage when it comes to programmer productivity Smalltalk’s IDE and runtime are amazingly powerful without the arcane complexity that you find in modern IDEs like Visual Studio, Eclipse, and Xcode. Here’s a demonstration: live coding Here are some great innovations: Smalltalk continues to evolve and improve in remarkable ways through the Pharo project. NativeBoost — having an inline assembler is definitely not a common feature in a dynamic language. NativeBoost has since evolved into Unified Foreign Function Interface (or UFFI). is a set of code visualisation tools containing many means to visualise and analyze code. Moose , which uses Pharo as the reference language, is a fantastic front-end web programming tool. Amber is another terrific front-end web programming tool. PharoJS are a wondrous feature in the Seaside web framework. Continuations — a way to serialize live objects, and for transporting your objects around. Fuel — allows one image to manipulate another one. Very good for debugging an image that has crashed and is unable to open/load. Oz lets you manage your Pharo images (launch, rename, copy and delete) and download image templates (i.e., zip archives) from many different sources to create new images from any template. Pharo Launcher is Pharo’s Github, but unlike Github it is fully open source and all Smalltalk. SmalltalkHub is a port of Scratch to Pharo — this is important because Scratch has been ported to HTML5 and this is an effort to keep Scratch Smalltalk-based. Phratch is an excellent platform for teaching kids how to code. Phratch — good support for Pharo in Emacs; you can replace the Pharo GUI with Emacs and enjoy the benefits of this most powerful editor. Shampoo Legacy Like Tesla, Smalltalk has a wonderful legacy. Its beautiful implementation of has directly influenced the design of nearly every object-oriented language we use today: Java, Python, C#, PHP, Ruby, Perl, Objective-C, Groovy, Scala, Dart, Erlang, CLOS. object-oriented programming Smalltalk introduced the world to the language . (No, it wasn’t the first but it was the best-known.) This is the same tech that underpins Java and .NET. virtual machine Smalltalk pioneered JIT (just-in-time) compilation. From Smalltalk came the first modern IDE (integrated development environment), which included a text editor, a system or class browser, an object or property inspector, and a debugger. Smalltalk was the first language tool to support live programming and advanced debugging techniques such as on-the-fly inspection and code changes during execution in an easily usable form. Since Smalltalk-80 (in 1980), it had first-class functions and closures which, oddly enough, make Smalltalk quite good for functional programming. Quite remarkable for a “pure” object-oriented language. (How long did it take Java, Python, C# and C++ to get this feature?) Smalltalk introduced the software architectural pattern MVC (Model-View-Controller). To a large extent, Smalltalk was responsible for giving us (or TDD) and extreme programming (or XP), which are both very influential in today’s standard agile practices. test-driven development Smalltalk made “duck typing” a household word. Duck typing is where “type checking” is deferred until runtime — when reflection capabilities are used to ensure correct behavior. We find duck typing in many languages today, including Java, Python, Go, Groovy, Objective-C, and PHP. Smalltalk pioneered the development of object databases of which is a great example. GemStone/S Smalltalk gave us the . first browser refactoring Smalltalk was instrumental in developing the (or GUI) and the “what you see is what you get” (WYSIWYG) user interface. graphical user interface People don’t realize this, but Smalltalk is every bit as extensible as Lisp! Read . Who needs Lisp macros? Lisp, Smalltalk, and the Power of Symmetry Apple’s success owes a lot to Smalltalk. It’s true! Objective-C has been the foundation of macOS and iOS, and Objective-C is essentially a cross between C and Smalltalk. MacOS evolved from NeXTStep which was built with Objective-C. to completely realign Apple’s strategy; the GUI was a direct outflow of Smalltalk work. Steve Jobs was inspired by Xerox PARC’s GUI and WIMP Whew! Nikola Tesla would’ve been proud! More than four decades later, Smalltalk enjoys far more commercial usage than any of the upstart languages we hear so much about (for example, Ceylon, Clojure, Crystal, D, Dart, Elixir, Elm, F#, Haskell, Haxe, Julia, Nim, Rust). Between them, they have many prominent Smalltalk users: still Cincom, Instantiations, and GemTalk are major Smalltalk vendors. JPMorgan Desjardins UBS Florida Power & Light Texas Instruments Telecom Argentina Orient Overseas Container Lines BMW Siemens AG Just to name a few. In my home country, Smalltalk is used by Communications Security Establishment (CSE), Canada’s national cryptologic agency. Pharo itself has prominent users, too, for example: ALLSTOCKER ATMs in Moscow streets LAMRC FINWorks YesPlan In the early 2000s, the U.S. joint military used Smalltalk to write a million-line battle simulation program called JWARS. It actually outperformed a similar simulation called STORM written in C++ by the U.S. Air Force. That by itself is an astonishing testament to the capabilities of the language. Here’s a commercial virtual reality application done in Smalltalk: . Magnificent! 3D Immersive Collaboration Smalltalk is good for and . machine learning data science Smalltalk is being used to fight Ebola! Smalltalk is used in . wide-scale data visualization for medicines in 16 countries Smalltalk was so good for business use that in the early 1990s, IBM chose Smalltalk as the centrepiece of their VisualAge enterprise initiative to replace COBOL. Unfortunately, Java came along in 1995 and put an end to that. And as we all know, today Java is the enterprise standard programming language. In fact, according to a 1995 IDC report, OO language market shares were: C++ — 71.3% Smalltalk — 15.1% Objective-C — 5.7% OO Pascal — 4.2% CLOS — 2.5% Eiffel — 1.1% all others — 0.2% Here’s a page from Computerworld, November 6, 1995, showing Smalltalk and C++ duking it out: Let’s look at a universities and research groups that use Smalltalk (Pharo): few University of Bern, Switzerland, http://scg.unibe.ch Uni. of Buenos Aires, Argentina Vrije Universiteit Brussels, Belgium Uni. de Bretagne Occidentale, France Uni. Catholic of Argentina, Argentina Federal University of Espírito Santo (UFES), Brazil Uni. of Chile at Santiago, Chile Czech Technical Uni., Czech CULS Prague, Czech DCyT — Universdad Nacional de Quilmes, Argentina http://www.unq.edu.ar Ecole des Mines de Douai, France http://ia.mines-douai.fr ENSTA Bretagne University of Hagen http://www.fernuni-hagen.de/ps/lehrveranstaltungen/k01814/ HackBo, Hackerspace en Bogotá, Colombia. http://hackbo.co Ivan Franko Nat. Uni. of Lviv, Ukraina Université de Montpellier, France IAE Savoie Mont-Blanc, France Uni. de Maroua, Cameroon Aalborg University, Denmark Northern Michigan Uni., USA Uni. Policnica de Catalunya, Spain Uni. of La Plata, Argentina Uni. of 20 Aout 55-Skikda, Algeria Uni. Technologica Nacional at Buenos Aires, Argentina Uni. Technologica Nacional at Cordoba, Argentina Uni. Nacional de San Martin, Argentina Tomsk State University, Russia http://www.csd.tsu.ru Université de Louvain la neuve, Belgium Université de Yaounde, Cameroon Universite di Cagliari, Italy (AR) Lafhis (AR) Institute of Veterinary Genetics IGEVET ASERG (CH) Software Composition Group (FR) CAR (FR) RMOD (IRD) Ummisco (CH) Reveal (CL) Pleiad (FR) OC CEA-List (FR) Laboratório e Observatório de Ontologias (BR) Uqbar (AR) (CZ) CCMI-FIT Lab-STICC UMR 6285 Lab-STICC (IT) Agile Group As you can see, Smalltalk is incredibly strong in industry academia! and So in the end, I am puzzled why Smalltalk gets so little press coverage, why Smalltalk is as forgotten as Nikola Tesla. These two deserve better fates.

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