Endless West

endlesswest

The Last Days of Marcus Karenin

Moby-Dick; or The Whale, by Herman Melville is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book . Chapter 13: Wheelbarrow here CHAPTER 13. Wheelbarrow. Next morning, Monday, after disposing of the embalmed head to a barber, for a block, I settled my own and comrade’s bill; using, however, my comrade’s money. The grinning landlord, as well as the boarders, seemed amazingly tickled at the sudden friendship which had sprung up between me and Queequeg—especially as Peter Coffin’s cock and bull stories about him had previously so much alarmed me concerning the very person whom I now companied with. We borrowed a wheelbarrow, and embarking our things, including my own poor carpet-bag, and Queequeg’s canvas sack and hammock, away we went down to “the Moss,” the little Nantucket packet schooner moored at the wharf. As we were going along the people stared; not at Queequeg so much—for they were used to seeing cannibals like him in their streets,—but at seeing him and me upon such confidential terms. But we heeded them not, going along wheeling the barrow by turns, and Queequeg now and then stopping to adjust the sheath on his harpoon barbs. I asked him why he carried such a troublesome thing with him ashore, and whether all whaling ships did not find their own harpoons. To this, in substance, he replied, that though what I hinted was true enough, yet he had a particular affection for his own harpoon, because it was of assured stuff, well tried in many a mortal combat, and deeply intimate with the hearts of whales. In short, like many inland reapers and mowers, who go into the farmers’ meadows armed with their own scythes—though in no wise obliged to furnish them—even so, Queequeg, for his own private reasons, preferred his own harpoon. Shifting the barrow from my hand to his, he told me a funny story about the first wheelbarrow he had ever seen. It was in Sag Harbor. The owners of his ship, it seems, had lent him one, in which to carry his heavy chest to his boarding house. Not to seem ignorant about the thing—though in truth he was entirely so, concerning the precise way in which to manage the barrow—Queequeg puts his chest upon it; lashes it fast; and then shoulders the barrow and marches up the wharf. “Why,” said I, “Queequeg, you might have known better than that, one would think. Didn’t the people laugh?” Upon this, he told me another story. The people of his island of Rokovoko, it seems, at their wedding feasts express the fragrant water of young cocoanuts into a large stained calabash like a punchbowl; and this punchbowl always forms the great central ornament on the braided mat where the feast is held. Now a certain grand merchant ship once touched at Rokovoko, and its commander—from all accounts, a very stately punctilious gentleman, at least for a sea captain—this commander was invited to the wedding feast of Queequeg’s sister, a pretty young princess just turned of ten. Well; when all the wedding guests were assembled at the bride’s bamboo cottage, this Captain marches in, and being assigned the post of honor, placed himself over against the punchbowl, and between the High Priest and his majesty the King, Queequeg’s father. Grace being said,—for those people have their grace as well as we—though Queequeg told me that unlike us, who at such times look downwards to our platters, they, on the contrary, copying the ducks, glance upwards to the great Giver of all feasts—Grace, I say, being said, the High Priest opens the banquet by the immemorial ceremony of the island; that is, dipping his consecrated and consecrating fingers into the bowl before the blessed beverage circulates. Seeing himself placed next the Priest, and noting the ceremony, and thinking himself—being Captain of a ship—as having plain precedence over a mere island King, especially in the King’s own house—the Captain coolly proceeds to wash his hands in the punchbowl;—taking it I suppose for a huge finger-glass. “Now,” said Queequeg, “what you tink now?—Didn’t our people laugh?” At last, passage paid, and luggage safe, we stood on board the schooner. Hoisting sail, it glided down the Acushnet river. On one side, New Bedford rose in terraces of streets, their ice-covered trees all glittering in the clear, cold air. Huge hills and mountains of casks on casks were piled upon her wharves, and side by side the world-wandering whale ships lay silent and safely moored at last; while from others came a sound of carpenters and coopers, with blended noises of fires and forges to melt the pitch, all betokening that new cruises were on the start; that one most perilous and long voyage ended, only begins a second; and a second ended, only begins a third, and so on, for ever and for aye. Such is the endlessness, yea, the intolerableness of all earthly effort. Gaining the more open water, the bracing breeze waxed fresh; the little Moss tossed the quick foam from her bows, as a young colt his snortings. How I snuffed that Tartar air!—how I spurned that turnpike earth!—that common highway all over dented with the marks of slavish heels and hoofs; and turned me to admire the magnanimity of the sea which will permit no records. At the same foam-fountain, Queequeg seemed to drink and reel with me. His dusky nostrils swelled apart; he showed his filed and pointed teeth. On, on we flew; and our offing gained, the Moss did homage to the blast; ducked and dived her bows as a slave before the Sultan. Sideways leaning, we sideways darted; every ropeyarn tingling like a wire; the two tall masts buckling like Indian canes in land tornadoes. So full of this reeling scene were we, as we stood by the plunging bowsprit, that for some time we did not notice the jeering glances of the passengers, a lubber-like assembly, who marvelled that two fellow beings should be so companionable; as though a white man were anything more dignified than a whitewashed negro. But there were some boobies and bumpkins there, who, by their intense greenness, must have come from the heart and centre of all verdure. Queequeg caught one of these young saplings mimicking him behind his back. I thought the bumpkin’s hour of doom was come. Dropping his harpoon, the brawny savage caught him in his arms, and by an almost miraculous dexterity and strength, sent him high up bodily into the air; then slightly tapping his stern in mid-somerset, the fellow landed with bursting lungs upon his feet, while Queequeg, turning his back upon him, lighted his tomahawk pipe and passed it to me for a puff. “Capting! Capting!” yelled the bumpkin, running towards that officer; “Capting, Capting, here’s the devil.” “Hallo, you sir,” cried the Captain, a gaunt rib of the sea, stalking up to Queequeg, “what in thunder do you mean by that? Don’t you know you might have killed that chap?” “What him say?” said Queequeg, as he mildly turned to me. “He say,” said I, “that you came near kill-e that man there,” pointing to the still shivering greenhorn. “Kill-e,” cried Queequeg, twisting his tattooed face into an unearthly expression of disdain, “ah! him bevy small-e fish-e; Queequeg no kill-e so small-e fish-e; Queequeg kill-e big whale!” “Look you,” roared the Captain, “I’ll kill-e you, you cannibal, if you try any more of your tricks aboard here; so mind your eye.” But it so happened just then, that it was high time for the Captain to mind his own eye. The prodigious strain upon the main-sail had parted the weather-sheet, and the tremendous boom was now flying from side to side, completely sweeping the entire after part of the deck. The poor fellow whom Queequeg had handled so roughly, was swept overboard; all hands were in a panic; and to attempt snatching at the boom to stay it, seemed madness. It flew from right to left, and back again, almost in one ticking of a watch, and every instant seemed on the point of snapping into splinters. Nothing was done, and nothing seemed capable of being done; those on deck rushed towards the bows, and stood eyeing the boom as if it were the lower jaw of an exasperated whale. In the midst of this consternation, Queequeg dropped deftly to his knees, and crawling under the path of the boom, whipped hold of a rope, secured one end to the bulwarks, and then flinging the other like a lasso, caught it round the boom as it swept over his head, and at the next jerk, the spar was that way trapped, and all was safe. The schooner was run into the wind, and while the hands were clearing away the stern boat, Queequeg, stripped to the waist, darted from the side with a long living arc of a leap. For three minutes or more he was seen swimming like a dog, throwing his long arms straight out before him, and by turns revealing his brawny shoulders through the freezing foam. I looked at the grand and glorious fellow, but saw no one to be saved. The greenhorn had gone down. Shooting himself perpendicularly from the water, Queequeg, now took an instant’s glance around him, and seeming to see just how matters were, dived down and disappeared. A few minutes more, and he rose again, one arm still striking out, and with the other dragging a lifeless form. The boat soon picked them up. The poor bumpkin was restored. All hands voted Queequeg a noble trump; the captain begged his pardon. From that hour I clove to Queequeg like a barnacle; yea, till poor Queequeg took his last long dive. Was there ever such unconsciousness? He did not seem to think that he at all deserved a medal from the Humane and Magnanimous Societies. He only asked for water—fresh water—something to wipe the brine off; that done, he put on dry clothes, lighted his pipe, and leaning against the bulwarks, and mildly eyeing those around him, seemed to be saying to himself—“It’s a mutual, joint-stock world, in all meridians. We cannibals must help these Christians.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Melville, Herman,2001. Moby-Dick: or The Whale. https://www.gutenberg.org/files/2701/2701-h/2701-h.htm#link2HCH0013 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Moby-Dick; or The Whale: Chapter 13 - Wheelbarrow 

Machine learning can be complex and overwhelming. Luckily Google is on its way to democratize machine learning by providing Google AutoML, a Google Cloud tool to handle all the complexity of machine learning for common use cases. Because of the ease of building a custom machine learning model with AutoML, has been reduced drastically. time-to-market If you upload your labelled dataset, AutoML will extract the patterns and create a custom machine learning model. AutoML can be used with tables, text, images, and video. The trained model can then be accessed through an API or client, enabling machine learning capabilities to your specific use-case. It’s like uploading a recipe for bread to a machine, putting in all the ingredients and within hours, a perfectly baked loaf of bread comes out, without even knowing how to bake. Recently, Google released AutoML for vision recognition. This leads to many new, innovative opportunities. Previously, you could only use API calls to access the cloud-hosted model, even though an API call to the model can take more than a second. However, with AutoML Edge, you can download the model and run it on your local machine, reducing the time to get predictions from the model to milliseconds. In this way, users get a real-time experience. Edge At Incentro, a digital change company, we always keep an eye out for new techniques that could result in a truly innovative advantage for our clients. As it turns out, AutoML Edge was the perfect match for a business case that we were working on. The customer is a global optical retailer. The business case was to design a ‘magic mirror’. The magic mirror is presented in offline stores, and it is able to guide the customers through their fitting session when they are shopping for glasses, and helps them to find the perfect pair of glasses. In this case, both the speed and convenience of the model were essential. Therefore, we made a Proof of Concept (PoC) to present to the client. In this blog post, I will tell you more about this magic mirror and how it came to be. The Case Who would ever buy something that’s going to be on their face every single day, without trying it out first? I sure wouldn’t. That’s the main reason why buying a new pair of glasses used to be a strictly offline experience for such a long time. Keeping up with fast-growing technology and innovations is crucial for companies to survive. Nowadays, countless retail stores go bankrupt, because they did not put enough emphasis on their e-commerce. Even when doing e-commerce nowadays, you’re barely keeping up with the competition. If one wants to get a headstart, one should connect online and offline by creating an omnichannel experience, which allows the company to interact with its customers on multiple levels, bundling their forces, to enhance the customer experience. In our case, the portal between the online and offline would be the ‘magic mirror’: an interactive mirror that guides customers through the fitting process. It would do so by using machine learning to detect the glasses someone’s trying on in the store and provides the customer with useful information, such as pricing, the brand story and other available colours. And it doesn’t stop there: the mirror could also recommend glasses based on the types of glasses someone tried on before, the shape of their face, their age, their gender and more. All these characteristics can be extracted using machine learning. This is where the speed and convenience part comes in: for an experience to feel truly immersive, the detection of these characteristics need to be instant. Luckily for us, AutoML Vision Edge was released recently. Time to check it out! Option AutoML When opting to use AutoML Vision, you immediately face some questions: classification or object detection? Edge or cloud-hosted? At first, I experimented with cloud-hosted object detection. However, I soon found out that labelling the objects (glasses in our case) by hand was very time-consuming: we had to draw a rectangle around each object, assign a label to it and then move on to the next one, over and over again for more than a thousand times. Luckily, the process could easily be automated by using the standard AutoML vision object detection API. The API would take care of labelling the glasses in the pictures and since I already knew which pictures featured which glasses, I could simply insert the name of the glasses into the label. Problem solved! Well… not quite. Sometimes the standard Google Vision API didn’t detect the glasses properly or detected multiple glasses in one image. The pictures therefore still needed to be checked manually — a time-consuming task that was quite discouraging. Wasn’t there a way for us to just sit back and enjoy the show? After labelling hundreds of images semi-automatically, uploading them to the cloud and hosting the model, I found that the API was easily implemented and already rendered reasonable results. However, one would have to pay for every hour the API is deployed. Maybe not the best solution in our case. But there are still some options on the table, like experimenting with the AutoML Vision Edge. That would make labelling a much easier task since we wouldn’t have to put a box around objects — the entire image would just get (multiple) labels. Using this classification option, our first results were shockingly bad. Unexpected? Not really, because the label only described about 5% of the image: the glasses. The images contained a lot of noise, which was troublesome for the model. However, this was something we could solve. Face recognition to reduce noise I asked myself: what if I just cut out the heads from the images and feed them into AutoML? That would reduce noise drastically! From my object detection experiment, I learned that I don’t like to do things manually. That’s why I decided to write some code to do the crops automatically. After some browsing on the internet, I found a basic algorithm using ‘haar cascade’ to indicate a face in an image. By using this code, I have created a function that crops out the head from the image and saves this as a new image of only one head wearing glasses. This is something I can feed back into our AutoML machine. Way of making pictures Just like the bread baking machine, AutoML needs its ingredients. In our case that’s images. Loads of them. For the PoC, I decided to train our model with 10 different pairs of glasses, some of them almost identical. A rule of thumb is that if a human eye should be able to see the difference between two pairs of glasses, as small as it may be, AutoML would be able to learn it as well. Filled with confidence, I started taking pictures with my mobile phone — about 20 per pair of glasses. I cropped out the heads to reduce noise, fed the almost noiseless pictures into AutoML and directly trained the first AutoML model. After an hour, the model was ready to deploy. Filled with excitement and suspense, I created a simple API using the built-in webcam of my MacBook, put one of the glasses on my face and … got some very confusing results, to say the least. It felt like the model made random guesses rather than a well-made prediction. What an anti-climax. After some minutes of thinking about what went wrong, I realised my MacBook’s webcam is about 1 MP, while the camera on my phone is like 12 MP. Maybe, I mused, it would help to use a better camera and to train the model with images that were made with that same camera. After the purchase of a more capable webcam, I got started to train an AutoML model again. This time, I thought it would be a smart decision to overload AutoML with pictures — and what faster way to make a lot of pictures than shooting a video? I asked about five of my colleagues to act as a model for my model (pun intended) and recorded a video of about 10 seconds for every single pair of glasses. With 30 frames per second, 10 seconds per video and 5 colleagues/models per pair of glasses, this resulted in 1500 images per pair of glasses, which had to be enough. After uploading and training the model again, waiting for another hour, downloading the model and implementing it in my code, I was ready to give it another go. Now, if I used the new webcam standing at my desk, the results were good. But as soon as I left my desk and tried the model somewhere else in the office, the results were not great, not terrible ( https://www.quora.com/What-does-not-great-not-terrible-mean ). So I got to thinking again. Maybe the problem was the variety within the images I fed into AutoML — or rather the lack of variety. The frames from the video’s I made, shot from a stationary spot, were (logically) almost identical. For the next couple of images, I wrote a script that would automatically take about 5 pictures per second, crop out the face, upload them to the cloud, label them and finally feed them to AutoML. I kindly asked my colleagues from before to model again and used a greater variety of lights, locations and head movements this time. As such, I made another couple of hundred pictures per pair of glasses, removed a great portion of the video frames to avoid overfitting and tried my luck again. This AutoML thing was becoming a familiar ritual: uploading, waiting for an hour, downloading the model, implementing it in my code and — poof! The model worked! The response time was lightning fast and the result was very reliable: the model recognized every single pair of glasses. Time to hook it up to the smart mirror and see how this works out. Serving the results to customers Now that the backend works properly, I can have a look at what happens in the frontend. Every 200 ms, the frontend will make a request to the backend via a REST-API call. The backend then takes a picture using the webcam inside the mirror and checks if there is a person in the image. If there is, the backend classifies if the person is wearing glasses and if so, classifies the glasses, finally sending back the data to the frontend — all in under 200 ms to handle the next request. Algorithm to detect the person in question After using the smart mirror for a couple of minutes, I called in some of my colleagues to show them the magic. They enthusiastically grabbed a pair of glasses from the shelves, put them on their faces and gathered together in front of the mirror. And in that moment, I encountered yet another challenge: with that many people in front of the mirror, it struggled to determine which of all those people to check for glasses. Therefore I had to find a smart way to determine which person standing in front of the mirror is most likely to be interacting with it. The way I chose to make this decision is based on two metrics: someone’s distance from the mirror and their distance from the centre of the mirror. When we tried the mirror again with this improved algorithm, it worked like a charm. Data collection This is just a Proof of Concept, but I can’t help but wonder over the endlessness of possibilities a smart mirror would open. In the first place, our objective is to enhance the customer experience by providing customers with additional information about the glasses they’re trying out, all while (in the background) extracting valuable data from the people interacting with it: gender, age, face shape, sentiment, and more. All this collected data could resulting in the mirror being able to give personal recommendations later on. This helps the customer to find their perfect pair of glasses within minutes of entering the store. The store will really know it’s customer and can use this information to better serve the customer. Next steps The next steps are to get the code into production, enlarge our training set of ten glasses to about a thousand and see how it will respond. The concept of smart mirrors is not unique to glasses and can be used for a variety of use-cases both in retail and other industries. It’s the speed and convenience that makes Google AutoML edge unique and the possibilities endless. If this kicked your mind into gear and you’re looking for someone to wonder about the possibilities, or if you’ve simply got a question, you know where to find me. For more cases have a look at www.incentro.com.

Face Recognition On The Wall; Google's AutoML Edge Democratizes ML For All

Reflecting on my company’s journey to Microservices, DevOps, and the Perfect Value Stream In the Manufacturing industry, the Value Stream Map (VSM) is a popular tool used to measure performance. The VSM was made famous by , and is essentially a flow chart that describes the necessary steps to produce value. Toyota For example, consider this simple VSM for a Chair Factory. An example VSM for a Chair Factory. This example illustrates the 4 steps our factory needs to produce a chair after receiving an Order: Welding, Painting, Assembling, and Inspection. There are a couple of things worth pointing out here. The steps must be done sequentially. In this example, Painting must come after Welding for each chair. The steps can be done in parallel for different work items. In this example, that means Painting and Welding can occur simultaneously, but for 2 different chairs. The total time for the VSM is the sum of all the steps. In this example, each chair requires 7 days from start to finish. The is how fast a work item can flow through the VSM. In this example, our factory could produce 1 chair every 3 days if each step always had a chair to work on. output rate Our Chair Factory working at full capacity can produce 1 chair every 3 days. The last point is especially noteworthy because it illustrates the difference between total time and . When deciding how to improve a process, the output rate explains why we should focus on the longest step (or biggest bottleneck). In this example, our workflow is stymied only by Inspection. output rate This example has been about chairs, but Value Steam Maps can also describe software delivery. Consider another VSM below; it’s similar to making chairs. An example VSM for software delivery. In this software example, Deploy is the longest step, which means the output rate is at least 3 days per Feature Request despite how fast the other steps are. In other words, this VSM is telling us that Deploy is effectively our only constraint. Both the above examples are oversimplified Value Stream Maps, but they illustrate why only the largest bottlenecks matter. And as I reflect on how my company’s architecture has evolved over the years, I’ve realized it’s these bottlenecks that have steered some of our biggest changes. The Story Of How Our Infrastructure Was Guided By Bottlenecks In the beginning, my company’s journey began with a few developers and not much else. Coordination was easy since the team was small, and there were no legacy systems to support because none existed. This provided the agility to figure out a business and product. Our Value Stream was clear. Just a couple of developers and a new Rails project Then our company grew. Users increased and product evolved, which meant new systems were needed. But managing these systems required us to take time away from development. This became our first bottleneck. Consequently, we introduced tools like and to manage our systems, and so our first big bottleneck was fixed. Capistrano Puppet More systems required required tools like Puppet and Capistrano to manage them. But our company continued to grow. More developers came, along with more deployments, hot-fixes, and configuration. Our automation helped, but many tasks still required human-assistance from our lone Sysadmin, who soon became overwhelmed. This became our second big bottleneck, and as a result more Sysadmins were hired and an Infrastructure (or ) team was formed. SRE More people and teams required an SRE team to deal with all their requests At this point, we mostly used Ruby and MySQL, and our SRE team allowed us to scale. But soon we would need JavaScript and Scala, with Kafka clusters and Cassandra databases. This turned into our next bottleneck because each new technology we introduced spawned unique requirements that hindered our SRE team with system specific procedures. Consequently, we migrated our systems onto Docker, and a , to give our Sysadmins a common interface to support our varied systems as our technology choices expanded. container orchestration (or Microservice) platform More diverse technologies required Docker to make it easier to support diverse technologies. Containers allowed us to support a breadth of technologies, but it also encouraged developers to be more involved with the operation of their systems. This meant that an increasing number of teams were asking Sysadmins for changes to their containers. Teams were building faster, and becoming more DevOps, but this dependency on the SRE team became the new bottleneck. Recognizing this trend in our organization led to the creation of a self-serve portal for developers to apply specific changes without a Sysadmin (even in production). Initially, this portal allowed users to set CPU and memory limits for their containers, the most popular request at the time, but features were added as other common requests (or bottlenecks) emerged. An early mock of our self-serve infrastructure portal for teams. This portal helped drive DevOps across our company by enabling teams to work freely, but also left us facing new challenges around cost management and access control, and needless to say, these challenges will not be the end. It’s likely that once these challenges are solved, we’ll find more bottlenecks again. The bottlenecks will never cease, but rest assured, that’s not a bad thing. The story of our infrastructure isn’t new, but what I’ve found enlightening is recognizing the endlessness of our journey. The perfect infrastructure doesn’t exist (the same can be said of any software product), but that doesn’t mean we should stop trying to improve. Reaching the finish line isn’t as important as moving forward. Continuous Bottlenecks Lead Towards Continuous Improvement Having bottlenecks to fix is a good thing. Bottlenecks represent an improvement that can be made to how we work. They guide our efforts by forcing us to constantly ask ourselves how we create value, and what that value is. In my from , Eduardo asks Mark when he’ll be done building Facebook so he can start monetizing it. Mark aptly replies that Facebook will never be done, similar to how fashion will never be done. favourite scene The Social Network The Facebook will never be done This scene resonated with me because I think it applies not only to Facebook, but to all forms of craft, whether it be software, art, music, or something else. Lessons can always be learned, growth can always be had, and in the case of software delivery, Value Streams uncover the bottlenecks that lead to the most meaningful improvements. I was told once that building software is like building railroads; the process begins with pioneering, followed by building, then finally optimization. While this comparison is sometimes useful, the finality of “optimization” felt lacking. Instead, I prefer comparing software development to Star Trek, and its mission of continual exploration, and like exploring the universe, building (and improving) is never done. We just need to decide which direction to go. The Real Difference Between Building Chairs And Writing Software Manufacturing and software share a continuous journey of improvement, but this is where the similarities end when it comes to the Value Stream Map. Manufacturing deals with the physical world. On a factory floor, you can see the work items, and the workflow itself. It might even be obvious which steps are bottlenecks and error prone. This is not so easy with software. With software, the work is electronic, and the factory floor is a room full of computers. It’s hard to see what someone is tasked, or what the workflow is. Software is invisible, which is what really differentiates it from manufacturing, and why tools like Jira are so important for this type of work. Progress is easier to spot in an assembly line than an office. Jira is a task system for software teams to describe and track work items, their progress, ownership, time spent, and other information that’s vital to understanding inefficiencies and building a meaningful Value Stream Map. These task management system are often thought of as being meant only for planning work, but a large part of their value is derived from how they chronicle the past, so we can rationalize it to make better decisions in the future. management future Creating a ticket to track a work item in Jira A lot of work has been done to understand developer productivity and its driving factors. In the age of DevOps, this has resulted in recognizing Deployment Frequency, Change Lead Time, and . However, in my experience nothing is as universally effective as simply journaling work and time. I strongly believe the benefits individuals receive from journaling and reflection, such as the ones below, applies to organizations too. other such developer metrics Learning from direct experience can be more effective if coupled with reflection . Keeping a diary to record wins and setbacks (that otherwise might slip through your memory) can provide motivation from realizing you are making progress, and help you think about how to get rid of inhibitors (even minor ones that recur) blocking your progress. The single most important thing for any software company to focus on is understanding how it creates value. Many companies get lost choosing between technologies, or organizational structures, but none of this intrinsically matter. The only thing that should matter is how value is created, and the obstacles that get in the way. And by citing these experiences (with Jira or something else), and reflecting upon them, we’re able to not only recognize the obstacles in our way, but reveal the path forward.

How Toyota Guides The Evolution Of Our Infrastructure (And Makes Me Appreciate Jira)

*Also published on uxmag.com* We’re not seeing movie recommendations really catch on with the world. Sites like and didn’t make it. What’s the hold up? Jinni Foundd Putting aside movie recommendation ‘accuracy’, the missing key is making recommendations a great experience. A record setting company’s app on a record setting company’s watch Just about every tech company is making experience a priority and acting on it. Apple recently had . Uber is currently . And entire e out of the experience of having more convenience. But recommendations are behind. the most profitable quarter in history the most valuable venture-backed private company in the world conomies have been born “I think subconsciously people are remarkably discerning. I think that they can sense care.” — Jony Ive Here are some characteristics with movie recommendations today along with the negative feelings you have associated with them: Endlessness (& Overload) Most movie recommendation sites give you the option to rate movies on a one to five star scale. I know, I know; the promise here is that the more you rate, the better your movie picks will be. Soon after you start rating, you’ll notice that the list of movies to rate never ends. Fatigue sets in and you quickly find rating more movies to be mundane. It’s a marathon that you can’t finish. But they tell you to keep going. As Janel Torkington insightfully puts it, an endless list “robs the user of a sense of finality.” Think about the sense of accomplishment when you finish a hard task. Now think about one that you can never get done. It’s not a delightful feeling. Immaterial Content (& Tedium) Inside the list of movies that they tell you to rate, you’re being presented with a lot of that you’ve never even seen. movies Obscure or niche flicks appear at the top, making you skip them before getting to something relevant to you. I was presented with and upon logging on to Foundd for the first time. Bridget Jones: The Edge of Reason Hitman Heart: Wrestling with Shadows Taken a step further, when you find movies that you’ve seen before they’re often ones that you saw Unless those movies really, really connected with you, you’re going to have trouble remembering what you thought of it. Your rating becomes a guess. years ago. Obvious Incongruity (& Dissonance) Then come your recommendations. The results that you put in effort to receive. Some sites will recommend even before you say a word about your movie preferences. Almost as striking, they’ll give you a big list of recommendations after just one or two ratings. The problem here is that In this example, was the only movie I rated (I gave it a 95). not enough is known about you to give you some picks that make sense. Donnie Darko OK, is a fit here. We can see the similarities. Impressive. Mulholland Drive Next: . Well…it sort of makes sense. There are some parallels, possibly. Suspiria Finally: and How do these movies relate to ? It feels incongruent. We expect good results but we get something that looks like it was picked from a hat. It’s a contradictory and dissonant feeling. Wedding Crashers Iron Man 2. Donnie Darko Feature Creep (& Inconvenience) Photo credit: https://www.flickr.com/photos/highwaysagency/6281302040/ A solution may be to let you search by every movie trait imaginable— your own personal God Mode. That’s not the answer either because it results in inconvenience. It requires too much work from you. We can get a bag of groceries at the push of a button. are spreading like wildfire outside of their Gen Y origins. We now expect this level of convenience outside of the lexicon of ‘Apps’, including recommendations. Our technology is pushing our expectations. The ideals of The Convenience Generation An example is Jinni. Before the shutdown, there were features, filters, options, specializations, and more available for users to play with. Some examples: ‘Now on TV for me’, ‘Entertainment Personality’, ‘Preferences’, ‘Story Tuners’, ‘Why close to you’, ‘My Guide’, ‘The Watch Wall’. 67+ Getting a user inside a movie they enjoy became overshadowed by the process of finding one. “Our goal is to try to bring a calm and simplicity to what are incredibly complex problems so that you’re not aware really of the solution.” — Ive The Surface Level (& Detachment/Disappointment) Tags attribute traits to a movie. They’re extra useful because they’re standard enough for a computer to be able to understand, so a website can use algorithms that play with them. Tags have the potential to be powerful, but the problem is that they’re usually very general. ‘Sci-Fi’, ‘Action’, ‘Space’, ‘Good vs. Evil’. We as humans don’t respond to a movie just because it has any of these traits. What does that action entail? How is Good and Evil portrayed? And once you have those answers, what about when we blend these specifics together? Instead, how do individuals respond and react to those traits? In some cases though that blend doesn’t make enough of a difference. Take and . Star Wars: A New Hope Star Wars: The Phantom Menace The tag-based formula was there. It’s Sci-Fi. It has a character named Anakin Skywalker in it. On the surface level, it’s being . But people hate the movie. Star Wars Star Wars If is getting it right, it means that the characters are giving us enough to invest in them. To along with them. To care so much that we want to see the continuation of their stories after 32 years. What makes Star Wars ‘Star Wars’ is how we feel when we watch and listen its characters and their stories. Star Wars feel desperately This transcends moving pictures on the screen and 1s and 0s inside a computer. We need to begin to make the tags identify and recognize the shades of this kind of human to human connectedness. We need to improve on uncovering the true things that make people like movies. And we need to honor users’ expectations and provide a seamless, easy experience for them. I think is starting to do just that. And Chill P.S. Achievement Unlocked. You’ve completed the article! If you found value in this, I’d really appreciate it if you recommend this post (by clicking the ❤ button) so other people can see it! Learn more about And Chill or share your insight by emailing me at jake@andchill.io P.P.S. “Is there something I can use to give me an unbelievable movie recommendation experience?” Yes, there is! Message And Chill at m.me/textandchill whenever you want to watch something. We think you’ll feel the attention to detail and care that we put in our product. You might also like “What Should I Watch”: 5 Examples of the Area Where Netflix is Forgetting All about Your Experience

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