The Game of Thrones star was photographed kissing British aristocrat Peregrine Pearson during a Parisian outing.

Sophie Turner Kisses Aristocrat Peregrine Pearson Amid Joe Jonas Split

Here’s your morning rewind for the Wednesday, Nov. 1, edition of Global News Morning Saskatoon. It’s a long-standing tradition in Saskatoon and it promises to be a very special event this year. The 69th annual Silver Plate Dinner is set to go on Nov. 7 at TCU Place.

Saskatoon morning news rewind: Wednesday, Nov. 1

The three senior talents featured in this PGT Rewind episode truly stole our hearts with their performances on Pilipinas Got Talent.

PGT Rewind: Top 3 scene-stealing performances of senior talents that prove age is just a number

Founded in 1894 and officially chartered in 1907, the Rod and Gun Club's mission was to promote fishing, hunting, and other outdoor sports.

Valley Rewind: Black Mountain Rod and Gun Club, 1937

If not for Penn State perfectly timing a blocked field goal against Ohio State seven years ago, the series between the Big Ten East powerhouses might be seen in a different light. After all, for all the instant classics produced, the results have largely ...

Sunday Rewind: Ohio State 20, Penn State 12

In our CBS 6 Rewind, we take you back to a nearly 100-year community favorite local department store, The Carl Company.

CBS 6 Rewind: The Carl Company's 95-year legacy in Schenectady

When Kelly Florence and Meg Hafdahl first met, they immediately bonded over their shared love of all things horror. So, seven years ago, the pair decided to start a podcast called Horror Rewind, which has become a huge success.

Knowing Your Neighbor: Horror Rewind

MFL MarMac’s football team lost in last season’s Class 1A state playoff quarterfinals to West Branch, 27-24, blowing an 11-point lead in the fourth quarter. That made Friday night’s 21-0 win in a 1A second-round game extra special.

Iowa high school football playoff rewind: MFL MarMac exacts revenge on West Branch

Check out some of these amazing photos of the Purdue University Lady Boilermakers ahead of the 2023-24 basketball season! The Purdue University Lady Boilermakers’ next game is against UCLA on Nov. 6. This gallery was compiled by Multi-Media Staff Writer ...

Recorder Rewind: Purdue University Lady Boilermakers

Here’s your morning rewind for the Tuesday, Oct. 31, edition of Global News Morning Saskatoon. Songs inspired by Oct. 31 are a big part of what makes Halloween special. From Monster Mash to Michael Jackson’s Thriller, there is no shortage of songs to get people in the Halloween spirit.

Saskatoon morning news rewind: Tuesday, Oct. 31

Rewind

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The 2021 AI Rewind: HackerNoon Edition

This video shows a curated list of the with a , , and . The videos include great commentary on the future of AI by many important people in the field such as Fei-Fei Li, Luis Lamb, Gary Marcus, and more. latest breakthroughs in AI by release date clear video explanation link to a more in-depth article code Enjoy the video! Chapters: Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff in 2021, I promise. 0:00 2020, A year in review 0:28 Where do you want AI to go? 9:06 References: Complete article: https://github.com/louisfb01/Best_AI_paper_2020 GitHub repo: https://github.com/louisfb01/Best_AI_paper_2020 Follow me for more AI content: Instagram: https://www.instagram.com/whats_ai/ LinkedIn: https://www.linkedin.com/in/whats-ai/ Twitter: https://twitter.com/Whats_AI Facebook: https://www.facebook.com/whats.artifi... Medium: https://medium.com/@whats_ai Youtube: https://www.youtube.com/channel/UCUzGQrN-lyyc0BWTYoJM_Sg/join Video Transcript 00:00 where do you want ai to go what what 00:02 would make you happy 00:03 what is the objective here the objective 00:05 function for those of us who are 00:06 building ai 00:08 um or trying to inform it from other 00:10 fields 00:11 what would count in success where do you 00:12 want to take it to 00:17 [Music] 00:50 [Music] 00:57 [Music] 01:18 [Music] 01:43 [Music] 01:54 [Music] 02:39 [Music] 03:24 [Music] 03:30 [Music] 03:51 [Music] 04:16 [Music] 04:29 [Music] 04:50 but it's a trick no no it's much more 04:52 than that uh whosoever be he were they 04:54 shall have the power 04:57 whatever man it's a trick please be my 05:00 guest 05:02 come on really yeah oh this is gonna be 05:05 beautiful clint you've had a tough week 05:07 we won't hold it against you if you 05:08 can't get it up you may have seen this 05:10 before 05:10 right yeah all right 05:39 [Music] 06:24 [Music] 06:30 [Music] 06:51 [Music] 07:16 [Music] 07:27 [Music] 08:13 [Music] 08:17 researchers and artists around the world 08:19 have been using style game 2 to do all 08:20 kinds of interesting work 08:22 at nvidia we've been pushing the 08:23 frontier of using gans to synthesize 08:25 extremely realistic images 08:28 the catch is that you need to train them 08:30 on extremely large quantities of data 08:32 so to tackle this challenge we've 08:33 invented a new approach for stylegan 2 08:35 that we call adaptive discriminator 08:37 augmentation or ada 08:38 this lets us reduce the number of 08:40 training images by a factor of 10 a 08:42 factor of 20 or more while still getting 08:44 great results 08:56 [Music] 09:06 as a scientist i want to 09:10 push the scientific knowledge and 09:12 principles of 09:13 ai further and further and the first 09:15 thing is start with 09:17 some really fundamental laws and 09:21 principles of 09:22 ai i keep saying that i still feel our 09:24 ai 09:25 era is pre-newtonian physics that we are 09:28 still studying phenomenology 09:30 and engineering but there is going to be 09:34 a moment 09:34 or a set of moments where we're starting 09:37 to understand 09:38 the principles of intelligence and that 09:41 is 09:42 the scientists in me we're in the head 09:45 of a citizen i guess 09:47 and directing stanford's human center ai 09:51 institute 09:52 i want this to be a technology that can 09:55 an idealistic way really better human 09:58 conditions 09:58 right it's so profound it's so 10:02 horizontal 10:03 it's so um it has so much human and 10:07 societal impact 10:10 it can be very very bad and can be very 10:12 very good 10:13 so there i would like to see a 10:16 framework of this technology being 10:19 developed and deployed 10:21 in the most benevolent way 10:25 what comes to mind very often so i hang 10:27 out because of the family i'm born into 10:30 with people who work at state 10:32 departments who deal with international 10:34 laws military laws etc 10:36 and there's a there's a there's a big 10:38 concern here with militarization of ai 10:40 right 10:41 danny just mentioned human decision 10:43 making humans in the loop why people are 10:45 trying to negotiate 10:46 treaties limiting should we limit should 10:49 they always be a human 10:50 in the loop when we when we build 10:52 dangerous machines 10:54 you know kill a robot as well as is 10:56 happening as we all know 10:58 in the defense world and and as will 11:01 happen so what about the growing threats 11:03 of militarization of ai which is ongoing 11:06 and what should we 11:07 uh should we do anything about it or 11:10 pessimistic can we do anything about it 11:12 or is that 11:15 genie out of the box i want the costs 11:18 and the benefits of ai to be 11:21 evenly distributed across society both 11:24 in the united states 11:26 and globally and i want the public to 11:29 trust that that's what's being done 11:31 and i think that's impossible without 11:34 changes to law 11:36 just impossible um i like one of the 11:38 sayings of michael raving 11:40 from harvard and hebrew university said 11:42 that it's great that 11:44 uh computer science has not been around 11:46 for 2000 years 11:48 and we are at a stage where very very 11:50 important results 11:51 occur in front of our eyes i also like a 11:54 saying by 11:56 alan turing in his mind paper where he 11:58 said we can only see a short distance 12:01 ahead 12:02 but we can see plenty there that needs 12:04 to be done 12:05 and as fei-fei said as scientists i want 12:08 ai to advance but since ai is having an 12:11 impact 12:12 as big as physics had in the 20th 12:15 century 12:15 engineering heading and 20th century all 12:18 the ethical issues 12:19 all the biases and all the social 12:21 implications that margaret and others 12:23 and here in francesca 12:25 have been studying they are key as 12:28 responsibility of our 12:29 ourselves as scientists and in terms of 12:32 uh 12:32 technical technically speaking what we 12:35 are trying to do 12:36 is to conciliate traditions of ai so 12:39 that we cannot see 12:41 and as you said gary before you've been 12:43 saying since the 12:44 late 90s we have to converge we have to 12:47 look for convergency 12:49 you cited the only colonial cited other 12:51 prominent scientists today 12:53 we need a way of seeing that several 12:56 techniques can contribute to this 12:57 endeavor of making ai 12:59 fairer ai less bias and ai 13:03 to make something very positive for us 13:06 as humanity we need uh as scientists to 13:09 see 13:09 our fields in a very uh human humanistic 13:13 way 13:14 so that not only the technical stuff 13:16 advances but we also have to 13:18 be guided by serious and by effective 13:20 ethical principles 13:22 laws and norms as ryan said it's hard to 13:25 do to do that at the moment we are at 13:27 the beginning of uh 13:29 an ai cambrian explosion as several 13:32 people mention here but we need to be 13:34 very aware 13:35 of the social ethical and global 13:38 implications 13:39 that ai has these days we have to be 13:41 concerned about the north south 13:44 divide about the different cultures in 13:46 order to regulate it properly 13:49 we cannot see it only from a single 13:52 cultural perspective so 13:53 that's what i want to see ai researchers 13:56 doing to be they have to be concerned 13:58 about the technical results 14:00 the outstanding results ai has been 14:02 showing but also 14:03 we have to be to care about other people 14:06 about 14:06 other people's other countries and over 14:09 and over all 14:10 for the global health of the planet 14:12 thank you very much 14:13 and merry christmas happy new year to 14:16 everyone thank you gary and vincent for 14:18 the 14:18 brilliant debate you brought today and 14:20 for the scientists 14:22 i am reminded of the old african proverb 14:24 that i'm sure you all know which is it 14:25 takes a village to raise a child 14:27 clearly it will take a village as we've 14:29 seen today 14:30 to raise an ai that is ethical robust 14:32 and trustworthy

AI Rewind: A Year of Amazing Machine Learning Papers  

Hello everyone! At Dashbouquet, we can’t stress enough the value of long-term relationships with our clients. We consider our success to be based on your solely — and we can’t be more proud to know that so many companies trust us with their projects. We’d also like to thank the readers of our blog: your comments and engagement inspire us to keep posting relevant information and share our expertise with those interested in technology and innovation. So we are saying a huge THANK YOU to our clients, readers and all the tech geeks out there who work and communicate with us. We are wishing you many exciting new projects, lots of fun and adventure and we hope that in 2019 our client base will expand. We love doing what we do and we are happy to share our passion with you. _Read writing from DashMagazine in Hacker Noon. Collection of posts from those who build Dashbouquet…_hackernoon.com DashMagazine - Hacker Noon 2018 has been busy for Dashbouquet. Here is a quick glance: 20 new people hired 5 new projects launched 64 articles published 1 new office opened 5 reviews received As for our blog, it’s been busy as well. We have collected the top-9 most read articles in case you missed any of them or simply want to re-read them: Top Ten Tools You Should Use In Your Software Workflow Web Development Trends that will Dominate in 2019 Usage of Reselect in a React-Redux Application 10 JavaScript Animation Libraries to Follow in 2018 Angular VS React in 2018 Top 5 JavaScript IDEs in 2018 Machine Learning and AI trends for 2018: What to Expect? Sex Toys and Artificial Intelligence — The Past, Present, and the Future How Blockchain Can Transform Artificial Intelligence Infographic: React vs Angular vs Vue.js Thanks again for staying with us — you inspire us to do more cool stuff! And as usual, we are always looking forward to receiving your feedback. So please, comment on our articles, hit us with an email or simply share content from our blog — it would make us feel really special. All the best wishes from the Dashbouquet team, have a very Merry Christmas and a Happy New Year! Originally published at dashbouquet.com . _Read writing from DashMagazine in Hacker Noon. Collection of posts from those who build Dashbouquet…_hackernoon.com DashMagazine - Hacker Noon

A Dashbouquet Rewind: How we Rolled through 2018 — and Together with you!

Christmas time is finished. New Year’s Eve is finished as well. I hope that everyone had a great time and you are ready to start working hard and learning new things! I want to start this year with a wonderful article-tutorial on how to create similar to fast rewind control. Periscope Requirements Xcode 7+ 8+ iOS At least basic Swift knowledge Passion to iOS and development High-level explanation To build this component we will have to use video player — . We will add to begin rewind and calculate finger translation — it will help us to change rewind speed and calculate time. While rewinding is in progress we will use to blur video, and to generate preview thumbnails. AVPlayer UILongPressGestureRecognizer UIVisualEffectView AVAssetImageGenerator OK. Let’s stop talking, . just do it Create single view application and create new class — subclass of UIViewController. Import and put this code inside class declaration: VideoViewController AVFoundation Everything we did here is quite easy: Declared all variables related to AVPlayer which we are going to use in the future; Created custom initializer with videoURL where we set URL and initialize all — AVURLAsset, AVPlayerItem, AVPlayer and AVPlayerLayer; In we add player to view sublayers; loadView In we simply start playing video; viewDidLoad In we return — it hides status bar if our status bar is based on view controller (it can be changed in file). prefersStatusBarHidden true Info.plist Simply set frame of playerLayer to view.bounds — we support both portrait and landscape orientations. OK. Our video view controller is ready for early stage test. In order to test it we have to do two simple steps: , extract and add folder to your project ; Download this ZIP file Resources (do not forget to select required targets) Open file and put this code inside: ViewController.swift Now run project on your device or simulator and make sure that video view controller is presented, video is playing, and all orientations are supported. Our next stage is to recognize touches and blur content. Declare these two variables in : VideoViewController Paste this code to the end of method: loadView Add this code to the end of method: viewWillLayoutSubviews Implement this function: The purpose of this function is to pause video and fade in visual effect view when touches began, and resume video and fade out visual effect view when touches cancelled, ended or failed. In the future we will add more logic to this method. Run project and test long press. That’s how it works on my device: Next step is to create timeline view and implement rewind functionality. But let’s have a very quick look at UI to understand what exactly our timeline should have and how it should work. Periscope As you can see — timeline goes from the left side to the right side of the view controller. It has a white dot which indicates where video was stopped when user started rewinding. If you compare screenshots you will notice, that interval width is different — it is because rewind speed (we will call it in our project) values are not equal. And the last thing to notice is that current time is always in the middle of the timeline. zoom Create as a subclass of UIView and declare these variables: TimelineView The purpose of each variable is obvious, but let’s make sure everyone understands: — total duration of a video in seconds; duration — seconds, when rewind began; initialTime — current time in seconds; currentTime — private variable which stores zoom value; _zoom — wrap on top of _zoom variable, so when new value is going to be assigned we can check whether it is in an acceptable range; zoom and — both variables define acceptable range for zoom; minZoom maxZoom — width of a line representing a specific time interval on a timeline. If zoom is not equal 1, then actual interval width equals to . Value will be used during rewind for calculations — for example, if zoom is 1, intervalWidth is 30 and intervalDuration is 15, then when user moves 10pixels left or right we will rewind by +5 or -5 seconds; intervalWidth intervalWidth * zoom — duration of an interval in seconds. If video is 55 seconds and interval is 15 seconds — then we will have 3 full intervals and one not full interval. Value will be used during rewind for calculations. intervalDuration minZoom, maxZoom, intervalWidth and intervalDuration could be constants, but I decided to make them vars — if you want to reuse this view controller with different videos you might want to adjust these values. Implement these functions: — calculates interval width depending on the zoom value; currentIntervalWidth — calculates time interval in seconds from passed width. Will be used to rewind by distance; timeIntervalFromDistance — calculates distance from given time interval. Will be used to calculate elapsed interval width; distanceFromTimeInterval — takes distance, calculates time interval from and adds this value to the current time. rewindByDistance Implement draw rect: Do not be scared. Everything here is simple :) I will explain only those things, which I think might need explanation: As you probably noticed before — when we rewind in Periscope timeline moves left-right. To have that movement we calculate value and apply to all our drawings; originX We draw first line which indicates full timeline; We draw second line which indicates elapsed time; We draw dot which indicates initial time; And last, we draw separators between intervals. And final bit on timeline view for now — implement new initializer and set to : opaque false I feel that our timeline is finished. Next stage is to add it to view controller and make it work! Add these variables: First variable is a content view, where all rewind related views/controls will be added. We will fade in and fade out this view when rewinding begins and finishes. Second variable is our actual timeline view which we implemented in a previous stage. And the last variable will help us to calculate how far finger has moved and how far we should rewind. Put this code to the end of method: loadView It adds all new subviews to their superviews and sets duration of the timeline to the duration of the asset. Update function with this code: longPressed We have added code to get current location of the finger; We calculate zoom value depending on the finger location. You can play with this calculation, but I prefer this calculation; We added logic to set initial time to the timeline when rewind begins; We call rewindByDistance method when gesture state is equal to .Changed; We fade in and fade out our newly created content view; We update previous location x if it is not equal to the current. Last stage before we try it is to layout our newly created views. Put this code to the end of : viewWillLayoutSubviews Run project on your device or simulator and check it out! That’s how it looks on my device: I really hope that you managed to do everything and it works exactly the same. If not — quickly go threw tutorial again. As you might have noticed it does not rewind when we release finger. In order to do that add update longPressed function with this code: We added 2 new lines — line 17 and line 18. With the first line we calculate new time, and with the second line we seek to that time. Run your project again.. and enjoy! It works now! Lazy people can close tutorial at this stage , because core stuff is built and next stage is mainly improvements and some nice-to-have features. (hope you are not lazy) Our goal is to add two new views: Preview image view which will show thumbnail of the video at current rewind time; Label, which will show current rewind time in format minutes:seconds. In order to update these views I decided to add closure to timeline view which will be triggered every time when value changes. currentTime Add this variable to : TimelineView Update variable implementation: currentTime Add these variables to the class: VideoViewController Add these two lines to the end of : init(videoURL: NSURL) Maximum size specifies the maximum dimensions for generated image. We want maximum height to be . Generated image is never scaled up. But in most of the cases video size is much higher than the expected thumbnail size. rewindPreviewMaxHeight Add these lines to the end of loadView method: Update function with this code: viewWillLayoutSubviews Feel free to change value. It indicates gap height between preview image view, current time label and timeline view. verticalSpacing And the last step in this section is to implement closure. Add this code just before adding timeline view to it’s superview: currentTimeDidChange We generate current time string and assign to rewindCurrentTimeLabel.text; We initialize requested time with current time and required timescale; We generate CGImages asynchronously. On completion we try to generate UIImage from CGImage, switch to main thread, set image to preview image view and ask to call layout subviews if needed. Run you project and enjoy! It looks fantastic! Let’s do the final touch! In Periscope they have a nice shadow behind preview image view. We will add it as well! Add this variable to : VideoViewController Add this block of code to the function just before setting up and adding rewindPreviewImageView to subviews : loadView (if you add it after rewindPreviewImageView, then it will be higher in a layer hierarchy and will be shown on top of imageView) For those who does not understand the purpose of line number 6 I would suggest to read about . iOS implicit animations Update viewWillLayoutSubviews: Run and check it out. Everything is great. But.. wait.. our preview image is pixelated! It is because expects us to provide pixels instead of points. Documentation on how points are different from pixels can be found . AVAssetImageGenerator.maximumSize here Let’s fix this minor issue. Update this line all over the class: with this: Now run project again. What’s going on.. Where is our shadow?! Where is our corner radius?! We forgot one little thing — returns us CGImage and we initialize UIImage with CGImage without providing required scale. In our case our needed scale is . AVAssetImageGenerator UIScreen.mainScreen().scale Find this line: And update with this: Ruuun again! Wonderful! Everything is back and it works great! Woop woop!!! Thank you for going threw this tutorial. If you liked it please press heart button and share it with your friends & colleagues. Source code from this tutorial with some improvements and more features can be downloaded . here See you in my next articles-tutorials! Bye!

Building Periscope fast rewind control for iOS

How to Deploy a Scala Play Application to Heroku

The way users engage with apps is shifting fast — and often not in the direction most teams anticipated. What once passed as innovation is now just noise. Features that used to impress are now ignored. Entire product categories, built on bold predictions and massive funding rounds, have quietly faded away. Today, user attention is scarce, switching costs are near zero, and tolerance for clunky UX is gone. Most importantly, people no longer explore apps with curiosity; they evaluate them immediately. If the value isn’t apparent immediately, they move on. In this environment, product teams are forced to rethink almost everything: how value is communicated, how habits are formed, and how technologies like AI are integrated. It’s no longer about building more — it’s about building less but better. more less So, what exactly stopped working? What expectations are replacing old norms? And how are the most adaptive teams delivering under these new constraints? Let’s break it down. 1. What No Longer Works 1. What No Longer Works Not every technology backed by billions has managed to earn its place in everyday life. Some of the most ambitious bets of recent years — AR, VR, and voice interfaces — failed the test of real-world usage. Not every technology backed by billions has managed to earn its place in everyday life. AR and Apple Vision Pro were pitched as the next layer of interaction. In reality, they were too expensive, uncomfortable, and limited by short battery life. Everyday use just wasn’t practical. A design built to impress couldn’t deliver where comfort and endurance mattered most. The technology meant to replace screens ended up as more of a showcase than a solution. In reality, In reality, Voice assistants faced a similar fate. Siri, Alexa, and their counterparts didn’t become the default interface. Their use stayed confined to narrow scenarios — playing music, checking the weather, or helping older users talk to a speaker. That’s not real behavioral adoption. The core issue behind both is the lack of an active, recurring user base. Without it, developers have no incentive to build; without developers, there’s no ecosystem of meaningful use cases. It’s a closed loop that stops the platform from becoming truly indispensable. The core issue behind both is the lack of an active, recurring user base. Without it, developers have no incentive to build; No Device Has Replaced the Smartphone No Device Has Replaced the Smartphone Another area that hasn’t lived up to the hype is the search for the “next device” after the smartphone. Startups like Rewind tried to reinvent how we consume digital services — but the phone remains firmly in place as the main, all-purpose interface. Building specifically for Oculus or Apple Vision Pro doesn’t make sense — these platforms lack the user base to justify serious developer investment. hasn’t lived up to the hype is the search for the “next device” after the smartphone. Attempts to turn AR into the next big user interface also fell flat. Facebook poured billions into the vision — after acquiring Oculus, up to 30% of the company’s developers were focused on AR and the Metaverse. But the idea never really took off. Users stayed grounded in reality, where devices still need to be practical, autonomous, and suited for everyday use — not experimental showcases. AI Remains Untrusted AI Remains Untrusted At the same time, skepticism toward AI-generated content is growing. People have a stronger emotional response to things made by humans — even when AI delivers the same output. The rise of synthetic influencers, AI-generated photos, and machine-written texts raises new questions: Where is the line between real and fake? Should AI content be labeled? And is it even healthy to form emotional connections with a simulation? This psychological barrier is starting to matter more than the technical one. Even the most intelligent support chatbots still get the same request: “Connect me to a human.” The desire to talk to real people hasn’t gone away, which limits how far AI can scale, no matter how efficient it gets. This psychological barrier is starting to matter more than the technical one. Even the most intelligent support chatbots still get the same request: “Connect me to a human.” The desire to talk to real people hasn’t gone away, which limits how far AI can scale, no matter how efficient it gets. The Superapp Illusion The Superapp Illusion The idea of a super app — combining multiple services into one interface — was long seen as a strategic goal for digital platforms. The logic seemed straightforward: if users can solve all their needs within a single product, retention and monetization should follow. But in many cases, that logic didn’t hold. Attempts to pack everything into one place often led to bloated interfaces, unclear user flows, and declining engagement. Instead of convenience, users encountered unnecessary complexity. As a result, the trend began to shift in the opposite direction. Platforms started breaking products apart again. For example, in February, reports emerged that Instagram considered turning Reels into a standalone app. Earlier, media reports indicated that TikTok was officially expanding into long-form content territory. Platforms started breaking products apart again. in February, in February, Earlier, Earlier, The focus has shifted from platform dominance to targeted competition across specific use cases — stories, short videos, and long-form content. The new approach is clear: success isn’t about doing everything, it’s about doing one thing better than anyone else. The focus has shifted from platform dominance to targeted competition across specific use cases — stories, short videos, and long-form content. The new approach is clear: success isn’t about doing everything, it’s about doing one thing better than anyone else. The focus has shifted from platform dominance to targeted competition across specific use cases — stories, short videos, and long-form content. 2. How User Behavior Has Shifted 2. How User Behavior Has Shifted Users don’t explore apps anymore — they scan them. If something doesn’t work or make sense instantly, they’re out. The space between opening and uninstalling has collapsed to seconds, and product teams now operate within this ever-shrinking window. Users don’t explore apps anymore — they scan them Attention spans have tightened Attention spans have tightened People spend less and less time inside individual products. The competition isn’t just between apps in the same category — it’s across all digital experiences.  Every product is fighting for a share of the same limited attention. Even time spent per app is shrinking, making retention a battle that starts the moment a user lands. Users need a reason to stay — not later, but right now. Product teams have to design for habit from day one. If an app doesn’t deliver immediate value or fit naturally into a user’s daily routine, people will simply move on. As the saying goes: "Make it a habit, or they’ll disappear fast." Users need a reason to stay — not later, but right now. Product teams have to design for habit from day one. If an app doesn’t deliver immediate value or fit naturally into a user’s daily routine, people will simply move on. As the saying goes: "Make it a habit, or they’ll disappear fast." Users need a reason to stay — not later, but right now. Users need a reason to stay — not later, but right now. Product teams have to design for habit from day one. If an app doesn’t deliver immediate value or fit naturally into a user’s daily routine, people will simply move on. As the saying goes: "Make it a habit, or they’ll disappear fast." First-touch experience has become critical First-touch experience has become critical No more “cold starts.” Apps are expected to deliver engagement immediately — without friction, tutorials, or delayed benefits. Each interaction needs to feel like a forward motion. The time users are willing to spend before they feel progress is near zero. Apps are expected to deliver engagement immediately — without friction, tutorials, or delayed benefits. Today, apps have to prove more than just user demand—they need to show how they’ll generate revenue early on. It’s no longer enough to simply grow a user base — you need to know how you’ll make money from day one. Today, apps have to prove more than just user demand—they need to show how they’ll generate revenue early on. It’s no longer enough to simply grow a user base — you need to know how you’ll make money from day one. Products with no clear path to monetization aren’t just risky — they’re dead on arrival. 3. What’s Working in 2025 — and What Will Keep Working 3. What’s Working in 2025 — and What Will Keep Working While some product bets are being wiped out, others quietly prove their value — not through hype but through everyday use. Successful apps make complex things simple, seamlessly blend into daily life, and keep the technology in the background. Successful apps make complex things simple, seamlessly blend into daily life, and keep the technology in the background. Financial services have gone mainstream Financial services have gone mainstream Uberization unlocked experiences once reserved for the elite. You no longer need to buy a helicopter — you can rent one. A ride in a Maybach? Just another tap in your app, not a luxury fantasy. What used to be a privilege has become a commodity. But that phase is already behind us. The “rent, don’t own” model has quietly evolved into the next stage: the democratization of complex financial tools. What once required a bank account, paperwork, and approval processes is now part of the product flow. Apps let you invest $3 in stocks or split your grocery bill into four payments — no forms, friction, or feeling that you’re “doing finance.” It all happens quietly in the background, without your effort. The “rent, don’t own” model has quietly evolved into the next stage: the democratization of complex financial tools. Subscriptions are the default for entertainment Subscriptions are the default for entertainment The less time people spend working, the more they spend consuming. Subscription is now a core model, aligned with the growing habit of continuous entertainment.People are willing to pay if a product fills free time without friction. Despite the flood of streaming content, TikTok, and YouTube, user time is still expanding — and the services that occupy it will continue to grow steadily. People are willing to pay if a product fills free time without friction. AI works best when it disappears The most useful AI isn’t in chatbots — it’s in small, built-in tools that solve problems before you notice them. Removing unwanted objects from photos, searching images by content, and auto-cleaning videos — these features save time without demanding attention. This kind of AI doesn’t draw the eye or sell itself as a feature — it just works. Not a showcase but a silent engine that makes the product smarter, faster, and cleaner. Apps ultimately become tools for saving time and taking over routine tasks. The most useful AI isn’t in chatbots — it’s in small, built-in tools that solve problems before you notice them. Do less, but do better. In an environment of shrinking attention and rising expectations, it’s not the feature-packed apps that win — it’s the ones that deliver immediate and specific value. Users don’t allow time for warm-ups — the benefit needs to be clear from the start, and the experience must feel natural and effortless. This way, you can win.

What Consumers Expect from Apps in 2025 and How to Deliver

Let’s face it: we’ve all been riding the wave of combining AI and blockchain like it’s the perfect futuristic cocktail. It sounds cool, it feels powerful, and on paper, it solves all our problems. AI gives us brains. Blockchain gives us trust. Put them together and bam! — you’ve got a decentralized, intelligent system that nobody can corrupt and everyone can believe in. Until it breaks. That’s when things get weird. That’s when the "who’s responsible?" question turns into a room full of finger-pointing. So let’s talk about what happens when AI fails on the blockchain. Not just a server error, not just a misclassified image or a chatbot that gave a dumb answer — but when a supposedly smart system, running on supposedly unbreakable rails, messes up. Big time. on And more importantly: who takes the fall? Why We Thought AI + Blockchain Was a Match Made in Heaven Before we get into the failings, let’s rewind for a second. The dream of mixing AI with blockchain comes from a place of good intentions. AI can process and act on huge amounts of data. Blockchain brings transparency and decentralization. In theory, you get systems that can think and adapt without needing a central authority. You get smart contracts that can do more than just wait for conditions to be met — they can analyze data, make predictions, and make decisions. analyze In practice? It’s a bit of a mess. A Simple Example: The Lending dApp from Hell Imagine a decentralized finance (DeFi) app that uses AI to score borrowers. It doesn’t just check your wallet history — it analyzes your trading behavior, past protocol interactions, and maybe even your social media presence (yes, it happens). It then gives you a credit score and lets you borrow accordingly. Now imagine the AI flags someone as a high-risk borrower because they once bought a meme coin that tanked. That person gets denied a loan, even though they’ve never defaulted on anything. Who’s at fault here? The developer? The data? The AI model? The blockchain? Who’s at fault here? The developer? The data? The AI model? The blockchain? Who’s at fault here? The developer? The data? The AI model? The blockchain? The smart contract executed the logic correctly. But the logic was flawed. Or the data was flawed. Or the training was flawed. Or maybe all of the above. The Blame Game: Who’s Actually Responsible? When things go sideways, everyone ducks. The Developer says, "We just integrated the AI service. It’s not ours."


The AI Provider says, "The model was trained on the data provided. Not our fault if the data was bad."


The User says, "I had no idea this thing was even deciding my loan eligibility."


The Blockchain says nothing — because it’s code. Immutable. Unfeeling. Unchanging. The Developer says, "We just integrated the AI service. It’s not ours." The Developer says, "We just integrated the AI service. It’s not ours." The Developer The AI Provider says, "The model was trained on the data provided. Not our fault if the data was bad." The AI Provider says, "The model was trained on the data provided. Not our fault if the data was bad." The AI Provider The User says, "I had no idea this thing was even deciding my loan eligibility." The User says, "I had no idea this thing was even deciding my loan eligibility." The User The Blockchain says nothing — because it’s code. Immutable. Unfeeling. Unchanging. The Blockchain says nothing — because it’s code. Immutable. Unfeeling. Unchanging. The Blockchain In most traditional systems, responsibility flows upward. If the app messes up, the company is accountable. They can patch it, apologize, issue refunds, whatever. But in the decentralized world, we’ve designed systems that intentionally remove central points of control. That’s great when you want to avoid censorship or single points of failure. But it also means there’s no one person, team, or entity to pin the failure on. intentionally That’s a feature — and a bug. According to a 2023 survey by PwC, only 27% of consumers say they fully trust AI systems, especially when used in financial decision-making. survey by PwC And trust drops even lower when you tell them the AI is also embedded in a decentralized system with "no customer support." This highlights the broader concern around AI statistics — they give us a snapshot of sentiment or performance, but not always the context or accountability behind those numbers. AI statistics So What’s Really Failing? It’s tempting to blame the AI or the blockchain, but those are just tools. What fails is usually the system design — how we decide to glue everything together. If you train an AI on garbage, it will make garbage decisions. If you deploy that AI on-chain via a smart contract, now you’ve made those garbage decisions permanent. system design The issue is that AI and blockchain operate under very different philosophies: AI is probabilistic. It guesses, estimates, learns, and improves.
Blockchain is deterministic. It executes exactly what it’s told, every time. AI is probabilistic. It guesses, estimates, learns, and improves. Blockchain is deterministic. It executes exactly what it’s told, every time. Mixing the two isn’t impossible. But it requires a level of design maturity that we often skip in the rush to ship the next hyped-up dApp. Legal and Ethical Minefields Here’s where things get spicy. In some countries, if an AI system makes a discriminatory decision — say, denies someone access based on race or gender — the organization using that system can be held liable. But what happens when the system isn’t run by a company, but by a DAO? Who do you sue?
Who pays damages?
Can you even reverse the decision if it’s on-chain? Who do you sue? Who pays damages? Can you even reverse the decision if it’s on-chain? reverse We’ve created systems with real-world impact but without real-world accountability.
That’s not innovation. That’s a lawsuit waiting to happen. We’ve created systems with real-world impact but without real-world accountability. We’ve created systems with real-world impact but without real-world accountability. That’s not innovation. That’s a lawsuit waiting to happen. That’s not innovation. That’s a lawsuit waiting to happen. So What Can We Do About It? Let’s not get too doomy. There are ways to make this combo work better. are 1. Transparent Models Use open-source AI models or, at the very least, offer transparency into how decisions are made. If someone’s loan or vote depends on a model’s output, they should know why. 2. Human-in-the-Loop Systems Just because a smart contract can execute an AI decision doesn’t mean it should. Allow human review — especially for high-stakes actions. 3. Model Versioning + Rollbacks If an AI model goes rogue or gets updated with better logic, we need mechanisms to update or roll back smart contracts accordingly. That’s tricky, but essential. 4. Off-Chain Computation, On-Chain Validation Do the heavy AI lifting off-chain, but feed the results into smart contracts via oracles. That way, you keep the blockchain clean and verifiable, without hardcoding the AI’s entire brain. In the End, Blame Is a Design Problem When AI fails on the blockchain, the question isn’t just "who do we blame?" It’s "how did we let this happen without knowing who’s responsible?" We need better system design, clearer accountability models, and yes — some real talk about the limits of decentralization when real humans are affected. Because in the end, it’s not about pointing fingers. It’s about building systems that don’t fail people in the first place.

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