Hi, friends! Welcome to Installer No. 87, your guide to the best and Verge-iest stuff in the world. (If you’re new here, welcome, happy It’s Officially Too Hot Now Week, and also you can read all the old editions at the Installer homepage.) This week, I’ve been reading about Sabrina Carpenter and Khaby Lame and intimacy coordinators, finally making a dent in Barbarians at the Gate, watching all the Ben Schwartz and Friends I can find on YouTube, planning my days with the new Finalist beta, recklessly installing all the Apple developer betas after WWDC, thoroughly enjoying Dakota Johnson’s current press tour, and trying to clear all my inboxes before I go on parental leave. It’s… going.I also have for you a much-awaited new browser, a surprise update to a great photo editor, a neat trailer for a meh-looking movie, a classic Steve Jobs speech, and much more. Slightly shorter issue this week, sorry; there’s just a lot going on, but I didn’t want to leave y’all hanging entirely. Oh, and: we’ll be off next week, for Juneteenth, vacation, and general summer chaos reasons. We’ll be back in full force after that, though! Let’s get into it.(As always, the best part of Installer is your ideas and tips. What do you want to know more about? What awesome tricks do you know that everyone else should? What app should everyone be using? Tell me everything: installer@theverge.com. And if you know someone else who might enjoy Installer, forward it to them and tell them to subscribe here.)The DropDia. I know there are a lot of Arc fans here in the Installerverse, and I know you, like me, will have a lot of feelings about the company’s new and extremely AI-focused browser. Personally, I don’t see leaving Arc anytime soon, but there are some really fascinating ideas (and nice design touches) in Dia already. Snapseed 3.0. I completely forgot Snapseed even existed, and now here’s a really nice update with a bunch of new editing tools and a nice new redesign! As straightforward photo editors go, this is one of the better ones. The new version is only on iOS right now, but I assume it’s heading to Android shortly.“I Tried To Make Something In America.” I was first turned onto the story of the Smarter Scrubber by a great Search Engine episode, and this is a great companion to the story about what it really takes to bring manufacturing back to the US. And why it’s hard to justify.. That link, and the trailer, will only do anything for you if you have a newer iPhone. But even if you don’t care about the movie, the trailer — which actually buzzes in sync with the car’s rumbles and revs — is just really, really cool. Android 16. You can’t get the cool, colorful new look just yet or the desktop mode I am extremely excited about — there’s a lot of good stuff in Android 16 but most of it is coming later. Still, Live Updates look good, and there’s some helpful accessibility stuff, as well.The Infinite Machine Olto. I am such a sucker for any kind of futuristic-looking electric scooter, and this one really hits the sweet spot. Part moped, part e-bike, all Blade Runner vibes. If it wasn’t $3,500, then I would’ve probably ordered one already.The Fujifilm X-E5. I kept wondering why Fujifilm didn’t just make, like, a hundred different great-looking cameras at every imaginable price because everyone wants a camera this cool. Well, here we are! It’s a spin on the X100VI but with interchangeable lenses and a few power-user features. All my photographer friends are going to want this.Call Her Alex. I confess I’m no Call Her Daddy diehard, but I found this two-part doc on Alex Cooper really interesting. Cooper’s story is all about understanding people, the internet, and what it means to feel connected now. It’s all very low-stakes and somehow also existential? It’s only two parts, you should watch it.“Steve Jobs - 2005 Stanford Commencement Address.” For the 20th anniversary of Jobs’ famous (and genuinely fabulous) speech, the Steve Jobs Archive put together a big package of stories, notes, and other materials around the speech. Plus, a newly high-def version of the video. This one’s always worth the 15 minutes.Dune: Awakening. Dune has ascended to the rare territory of “I will check out anything from this franchise, ever, no questions asked.” This game is big on open-world survival and ornithopters, too, so it’s even more my kind of thing. And it’s apparently punishingly difficult in spots.CrowdsourcedHere’s what the Installer community is into this week. I want to know what you’re into right now as well! Email installer@theverge.com or message me on Signal — @davidpierce.11 — with your recommendations for anything and everything, and we’ll feature some of our favorites here every week. For even more great recommendations, check out the replies to this post on Threads and this post on Bluesky.“I had tried the paper planner in the leather Paper Republic journal but since have moved onto the Remarkable Paper Pro color e-ink device which takes everything you like about paper but makes it editable and color coded. Combine this with a Remarkable planner in PDF format off of Etsy and you are golden.” — Jason“I started reading a manga series from content creator Cory Kenshin called Monsters We Make. So far, I love it. Already preordered Vol. 2.” — Rob“I recently went down the third party controller rabbit hole after my trusty adapted Xbox One controller finally kicked the bucket, and I wanted something I could use across my PC, phone, handheld, Switch, etc. I’ve been playing with the GameSir Cyclone 2 for a few weeks, and it feels really deluxe. The thumbsticks are impossibly smooth and accurate thanks to its TMR joysticks. The face buttons took a second for my brain to adjust to; the short travel distance initially registered as mushy, but once I stopped trying to pound the buttons like I was at the arcade, I found the subtle mechanical click super satisfying.” — Sam“The Apple TV Plus miniseries Long Way Home. It’s Ewan McGregor and Charley Boorman’s fourth Long Way series. This time they are touring some European countries on vintage bikes that they fixed, and it’s such a light-hearted show from two really down to earth humans. Connecting with other people in different cultures and seeing their journey is such a treat!” — Esmael“Podcast recommendation: Devil and the Deep Blue Sea by Christianity Today. A deep dive into the Satanic Panic of the 80’s and 90’s.” — Drew“Splatoon 3 (the free Switch 2 update) and the new How to Train Your Dragon.” — Aaron“I can’t put Mario Kart World down. When I get tired of the intense Knockout Tour mode I go to Free Roam and try to knock out P-Switch challenges, some of which are really tough! I’m obsessed.” — Dave“Fable, a cool app for finding books with virtual book clubs. It’s the closest to a more cozy online bookstore with more honest reviews. I just wish you could click on the author’s name to see their other books.” — Astrid“This is the Summer Games Fest week (formerly E3, RIP) and there are a TON of game demos to try out on Steam. One that has caught my attention / play time the most is Wildgate. It’s a team based spaceship shooter where ship crews battle and try to escape with a powerful artifact.” — Sean“Battlefront 2 is back for some reason. Still looks great.” — IanSigning offI have long been fascinated by weather forecasting. I recommend Andrew Blum’s book, The Weather Machine, to people all the time, as a way to understand both how we learned to predict the weather and why it’s a literally culture-changing thing to be able to do so. And if you want to make yourself so, so angry, there’s a whole chunk of Michael Lewis’s book, The Fifth Risk, about how a bunch of companies managed to basically privatize forecasts… based on government data. The weather is a huge business, an extremely powerful political force, and even more important to our way of life than we realize. And we’re really good at predicting the weather!I’ve also been hearing for years that weather forecasting is a perfect use for AI. It’s all about vast quantities of historical data, tiny fluctuations in readings, and finding patterns that often don’t want to be found. So, of course, as soon as I read my colleague Justine Calma’s story about a new Google project called Weather Lab, I spent the next hour poking through the data to see how well DeepMind managed to predict and track recent storms. It’s deeply wonky stuff, but it’s cool to see Big Tech trying to figure out Mother Nature — and almost getting it right. Almost.See you next week!See More:

In-app bidding has automated most waterfall optimization, yet developers still manage multiple hybrid waterfalls, each with dozens of manual instances. Naturally, this can be timely and overwhelming to maintain, keeping you from optimizing to perfection and focusing on other opportunities to boost revenue.Rather than analyzing each individual network and checking if instances are available at each price point, breaking down your waterfall into different CPM ranges allows you to visualize the waterfall and easily identify the gaps.Here are some tips on how to use CPM buckets to better optimize your waterfall’s performance.What are CPM buckets?CPM buckets show you exactly how much revenue and how many impressions you’re getting from each CPM price range, giving you a more granular idea of how different networks are competing in the waterfall. CPM buckets are a feature of real time pivot reports, available on ironSource LevelPlay.Identifying and closing the gapsTypically in a waterfall, you can only see each ad network’s average CPM. But this keeps you from seeing ad network distribution across all price points and understanding exactly where ad networks are bidding. Bottom line - you don’t know where in the waterfall you should add a new instance.By separating CPM into buckets, (for example, seeing all the ad networks generating a CPM of $10-$20) you understand exactly which networks are driving impressions and revenue and which CPMs aren’t being filledNow how do you do it? As a LevelPlay client, simply use ironSource’s real time pivot reports - choose the CPM bucket filter option and sort by “average bid price.” From here, you’ll see how your revenue spreads out among CPM ranges and you’ll start to notice gaps in your bar graph. Every gap in revenue - where revenue is much lower than the neighboring CPM group - indicates an opportunity to optimize your monetization strategy. The buckets can range from small increments like $1 to larger increments like $10, so it’s important to compare CPM buckets of the same incremental value.Pro tip: To best set up your waterfall, create one tab with the general waterfall (filter app, OS, Ad unit, geo/geos from a specific group) and make sure to look at Revenue and eCPM in the “measures” dropdown. In the “show” section, choose CPM buckets and sort by average bid price. From here, you can mark down any gaps.But where do these gaps come from? Gaps in revenue are often due to friction in the waterfall, like not enough instances, instances that aren’t working, or a waterfall setup mistake. But gaps can also be adjusted and fixed.Once you’ve found a gap, you can look at the CPM buckets around it to better understand the context. Let’s say you see a strong instance generating significant revenue in the CPM bucket right below it, in the $70-80 group. This instance from this specific ad network has a lot of potential, so it’s worth trying to push it to a higher CPM bucket.In fact, when you look at higher CPM buckets, you don’t see this ad network anywhere else in the waterfall - what a missed opportunity! Try adding another instance of this network higher up in the waterfall. If you’re profiting well with a $70-80 CPM, imagine how much more revenue you could bring at a $150 CPM.Pro tip: Focusing on higher areas in the waterfall makes a larger financial impact, leading to bigger increases in ARPDAU.Let’s say you decide to add 5 instances of that network to higher CPM buckets. You can use LevelPlay’s quick A/B test to understand if this adjustment boosts your revenue - not just for this gap, but for any and all that you find. Simply compare your existing waterfall against the new waterfall with these 5 higher instances - then implement the one that drives the highest instances.Božo Janković, Head of Ad Monetization at GameBiz Consulting, uses CPM buckets "to understand at which CPMs the bidding networks are filling. From there, I can pinpoint exactly where in the waterfall to add more traditional instances - which creates more competition, especially for the bidding networks, and creates an opportunity for revenue growth."Finding new insightsYou can dig even deeper into your data by filtering by ad source. Before CPM buckets, you were limited to seeing an average eCPM for each bidding network. Maybe you knew that one ad source had an average CPM of $50, but the distribution of impression across the waterfall was a black box. Now, we know exactly which CPMs the bidders are filling. “I find ironSource CPM buckets feature very insightful and and use it daily. It’s an easy way to identify opportunities to optimize the waterfall and earn even more revenue." -Božo Janković, Head of Ad Monetization at GameBiz ConsultingUnderstanding your CPM distribution empowers you to not only identify your revenue sources, but also to promote revenue growth. Armed with the knowledge of which buckets some of their stronger bidding networking are performing in, some publishers actively add instances from traditional networks above those ranges. This creates better competition and also helps drive up the bids from the biddersThere’s no need for deep analysis - once you see the gaps, you can quickly understand who’s performing in the lower and higher buckets, and see exactly what’s missing. This way, you won’t miss out on any lost revenue.Learn more about CPM buckets, available exclusively to ironSource LevelPlay here.

India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from $55.6 billion in 2010-11 to $118.7 billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach $160 billion in 2029. This means there is an increasing market for digitalized banking experiences for non-resident Indians(NRIs), ranging from remittances to investing in different assets back home. Aspora (formerly Vance) is trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances. “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said. In the last year, the company has grown the volume of remittances by 6x — from $400 million to $2 billion in yearly volume processed. With this growth, the company has attracted a lot of investor interest. It raised $35 million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at $150 million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money. The company announced today it has raised $50 million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at $500 million. The startup has raised over $99 million in funding to date. Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | July 15 REGISTER NOW After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates. The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year. Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs. “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch. He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home. In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this. Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance. Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet. Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge. “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.”

Color Lim is a puzzle platformer where you need to use your slimy color-switching ability to solve puzzles and save a small village. This is a fantastic, smooth puzzle platform that has you playing a colorless slime called Lim who is looking to find their true color. At first, you aren’t really colorless – you are blue, but this isn’t your true and only color, as you can change and adapt when needed. Why is that important? Because in this world, color determines everything. Being able to absorb and transform to different colors allows you to go through specific platforms or to spray slime and then slide into the walls of the world, finding a new way to navigate around. There isn’t a limit on your own slime, so you are able to blast out some goop and then inject yourself into the walls, moving quickly around levels and solving puzzles. There are enemies that are looking to harm you, too, but these can be easily avoided most of the time. Color Lim doesn’t just have endless platforms to enjoy, but also has a little story showcased through cute characters who seem quite helpful! In this world, something bad has happened, and now there is a small village that is rebuilding, needing your help. As someone with such a great ability, you can find yourself using your colors to help them. I got to play a short demo of Color Lim at Pocket Gamer Connects Barcelona where I really liked how sleek and fast the movement felt for the game. The cute characters and little hints of a story captivated me, especially when exploring the town. However, I did feel that sometimes it wasn’t obvious what to do next or where to go – especially in the town where the platforms were hard to determine against what was just the background. Hopefully, these minor issues will be fixed before release. Color Lim is currently in development, but in the meantime, you can add it to your Steam Wishlist. About The Author

Development and Testing  Rate this post Sorting is a basic task in programming. It arranges data in order. There are many sorting algorithms. Selection Sort is one of the simplest sorting methods. It is easy to understand and code. But it is not the fastest. In this guide, we will explain the Selection Sort Time Complexity. We will cover best, worst, and average cases. What Is Selection Sort? Selection Sort works by selecting the smallest element from the list. It places it in the correct position. It repeats this process for all elements. One by one, it moves the smallest values to the front. Let’s see an example: Input: [5, 3, 8, 2]Step 1: Smallest is 2 → swap with 5 → [2, 3, 8, 5]Step 2: Smallest in remaining is 3 → already correctStep 3: Smallest in remaining is 5 → swap with 8 → [2, 3, 5, 8] Now the list is sorted.How Selection Sort Works Selection Sort uses two loops. The outer loop moves one index at a time. The inner loop finds the smallest element. After each pass, the smallest value is moved to the front. The position is fixed. Selection Sort does not care if the list is sorted or not. It always does the same steps. Selection Sort Algorithm Here is the basic algorithm: Start from the first element Find the smallest in the rest of the list Swap it with the current element Repeat for each element This repeats until all elements are sorted. Selection Sort Code (Java Example) javaCopyEditpublic class SelectionSort { public static void sort(int[] arr) { int n = arr.length; for (int i = 0; i < n - 1; i++) { int min = i; for (int j = i + 1; j < n; j++) { if (arr[j] < arr[min]) { min = j; } } int temp = arr[min]; arr[min] = arr[i]; arr[i] = temp; } } } This code uses two loops. The outer loop runs n-1 times. The inner loop finds the minimum. Selection Sort Time Complexity Now let’s understand the main topic. Let’s analyze Selection Sort Time Complexity in three cases. 1. Best Case Even if the array is already sorted, Selection Sort checks all elements. It keeps comparing and swapping. Time Complexity: O(n²) Reason: Inner loop runs fully, regardless of the order Example Input: [1, 2, 3, 4, 5] Even here, every comparison still happens. Only fewer swaps occur, but comparisons remain the same. 2. Worst Case This happens when the array is in reverse order. But Selection Sort does not optimize for this. Time Complexity: O(n²) Reason: Still needs full comparisons Example Input: [5, 4, 3, 2, 1] Even in reverse, the steps are the same. It compares and finds the smallest element every time. 3. Average Case This is when elements are randomly placed. It is the most common scenario in real-world problems. Time Complexity: O(n²) Reason: Still compares each element in the inner loop Example Input: [3, 1, 4, 2, 5] Selection Sort does not change behavior based on input order. So the complexity remains the same. Why Is It Always O(n²)? Selection Sort compares all pairs of elements. The number of comparisons does not change. Total comparisons = n × (n – 1) / 2 That’s why the time complexity is always O(n²).It does not reduce steps in any case. It does not take advantage of sorted elements. Space Complexity Selection Sort does not need extra space. It sorts in place. Space Complexity: O(1) Only a few variables are used No extra arrays or memory needed This is one good point of the Selection Sort. Comparison with Other Algorithms Let’s compare Selection Sort with other basic sorts: AlgorithmBest CaseAverage CaseWorst CaseSpaceSelection SortO(n²)O(n²)O(n²)O(1)Bubble SortO(n)O(n²)O(n²)O(1)Insertion SortO(n)O(n²)O(n²)O(1)Merge SortO(n log n)O(n log n)O(n log n)O(n)Quick SortO(n log n)O(n log n)O(n²)O(log n) As you see, Selection Sort is slower than Merge Sort and Quick Sort. Advantages of Selection Sort Very simple and easy to understand Works well with small datasets Needs very little memory Good for learning purposes Disadvantages of Selection Sort Slow on large datasets Always takes the same time, even if sorted Not efficient for real-world use When to Use Selection Sort Use Selection Sort when: You are working with a very small dataset You want to teach or learn sorting logic You want stable, low-memory sorting Avoid it for: Large datasets Performance-sensitive programs Conclusion Selection Sort Time Complexity is simple to understand. But it is not efficient for big problems. It always takes O(n²) time, no matter the case. That is the same for best, worst, and average inputs. Still, it is useful in some cases. It’s great for learning sorting basics. It uses very little memory. If you’re working with small arrays, Selection Sort is fine. For large data, use better algorithms. Understanding its time complexity helps you choose the right algorithm. Always pick the tool that fits your task. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Set in the glitzy world of Hollywood in the late '70s, Confidential Killings have you investigate a series of gruesome murders that seem connected. There are rumours about a mysterious cult behind them...  Explore the crime scenes, use your detective skills to deduce what's going on! Wishlist on Steam! https://store.steampowered.com/app/2797960/Confidential_KillingsJoin our discord: https://discord.gg/xwFXgbb2xfMore informationDownloadDevelopment logDemo out! 10 days agoCommentsLog in with itch.io to leave a comment.I LOVE it! The art, the gameplay, the story, it's so much fun!ReplyFirst I was like "nah, so you just want to check if I have read everything, or what?" but later it made sense with the twists and hunting for the word you already know but need to find elsewhere.ReplyPicto Games21 hours ago(+2)the cursor is blinking it is very disturbing and the game very goodReplyBRANE15 hours ago (1 edit) (+1)I recommend trying the desktop builds if you'd like to play without this issue. Or putting more fire on this PR of Godot:https://github.com/godotengine/godot/pull/103304ReplybeautifulDegen1 day ago(+1)Nice gameReplylovedovey6661 day ago(+1)I love this game! i like the detective games and this is perfect :3ReplyThis is a great game! The old style detective game ambientation is superb, and the art sublime. The misteries were pretty entertaining and interesting to keep you going as you think what truly happened!ReplyI had to take notes.... my memory aint great lol really enjoyed it ReplySebbog1 day agoThis game is kind of like the detective games the Case of the Golden Idol and its sequel, the Rise of the Golden Idol, from 2022 and 2024 respectively. It's not just bullshit. It has a coherent story. If you haven't heard of the Golden Idol games, then it's basically a game where you investigate mysterious deaths and fill in the blanks of the story. You can navigate from multiple different scenes and click on people and objects to gather important clues. I think it was a good game. I like that it's similar to the Golden Idol games. I also liked that you could see the exact amount of wrong slots when it's less than or equal to 2. It said either two incorrect or one incorrect. This isn't how it works in the Golden Idol games. Although, this might make the game too easy. I am not sure tho. I also streamed this game on YouTube: ReplyMV_Zarch3 days agoI’m so happy I found this game. Amazing! The mysteries are just so good and well done. The art is beautiful and really sets up the atmosphere well. I am really interested to see the full game.Replyreveoncelink5 days agoIt was amazing!! Perfect gameplay and so many clues to connect the dots. Amazing.ReplyHey, this is a great game except for the flickering of the cursor. It’s the same for your other games (We Suspect Foul Play afaik). Hope this gets fixed! (I’m on chrome) ReplyBRANE6 days agoheya! For the flickering issue I'm not really sure what's the problem, but having a screen recording of it could help.Other than that we're not that focused on fixing the web build as it's going to be a PC game - so I suggest trying the Windows buildReplyReally fun! Wishing you guys lots of luck!ReplyThank you!Replykcouchpotato8 days ago(+1)This game is so awesome!! I've wishlisted it on steam.ReplyBRANE8 days agoThank you!Reply

Artificial intelligence is already rewriting the rules of wireless and telecom — powering predictive maintenance, streamlining network operations, and enabling more innovative services. As AI scales, the disruption will be faster, deeper, and harder to reverse than any prior shift in the industry. Compared to the sweeping changes AI is set to unleash, past telecom innovations look incremental. AI is redefining how networks operate, services are delivered, and data is secured — across every device and digital touchpoint. AI Is Reshaping Wireless Networks Already Artificial intelligence is already transforming wireless through smarter private networks, fixed wireless access (FWA), and intelligent automation across the stack. AI detects and resolves network issues before they impact service, improving uptime and customer satisfaction. It’s also opening the door to entirely new revenue streams and business models. Each wireless generation brought new capabilities. AI, however, marks a more profound shift — networks that think, respond, and evolve in real time. AI Acceleration Will Outpace Past Tech Shifts Many may underestimate the speed and magnitude of AI-driven change. The shift from traditional voice and data systems to AI-driven network intelligence is already underway. Although predictions abound, the true scope remains unclear. It’s tempting to assume we understand AI’s trajectory, but history suggests otherwise. Today, AI is already automating maintenance and optimizing performance without user disruption. The technologies we’ll rely on in the near future may still be on the drawing board. Few predicted that smartphones would emerge from analog beginnings—a reminder of how quickly foundational technologies can be reimagined. History shows that disruptive technologies rarely follow predictable paths — and AI is no exception. It’s already upending business models across industries. Technological shifts bring both new opportunities and complex trade-offs. AI Disruption Will Move Faster Than Ever The same cycle of reinvention is happening now — but with AI, it’s moving at unprecedented speed. Despite all the discussion, many still treat AI as a future concern — yet the shift is already well underway. As with every major technological leap, there will be gains and losses. The AI transition brings clear trade-offs: efficiency and innovation on one side, job displacement, and privacy erosion on the other. Unlike past tech waves that unfolded over decades, the AI shift will reshape industries in just a few years — and that change wave will only continue to move forward. AI Will Reshape All Sectors and Companies This shift will unfold faster than most organizations or individuals are prepared to handle. Today’s industries will likely look very different tomorrow. Entirely new sectors will emerge as legacy models become obsolete — redefining market leadership across industries. Telecom’s past holds a clear warning: market dominance can vanish quickly when companies ignore disruption. Eventually, the Baby Bells moved into long-distance service, while AT&T remained barred from selling local access — undermining its advantage. As the market shifted and competitors gained ground, AT&T lost its dominance and became vulnerable enough that SBC, a former regional Bell, acquired it and took on its name. It’s a case study of how incumbents fall when they fail to adapt — precisely the kind of pressure AI is now exerting across industries. SBC’s acquisition of AT&T flipped the power dynamic — proof that size doesn’t protect against disruption. The once-crowded telecom field has consolidated into just a few dominant players — each facing new threats from AI-native challengers. Legacy telecom models are being steadily displaced by faster, more flexible wireless, broadband, and streaming alternatives. No Industry Is Immune From AI Disruption AI will accelerate the next wave of industrial evolution — bringing innovations and consequences we’re only beginning to grasp. New winners will emerge as past leaders struggle to hang on — a shift that will also reshape the investment landscape. Startups leveraging AI will likely redefine leadership in sectors where incumbents have grown complacent. Nvidia’s rise is part of a broader trend: the next market leaders will emerge wherever AI creates a clear competitive advantage — whether in chips, code, or entirely new markets. The AI-driven future is arriving faster than most organizations are ready for. Adapting to this accelerating wave of change is no longer optional — it’s essential. Companies that act decisively today will define the winners of tomorrow.

The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy problems and slower, analytical thinking for complex ones. While LRMs mimic slow, logical reasoning, they generate significantly longer outputs, thereby increasing computational cost. Current methods for reducing reasoning steps lack flexibility, limiting models to a single fixed reasoning style. There is a growing need for adaptive reasoning that adjusts effort according to task difficulty.  Limitations of Existing Training-Based and Training-Free Approaches Recent research on improving reasoning efficiency in LRMs can be categorized into two main areas: training-based and training-free methods. Training strategies often use reinforcement learning or fine-tuning to limit token usage or adjust reasoning depth, but they tend to follow fixed patterns without flexibility. Training-free approaches utilize prompt engineering or pattern detection to shorten outputs during inference; however, they also lack adaptability. More recent work focuses on variable-length reasoning, where models adjust reasoning depth based on task complexity. Others study “overthinking,” where models over-reason unnecessarily. However, few methods enable dynamic switching between quick and thorough reasoning—something this paper addresses directly.  Introducing OThink-R1: Dynamic Fast/Slow Reasoning Framework Researchers from Zhejiang University and OPPO have developed OThink-R1, a new approach that enables LRMs to switch between fast and slow thinking smartly, much like humans do. By analyzing reasoning patterns, they identified which steps are essential and which are redundant. With help from another model acting as a judge, they trained LRMs to adapt their reasoning style based on task complexity. Their method reduces unnecessary reasoning by over 23% without losing accuracy. Using a loss function and fine-tuned datasets, OThink-R1 outperforms previous models in both efficiency and performance on various math and question-answering tasks.  System Architecture: Reasoning Pruning and Dual-Reference Optimization The OThink-R1 framework helps LRMs dynamically switch between fast and slow thinking. First, it identifies when LRMs include unnecessary reasoning, like overexplaining or double-checking, versus when detailed steps are truly essential. Using this, it builds a curated training dataset by pruning redundant reasoning and retaining valuable logic. Then, during fine-tuning, a special loss function balances both reasoning styles. This dual-reference loss compares the model’s outputs with both fast and slow thinking variants, encouraging flexibility. As a result, OThink-R1 can adaptively choose the most efficient reasoning path for each problem while preserving accuracy and logical depth.  Empirical Evaluation and Comparative Performance The OThink-R1 model was tested on simpler QA and math tasks to evaluate its ability to switch between fast and slow reasoning. Using datasets like OpenBookQA, CommonsenseQA, ASDIV, and GSM8K, the model demonstrated strong performance, generating fewer tokens while maintaining or improving accuracy. Compared to baselines such as NoThinking and DualFormer, OThink-R1 demonstrated a better balance between efficiency and effectiveness. Ablation studies confirmed the importance of pruning, KL constraints, and LLM-Judge in achieving optimal results. A case study illustrated that unnecessary reasoning can lead to overthinking and reduced accuracy, highlighting OThink-R1’s strength in adaptive reasoning.  Conclusion: Towards Scalable and Efficient Hybrid Reasoning Systems In conclusion, OThink-R1 is a large reasoning model that adaptively switches between fast and slow thinking modes to improve both efficiency and performance. It addresses the issue of unnecessarily complex reasoning in large models by analyzing and classifying reasoning steps as either essential or redundant. By pruning the redundant ones while maintaining logical accuracy, OThink-R1 reduces unnecessary computation. It also introduces a dual-reference KL-divergence loss to strengthen hybrid reasoning. Tested on math and QA tasks, it cuts down reasoning redundancy by 23% without sacrificing accuracy, showing promise for building more adaptive, scalable, and efficient AI reasoning systems in the future.  Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. 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