May 13, 20256 min readDeep Math from String Theory Appears in Clashing Black HolesResearchers have shown that abstract mathematical functions from the frontiers of theoretical physics have a real-world use in modeling gravitational wavesBy Ramin Skibba edited by Lee BillingsAn illustration of two black holes about to merge and emitting copious gravitational waves. Chris Henze/NASA/SPL/Getty ImagesA decade ago astrophysicists at the Laser Interferometer Gravitational-Wave Observatory (LIGO), operated by the California Institute of Technology and the Massachusetts Institute of Technology, managed to detect subtle ripples in spacetime called gravitational waves, released by a pair of black holes spiraling into each other, for the first time. That impressive discovery—which earned the 2017 Nobel Prize in Physics—has since become commonplace, with researchers regularly detecting gravitational waves from myriad far-distant celestial sources.And as the numbers of gravitational-wave observations have increased, physicists’ careful modeling is revealing new details about their mysterious origins. Some of the most intriguing gravitational-wave events, it turns out, could arise not from catastrophic collisions but rather from near misses. Furthermore, these cosmic close calls might be best understood using concepts derived from string theory—a notional theory of everything that posits that all of nature is fundamentally composed of countless, wriggling subatomic strings. This arguably marks the first linkage to date between a core mathematical aspect of the arcane theory and real-world astrophysics.At least, that’s the conclusion of an international team of researchers that applied geometric structures inspired by particle physics and string theory to the behavior of black holes when the colossal objects closely pass and deflect each other. Such interactions between black holes or neutron stars (compact remnants of exploded massive stars) can be studied through the deflection angle, the energy released through the near miss and the momentum of the objects’ recoil—all of which may be discerned in gravitational waves. The team’s results were published in the journal Nature on Wednesday.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.Black Holes as ParticlesIn their study, the researchers used an obscure class of abstract mathematical functions to solve the formidable equations involved in determining the radiated energy from a near miss. “You need these new functions, which, in math and mathematical physics, have been studied intensively but, to date, have not appeared in any real physical observable. That’s what makes it quite interesting,” says Jan Plefka, a theoretical physicist at the Humboldt University of Berlin and a co-author of the new study. Those obscure functions, known as six-dimensional Calabi-Yau manifolds, had never been shown as directly relevant to descriptions of real astrophysical phenomena before.In the years since LIGO’s initial detection, two additional major gravitational-wave observatories, Europe’s Virgo and Japan’s Kamioka Gravitational-Wave Detector (KAGRA), have come online. Together, they form the international LIGO-Virgo-KAGRA collaboration and have amassed detections of nearly 300 gravitational-wave events over the past decade, mostly from colliding pairs of black holes. Also called black hole “mergers,” such events are the cacophonous moment when these dense gravitational behemoths smash together to form a single, larger beast. Plefka and his colleagues are studying different interactions known as “scattering” events, which occur when paired black holes slip by each other, usually in prelude to their eventual coalescence. During these close encounters, the clashing gravity of the black holes causes each to accelerate past the other, generating a significant gravitational-wave signal, but the objects are sufficiently separated to avoid merging.It’s no coincidence that this resembles elementary particles deflecting each other. “You can use the techniques developed for the scattering of microscopic objects to describe this scattering of macroscopic ones,” Plefka says. Considered from far enough away, well beyond the event horizon—that pivotal region within which neither matter nor light can escape—a black hole can be modeled as a particlelike point with mass and spin, albeit one that generates gravitational rather than electromagnetic waves.On that basis, Plefka and his colleagues applied techniques from quantum field theory that are more typically used to analyze the behavior of elementary particles. “We’re building on decades of work that has been done to make predictions for collider experiments,” says Gustav Mogull, a particle physicist at Queen Mary University of London and one of Plefka’s co-authors.Closer to Complex RealitiesThe team’s goal was to bring its numerical approximations as close as possible to mirroring reality—which, of course, tends to be more messy. To do that, Mogull, Plefka and their team toiled to crank up the complexity of their calculations. In this work, the researchers incorporated five levels of that complexity—to what is known as the fifth post-Minkowskian order of precision—for describing the scattering angles of black hole pairs, their radiated energies and their recoils.This is where the Calabi-Yau geometric structures, normally associated with string theory, come in. In string theory, Calabi-Yau geometries involve the compactification of higher dimensions. Here, they are not merely abstractions but instead emerge from the researchers’ calculations of black hole scattering. It’s perhaps ironic that string theory, notoriously derided as untestable, has given rise to mathematical structures of relevance to measurable physics far from the rarefied realm of strings.A visualization of two black holes scattering off each other and emitting gravitational waves, which are rendered in shades of blue (darker shades correspond to higher energies). This visualization was computed with the aid of advanced mathematical functions known as Calabi-Yau manifolds.Mathias Driesse/Humboldt Universtität zu BerlinAny mathematical function is associated with some kind of geometry, Mogull explains—and as the function increases in complexity, so, too, does its geometry. In the case of something basic, such as the sine or cosine functions used in trigonometry, that geometry is a simple circle. Elliptic functions, on the other hand, imply a doughnut-shaped geometry called a torus, which is also a Calabi-Yau onefold. It turns out that the functions Mogull, Plefka and their team developed for black hole scattering are associated with Calabi-Yau threefold structures, which involve six-dimensional surfaces. “I don’t think the appearance of Calabi-Yaus was that unexpected within our community. I would say this represents confirmation of something that people had suspected but was yet to be verified,” Mogull says.To demonstrate the utility of their approach in their study, Plefka, Mogull and their colleagues compare their approximations of black hole scattering angles to other, presumably more precise ones that were derived from numerical simulations. Such simulations can be time-consuming to run, even on state-of-the-art supercomputers—hence the search for accurate approximations. The team’s highest-order approximation closely matches the results of supercomputer number crunching for cases of black holes that gently deflect each other across great distances. But when the black holes come closer to a head-on collision, the team’s calculations begin diverging from the numerical simulations.Such work may seem to be a purely academic exercise, but in fact, the research could prove vital for making new discoveries. Signals from scattering black holes and neutron stars should be within reach of the next generation of gravitational-wave detectors that are set to come online in the late 2030s. These detectors, which will also need a new generation of models called waveform templates to discern true gravitational-wave signals from a sea of cosmic and terrestrial noise, include the proposed Einstein Telescope in Europe and Cosmic Explorer in the U.S. The latter, like LIGO, is supported by the National Science Foundation, and so far these kinds of gravitational-wave projects have avoided being directly targeted by the Trump administration’s aggressive proposed cuts to federally funded science.The work’s prospect for enhancing our understanding of gravitational-wave sources excites scientists who are gearing up for this new wave of detectors. They include Jocelyn Read, a physicist at California State University, Fullerton, who works with the Cosmic Explorer project. “Next-generation facilities can measure nearby signals with exquisite fidelity,” she says. (Here “nearby” means “within a few billion light-years.”) “So having very accurate and precise predictions from our current theories is definitely needed to test them against those kinds of future observations,” Read adds.Yet she also urges caution when assessing the significance of Plefka, Mogull and their colleagues’ work. “If they’re talking about implications for gravitational-wave astronomy, there are a few more steps that are needed,” she says. And their team has competitors, too, including some who have been deploying numerical simulations of their own.These kinds of approximate methods could eventually inform the waveform templates that are so crucial for filtering out noise in upcoming gravitational-wave detectors, Plefka says. Geraint Pratten, a LIGO physicist at the University of Birmingham in England, agrees. “I think it’s a heroic calculation by the group. It will provide a lot of insight into how we can structure next-generation waveform models,” he says. Pratten adds that more work will need to be done to move past the new study’s limitations. For example, the paper focuses on black holes without spin and those that undergo “unbound” scattering, which means that they deflect each other and never meet again. In reality, most, if not all, black holes are thought to spin, and usually scattering events precede an eventual merger.But in any case, he believes some gravitational waves from black hole and neutron star deflections will eventually be detectable, such as through observations of globular clusters, where these dense objects are packed together in a small space, cosmically speaking.For Plefka, Mogull and their peers, this macroscopic version of quantum field theory is still a young field, and there are many new types of astrophysically relevant calculations that they and others can do. These esoteric Calabi-Yau structures, formerly on the frontier of theoretical physics, could be just the beginning. “You’ve had this whole new class of mathematical functions—these theoretical things that had appeared in string theory,” Mogull says. “And we’re saying, ‘Look, this is tangible. This [radiated energy from scattering] is something you can try to detect, try to measure. This is real physics now.’”

Redesigning constantly doesn’t make your product better. It makes it harder to use and easier to forget.In many startups, there’s an unsettling ritual: every month brings a redesign, a new feature set, or a completely reinvented flow. It’s like product teams believe that if nothing changes, nothing is improving.But this constant churn isn’t innovation. More often, it’s anxiety disguised as iteration. And far from making your product better, it may be killing it slowly.Featuritis: the silent disease of modern product designOriginally coined by Alan Cooper, the term describes the compulsive addition of new features under the false belief that more equals better. Teams keep launching, tweaking, and adding. The roadmap turns into a crowded highway of ideas, none of which are allowed to mature.This behavior isn’t new. In HCI, it’s known as Featuritis (or creeping featurism), a pattern that leads to bloated systems disguised as progress.Visual example of a featuritis (source)You’re not building a stronger product. You’re building a bloated one.The result? A busy roadmap, stressed teams, and a product that’s harder to maintain, harder to use, and harder to love.There are some good case studies on how very basic functionality can end up broken because of this..The illusion of progress: why more doesn’t mean betterIn fast-paced environments, doing more feels like winning. Shipping fast becomes a badge of honor. But quantity ≠ impact.Great UX isn’t about how much you add. It’s about how little you need to add to make something useful, delightful, and memorable.When adding more features feels like progress, but leads nowhere.Ask yourself:How many features are used daily by your core users?When was the last time you removed something from your product?Do you measure usage, or just output?Too often, teams celebrate shipping without checking if anything actually lands.The hidden cost of unused functionalityEvery new feature comes with a hidden bill:Design timeDevelopment timeQA testingDocumentationSupport overheadMaintenance debtThese features don’t just consume resources; they also add sunk costs to your product, making it harder to remove them later, even if they fail.Low usage, high cost. A silent product tax.Worse, unused features clutter the interface and compete for the user’s attention. They dilute the core value of the product. They make everything feel heavier.Every unused feature is a lost resource. A tax on clarity. A distraction from what matters.The confused user: from intuitive to overwhelmingWhen your product changes every month, your users have to constantly relearn it. That’s not iteration, it’s erosion.Simplicity is what makes people feel confident. Familiarity builds trust. And trust drives retention.When too many features get in the way.If your product feels different every time they open it, don’t be surprised when they stop opening it altogether.So what should you do instead?Consistency isn’t stagnation. It’s a strategy.Here’s what teams with clarity do:Listen before you build ✅ Not all user requests are worth turning into features.Prioritize with data, not opinions ✅Well-formed decisions bring greater impact and fewer mistakes.Iterate with intent ✅Change with purpose, not pressure.Establish a product vision ✅ Without one, everything feels urgent (and nothing truly is).This is the hierarchy great teams follow:Start with the vision, end with the UI.Products like Linear, Notion, or Figma haven’t won because they change constantly. They’ve won because they improve with surgical precision, guided by purpose.Build less. Mean more.In the race to grow, many teams forget this:You’re not just building features. You’re shaping perception.Every decision you make (what you launch, remove, or keep consistent) shapes how users perceive your product.If you build with intention, they’ll trust it.If you change too often, they’ll leave it.So next time someone says “Let’s redesign this,” pause and ask:Is this better, or just newer?Have you ever felt pressure to change your product constantly just to look like you’re making progress?I’d love to hear how you or your team handle this kind of mindset → 💬Don’t change your app every month was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

In an industry where typefaces are designed to be used by thousands, a radical new concept has emerged that turns conventional type distribution on its head. David Březina, managing director at Rosetta Type, has created a typographic experiment called Only Yours: a typeface system that generates unique variants available exclusively to individual customers. The premise is brilliantly simple yet revolutionary: nine new variants of the typeface are released every day. Once purchased, a variant is permanently removed from circulation, ensuring that no one else will ever own that specific design. "The idea for a typeface that would be different for each customer has been on my mind for a few years," explains David. "According to surveys, most designers do not know how to use OpenType features and hence letter alternates. I'd been working on a still-unreleased typeface called Sacco, and at some point, it was so easy to create more and more letter alternates that it seemed like a shame to keep them tucked away somewhere in the font where no one would look." This observation sparked a more profound question: what if instead of hiding alternates within a font's features, the alternates themselves became the foundation for entirely unique typeface variants? Inspired by Dylan The concept lay dormant, though, until a surprising moment of inspiration. "I sat on it for a few years," recalls David. "The project seemed intimidating in scope, and I didn't really see a good reason to do it. "Then I went to see Bob Dylan, who's known for constantly rewriting his songs, in concert. It occurred to me that having so many perceivably different variants would bring more authenticity to the users. They could each have a unique font, in the same way concert-goers can attend a unique performance." This parallel between the legendary musician's ever-evolving performances and the notion of typographic uniqueness crystallised the concept. What began as design work in December 2025 led to a soft launch at the Automatic Type Design conference in Nancy, France, in February 2025. Science behind the system Only Yours isn't merely a stylistic exercise; it's grounded in David's PhD research on letter similarity perception. "I studied how they are clearly different, yet, in a well-designed font, they also look visually coherent," he explains. Technical implementation is where things become fascinatingly complex. Rather than designing alternative letters, David took a more fundamental approach. "Similarity perception is explained in terms of distinctive visual features that are shared by some of the letters, but not necessarily all of them," he explains. "More shared features mean stronger perceived similarity between the letters. It occurred to me that I could hack into this system and design alternates, not for the letters as we're used to from contemporary fonts, but for the individual features." While keeping some aspects of the generation process proprietary, David confirms that this feature-based approach allows for remarkable diversity while maintaining design coherence. "Using a reverse-contrast design remotely related to Antique Olive also helped to incorporate some rectangular elements more easily. The coherence does not need to be perfect, after all. Some of my favourite variants come from unexpected violations of the overall coherence." Scarcity as a feature, not a bug In an era of unlimited digital reproduction, Only Yours deliberately introduces scarcity. With over a million variants, the decision to release just nine per day transforms typography selection into something akin to a daily treasure hunt. "We didn't want to design a system for users to pick and choose," David notes. "The value of the project is not in providing all the options. It is in exclusivity. "Originally, I wanted the variant to be chosen randomly for the customers," he added. "But that did not seem right, so we settled on a compromise: offering nine new variants every day, which also turns it into a game. They're never shown again, so if you like one, you better get it." This approach creates a fascinating tension between the digital nature of typefaces and the artificial scarcity that makes each variant valuable. Once a customer purchases a weight from a variant, all weights within that variant become exclusive to them, preventing anyone else from fragmenting their unique typographic identity. Redefining typographic identity When sharing his concept with fellow designers before launch, David received some scepticism. "When I tried to discuss this idea with some friends, graphic designers, in November, they told me that it would be 'totally useless for branding'," he recalls with amusement. I disagree: since every variant is exclusive, it is perfect for branding!" Far from being an obstacle to professional use, the consistency within each variant across weights ensures practical usability while maintaining uniqueness. "Each variant of Only Yours is consistent across multiple weights, so it should work just fine in professional contexts," he asserts. The potential applications extend beyond individual designers. "Creating unique experiences in branding is not new. There is Freitag with their bags, each original, or even Coca-Cola with their named cans, which is a step in that direction," David points out. "Perhaps Only Yours could make it easier in some cases. We could, theoretically, generate thousands of unique variants for someone to licence as a bulk. Or a magazine could get a new variant for each issue." Looking to the future: Softly Yours Building on the success of Only Yours, Rosetta Type has already announced plans for Softly Yours, a rounded version using the same generative framework. "We set the price quite affordable to be more flexible," David adds. "We do have customers who already purchased multiple variants." Overall, this project represents a fascinating intersection of typographic design, computational creativity and exclusivity marketing. In a world increasingly concerned with digital ownership and authenticity, Rosetta Type has created something that feels almost paradoxical: a digital asset that can't be perfectly duplicated. For graphic designers and branding specialists, Rosetta Type offers something truly novel: the guarantee that your typographic voice won't be inadvertently duplicated by competitors. In an industry constantly searching for uniqueness, David has engineered it directly into the fabric of his typeface. As the nine variants change daily and customers claim their exclusive pieces of typographic history, Only Yours and Softly Yours aren't just new fonts. They represent a new relationship between designers and their tools, where scarcity, exclusivity and personal connection have been reintroduced to the digital realm.

Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Start your entry today, and take advantage of the Early Entry prices! When the AI wave first crashed into architecture, many feared the end was near. Digital tools like Midjourney, DALL-E and Stable Diffusion could sketch, render and visualize ideas faster than most human teams. Overnight, centuries of craft risked being reduced to a series of prompts and algorithms. But not everyone panicked. Some adapted and evolved. A few, like Amir Hossein Noori, were already living in the future and gaining recognition for it. In 2023, Noori earned Special Mentions at the Architizer Vision Awards, both in the Architect Creator of the Year category and for his AI-assisted visualization project, Cave of Wonders. His early work showed that architects could engage with AI critically and creatively, instead of defensively. He is part of a new generation of architects paving the way forward for a future of architecture with AI. Project by AHN Studio | Images courtesy of Amir Hossein Noori Rather than treating AI as a threat, Noori embraced it as a design partner. As the Cofounder and CEO of AI Hub, he has built a system that helps architects and designers survive in a creative industry that is changing faster than anyone expected. Through AI Hub, Noori has been quietly reshaping what an architect’s toolkit looks like. Instead of offering off-the shelf solutions, his team builds custom AI tools tailored to each firm’s needs. They train design teams to find and use the right technologies, create private AI models to protect creative identity and help architects win more projects, save time, and work smarter. The early days of AI’s arrival were chaotic. Many architects worried that machine-generated design would dilute the craft or even eliminate the need for human designers altogether. I for one, was worried about the fact that it further encouraged the wrong notion that everything done quickly was good, even if it really wasn’t. For Noori,  the panic missed because he was way ahead of time. Architects, he believed, could either resist it or lead it. Project by AHN Studio | Images courtesy of Amir Hossein Noori “My design process is a combination of manual and hand drawing, software, and artificial intelligence. In the initial phase of the project, the project brief is established through multiple client discussions,” says Amir Hossein Noori. “Using the brief and developing a narrative that can support the project’s design concepts, the procedure continues. After refining the narrative, I begin to visualize the ideas using sloppy sketches and diagrams, which are then translated into powerful visuals using artificial intelligence and other software.” Just like Noori, survival for architects should not be about working harder or faster. It should be about working differently. Architects who once spent days refining 3D models now collaborate with AI systems that generate dozens of options in hours. Those who once viewed design as a linear, painstaking process now see it as an agile, evolving conversation between human intention and machine intelligence. The shift has not been easy. Many traditional architects resist the idea of AI participating in the sacred creative process. But some architects are not clinging to the standard definitions of authorship. They are reframing their role and are moving from sole creators to creative directors. They guide and collaborate with AI. Their tools simply changed but the vision is still theirs. Project by AHN Studio | Images courtesy of Amir Hossein Noori Noori’s work shows that AI is not here to replace architects; it is here to extend them. By the work he has done with AI hub, he is showing that the future of architecture belongs to those who know how to lead both people and machines. Its not about losing control. It is about changing the reins and spending less time on the mundane tasks that bog down creativity and more time on the things that matter. Architecture education, too, will need to evolve. As AI becomes a standard design tool, future architects will need to be fluent in form-making, prompt engineering, data interpretation, and creative collaboration with non-human systems. Being a great designer will no longer mean just drawing faster. It will mean thinking broader. Project by AHN Studio | Images courtesy of Amir Hossein Noori At its core, architecture has always been a negotiation between art and technology. From the invention of squares and tables to the first CAD programs, technology has pushed the boundaries of design. AI is simply the next tool in that continuum. Like every tool before it, it demands adaptation and leadership, not fear. The architects who resisted AI may still be standing for now. But those who have embraced it, like Amir Hossein Noori, are already building the next generation of practice. And they are making it clear that in the next era of architecture, vision is still the most powerful tool. Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Start your entry today, and take advantage of the Early Entry prices! Top image: Amir Hossein Noori The post Amir Hossein Noori and the Rise of the Future-Ready Architect appeared first on Journal.

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Hades II introduces a new mechanic called Forget-Me-Not, which allows players to pin recipes they don't have enough crafting reagents for. While they're out in the world, the path to those resources will be highlighted, making it easier to keep track of what you need. I love this feature, in no small part because I've been making my own version of it for other games for years. It's just one of the many ways I use spreadsheets to improve my gaming.To be clear, I am not recommending that you follow in my footsteps and create a spreadsheet for every game you play. I am deeply unwell, the world is on fire, and creating little charts and tables is how I exert some semblance of control over a chaotic world. Also, not every game needs a spreadsheet.For some games, though, it's just more helpful to have your own goal tracker. Especially in sandbox games, where the quest markers are only the start of what you can do. For my purposes, I'll either use a typical Google Sheet, or if I'm feeling real spicy, I'll branch out to something like Notion for some more flexible tools. Here are just a few examples of what I do.Tracking Tears of the Kingdom armor upgradesThere are so many armor sets in Tears of the Kingdom, and tracking down every piece is super rewarding both in terms of exploration and the powers you get from different sets. What's less rewarding is upgrading the ones you already have. You need to visit Great Fairies with the right amount of ingredients, some of which are super rare or only drop from specific enemies. Some of which you might've otherwise walked right past if you didn't know you needed them. (Looking at you, Lizalfos tails.) That's why I made this armor upgrade tracker. I gave myself sections to mark off which sets I'm most interested in collecting, how many pieces I still have left to claim, what upgrade level each of them is currently at, and whether there's a set bonus I haven't unlocked yet. Most of this info is technically visible in the UI, but this way I don't have to dig through menus to see whether I've finished upgrades on the Froggy armor before I go collect a bunch of sticky lizards.I also gave myself space to write down what ingredients I need for an upgrade, which is low-key the most helpful part for me. Almost everything in Tears of the Kingdom is used to upgrade some armor set or another, but I don't always remember which ones I care about. And the only way in-game to see what ingredients are needed for an upgrade are to visit the Great Fairy directly.This saved me tons of wasted effort because, when I'm out adventuring, I can quickly look at my notes to see whether I still need a particular item when I come across it. Not only is this handy as reference, but it saves me from having to go on a farming run later for the five Thunderwing butterflies I walked right past hours earlier.Running an outpost empire in Starfield Credit: Eric Ravenscraft I have my complaints about Starfield, but that didn't stop me from spending dozens of hours building outposts to farm as many resources as possible. Is there a point to this? Not really. Most resources you need in Starfield, you can either buy or farm easily. But I like building my own mini-economies. The only trouble is, the game isn't built to be the overseer of dozens of outposts.Enter my Notion table. For this one, I gave myself at-a-glance reference points for the resources each planet has, which of those resources are available at my outpost, and which of those I'm currently harvesting. Since you get a bonus when you've fully surveyed a planet, I've also marked whether I've completed that planet's survey. (Note: I wasn't able to easily make this into a template, so you'll have to make your own version from scratch. This page from Notion will help you understand the basics, but there are plenty of other YouTube tutorials to check out if you need more guidance.)Granted, this is my own personal Sisyphean boulder, since the only reason to spend this much effort organizing Starfield outposts is for the joy (?) of outpost organizing. However, I've done similar things for games like Stardew Valley, which are a bit more objective-oriented.I used Notion for this one because I liked its approach to databases a little better than Google Sheets. You can create a full database with entries for all the data you need, but then create alternate views that only focus on some of the data you've created. For example, I also tracked what flora and fauna grew on each planet, but since I didn't need that while managing my outposts, I decided to create a view that didn't include it.Quantifying my time wasted in Overwatch 2Of all the spreadsheets I've made for the games I play, few have brought me as much pain as this one. Overwatch 2 switched to a battle pass system, with rewards that will disappear if you don't claim them by the end of the season. I play a lot of this game already, but I wanted to know just how much time it would take to complete certain rewards.So, I made myself a little calculator. I started by tracking the average amount of time it took to complete a game and how much XP I got for each game. Then I gave myself a few boxes that calculate how much time it would take to reach a reward tier, based on how much XP I'd earned so far. I got it down to a point where I only had to fill in three blocks (highlighted in blue in the sheet) to see how much more time I was planning to waste in a game for a cosmetic reward I wouldn't even use.Now, I don't need to be told to play Overwatch 2. I love playing it with my friends, and I'll gravitate to it regardless of how much it mistreats me. The real value here is that, sometimes, this spreadsheet discourages me from playing. If the game says "You're just 50,000 XP away from getting a reward!" it might seem like a good idea. The game wants you to keep playing after all. Instead, my spreadsheet says "You will have to play 41 more hours of this game to reach that level. So I sigh and close the game.This has actually helped me kick some of that "Just one more game" habit that's so easy to fall into. Every online game these days has a litany of rewards and points and blips and bloops designed to give you just a little dopamine every time you decide to keep playing. My calculator tool wasn't designed to save me time, but it did end up being more honest about it than Blizzard would ever be.Other spreadsheet ideasThese are just a few of the many examples I've made over the years. I've made sheets that calculate how much my Stardew Valley crops will yield over time, taking the guesswork out of buying from Pierre's store. (I'm also far from the first with this idea.) I've also made sheets similar to my Tears of the Kingdom tracker for upgrades in games from Skyrim to Horizon Forbidden West. Even if you're not into making your own, there's a chance some enterprising gamer out there has made arcane spreadsheets for things like how much an armor buff affects your favorite hero. Any time there's some math I want to do that the game doesn't do for me, or resource requirements I'm sick of Googling over and over, I'll turn to my trusty spreadsheets—and now you can (should?) too.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Productivity platform Notion is betting on large language models (LLMs) powering more of its new enterprise capabilities, including building OpenAI’s GPT-4.1 and Anthropic’s Claude 3.7 into their dashboard. Even as both OpenAI and Anthropic start building productivity features into their respective chat platforms, bringing these LLMs into a separate service shows how competitive the space is.  Notion announced its new all-in-one AI toolkit inside the Notion workspace today, including AI meeting notes, enterprise search, research mode and the ability to switch between GPT-4.1 and Anthropic’s Claude 3.7. One of the new features lets users chat with LLMs inside the Notion workspace and switch between models. Right now, Notion only supports GPT-4.1 and Claude 3.7. The idea is to reduce window and context switching. The company said early adopters of the new feature include OpenAI, Ramp, Vercel and Harvey.  Model mixing and fine-tuning Notion built the features with a mix of LLMs from OpenAI and Claude, and its own models.  The move away from pure reasoning models makes the model choice interesting for Notion. GPT-4.1 is technically not a reasoning model, while Claude 3.7 is a hybrid model— capable of acting as a regular LLM and a reasoning model.  Reasoning models are having a moment, though many warn that these models can still sometimes lie. However, while reasoning models like OpenAI’s o3 (and yes, Claude 3.7 Sonnet) take their time to answer, going through different scenarios, they are not the best for quick thinking and data-gathering tasks. Many productivity tasks, like meeting transcriptions or searches for task data, don’t need the power of a reasoning model behind them.  Sarah Sachs, Notion AI Engineering Lead, told VentureBeat in an email that the company aimed for features that didn’t sacrifice accuracy, safety and privacy, along with responding to queries in the speed enterprises need.  “In order to achieve a low-latency experience, we fine-tuned the models with internal usage and feedback from trusted testers, in order to make the AI specialized in Notion retrieval tasks,” Sachs said. “This setup helps Notion AI understand business needs, give relevant answers, serve customers with sub-second latency, and keep customer data safe and compliant.” Sachs said hosting and building with different models allows users to “pick the option that best fits their needs— whether that’s a more conversational tone, better coding capabilities, or faster response times.” AI meeting notes and more Notion AI for Work tracks and transcribes meetings for users, especially if they added Notion to their calendar, and it can listen in on calls.  Users can use Notion for enterprise search by connecting apps like Slack, Microsoft Teams, GitHub, Google Drive, Sharepoint and Gmail. Sachs said Notion AI will search an organization’s internal documents, databases and the connected apps.  The enterprise search results, along with other uploaded documents or a web search, allow Notion users to access the new Research Mode. It will draft documents directly from Notion while “analyzing all of your sources—plus the web—and think through the best response.” Notion also added both GPT-4.1 and Claude 3.7 as chat options. OpenAI noted that Notion users can chat with GPT-4.1 on the workspace and create a Notion template directly from the conversation. Sachs said the company is working on adding more models to its chat feature.  Subscribers to Notion’s Business and Enterprise plans with the Notion AI add-on get immediate access to the new features.   Compete with the model providers Even though Notion users can access both Anthropic and OpenAI on the platform, Notion still has to compete with model providers.  OpenAI’s Deep Research has been hailed as a game-changer for agentic retrieval augmented generation (RAG). Google also has its version of Deep Research. And Anthropic can search the internet for you.  Not to mention, Notion needs to compete with other platforms that already leverage AI. The meeting space is chock full of companies tracking, transcribing, summarizing and pulling insights from calls with AI. However, Notion’s big selling point is that it has all these capabilities on one single platform. Enterprises can use all those different services but live outside their chosen productivity platform. Notion said having all of these features in one place, with one all-in-one pricing, will save enterprises from subscribing to different platforms.  Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Social media was mankind’s first run-in with AI, and we failed that test horribly, according to tech ethicist Tristan Harris, whom The Atlantic called “the closest thing Silicon Valley has to a conscience.” A recent survey found nearly half of Gen Z respondents wished social media had never been invented. Yet, 60% still spend at least four hours daily on these platforms.  Bullying, social anxiety, addiction, polarization, and misinformation—social media has become a cocktail of disturbing discourse. With GenAI, we have a second chance to ensure technology is used responsibly.  But this is proving difficult. Major AI companies are now adopting collaborative approaches to address governance challenges. Recently, OpenAI announced it would implement Anthropic’s Model Context Protocol, a standard for connecting AI models to data sources that’s rapidly becoming an industry norm with Google following suit.  With any new technology, there are unexpected benefits and consequences. As Harris put it, “whatever our power is as a species, AI amplifies it to an exponential degree.”  While GenAI helps us accomplish more than ever before, dangers exist. A seemingly safe large language model (LLM) can be manipulated by bad actors to create harmful content or be jailbroken to write malicious code. How do we avoid these harmful use cases while benefiting from this powerful technology? Three approaches are possible, each with its own merits and drawbacks.  3 ways to benefit from AI while avoiding harm  Option #1: Government regulation  The automobile brought both convenience and tragedy. We responded with speed limits, seatbelts, and regulations—a process spanning over a century.  Legislators worldwide are attempting similar safeguards with AI. The European Union leads with its AI Act, which entered into force in August 2024. Implementation is phased, with some provisions active since February 2025, banning systems posing “unacceptable risk” like social scoring and untargeted scraping of facial recognition data.  However, these regulations present challenges. European tech leaders worry that punitive EU measures could trigger backlash from the Trump administration. Meanwhile, U.S. regulation develops as a patchwork of state and federal initiatives, with states like Colorado enacting their own comprehensive AI laws.  The EU AI Act’s implementation timeline illustrates this complexity: Some bans started in February 2025, codes of practice follow nine months after entry into force, rules on general-purpose AI at the 12-month mark, while high-risk systems have 36 months to comply.  A real concern exists: Excessive regulation might simply shift development elsewhere. Building a functional LLM model costs only hundreds of millions of dollars—within reach for many countries.  While regulation has its place, the process is too flawed for developing good rules currently. AI evolves too quickly, and the industry attracts too much investment. Resulting regulations risk either stifling innovation or lacking meaningful impact.  So, if government regulation isn’t the panacea for AI’s dangers, what will help?  Option #2: Social discourse  Educators are struggling with GenAI and academic honesty. Some want to block AI entirely, while others see opportunities to empower students who struggle with traditional pedagogy.  Imagine having a perpetually available tutor answering any question—but one that can also complete your assignments. As Satya Nadella put it recently on the Dwarkesh Podcast, his new workflow is to “think with AI and work with my colleagues.” This collaborative approach to AI usage could be a model for educational settings, where AI serves as a thinking partner rather than a replacement for learning.  In homes, schools, online forums, and government, society must reckon with this technology and decide what’s acceptable. Everyone deserves a voice in these conversations. Unfortunately, internet discussions often devolve into trading sound bites without context or nuance.  For meaningful conversations, we must educate ourselves. We need effective channels for public input, perhaps through grassroots movements guiding people toward safe and effective AI usage.  Option #3: Third-party evaluators   Before the 2008 financial crisis, credit rating agencies assigned AAA ratings to subprime mortgages, contributing to economic disaster. The problem? Industry-wide self-interest.  When it comes to AI regulators, of course, we run the risk of an incestuous revolving door that does more harm than good. That doesn’t have to be the case.   Meaningful and thoughtful research is going into AI certifications and third-party evaluators. In the paper AI Certification: Advancing Ethical Practice by Reducing, Peter Cihon et al. propose several notions.   First, because AI technology is advancing so quickly, AI certification should emphasize evergreen principles, such as ethics for AI developers.   Second, AI certification today lacks nuance for particular circumstances, geographies, or industries. Not only is certification homogenous, but many programs treat AI as a “monolithic technology” rather than acknowledging the diverse types, such as facial recognition, LLMs, and anomaly detection.  Finally, to see good results, customers must demand high-quality certifications. They have to be educated about the technology and the associated ethics and safety concerns.  The path forward  The way forward requires multistakeholder, multifaceted conversations about societal goals and preventing AI dangers. If government becomes the default regulator, we risk an uninvestable marketplace or meaningless rubber-stamping.  Independent third-party evaluators combined with informed social discourse offers the best path forward. But we must educate ourselves about this powerful technology’s dangers and realities, or we’ll repeat social media’s errors on a grander scale.  Peter Wang is chief AI and innovation officer at Anaconda. 

Notion used OpenAI's GPT-4.1 and Anthropic's Claude 3.7 for its new AI features for enterprises, moving away from reasoning models.Read More

Notion used OpenAI's GPT-4.1 and Anthropic's Claude 3.7 for its new AI features for enterprises, moving away from reasoning models.Read More