Photographer Paul Clemence has unveiled a new photo series documenting Le Corbusier's Notre Dame du Haut in Ronchamp, France. The collection revisits one of the 20th century's most influential works of religious architecture through an exploration of light, materiality, and atmosphere, highlighting the sculptural qualities of the chapel and the changing relationship between its concrete surfaces and natural illumination. Combining black-and-white exterior photographs with color interior images, the series presents a new documentation of a building that continues to shape architectural discourse more than seven decades after its completion.

The idea of a "Great Green Wall" designates a large-scale reforestation initiative undertaken specifically to halt desertification. Currently underway in Africa to prevent the advancement of the Sahara Desert and in China around the Gobi Desert, it is a landscape, agriculture, economic, and social development project in reaction to the rise of global temperatures, over-farming, and unsuitable land management. Both projects, currently underway, are intersectional initiatives involving governments from different countries, civil society organisations, and both individual volunteers and organised groups, guided by the idea of a unified response to global environmental issues through wildlife restoration. It is a large-scale, infrastructural application of a 'green' or 'wildlife corridor': a linear strip of vegetation or natural landscape that connects fragmented ecosystems and wildlife populations separated by human development on a continental scale.

While pyramidal geometry appears in monumental historical structures, its contemporary application remains limited. The primary constraint is the sloping envelope, which contracts the building's floor area as it ascends and reduces usable interior volume compared to orthogonal or cylindrical forms. However, from a structural standpoint, the geometry offers lateral stability against wind and seismic loads due to its broad base, central mass distribution, and self-bracing triangular faces. Nonetheless, applying this shape at a residential scale introduces several layout constraints. These slanted walls limit vertical wall surfaces, which complicates the placement of standard windows, doors, and floor partitions. At the same time, the low clearance along the perimeter also creates unusable floor area at the edges of the room.

A war can erase a skyline overnight. Roofs collapse, streets become impassable, and landmarks that once oriented everyday life are reduced to rubble. The most profound loss often remains invisible. Buildings can be surveyed, photographed, scanned, and, eventually, rebuilt. The knowledge that produced them is far more fragile. It survives in the hands of stonemasons who understand how a limestone vault carries its load, carpenters who know how timber joints respond to seasonal movement, plasterers who mix lime by memory rather than measurement, and residents whose everyday rituals keep these buildings in use. When these people are displaced, killed, or forced to abandon their trades, reconstruction inherits a problem that no drawing or digital model can solve. Stone can always be quarried again. Recovering the knowledge to shape it takes much longer.

Designers have spent years saying they would do better work if the organisation got out of the way. Not always in those exact words, obviously. It usually comes out as something more reasonable: we didn’t get enough engineering time, product had already decided the solution, the roadmap was too packed, leadership only cared about this quarter’s numbers, research got cut, the experiment was never run properly, the design debt was known about, but nobody wanted to spend a sprint fixing it.

Much of this is true. Most designers have worked inside that awkward middle space between product and engineering. Product frames the problem, or at least thinks it does. Engineering decides what is feasible, or at least what is affordable. Design is expected to make the thing clearer, simpler, more coherent, more usable, and occasionally more desirable, while also being careful not to disrupt the plan too much.

That position has always been uncomfortable.

Designers are told to think strategically, but often lack the power to act strategically.

They can spot the broken onboarding flow, the confusing upgrade path, the empty state that makes users feel stupid, the feature that looks reasonable in a product review but makes no sense in real use. Seeing the problem is one thing. Getting it fixed is another.

So design often becomes an argument. You make the case. You annotate the flow. You bring the research clip. You point to the support tickets. You show the Figma prototype. You explain why the “small edge case” is actually the first-run experience for half your new users. Then everyone nods, agrees it matters, and moves on to whatever had already made it onto the roadmap.

This is one reason AI is more interesting for design than the usual “will it replace designers?” debate suggests. The real change is not that designers can make more screens. Nobody needs more screens. The interesting change is that designers may need less permission.

A good designer can now move from “we should fix this” to “I fixed this, and pushed it live.” They can prototype the alternative onboarding flow, write and test clearer product copy, build a rough working version of the interaction, clean up small pieces of design debt without waiting three months for a roadmap slot, and make the better thing visible enough that it becomes harder to ignore.

That changes the politics of the work. Design has often relied on persuasion because designers lacked direct means of production. AI weakens that dependency. Not everywhere, and not for everything. Complex products still have architecture, infrastructure, data models, permissions, security, compliance, legacy systems, and all the other unglamorous reasons software is hard. But the boundary is moving.

More of the gap between having the idea and making the idea real can now be crossed by a motivated designer with the right tools.

In this version of the future, designers become less permission-dependent: less reliant on product to bless the problem, less reliant on engineering to make every small improvement real, less trapped in the role of internal critic, taste-provider, or Figma operator. More able to make, test, repair, and ship.

The best designers start to look less like traditional product designers and more like hybrid product leaders. They still care about interaction, hierarchy, language, flow, brand and craft, but they also understand the commercial shape of the problem. They can make trade-offs. They can prototype in code, or close enough to code. They can use AI to explore options quickly, then use judgment to throw most of them away. They can sit with a founder or PM and move from a vague product concern to something tangible by the end of the day.

There may be fewer of these people, but they will be harder to ignore. The current design-org model was partly built around scarcity: scarce engineering time, slow production, expensive prototypes, handoffs between specialists, heavy coordination across teams. If AI reduces some of that scarcity, it probably reduces the need for some of the roles that grew around it. The optimistic case is not that every designer keeps their job and gets a productivity boost. That feels like wishful thinking. The more believable version is that the total number of designers goes down, but the designers who remain have more direct influence over the product.

That is not a bad outcome for the strongest designers. It may even be the thing many of them have wanted for years.

Autonomy has teeth. If AI gives designers more room to act, it also removes some of the cover. The same constraints that held good designers back have also protected weaker ones from being tested too directly.

For years, it has been easy to say: I had a better idea, but we never got the engineering time. Sometimes that was exactly what happened. Sometimes the better idea was never really more than a critique. It had not been made concrete. It had not been tested. It had not dealt with the awkward trade-offs. It sounded strong because it lived safely in opposition to the shipped thing.

A lot of designers are good at noticing what is wrong. Fewer are good at deciding what should happen instead. Fewer still can make that alternative real enough for other people to judge. AI will expose this gap.

If you can prototype the recommendation, the recommendation has to get better. If you can make the alternative flow, the flow has to survive contact with details. If you can test the product copy, you have to care what happens when users read it. If you can fix the small piece of design debt, you have to decide whether it was really worth fixing.

Some designers are not as strategic as they think they are. They have learned the language of strategy without the discomfort of owning outcomes. They can talk about user needs, business goals, systems thinking, and product quality, but struggle when asked to make a call. They want influence, but not the exposure that comes with it.

The profession has spent a long time arguing that design deserves more power. Fine. But more power means fewer excuses. It means the work is judged less by the elegance of the argument and more by the quality of the thing you made, tested, or changed. That is a better standard, but it will not be kind to everyone.

There is a second bear case, and it is probably the one large design teams should worry about most. Product and engineering already have more institutional power than design in most companies. They own the roadmap, the technical architecture, the sprint machinery, the metrics, and usually the language leadership understands. Design often has to translate its concerns into someone else’s terms before they count.

AI may not rebalance that power. It may hand product and engineering enough design capability to make design easier to bypass. A PM who can generate a decent flow, decent copy, and a decent prototype may not feel the same need to involve design early. An engineer who can use AI to produce a reasonable interface may decide the design system covers enough of the decision-making. A founder who can get to a polished demo in an afternoon may confuse polish with product thinking.

The problem is not that these people will suddenly become great designers. The problem is that many companies do not know the difference between great design and plausible design. Plausible design is dangerous. It looks coherent in a product review. It uses the right components. The spacing is fine. The copy is not embarrassing. The flow mostly works. Nobody in the meeting feels strongly enough to object. So it ships.

A lot of bad product decisions already survive because they look plausible. AI will produce more of them. This is where design could lose ground quickly: not because taste, judgment, research, and interaction thinking stop mattering, but because the visible outputs of design become easier for other functions to imitate.

If a company already thinks design is mostly screens, prototypes, and polish, AI gives it a cheaper way to get those things.

In that world, design does not gain more agency. It gets narrowed. The remaining designers manage the design system, police component usage, review flows that have already been decided, tidy the interface, maintain brand consistency, and get pulled into high-stakes launches, executive demos, and the occasional messy cross-platform problem. Useful work, but a smaller surface area. Less shaping the product, more maintaining the furniture.

This is why the “AI will automate the boring 20%” argument feels too comforting. In some companies, perhaps that is what happens. But in large tech organisations, where design teams grew around coordination, production and process, the cut could be much deeper. Not 20%. Maybe 50%. Maybe more. Especially in places where leadership never really understood why the design team had grown so large in the first place.

The painful part is that both futures can be true at the same time. AI can make the best designers more capable and many average designers less necessary. It can give design more agency while reducing design headcount. It can help a small number of designers move closer to product leadership while pushing others into governance and clean-up work. It can free designers from waiting for permission, then reveal that some were more comfortable waiting than acting.

The designers who do well will not be the ones who merely use AI to produce more options. Options are cheap now. They will be the ones who know which option is worth pursuing, why it matters, how to test it, what to cut, where the product is lying to itself, and when “good enough” is quietly damaging the business.

They will have taste, but taste will not be enough. They will need product judgment, technical curiosity, commercial awareness and the nerve to make decisions before every variable is settled. They will need to be comfortable moving between a customer conversation, a prototype, a pricing concern, a brand question, and a messy implementation detail without insisting that all of those belong to someone else.

I’m not completely sure where we end up. I hope it is closer to the bull case: fewer permission structures, more making, more agency, better designers finally able to show what they can do without being held back by the machinery around them.

I fear it may be closer to the bear case: product and engineering absorb much of the work, companies decide plausible design is good enough, and design loses status, headcount, and strategic ground.

In reality, it will probably be some uncomfortable mix of the two. Some designers will use AI to gain more agency. Some companies will use it to need fewer designers. Some teams will produce better work because the distance between judgment and execution gets shorter. Others will ship more plausible mediocrity because nobody in the room can tell the difference.

For years, designers have said they could create more value if they were less constrained by the organisation around them. AI is about to test that claim. Some will finally get to prove it. Some will find out the constraints were doing them a favour.

Further Resources

• “Good from Afar, But Far from Good: AI Prototyping in Real Design Contexts,” Huei-Hsin Wang and Megan Brown (NN/Group)
  The UX design field has been flooded with AI-powered prototyping tools that generate interfaces from natural-language prompts. Despite the huge marketing hype, an evaluation with real design scenarios revealed that while these tools can follow instructions to achieve a general goal, they often lack the sophistication to weigh design tradeoffs and to produce thoughtful, high-quality designs without extensive guidance from humans.
• “AI Design Tools Are Marginally Better: Status Update,” Megan Brown, Caleb Sponheim and Taylor Dykes (NN/Group)
  AI-powered design tools have improved, yet we’re still nowhere near the usefulness we’ve been promised. This article reviews several AI tools and features, including: Figma’s Rename Layers, Rewrite This, Find More Like; Khroma Color; and Midjourney. The authors also take a look at the wireframe and prototype generation capabilities of some AI tools.
• “Using AI for UX Work: Study Guide,” Tanner Kohler (NN/Group)
  Unsure where to start? This curated collection of links to articles and videos about the best ways to introduce artificial intelligence for UX design work should help you.
• “I used AI for every task for two weeks,” Joanna Otmianowska (DEV Community)
  The author (who is a front-end developer) tried to use Claude Code for every task at work. This turned into a full-on experiment. In the article, Joanna shares all the details about the experience.
• “How AI will Affect the Design Industry,” Andy Budd
  It is likely that AI is not going to "kill design" in the next few years, as some are claiming. However, these are definitely times of change, and change means that there will be big opportunities for those who embrace new technologies early.
• “Design has been too settled for too long,” Andy Budd
  For a discipline that talks so much about change, design has been running on a surprisingly settled operating model. AI is starting to break that model. In this article, Andy reviews in detail the current trends regarding adopting AI in the daily workflows of design teams.
• “What Designers Should Take From Benedict Evans’ Latest AI Deck,” Andy Budd
  Benedict Evans has a useful habit of standing slightly away from the noise. For years, his big strategy decks have acted as a kind of weather map for the technology industry: mobile, media, ecommerce, platforms, regulation, capital flows, and now AI. They are not predictions in the cheap sense — they are attempts to show the shape of the system: where the money is going, what assumptions people are making, which comparisons are lazy, and where the industry may be fooling itself.
• design + AI conference

Many companies silently assume that everybody wants more AI in their lives. That people are craving new AI features, new AI products, new AI workflows — that would all magically replace all existing outdated practices and broken ways of working.

But in reality, it seems like people don’t want more AI at all — at least not in the way most AI leaders envision it. Unsurprisingly, many AI features have low adoption and retention — at a very high cost of delivery, and a high risk of reputation damage.

It’s remarkably difficult to make a strong argument with senior leadership, but AI is not a value proposition. New AI features don’t magically make for happy or excited customers. Because AI features are often bolt-ons and separate tools for employees to use, they typically take people out of their regular way of working.

AI is pretty good at amplifying shortcuts and shortcomings in organizations — from data quality to decision making. It can’t magically fix years of accumulated quick patches, technical debt, broken culture and internal politics. If anything, they become more visible with AI as inconsistencies or conflicting priorities and get handed directly to users, who are then left to make sense of the mess themselves.

Because in most organizations, work typically requires hopping on and off between plenty of disconnected and fragmented systems, with a new AI tool, they now have yet another system that they also need to hop on and off. Often it produces more work, and typically it’s not particularly rewarding work either.

On top of that, people are very much aware of the cost of finding and fixing AI hallucinations. Asking AI to generate a response might feel easier than writing from scratch, but it has a cost:

• Skim through the entire AI output,
• Spot key points to focus attention on,
• Review/verify key points, one-by-one,
• Check rationale for what follows next,
• Articulate corrections + regenerate,
• Review the response (a number of times).

For many people, AI isn’t something they can proactively choose and explore on their own — it arrives uninvited, at someone else’s pace. On top of that, plenty of messages amplify fears and worries about AI replacing work — so it’s hardly surprising that the perception of AI isn’t excitement. It’s resistance to change and deep anxiety about one’s place in a world that seems to be changing without them.

At best, AI features might be silently accepted or nodded away. At worst, AI raises concerns, doubts, caution — and calls for a healthy dose of skepticism. And sometimes it’s perceived as a threat or liability — because unlike other features, AI is neither predictable nor reliable.

People don’t dream of AI art museums or AI fridges or AI hotel reception or AI-narrated children’s books. They don’t want their children to have romantic AI partners. Most people don’t want to actively manage (and clean up after) a swarm of AI agents roaming in their bank accounts and acting on their behalf in the real world. And most notably, people don’t really want a magical box to speak to or type into all the time.

I’m always puzzled by the comparison of AI features with how unreliable humans are. But people don’t compare software with other people. They compare features with features — and if one feature in one product is unreliable, while a similar feature works flawlessly in another, they choose the latter. It’s not about AI or not AI, but rather what works consistently and reliably, and what doesn’t.

Many conversations about AI are conversations about the speed of delivery. But to many people, there is little value in increasing the speed of delivery. They want to do things well, with enough time to think and make good decisions. They also want to enjoy the time they spend working on things, rather than just ship faster. There is an enormous feeling of reward and achievement that slowly disappears, one vibe-coded change at a time.

People don’t change much. And after all these years, they (still) want features that are fast, accessible, reliable, predictable and useful — every single time. And ideally not the ones that replace their entire workflow, but that augment their way of working — and that take over the most mundane, annoying, and boring tasks that they find no pleasure in.

Many jobs are exposed to AI automation, but in many of them there is a rewarding, unique, creative part that requires taste, point of view, and perhaps even human intuition. And if AI automates boring parts of it, that’s an advantage for everyone. That’s also what enhances productivity and brings more joy in daily life.

When AI automates tedious and mentally exhausting tasks, its value is much easier to grasp. But for that, AI shouldn’t feel like a bolt-on. It should be deeply integrated into people’s existing workflows. It must also match existing mental models that they have developed and fine-tuned for years or decades. AI should adapt to how people think and make decisions, not the other way around.

And it doesn’t really matter if these features are branded as “AI”, “smart” or “automation”. However, they must work well for people using them. And that means that people must be aware of use cases where it actually helps them, and be inspired to find more use cases on their own.

Ironically, tools that work well there aren’t “AI-first” — they are “AI-second”. Subtle, humble, calm, ambient, taking a supportive role in the background for work that otherwise is remarkably dull and unnecessary.

I don’t want to read books written by AI. I don’t want to gaze upon paintings by AI. I don’t want AI to teach my children. I don’t want to have an AI therapist. I don’t want AI making my medical decisions. I want AI to do all the physical and mental labor that taxes me so I can read books written by humans and go to art galleries to engage with art made by humans. I want AI that makes my life easier rather than forces me to change myself.

— Bo Young Lee

Perhaps I’m missing a bigger picture, and perhaps I’m just old school — but I really do like people. Their stories, their thinking, their emotions, their enthusiasm, their laughing. AI can be remarkably helpful in many situations, but so are people. And between the two, I would favor spending time with a human — however imperfect they are — every single time.

No, people don’t need more AI in their lives — they need AI to automate all the boring stuff they have to deal with every day, so they have more time and headspace to do things that they actually love and enjoy doing. That doesn’t mean spending more time with AI — but spending more time with people they love.

Meet Design Patterns For AI Interfaces, Vitaly's new video course with practical examples from real-life products — with a live UX training happening soon. Jump to a free preview.

• AI Adoption Gap: IBM 2026 Study, by MindStudio
• Powered by AI Is Not a Value Proposition, by Nielsen Norman Group
• AI Chatbots Discourage Error Checking, by Nielsen Norman Group
• The Jobs Most Exposed to AI Automation, by The Washington Post
• On AI and What We Actually Want From It, by Bo Young Lee

The project involved the transformation of an industrial site in the small town of Limoux in the Aude department, and at the heart of a community of municipalities with 30,000 inhabitants. It is a threshold town between urban and rural, a situation that exemplifies the future of regions that have been left behind by major economic trends.

Shenzhen Natural History Museum is one of the world's largest new natural history museums and the largest in South China.

Recently we've covered a number of robots, ranging from creepy to nifty:

UBTECH's U1 companion robots.

Unitree's crazy off-road AS2-W.

Hyundai's parking robots.

AGIBOT's Expedition A3 packing itself away for transport.

The Swiss LEVA cargo-carrying dog-bot.

Unitree's GD-01 mecha-bot.

As of today, they all have one thing in common: They're all banned by the United States government. This week the Federal Communications Commission issued a ban on foreign-made "advanced robotic devices," citing security concerns:

"The networked capabilities of advanced robotic systems create extensive vulnerabilities and vectors for attacks that can manipulate the data and physical operation of the advanced robotic system. Relying on foreign-produced advanced robotic devices presents unacceptable supply chain and cybersecurity vulnerabilities . . . Advanced robotic devices collect data that could be leveraged by malign actors to surveil Americans, enhance the capabilities of foreign intelligence services, or to remotely commandeer the robots."

They've also banned foreign-made (networked) power inverters, as they could potentially provide a backdoor into the power grid for cyberwarriors.

The ban is effective immediately, but those who already own such components or robots are exempt.

Screen time on tablets has never been higher, but comfort hasn’t kept pace. Most tablet displays are still optimized for brightness and color gamut, with eye protection largely an afterthought or a toggle buried in settings. Reading long documents, annotating notes, or watching videos for hours on a typical panel can leave your eyes feeling the aftermath in ways that aren’t exactly encouraging.

TCL’s TAB A1 Plus NXTPAPER approaches that problem with something more deliberate than a simple blue-light filter. Built around the brand’s fourth-generation NXTPAPER display technology, it brings a large 12.2-inch 2.4K panel that combines seven integrated eye-comfort technologies, including Nano Crystal Shield Glass and an OmniSoft Light System, to more closely replicate the gentler, less fatiguing qualities of reading on paper.

Designer: TCL

The aspect ratio is worth noting too. At 3:2, the screen is taller than typical tablets, which means more of a document or webpage fits on screen without endless scrolling. That might sound like a minor detail until you’re cross-referencing notes during a study session or keeping track of multiple open documents across a long work afternoon. The adaptive 120Hz refresh rate keeps every interaction smooth throughout.

Switching between display modes is handled by the dedicated NXTPAPER Key, which cycles through paper-like and standard color modes without digging into settings menus. The 3-in-1 VersaView feature goes a step further, letting you pin specific apps to stay in full color while the rest of the interface remains in the easier-on-the-eyes paper mode. It’s a level of personalization that’s genuinely thoughtful.

NXTPAPER isn’t attempting to replicate E Ink or compete with dedicated e-readers on texture alone. It preserves the advantages of a conventional color display, including smooth video playback and vivid visuals, while adding the glare reduction and light softening that make extended reading sessions less punishing. The result is a screen that can move from a video stream to a study session without demanding any trade-off.

A suite of built-in AI tools handles the more tedious parts of daily work. Google Gemini and Circle to Search are onboard, alongside Writing Assist, Text Assist, a Smart Translator, and Smart Voice Memo. The tablet also runs Android 16 with Split Screen, Floating Windows, and Sidebar features built in, which means going from writing a draft to pulling research doesn’t require closing anything.

Under the hood, the Snapdragon 4 Gen 2 chip pairs with up to 16GB of RAM and 256GB of storage, expandable to 2TB via microSD. The 10,000mAh battery is large enough to carry through extended sessions without anxiety, and the 33W fast charging means a dead tablet doesn’t stay that way for long. The metal unibody keeps the whole package under 6.70mm thick.

The IP54-rated splash and dust resistance is a practical safeguard for a device that moves with you, and the build weighs in at 556 g. It comes in Aerolite Grey, and the box includes the tablet, a stylus, and a flip case, making the $399.99 asking price a fairly well-rounded deal for a package that doesn’t require additional accessories just to get started.

The post This $400 Tablet Comes With a Stylus, a Case, and Eyes That Don’t Hurt first appeared on Yanko Design.

Portable audio has long been a game of compromise. DAC dongles give you a modest step up from your phone’s built-in output, but connect anything more demanding than a pair of featherweight earbuds, and they start to run out of steam. Desktop amplifiers solve the power problem but chain you to a desk, a power outlet, and a bag heavy enough to make you think twice about bringing them anywhere.

The Khadas Tea Pro sits firmly in the space between those two options, offering enough amplification for serious headphones without the bulk or the cable tangle. At 7.85 mm thin and 96 grams, it genuinely disappears into a pocket, and its magnetic back panel snaps directly onto any iPhone 12 or later, turning your phone and DAC into a single, tidy unit that doesn’t need a pouch of its own.

Designer: Khadas

The connection is handled by 19 N56SH magnets, holding the Tea Pro against the back of the phone without straps or brackets. Android users and those with older iPhones can attach a magnetic ring to their case and get the same result. A premium wear-resistant leather panel covers the back, giving the device a tactile quality that’s a step removed from the usual plastic-and-aluminum DAC dongle aesthetic.

Inside, an ESS ES9039Q2M DAC chip works alongside an XMOS XU316 USB bridge for wired playback, delivering PCM up to 768kHz at 32-bit and DSD up to 512 native. The signal chain runs through TI OPA1612 op-amps and Ricore RT6863 amplifier chips, achieving a total harmonic distortion of -118 dB, a specification more at home in a studio rack than a 96-gram device that slips into a shirt pocket.

Both a 4.4mm balanced and a 3.5mm unbalanced output are on board, with the balanced jack putting out 165mW into 33 ohms and the unbalanced delivering 120mW. The Tea Pro also manages 41.67mW into 600 ohms, meaning even demanding full-size headphones with high impedance get a fully powered signal rather than an underpowered afterthought by a device that’s trying too hard to be small.

Wireless connectivity comes via a Qualcomm QCC5181 Bluetooth 5.4 chip that covers SBC, AAC, aptX, aptX Adaptive, aptX HD, and LDAC, keeping the Tea Pro compatible with virtually any device. The Khadas app adds a feature-rich equalizer that works over Bluetooth, letting you shape the sound to your taste without touching the hardware. There’s also a built-in microphone, so taking a call doesn’t require unplugging anything.

A 2100mAh battery supports up to 11 hours of wireless playback or just over eight hours wired, enough to outlast a full day of commuting without reaching for a charger. A 0.95-inch AMOLED color display on the front keeps output format, volume, and battery status visible at a glance, while independent power management keeps the battery from draining your phone when you aren’t connected to a power source.

Volume control runs through a dedicated analog chip rather than a software slider, keeping noise out of the signal path. The Tea Pro is priced at $199, which puts it well above the typical dongle but far below a battery-powered desktop DAC/amp that requires its own carrying case. For a device that fits in a jacket pocket and holds itself to the back of your phone, that’s a narrowing of the gap that’s hard to argue with.

The post Khadas Just Made a $199 Amp That Snaps to Your iPhone Like a Wallet first appeared on Yanko Design.

Casa en el Aire is a single-family home located in San Mateo de Alajuela, Costa Rica, on a steeply sloping site with dominant landscape views. Its primary strategy is to address housing through minimal intrusion: rather than resting heavily on the ground, the house is elevated on stilts to reduce earthwork, allow ecological continuity beneath the structure, maintain natural runoff, and prevent flooding, making a complex site habitable without sacrificing accessibility or comfort for its elderly owners.

The Vertical Playgarden was built in the courtyard of the Bajza Street Primary School in Budapest's 6th district, one of the densest inner-city areas of the Hungarian capital. More than 500 children use the school every day, yet the courtyard had long functioned as a mono-functional sports field: a rubber-covered surface enclosed by school buildings and a swimming hall, offering little opportunity for retreat, free play, nature, or differentiated use.