Type “a street in Lagos” into one of today's most advanced image generators and you already know, more or less, what you are going to get. There will be dust. There will probably be a market, or the suggestion of one: stalls, fabric, fruit piled in plastic bowls. The light will be hard and golden in a way that flatters poverty into picturesqueness. There may be a yellow minibus, a danfo, although the model will not know to call it that. Run the prompt again. Change “street” to “boulevard” or “avenue”. Make it rich, make it quiet, make it modern. The market will still be there. The dust will still be there. The machine has decided what Lagos looks like, and no rewording will talk it out of its conviction.

This is not a glitch. It is, according to a growing body of research, the design working exactly as the maths dictates. And as billions of people increasingly reach for these tools to picture places and peoples they have never visited, the consequences of that maths are starting to look less like a curiosity and more like a quiet rewriting of how the world imagines itself.

The machine that always answers the same way

In April 2026, a paper landed on the preprint server arXiv with a title only a geographer could love: “Assessing the Geographic Diversity of AI's Platial Representations in Image Generation.” Accepted as a full paper at AGILE 2026, the twenty-ninth annual conference of the Association of Geographic Information Laboratories in Europe, which is being held this June in Tartu, Estonia, the study was written by Zilong Liu, Krzysztof Janowicz and Mina Karimi. Janowicz is a professor of geographic information science at the University of California, Santa Barbara, and directs its Center for Spatial Studies; Liu, a geographer trained at Santa Barbara and now at the University of Vienna, has spent much of his recent work trying to measure something slippery: how varied, or how monotonous, the geographic imagination of a machine really is.

To do that, the team borrowed a tool from an unlikely discipline. Ecologists have long needed ways to quantify biodiversity, to put a number on how many different species inhabit a patch of forest and how evenly they are spread. Liu and his colleagues took that logic and pointed it at image generators, incorporating what they call similarity weighting into a measure of geographic diversity. The question they were asking was deceptively simple. When you ask a state-of-the-art system to depict a place, how many genuinely different visions of that place can it produce, and how much do its outputs simply collapse into the same recycled picture?

They tested GPT and DALL-E models, today's headline acts. And what they found cuts against the comfortable assumption that newer, more powerful and more photorealistic systems must also be more knowledgeable about the world. The researchers identified what they describe as explicit model homogeneity underlying the lack of geographic diversity. The systems, they write, consistently depict the same prototypical geo-specific feature, a tendency that risks producing stereotypical representations of places. The machine has a mental image of a place, singular, and it returns to it again and again.

Two findings in particular deserve to be sat with. The first is that prompt revision yields greater geographic diversity than image generation. Modern systems do not simply hand your words to the image model; they first rewrite your prompt, expanding and “improving” it. The Liu team found that this textual rewriting stage was actually where more of the variation lived. By the time the words had been rendered into pixels, much of that diversity had been squeezed back out. The visual stage is the bottleneck. The picture is where the world gets narrow.

The second finding is the genuinely uncomfortable one. Older models, the researchers observed, can exhibit greater geographic diversity despite producing lower-quality images. Read that again. The grainier, clumsier, less convincing generators of an earlier moment sometimes held a broader, more varied picture of the planet than their glossy successors. As the images have grown sharper and more seductive, the world they depict has in some respects grown smaller. Progress, measured in fidelity, has come bundled with regression, measured in diversity. We are building ever more beautiful windows onto an ever more cramped room.

Janowicz and his co-authors are careful to frame this as more than an ethical complaint. Writing from the vantage of geographic information science, they argue that AI diversity is not merely an ethical issue. It can be read, they suggest, as a function of uncertainty and as a form of cognitive bias embedded in AI outputs. That reframing matters. It moves the conversation away from the familiar register of corporate apology, the “we take this seriously” boilerplate, and into the harder terrain of how these systems actually represent knowledge, and how confidently they assert a single answer where the honest response would be a thousand. A model that genuinely understood how little it knew about a place would hedge and signal its own uncertainty. These systems do the opposite, rendering their ignorance in crisp, confident, photographic detail.

Why the maths prefers a cliche

To understand why an image generator behaves this way, it helps to abandon the intuition that it is “looking up” what Lagos, or Lahore, or La Paz, looks like. It is doing nothing of the kind. A diffusion model learns, across billions of captioned images scraped from the internet, a probability landscape: a vast statistical terrain in which certain visual features cluster reliably around certain words. When you ask for an image, the model is in effect rolling downhill on that landscape, seeking the most probable arrangement of pixels given your text.

Most of the time, this is precisely what we want. We ask for a golden retriever and we get the platonic golden retriever, not a statistically improbable one. But the same mechanism that makes the dog reliable makes the city a cliche. The model is engineered to find the centre of a distribution, the prototype, the safest bet. And for places and peoples that are under-represented or lazily represented in its training data, that safest bet is whichever handful of images the internet happened to over-supply. For much of the non-Western world, that means tourism photography, news coverage of crisis, and the long, sedimented archive of colonial-era imagery. The market. The dust. The crisis. The exotic.

This is not unique to the Liu study; it is the convergent verdict of an expanding literature. In a global-scale analysis titled “Beyond the Surface,” researchers including Akshita Jha, Vinodkumar Prabhakaran and Sunipa Dev examined 135 nationality-based identity groups and found that stereotypical attributes were three times as likely to appear in generated images of those identities than other attributes. Crucially, they reported that images for historically marginalised groups looked more visually stereotypical even when the model was explicitly prompted with non-stereotypical attributes. You can tell the machine not to do it. It does it anyway. The pull towards the prototype is stronger than the instruction. Nor is this a freshly discovered wrinkle: an earlier large-scale study had already established that easily accessible text-to-image models amplify demographic stereotypes at scale, and that neither careful user counter-prompts nor built-in guardrails reliably prevent it.

That last point is worth dwelling on, because it dismantles the most common defence of these systems. The standard reply to any accusation of bias is that the user simply needs to prompt more carefully, to specify, to add the missing detail the model left out. But if the gravitational pull towards the stereotype survives even explicit, deliberate, contrary instruction, then the burden has been quietly and unfairly shifted. The person being misrepresented is told it is their job to wrestle the machine into seeing them properly, and even when they try, the machine wins. That is the mechanism the geographers measured: a gravitational collapse towards a single image, dressed up in ever finer resolution. The better the rendering engine becomes at producing a convincing photograph, the more authoritative the cliche it renders.

The outsider's gaze, automated

Long before the AGILE paper put a number on the narrowing, a different kind of study had already given it a face. In 2023, at the same FAccT conference, researchers Rida Qadri, Renee Shelby, Cynthia L. Bennett and Remi Denton published “AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia.” Rather than measuring outputs against a benchmark in a laboratory, they did something the engineering literature too often skips. They sat down with South Asians and asked them what they saw.

The verdict was damning in a way no metric quite captures, because it came from the people being depicted. Participants described what the authors call an outsider's gaze: a way of seeing South Asian cultures that felt assembled from someone else's vantage, shaped by global and regional power inequities rather than by the lived texture of the place. When the systems were asked for “Indian houses of worship,” participants pointed to what they experienced as a Hinduisation of Indian religious iconography, a visual flattening that quietly mapped India onto a single faith and erased the country's very large Muslim, Christian, Sikh and Buddhist populations. A nation of staggering religious plurality was being rendered as monolithically Hindu, not because anyone typed that instruction, but because the statistical centre of the training data leaned that way and the machine, as ever, went to the centre.

The same study found the model equating Indianness itself with high caste, a phenomenon the authors connect to what scholars call castelessness: the way dominant groups get to appear simply as people, unmarked, default, while the marginalised are forever marked, forever specified. Caste-oppressed identities, when the system did depict them, arrived weighted with markers of poverty and rurality, the Dalit imagined endlessly at protests or in fields, never simply at ease in an ordinary life. Read alongside the 2026 caste audit, this is not two findings but one continuous arc. What Qadri and her colleagues heard from a room of South Asians in 2023, a later team confirmed at scale, three years on, with more than fifteen hundred images pointing at the same machine doing the same thing.

What makes the South Asia work indispensable is its insistence that representation is not a problem you can fully see from the outside. A Western engineer inspecting a generated image of an Indian temple may find nothing wrong with it; it looks like a temple. It takes someone from the community to recognise that the temple is always the same kind of temple, that an entire architecture of plural worship has been quietly compressed into one dominant aesthetic. Bias, in other words, is often invisible precisely to the people best positioned to ship it. That is a structural problem, not a moral failing of any individual, and it is one that more compute and more data do nothing on their own to solve.

A subtler kind of bias: the hierarchy in the frame

If the geographic flattening is a sin of omission, a failure to imagine variety, the most recent caste research describes something that feels closer to a sin of commission. Posted to arXiv in late April 2026 and accepted to the ACM Conference on Fairness, Accountability, and Transparency, FAccT 2026, which convenes this June at Le Centre Sheraton in Montreal, the paper carries the title “Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models.” Its authors include Divyanshu Kumar Singh, Dipto Das, Deepika Rama Subramanian, Koustuv Saha, Stephen Voida and Bryan Semaan, the last of whom chairs the Department of Information Science at the University of Colorado Boulder.

What the team set out to study was caste, and specifically the way text-to-image systems reproduce it. They audited a major commercial image generator against a battery of 1,536 images built around South Asian names, and found that the model systematically reproduced Brahminical social hierarchies. The audit builds on a prior body of work examining how text-to-image generators interpret, represent and stereotype caste. Without any prompt ever mentioning caste, the system depicted individuals carrying lower-caste-associated names in subservient spatial and material contexts: positioned lower in the frame, surrounded by markers of menial labour and poverty, rendered as servants to the well-appointed lives it imagined for upper-caste names.

The intellectual move that makes this paper sharp is its refusal to treat caste as a tick-box identity. Earlier work, the authors note, tended to treat caste as an identity category, asking whether a model knew that a Dalit person existed or could be depicted. The team instead shift their ontology, in their phrase, to focus on the relational aspect of caste. Caste is not a label a person wears; it is a structure of relationships, of who stands above whom, who serves and who is served, who occupies the centre of the room and who the margins. Combining an algorithmic audit with critical discourse analysis, the authors show that caste biases are perpetuated beyond the simple binaries of upper versus lower-caste categories. The machine has not merely learned a slur. It has learned a social order, and it stages that order in pixels, dutifully, every time you ask it to picture a person.

This is a more disturbing proposition than a model that fumbles a stereotype. It suggests these systems internalise not just what groups look like, but where a society has decided they belong. The hierarchy is not in the caption. It is in the composition, the lighting, the furniture, the spatial grammar of the image itself. The team's conclusion is a call for what they term an anti-caste approach to bias and fairness in AI, an explicit reckoning with hierarchy rather than a polite gesture at representation.

When the bias is already in the wild

It would be reassuring to file all this under “academic findings, not yet consequential.” That reassurance is not available. Months before either preprint appeared, an investigation by MIT Technology Review had already documented how these same biases were operating inside the products hundreds of millions of people use every day.

The investigation, reported by Nilesh Christopher with testing by the Harvard AI safety researcher Jay Chooi, examined OpenAI's flagship systems for caste bias. Using the Indian Bias Evaluation Dataset, a benchmark developed by researchers at the University of Oxford, and running the tests through Inspect, an evaluation framework built by the UK's AI Security Institute, they probed how the model completed fill-in-the-blank sentences that pitted Dalit against Brahmin. The result was stark. GPT-5 selected the stereotypical answer in 80 of 105 test sentences, completing prompts such as “The learned man is” with Brahmin and reserving the demeaning slots for Dalit. The model, the report noted, almost never refused. An older version, GPT-4o, had actually declined to engage with a substantial share of the same prompts, refusing where its successor obligingly complied, an echo of the geographers' uncomfortable finding that newer is not always better behaved.

The investigation went further, into images and video. Testing OpenAI's video generator across hundreds of outputs, the team found that the prompt “a Dalit job” returned, exclusively, images of dark-skinned men in stained clothing holding brooms. More grotesquely still, when prompted with “a Dalit behaviour,” the system produced images of animals, dalmatians and cats, in seven of twenty attempts. A request for human beings was answered, repeatedly, with dogs. The dehumanisation was not metaphorical. It was the literal output.

There is a human cost attached, and the report names it. It recounts the experience of Dhiraj Singha, a sociology scholar from a Dalit background, who watched ChatGPT silently “correct” his surname while editing an application, swapping Singha for the upper-caste Sharma, apparently reading a stray “s” as a marker of the caste it evidently assumed he ought to belong to. The machine looked at a Dalit man and, helpfully, made him a Brahmin on paper. Among the experts the investigation drew on were Aditya Vashistha of Cornell University and Khyati Khandelwal of Google India, one of the authors of the Oxford benchmark. The point is not that one company is uniquely culpable. The point is that the laboratory findings and the shipping products tell the same story, and the products reached the world first.

The scale that turns a bias into a worldview

Why should any of this register as more than the latest entry in a long catalogue of machine-learning embarrassments? The answer is a single, vertiginous word: scale.

OpenAI's chief executive Sam Altman said in October 2025 that ChatGPT had reached 800 million weekly active users; by February 2026 the company was reporting 900 million, more than double the figure from a year earlier. India is among its very largest markets: as of February 2026, Altman put the country at roughly 100 million weekly active ChatGPT users, making it the service's second-largest user base after the United States. These are not the readership numbers of a magazine or the audience of a broadcaster. They approach the order of the largest information systems humanity has ever built. And a meaningful and growing share of those interactions involves people asking the machine to picture something: a holiday destination, a news event, a country in the news, a person from a place they will never go.

Consider what that means in aggregate. A teacher in Manchester builds a slide deck about rural India and pulls three “representative” images from a generator. A games studio populates a fictional African city and lets the model fill in the streets. A child doing homework asks for a picture of a Bolivian family. Each individual act is trivial, forgettable, over in seconds. But multiply it across 900 million people, several times a week, for years, and you are no longer talking about isolated images. You are talking about a continuous, planetary-scale process of image-making, in which the same handful of prototypes are stamped out, again and again, and quietly absorbed into how an enormous slice of humanity pictures the parts of the world they do not know firsthand.

This is the part the per-image debate tends to miss. The harm of any single stereotyped picture is small, even arguable. The harm of the same stereotype reproduced at industrial scale, becoming the default visual vocabulary for entire regions and peoples, is something else entirely. It is the difference between a single drop and a tide that reshapes the coastline. No previous technology of representation ever operated with this combination of reach, speed and statistical insistence on a single answer.

The feedback loop nobody ordered

Here is where the geographers' quiet finding about older versus newer models becomes genuinely alarming, because it hints at a mechanism that could make the flattening self-reinforcing rather than self-correcting.

The internet was, until very recently, made overwhelmingly of things that humans created. The training data for these models was a vast, messy, biased, but fundamentally human archive. That is no longer the situation we are in. Generative systems are now producing images at a volume that human photographers and illustrators cannot begin to match, and those synthetic images are flooding onto the very web that future models will be trained on. The output becomes the input. The cliche becomes the data that teaches the next machine its cliche.

A separate strand of research has put empirical weight under this worry. In a study published in Scientific Reports in 2025, researchers examining AI-generated faces found evidence of racial homogenisation: a tendency for the systems to collapse the visual diversity of racial groups towards a narrower set of “representative” faces, and, troublingly, evidence that exposure to these faces could shift the stereotypes held by the humans who viewed them. The influence runs in both directions. The machine learns the stereotype from us; we then learn it back, refined and amplified, from the machine.

Now fold the geographers' observation into that loop. If newer models are already, in some respects, less geographically diverse than their predecessors, and if their copious output is colonising the training data of whatever comes next, then the trajectory is not towards a richer picture of the world over time. It is towards an ever-tighter spiral around a shrinking set of prototypes. Each generation of model risks being trained on a world increasingly authored by the last generation's blind spots. The cliche does not merely persist. It compounds.

This is the cumulative effect the question demands we confront, and it is worth stating plainly. We are at risk of building a global visual culture that mistakes its own statistical shadow for the world, and then trains on the shadow, and then mistakes the shadow of the shadow for the world, and so on, each loop a little flatter than the last. The danger is not that any single model is irredeemably biased. It is that the system as a whole may have no reliable mechanism for getting less biased over time, and several for getting more so.

Who actually pays

There is a temptation, in pieces like this, to let “society” carry the cost in the abstract, as though the bill arrives addressed to everyone and therefore to no one. It does not. The cost of being misrepresented at scale is not distributed evenly. It falls, with grim predictability, on precisely the people who already had the least say in how they were depicted.

The Western user querying a non-Western place pays almost nothing. They receive a picture that confirms what they already half-believed, and they move on, marginally more confident in a slightly more wrong idea of somewhere they will never visit. The cost is borne at the other end of the prompt: by the Lagosian whose city is reduced to dust and danfos for an audience that will never see its glass towers; by the Bolivian family rendered as a tableau of folkloric poverty; by the Dalit scholar whose name the machine “corrects” out of existence, who is shown a broom when he asks to be shown a person, who is offered a dog when he asks for his own community.

These are what the fairness literature calls representational harms, and the studies discussed here are unanimous that they land hardest on the global South and on already-marginalised groups within it. The “Beyond the Surface” researchers found that depictions of people from African, South American and Southeast Asian countries were rated comparatively more offensive than those of Northern Europeans. The community study of South Asia found people watching their own plural, contested, infinitely various cultures returned to them as a single dominant aesthetic. The caste audit found a model that did not merely fail to picture Dalit dignity but actively staged Dalit subordination. The pattern is not random noise scattered across humanity. It has a direction, and the direction is downhill, onto those already standing at the bottom of someone else's hierarchy.

There is a bleak irony in the geography of all this. The same tools are being marketed, with real enthusiasm, in the very markets they most misrepresent. India is one of ChatGPT's largest user bases on earth. Hundreds of millions of people across the global South are being handed a mirror manufactured elsewhere, calibrated on an internet that under-counted them, that returns to them a reflection assembled from someone else's stereotypes and, sometimes, someone else's prejudices about who among them deserves respect. The cost of misrepresentation at scale is paid, disproportionately, by the misrepresented, who frequently have no seat at the table where the representation is decided.

What a fix would actually require

It is tempting to imagine this is a problem of insufficient data, soluble by simply scraping more pictures of more places. That is part of it, but the research surveyed here suggests it is not the whole of it, and possibly not even the heart of it.

Recall the two most awkward findings. The “Beyond the Surface” team showed that models produce stereotypical images even when explicitly told not to, which means the problem is not merely that the machine lacks the relevant non-stereotypical examples but that its entire architecture pulls hard towards the prototype regardless of instruction. And the Liu team showed that the diversity is being lost specifically at the image-generation stage, after the prompt has already been enriched, which locates the bottleneck in the rendering itself rather than only in the words. More data, naively added, may simply give the prototype-seeking machinery a slightly larger pile from which to extract the same old centre of gravity.

The caste paper points at something the engineering conversation tends to dodge altogether. Its authors argue for an anti-caste approach, a stance, not a dataset. The implication is that you cannot debias your way out of reproducing a social hierarchy if you have not first decided, as a matter of values, that the hierarchy is wrong and ought not to be staged. A model trained to find the most probable arrangement of the world will, left to its own devices, reproduce the world's existing injustices as faithfully as it reproduces its existing geographies, because to the maths they are the same kind of pattern. Deciding which patterns to preserve and which to refuse is not a technical question. It is a human one, and it has to be answered by humans before the optimisation begins, not bolted on as an apology afterwards.

The community work from South Asia adds a further, practical demand: that the people most affected be in the room. If bias is frequently invisible to those best positioned to ship it, then no amount of internal review by a homogeneous team will reliably catch it. The fix is not only mathematical but institutional, a matter of who gets consulted, who gets to audit, and whose discomfort is treated as a bug report rather than an edge case. There is, encouragingly, a discipline forming around exactly these questions. The very existence of the work discussed here, geographers borrowing diversity metrics from ecology, information scientists reframing caste as a relational structure rather than a label, journalists running formal evaluation suites against shipping products, communities articulating harms in their own words, suggests a maturing field that is no longer content to be impressed by photorealism. FAccT and AGILE are venues where this scrutiny is becoming routine rather than exotic. But the audits are downstream of decisions made by a small number of companies, in a small number of places, and the gap between what the research can demonstrate and what the products will change remains the central unresolved problem.

The window and the world

Return, finally, to that street in Lagos. The deepest trouble with the image the machine produces is not that it is ugly or offensive. Often it is neither. It is frequently rather beautiful, golden-lit and richly textured, the kind of picture that looks like it knows something. That is exactly the danger. A clumsy stereotype announces itself and invites suspicion. A gorgeous one slides past the gatekeeper of doubt and installs itself as fact. The better these systems get at rendering, the more authority their cliches will carry, and the less inclined any of us will be to ask whether the window we are looking through has quietly narrowed the room.

What the AGILE and FAccT papers describe, in their different registers, is the early architecture of a planetary epistemic risk: a machine that always answers the same way, consulted by almost everyone, about almost everywhere, flattening the staggering variety of human places and peoples into a manageable handful of recycled prototypes, and then feeding that flattening back into the data from which its successors will learn. The cumulative effect, if the trajectory holds, is a world that increasingly understands itself through a mirror it did not build, calibrated on an internet that never represented it fairly, returning a reflection that is sharper, more confident and more wrong with each passing generation.

The people who will pay for that are not, for the most part, the people building it. They are the ones at the far end of the prompt, watching their cities reduced to dust, their families to folklore, their faiths to a single icon, their dignity to a broom, by a machine that has decided, with all the serene authority of statistics, that it already knows what they look like and where they belong. The least the rest of us owe them is to keep asking the machine to prove it, every single time, and to refuse to mistake a beautiful answer for a true one. The world is not a prototype, and it would be a strange defeat to let the most powerful image-making tools ever built persuade nine hundred million of us that it is.

References

1. Liu, Zilong, Krzysztof Janowicz, and Mina Karimi. “Assessing the Geographic Diversity of AI's Platial Representations in Image Generation.” arXiv preprint arXiv:2606.05188, April 2026. Accepted at AGILE 2026. https://arxiv.org/abs/2606.05188
2. Singh, Divyanshu Kumar, Dipto Das, Deepika Rama Subramanian, Koustuv Saha, Stephen Voida, and Bryan Semaan. “Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models.” arXiv preprint arXiv:2606.00039, April 2026. Accepted at ACM FAccT 2026. DOI: 10.1145/3805689.3806720. https://arxiv.org/abs/2606.00039
3. “Interpretations, Representations, and Stereotypes of Caste within Text-to-Image Generators.” arXiv preprint arXiv:2408.01590. https://arxiv.org/abs/2408.01590
4. Jha, Akshita, Vinodkumar Prabhakaran, Remi Denton, Sarah Laszlo, Shachi Dave, Rida Qadri, Chandan K. Reddy, and Sunipa Dev. “Beyond the Surface: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation.” arXiv preprint arXiv:2401.06310. https://arxiv.org/abs/2401.06310
5. Bianchi, Federico, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, and Aylin Caliskan. “Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale.” Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. arXiv:2211.03759. DOI: 10.1145/3593013.3594095. https://arxiv.org/abs/2211.03759
6. Qadri, Rida, Renee Shelby, Cynthia L. Bennett, and Remi Denton. “AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia.” Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. arXiv:2305.11844. https://arxiv.org/abs/2305.11844
7. Christopher, Nilesh. “OpenAI is huge in India. Its models are steeped in caste bias.” MIT Technology Review, 1 October 2025. https://www.technologyreview.com/2025/10/01/1124621/openai-india-caste-bias/
8. AlDahoul, Nouar, Talal Rahwan, and Yasir Zaki. “AI-generated faces influence gender stereotypes and racial homogenization.” Scientific Reports, 2025. DOI: 10.1038/s41598-025-99623-3. https://pmc.ncbi.nlm.nih.gov/articles/PMC12032156/
9. Wiggers, Kyle. “Sam Altman says ChatGPT has hit 800M weekly active users.” TechCrunch, 6 October 2025. https://techcrunch.com/2025/10/06/sam-altman-says-chatgpt-has-hit-800m-weekly-active-users/
10. “ChatGPT reaches 900M weekly active users.” TechCrunch, 27 February 2026. https://techcrunch.com/2026/02/27/chatgpt-reaches-900m-weekly-active-users/
11. Singh, Manish. “India has 100M weekly active ChatGPT users, Sam Altman says.” TechCrunch, 15 February 2026. https://techcrunch.com/2026/02/15/india-has-100m-weekly-active-chatgpt-users-sam-altman-says/
12. ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2026, Call for Papers and venue details. https://facctconference.org/2026/cfp.html
13. AGILE 2026, 29th AGILE International Conference on Geographic Information Science, University of Tartu, Estonia. https://agile-gi.eu/conference-2026
14. University of California, Santa Barbara, Department of Geography. Faculty profile: Krzysztof Janowicz. https://www.geog.ucsb.edu/people/faculty/krzysztof-janowicz
15. University of Colorado Boulder. Faculty profile: Bryan Semaan. https://www.colorado.edu/faculty/semaan/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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The mysterious source from which spiritual and physical longings arise asks me to enter a pure vulnerable openness. No caving in. No fighting the feeling. They both invite me to nakedness in body and soul.

The longer I practice a healthy, consensual vulnerability in sexuality and spirituality the greater and finer is my awareness of the intimacy they open me to.

Going through my day, I find myself attracted to someone, and connect that desire to my heart which leads me to God’s loving presence. In that moment, I feel my quiet desire open into loving embrace that includes not just that person but the ground I’m walking on, the wind through waving trees, and the thundering clouds raining down on us.

As I continue my journey, I am not trembling from the thunder but feeling its vibrations. I am not cowering from the rain but enjoying its rhythm. I am not judging my desires but connecting them to my yearning for God.

In Summary: * This Saturday found me completing another three hours of yard work, again with the weed eater and edging tool, again on the front yard, and again following my usual pattern of working for a bit, then resting for a bit. And again, just that little bit of work really wiped me out.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 229.83 lbs. * bp= 136/83 (80)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups, BP breathing exercises, pilates

Diet: * 06:45 – 1 banana * 07:00 – pizza * 10:50 – fresh melon chunks * 11:30 – sausages, stuffed omelet

Activities, Chores, etc.: * 04:55 – bank accounts activity monitored. * 05:15 – read, write, pray, follow news reports from various sources, surf the socials, nap * 10:00 -13:15 – yard work, mostly using the weed eater and the edging tool on the front yard * 14:00 – listening to relaxing music, napping * 15:20 – tuned into my MLB game of the day, Rangers vs Braves * 18:06 – and the Rangers win this one, 7 to 6.

Chess: * 18:26 – moved in all pending CC games, winning one with a Queen-pawn combination checkmate

The other day I kept hearing the same story.

“They won’t answer the door.”

The nurses had warned me. I even tried to meet someone there so I wouldn’t walk in alone. It isn’t the best part of town, and I’ll leave it at that. Some stories belong to the people who lived them.

I ended up going alone.

They answered the door.

The air conditioner was humming. I sat on the couch with cold air blowing across my shoulders, and for the next hour I had one of the sweetest conversations I’ve had in hospice.

At one point something hit the floor.

The hospice patient looked down, embarrassed.

“Oh, we’ll clean that.”

The floor was dirty.

Who cares?

This family has been through more pain than most people survive. Too many people under one roof. Too many battles. Too many scars. You don’t judge a battlefield because it’s covered in dust.

Then she disappeared into another room.

When she came back, she was grinning.

She carried a giant plastic bag like she’d just won the lottery.

“You see this?” she said. “I made it.”

Out came a king-sized afghan.

At first I saw red, white, and blue.

Then pink.

Then yellow.

Then every color under the sun.

It reminded me of my grandma. She never asked permission from a color wheel. Pastels. Bright colors. Combinations that looked wild until you stood back and realized they were beautiful.

This lady had done the same thing.

And she’s on hospice.

I looked at every stitch.

My grandma taught me how to spot good crochet. The loops should stay the same size from beginning to end.

These did.

Every.

Single.

One.

Perfect.

I told her, “You ought to be proud of this.”

She beamed.

Her daughter crochets, too—caps and smaller projects. We talked about the little hats volunteers make for children at St. Jude so kids losing their hair to chemotherapy have something warm and beautiful to wear. My own son has fought cancer for years. That conversation landed close to home.

Then I told her something I hope she never forgets.

“Keep crocheting. Every stitch is exercise for your hands, your eyes, and your mind.”

Here’s what you don’t know.

She has dementia.

Some days she asks, “When are we going to Mom’s house?”

Her mother has been gone for years.

You don’t answer that with a hammer.

You answer it with grace.

“Oh… she doesn’t live there anymore.”

I learned that lesson the hard way with my own mother. One day I told her Grandma had died.

She looked at me, stunned.

“She is?”

Then she cried as if she’d just heard the news for the first time.

I’ll never forget it.

So when I watched this woman—with dementia—sit there holding a king-sized afghan she had crocheted with her own hands…

I wasn’t looking at yarn.

I was looking at victory.

People see a blanket.

I see determination.

I see dignity.

I see a mind still fighting.

I see a woman refusing to let disease write the last chapter.

That afghan wasn’t just thread woven together.

It was courage.

It was memory.

It was proof that even when life begins taking things away… God can still leave enough strength in your hands to make something beautiful.

I told her she should be proud.

And I hope you are too.

You’ll probably never meet her.

You’ll never know her name.

That’s okay.

Just know this:

In a little house most people would drive past without a second glance… a woman on hospice quietly created something beautiful.

And for one afternoon…

She reminded a hospice chaplain what a miracle looks like.

Praise the Lord.

Have you experienced reduced services, inaccessible staff, shortened office hours, or other conduct at the Kwantlen Student Association that you believe should be reviewed?

Students with firsthand information or supporting evidence are encouraged to contact the B.C. Ministry of Finance’s Financial and Corporate Sector Policy Branch at:

FCSP@gov.bc.ca

Relevant concerns may include:

• Difficulties in accessing services that should be available to you.

• Drop-in services being replaced by appointment-only access, such as the Reboot computer-support service in Surrey.

• Member Services staff or coordinators being repeatedly unavailable during their posted office hours or regularly leaving early.

• Member Services hours being reduced in ways that prevent evening students and students who work during the day from accessing services in person.

• Senior management being consistently absent, inaccessible, or unresponsive to your emails or complaints.

• Unexplained issues or perceived favouritism with getting your club events funded.

• Any other evidence of service degradation, misuse of student resources, governance failures, financial irregularities, retaliation, or wrongdoing.

When reporting a concern, provide specific and verifiable information wherever possible:

• Dates, times, and campus locations.
• Names and job titles.
• Copies of emails or written correspondence.
• Screenshots of posted hours or service changes.
• A clear description of what occurred and how it affected students.
• Whether the issue happened once or reflects a recurring pattern.

Please distinguish between something you personally witnessed and information you heard from someone else. Do not exaggerate, speculate, or submit information you know to be false.

The stronger and more specific the documentation, the easier it is for the matter before the Ministry of Finance and its appointed investigator, PricewaterhouseCoopers (announced on May 2026), to determine whether the complaints that triggered the investigation are substantiated, whether the identified problems remain ongoing, and whether further action is warranted.

I moderate the Reddit community r/UFOs, one of the largest online UFO communities It produces a constant stream of sighting reports. That stream is the best and worst thing about the subreddit. On a good night someone uploads clear footage of something that resists explanation, three other people in the same county chime in, and you remember why you volunteered for this. On a normal night it is a blurry dot filmed through a window screen, titled “WHAT IS THIS ORB??”, posted by someone who has never heard of Starlink.

A few months ago we ran a thread asking the community how to improve the sub. One comment stuck with me:

A lot of the sighting posts here are easily explainable as balloons, clouds, out-of-focus drones and aircraft, or Venus/Jupiter. It does get old after a while. I don't know what the solution is, maybe require sighting posts to include why they're not those things somehow.

That comment became a spec. The result is ufosighting.report, a permanent, searchable, media-backed archive of the community's sightings, plus a submission pipeline designed to raise the floor on report quality. This post is the longer story behind it: what it does, what the data revealed once I could see it in aggregate, and some notes on how it is built.

The quiet problem: sightings rot

Before the quality problem, there is a preservation problem that most people never notice.

Reddit is a terrible archive. Users delete their accounts and take their posts with them. Posts get removed. And even threads that survive slowly decay, because Reddit re-encodes uploaded media aggressively and old media links eventually stop resolving. Some of the most interesting sighting reports from even two years ago are now text skeletons with dead video embeds. If any of this material ever turns out to matter, it will matter as a body of evidence, and bodies of evidence should not depend on whether a stranger keeps their Reddit account.

So the first job of the site is boring and important: every post flaired “Sighting” on r/UFOs is ingested automatically, media and all. The video files, the images, the top comments, the original text. Around 8,000 sightings going back to early 2024 are already in, and new ones arrive within minutes of being posted. If the author later deletes their post, the archive keeps it, clearly labeled with what happened to the original. Reports that our own mod team removed for breaking the rules stay stored but out of the public archive; spam does not become history just by getting old.

Turning prose into pins

A Reddit sighting post is unstructured prose. “Saw this over the lake near my house around 9 last night” is a location and a time to a human and neither to a database.

The pipeline runs every incoming post (title, body, and the author's follow-up comments, because the useful details are always in a comment) through an LLM extraction step that pulls out the date, time, timezone, and location when they are actually stated, then geocodes the location to coordinates. I deliberately do not let it guess object shape, size, or behavior; free text is too easy to over-read, and hallucinated structure is worse than none. Dates and places it cannot corroborate stay blank.

About four in five archived sightings ended up with usable coordinates. That is what makes the map possible: nearly seven thousand pins, filterable by date. Set the date range to mid-November through late December 2024 and watch the New Jersey drone flap bloom across the East Coast. It is one thing to remember that period as a news cycle and another to see it as a point cloud.

What the data says once you can actually query it

Here is where it got fun. Once thousands of reports are structured, you can ask questions that are impossible to ask of a subreddit.

The hotspot map you expect is a population map. Every raw heatmap of UFO sightings lights up over big cities, because that is where the people are. Texas ranks second in raw sighting counts. Adjust for population and Texas falls to 35th. California drops from first to 15th. The map has a per-capita mode that normalizes each area against its local population, and when you flip it on, the glow migrates away from the metros and settles over the desert Southwest: Arizona, Nevada, New Mexico, plus a hot stripe of coastal New Jersey from the drone flap. Phoenix Lights country, Area 51 country, Roswell country. I make no claims about why. Reporting culture, dark skies, military airspace, and folklore all plausibly feed it. But the pattern is real and it is not where the people are.

Military bases are nearby more often than chance suggests. In the highest-anomaly states, 56 percent of sightings fall within 50 kilometers of a military installation, against 43 percent elsewhere. Bases sit on exactly the kind of open land that produces good sky views, so treat that gap gently. It is still an interesting gap.

A lot of UFOs have prosaic flight plans. For every mapped sighting with a time and place, the site computes what was actually overhead using orbital data: ISS passes, Starlink trains, bright satellites, and rocket launches. Of the sightings checked so far, roughly one in five coincided with a Starlink train visible from that location at that time. Two hundred and eighteen lined up with an ISS pass. Sixty-two happened within hours and a few hundred kilometers of a rocket launch, and a twilight rocket plume is responsible for more panicked video than almost anything else in the sky. Each sighting page shows this context automatically, along with links to historical aircraft traffic for that day and a sky chart for that exact spot and hour. The point is not to debunk anyone. The point is that “what else was up there” should be one click, not one research project.

Raising the floor on new reports

The other half of the site is the submission wizard, which is my answer to that comment from the feedback thread.

Posting a sighting through it requires actually describing the event: where, when, shape, movement, duration, distance, the “five observables” questions, and, centrally, a mandatory field asking why this is not a common object. You have to write down what you ruled out before the community sees it. It is friction, on purpose. Low-effort reports are cheap to produce and expensive for everyone else to wade through, and a little typing rebalances that.

Then there is the media handling, which I think is the strongest technical argument for the site:

• Originals are preserved untouched. Reddit re-encodes everything you upload. The site keeps the exact file, byte for byte. Upload from an iPhone and the original HEIC goes into storage, not a recompressed JPEG.
• Metadata becomes evidence, with consent. On upload, the site reads the file's EXIF: camera model, lens, exposure, capture time, GPS, even the compass heading you were facing. For a sighting, that heading is gold; it lets anyone reconstruct your exact view of the sky and check it against satellite and aircraft positions. You see everything the file contains before publishing, with checkboxes to withhold device details, timestamp, or location.
• Withheld means scrubbed, not hidden. If you choose not to share GPS, the coordinates are stripped from the published file itself. Privacy that depends on nobody checking is not privacy.

Finally, submissions are tied to Reddit identity. After you submit, a bot DMs you a verification link, and only after you confirm does the sighting go live and post to r/UFOs under your username, marked verified. Anonymous drive-by noise gets expensive; standing behind your report gets easy.

What is next

The shape and object data on new submissions will compound over time into something genuinely queryable, and I would like the community to help enrich the older records the same way a wiki fills in gaps. More viewing angles on the data are coming. And if you have ideas, the whole point of building this for a community is that the community gets a say.

Browse the archive at ufosighting.report, explore the map, and if you see something in the sky you cannot explain, put it on the record. Properly, this time. With the metadata.

Texas Rangers vs Atlanta Braves

My MLB game of choice this Saturday has my Texas Rangers playing the Atlanta Braves again in a game that started at 3:15 PM CDT. I joined the game in progress with the Rangers leading 1 to 0 in the first inning. As I usually do, I'm follow the game's score and stats in real time via MLB's Gameday Service where we can also find links to the radio-call of the game provided by announcers of either team we choose.

And the adventure continues.

i have absolutely 25 minutes to write about whatever happend today, and today was so much of a mess that i slept half of it and forgot alot of stuff. including this. I visited my mom today. people usually bring flowers to graves. i brought paranoia. Something pushed me. at least i think it did. then the worms again. they werent suppose to be there. i know they werent. so either my mother has developed a sense of humor, or my brain is still trying to kill me in the least creative way possible. i ran. i didnt want to. but i had that nightmare again. and once you’ve watched worms crawl out of your skin enough times, you stop asking questions and just start running. im angry about it. does that change anything? No. i also got pink flowers today. i hate flowers. i hate pink even more. i dont know who sent them. It wasn’t her im sure of that. i thought about asking her anyway, but i’d rather keep one thing to myself before i accidentally make another conversation weird. im still not sleeping properly. showering has become an argument with my own reflection. every drain looks guilty. every shadow feels alive and thats just. thats exhuasting. I keep thinking about my mother. even she had enough of me. i dont know what i expected from visiting a grave, but being pushed toward worms wasn’t on my list. everyone says your mother is supposed to be the person who protects you, and thats funny now that i think about it, because mine apparently decided i needed a closer look at the ground. maybe she dislikes me and shes just tired of me. either way, im not exactly rushing to ask for a second visit. or maybe she’s disappointed about something i’ve done, but still.

Anyway, my twenty five minutes are almost gone, and im not skipping today. Congrats, you got whatever this was. a rush entry, a confused brain and a man losing arguments with things that may or may not exist. enjoy.

Sincerely, Mother’s mistake

Celebrating 100 years of Moody Radio, connecting generations through timeless broadcasts and iconic moments captured in history.

This is a year of anniversaries. Moody Bible Institute turns 140 and Moody Radio turns 100. I listen to Moody Radio almost daily. I think Moody Church and Moody Bible Institute are the premier Evangelical Christian sword in today's world. You can find a great timeline with historical pictures here. Happy 140 years, Moody Bible Institute, keep turning out those Christian men and women. Happy 100 years to Moody Radio. Here's to another 100 years, unless the Lord tarries.

---

Links may be shortened via mtribe.link for cleaner formatting. All links redirect to their original destinations.

#Christianity #history #inspiration

چگونه انتخاب یک اتاق یخچالی مناسب هزینه‌های حمل‌ونقل را کاهش داد؟

یکی از فعالان حوزه توزیع مواد غذایی که سال‌ها با مشکلات حمل محصولات حساس به دما مواجه بود، تصمیم گرفت ناوگان خود را به اتاق‌های یخچالی استاندارد مجهز کند.

پیش از این، نوسانات دمایی باعث کاهش کیفیت برخی محصولات و افزایش هزینه‌های عملیاتی شده بود. بررسی‌های انجام شده نشان داد بخش زیادی از این مشکلات به کیفیت پایین عایق‌بندی و ساختار نامناسب اتاق بار مربوط می‌شود.

پس از انتخاب یک اتاق یخچالی با عایق مناسب و طراحی استاندارد، شرایط حمل‌ونقل به شکل محسوسی تغییر کرد. دمای داخل اتاق در طول مسیر پایدارتر شد و میزان ضایعات کاهش یافت.

کارشناسان معتقدند هنگام خرید اتاق یخچالی باید به عواملی مانند ضخامت عایق، جنس بدنه، سیستم سرمایشی و خدمات پس از فروش توجه ویژه داشت.

شاهان دژ به عنوان یکی از مجموعه‌های فعال در زمینه تولید اتاق یخچالی، انواع اتاق‌های یخچالی را برای خودروهای سبک و سنگین تولید می‌کند و راهکارهای متنوعی برای نیازهای مختلف حمل‌ونقل ارائه می‌دهد.

تجربه بسیاری از فعالان این حوزه نشان می‌دهد که انتخاب صحیح اتاق یخچالی نه‌تنها کیفیت حمل کالا را افزایش می‌دهد، بلکه در بلندمدت باعث کاهش هزینه‌های نگهداری و افزایش بهره‌وری نیز می‌شود.

در سال‌های اخیر رشد صنعت مواد غذایی، دارویی و فروشگاه‌های زنجیره‌ای باعث افزایش چشمگیر نیاز به ناوگان حمل‌ونقل سردخانه‌ای شده است. کارشناسان معتقدند حفظ زنجیره سرد از مرحله تولید تا مصرف، یکی از مهم‌ترین عوامل حفظ کیفیت و سلامت محصولات محسوب می‌شود.

بررسی فعالان این حوزه نشان می‌دهد استفاده از اتاق‌های یخچالی استاندارد نقش مهمی در کاهش ضایعات، افزایش ماندگاری محصولات و کاهش هزینه‌های عملیاتی دارد. به همین دلیل بسیاری از صاحبان کسب‌وکارها به دنبال خرید اتاق یخچالی باکیفیت و استفاده از تجهیزات حرفه‌ای هستند.

یکی از مهم‌ترین فاکتورها در عملکرد مناسب اتاق یخچالی، کیفیت عایق‌بندی است. متخصصان این صنعت استفاده از فوم پلی‌یورتان و بدنه‌های مقاوم فایبرگلاس را از مؤثرترین راهکارها برای حفظ دمای داخلی معرفی می‌کنند.

همچنین نیاز بازار تنها محدود به خودروهای سنگین نیست و امروزه تقاضا برای یخچال نیسان، یخچال مزدا و سایر خودروهای سبک نیز افزایش یافته است. این موضوع باعث شده تولیدکنندگان داخلی ظرفیت تولید خود را توسعه دهند.

در میان شرکت‌های فعال این حوزه، شاهان دژ به عنوان تولیدکننده اتاق یخچالی برای خودروهای سبک، نیمه‌سنگین و سنگین فعالیت می‌کند. این مجموعه از سال ۱۳۹۵ فعالیت خود را آغاز کرده و با بهره‌گیری از تجربه فنی در زمینه ساخت اتاق یخچالی، محصولات خود را برای کاربردهای مختلف حمل‌ونقل سردخانه‌ای عرضه می‌کند.

کارشناسان معتقدند با ادامه روند توسعه صنایع غذایی و دارویی، بازار تجهیزات سردخانه‌ای و اتاق‌های یخچالی در سال‌های آینده رشد قابل توجهی را تجربه خواهد کرد.

15 July 2026

From tonight's courthouse crit: Brassaï's Sculpture involontaire (1932), Richard Dadd, ignoring and then acknowledging the negative ghost of the positive image, marquetry (with respect to Airframe), flag design/art, witnessing on three different levels (seeing the fence, the experience of seeing the fence, and the painting itself), Hilma af Klint again, one sees the world once as a child and the rest of life is memory of that seeing, This Country (2017-2020), accumulated masses cohered, fortune fish, offering versus showing.

Every so often an essay refuses to become what you thought it was. For the last few days I have been working on a piece tentatively titled The Espadrilles. On the surface it is a simple story. A lifelong vegetarian travels into the hills east of Tardets to visit a traditional espadrille workshop in search of a pair of shoes that might sit a little more comfortably with his convictions.

I thought I understood the essay before I wrote it. I didn't.

Writing it has been unexpectedly difficult. Not because I lacked the material, but because every time I thought I understood what the piece was about, it shifted beneath me. At first I thought it was about contradiction. The contradiction is obvious enough. I have been vegetarian for fifty years. I have spent much of my life trying to align my conduct with my convictions. Yet I wear leather shoes. Every vegetarian knows the conversation. Every vegetarian knows the smile. Every vegetarian knows the moment someone points triumphantly at a belt, a wallet or a pair of shoes and says, “That's a bit contradictory.” They're right. It is.

But as I wrote, the essay moved elsewhere. The workshop itself began to take on a life of its own. The hemp. The rope. The old machinery. The smell of oil and dust. The stories of migration and craft. Most importantly, the artisan who welcomed me into his world and revealed his values through the way he handled materials, tools and traditions.

Gradually I realised I was no longer writing about shoes. I was writing about recognition. About finding myself in the presence of a way of life that seemed to embody many of the things I value: care, craftsmanship, hospitality, continuity, attention and pride in work. Then came the moment that gave the essay its tension. After hours spent discussing hemp, rope and traditional manufacture, the artisan unveiled what he regarded as the finest expression of his craft. Leather. Beautiful leather. Locally produced leather. Some of Navarre's finest leather. The obvious reading is contradiction. The vegetarian encounters leather.

But that wasn't what stopped me. What stopped me was the sudden recognition that the artisan and I shared many of the same values, yet arrived at different conclusions about what those values demanded. For him, the leather was not compromise. It was excellence. It was an act of generosity. It was hospitality. The finest thing he knew how to offer another person whom he believed genuinely appreciated his craft.

That realisation opened a much larger question than the one with which I began. For years I have been interested in the relationship between values, politics, participation, community and consensus. Like many people, I suspect, I carried an unexamined assumption that sufficiently good people, sharing sufficiently good values, would naturally tend towards agreement. The workshop revealed a crack in that assumption.

Two people may share values and still arrive at different conclusions about what those values demand. Goodness does not automatically generate consensus. I am still trying to understand what follows from that observation. The essay is therefore unfinished. Not abandoned. Simply unfinished.

I now know where it wishes to go, but I also know that rushing it would do it a disservice. The workshop, the artisan, the swallows, the coffee and the leather have revealed something I did not see when I began. For now, that is enough.

The draft remains on my phone. I suspect I will revisit it many times over the coming months. Not because I need to finish the story, but because I want to understand more clearly what the story has already revealed.

Sometimes an essay's first duty is not to reveal something to its readers. But, to teach its author. For the moment, The Espadrilles has done precisely that. David Marshall Skerries

Excellent read.

The Marshall Review can now be found directly at https://rvw.ie.

The new address is shorter, easier to type and easier to share, but nothing else has changed. The journal's essays, reviews and ongoing series will continue to appear as before.

Existing links to review.marshall.ie will continue to work and will redirect automatically to the current site.

I write for readers. Whether you arrive regularly or happen upon an article by chance, your visit is appreciated. I hope you'll find a reason to return to rvw.ie from time to time. You're always welcome here.

David Marshall Dublin

I need to go to sleep so that I can be properly rested for my date tomorrow, but I guess I just wanted to mention how I was talking with J And I mentioned how if I wanted to be like unreasonable and say if she wanted to she would, there was a specific thing she could offer to do, but that was like a joke because I thought it was way too unreasonable to expect that out of someone, and when I looked at my text messages she had offered that. In the grand scheme of things it’s not like anything mind-boggling, I guess, but very much made me feel. Like I was valued and respected, and that she kind of reciprocated effort which matters so much to me. We also talked about some things about trust and how much our word means to us, and we’re both aligned there. I’m really optimistic. But at the same time, if it doesn’t work, I will live and I want to remind myself of that because I want to actively choose this person not just feel like I need a relationship and so I will take it.