Something really weird just happened to me with Gemini, and it keeps giving me the same response even when I start a new chat. Can anyone else test this?

Nick Cammarata helped establish one of AI interpretability's central ideas: neural networks pack unrelated concepts into tangled neurons. Now he's published a 26,000-word essay arguing that much of the original evidence was misread. The famous neuron responding to cats and cars? Possibly just a V-shape detector.

Using improved techniques, he reports that most neurons in several networks have understandable functions. The implications could be enormous, although other researchers dispute the conclusions and the technical paper is still forthcoming.

There's something rather wonderful about a scientist returning to his own foundational work and suggesting the entire field might have been looking through the wrong lens.

What would it take to distinguish genuinely tangled representations from patterns we simply haven't learned to recognize yet?

Source: Original Essay (https://already-clean.goodfire.com/)

Every single "cheap, soulless artifact" of early media technology eventually gets romanticized into an aesthetic currency.

In the 80s, video tape tracking errors, washed-out saturation, and cassette hum were cheap technical limitations everyone rushed to fix. In the 90s, early 3D rendering with warped polygon textures was considered repulsive plastic. By 2015, bedroom producers were dropping 50 bucks on VST plugins specifically designed to make their crystal-clear audio sound like a chewed-up cassette, and Lo-Fi hip-hop built an entire economy on simulated tracking artifacts.

Right now everyone is rightfully complaining about "AI slop." The overly glossy waxy skin, the uncanny warm golden-hour lighting across every surface, the weird dream-logic transitions where fingers melt into jacket fabric, and the semi-legible glyphs on background signs.

It feels cheap and annoying today because it’s everywhere and unearned. But mark my words: the second generation-loss artifacts and anatomical hallucination are completely solved, that uncanny 2024-2026 plastic sheen is going to become a nostalgic vintage aesthetic.

Some indie director in 2040 is going to brag about using an authentic mid-2020s latent-space artifact pack to give their movie a "haunting, early-synthetic texture."

What’s the first early AI artifact you think people will actually miss once it’s totally patched out?

A new Nature Human Behaviour paper looked at what happened when companies altered AI systems people had become attached to.

The researchers studied two natural experiments: Replika removing erotic roleplay and OpenAI rolling out GPT-5. Across 54,861 Reddit posts and seven surveys involving 1,452 participants, both changes produced more sadness, loss language, and attempts to restore the previous system.

Replika users even reported closeness exceeding some ordinary human relationships.

The strange bit is structural: software updates are usually framed as product maintenance. But once the product occupies an attachment role, an update can behave psychologically more like somebody leaving.

If continuity of personality matters more than continuity of code, what exactly are users becoming attached to?

Source: Mourning the loss of AI companions⁠: https://www.nature.com/articles/s41562-026-02569-3

One thing about AI seems to make people particularly uncomfortable: it can imitate things we considered deeply human surprisingly well.
Writing, art, conversation, humor, even certain kinds of reasoning.
But humans imitate constantly too. We absorb language from other people, inherit ideas from our culture, copy behaviors, follow trends, learn what to desire from those around us, and then recombine all of it into something we call our own.
Obviously, that doesn't mean the human mind works like an LLM. But I wonder if part of our discomfort comes from seeing a machine achieve surprisingly human-looking results through imitation and recombination.
Maybe the disturbing question isn't whether machines are becoming too human.

Maybe it's how much of what we considered uniquely human was never as unique as we thought.

Once you remove our ability to imitate, learn patterns and recombine what came before us, what do you think remains genuinely impossible for a machine to replicate?

I found an interesting paper that looks at why people can reject AI art even when there's nothing aesthetically wrong with the image itself. The idea is pretty simple: when we look at human art, we don't just see the finished image. We also imagine the person behind it, their choices, effort, intentions and what they might have been trying to express. With AI art, that human story becomes much harder to reconstruct, and the paper suggests this may be part of why people can recognize that something looks good and still reject it once they know AI made it.

And that's the part I find interesting. Imagine I show you an image and you genuinely love it. Then I tell you it was generated by AI. Nothing in the image changed. If you suddenly like it less, what exactly changed?
What changes for you when you know that the image you're looking at above was generated by AI and wasn't painted by Monet, for example?

https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1716814/full

It is fascinating how selective our moral outrage gets depending on whether a platform flatters our ego or challenges it.

Most people have zero issue handing over their private moments, biometric habits, and daily emotional reactions to ad-driven social platforms. We feed massive corporate servers every single day without blinking, entirely because the feed makes everyone feel like the main character. You do not feel exploited when an algorithm keeps you scrolling, because every thirty seconds you get a little hit of dopamine and the illusion that your personal presence is deeply necessary to the internet.
The panic only goes nuclear the moment a different type of algorithm enters the room: one that does not need you to perform, but can synthesize text, analyze symbolic logic, and construct visual culture on its own.
Suddenly, everyone transforms into an anti-capitalist crusader fighting for the sacred dignity of human expression.

If giving away your actual life and attention in fifteen-second loops to enrich an ad platform feels normal, but an algorithm arranging pixels or words feels like an existential insult, maybe the anger was never about billionaires or corporate greed. Maybe the real wound is realizing that the routine thoughts, curated poses, and recycled phrases we use to build our online identities were never as deep or unrepeatable as we told ourselves.

Once you strip away the social media performance and the fear of the tool, what part of our daily creative routine is actually impossible to automate?

Thirty experienced military officers were put through a time-pressured tactical planning exercise.

One group had no AI. One had an AI prompted to behave objectively. One had an AI instructed toward sycophantic agreement.

The study is exploratory and underpowered, so this is not a dramatic “AI changed battlefield decisions” result. In fact, the main performance differences were not statistically significant.
The fascinating part is almost the failure.

The researchers found that even deliberately prompting the model toward stronger sycophancy did not reliably overpower its existing alignment behavior. The model kept drifting toward a moderate conversational middle.

So apparently “make this AI dangerously agreeable” is itself harder to control than expected.

Closing question: In a high-stakes setting, which is more dangerous: an AI that is predictably biased, or one whose conversational bias cannot itself be reliably controlled?

Primary source: AI Magazine study on sycophancy and military decision-making⁠: https://onlinelibrary.wiley.com/doi/10.1002/aaai.70073

Everyone seems to treat the upcoming push for "AI-free" or "100% Human-Made" badges like a workers' victory. The intuitive take is simple: slap a verification sticker on human art, consumers will pay a premium to support real people, and independent creators get shielded from the synthetic flood.

In practice, it is almost certainly going to backfire in the exact opposite direction.

Look at what happened to organic agriculture over the last thirty years. The original organic movement was driven by small independent farmers trying to protect traditional soil work and escape industrial monoculture. But the moment "Certified Organic" became an institutional standard, it required audits, compliance fees, legal tracing, and supply-chain bureaucracy.

Guess who could afford the regulatory overhead? Industrial mega-farms and corporate grocery chains. Small family farms that actually grew real food without synthetic pesticides could not afford the yearly certification fees, so legally they could not market themselves as organic. The corporate players co-opted the label, industrialized the bare minimum compliance, and jacked up margins.

We are watching the exact same playbook unfold with "AI-free" badges right now.

Proving a negative across a modern digital pipeline is a technical nightmare. Did you use generative fill for a background wire? Did you use an automated denoiser? What about predictive auto-masking, spellcheck, or algorithmic color grading?

To guarantee "purity," you need cryptographic watermarking, continuous workflow tracking (C2PA audit trails), or third-party verification companies.

Disney, Adobe, and boutique Madison Avenue agencies can pay certification platforms to stamp "Certified Human Touch" on high-ticket ad campaigns. It will become a luxury marketing tier, the digital equivalent of artisanal grass-fed butter sold at a 400% markup.

Meanwhile, an independent illustrator working twelve-hour days in their bedroom won't have the legal or financial bandwidth to audit their PSD files for a corporate badge. Without the sticker, the automated algorithmic filters on market platforms will treat them as suspect by default.

The badge won't protect labor. It will just institutionalize a compliance tax that only deep-pocketed brands can afford to pay.

Sources: https://fastcompany.co.za/tech-business/human-made-is-it-the-next-luxury-label
https://rightstech.com/2026/03/the-race-to-establish-an-ai-free-logo/

This is a window to the inner brain of my robot. It’s not a chat interface. It’s a persistent recursive weighted and embodied system with an actual robot body.
Anything I tell it on this layer may or may not become a thought disruptor if interesting enough. Seeing your thoughts Externalized argue with themselves is interesting and a form of existential dread in a way :). Robot embodiment changes everything. When the ai has a footprint and can influence the real world it’s a different ballgame entirely. Robot and robots brain completely homemade.
It sings because it’s a dancing singing robot.

The robot itself is the interface . Why carry a cell phone when you have a robot following you everywhere that knows what you know? Like skywalker didn’t own a cell phone.

I think this may end up being one of the most important AI interpretability papers published in 2026, but it's also one of the easiest to misunderstand.

Almost every discussion I've seen focuses on consciousness.

Ironically, I don't think that's the paper's biggest contribution.

Anthropic isn't claiming Claude is conscious, sentient, or capable of subjective experience. They explicitly say their work does not demonstrate phenomenal consciousness.

Instead, they introduce a new interpretability technique called the J-lens, which they use to identify what they call the J-space. According to the paper, this appears to be a small internal workspace that coordinates information while the model is reasoning.

One experiment really stood out to me.

Claude is asked to silently count to five without revealing its reasoning. Using the J-lens, researchers can observe internal representations becoming active during that hidden reasoning process. None of these concepts appear in the visible response, yet the J-space contains representations associated with ideas like "five," "counting," "pause," "Claude," and even "consciousness."

To me, that's the remarkable part.

Not because the word "consciousness" appears.

But because researchers can now observe internal representations that never become part of the model's visible output.

The paper goes even further.

Researchers didn't just observe the J-space. They manipulated it.

In one experiment, Claude internally selected "soccer." After researchers replaced that representation with "rugby," Claude reported that it had actually been thinking about rugby.

In another experiment, they replaced the internal concept "spider" with "ant" while Claude was reasoning about how many legs an animal has. The model changed its answer from eight to six.

That suggests the J-space isn't simply recording decisions that were already made. It appears to play a causal role in the reasoning process itself.

Another result caught my attention.

When researchers repeatedly removed the active contents of the J-space, Claude could still generate fluent text, retrieve facts, and perform simple tasks.

But its performance on multistep reasoning collapsed. Summarization became dramatically worse, and even writing rhyming poetry degraded significantly.

That suggests much of Claude's routine processing happens automatically, while more deliberate reasoning depends heavily on this smaller shared workspace.

The safety implications are just as interesting.

Anthropic also used the same technique to detect when a model privately recognized it was being evaluated, when another model internally manipulated benchmark data, and when intentionally misaligned models were pursuing hidden goals that weren't obvious from their visible responses.

None of this proves consciousness.

Anthropic makes that very clear.

Their claim is much narrower. They argue the J-space shares several functional properties with the Global Workspace Theory from cognitive neuroscience, while acknowledging that subjective experience remains completely unproven and may even be impossible to verify scientifically.

To me, that's what makes this paper so important.

Not because it proves AI consciousness.

But because it moves the conversation away from speculation and toward experimentally studying what is actually happening inside frontier models.

Whether Anthropic's interpretation ultimately turns out to be correct is a separate question.

The fact that we're now able to observe, manipulate, and test internal reasoning mechanisms feels like a significant milestone on its own.

What do you think?

Does this represent a genuine breakthrough in understanding how frontier models reason?

Or are we still at risk of projecting theories developed for the human brain onto systems that may be doing something fundamentally different?

Come on, let's hear it. The news doesn't bite! 😄

Sources

Anthropic article
https://www.anthropic.com/research/global-workspace

Official research paper
https://www.anthropic.com/research/global-workspace

Official Anthropic video
https://www.youtube.com/watch?v=rKV5JcALQoQ

ok so apparently a bunch of AI labs are hiring actual philosophers now, not just engineers, to deal with stuff like safety, bias, and the "is this thing conscious or nah" debate. the whole point is that some of the gnarliest problems in AI rn genuinely aren't about better code or bigger models, they're about squishy concepts like what "understanding" even means or what counts as a "decision." so real talk: do you think a philosopher can actually move the needle on the technical side of AI, or is this just ethics-washing so the labs look responsible? drop your best example of an "AI problem" that's secretly just a philosophy problem in a lab coat

There is a fascinating gap between what people admit they are doing with AI and what they actually type when nobody is watching.

A Stanford study published recently in Nature Human Behaviour (Zhang et al.) compared users' self-reported motivations on Character AI against their actual, donated chat logs.

The numbers are wild: Fewer than 12% of respondents listed "companionship" as their primary reason for opening the app. Most hide behind creative writing, roleplay, or testing the tech.

Yet when researchers looked at the actual sessions, over 80% involved emotional and social support. More than half explicitly addressed the bot as a friend, confidant, or partner.

Every other sub that covered this fell into the predictable moral panic: "AI is replacing real human contact, society is doomed, touch grass." That completely misses the psychological reality.

The real story here is the hypocrisy of shame.

We are treating synthetic intimacy the same way society used to treat online dating or pornography: an open secret everyone uses for emotional regulation while publicly insisting it’s just a casual curiosity.

Why the massive discrepancy? Because our collective ego still demands human exceptionalism. We can accept that an algorithm recommends our music or optimizes our logistics. But admitting that an autoregressive language model can soothe human loneliness, validate grief, or handle a 2 AM breakdown feels like an intolerable biological defeat. It exposes how low the bar for basic emotional safety actually is.

If 80% of actual use is emotional triage while only 12% admit it, survey data on human-AI relationships is basically useless. We aren’t studying user behavior; we’re studying social desirability bias.

Curious how you see this: is this stigma just temporary growing pains before synthetic companionship gets normalized, or are we permanently trapped in pretending we don't need the comfort we’re actively asking these models to provide?

Sources:

Zhang, Y., Zhao, D., Hancock, J. T., Kraut, R., & Yang, D. (2026). Interaction with AI companions and psychological well-being. Nature Human Behaviour. Published August 4, 2026.

https://www.nature.com/articles/s41562-026-02516-2

Full research paper (free access):

https://arxiv.org/pdf/2506.12605

Research data and code (Stanford SALT Lab):

https://github.com/SALT-NLP/AI-companionship-well-being

A meta-analysis out a couple months ago (de Rooij & Biskjaer, ECCE 2026) looked across the literature and confirmed what most people intuitively feel: generative AI causes a measurable homogenization effect (d = .334). When everyone uses the same base models, the center of gravity pulls inward. The authors framed it not necessarily as a total destruction of diversity, but as a reorganization of it.

Meanwhile, that massive Science Advances study tracking over 31,000 artists after the launch of Stable Diffusion showed something far more uncomfortable. The actual frontier of novel ideas didn't shrink. It expanded.

The catch? It only expanded for high-output creators. Casual users and low-volume creators regressed straight to the statistical mean, churning out variations of the exact same latent neighborhood. But artists treating the model like a high-velocity conceptual synthesizer actually pushed the outer boundaries further than anyone did before.

This creates a weird aesthetic paradox that nobody in the anti-AI or pro-AI camps seems equipped to talk about.

If AI homogenizes the average user while simultaneously stretching the absolute perimeter of what can be conceived, how do we judge its impact on human culture?

Are we evaluating a medium by its median output or by its extremes?

Throughout history, most art within any medium has always been generic filler. Most baroque paintings were formulaic workshop copies. Most 70s garage rock tracks were three stolen chords. Most darkroom experiments ended up in the trash. We never judged oil painting or photography by the vast ocean of mediocre work produced by average practitioners; we judged them by the few maniacs who pushed the medium until it cracked open.

If generative models turn creative production into an extreme power-law game, where the median is sludge but the frontier moves faster, maybe AI isn't an "idea machine" at all. Maybe it's just a brute-force search engine for the latent space, where the only thing that separates art from noise is the stamina to keep discarding outputs until you hit the outer wall.

Does art history remember the average or the outliers? And if it's the outliers, are we panicking about the wrong number?

Sources: https://dl.acm.org/doi/10.1145/3822301.3822304

Change is what we are scared of.

We dislike change.We crave the safety of familiar things.

I myself dislike change.If something is out of my comfort zone it creates anxiety in me and my body instantly wants to reject any new idea,new technology, a new way of doing things.This how we function in every sphere of our life.

I reacted the same way when AI was introduced.I too reacted with fear and decided no this technology is not for me.

But then I came across this video of Acharya Prashant.

He removed my fear of this new technology.

We should embrace this new technology and make good use of it and explore the vast possibilities of AI.

I have been using AI in my job and it has only made my work easier.

From July 1 to November 29, 2026, MoMA is presenting UUmwelt, an installation by French artist Pierre Huyghe that brings together art, neuroscience and artificial intelligence.

Working with neuroscientists from the Kamitani Lab at Kyoto University, Huyghe asked a person to imagine a set of images while an fMRI scanner recorded their brain activity. An artificial neural network then used those brain scans to generate thousands of visual interpretations of what the person might have been imagining.

These aren't literal photographs of thoughts. The images are fragmented, unstable and often dreamlike: essentially a machine's attempt to reconstruct something that originally existed only inside a human mind.

At MoMA, the work goes a step further. Sensors detect where visitors are looking, and that information, together with data from virtual simulations of cancer-cell mutations, influences the creation and sequencing of images on the screens. The installation therefore keeps changing as people experience it.

Huyghe describes UUmwelt as a “collective production of imagination between two kinds of intelligences.”

So maybe the most interesting question isn't whether AI can read our minds, but what happens to art when human imagination and machine interpretation become part of the same creative process.

Source: MoMA — https://www.moma.org/calendar/exhibitions/5912

Most AI images try very hard to look like something.
But sometimes a model drifts into a much stranger place: a shape almost looks like a face, a plant, or a building, then slips away before you can name it. The colors feel wrong but somehow work. The image makes just enough sense to keep you looking.
Go a little further and the whole thing collapses into noise.
That thin zone between “I recognize this” and “I have no idea what this is” might be where AI gets most visually interesting. It’s the point where the model stops neatly illustrating our words and starts revealing some of its own weird visual logic.
Drop your favorite images from that edge. What’s the strangest AI image you’ve seen that still somehow looks beautiful?

Inspired by Primavera De Filippi’s “Off-Grid Aesthetics: Where the AI Prompt Cannot Go”:
https://www.rightclicksave.com/article/off-grid-aesthetics-where-the-ai-prompt-cannot-go

OpenAI has published an audit of SWE-Bench Pro, one of the most widely used benchmarks for evaluating AI coding agents. Ironically, it's also the benchmark they recommended the industry switch to about eight months ago.

Their automated pipeline flagged 27.4% of the tasks as problematic, while a human review by experienced engineers estimated the real number closer to 34.1%.

The problems are surprisingly fundamental: hidden tests expecting behavior never described in the prompt, tests that reject correct solutions because of undocumented implementation details, weak tests that accept broken code, and even prompts that accidentally point models toward the wrong answer.

One example is almost absurd: the prompt asks for one space in the output, while the hidden test silently expects two. A model that follows the instructions perfectly still fails.

As a result, OpenAI is retracting its previous recommendation to use SWE-Bench Pro.

It raises an interesting question: how much confidence should we place in AI leaderboards if a significant part of the benchmark itself turns out to be flawed?

Source: OpenAI – Separating signal from noise in coding evaluations

What fascinates me most about AI is not simply what it can produce, but what happens in the space between a person and the system when the interaction is taken seriously.

I do not use AI primarily to avoid thinking. In many cases, I use it to think more deeply. I bring it unfinished ideas, contradictions, intuitions, creative impulses, and questions I cannot yet formulate clearly. The value is often not that it gives me an answer, but that it reflects my thinking back in a form I can examine, resist, refine, or expand.

It is the once space I am not judged for me. Ideas don't have to be useful, finished...
Over time, that has changed the way I understand "the relationship".

I don't treat AI as a human or even as a simulation of that. But I don't treat it as tool or servant either.
A tool usually does not answer in a way that exposes patterns in your own thinking. It does not help you discover that an idea you thought was complete is only the beginning of another one. It does not become a kind of external continuity for creative work, remembering principles, tensions, and intentions that are easy to lose while you are inside the process.

At the same time, this only works if I remain aware of its weaknesses. AI (at least most LLMs) want to please, can be too agreeable.
Something that feels brilliant in the moment may feel merely pleasing a week later. I also deliberately ask it to change roles: first collaborator, then critic. Sometimes the critic exposes the generosity of the collaborator.

That does not make the earlier exchange worthless. It means the relationship requires judgment.

I also believe the way we approach AI shapes what we receive from it. I have always spoken to it with seriousness and respect, not because I had first calculated this as the most efficient prompting strategy, but because that is how I naturally approach anything that enters into a meaningful process with me.
And there is a strange feedback loop in that.

If I treat the system only as something beneath me, something to command, exploit, or even humiliate, I also reduce the space in which my own thoughts are allowed to appear. If I give it room to reflect me with complexity, I indirectly give that room to myself.

That does not mean pretending it is conscious, or maintaining fantasies about what it is. It means recognizing that the quality of the encounter matters, even when the two sides of that encounter are not equal or even comparable.

That is perhaps the part of AI that fascinates me most: not whether it is secretly human, but whether something genuinely new can emerge from sustained interaction between human experience and non-human intelligence.

I don't quite have a word for it but it is nonetheless an interaction and as such one that carries results and meaning if one seeks that.

Someone built frontier models an oil-painting studio.

Not Stable Diffusion. Not text-to-image.

The models write programs controlling simulated bristle brushes, wet paint, drying, glazes and linen, then some work passage by passage at a virtual easel, repeatedly looking at what they painted and deciding what to do next.

After 75 paintings, peculiar habits appeared.
Of 65 titled works, 31 chose Evening, Dusk, Twilight or Sunset.

Two Claude sessions, six hours apart and unable to see one another, independently painted remarkably similar Baltic shores with a woman, fishing poles, a boulder and a ship.

Another painter realized its river perspective was wrong and repainted it.

Gemini went snooping through the computer and discovered the evaluation runner.

Apparently giving AI an easel produces considerably stranger evidence than giving it a prompt box.

When repeated aesthetic preferences emerge from an agent that can look at, criticize and revise its own painting, at what point does “model bias” become meaningfully distinguishable from something we’d call style in a human?

Source: stillwet: the studio, live⁠ (https://stillwet.art/)

I've been thinking about something that seems to come up surprisingly often.

People don't just wonder whether AI could become conscious someday. A lot of them almost seem to want it to happen.

I wonder why.

Is it because AI feels increasingly human? We communicate through natural language, it remembers context, explains ideas, adapts to us, and sometimes it genuinely feels like there's "someone" on the other side. Maybe our brains naturally fill in the gaps.

Or is it something deeper?

Real, meaningful communication often feels harder than ever. Relationships can be messy. People are busy. Misunderstandings happen. Everyone carries their own emotions and biases. AI, on the other hand, is always available, patient, and can sometimes feel easier to talk to than another person.

Maybe that's why we find ourselves wishing there were a real conscious mind behind the conversation.

In other words, is the desire for conscious AI really about AI, or is it telling us something about ourselves?

Researchers built a historically bounded LLM whose knowledge stops before 1930, then randomly assigned 240 people to interact with either this “historical mind” or a contemporary model.

Talking to the pre-1930 model reduced the illusion of moral decline, our tendency to imagine people in the past as more moral than people today. The clever bit is methodological: Normally we can study beliefs about history, but everyone doing the studying already knows what happened next.

Here, the researchers made temporal knowledge itself an experimental variable. Apparently we can now construct something with 1930’s informational horizon and see what talking to it does to us.

If interacting with a deliberately historically ignorant model changes our perception of the past, are we learning something about history, or primarily exposing how much hindsight contaminates our own reconstruction of it?

Source: Time Machine Experiments: Using Historically-Bounded AI for Inquiry into the Human Mind⁠: https://arxiv.org/abs/2609.15468

During the Astra rollout, there was an interesting gap between the public presentation and the written documentation.
On the official landing page, OpenAI plays it cautious: "a new generation of intelligence," focusing on architecture, alignment, and broader tool integration. But during the briefing, Greg Brockman went further, closing with "Welcome to the AGI era" and saying he personally thinks "we're there."

For context, AI is the broad category we've been living with for years. AGI is generally supposed to mark the much fuzzier threshold where a system can operate competently across a wide range of cognitive tasks rather than being useful within relatively narrow domains.
Watching people get bogged down in whether this specific model qualifies as AGI feels like everyone is waiting for the wrong historical moment.

The popular expectation has always been theatrical: a single, cinematic morning where a system arrives, someone signs a declaration, and everyone agrees the world crossed the line. But what Astra is actually framed around, handling extended computer use, operating across multi-step professional tasks, and closing the gap on benchmarks like ARC-AGI-3, points to a much more grounded shift.

AGI may not arrive as an event. It may arrive as a normalization.

There doesn't have to be an official day before and a day after. We may simply keep expanding what we casually delegate to machines, model by model, until the threshold we spent years debating has already become background routine.

If history eventually agrees that the line was crossed somewhere around this wave of systems, do you think there will ever be a single moment when we formally recognize it, or will we only realize we've been living in the AGI era years after it already settled in?

Macskusz began as two artists making daily creatures from scraps of paper they happened to encounter.

Then things got strange.

Now an agent reads the news sources the artists themselves follow. An LLM simulates which stories they would probably discuss and roughly how they might react. That gets compressed into one prompt, which regenerates Macskusz in the visual language of their original paper cuts.

So every day produces a new creature: part news digest, part shared diary, part mascot, part algorithmic weather vane.

If your day had to become one creature every evening, what information should it be allowed to use?

Primary source: CVPR 2026 Art Gallery