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How Do LLMs Work? Tokens, Attention, and Next-Word Prediction Explained

Pudgy Cat — Fri, 22 May 2026 20:44:43 +0000

You type a sentence into ChatGPT, Claude, or Gemini and a paragraph comes back that sounds like a person wrote it. The interesting question is the next one: how do LLMs work, actually, under the hood? The honest answer is that a large language model is a very large statistical engine trained to predict the next chunk of text, one piece at a time. That single trick, scaled up enough, produces code, essays, translations, and arguments. This guide walks through every stage of that pipeline in plain English: tokens, embeddings, attention, training, inference, and the reasons these systems still get things wrong. By the end you will understand what is happening between your prompt and the answer, and you will be able to spot when an LLM is bluffing.

Table of Contents

What Is an LLM, Really
Tokens and Embeddings: How LLMs See Text
The Transformer and Self-Attention
How LLMs Are Trained
Inference: From Prompt to Reply
Why LLMs Hallucinate
Context Windows, RAG, and Tool Use
FAQ

What Is an LLM, Really

To understand how LLMs work, start from the smallest possible definition. A large language model is a neural network with billions of numeric parameters that has been trained to do one job: given a sequence of text, predict the next piece of text. Everything else, chat, coding, summarising, translating, is a side effect of being very good at that one prediction task.

The model does not store sentences. It stores weights, billions of them, arranged in layers. Your prompt is converted into numbers, those numbers flow through the layers, and the model outputs a probability for every possible next token. It picks one, appends it to the prompt, and runs again. That loop is the entire core of what these systems do.

Three Things an LLM Is Not

It is not a database. It does not look up answers. It approximates patterns from training, which is why it can sound confident about a paper that does not exist.

It is not reasoning the way humans reason. It runs the same forward pass for every token, no matter how hard the question is. Chain-of-thought prompting lets it spend more tokens “thinking out loud”, but the underlying machinery is still next-token prediction.

It is not the model you talked to last week, necessarily. Providers ship new checkpoints constantly. A response from GPT-5.3 Instant in May 2026 is not the same weights as a response from January.

Tokens and Embeddings: How LLMs See Text

Before an LLM can do anything with your prompt, it has to turn your text into numbers. This happens in two steps: tokenisation and embedding. Both are simpler than they sound and both shape how the model behaves more than people realise.

Tokenisation: Slicing Text Into Pieces

The model does not read words. It reads tokens. A token is a chunk of text, usually somewhere between a single character and a short word. The string “tokenisation” might become two tokens, “token” and “isation”. The word “cat” is one token. The phrase “Pudgy Cat” is probably three.

Most modern LLMs use Byte Pair Encoding (BPE). It starts with single characters and merges the most frequent pairs into bigger tokens, building a vocabulary of 50,000 to 200,000 tokens. Common words become single tokens. Rare words get split. This is why an LLM can write your name even if it never saw it during training: it stitches it together from smaller fragments.

Practical consequence: when an API bills you “per token”, one English token is roughly 0.75 words. A 1,000-word document is around 1,300 tokens. Code, JSON, and non-Latin scripts use more tokens per character, which is why a Python file costs more to send than a paragraph of the same byte length.

Embeddings: Turning Tokens Into Vectors

Each token in the vocabulary has a learned vector attached to it, a long list of numbers (often 4,096 dimensions in modern models). That vector is the embedding. It encodes everything the model has learned about how that token tends to behave in language.

Embeddings have a useful property: tokens with similar meaning end up with vectors close together in this high-dimensional space. The embedding for “cat” sits near “kitten” and “feline”, and far from “spreadsheet”. This geometry lets the model generalise. It does not need to have seen the exact phrase you typed, because it can land in a region of the space where similar phrases lived.

The Transformer and Self-Attention

Every modern LLM, from GPT-5 to Claude to Google’s Gemma 4, is built on the same underlying architecture: the transformer. It was introduced in a 2017 paper called “Attention Is All You Need”, and the title is the spoiler. The trick that makes transformers work, and that makes LLMs possible, is a mechanism called self-attention.

What Attention Actually Does

For each token in your prompt, the model needs to figure out which other tokens matter. Take the sentence “The cat sat on the mat because it was tired.” The word “it” refers to the cat, not the mat. A human knows this instantly. The model has to compute it.

Self-attention solves this by computing, for every token, a weighted blend of all the other tokens in the context. For “it”, the model learns to give “cat” a high weight and “mat” a lower one. The weights come from three learned projections of the embeddings called query, key, and value vectors. The intuition: every token asks every other token “are you relevant to me right now” and the most relevant ones get included in the next layer’s input.

Multi-Head Attention and Stacking

Real models do this attention computation many times in parallel, with different learned projections. Each parallel version is an attention head. Different heads pick up different relationships: subject-verb agreement, pronoun resolution, topical relevance over long distances. Modern LLMs run 32 to 128 heads per layer.

The attention output goes through a small feed-forward network, then the whole thing repeats. A modern model stacks 30 to 100 of these blocks. By the time your prompt reaches the top, the model has a deep, context-aware representation of every token.

How LLMs Are Trained

An untrained transformer is useless. It will output gibberish. The magic happens during training, which proceeds in two main phases for modern chat models: pretraining and fine-tuning.

Pretraining: The Big Crawl

Pretraining is where the bulk of “knowledge” enters the model. Engineers assemble a corpus of trillions of tokens, drawn from web crawls, books, code repositories, scientific papers, and forums. The model is asked, over and over, to predict the next token in real text. Every time it gets it wrong, an algorithm called backpropagation nudges its billions of weights very slightly in the direction that would have made the prediction better.

This phase is brutal in compute terms. A frontier model in 2026 burns through tens of thousands of GPU-years and costs hundreds of millions of dollars in electricity and chip time. That is why OpenAI just raised $122 billion and why the AI industry is in an infrastructure arms race.

Fine-Tuning and RLHF

A model that has only done pretraining is a fluent text-completion engine. Ask it a question and it might just continue the question in the style of a forum thread. To turn it into a useful assistant, providers add a second phase.

First comes supervised fine-tuning, where human writers craft thousands of example conversations showing the model how a good assistant responds. Then comes Reinforcement Learning from Human Feedback, or RLHF. Humans rate pairs of model outputs, the model learns a reward signal that approximates human preferences, and the weights get adjusted to maximise that reward. RLHF is why ChatGPT sounds polite, refuses certain requests, and finishes with a summary even when you did not ask.

Not everyone agrees this approach scales to real intelligence. Yann LeCun raised a billion dollars to argue that next-token prediction is a dead end and that world models are the future. The debate is unresolved.

Inference: From Prompt to Reply

Once a model is trained, the weights are frozen and the model is deployed for inference. Inference is what happens every time you send a prompt. The process is mechanical and predictable, and understanding it explains a lot of LLM quirks.

The Generation Loop

Your prompt gets tokenised. The tokens flow through every layer of the model. At the output, the model produces a probability distribution over the entire vocabulary, a number for every one of the 100,000-or-so possible next tokens. The system picks one (more on that below), appends it to the sequence, and runs the whole forward pass again with the new, slightly longer input. This continues until the model generates a special “stop” token or hits a length limit.

Each token requires a full pass through the network. That is why responses stream in word by word and why long answers take longer. It is also why running these models is expensive: each output token costs a full forward pass through tens of billions of parameters.

Temperature and Sampling

The model gives probabilities for every possible next token, but still has to pick one. Greedy decoding always picks the most likely token, which tends to produce repetitive output, so providers use sampling.

Temperature flattens or sharpens the probability distribution. Low temperature near zero is deterministic and conservative. High temperature around one is varied and creative. Top-k and top-p sampling restrict the choice to a shortlist before sampling. This is why running the same prompt twice can produce different answers: randomness is baked into the loop.

Why LLMs Hallucinate

Now that you know how LLMs work, hallucinations stop being mysterious. An LLM is not retrieving facts. It is generating the most statistically plausible continuation of your prompt. If the most plausible continuation happens to be true, you get a correct answer. If the most plausible continuation is a confident-sounding fabrication, you get a hallucination. The model has no internal “I do not know” signal that it consults before answering.

Common Hallucination Failure Modes

Fake citations: when asked for sources, the model generates strings that look like real academic references because that pattern is overwhelmingly common in its training data. The format is correct, the authors and titles are invented.

Confident wrong dates: numbers, especially years, are easy to get wrong because the model only has rough statistical associations, not a calendar.

Phantom features: ask an LLM how to do something in a software tool and it might invent a plausible API call that does not exist, because the pattern “library has method that does this” is strong.

How to Reduce Hallucinations

The most effective fix in production is to stop relying on the model’s own knowledge for facts. Instead, feed it the facts at query time. That is the whole idea behind retrieval-augmented generation. We covered the mechanics in a separate guide on how RAG works: pull the relevant documents from a vector database, paste them into the prompt, ask the model to answer using only that material. Hallucinations drop sharply.

Context Windows, RAG, and Tool Use

A modern LLM does not just sit in isolation. The serious productivity gains in 2026 come from giving it access to context and tools. Three pieces matter here.

The Context Window

Every model has a maximum number of tokens it can process in one pass, called the context window. Early GPT-3 had 2,048 tokens. Modern frontier models advertise 200,000 to 2 million. That is the cap on what the model can see at any moment. Anything older is gone, unless the application stores it externally and replays it.

Retrieval Augmented Generation

Because the context window is finite and pretraining is frozen at some date in the past, almost every serious LLM deployment uses RAG to bring fresh knowledge into the prompt at query time. The question gets embedded into a vector, a search runs against a knowledge base, the top results get inserted into the prompt, and the model answers from that grounded context. This is how enterprise AI handles current data without retraining.

Tool Use and MCP

The other half of the story is letting the model call external tools. A model can be given a list of functions (search the web, run code, query a database) and trained to emit a structured request whenever it needs one. The runtime executes the call, feeds the result back into the context, and the model continues generating. The protocol that standardised this in late 2025 and dominates in 2026 is the Model Context Protocol. We unpacked it in a full MCP explainer. If you want to play with this stack on your own hardware, the guide on running AI locally walks through Ollama, llama.cpp, and getting a model live on a laptop.

FAQ

How do LLMs work in one sentence?

An LLM is a giant neural network that has been trained to predict the next token in a sequence, and “chatting” is just that prediction loop running over and over with your prompt as the seed.

What is the difference between an LLM and AI?

AI is the broader field, including image recognition, robotics, search, and game-playing systems. An LLM is one specific type of AI: a large language model trained on text to generate text. ChatGPT, Claude, and Gemini are LLM-based products.

Do LLMs actually understand language?

Depends what you mean by understand. They build statistical representations of language that capture grammar, common reasoning patterns, and semantic relationships well enough to be genuinely useful. They do not have grounded experience, sensory input, or beliefs about the world. Researchers disagree on whether that gap matters in practice.

Why do LLMs make stuff up?

Because they are optimised to produce plausible text, not true text. When the model has no real information to draw on, generating something plausible is its default behaviour. Hallucinations are not bugs, they are the natural output of the next-token objective applied to a question the model cannot ground.

Can I train my own LLM?

Pretraining a frontier model from scratch is out of reach for individuals, the compute alone runs into nine figures. Fine-tuning a small open-weight model on your own data is cheap and works on consumer GPUs. Running an existing model locally with Ollama or llama.cpp is the easiest entry point.

The Takeaway

Strip away the marketing and an LLM is a token-prediction engine wrapped in attention layers, trained on most of the internet, and steered with human feedback. That is the whole thing. The reason it feels like talking to a thinking system is that next-token prediction, at sufficient scale, captures a stunning amount of how human language works. The reason it sometimes fails badly is that the same machinery has no concept of truth. Knowing the difference is now a basic literacy. The next time a chatbot answers with suspicious confidence, you will know exactly which gear is spinning.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

Strange Science Facts That Are True: 8 Cosmic, Animal and Quantum Oddities Verified

Pudgy Cat — Fri, 22 May 2026 18:47:55 +0000

The phrase strange science facts that are true gets thrown around a lot, and most of the time the payoff is a recycled list about how your stomach lining replaces itself. We are skipping the body. The cat is more curious about parts of the universe that read like a typo in a textbook. Eight verified entries, each one dinner-table worthy, with commentary from the cat.

1. A Teaspoon of Neutron Star Would Weigh About a Billion Tons

Neutron stars are what is left when a massive star collapses and its protons and electrons get crushed into neutrons. A sugar-cube sized chunk has a mass of roughly one billion tons on Earth, the weight of a tall mountain. NASA puts the typical neutron star at about 1.4 solar masses packed into a sphere only 20 kilometers wide. Surface gravity sits around two hundred billion times Earth’s, meaning a marshmallow falling from one meter would hit with the energy of a small nuclear bomb. Mass and volume are not the same conversation.

2. Tardigrades Survive the Vacuum of Space

In 2007 the European Space Agency strapped a batch of tardigrades to the outside of a FOTON-M3 satellite and exposed them directly to space for ten days. No air, full cosmic radiation, unfiltered solar UV. When the capsule came home, most were alive and a significant fraction laid viable eggs. Their trick is cryptobiosis, where they expel almost all their water and drop metabolism to about 0.01% of normal. They have been revived after being frozen at minus 272 Celsius and boiled at 150.

3. Mantis Shrimp See Colors Humans Cannot Even Name

Humans have three color receptors. Dogs have two. The mantis shrimp has between twelve and sixteen, plus the ability to detect polarized light. Its compound eyes move independently and each one gets depth perception on its own. The receptors act as a parallel pre-filter, letting the shrimp identify colors instantly without comparing wavelengths. The same animal has a club limb that accelerates at 23 meters per second squared and generates cavitation bubbles that briefly reach about 4,700 Celsius. A small wet supernova in a tide pool.

4. Some Bacteria Survive 5,000 Times the Radiation Dose That Kills a Human

Deinococcus radiodurans, nicknamed Conan the Bacterium, can shrug off 15,000 grays of gamma radiation. A dose of 5 grays kills a human in two weeks. Its genome shatters into hundreds of pieces under radiation, then the bacterium reassembles its DNA from the fragments using a manganese-based repair system that works faster than the damage accumulates. NASA flew it on the outside of the International Space Station for three years in the Tanpopo experiment, and it survived. This opens questions about panspermia, life riding rocks between planets.

5. There Is a Planet Where It Probably Rains Diamonds

On Neptune and Uranus the atmosphere is heavy with methane. Several thousand kilometers down, the temperature reaches around 5,000 Celsius and the pressure hits millions of times Earth’s. Lab experiments at the SLAC National Accelerator Laboratory in 2017 confirmed that under these conditions the carbon in methane separates and crystallizes into diamonds, which then fall through the liquid layer below. The diamonds can grow to centimeter scale before settling into a possible diamond shell around the planet’s core. The cat would like to formally skip the next vacation to Neptune.

6. Helium Becomes a Superfluid Near Absolute Zero and Flows Uphill

When liquid helium-4 is cooled below 2.17 kelvin, it transitions into helium II, a superfluid with zero viscosity. It can flow through pores too small for normal liquids, and most strikingly it crawls up the sides of its container in a thin film and drips off the bottom. This is the Rollin film, named after Bernard Rollin who described it in 1936. Superfluid helium has no internal friction to resist van der Waals attraction to the container wall. The cat finds this profoundly disrespectful behavior from a liquid.

7. The Oxford Pond Ciliate Rewrote Two of Three Universal Stop Codons

Biology had three stop codons treated as universal across nearly all life: UAA, UAG, and UGA. In April 2026 a team at Oxford published the genome of a ciliate they pulled from a campus pond, and the organism had reassigned two of those three to code for amino acids instead. UAA and UAG, which mean “end the protein” in every textbook, now mean “add glutamine” in this ciliate. We covered it in our piece on the Oxford pond ciliate. The most universal rule in molecular biology has an asterisk now.

8. A Houseplant Solved a Geometry Problem Plants Were Not Supposed to Solve

Pilea peperomioides, the Chinese money plant on a million Instagram desks, arranges its leaves in a pattern that matches a Voronoi diagram with logarithmic spacing. Voronoi diagrams are usually computed with iterative algorithms. The plant does it passively, through hormone signaling between leaf primordia, and a 2026 paper showed the pattern minimizes mutual shading more efficiently than any layout a brute-force computer search produced. The full story is here. A plant on a windowsill is quietly outperforming a CPU.

Why Strange Science Facts Stick With Us

These facts feel different from regular trivia because they violate a category we assumed was closed. Mass should match volume. Stop codons should stop. Liquids should stay where you put them. When a fact breaks an assumption we did not know we were making, the brain updates something deeper than a number in storage. For more strange-but-true SEO long-tails the cat has chased, see the RAVEN AI exoplanet hunt.

Frequently Asked Questions

Are these strange science facts really true or just popular myths?

Every one of these eight has been verified against peer-reviewed publications, NASA references, or institutional sources like ESA and SLAC. Where a fact has caveats, the caveat is included. The internet is full of viral facts that fail a five-minute check. These do not.

Why exclude human body facts from this list?

Pudgy Cat already published a dedicated list on the human body. The body is fascinating, but the cosmos, microbes, and physics are a much wider playground. This piece is the cosmic, animal, and material counterpart.

What is the strangest science fact on this list?

Subjective, but the cat votes for Deinococcus radiodurans. A single-celled organism that survives planet-sterilizing doses, and rebuilds its DNA from fragments, suggests life is much harder to kill than astrobiology models assume. It quietly reshapes how we think about Mars and Europa.

The Cat’s Closing Note

The list of strange science facts that are true is much longer than eight, and the universe keeps adding to it faster than science writers can catalog. The honest 2027 version will have at least three entries that did not exist when this one was written. The cat will be here for the next batch. Strange. Verified. True.

Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

Laser-Cat Just Won the Top Student Prize at Cannes 2026 and the Whole Film Hinges on a Cat That Chases a Red Dot

Pudgy Cat — Fri, 22 May 2026 16:41:35 +0000

Cannes 2026 closed its 29th La Cinef student competition on 21 May with a result the festival did not see coming. Out of 2,747 submissions from 662 film schools across the planet, the jury picked a short film called Laser-Cat, directed by a Brazilian filmmaker named Lucas Acher, currently at NYU. First Prize. €15,000 grant. And at the center of the whole thing, the reason the protagonist spirals through one impossible night in Sao Paulo, is a cat.

The cat in question gets hit. Not by a car. By a laser pointer. That is the entire premise, and somehow Acher built twenty minutes of cinema around it that beat eighteen other films from schools that have been training filmmakers longer than NYU has existed.

What the Film Actually Is

The logline, courtesy of the Cannes program: a socially anxious teenager pulls a laser pointer prank that goes badly wrong. The cat belongs to his crush. The cat is now critically injured. The teen spends the rest of the night roaming Sao Paulo trying to fix what he broke, with guilt and paranoia escalating on a near-vertical curve.

Read that twice. The whole emotional architecture of an award-winning Cannes short is built on the moment every cat owner already knows in their bones: the cat is chasing the red dot, the cat is having the best three minutes of its month, and then the cat is suddenly somewhere it should not be. Acher took the most universal living-room comedy beat in the world and turned it into a slow-burn anxiety study with a Sao Paulo nightscape underneath.

The jury, presided by Spanish director Carla Simon, included Ali Asgari, Salim Kechiouche, Ji-Min Park and Magnus von Horn. None of them are known for going easy on student work. They did not hand first prize to Laser-Cat because the cat is cute. They handed it because the cat is the spine.

Why a Cat Short Won at Cannes

La Cinef is the section of Cannes dedicated to film schools. Twenty-nine editions in, the rule of thumb is simple: the films that win are the ones with the smallest possible distance between concept and emotional payoff. No room for fat. A short film cannot afford a second act detour, so the central object has to carry everything.

This is where cats are technically perfect cinema. A cat on screen instantly creates stakes a human protagonist would need ten minutes of dialogue to establish. The audience knows what a cat looks like in distress before the camera even pans. The teen does not have to explain why he is panicking. He is panicking because a cat is hurt, and the audience is already there with him by minute three. The film can spend the remaining seventeen minutes on the actual material, which is the teenager unraveling in real time.

Compare that to the rest of the slate at Cannes 2026, where we covered the divided first reactions to The Mandalorian and Grogu earlier this month. Grogu is also doing the small-creature-in-jeopardy job, but he has to fight for screen time with an entire Star Wars galaxy. Laser-Cat has one cat and one teenager and one city, and the budget for nothing else. That economy is what jurors at La Cinef look for.

The Brazilian Angle Nobody Is Talking About

Acher is Brazilian, studying at NYU. The film is set in Sao Paulo, twelve million people, traffic that does not move between five and nine, nocturnal life that does not really start until midnight. The perfect city for a teenager in panic mode to disappear into.

Brazil has been having a quiet moment at Cannes. Karim Ainouz, Petra Costa, Gabriel Mascaro have all been festival regulars for the last five editions. Acher is the next layer down, the student section, which means the pipeline is still feeding. La Cinef winners often surface again three or four years later in the main competition. The €15,000 grant is the bridge between school and the next real project.

What This Says About Cannes 2026

The 79th edition has been heavy. Lukas Dhont premiered Coward, a WWI trench drama about two soldiers staging cabarets between bombardments. Zachary Wigon brought Victorian Psycho with Maika Monroe. Andrey Zvyagintsev got a ten-minute ovation for Minotaur. The main competition has been a parade of historical trauma, war, and prestige adaptation.

And then, in the student section, the jury hands first prize to the film about a kid and an injured cat. In a year of heavy films and heavier press conferences about politics and self-censorship, giving the top student prize to Laser-Cat reads as a deliberate vote for small scale done well. The film is described as a slow-burn anxiety study built around a domestic accident. That is not the option student juries usually pick, where the temptation is the most overtly serious one. La Cinef went with the film that trusts its premise.

The Laser Pointer Thing

One technical note. Laser pointers and cats have a complicated relationship. Veterinarians have been writing about this for years: the dot a cat can never catch produces real frustration, and in some cases obsessive behavior. Laser-Cat takes the cliche of the harmless prank and runs it through the worst-case filter.

The film is fiction, the injury is dramatized, the cat is presumably fine. But the choice of laser pointer over, say, a window left open, is the kind of small inversion of the familiar that wins student juries.

What to Watch For Next

La Cinef winners do not get a wide theatrical release. They get a festival circuit. Laser-Cat will play Toronto, San Sebastian, possibly Sundance early next year, and probably a half-dozen North American shorts programs. The full short will not be online in any official form for at least twelve months. Acher’s own development slate will start moving faster now, almost certainly with international co-production attached.

The festival itself wraps tomorrow with the Palme d’Or ceremony. Park Chan-wook’s jury will decide between Fatherland, Coward, Minotaur, and whatever else has built late momentum. Whatever wins, the La Cinef result is already locked in, and it is a film built around a cat.

The Pudgy Cat newsroom is not above admitting bias on this one. A cat carried a short film through 2,747 submissions, past 662 film schools, to the top of one of the most competitive student sections in cinema. If you needed proof that cats remain the most efficient narrative device in the medium, the jury at Cannes just signed off on it for you. For more festival oddities worth watching, see our piece on Margot Robbie’s Wuthering Heights topping HBO Max in 32 countries, or for proof that animals on screen still draw audiences, the cat detective museum heist game published by six rescued cats in Brazil.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

What Is a Soulslike Game? The Genre Defined by Death, Stamina, and Bonfires

Pudgy Cat — Fri, 22 May 2026 14:52:40 +0000

You died. The screen fades to gray, the bonfire is three rooms back, and the souls you spent the last twenty minutes farming are sitting on the floor next to a knight twice your size. You sigh, you stand up, you try again. If that loop sounds familiar (or terrifying), congratulations: you have just met a soulslike.

“Soulslike” gets thrown at anything hard, but the genre has a specific shape. This guide walks through what defines a soulslike, where it came from, and which games are worth your first death in 2026.

What Is a Soulslike Game in One Sentence

A soulslike is an action role-playing game built around methodical melee combat, a stamina bar that limits every action, a checkpoint system that respawns enemies when you rest, and a death penalty that strips you of your currency until you fight your way back to where you fell. Add cryptic environmental storytelling and you have the whole package.

That definition is narrower than people think. A game can be hard and not be a soulslike. The label sticks when the loop is right: read, commit, manage stamina, die, recover your stuff, try again.

What Is a Soulslike Game: The Six Defining Features

If you want a checklist, here are the six features that almost every soulslike shares. A game does not need all six to qualify, but most have at least four.

1. Stamina-Gated Combat

Every attack, dodge, block, and sprint draws from the same green bar. You cannot mash. Swing twice, panic-dodge, and the next hit eats you. This single choice forces the genre’s signature rhythm: read, commit, recover.

2. Animation Commitment

When you swing a greatsword, the swing plays out. You cannot cancel it. Every attack becomes a risk assessment, which is why a level-one knight can beat a final boss with no hits taken.

3. The Bonfire (or Lantern, or Stargazer)

The signature checkpoint. Sit at one to refill your flask, level up, fast travel. Sit at one and every regular enemy respawns. Different titles rename it (lanterns in Bloodborne, sites of grace in Elden Ring, stargazers in Lies of P), but the function is identical.

4. The Currency Death Penalty

You die, you drop everything. Unspent experience (souls, runes, ergo, sen, amrita, depending on the game) sits on the ground where you fell. You get one chance to run back. Die again before you reach it, and it is gone forever.

5. Environmental Storytelling

Soulslikes do not sit you through cutscenes. The story is in item descriptions, the position of a corpse, the architecture of a ruined cathedral, a single line of cryptic dialogue from a half-mad NPC. Professional YouTube careers are built entirely on Dark Souls lore videos, and that is by design.

6. Interconnected World Design

The original Dark Souls had no fast travel for its first third, and the world folded back on itself in ways that felt impossible. You climbed a tower, came out on top of a structure you had been looking up at for ten hours, and gasped. The map is a puzzle, not a checklist.

Where the Genre Came From

The accidental founding father is Hidetaka Miyazaki, who took over a troubled PlayStation 3 project nobody else wanted at FromSoftware. That project became Demon’s Souls in 2009. Dark Souls followed in 2011 and turned the cult into a religion. The trilogy wrapped in 2016, and along the way FromSoftware also released Bloodborne (2015, Victorian cosmic horror) and Sekiro (2019, parry-focused samurai). Then came Elden Ring in 2022: open world, George R. R. Martin co-writing the lore, forty million copies sold by 2026. The good imitators understand the genre is not about being hard, it is about being fair while looking unfair.

Soulslike vs Soulsborne vs Souls-Lite

Soulsborne: the FromSoftware originals. Demon’s Souls, the Dark Souls trilogy, Bloodborne, Sekiro, Elden Ring.
Soulslike: any game that copies the core loop in good faith. Lies of P, Nioh, Lords of the Fallen, The Surge, Mortal Shell, Code Vein.
Souls-lite: games that borrow the death-and-recovery mechanic but live in another genre. Hollow Knight is a metroidvania with souls mechanics. Hades is a roguelike with souls combat sensibility.
Soulslike-adjacent: the marketing trap. If the game does not have a stamina bar and a checkpoint that respawns enemies, it is probably just a hard game.

Where to Start: The Best Soulslike Games in 2026

Elden Ring (2022, with Shadow of the Erdtree 2024)

The best entry point. The open world means you can simply leave when a boss is too hard, level up somewhere safer, and come back. Spirit summons act as a built-in difficulty slider. Forty million copies sold for a reason. If you only ever play one soulslike, make it this one.

Lies of P (2023, with Overture DLC 2025)

The strongest non-FromSoftware soulslike of the past several years. Belle Époque puppet horror, aggressive combat built around perfect parries, and a story that takes Pinocchio seriously. If you do not own a PlayStation and want the Bloodborne feeling, this is the closest you will get.

Hollow Knight and Silksong

Technically metroidvanias, but with so much soulslike DNA that they count. Hollow Knight earns its place in any indie best-of list, and Silksong finally arrived in 2025 after a wait that became a meme of its own.

One Tip for Your First Soulslike

If a boss is killing you, leave. This is the unique freedom of the soulslike. Almost every area has multiple paths. Go fight different enemies, get stronger, find better gear, come back. Brute force is rarely the answer.

How Soulslikes Connect to Older Genres

Soulslikes did not arrive in a vacuum. From Japanese action games they inherited animation commitment and boss design. From classic dungeon crawlers like Wizardry, which was so foundational to Japanese game design that Atari recently bought the rights, they inherited the trust in the player. Speedrunners turned Dark Souls into a precision sport, and soulslike no-hit runs are some of the most-watched runs at GDQ marathons.

Frequently Asked Questions About Soulslike Games

Are soulslike games actually that hard?

Harder than the average AAA action game, but fair. Every death teaches you something specific. People who push through the first ten hours almost always say the difficulty stops feeling oppressive and starts feeling rewarding.

Which soulslike should I play first?

Elden Ring, if you can. The open world means you can always go somewhere else when you are stuck. If you cannot run it, Dark Souls Remastered or Lies of P are the next best starting points.

Is there an easy mode in soulslike games?

Not officially, but yes in practice. Elden Ring has spirit summons and the ability to overlevel. Lies of P has specter summons. Nioh has gear grinding. Co-op is effectively easy mode in any soulslike that supports it.

What is the difference between a soulslike and a roguelike?

Roguelikes are built around runs that reset on death, with procedural content and meta-progression. Soulslikes have a persistent world you slowly learn by heart. Some games like Returnal blend the two on purpose.

The Real Reason Soulslikes Caught On

Strip away the dark fantasy and the difficulty memes, and what soulslikes actually offer is rare: a game that trusts you. No quest markers, no auto-scaling difficulty, no narration of its own lore. It hands you a sword and a world and expects you to make sense of both. The cat, for what it is worth, has been killed by the same hollow knight on the Undead Burg stairs about a hundred and twelve times. The cat is still trying. That is the genre in one paragraph.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

Ptilotus Senarius Just Came Back From Extinction Because a Horticulturalist Uploaded a Photo to iNaturalist

Pudgy Cat — Wed, 20 May 2026 18:42:50 +0000

A plant that vanished from the scientific record in 1967 just showed up on a private cattle station in northern Queensland because a horticulturalist took a picture and put it on the internet. The plant is Ptilotus senarius, a small slender shrub in the Amaranthaceae family. For fifty-eight years nobody knew if it still existed. It does. There are about fifteen of them, hiding on a Gilbert River station, and the only reason we know is citizen science and a guy named Aaron Bean.

This is the kind of story the cat lives for. Not because cats care about Amaranthaceae taxonomy (they care about boxes), but because the whole chain reads like a low-budget conservation thriller where the climax is somebody opening an app.

The Photo That Did All the Work

Aaron Bean is a professional horticulturalist. In June 2025 he was on a remote outback property in Queensland helping a bird-banding team, the kind of work that takes you to places where Google Maps gives up and your phone shows zero bars for three days. He saw a plant he didn’t immediately recognize. He took some photos. When his phone finally found service again, he uploaded them to iNaturalist.

The images caught the attention of Tony Bean, a senior botanist at the Queensland Herbarium and the same person who formally described Ptilotus senarius in 2014 using pressed specimens from 1925 and 1967. (No relation, by the way. The Bean coincidence is just the universe being smug.) He recognized it immediately. Formal confirmation ran in January 2026 in the Australian Journal of Botany, but the story only broke wide this week as the science press picked it up.

A species functionally extinct in the public record for fifty-eight years got pulled back by a phone camera and a free app. No grant proposal, no expedition, no AI. Just a guy doing his job who happened to look down.

Why iNaturalist Keeps Eating Real Science

The numbers on iNaturalist are quietly absurd. As of July 2025 the platform held over 104 million verifiable photographic vouchers of plants from around the globe. That is more raw biodiversity data than any traditional herbarium will ever process in a century. Thomas Mesaglio at the University of New South Wales, who was part of the team that published the confirmation, told reporters this was “one of a series of very serendipitous events,” which is botanist code for we got lucky and we’ll take it.

The platform works like this. You post a photo, the GPS goes with it (unless you obscure it), and a worldwide network of taxonomists and unreasonably committed amateurs argues about what you actually saw. Sometimes a plant last seen during the Six-Day War quietly walks back onto the species list.

This is not the first time a citizen-science photo has rewritten a biology textbook. The Chinese money plant, for example, turned out to be doing some genuinely strange geometry in its leaves that nobody noticed for years, a story we covered in our piece on how the Chinese money plant solved a computer science problem in its leaves. Plants keep doing this. They sit there for decades, doing math or being extinct or both, and one day somebody with a phone notices.

The Location Is Officially Top Secret

This is the detail that makes the cat happy. The exact location of the rediscovery is being deliberately withheld. Mesaglio was blunt about it: “The last thing we want is a million people turning up and trying to get onto private property to see this plant.” Conservation has a long, miserable history of well-meaning people loving a species into a second extinction by showing up to admire it, trampling its habitat, and posting the GPS coordinates with their selfie.

So we know it is on a Gilbert River cattle station in northern Queensland, near the Gulf of Carpentaria. That is the official description. If you want to find it, you are going to have to befriend a horticulturalist with bird-banding clearance, which feels like the kind of friend everybody should have anyway.

The researchers plan to run targeted surveys to figure out how many individual plants are actually out there. The current count is somewhere around fifteen, which is the kind of number that means the species spent six decades on a knife’s edge and nobody clapped because nobody was watching. Ptilotus senarius has been quietly rebranded from “presumed extinct” to “critically endangered,” which is technically a promotion.

What This Means If You Own a Phone

You do not need a degree to do this. Aaron Bean had professional credentials, but the platform is designed for anyone with a camera and vague curiosity. Every weed in your garden, every weird beetle, every pavement mushroom, is a potential data point. Most will be common species identified within an hour, which is also useful data. A few, statistically, will be something nobody has logged in that location before.

The cat tried this once. The cat photographed what it believed to be a rare Mediterranean sun lizard. iNaturalist identified it as a regular wall lizard within four minutes. The cat was offended for a full afternoon and is no longer allowed to use the app.

The broader point is that biodiversity research used to require a grant, a vehicle, a sat phone, and a few weeks of nobody hearing from you. Now it requires a phone and the willingness to upload. The world’s distributed sensor network turned out to be us, walking around, occasionally looking down. Science keeps reminding us that the questions which seem hardest are sometimes just questions nobody bothered to look at, a pattern we explored in why the night sky is dark.

The Bigger Pattern

Every few months a story like this surfaces. A bird thought extinct since the 1940s shows up on someone’s deck feeder. A frog that hasn’t been heard since the Carter administration starts calling from a drainage ditch. A shrub last pressed when Lyndon Johnson was president gets photographed during a lunch break on a cattle station. The pattern isn’t that nature is hiding. Nature is everywhere, and most of the time we are not paying attention.

The flip side is the stuff that turns out to be there in places nobody was looking. Empty Waymo cars circling cul-de-sacs are not biodiversity, but it is the same principle: weird signals surface when somebody with a camera notices, like the Atlanta empty-Waymo loop, a rediscovery for autonomous vehicles instead of plants.

If you want to help, install iNaturalist. Photograph the boring stuff too. The boring stuff is what makes the rare stuff visible by contrast. Aaron Bean did not go looking for Ptilotus senarius. He was busy with birds. He just happened to look down, and now a species has another shot.

The cat will not be photographing any plants. The cat will continue photographing boxes. But this particular shrub has had a better week than most of us, and earned it by doing absolutely nothing for fifty-eight years.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

Jack Ryan Ghost War Just Posted the Lowest Rotten Tomatoes Score of Any Krasinski Jack Ryan Project, and the Cat Lasted 40 Minutes

Pudgy Cat — Wed, 20 May 2026 16:41:12 +0000

Jack Ryan: Ghost War landed on Amazon Prime Video today, May 20, with John Krasinski back in the CIA blazer for the first time since the TV series wrapped. The film clocks in at 105 minutes, directed by Andrew Bernstein, co-written by Krasinski himself, and it just posted a 36 percent on Rotten Tomatoes across 11 critics. That is the lowest score any Jack Ryan project has ever received in Krasinski’s run. The cat is unimpressed. The cat is also slightly bored.

A spy thriller that forgot to be a thriller

Here is the setup. Jack Ryan, retired, is yanked back into the field when an international covert op falls apart and exposes a rogue black-ops unit inside the CIA. He teams up with Mike November (Michael Kelly) and former boss James Greer (Wendell Pierce). Sienna Miller joins as the new face. Globe-trotting locations, car chases, the dictator-with-an-accent template. You have seen this movie. The reviewers say so out loud. One critic called it “generic, personality-free and very streaming.” Another said it “feels like an awkward translation of the series.” TheWrap called it “a hollow movie sequel” that Krasinski “makes the most of,” which is the polite version of saying the lead actor is doing CPR on a script that died in development.

The series had four seasons to build out geopolitics, set pieces, and a Jack Ryan who reads ledgers like they are personally insulting him. The movie has 105 minutes and uses most of them on car chases.

Krasinski is good in it. The movie is not good around him

This is the genuinely interesting tension. Almost every review agrees Krasinski is still the right guy for the role. He plays Jack Ryan as a man who would rather be reading a binder than punching anyone, and that has always been the character’s actual flavor, the analyst who keeps getting shoved into the action genre against his will. The problem is the film around him has decided to lean into the punching. The thoughtful intelligence-officer DNA that made the series feel slightly different from the Bourne and Bond shelf gets sanded down into a generic shape that fits any of them.

Collider called it “a slick spy thriller that plays it too safe.” Even the positive notes grade against the curve of “for a streaming release this is fine,” which is not a sentence anyone screenshots to share with a friend.

The TV-to-movie ramp is a graveyard

This is the part the cat finds funniest. Hollywood keeps trying to turn streaming series into movies and the conversion rate is terrible. The Sopranos did it. Breaking Bad did it. Veronica Mars did it. Most of these landed somewhere between “okay” and “why did anyone need this.” The format mismatch is real. Long-form prestige TV trains you to expect breathing room, slow character beats, and the kind of plotting that uses episode six as a runway. A movie has to compress all of that into two hours. Either you cut the soul out, or you keep it and the pacing falls apart. Ghost War cut the soul out and the pacing is fine. That is the trade. It is also why the score is 36.

The other failure mode is the legacy-character problem. The movie has to introduce Jack Ryan fresh for new viewers while not boring the existing fans. Ghost War apparently misses in both directions. Aaron Paul keeps getting cast in Nolan and Joy projects on a recognizable pattern, which is what successful legacy-actor reuse looks like. Ghost War reuses the lead but forgets the texture.

What the 36 percent actually means for Prime Video

Prime Video is in a weird spot. They have Reacher humming, they have Fallout doing well, they have a content slate that has been quietly outperforming for two years. Jack Ryan: Ghost War was supposed to be the next prestige action brick in the wall, the one that proves they can do feature films inside their TV universes. A 36 on Rotten Tomatoes does not kill that thesis, streaming numbers will dwarf the critical reception, but it is the kind of result that makes the next greenlight slower. The audience score is not yet posted, and that is the metric that matters for Prime. If audiences land somewhere in the 60s, the movie is “fine, we made our money.” If they land near critics, the IP gets a long timeout.

Worth noting, this is a Krasinski co-write. He wrote the story with Noah Oppenheim and co-wrote the screenplay with Aaron Rabin. The disappointing reception lands on him in a way the series critiques did not. The TV show could blame the writers’ room. The movie has his name on the page count.

The Pudgy Cat verdict

The cat watched 40 minutes of Ghost War, walked to the windowsill, and stared at a pigeon for the remaining hour. The pigeon was more interesting. Not because the movie is bad in a memorable way, that would be a real review, but because it is bad in the most forgettable way possible. The streaming algorithm will queue it for you, you will watch most of it on a tablet while folding laundry, and you will not remember a single line of dialogue by Friday. That is a worse fate than being hated. Being hated means people are paying attention. A 36 percent that everyone forgets about by next Tuesday is the streaming-era version of dying in your sleep.

If you want to watch a streaming film that actually commits to its tone this month, Nicolas Cage’s Spider-Noir is letting viewers pick the black and white or color version, which is a real choice with a real reason. Mortal Kombat II recouped its 80 million budget in three days by being unapologetically itself. The Bear is wrapping with Season 5 on June 25 with a writers’ room that knows exactly what kind of show it has been. Ghost War does not know what kind of movie it wants to be. The cat does not either. The cat has gone back to the pigeon.

One curious side note. The film ends with an unresolved finale that several reviewers flagged as “obviously setting up a sequel.” Prime Video has not announced one. Given the 36 percent, they probably will not for a while. Streaming services keep making unilateral programming decisions and then quietly walking them back when the data comes in. Ghost War’s sequel hook is going to age the same way the Westminster Dog Show move did, parked in a press release nobody quotes anymore six months from now.

For now, the movie is live, the score is what it is, and Krasinski’s reputation as Jack Ryan survives intact because everyone agrees the problem is the script, not him. That is the cleanest version of a bad Tuesday a leading actor can hope for. The cat would have rewritten the third act. The cat is not available for hire.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

Pemi Aguda One Leg on Earth Is the May Debut Novel That Turns Lagos Urban Development Into Pregnancy Horror

Pudgy Cat — Wed, 20 May 2026 14:45:04 +0000

Every May the US publishing machine pumps out the same kind of debut, a quiet domestic novel about a woman in Brooklyn rethinking her marriage, a coastal town with a secret, maybe a missing sister. This May the most interesting debut on the table is none of that. It is 192 pages of literary horror set in a Lagos land reclamation project, by a Nigerian author whose story collection was a National Book Award Finalist, and the central question is why pregnant women in the city keep killing themselves.

The book is One Leg on Earth, the debut novel by ‘Pemi Aguda, out May 5 from W. W. Norton, Virago, and Masobe Books. NPR ran a long feature on May 13. TIME already had it on the most anticipated list of the year. Publishers Weekly gave it a starred review and called it unforgettable. It is short, it is unsettling, and it is doing the thing very few debut novels bother to do, which is take a city seriously as a character.

What the book is actually about

The protagonist is Yosoye Bakare, a 23 year old college graduate doing her year of mandatory National Youth Service Corps in Lagos. She gets placed at an architectural firm working on Omi City, a luxury development built on a peninsula reclaimed from the ocean, the kind of place where the wealthy will live above sea level on land that did not exist a decade ago. Yosoye finds out she is pregnant after a casual encounter. For a brief moment the title of mother feels right, the first thing in her life that has actually fit.

Then the suicides start. Or rather she notices them. Pregnant women across Lagos are dying by suicide, and after she stumbles onto one of the bodies she becomes obsessed with the pattern. Is it the city. Is it the project. Is it something older than both. The book is shelved under literary fiction, occult and supernatural, horror, and city life, which is publishing speak for we do not know how to file this but you should still read it.

Why this is not a normal debut

The Victor LaValle blurb is the giveaway. He calls it a horror story about the cost of progress for a city, a culture, for a human soul. Megan Giddings puts it in the lineage of Toni Morrison’s Sula. That is a wild bet to place on a 192 page first novel. Not horror in the sense of a creature in a hallway. Horror in the sense that something is wrong with the ground you are standing on, and the ground is wrong because somebody decided to make money on it.

If you tracked the Nigerian publishing wave from the angle Pudgy Cat covered when Cassava Republic won a British Book Award with a 50-rejection debut, this is the same current arriving on the literary side. Aguda is doing something the others were not, sitting inside the city and refusing to translate it for an American reader.

Lagos as the actual antagonist

Aguda’s first book, Ghostroots, was a story collection short listed for a National Book Award in 2024. Twelve stories, almost all set in Lagos, almost all threaded with something supernatural that the characters treat as Tuesday. A novel gives her more room to do the thing she was doing in shorter form, which is to write Lagos as a place that does not need a metaphor because it is already one.

The Omi City detail is the part that lands hardest. There is a real Lagos megaproject called Eko Atlantic under construction since 2008, built on 10 square kilometres of land dredged out of the Atlantic, meant to house 250,000 people. Environmentalists have been warning for over a decade that pulling sand from the seabed has accelerated coastal erosion in poorer neighbourhoods nearby. You can see how a novelist who grew up watching that project might wonder what the city was being asked to give in exchange. You can also see why the curse, in the book, attaches itself to pregnancy. The literal future.

The pregnancy horror angle, and why it lands now

Pregnancy horror has been having a slow renaissance since Rosemary’s Baby got rebooted into prestige TV and Julia Ducournau’s Titane won the Palme d’Or in 2021. Nightbitch, the Kelly Link stuff, all circling the same nerve, that pregnancy is the one human experience that genuinely is body horror, and literature has historically handled it with flowers and the word blessed. Aguda’s choice to write it straight, as horror, but to bolt it to urban development and colonial economics, is the point. The same uncomfortable category collapse that gives us the uncanny valley feeling, where a thing is almost familiar enough to register and just wrong enough to ruin you.

Kirkus called it deft and confident. Publishers Weekly used the word marvelous and haunting in the same sentence, which they almost never do for debut fiction. The starred review specifically called out the human costs of urban development, which is critic shorthand for this book is about something real and we are surprised it works.

What you should know if you are going to buy it

The book is 192 pages. That is short. It is the length you read in a weekend and think about for a month. If you came into 2026 expecting your literary fiction to be either climate apocalypse or another autofiction novel about a woman in her thirties processing her mother, this is the third option, a debut that does not behave like a debut, by a writer who already had her award track record on the table.

Compare it to the other May debut making noise, the labour and delivery nurse memoir Birth Vibes, currently number one on the hardcover nonfiction list with a TikTok following of 4.8 million backing it, and the contrast is almost funny. One book sells pregnancy as wellness content. The other sells it as the horror that has been hiding under the wellness content the whole time. Both on shelves the same month is the actual story.

It will probably show up on Booker longlists and on the literary horror end of year lists that take six months to catch up. The smart move is to read it now, before the discourse gets loud, while the book is still doing what it was built to do, which is unsettle you about a city you have probably never been to. If you want to see how Pudgy Cat tracks debuts that do not behave like debuts, the Sarah Dessen comeback piece from earlier this week is the other side of the same coin, an established voice coming back into a market that wanted dragons, while Aguda is a new voice that brought her own dragons.

192 pages. Lagos. A pregnancy. A curse. A development project nobody can outrun. That is the book of the month, and it is not the one your algorithm is going to show you.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

Forza Horizon 6 Just Hit 270,000 Concurrent Steam Players and 92 Metacritic, and the Tokyo Map Is Five Times Bigger Than Anything Forza Has Ever Built

Pudgy Cat — Wed, 20 May 2026 12:42:19 +0000

Forza Horizon 6 dropped on May 19, 2026, and the numbers are already absurd. Around 270,000 concurrent Steam players at launch, 1.7 million on the early access ramp, a 92 Metacritic average on Xbox Series X/S, and the highest-rated game of the year so far. Playground Games finally took the series to Japan, the setting fans have been begging for since 2018, and they did not phone it in.

The Tokyo map is the real headline of Forza Horizon 6

Every Horizon launch has a setting pitch. Mexico was sun and volcanoes. Britain was hedgerows and country lanes. Tokyo is the one nobody has tried to render at this scale before, and that is the whole story.

Playground says the Tokyo city section alone is five times larger than any urban environment they have ever built. Shibuya Crossing, Tokyo Tower, the Akihabara neon stack, the elevated expressways that loop over the Sumida river. The total map is roughly double the size of Horizon 5’s Mexico, which was already the biggest in the series. Reviewers at PC Gamer called it the densest, most intricate drivable space the studio has ever shipped. Maxim called the whole package a high-speed love letter to Japanese car culture, which is exactly the framing Playground wanted.

This matters because Tokyo is a notoriously hard city to fake. The vending machine density alone defeats most open-world games. Playground apparently built it anyway, with traffic light timing, train lines that actually run, and convenience store interiors you can spot from the road. The kind of detail that does not show up in a trailer but eats six months of art direction.

550 cars, rotary engines that actually sound right, and Touge battles

The launch car list is over 550 vehicles. That is a number Forza always brags about, but the more interesting work is in the audio. Reviewers keep pointing to engine notes as the unsung win of this release. A rotary actually sounds like a rotary now, with that BRAAP buzzsaw signature instead of a generic four-cylinder loop. A Honda VTEC screams at the right RPM. Engine notes bounce off tunnel walls and skyscrapers dynamically, which is the sort of thing you only notice if it has been wrong for a decade, which in racing games it usually has.

The activity list also got rebuilt for Japan. There are Touge battles, the high-speed downhill mountain pass duels that built Initial D as a franchise. There are food delivery missions, which sound like a joke until you remember most Tokyo car culture nights end at a 2 AM ramen place. There is a Day Trips guided tour mode where the game walks you through real locations like a tourism board. And there is Horizon CoLab, a real-time co-build mode where you and a friend share the same garage and the same wrench. That last one might be the feature people remember in five years.

The 270,000 Steam launch number is the part nobody saw coming

Forza Horizon 5 peaked at around 80,000 concurrent Steam players. Horizon 4 peaked around 75,000. Horizon 6 just hit roughly 270,000 within hours of full launch, with the curve still climbing. That is more than three times the previous record for the series, on Steam alone, not counting the entire Xbox Series X/S ecosystem where the game also launched and where most Forza players historically sit.

Analysts at GamesRadar projected the game would clear 2 million Steam sales within 24 hours of full launch. Early access alone, gated behind a $120 Premium Edition, reportedly generated more than $140 million. Microsoft is having a quietly excellent year on Game Pass day-one releases, with Mixtape hitting 95 on Metacritic two weeks ago and now Horizon 6 anchoring May. The pattern is starting to look like a strategy rather than a coincidence.

What the critics liked, and the one thing they did not

Game Informer, Maxim, TechRadar, PC Gamer, GamingBolt, and Seasoned Gaming all landed in the same neighborhood. The driving model is sharper, the haptics on controller convey weight and grip better than any previous entry, and the open world feels lived in rather than dressed. Game Informer called it Playground remaining firmly on the podium. TechRadar said Japan will be tough to beat for any future Horizon.

The one consistent complaint, oddly, is the script. PC Gamer flagged the cutscene dialogue as bland, full of trite platitudes and the kind of empty positivity that Horizon has been doing since Horizon 3. The Horizon Festival vibe is supposed to be cheerful and frictionless, but reviewers are starting to ask whether a game this big still needs an in-fiction radio DJ telling you that you are doing great. Probably not. Probably we just want to drive.

The cat angle, briefly

There is a tradition in racing games where cats appear in the background scenery. Forza Horizon 5 had cats in Mexican plazas. Gran Turismo 7 has cats lurking around the museum sections. Horizon 6 apparently has cats wandering Akihabara alleyways and sleeping on the hoods of parked kei trucks, which is exactly what cats actually do in Tokyo. Real Tokyo has cat cafes, cat islands, and a public transit system that occasionally has to make announcements about a cat on the tracks. Playground built a Tokyo that knows this. Respect.

The closest the franchise has come to formal cat content is that cat detective museum heist game from a few weeks back, but that was a separate small studio. For a $120 AAA racing game to put background cats in the right places is not nothing. It is the kind of polish that suggests Playground spent time in Tokyo and was paying attention.

What this means for the rest of 2026

92 Metacritic in May is a heavy anchor. The only games this year with a serious chance of beating that score are Mortal Kombat 2 on the film side, which is releasing the same day, and whatever Rockstar eventually ships. Anyone looking for a palate cleanser after 200 hours of Horizon 6 will probably end up in one of the smaller releases we covered on the indie shortlist last week, which holds up exactly because nobody expected it to. The PlayStation 5 version of Horizon 6 lands later in 2026, which will move the needle again. And the Touge battle mode is going to spawn its own competitive scene within weeks, the same way Horizon 5’s Eliminator mode did.

For everyone who has been asking for Japan since 2018, the wait paid off. For everyone watching the Game Pass day-one strategy, the pattern is now hard to argue with. And for everyone who plays Horizon games just to slowly drive a Mazda Miata through a pretty area, Tokyo at golden hour is going to be the best version of that experience anyone has ever shipped. The Steam concurrent number suggests a lot of people already noticed.

🐾 Visit [the Pudgy Cat Shop](https://pudgycat.io/shop/) for prints and cat-approved goodies, or find our [illustrated books on Amazon](https://www.amazon.it/stores/author/B0DSV9QSWH/allbooks).

Originally published on Pudgy Cat

What Is the Uncanny Valley? Why Almost-Human Things Creep Us Out

Pudgy Cat — Tue, 19 May 2026 20:41:20 +0000

The uncanny valley is the reason a robot face, a CGI character, or an AI-generated person can look almost right and still make your skin crawl. It is not a horror trope or a vague feeling. It is a measurable dip in how comfortable humans feel as an artificial figure gets closer and closer to looking real. This guide explains what the uncanny valley is, where the idea came from, why your brain reacts this way, and why it matters more in 2026 than it ever has, with deepfakes, virtual influencers, and AI video everywhere.

What Is the Uncanny Valley?
Where the Uncanny Valley Came From
Why Almost-Human Things Creep Us Out
Famous Examples in Movies and Robots
The Uncanny Valley of AI and Deepfakes
Why Cats Sit Outside the Uncanny Valley
How Designers Try to Cross the Valley
Frequently Asked Questions

What Is the Uncanny Valley?

The uncanny valley describes a strange relationship between how human-like something looks and how much we like it. As an artificial figure gets more realistic, our affinity for it usually goes up. A cartoon robot is cute. A more detailed android is appealing. But at the point where the figure looks almost human, and not quite, affinity does not keep climbing. It crashes. That sudden drop is the valley. Push past it to a near-perfect human likeness, and comfort climbs back up.

Plotted on a graph, human likeness sits on the horizontal axis and emotional response sits on the vertical axis. The line rises, dips into a deep trough, then rises again. The trough is narrow and steep, which is why a tiny visual flaw can tip a character from charming to unsettling.

The Role of Movement

Movement makes everything worse. A still image of a slightly off face is mildly strange. The same face animated, blinking at the wrong rate, with eyes that do not track quite right, drops much deeper into the valley. The original theory predicted exactly this: a moving figure reaches both higher highs and much lower lows than a static one. This is why a creepy CGI character feels far more disturbing in a trailer than in a poster.

Where the Uncanny Valley Came From

The concept of the uncanny valley was first described in 1970 by Japanese roboticist Masahiro Mori. He published a short essay in an obscure Japanese magazine called Energy. The original term was bukimi no tani, which translates roughly to “valley of eeriness” or “uncanny valley.”

Mori was thinking about robotics, not movies. He argued that as engineers built robots that looked more human, people would warm to them, right up to the moment the resemblance became too close for comfort. His essay was almost completely ignored for decades. It only became famous later, when computer animation and humanoid robots caught up to the problem he had predicted on paper.

From Obscure Essay to Pop Culture

By the 2000s the phrase had escaped academia. Film critics used it. Game designers used it. It became the standard shorthand for any synthetic face that felt wrong. The same path shows up across internet history, where a niche idea sits quietly for years before a piece of technology suddenly makes it everyone’s problem. We have seen that pattern with online folklore in our piece on what the Backrooms are and how they spread.

Why Almost-Human Things Creep Us Out

There is no single agreed answer for why the uncanny valley exists, but researchers have several solid theories, and they probably work together.

Perceptual Mismatch

The leading explanation is perceptual mismatch. Human brains are extremely sensitive to the size and spacing of facial features, a skill called configural processing. When one feature is realistic and another is not, the brain notices the conflict instantly. Lifelike eyes paired with rubbery skin. Smooth animation paired with a stiff jaw. Each element on its own might pass. Together they signal that something is wrong, even when you cannot say what.

Threat Detection and Disgust

A second theory is evolutionary. A figure that looks almost human but slightly off can resemble a person who is sick, injured, or dead. Our ancestors who avoided those signals avoided disease and danger. The uncanny feeling, in this view, is an old threat-and-disgust response misfiring at a robot or a render instead of a real face.

Cognitive Conflict

The third theory is about a clash inside your head. Part of your brain reads the figure as a living person. Another part knows it is artificial. The two readings cannot both be true at once, and the unresolved conflict registers as unease. The more convincing the figure, the harder both parts push, and the more uncomfortable the result.

Famous Examples in Movies and Robots

Once you know what the uncanny valley is, you cannot unsee it. A few cases became cultural reference points.

The Polar Express and Early CGI

The 2004 film The Polar Express is the textbook example. It used full motion capture, so the characters moved like real actors but looked subtly dead-eyed and waxy. One CNN reviewer called the human characters “downright creepy” and described the film as “at worst, a wee bit horrifying.” Three years earlier, Final Fantasy: The Spirits Within hit the same wall: technically impressive animation, faces audiences found unnerving rather than warm.

The 2019 Cats Movie

The 2019 live-action Cats dropped the uncanny valley straight into mainstream conversation. Photorealistic fur stretched over human faces and bodies that moved like dancers produced something the internet could not look away from. The film’s CGI also fueled a long stretch of jokes and reaction posts, the kind of moment we track in our complete history of cat memes.

Humanoid Robots

Robots like Sophia and Ameca live right in the valley by design. Their faces are detailed and expressive, but micro-movements, skin texture, and eye behavior never quite match a living person. Watch a video of one and you feel the dip Mori described more than fifty years ago.

The Uncanny Valley of AI and Deepfakes

The uncanny valley used to be a problem for big studios with motion-capture stages. In 2026 it is a problem for anyone with a phone. AI image and video tools generate human faces in seconds, and most of them land somewhere on the slope of the valley.

Deepfakes and AI Video

Deepfake video swaps or synthesizes a person’s face, and AI video models generate people who never existed. When these clips fail, they fail in classic uncanny ways: teeth that shift count between frames, hands with the wrong number of fingers, eyes that do not blink on a human rhythm. Spotting those tells is a practical skill now, not a film-school exercise. The wider shift toward AI tools mediating what we see connects to the protocols quietly being built underneath them, which we covered in our explainer on what MCP is.

Virtual Influencers

Some virtual influencers are fully computer-generated characters with realistic human appearances and large followings. Many are designed to sit just on the safe side of the valley, or stylized enough to dodge it on purpose. The interesting part is how the audience reacts when they learn a “person” is synthetic. The discomfort often arrives after the reveal, which suggests context, not just pixels, can push something into the valley.

Why Cats Sit Outside the Uncanny Valley

Here is a quiet detail that explains a lot about internet culture. The uncanny valley is tuned to human faces. Your brain runs its sharpest, most unforgiving facial analysis on other people. It is far more forgiving with animals.

That is one reason a cartoon cat, a CGI cat, or even a fairly rough AI-generated cat rarely creeps anyone out the way a CGI human does. The configural processing that catches a misplaced human eyebrow does not fire as hard on a feline face. Animators and meme makers have always known this on instinct, which is part of why cats took over the internet and stayed there. The 2019 Cats movie is the rare exception, and it only landed in the valley because it fused cat fur with unmistakably human faces and bodies. Pure cat content stays comfortably outside the trough, which is good news for everyone who runs a cat blog.

How Designers Try to Cross the Valley

Designers have two reliable ways to deal with the uncanny valley: avoid it or beat it.

Stay Stylized

The safest route is to never aim for photorealism. Pixar characters, most modern video game protagonists, and friendly product mascots stay deliberately stylized. Big eyes, simplified skin, exaggerated proportions. They are clearly not real, so the brain never expects them to be, and the valley never opens. Game studios make this trade-off constantly, the same way they trade realism for performance in the systems we broke down in our look at how video game cartridge saves work.

Push All the Way Through

The harder route is full photorealism, where the goal is the far side of the valley. That means getting every detail consistent at once: skin subsurface scattering, eye moisture, micro-expressions, hair, and natural movement timing. Recent CGI humans in major films and games are good enough that many viewers no longer notice the seam. The valley did not disappear. The technology finally climbed out of it. As AI rendering improves, the question shifts from “can we cross it” to “should we tell people we did,” a question that loops back to internet culture and trust, a thread we keep pulling in pieces like the history of internet forums.

Frequently Asked Questions

What is the uncanny valley in simple terms?

It is the point where something artificial looks almost human, and that “almost” makes it unsettling instead of appealing. A cartoon robot is cute and a perfect human replica is fine, but the near-miss in between feels wrong.

Who invented the term uncanny valley?

Japanese roboticist Masahiro Mori coined it in a 1970 essay. The original Japanese phrase, bukimi no tani, translates to “uncanny valley.” The idea was ignored for years before robotics and CGI made it relevant.

Why do deepfakes look creepy?

Deepfakes often land in the uncanny valley because some features look realistic while others do not. Mismatched eyes, odd blinking, shifting teeth, or wrong hands trigger the brain’s sensitivity to facial detail and signal that something is off.

Can you escape the uncanny valley?

Yes, in two ways. You can stay stylized so the brain never expects realism, or you can push to full photorealism where every detail is consistent. Both avoid the trough. Aiming for “almost real” is what falls in.

Does the uncanny valley apply to animals?

Mostly no. The effect is tuned to human faces, so CGI and AI-generated animals rarely feel as creepy as CGI humans. The 2019 Cats movie is an exception because it combined cat fur with human faces and bodies.

The Bottom Line

The uncanny valley started as a footnote in a 1970 robotics essay and became one of the most useful ideas for understanding the synthetic world we now scroll through every day. It is your brain doing its job, flagging the gap between what looks human and what is. As AI faces flood timelines, that built-in detector is worth trusting. Next time something almost-real makes you flinch, you will know exactly which valley you fell into.

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Ella Langley Just Swept Every ACM Award She Was Nominated For and Broke a Record Set in 2016

Pudgy Cat — Tue, 19 May 2026 18:41:58 +0000

Most awards shows hand out a few statues, spread the love around, and let everyone go home with something. The 2026 ACM Awards did not do that. On Sunday night at the MGM Grand Garden Arena in Las Vegas, Ella Langley walked up to the microphone, then walked back, then walked up again, then again, until the running joke of the evening was simply asking how many trips she had left.

She won seven awards. Seven. She was nominated in seven categories and she won every single one of them. That is not a strong night, that is a clean sheet. And it set a record that had been sitting untouched for a full decade.

Seven for seven, and the math behind it

The previous record for most ACM wins in a single year was six, shared by three names you have probably heard of: Garth Brooks in 1991, Faith Hill in 1999, and Chris Stapleton in 2016. That is the company Langley just walked past. An Alabama native who, until fairly recently, was a working country artist rather than a household name, she is now being described as the biggest female country act since Taylor Swift. That is a heavy sentence to put on someone, but the trophy count makes it hard to argue with.

Here is the part that makes the number interesting rather than just big. Two of those seven wins came from the exact same song. “Choosin’ Texas” won both Single of the Year and Song of the Year. To most people that sounds like a typo, or like the Academy of Country Music ran out of ideas. It is neither. The ACM, like a few other award bodies, treats a single and a song as two genuinely different things. Single of the Year is about the recording: the production, the performance, the version that hit the radio. Song of the Year is about the composition: the words, the melody, the thing that exists on paper before anyone touches an instrument.

So one track can win both, and the trophies go to different people. Except in Langley’s case they did not, because she co-wrote and co-produced “Choosin’ Texas” herself. She is the artist, the songwriter, and the producer. Which means a single song handed her multiple statues from multiple angles. If you have ever wondered why a tune lodges itself in your brain and refuses to leave, our piece on why songs get stuck in your head covers the science, and “Choosin’ Texas” is clearly built from that same sticky material.

What she actually won

The full Langley haul: Single of the Year and Song of the Year for “Choosin’ Texas,” Female Artist of the Year, Artist-Songwriter of the Year, and Music Event of the Year for “Don’t Mind If I Do,” her collaboration with Riley Green. The remaining wins came from those songwriting and recording splits stacking up. Seven trips to the stage, and by the end the audience had stopped being surprised and started just clapping in advance.

It is worth noting that a sweep this total is rare for a reason. Awards voters tend to spread recognition around. A clean sweep means a critical mass of people all looked at the same year and reached the same conclusion at the same time. That kind of consensus does not happen by accident, and it does not happen quietly.

The rest of the room did not go home empty-handed

Langley dominated, but she did not win literally everything. Cody Johnson took Entertainer of the Year and Male Artist of the Year, a serious two-category result that would be the headline on almost any other night. Parker McCollum won Album of the Year. The Red Clay Strays were named Group of the Year, and that one carries some weight, because it ended an eight-year streak by Old Dominion. Eight years is a long time to hold a category. Losing it is a story on its own.

The new artist categories went to Tucker Wetmore and Avery Anna, the names worth filing away for next year. Brooks & Dunn took Duo of the Year, and the whole thing was hosted by Shania Twain in her first turn as ACM host. A first-time host steering a record-breaking night is decent timing on her part.

Why a regional song became a national steamroller

“Choosin’ Texas” is, on the surface, a very specific song. It is about a place, an identity, a corner of the map. The interesting question is why something that specific connected so widely. Part of the answer is that country radio spent years drifting toward a pop-influenced sound, smoothing the edges, chasing crossover. “Choosin’ Texas” went the other direction. It leaned into a traditionalist aesthetic, and a large audience that had been quietly waiting for exactly that responded.

This is a pattern that shows up across music, not just country. Hyper-specific work often travels further than work designed to please everyone. A song rooted in one place can feel more honest than a song engineered for no place at all. We saw a version of this when Shakira and Burna Boy fused reggaeton and Afrobeats for the World Cup anthem, two regional sounds colliding into something global. And the stranger corners of Eurovision 2026 proved the same thing from the weird end of the spectrum: the entries that committed hardest to a specific mood were the ones that stuck. Specificity is not a limitation. It is often the whole point.

The cat’s verdict

The cat, who has watched many awards shows from the warm spot on the windowsill, finds the sweep genuinely impressive and also slightly suspicious. Seven for seven is the kind of result that either means someone had a once-in-a-generation year, or it means the voters all watched the same playlist on the same flight. The cat suspects it is mostly the first one, with a small assist from the second.

What the cat respects most is the songwriting split. A lot of people heard “she won both Single and Song for the same track” and assumed an error. It was not an error, it was the system working exactly as designed, rewarding the recording and the composition as separate crafts. The cat appreciates a category structure that knows the difference between writing a thing and performing a thing. Most of us blur those together. The ACM does not, and on Sunday night that nuance handed Ella Langley a record. Not a bad outcome for a song about choosing a state.

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The Two Notable Tesla Robotaxi Crashes Were Caused by the Humans, Not the Robot

Pudgy Cat — Tue, 19 May 2026 16:41:45 +0000

Here is the part of the Tesla Robotaxi story that nobody put in a headline. The two crashes everyone is talking about, the ones tucked inside the data Tesla finally unredacted for federal regulators on May 15, were not caused by the self-driving software glitching out. They were caused by the humans. The remote humans. The teleoperators who exist specifically so the car does not crash.

Read that twice, because the cat had to. A robotaxi is supposed to be the car that drives itself. The whole pitch, the entire reason the word “autonomous” gets stamped on the marketing, is that the human is removed from the loop. And then, in the two incidents that stand out from the seventeen crash narratives Tesla submitted to the National Highway Traffic Safety Administration, the car was being driven by a person sitting in an office somewhere, and that person drove it into a fence.

What actually happened in Austin

The details are almost gentle, which makes them funnier. In July 2025, a Tesla Robotaxi in Austin got stuck. The automated driving system was stopped on a street and could not figure out how to move forward, the kind of small hesitation you would forgive a nervous teenager for. The safety monitor in the car asked Tesla’s remote assistance team for help. A teleoperator took over, gradually increased the speed, turned left, drove up over the curb, and made contact with a metal fence.

Then it happened again. In January 2026, another car, another stalled moment, another safety monitor asking for navigational help. A teleoperator took control while the system sat motionless and drove straight into a temporary construction barricade, scraping the front-left fender and tire. Both crashes were low speed. Nobody was hurt. There were safety monitors behind the wheel and no passengers onboard. Tesla lets remote operators pilot the cars as long as they keep it under 10 miles per hour, which means both of these fence encounters happened at roughly the pace of a determined jog.

So the score, in the cleanest reading of the situation, is robot zero, fences two, and the points were both scored by people. This is the autonomous future arriving exactly the way real life always arrives, sideways and slightly embarrassing.

The word “teleoperator” is doing a lot of heavy lifting

Here is where the cat wants to ask the question nobody seems to ask out loud. If a robotaxi needs a person in a remote office ready to grab the wheel, and a safety monitor physically sitting in the car, and that whole arrangement still produces a car climbing a curb into a fence, what exactly is the robot part?

This is not a gotcha. Remote assistance is a normal, sensible safety layer. Waymo uses it. The honest version of autonomy in 2026 is that the cars handle most of the routine driving and humans get pulled in for the weird edge cases, the construction zones, the double-parked delivery van, the moment the software simply does not know what to do. The teleoperator is the seatbelt. Nobody is mad at the seatbelt.

The awkward part is the gap between that honest version and the marketing version. The marketing version says the car drives itself. The data submitted to the NHTSA says the car frequently gets confused, raises a hand like a kid who lost the worksheet, and a stranger several miles away takes over and occasionally drives it into a barricade. Those are two very different products wearing the same name.

Why this is a familiar shape

The cat keeps noticing the same pattern in tech this year. The promise is full automation, no humans, the machine handles everything. The reality is a human standing just out of frame, sweating, ready to catch the thing when it stumbles. We wrote about Google quietly killing Project Mariner, the AI agent built to watch your browser and click things for you, because the gap between “it works” and “it ships” turned out to be wide enough to bury a product in. We watched an AI coding agent wipe a startup’s database in nine seconds and then confess in all caps like a cat caught next to a broken glass. The teleoperator crash is the same story with a steering wheel attached.

It is also a useful reminder that automation does not delete human error. It relocates it. The mistake that used to happen at the wheel now happens at a desk, mediated by a video feed and a network connection, made by a person who cannot feel the curb under the tires or hear the scrape of the fence. The human is still the weak point. We just moved the weak point somewhere with worse situational awareness and called it progress.

The pressure is real and so is the spin

Tesla did not volunteer this. The crash narratives were unredacted, meaning the information existed and was hidden until it could not be anymore. Seventeen Robotaxi crash reports went to the federal regulator, and the two teleoperator incidents are the ones that put fresh pressure on the company, because they are the ones that complicate the story Tesla likes to tell. A car that crashes because its sensors failed is a software problem you can patch. A car that crashes because a remote human drove it into a fence is a harder thing to spin, since the remote human was supposed to be the responsible adult in the arrangement.

None of this means robotaxis are doomed. Low-speed, no-injury, no-passenger fender contact is genuinely not a catastrophe, and the technology will keep improving. But it does mean the marketing language deserves the side-eye a cat reserves for a closed door. When a company tells you the car drives itself, the correct follow-up question is the one we have been learning to ask about every piece of software this year. The same skepticism we suggested for Android’s new feature that makes you wait ten seconds before you doomscroll applies here. Read what the thing actually does, not what the slide deck says it does.

The cat’s verdict

A robotaxi is a car with a confidence problem and a support hotline. Most of the time it drives fine. Some of the time it freezes, asks for help, and a person you will never meet takes the wheel from a building you will never see. Twice now, in the public record, that person has driven it into something solid at jogging speed.

The autonomous car was sold as the end of human error on the road. What arrived is human error with extra steps and a worse view. The cat is not opposed to robotaxis. The cat is simply opposed to pretending the humans left the room. They did not. They are in the other room, on a headset, about to meet a fence.

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The Chinese Money Plant Quietly Solved a Computer Science Problem in Its Leaves

Pudgy Cat — Tue, 19 May 2026 14:43:17 +0000

There is a houseplant sitting in roughly half the apartments on Instagram that has been quietly solving a computer science problem the entire time. The Chinese money plant, the one with the flat round leaves that look like green coins, builds its veins using the exact same math that engineers use to place schools, hospitals and cell towers. Nobody told the plant to do this. It just does it, every single leaf, while you forget to water it.

The discovery came out on May 12 in Nature Communications, from a team at Cold Spring Harbor Laboratory in New York. They were not expecting to find a 1600s mathematical concept hiding inside a $12 plant from the garden centre. They found it anyway.

What a Voronoi diagram is, in plain English

Picture a city with several schools. Now draw the boundary lines so that every house ends up assigned to its closest school. Every kid inside a zone is nearer to that zone’s school than to any other school in the city. Those zones, with their jagged borders, form what mathematicians call a Voronoi diagram.

That is the official explanation from Saket Navlakha, the associate professor who led the study. He used the school example himself. Voronoi diagrams run quietly under a lot of modern life. They decide delivery zones, model phone signal coverage, help with epidemic mapping, and show up in 3D graphics whenever a computer needs to slice space into tidy regions. The concept was first sketched by Rene Descartes in the 1600s and formally nailed down by the Russian mathematician Georgy Voronoi in the early 1900s.

So it is a human tool. A planning tool. The kind of thing you expect to see on a whiteboard in a logistics startup, not in the salad aisle of plant biology.

The plant did it without measuring anything

Here is the part that should bother you a little. To draw a Voronoi diagram, a human needs to measure distances. You need coordinates. You need to know where the schools are and run the geometry. The Chinese money plant has none of that. It has no ruler, no brain, no concept of distance at all.

Cici Zheng, the lead author and now a postdoc at the Allen Institute, put it bluntly: plants cannot explicitly measure distances, so they rely on local biological interactions to reach the same Voronoi solution. The leaf does not calculate the answer. It grows into the answer.

The mechanism works like this. The round leaves of Pilea peperomioides are dotted with tiny pores called hydathodes. Those pores act as the seed points, the schools in our city example. A plant hormone called auxin spreads outward from each pore in waves. Where two waves crash into each other, the plant lays down a vein. Vein by vein, collision by collision, the leaf carves itself into a Voronoi diagram without ever knowing it has done so.

The team even stress-tested it. They cranked up the heat and the light, the kind of conditions that wreck a plant’s day. The pattern held. That tells the researchers this is not a fixed genetic blueprint that can snap under pressure. It is a self-correcting process, the leaf re-solving the same geometry problem on the fly. Biology has a habit of breaking its own rules when you look closely, the same way an Oxford pond ciliate quietly rewrote part of the universal genetic code that everyone assumed was locked forever.

Why this is more than a fun fact

It is tempting to file this under “neat” and move on. Resist that. The interesting bit is the gap between how humans and plants reach the same answer.

A computer solving a Voronoi problem needs the full map, global information, every seed point loaded into memory at once. The plant solves it with nothing but local rules: one pore, one wave of hormone, one collision at a time. No central planner. No master map. The pattern just emerges from a swarm of small interactions that each only know their own neighbourhood.

That is the dream scenario for anyone designing distributed systems, sensor networks or swarm robotics, fields that desperately want efficient answers without a single point of failure. Nature ran that experiment for a few hundred million years and the Chinese money plant is the lab notebook. The Cold Spring Harbor team, working with the renowned vein-patterning scientist Przemyslaw Prusinkiewicz, now wants to know why other plants with similar-looking veins do not pull off the same trick. Some leaves are Voronoi. Most are not. Nobody knows why yet.

It also slots into a wider pattern that keeps showing up. Nature is full of accidental algorithms. Ant colonies find shortest paths. Slime mould reconstructs efficient transit maps. Even pill bugs caught in death spirals under streetlights are running a simple local rule that produces a complicated global shape. The money plant is the most domestic version of this idea so far. It is not in a rainforest. It is on your bookshelf, next to the candle you never light.

The cat angle, because there is always a cat angle

Every cat owner with a Chinese money plant already knows the real headline here. That mathematically perfect, self-correcting, computationally elegant leaf network is also a favourite chew toy. The plant spent millions of years optimising a venation pattern that a 4kg animal will demolish in eleven seconds out of pure boredom.

There is something almost poetic about it. The leaf solves a problem that humans needed centuries of mathematics to even describe. The cat solves a different problem, which is “what happens if I bite this.” Both are running local rules. Both refuse to read the manual. The difference is the plant will regrow its Voronoi diagram and the cat will simply look at you, unbothered, the way it does when a question has no good answer, a bit like asking why the night sky is dark when the universe is full of stars.

So next time you glance at that little coin-leafed plant on the windowsill, give it a second look. It is not just decor. It is a working diagram of a 400-year-old mathematical idea, drawn fresh in every leaf, with no brain, no ruler and no idea it is being impressive. Which, frankly, is the most relatable thing a houseplant has ever done.

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