One More Branch #1

On By Jon UreñaIn Escritura, gaming, Inteligencia artificial1 Comment

Two nights ago I endured the kind of insomnia that forces you to roll around in bed under a barrage of intrusive thoughts, but also receiving some compelling ideas from the girl in the basement. And a new idea excited me immediately. In case you’ve been following my blog, which likely only a couple of people worldwide do at the most, you may have read recent posts about a project about evolving board games. Well, I’m growing out of it already. Don’t know what to tell you. Thankfully we’re in the era of Codex and Claude Code, and you can program whole new apps in a couple of days, which is what I’ve done for the new one.

In summary: as a kid I loved those “Choose Your Own Adventure” books. I devoured them. I resent the fact that I lent one to a guy I ended up hating, and I never saw that book again; it was my favorite of those kinds of books, too (it was about a guy, a knight or something, exploring a vast subterranean complex of caves. I don’t remember much of it other than he fought monsters and at the end there was a gorilla. I never found out the title of that book again). We’re in the era of large language models that can write better than 99% of writers, so why not task it with producing such interactive branching stories in a structured manner?

Well, it’s done. Here’s the repository: https://github.com/joeloverbeck/one-more-branch . You give the system a character concept, some worldbuilding details, the tone/genre, and it creates the first page of narrative, with 2-4 meaningful, distinct choices at the end. You click one, and the corresponding page gets generated. If you restart, you can navigate already explored branches without generating anything, but any new branches will be generated and stored. I have let the AI decide when branches end. Haven’t seen that yet.

There’s currently an issue: I store information about the characters involved, the canon facts about them, globally. And I found out that the information can bleed from branch to branch when I didn’t mean it; for example, in one branch a character gave the other a map or something to that effect, and it was registered globally, which would have contaminated new branches. Such issues are the kinds that you routinely fix while working on an app. It will involve distinguishing between global canon facts about characters and state changes involving characters.

Anyway, I present you some branches of a test narrative. You can click the choices that have been explored, and they will lead you to the appropriate header, like in one of those “Choose Your Own Adventure” books but automatically. Other branches haven’t been explored so they aren’t clickable.

Page 1

The Drowned Serpent tavern reeks of canal-water and desperation, which means the crowd is perfect. You are Vespera Nightwhisper—at least, that’s the name you’re wearing tonight—and you’ve been working this room for two hours, your hybrid lute-viol singing melodies that make dock workers weep into their ale while their coin purses grow lighter. Your whiskers twitch slightly as you modulate your voice into something breathy and inviting, finishing a ballad about star-crossed lovers. The amber-gold eye catches firelight while the ice-blue one tracks the room’s exits. Your tail sways in time with the final chord. The applause comes with the satisfying clink of copper and silver hitting your open instrument case. But it’s all… fine. Competent. Safe. The music isn’t reaching that place you need it to reach—that breakthrough you can taste but can’t fucking touch.

You’re prowling through the crowd afterward, ears decorated with silver charms catching the lamplight, when a hooded figure intercepts you near the bar. Beaver-folk, you note instantly—the broad flat tail is unmistakable beneath the cloak. His breathing is shallow, panicked. Prey behavior.

“You’re the bard, mmh?” His voice cracks. “The one who takes… contracts?”

Your whiskers perk forward with interest. He slides a leather pouch across the bar—it clinks heavily. Gold, not copper.

“My business partner, Greaves. He’s been… changed. Cursed item from a dredger haul. The Ash-Seal company says it’ll take two weeks to dispatch someone, but he’s—” The beaver-folk’s voice drops to a whisper. “—he’s killing people at night. Doesn’t remember it come morning. I need someone who can track him, stop him, and keep it quiet before the constables get involved.” His eyes are desperate, searching your face. “They say you don’t flinch.”

You can already hear it—the chord progression that would capture this moment. Minor key, building tension, the way his breath hitches like a string about to snap. Part of you is cataloging his tells: the way he won’t meet your heterochromatic gaze, how his fingers drum against the bar—guilt rhythm, not just fear. He knows something he’s not saying. The other part of you feels that familiar clarity starting to crystallize. Danger. Blood. The prospect of something that matters. Your hand rests casually on your theatrical rapier’s pommel.

What do you do?

Page 2

The beaver-folk’s whiskers flatten against his muzzle. “You want to see it? The thing that’s turning my partner into—” He cuts himself off, glances around the tavern. Too many ears. “Not here. Come.”

You follow him out into Brinewick’s fog-thick night, your tail swishing with anticipation. The canals reflect lamplight in fractured gold as he leads you through three twisting streets to a cramped warehouse office that smells of timber and wet fur. He bolts the door behind you.

From a locked chest beneath his desk, he produces a wooden box wrapped in chains. Your whiskers twitch involuntarily—even through the wood and iron, you can feel something wrong radiating from it. Not sound, exactly. More like the absence of sound, a void where music should be.

“Greaves pulled this from a canal dredge two weeks ago,” the beaver-folk says, his paws shaking as he sets it on the desk. “Thought it was just an old mirror. Looked into it once. Just once. Now every night when the moon rises, he… changes. Becomes something else. Hunts.”

He unwraps the chains with the care of someone handling a venomous snake. Inside the box: a hand mirror, its silver surface tarnished black except for veins of sickly green that pulse with their own light. Your heterochromatic eyes widen—the amber one burns, the blue one goes cold. The reflection it shows isn’t the room. It’s something moving. Writhing.

Your fingers hover over your lute-viol’s case. Part of your brain is already cataloging this—the way the green light pulses in 6/8 time, the subsonic hum that makes your teeth ache. But another part, the part that stays sharp when blood starts flowing, is calculating. This isn’t just a curse. This is a weapon. And the beaver-folk’s breathing has changed—faster now, excited beneath the fear.

“The music of the curse,” you murmur, and your voice has gone flat, dangerous. The cute affect has vanished entirely. “Interesting phrase for someone who just needs his partner stopped, mmh?”

The beaver-folk’s tail twitches. “I don’t—”

“You’re lying.” Your whiskers are arrow-straight, reading every microexpression. “Your breathing shifted when you opened that box. Not fear. Anticipation. So let’s try this again, and this time don’t waste my fucking time: what do you actually want?”

What do you do?

Page 3

The beaver-folk’s pupils dilate. His breathing shifts—shallow now, caught. You watch his paws clench and unclench against the desk edge, claws scraping wood. He’s calculating whether to lie, and you can see the exact moment he realizes you’ll know.

“Smart cat,” he mutters, and his voice drops the desperate merchant routine entirely. What’s left is sharper. Hungrier. “Fine. Yes. Greaves needs to be stopped—that part’s true. But the mirror…” His eyes flick to the pulsing green light, and there’s reverence there. Want. “Do you know what Ash-Seal pays for functional cursed items? Not the broken ones they have to dispose of. The ones that still work.”

Your tail goes still. “You want to sell it.”

“I want to profit from two weeks of my business partner trying to eat dock workers, yes.” He leans forward, and now you can smell it—not just fear, but excitement, the musk of someone who’s found an angle. “But here’s the problem. Ash-Seal won’t pay premium unless they can verify it’s functional and contained. Which means I need someone who can demonstrate control. Someone who can look into it, trigger the transformation, and prove they can handle what comes after.”

The mirror pulses. Six beats. Rest. Six beats. Your mind is already turning it into a waltz in a minor key, something that would sound beautiful if it weren’t so wrong.

“You want me to become the monster,” you say, and your voice is flat as a blade. “To prove it works.”

“I want you to show you can survive it.” His whiskers twitch—he’s lying about something, but not about the core truth. “You’re a bard who fights. You’ve got discipline, control. If anyone can look into that thing and come back unchanged—or at least, come back in control—it’s someone like you. Do that, help me negotiate with Ash-Seal, and I’ll split the sale. Sixty-forty.”

Your amber eye burns. Your blue eye stays ice-cold. Part of you—the part that chases the crystalline clarity that comes after violence—is already wondering what kind of music would pour out of you if you let that curse in. What you could compose if you survived it. The rest of you knows this is the kind of choice that draws a line you can’t uncross.

The beaver-folk mistakes your silence for negotiation. “Seventy-thirty. Final offer. And I’ll throw in information about where Greaves hunts tonight. You can stop him, play hero for the constables, build your reputation. Then we do the demonstration for Ash-Seal tomorrow. Everyone wins.”

Your fingers drift to your rapier’s pommel. The mirror keeps pulsing. Six beats. Rest. Six beats. Like a heartbeat. Like a song you haven’t written yet.

Page 4

You lean in close enough that your whiskers nearly brush his cheek, letting your tail curl around his wrist where it rests on the bar. The beaver-folk goes rigid—prey instinct warring with something else. Good.

“Mmh, they say a lot of things about me,” you purr, tracing one claw along the edge of the coin pouch without opening it. “But ‘cheap’ isn’t one of them, sweetness.” Your heterochromatic eyes lock onto his—amber-gold and ice-blue pinning him in place. You can read him like sheet music: the way his breath catches, pupils dilating despite the fear-scent rolling off him. Desperate, yes. But there’s something else underneath. Anticipation?

“Triple,” you say, voice dropping into that register that makes people forget how to negotiate. “Your partner’s killing people, you need discretion, and you need someone who won’t run screaming when things get messy.” You let your fingers walk up his forearm. “That’s specialty work, darling. Premium rates.”

He swallows hard. “I—I don’t have that kind of—”

“Mrow, don’t lie to me.” Your whiskers flatten slightly, just enough to shift from seductive to dangerous. “You’re not coming to a tavern bard with a cursed-item problem because you’re poor. You’re coming because you need this handled quietly, and quiet costs.” You tilt your head, studying the micro-expressions dancing across his face. “So either you pay what I’m worth, or I walk, and you explain to the constables why you waited two weeks while bodies piled up.”

The beaver-folk’s breathing changes—faster, shallower. But he’s not breaking. He’s… calculating. Your ears swivel forward.

“Seventy-thirty,” he says suddenly.

“What?”

“The mirror—the cursed item. It’s functional. Controllable, in a sense.” His voice gains confidence, which sets your whiskers twitching. “Ash-Seal pays premium for items they can study and demonstrate. We contain it, prove what it does, I sell it to them. Seventy-thirty split. You get thirty percent.”

Your mind catalogs this instantly: He’s not trying to destroy the curse. He’s trying to profit from it. And he needs you to—what? Demonstrate it? Contain Greaves while he still transforms? The chord progression in your head shifts to something darker, more complex.

“You want me to help you sell the thing that’s turning your partner into a killer.” You don’t phrase it as a question. “While he’s still useful as proof of concept.”

The beaver-folk meets your eyes. “Greaves doesn’t remember anyway. And the money would set us both up for years. I know where he hunts. I know the pattern. We just need… documentation. Containment. Someone who can handle the dangerous part.”

Your tail lashes once. The dangerous part. The part where you’d be close to something curse-twisted and violent, where blood would likely spill, where that crystalline clarity might finally arrive and the music you’ve been chasing would bloom perfect and terrible in your skull.

Your whiskers are already trembling with something that isn’t quite fear.

What do you do?

Page 5

You pull back like he’s venomous, tail snapping away from his wrist. The predatory warmth drains from your expression, leaving something colder, clearer. “No.”

The beaver-folk blinks. “What?”

“No.” You stand, smoothing your tail fur with one hand—the nervous gesture betraying what your voice doesn’t. “I’ve crossed lines, sweetness. Plenty of them. But this?” Your whiskers flatten completely. “You’re not trying to stop your partner. You’re trying to profit off him. Off the people he’s killed.” The words taste wrong in your mouth, and that wrongness is what decides it. “That’s too fucking twisted even for me.”

You can see the calculation die in his eyes, replaced by panic. “Wait—you can’t—”

“Watch me.” You’re already moving, slinging your lute case over your shoulder. The rapier’s weight at your hip feels necessary, grounding. Your mind is racing, converting the situation into rhythm—6/8 time, the mirror’s pulse, the pattern of a trap you almost walked into. The melody forming is discordant, unfinished, and it makes your chest tight.

The night air hits you as you push through the tavern door. Brinewick’s canal-smell wraps around you—brine and rot and fish. The constabulary station is three blocks west, near the merchant district. You know because you’ve made a point of knowing where law enforcement operates in every city you work.

Your ears swivel back. The beaver-folk hasn’t followed. Smart. But now you’re walking toward the constables with a story about cursed mirrors and nighttime killings, and you’re a traveling bard with a reputation for not flinching at violence. They might listen. They might arrest you for wasting their time. They might already know about the bodies and wonder why you know details.

Your whiskers twitch. The smart play would’ve been taking the contract, documenting everything, then selling both the mirror and the evidence. Control the narrative. Instead you’re about to hand your leverage to people who don’t owe you anything.

But the alternative was becoming part of something that made your skin crawl, and apparently you still have limits. Who knew?

The constabulary station’s lamplight glows ahead. You can see two constables outside—a tall human woman and a tortoise-folk man, both armed. This is the moment. Once you walk up those steps and start talking, you’re committed. No take-backs, no negotiations, no performance that lets you slip away clean.

Unless you don’t. Unless you find Greaves yourself first, before the beaver-folk can move the mirror or disappear. Unless you handle this your way—not for profit, but because someone should, and the constables are slow and the bodies are real.

Page 6

You slide onto the bar stool beside him, close enough that your tail brushes his leg—calculated intimacy, the kind that makes people spill secrets. “I’m listening, mrow,” you purr, letting your voice do that thing where it sounds like an invitation and a threat at the same time. Your whiskers angle forward, reading the micro-tensions in his posture. “But I need details. When did this start? What does he become? Where does he hunt?” Your clawed finger taps the bar in rhythm—already your mind is converting his panic into tempo.

The beaver-folk glances around nervously before leaning closer. His breath smells like fear and wood shavings. “Two weeks ago. Greaves bought a dredger haul—mostly junk, rusted tools, broken pottery. But there was a mirror.” His voice drops further. “Ornate thing, silver frame with strange engravings. He looked into it once, just once, and that night…” He swallows hard. “He came back covered in blood. Didn’t remember leaving the warehouse. Said he’d slept through the night.”

“And the pattern?” you press, your ice-blue eye fixed on his face while your amber one tracks the tavern’s crowd. Your tail coils around the stool leg—anchoring yourself while your mind races ahead to chord structures, to the way terror sounds when you pluck it just right.

“Every night since. Always between midnight and dawn. He goes to the canal district—the old warehouse rows near Southwick Bridge. Three bodies so far that I know of.” The beaver-folk’s paws tremble as he slides a folded paper across to you. “This is where we store the timber. The mirror’s there, locked in my office. I… I haven’t destroyed it because—” He hesitates, and something flickers across his face. Not just fear. Anticipation? “—because Ash-Seal pays premium prices for functional cursed items. If you can prove it works, prove it can be controlled or contained, we split the sale. Seventy-thirty, your favor.”

There it is. The real melody beneath the desperate harmony. He wants to profit from this. Your whiskers twitch—not quite a lie, but not quite honest either. “Show me the mirror first,” you say, your voice dropping the sultry performance into something flat and surgical. “Then tell me everything about how Greaves hunts. Does he track by scent? By sound? Does he remember his victims come morning, or is it complete blackout?” Your claws drum against the bar—six-eight time, you note distantly. Everything is music if you listen right.

The beaver-folk nods rapidly. “Complete blackout. He wakes up in his bed, no memory of leaving. But there’s always blood under his claws, dirt on his feet. And the mirror—” His voice catches. “—it glows. Sickly green light, pulsing like a heartbeat when the sun goes down.”

Your breath catches. Not from fear—from recognition. That’s the rhythm you’ve been chasing. The one that arrives right after violence, right after the kill, when everything becomes crystalline and clear. You can already hear it: the pulse of cursed light translating into bass notes, the transformation into a crescendo, the hunt into a bridge that builds and builds until—

You realize you’ve been silent too long. The beaver-folk is staring at you, uncertainty creeping into his expression. Your whiskers flatten slightly as you force yourself back into the moment. “The warehouse,” you say simply. “Take me there. I want to see this mirror before I commit to anything.”

What do you do?

Page 7

You lean back slightly, letting your heterochromatic gaze bore into him—amber and ice reading every microexpression. Your whiskers flatten just enough to signal you’re not buying the full story. “Seventy-thirty sounds lovely, mrow,” you say, voice silk over razors, “but I don’t commit to hunting cursed problems without seeing what I’m actually dealing with.” Your tail uncurls from the stool, tip flicking in that particular pattern that means you’re calculating angles. “Tonight. I watch the transformation from a safe distance. See what your partner becomes, how the curse actually works, whether this is a containment job or a mercy killing.”

The beaver-folk’s whiskers twitch—there, that tells you he was hoping you’d just take his word for it. “That’s… reasonable,” he admits slowly. “But if Greaves catches your scent while he’s changed, I don’t know what he’ll do. The transformation makes him… wrong. Predator-wrong. His eyes go black, his teeth sharpen, and he moves like something that learned to hunt before it learned to think.”

“I can handle myself,” you say flatly, though your mind is already composing—6/8 rhythm, that green pulse he mentioned, the way terror sounds different when the monster used to be someone you knew. “Where exactly should I position myself? Rooftop? Alley? I need sightlines and escape routes.”

He pulls out a second scrap of paper, sketching quick lines with a charcoal nub. “The warehouse district near Southwick Bridge. There’s a three-story grain storage building with a flat roof—here.” He marks an X. “You’ll see the timber yard from there, and the route Greaves takes toward the old canals. He leaves around midnight, returns just before dawn.” His paw hesitates over the sketch. “The mirror stays in my office. It glows when the curse activates—sickly green light, pulses like a heartbeat. You’ll know when it starts.”

Your ears swivel forward. That rhythm again. Your fingers itch for your lute’s strings. “And you’ll be where during all this?”

“Safe in my quarters, door barred, pretending I don’t hear him leave.” The beaver-folk’s voice cracks slightly. “Same as every night for two weeks.”

You study him a moment longer—the way his shoulders hunch, the slight tremor in his webbed fingers, the calculating gleam buried under the fear. He’s not just desperate. He’s invested. This curse is worth money to him, and you’re the instrument he’s hoping will make it profitable. Fine. You can work with that. You’ve been worse things than someone’s profitable tool.

“Midnight, then,” you say, sliding off the stool with feline grace. “I’ll be on that roof. And beaver?” You lean close enough that your whiskers nearly brush his cheek. “If you’re lying about any detail that gets me killed, I’ll haunt you so thoroughly you’ll beg for whatever Greaves does to his victims. Understood, mrow?”

He nods quickly, and you catch the genuine fear there—good. You take the sketched map and the warehouse address, tucking both into your hidden pockets. The night air outside will be cold, and you have hours to kill before midnight. Your lute case feels heavy on your shoulder, and part of you wants to find a quiet corner to play, to chase that 6/8 rhythm that’s been circling your mind since he mentioned the mirror’s pulse. But another part—the part that keeps you breathing in dangerous cities—knows you should prepare. Scout the location in daylight. Check your medical kit. Maybe sharpen your rapier. Or you could visit the constabulary, see what they know about the bodies. Information is leverage, and leverage is survival.

The Drowned Serpent’s door swings shut behind you as you step into Brinewick’s lamp-lit streets, the canal-smell thick in the air, your mind already three moves ahead.

Life update (02/02/2026)

On By Jon UreñaIn Escritura, Slice of lifeLeave a comment

These last four days I’ve felt the darkness gathering at the edges of my being. Losing any intention of going outside. Lying in bed and hoping I wouldn’t get to wake up and endure any more.

A couple of hours ago I lay down, put on my VR headset, and tried to concentrate on watching a movie from the seventies (concretely Serpico). The other day was The Conversation. For whatever reason, I’ve always felt a pull toward the 1970s, even before Alicia Western. A feeling that somehow I belong to that time. Experiencing things from that era fills me with a nostalgia that hollows out my chest. The strong notion that I should have been there, should still be there. Another one of the many things in my life I haven’t understood about myself.

I’ve always felt uncomfortable among human beings, likely due to autism, and that doesn’t change much when I have to see people on a screen. To focus on a movie I have to get over a base ickiness, a discomfort. So much of what I see on a screen feels alien to me: how people interact with each other, how they react to things. Watching stuff from the 1970s adds a layer on top of that; it’s already been fifty fucking years, but it feels like it a whole different era. As if everyone from back then had been dead for a long, long time. And there are the absurd pains, like a moment when Al Pacino as this Serpico dude walks down the street and touches a girl’s head, and I wonder what happened to that person’s life. Her next fifty years of enduring on this earth. Is she alive or is she dead.

I haven’t been able to watch any of the movies I’ve tried recently for more than twenty minutes at a time or so. Maybe it’s depression-induced anhedonia. Maybe I’ve genuinely been losing my ability to enjoy things. Novels haven’t said much to me in a long time, and the only ones I cared for in the last few years or so were McCarthy’s works, someone whose soul was tragically anchored in the seventies. I’m no longer at an age in which I can lose myself in videogames; I know there are great stories waiting for me in stuff like Red Dead Redemption 2, but whenever I reinstall it, I play it once for like four hours, and then I can’t bring myself to launch it again.

I was born in Spain but I’ve never felt like I belong here. Technically I was born in the Basque Country region, but I’m not a separatist. I don’t connect with the locals. Things are so fucking bad here; we’re easily the most retarded country in Europe, that in no time will get even worse than the UK, France and Belgium when it comes to ethnic cleansing of the indigenous people. I have no hope for Spaniards, as I’ve had to work with your average one; all of them hooked to the state-sponsored media. They smugly spout the socialist garbage they’ve been fed as if they couldn’t conceive anyone thinking differently. They don’t even see it as politics; for them, that’s the natural state of things, and if you disagree, you’re a freak. The few times I’ve made the mistake of giving them an inch, hearing their thoughts beyond work-related matters reminded me again why I shouldn’t have.

In general, I feel like I’ve been dead for a long time and my body is taking decades to figure it out. Whenever that actually comes, I don’t think I’ll miss or feel any particular attachment to the stuff that at the time seemed so important to me: the stories I’ve written, the music I’ve loved, other projects of mine. It served its purpose while they happened, then they ceased being mine. I’m around because I’m around, then at some point I’ll cease to be and that’ll be that.

In a month or so I’ll have to start looking for a job. I don’t believe I’ll get hired as a forty-year-old programmer in this new era in which AI can do the work of a whole office of programmers. I’ll probably have to look for protected job as someone with a 52% disability. And I won’t do it for any other reason than the money. It seems there are people out there that get other benefits from the job: interacting with people, dealing with responsibilities… I want none of that. Working has always been a hell I had to get through merely to receive money at the end of the month.

Last time I spoke with my mother she asked me about work. I told her again that I don’t care about any of it. These “normal” people always try to deceive you, maybe because they deceive themselves, by going on about how jobs are more than things you endure because of money. And maybe it is for some people, but not for me. The last time they called me for a job in IT, which ended up falling through, I suffered a panic attack, my whole body telling me that I couldn’t return to that hell that put me thrice in the ER for heart and brain issues. I can’t allow myself to suffer the levels of stress I endured. No amount of money is worth that.

I guess that’s all I had to say at the moment. Not sure why I felt like saying any of it, who do I think is reading any of this, or why they would care about it.

LudoForge #4

On By Jon UreñaIn gaming, Inteligencia artificial, ProgramaciónLeave a comment

Now that the evolutionary process to grow game definitions is progressing at a steady pace in my app, named LudoForge, I fed the architectural docs to ChatGPT so that it would write a good explanation on how it works. It may be interesting to those curious about how complex systems can grow organically through an evolutionary algorithm that mimics biological evolution.

Teaching a computer to invent tabletop games (and occasionally rediscover the classics)

If you squint, evolving game designs is a lot like evolving creatures: you start with a messy ecosystem of “mostly viable” little organisms, you test which ones can survive in their environment, you keep the best survivors—but you also keep a diverse set of survivors so the whole population doesn’t collapse into one boring species.

In our system, the “organisms” are game definitions written in a small, strict game DSL (a structured way to describe rules: players, state variables, actions, effects, win/lose conditions, turn order, and so on). Each candidate game definition is wrapped in a genome: basically an ID plus the full definition.

From there, the evolutionary loop repeats: seed → simulate → score → place into niches → mutate elites → repeat.

1) Seeding: where the first games come from

Evolution needs a starting population. We can generate seeds automatically, or import them from disk, or mix both approaches. The important bit isn’t “are the seeds good?”—it’s “are they valid and diverse enough to start exploring?”

So seeds must pass two kinds of checks before they’re even allowed into the ecosystem:

  • Schema validation: does the JSON structure match the DSL’s required shape?
  • Semantic validation: does it make sense as a playable ruleset (no broken references, impossible requirements, etc.)?

And there’s a third, subtle filter: when we place games into “niches” (more on that below), seeds that land only in junk bins like unknown/under/over are rejected during seed generation, because they don’t help us cover the design space.

Think of this as: we don’t just want “a bunch of seeds,” we want seeds scattered across different climates so evolution has many directions to run in.

2) Playtesting at machine speed: simulation as the “environment”

A human can’t playtest 10,000 games a day. A simulation engine can.

For every candidate game, we run automated playthroughs using AI agents (simple ones like random and greedy are enough to expose lots of structural problems). The engine repeatedly:

  1. Lists the legal moves available right now
  2. Checks termination (win/lose/draw conditions, cutoffs, loop detection)
  3. Lets an agent pick an action (with concrete targets if needed)
  4. Applies costs and effects, recording what happened
  5. Advances the turn/phase according to the game’s turn scheduler

Crucially: when a complex game definition tries to do something illegal (like decrementing below a minimum, or targeting something that doesn’t exist), the engine records skipped effects/triggers instead of crashing, so the system can observe “this design is broken in these ways” rather than just failing outright.

This is the equivalent of an organism interacting with the world and leaving tracks: “it tried to fly, but its wings didn’t work.”

3) Turning playthroughs into numbers: metrics, degeneracy, and fitness

After simulation, we compute a set of analytics that act like proxies for design quality—things like:

  • Agency: did players have meaningful choices, or were they railroaded?
  • Strategic depth (proxy): how large is the typical decision space?
  • Variety: do many different actions get used, or does one dominate?
  • Interaction rate: are players affecting each other or only themselves?
  • Structural complexity: is this a tiny toy, or something richer?

These are not “fun detectors.” They’re sensors.

Then we run degeneracy detection: filters that catch the classic failure modes of randomly-generated rulesets:

  • infinite loops / repeated states
  • non-terminating games (hits max turns/steps)
  • games with no real choices
  • trivial wins, dominant actions, excessive skipped effects, etc.

Some degeneracy flags cause an outright reject (hard gate), others apply a penalty (soft pressure), and too many penalties at once can also trigger rejection.

Finally, all of this becomes a feature vector, and we compute an overall fitness score—the number evolution tries to increase.

4) “Growing in niches”: why we don’t keep only the top 1%

If you only keep the single highest-scoring game each generation, you get premature convergence: the population collapses into one design family and stops surprising you.

Instead, we use MAP-Elites, which you can picture as a big grid of “design neighborhoods.” Each neighborhood is defined by a few chosen descriptors (think: agency bucket, variety bucket, etc.). Each candidate game gets “binned” into a niche based on its descriptor values, and then it competes only with others in that same niche.

  • Each niche keeps its best resident (the “elite”).
  • Over time, the map fills with many different elites: fast games, slow games, chaotic games, skillful games, high-interaction games, and so on.

This is how you get a museum of interesting survivors, not one monoculture.

5) Reproduction: mutation (and why mutation is structured, not random noise)

Once we have elites across niches, we generate the next generation by mutating them.

Mutation operators aren’t “flip random bits.” They are rule-aware edits that make plausible changes to game structure, such as:

  • tweak a number (thresholds, magnitudes)
  • add/remove/duplicate actions (with safety guards so you can’t delete the last action)
  • add/remove variables (while rewriting dangling references)
  • change turn schedulers (round-robin ↔ simultaneous ↔ priority-based, etc.)
  • add triggers and conditional effects
  • modify termination rules (win/lose/draw conditions)

The key is: the operator library is rich enough to explore mechanics, not just parameters.

Mutation retries (because many mutations are duds)

Some mutations do nothing (“no-op”), or produce something that can’t be repaired. The runner will retry with a different operator a few times; if it still can’t produce a productive mutation, it falls back to keeping the parent for that offspring slot.

This keeps evolution moving without pretending every random change is meaningful.

6) Repair, rejection reasons, and staying honest about failure

After mutation, we may run repair (optional), then validation and safety gates. If a candidate fails, it’s not just dropped silently—the system classifies why it failed:

  • repair failure
  • validation failure
  • safety failure
  • evaluation error
  • evaluation returned null fitness/descriptors

And it persists these outcomes for observability and debugging.

This matters because “evolution” is only useful if you can tell whether the ecosystem is healthy—or if you’ve started breeding nonsense.

7) Adaptive evolution: learning which mutations are actually useful

Not all mutation operators are created equal. Some will be reliably productive; others will mostly create broken genomes.

So the runner tracks per-operator telemetry every generation: attempts, no-ops, repair failures, rejection counts, and how often an operator actually helped fill a new niche or improve an elite. evolution-runner

Those stats feed into adaptive operator weighting, so the system gradually shifts its mutation choices toward what’s working in the current region of the design space—without hardcoding that “operator X is always good.”

8) Optional superpower: motif mining (stealing good patterns from winners)

Sometimes an evolved game contains a little “mechanical phrase” that’s doing real work—like a neat resource exchange loop, or a repeating pattern of effects that creates tension.

When motif mining is enabled, we:

  1. select elites (top per niche + global best)
  2. re-simulate them to extract trajectories
  3. mine repeated effect sequences (“motifs”)
  4. convert those motifs back into DSL effects
  5. feed them into a special mutation operator that can inject those motifs into new games

That’s evolution discovering a useful mechanic, then turning it into reusable genetic material.

9) Human taste enters the loop (without turning it into manual curation)

Metrics are helpful, but “fun” is subjective. So we can add human feedback:

  • ratings (“how good is this?”)
  • pairwise comparisons (“A or B?”)

Rather than asking the human to judge random games, the system uses active learning to choose the most informative comparisons—especially cases where its preference model is uncertain, and it tries to include underrepresented niches so taste is learned across the map.

Under the hood, the preference model is an ensemble trained online (so it can update continuously) and its uncertainty controls how much feedback to request per generation (adaptive budget).

Fitness can then blend:

  • “objective-ish” signals (metrics/degeneracy)
  • “human preference” signals

So the system doesn’t just breed games that are non-broken—it breeds games that align with what you actually enjoy.

10) Why this can rediscover known games and invent new ones

If your DSL is expressive enough to describe the rules of an existing game, then in principle there exists a genome that encodes it. Evolution doesn’t need to “know” the game—it only needs:

  1. Search operators that can reach that region of the rulespace (structural mutations, not just numeric tweaks)
  2. Selection pressure that rewards the behaviors that make that game work (choice, balance, interaction, clean termination, etc.)
  3. Diversity preservation so the system keeps exploring many styles instead of collapsing early

Once those are in place, rediscovery becomes a side-effect of searching a huge space under the right constraints. And invention is what happens when evolution stumbles into combinations nobody tried on purpose—then keeps them because the ecosystem rewards them.

The simplest mental model

If you want the non-technical version in one breath:

We generate lots of small rulesets, machine-play them thousands of times, score them for “does this behave like a real game?”, sort the survivors into many different “design neighborhoods,” keep the best in each neighborhood, then make mutated children from those survivors—occasionally learning from human taste and reusing discovered mechanics—until the map fills with strong and varied games.

That’s the evolutionary process: not magic, not random, but a relentless loop of variation + playtesting + selection + diversity.

LudoForge #3

On By Jon UreñaIn gaming, Inteligencia artificial, Programación1 Comment

If you don’t know about LudoForge, you should probably read the previous posts. In summary: I’m developing an app to evolve tabletop game prototypes according to a “fun” factor made up of plenty of fun proxies like player agency, strategic depth, etc. The code is now mature enough to run X evolutionary generations on demand, produce shortlists of the best games, and finish. That’s great because it means the code works well, although there’s always room for improvement. But the shortlist of four winners is absolutely terrible in a way that has had me giggling for a while. I’ll let ChatGPT explain these “games.”

Game 1: “The Shared Counter Chicken”

Two players take turns. There’s a shared number on the table that starts at 0 and can be increased up to 15. The “win condition” is simple: whoever makes it hit 15 wins instantly.

The game also includes a couple of other buttons that change other numbers… but those numbers can never actually produce a win. They’re basically decorative dials. One of them always gets set to 12, but the victory threshold for it is 20, so it’s like having a “Launch Rocket” button that always fuels you to 60% and then stops forever.

So what do players really do? They increment the shared counter… and then start playing a tiny psychological game of chicken: “I could push the counter closer to 15… but then you might get the final move.” So the game hands them some useless actions that function as “stalling.” If both players are stubborn, they can just keep stalling until the match times out.

It’s not strategy so much as who blinks first.

Game 2: “The Game Where You Can’t Move”

This one is my favorite kind of failure because it’s so clean.

The game defines two counters. The only action in the entire game says: “Decrease both counters.”

But the action is only allowed if both counters are greater than zero.

And the game starts with both counters at zero.

So on turn one, a player looks at the rules and… there is literally nothing they’re allowed to do. No move exists. The game doesn’t even fail dramatically. It just sits there like a vending machine with the power unplugged.

In other words: it’s a tabletop game prototype that begins in a stalemate.

Game 3: “First Player Loses: The Speedrun”

This one actually runs, which makes it even funnier.

There are two counters. One counter is the only one that can possibly lead to a win — the other one only goes downward forever, so its “win condition” is a permanent lie.

The “real” counter starts at 0, and there’s an action that increases it by 2. The victory threshold is 4.

Here’s what happens:

  • Player 1 goes first. Their only sensible move is: 0 → 2.
  • Player 2 goes next and does the same move: 2 → 4.
  • Player 2 instantly wins.

So the entire “game” is basically:

“Player 1 sets up the win for Player 2.”

It’s like a sport where the first player is forced to place the ball on the penalty spot and then politely step aside.

Game 4: “The Unwinnable Ritual”

Three players this time. There’s one shared counter. Winning requires the counter to reach 4.

But the rules only let you do two things:

  • Set it to 2.
  • Decrease it by 2 (if it’s above zero).

Notice what’s missing: any way to make it bigger than 2.

So the win condition is a castle in the sky. The game is a ritual where players take turns setting the number to 2, or knocking it back down toward 0. It can never, ever reach 4. No amount of cleverness changes that.

It’s essentially a machine that cycles between “2” and “less than 2” until you get bored or the turn limit ends it.

ChatGPT finished its report with this note: “The first evolutionary run didn’t produce brilliant board games. It produced life, in the same way early evolution produced algae and sea foam. These were rule-sets that technically existed… but didn’t yet deserve to.”

These idiotic games already provided fun by existing, so as far as I care, this app has already been a success. The good thing is that I have code to handle degeneracy, fitness, etc., so I simply have to tighten that code so some of these nonsensical points would either kill a genome or penalize its fitness.

By the way, earlier tonight I was playing tennis in VR with people across Europe while I waited for Claude Code to work through the tickets of one of the app’s new features. And yet the world is terrible. We live in the lamest cyberpunk dystopia imaginable.

LudoForge #1

On By Jon UreñaIn Inteligencia artificial, Programación1 Comment

Two nights ago I was rolling around in bed trying to sleep when a notion came into my head, one that has returned from time to time: some of the most flow-like fun I’ve ever had was playing tabletop games. I’m a systems builder by nature, and I love to solve problems with a variety of tools. Tabletop games are complex problems to solve with specific series of tools. My favorite tabletop game is Arkham Horror LCG, although I’ve loved many more like Terraforming Mars, Ark Nova, Baseball Highlights: 2045, Core Worlds, Imperium, Labyrinth, Renegade… But none of them fully captured me. Like some potential game exists that has exactly every feature my brain yearns for, but that game doesn’t exist. I’ve cyclically thought that I should create that game, but I never know where to start. I don’t even know what exactly I want, other than knowing that what I’ve experienced isn’t enough.

These past few weeks I’ve been implementing extremely-complex analytics reports generators for my repository Living Narrative Engine. I was surprised to find out that it’s feasible to mathematically find gaps in extremely complex spaces (dozens of dimensions) as long as they’re mathematically defined. I guess Alicia was justified to be obsessed with math. So I started wondering: what makes a tabletop game good? Surely, the fun you have with it. Can “fun” be mathematically defined? Is it the agency you have? The strategic depth? The variety? If any of such metrics could be mathematically defined, then “fun” is a fitness score that combines them.

And what if you didn’t need to design the game yourself? If you can map a simulated game’s activity to metrics such as the agency per player, the strategic depth, the variety… Then you can evolve a population of game definitions in a way that, generation after generation, the “fun” score improves. If you can turn all game mechanics into primitives, the primitives will mutate in and prove their worth throughout the generations, composing coherent mechanics or even inventing new ones. Initially, a human may need to score game definition variants according to how “fun” the playthrough of those games were, but in the end that could be automated as well.

Because this is the era of Claude Code and Codex, I’ve already implemented the first version of the app. I’ve fed ChatGPT the architectural docs and told it to write a report. You can read it down below.

LudoForge: evolving tabletop games with a deterministic “taste loop”

I’m building LudoForge, a system that tries to answer a pretty blunt question:

What if we treated tabletop game design like search—simulate thousands of candidates, kill the broken ones fast, and let a human “taste model” steer evolution toward what’s actually fun?

Under the hood, it’s a seeded-population evolution loop: you start with a set of game definitions (genomes), run simulations, extract metrics, filter degeneracy, blend in learned human preferences, and then evolve the population using MAP-Elites and genetic operators. Then you repeat.

The big picture: the loop

LudoForge is structured as a pipeline with clean seams so each layer can be tested and swapped without turning the whole thing into spaghetti. The stages look like this: seed → evaluate → simulate → analytics → (optional) human feedback → fitness → MAP-Elites → (optional) mutate/crossover/repair → next generation. pipeline-overview

A key design choice: the core expects a seeded population. There’s no “magic generator” hidden inside that invents games from scratch. If you want a generator, you build it outside and feed it in. That keeps the engine honest and debuggable. Note by me after rereading this part of the report: this will change soon enough.

Games as genomes: a DSL that can be validated and repaired

Each candidate game is a genome: { id, definition }, where definition is a DSL game definition. Before any evaluation happens, the definition goes through schema + semantic validation—and optionally a repair pass if you enable repair operators. Invalid DSL gets rejected before it can contaminate simulation or preference learning.

Repair is deliberately conservative: it’s mostly “DSL safety” (e.g., clamp invalid variable initial values to bounds). Anything that’s “this game is technically valid but dumb/unplayable” is handled by simulation + degeneracy detection, not by sweeping edits that hide the real problem.

The simulation engine: deterministic playthroughs with real termination reasons

The simulation layer runs a single playthrough via runSimulation(config) (or wrapped via createSimulationEngine). It builds initial state from the definition, picks the active agent, lists legal actions, applies costs/effects/triggers, advances turns/phases, and records a trajectory of step snapshots and events.

It’s also built to fail safely:

  • No legal actions → terminates as a draw with terminationReason = "stalemate".
  • Max turns exceeded → terminationReason = "max-turns" with an outcome computed in that cutoff mode.
  • Loop detection (optional hashing + repetition threshold) → terminationReason = "loop-detected".

Most importantly: runs are reproducible. The RNG is a seeded 32-bit LCG, so identical seeds give identical behavior.

Metrics: cheap proxies first, expensive rollouts only when you ask

After simulation, LudoForge summarizes trajectories into analytics: step/turn counts, action frequencies, unique state counts, termination reasons, and sampled “key steps” that include legalActionCount.

From there it computes core metrics like:

  • Agency (fraction of steps with >1 legal action)
  • Strategic depth (average legal actions per step)
  • Variety (action entropy proxy)
  • Pacing tension (steps per turn)
  • Interaction rate (turn-taking proxy)

Extended metrics exist too, and some are intentionally opt-in because they’re expensive:

  • Meaningful choice spread via per-action rollouts at sampled decision points
  • Comeback potential via correlation between early advantage and final outcome

Here’s the honest stance: these metrics are not “fun”. They’re proxies. They become powerful when you combine them with learned human preference.

Degeneracy detection: kill the boring and the broken early

This is one of the parts I’m most stubborn about. Evolution will happily optimize garbage if you let it.

So LudoForge explicitly detects degeneracy patterns like:

  • loops / non-termination
  • stalemates
  • forced-move and no-choice games
  • dominant-action spam
  • trivial wins metrics-and-fitness

By default, those flags can reject candidates outright, and degeneracy flags also become part of the feature vector so the system can learn to avoid them even when they slip through.

Human feedback: turning taste into a model

Metrics get you a feature vector. Humans supply the missing ingredient: taste.

LudoForge supports two feedback modes:

  1. Ratings (1–5) with optional tags and rationale
  2. Pairwise comparisons (A/B/Tie) with optional tags and rationale

Pairwise comparisons are the main signal: they’re cleaner than ratings and train a preference model using a logistic/Bradley–Terry style update. Ratings still matter, but they’re weighted lower by default.

There’s also active learning: it selects comparison pairs where the model is most uncertain (predicted preference closest to 0.5), while reserving slots to ensure underrepresented MAP-Elites niches get surfaced. That keeps your feedback from collapsing into “I only ever see one genre of game.”

Fitness: blending objective proxies, diversity pressure, and learned preference

Fitness isn’t a single magic number pulled from the void. It’s a blend:

  • Base composite score from metrics (weighted sum/objectives)
  • Diversity contribution (pressure toward exploring niches)
  • Preference contribution from the learned preference model (centered/capped, with bootstrap limits early on)

Feature vectors are keyed by metric id (not positional arrays), which matters a lot: adding a new metric doesn’t silently scramble your model weights. Renaming metrics, though, becomes a migration event (and that’s correct—you should feel that pain explicitly).

Evolution: MAP-Elites + mutation/crossover that respect DSL validity

Instead of selecting “top N” and converging into a monoculture, LudoForge uses MAP-Elites: it bins candidates into descriptor niches and keeps the best elite per niche.

Descriptor binning is explicit and deterministic (normalize → floor into bin count; clamp to range), and niche ids serialize coordinates like descriptorId:bin|....

Then you can evolve elites with genetic operators:

  • Mutations like numeric tweaks, boolean toggles, enum cycling, duplicating/removing actions, nudging effect magnitudes, adding/removing phases, rewriting token/zone references safely, etc.
  • Crossover via subtree swaps of state.variables or actions, followed by DSL re-validation.

Optional “shortlisting” exists too: it picks a diversified subset of elites for human review using a max-min distance heuristic over descriptor coordinates.

What’s already proven (and what isn’t yet)

This isn’t vaporware; the end-to-end tests already prove key behaviors like:

  • the ordered phases of the pipeline
  • invalid DSL rejection before evaluation
  • safety cutoffs (max-turns) and deterministic seeded outputs
  • human prompt loops and legality enforcement
  • deterministic state transitions
  • MAP-Elites producing stable ids
  • active learning selection behavior
  • mutation + repair at scale, including crossover

And there are explicitly documented gaps—like extended metrics aggregation and worker-thread batch simulations.

The point of LudoForge

I’m not trying to build a “game designer replacement.” I’m building a design pressure cooker:

  • Simulate hard
  • Reject degeneracy ruthlessly
  • Measure what you can
  • Ask humans the right questions
  • Let evolution explore breadth, not just a single hill

If you’re into procedural design, evolutionary search, or just enjoy the idea of treating “fun” as something you can iteratively approximate with a human-in-the-loop model, that’s what this project is for.

Living Narrative Engine #19

On By Jon UreñaIn Arte, Escritura, gaming, Inteligencia artificial, ProgramaciónLeave a comment

I have quite the treat for you fuckers. I’ve recorded myself playing through my test scenario involving Alicia Western. More than an hour of me speaking in my accented English even though I rarely speak in real life, and showing off a fun, frustrating playthrough that made me hungry.

This is, of course, related to my beloved Living Narrative Engine. Repo here.

Living Narrative Engine #18

On By Jon UreñaIn Inteligencia artificial, Programación1 Comment

I’m building a browser-based app to play immersive sims, RPGs and the likes. In practice, I use it to set up short story scenarios or elaborate gooning sessions. I dared myself to build the most comprehensive psychological system imaginable, so that Sibylle Brunne, a 34-year-old orphan living in her parents rustic home somewhere in the Swiss mountains, while controlled by a large language model, would realistically bring her blue-eyed, blonde-hair-braided, full-breasted self to seduce my teenage avatar who is backpacking through the country, eventually convincing me to stay in her house so she can asphyxiate me with her mommy milkers.

Here’s a visual glimpse of the current complexity:

Alicia has become my test subject, as if she didn’t have enough with freezing to death. The system works like this: at the base you have mood axes (like pleasant <-> unpleasant), which change throughout a scene. Actors also have permanent biological or personality-based traits like aversion to harm. Together, mood axes and affect traits serve as weights and gates to specific emotion prototypes like disappointment, suspicion, grief. Delta changes to those polar mood axes naturally intensify or lessen the emotions. I also have sexual state prototypes, which work the same as the emotional states.

These emotional and sexual states serve as the prerequisites for certain expressions to trigger during play. An expression is a definition that tells you “when disappointment is very high and suspicion is high, but despair is relatively low, trigger this narrative beat.” Then, the program would output some text like “{actor} seems suspicious but at the same time as if they had been let down.” The descriptions are far better than that, though. The actors themselves receive in their internal log a first-person version of the narrative beat, which serves as an internal emotional reaction they need to process.

It all works amazingly well. However, to determine if I was truly missing mood axes, affect traits or prototypes, I had to create extremely complex analytics tools. I’ve learned far too much about statistical analysis recently, and I don’t really care about it other than for telling a system, “hey, here are my prototype sets. Please figure out if we have genuine gaps to cover.” Turns out that to answer such a request, some complex calculations need to map 20-dimensional spaces and find out diagonal vectors that run through them.

Anyway, I guess at some point I’ll run my good ol’ test scenario involving Alicia, with her now showing far more emotion than she used to before I implemented this system. That’s a win in my book.

Life update (01/25/2026)

On By Jon UreñaIn Escritura, Slice of lifeLeave a comment

I had lunch with my parents earlier today, and ended up having a nasty political argument. My father is already about 76 years old and looking the part. As far as I can tell, he sits all day hooked up to socialist political talk shows. Barely talks to anyone, let alone his wife, on account of her psychological abusing him for decades. Anyway, during lunch, they had the local socialist radio on going on about disinformation. Basically that anything you see online that the government disagrees with are malicious lies, often AI-generated. In fact, the utter piece of garbage, traitorous bastard we have for a president (who likely stole the elections) was at Davos claiming that we should have a digital ID to end online anonymity.

I pointed out that a recent government organization had said that, according to an autopsy report regarding the forty-something people dead in a recent train crash (we had four in like five days), they all had died on impact. As if the WTC towers had fallen on them instead of these people being in different train cars. That whole thing about them dying on impact is a blatant lie, if only because survivors of the accident are on video and radio speaking about how they tried to assist others and had to leave behind folks who they know ended up dying. My mother mentioned that this was to hide the fact that help came about an hour later. Some recent report had even blamed the train conductor, even though several previous train conductors had alerted about the fact that the track involved in the accident had serious issues.

My father got this irate tone on and spoke up, which he rarely does, and asked where I got the information. I repeated the fact that victims are on video saying this, so the autopsy report must be either incompetence or deliberate lies. Then he brought up how when some natural disaster hit a part of the country governed by a non-socialist leader, their response wasn’t questioned this much. Then he got onto the US, as in “look what that piece of shit nutcase is doing, they’re the same ones that stormed the Capitol, they’re now shooting innocent people who were just trying to take photos, and this lady who they believe had guns in her car, but she only had a teddy bear.” Pretty sure there’s a video of the woman trying to run over an ICE agent after having led a movement to prevent them from deporting people who had no business being in the country. And although I’m not sure on the latest shooting, the video does show him reaching wildly for something in his pocket as the agents are trying to reduce him.

I disagree with Trump on many accounts, but not on which most people seem to from both sides of the political aisle, particularly what we see in the US. He’s right that illegal immigrants and even legal immigrants who are a detriment to the country (criminals for sure, but not necessarily) should get deported. We should do it all over the West. We’ve been deliberately ethnically cleansed for the last couple of decades; it’s been organized in a distributed, systematic manner to make this happen. In many major European capitals, ethnic Europeans are the minorities. In Spain, about 40% of under 18, if not more, are of foreign origin. This has never happened before in the history of mankind unless it was an overt genocide, like in the case of the Bell Beaker culture invading Iberia from somewhere in Europe, taking all women for themselves and preventing the local men from reproducing; the influence of male genes from those ancient Iberian peoples went down to damn near zero. Same thing is happening now. “Don’t have children; for the environment! Also, mass import violent third-world men while promoting miscegenation!”

Marxists implanted in the culture this whole racism nonsense, a word they invented. Human populations are biologically different, and therefore are better at some things and worse at others. Then, they declared that all ethnic Europeans are racist, from which follows that ethnic Europeans, the male ones at least, need to disappear. Again, overt ethnic cleansing. The existence and prosperity of ethnic Europeans should not be argued nor negotiated.

My issue with Trump is that he’s supposedly a christian, which I don’t like to begin with because it’s utter nonsense, but that in practice he’s a jew. It’s not Make America Great Again, but Make Israel Great Again. Israel and jews in general have been busy with propaganda these last hundred years or so to paint themselves as these blameless, put-upon group, but they hate our guts even more than they hate muslims, and they’ll eagerly join forces with muslims to Gaza us all. They aren’t our friends. Look up that recent video in Davos about a rabbi referring to us ethnic Europeans as “old Europeans,” and how jews and muslims should join forces against “antisemitism” and “islamophobia.”

I don’t believe in arguing because there’s no point. Ultimately people are built to hold the moral, political, philosophical positions they have. There was a study that surfaced somewhat recently that proved, although without a massive number of participants, that men’s empathy for someone decreased massively and their satisfaction increased when a cheater was punished, while in the case of women, their empathy was completely unrelated to the behavior, including crimes, of their targets of empathy. This was proven with neuroimaging or shit like that. In such feminized societies as ours have become, you only have to watch how they keep marching for mass immigration and the poor military-age browns even after thousands upon thousands of ethnic European girls have been raped at an industrial scale by gangs of muslims. Girls who wandered bloodied and dripping out of gang dens, having been raped by several men, and asked for help to the first man they saw in the streets, only for that other man, a muslim, to lock her in his flat and call over his cousins to rape her again for hours.

I remember an incident in a course I attended. I’ve mentioned it several times already. The organizers had implanted in the course a muslim male of about twenty years old, who was seemingly “in risk of societal exclusion,” which is how the traitors in charge label these individuals who are here to deliberately ruin the country. During a forced talk, a local non-attractive man, who was disabled, said that if he could choose whom to date, he would prefer not to date a disabled woman, because he already had a lot to deal with regarding his own disability, and it would be hard for him to handle. Two women in the course immediately berated him, saying how that was insensitive and offensive of him. Then the muslim man started talking about how in the weekends he went to clubs and accosted women. “They say no, but when a woman says no, more often than not they mean yes.” The same women who had berated the first local man were now giggling at the foreign invader who was spouting something that supposedly these same women have been up-in-arms against for decades.

None of this has any solution other than segregation. And I don’t mean necessarily of races (although yes, we should). You have to segregate yourself alongside other people whose brain wiring produce results that don’t screw up yours, then build walls around you so that outsiders can’t ruin it. 99,999% of humanity throughout the last 200,000 years or so we’ve had an anatomically modern brain already knew this.