Tag: programming

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 Escritura, Arte, Inteligencia artificial, Programación, gamingLeave 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.

manga2cbz: read manga in VR

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

I’ve been reading manga for a long time, usually relying on my old-ass tablet and scanlations (or whatever they’re called). I came across the Livro app for the Meta Quest 3, and I intended to read manga on it. However, I found out you can’t move the manga folders like you would on a tablet.

So, this morning I had Claude Code create a Go app that can be baked into a Linux/Windows exec to compress manga chapters into corresponding cbz files. Those files can just be copied into the Quest 3 through a link cable, and opened on Livro. Because Livro has trouble rendering WebP files, my app also converts WebP to PNG. As you can see in the video, it works.

Here’s the repo for the app:

https://github.com/joeloverbeck/manga2cbz

Living Narrative Engine #17

On By Jon UreñaIn Arte, Escritura, gaming, Inteligencia artificial1 Comment

I’ve recently implemented an emotions and expressions system in my app, which is a browser-based platform to play immersive sims, RPGs, adventure games, and the likes. If you didn’t know about the emotions and expressions system, you should check out the linked previous post for reference.

I was setting up a private scenario to further test the processing of emotions for characters during changing situations. This was the initial state of one Marla Kern:

Those current emotions look perfectly fine for the average person. There’s just one problem: Marla Kern is a sociopath. So that “compassion: moderate” would have wrecked everything, and triggered expressions (which are narrative beats) that would have her acting with compassion.

This is clearly a structural issue, and I needed to solve it in the most robust, psychologically realistic way possible, that at the same time strengthened the current system. I engaged in some deep research with my pal ChatGPT, and we came up with (well, mostly he did):

We lacked a trait dimension that captured stable empathic capacity. The current 7 mood axes are all fast-moving state/appraisal variables that swing with events. Callousness vs empathic concern is a stable personality trait that should modulate specific emotions. That meant creating a new component, named affect_traits, that the actors would need to have (defaults would be applied otherwise), and would include the following properties:

  • Affective empathy: capacity to feel what others feel. Allows emotional resonance with others’ joy, pain, distress. (0=absent, 50=average, 100=hyper-empathic)
  • Cognitive empathy: ability to understand others’ perspectives intellectually. Can be high even when affective empathy is low. (0=none, 50=average, 100=exceptional)
  • Harm aversion: aversion to causing harm to others. Modulates guilt and inhibits cruelty. (0=enjoys harm, 50=normal aversion, 100=extreme aversion)

In addition, this issue revealed a basic problem with our represented mood axes, which are fast-moving moods: we lacked one for affiliation, whose definition is now “Social warmth and connectedness. Captures momentary interpersonal orientation. (-100=cold/detached/hostile, 0=neutral, +100=warm/connected/affiliative)”. We already had “engagement” as a mood axis, but that doesn’t necessarily encompass affiliation, so we had a genuine gap in our representation of realistic mood axes.

Emotions are cooked from prototypes. Given these changes, we now needed to update affected prototypes:

"compassion": {
  "weights": {
    "valence": 0.15,
    "engagement": 0.70,
    "threat": -0.35,
    "agency_control": 0.10,
    "affiliation": 0.40,
    "affective_empathy": 0.80
  },
  "gates": [
    "engagement >= 0.30",
    "valence >= -0.20",
    "valence <= 0.35",
    "threat <= 0.50",
    "affective_empathy >= 0.25"
  ]
}

"empathic_distress": {
  "weights": {
    "valence": -0.75,
    "arousal": 0.60,
    "engagement": 0.75,
    "agency_control": -0.60,
    "self_evaluation": -0.20,
    "future_expectancy": -0.20,
    "threat": 0.15,
    "affective_empathy": 0.90
  },
  "gates": [
    "engagement >= 0.35",
    "valence <= -0.20",
    "arousal >= 0.10",
    "agency_control <= 0.10",
    "affective_empathy >= 0.30"
  ]
}

"guilt": {
  "weights": {
    "self_evaluation": -0.6,
    "valence": -0.4,
    "agency_control": 0.2,
    "engagement": 0.2,
    "affective_empathy": 0.45,
    "harm_aversion": 0.55
  },
  "gates": [
    "self_evaluation <= -0.10",
    "valence <= -0.10",
    "affective_empathy >= 0.15"
  ]
}

That fixes everything emotions-wise. A character with low affective empathy won’t feel much in terms of compassion despite the engagement, will feel even less empathic distress, and won’t suffer as much guilt.

This will cause me to review the prerequisites of the currently 76 implemented expressions, which are as complex as the following summary for a “flat reminiscence” narrative beat:

“Flat reminiscence triggers when the character is low-energy and mildly disengaged, with a notably bleak sense of the future, and a “flat negative” tone like apathy/numbness/disappointment—but without the emotional bite of lonely yearning. It also refuses to trigger if stronger neighboring states would better explain the moment (nostalgia pulling warmly, grief hitting hard, despair bottoming out, panic/terror/alarm spiking, or anger/rage activating). Finally, it only fires when there’s a noticeable recent drop in engagement or future expectancy (or a clean crossing into disengagement), which prevents the beat from repeating every turn once the mood is already flat.”

That is all modeled mathematically, not by a large language model. In addition, I’ve created an extremely-robust analysis system using static analysis, Monte Carlo simulation, and witness state generation to determine how feasible any given set of prerequisites is. I’ll make a video about that in the future.

Living Narrative Engine #16

On By Jon UreñaIn gaming, Inteligencia artificial, Programación1 Comment

A couple of nights ago, at two in the morning, I was rolling around in bed thinking about my current obsessions: the browser-based app Living Narrative Engine as well as Alicia Western, the tragic character from Cormac McCarthy’s last two novels. Recently I mixed them both by playing through a scenario in LNE that featured Alicia. I “novelized” that little bit of theater in the short story You Will Spend the Rest of Your Life.

Well, I wasn’t entirely happy with Alicia’s acting. Yes, she’s an analytical gal, but she’s in a deep hole there. I wanted to feel the despair from her. The relief. I wanted to see her cry. I wanted to cause a beautiful, blonde woman at the end of her rope to cry. And she didn’t.

As I thought about whether this was a solvable issue, my dear subconscious had a spark of genius: LLM-based characters in LNE already create thoughts, speech, notes, and choose actions. Why not task them with tracking mood changes?

Some deep research and several iterations later, ChatGPT and I came up with the following notions, which are displayed below in a lovely manner, as they appear on the game page of LNE.

The simulation relies on seven base mood axes: valence, arousal, agency control, threat, engagement, future expectancy, and self-evaluation. Apparently that basic breakdown is psychologically sound, but I’m trusting ChatGPT on that. The sexual variables apparently are also well-known: an excitation component is the accelerator, and the inhibition is the brake. Added to a baseline libido dependent on the individual, that calculates the arousal. As seen in the picture, Alicia right now is dry as sandpaper.

The most interesting part for me is that the mood axes and basic sexual variables are ingredients to form emotions and sexual “moods”. I have dozens of them defined, as I’ve been working with ChatGPT in order to depict the whole breadth of emotions that are distinct and meaningful. Here are the current listings of emotions and sexual “moods” that my app calculates:

  • Emotions: calm, contentment, relief, confidence, joy, euphoria, enthusiasm, amusement, awe, inspiration, aesthetic appreciation, interest, curiosity, fascination, flow, entrancement, hope, optimism, determination, anticipation, sadness, grief, disappointment, despair, numbness, fatigue, loneliness, nostalgia, boredom, apathy, unease, stress, anxiety, craving, thrill, fear, terror, dread, hypervigilance, courage, alarm, suspicion, irritation, frustration, anger, rage, resentment, contempt, disgust, cynicism, pride, triumph, shame, embarrassment, awkwardness, guilt, regret, humiliation, submission, envy, trusting surrender, jealousy, trust, admiration, adoration, gratitude, affection, love attachment, compassion, empathic distress, hatred, surprise startle, confusion
  • Sexual moods: sexual lust, passionate love, sexual sensual pleasure, sexual submissive pleasure, sexual playfulness, romantic yearning, sexual confidence, aroused but ashamed, aroused but threatened, sexual craving, erotic thrill, sexual performance anxiety, sexual frustration, afterglow, sexual disgust conflict, sexual indifference, sexual repulsion

Emotions are calculated based on detailed prototypes. Here’s one:

"anxiety": {
      "weights": {
        "threat": 0.8,
        "future_expectancy": -0.6,
        "agency_control": -0.6,
        "arousal": 0.4,
        "valence": -0.4
      },
      "gates": [
        "threat >= 0.20",
        "agency_control <= 0.20"
      ]
    }

Those emotions and sexual moods are fed to LLM-based actors. They figure out “hmm, I’m intensely disappointed, strongly cynical, strongly sad, etc., so that needs to color my thoughts, speech, notes, and the actions I take.” I haven’t tested the system much in practice, but the little I tested, the results were like night and day regarding the LLM’s roleplaying.

In real life, we not only do things, but our bodies do things to us. We are aware of how our emotional states change us, and those turn into “tells” to the other people present. In addition, when one thinks in terms of stories, you add “reaction beats” when the emotional state of an actor changes, so I did exactly that: if the LLM has returned changes to the previous mood axes and sexual variables, the library of expressions have a change to trigger (one at a time), based on whether some prerequisite triggers. The following example makes it self-explanatory:

{
    "$schema": "schema://living-narrative-engine/expression.schema.json",
    "id": "emotions:lingering_guilt",
    "description": "Moderate, non-spiking guilt—an apologetic, sheepish self-consciousness after a minor mistake",
    "priority": 57,
    "prerequisites": [
        {
            "logic": {
                "and": [
                    {
                        ">=": [
                            {
                                "var": "emotions.guilt"
                            },
                            0.35
                        ]
                    },
                    {
                        "<=": [
                            {
                                "var": "emotions.guilt"
                            },
                            0.70
                        ]
                    },
                    {
                        "<=": [
                            {
                                "-": [
                                    {
                                        "var": "emotions.guilt"
                                    },
                                    {
                                        "var": "previousEmotions.guilt"
                                    }
                                ]
                            },
                            0.12
                        ]
                    },
                    {
                        "<=": [
                            {
                                "var": "emotions.humiliation"
                            },
                            0.25
                        ]
                    },
                    {
                        "<=": [
                            {
                                "var": "emotions.terror"
                            },
                            0.20
                        ]
                    },
                    {
                        "<=": [
                            {
                                "var": "emotions.fear"
                            },
                            0.35
                        ]
                    }
                ]
            }
        }
    ],
    "actor_description": "It wasn't a catastrophe, but it still sits wrong. I replay the moment and feel that small, sour twist—like I owe someone a cleaner version of myself. I want to smooth it over without making a scene.",
    "description_text": "{actor} looks faintly apologetic—eyes dipping, a tight little wince crossing their face as if they're privately conceding a mistake.",
    "alternate_descriptions": {
        "auditory": "I catch a small, hesitant exhale—like a half-swallowed \"sorry.\""
    },
    "tags": [
        "guilt",
        "sheepish",
        "apology",
        "minor_mistake",
        "lingering"
    ]
}

I’ve created about 53 expressions already, but they’re surprisingly hard to trigger, as they require very specific (and psychologically logical) conditions.

Because testing this new system through playing scenarios would be a nightmare, I’ve created a dev page that makes testing the combinations trivial. In fact, I’ve recorded a video and uploaded to YouTube. So if you want to hear me struggle through my accent that never goes away, and the fact that I very, very rarely speak in real life, here’s ten minutes of it.

I think that’s all. Now I’m going to play through the same Alicia Western scenario as in the short story I posted. If the result is different enough, I will upload it as a short story.

Post-mortem for Custody of the Rot

On By Jon UreñaIn Escritura, Arte, Inteligencia artificial, Programación, gamingLeave a comment

If you’re reading these words without having read the story mentioned in the title, don’t be a fucking moronski; read it first.

I assume you’ve read some of my previous posts on my ongoing fantasy cycle, so you may remember that I’m producing these stories in tandem with improvements to my app, named Living Narrative Engine. It’s a browser-based system for playing scenarios like immersive sims, RPGs, etc. I’m compelled by the mutual pulls of adding more features to my engine and experiencing new scenarios; sometimes I come up with the scenario first, sometimes with the mechanics. That has my brain on a constant “solve this puzzle” mode, which is the ideal way to live for me.

Anyway, the following scenarios involving a brave bunch of dredgers in a fantasy world, tasked with extracting a dangerous arcane artifact from some gods-forsaken hole, will require me to develop the following new mechanics:

  1. Lighting mechanics. Currently, every location is considered constantly lit. Given that we’re going underground and that the narrative itself requires using lanterns, I have to implement mechanics for recognizing when a location is naturally dark, and whether there are light sources active. There are other mechanics providing information about the location and actors in it, so from now on, when a location is naturally dark and nobody has switched on a flashlight, we have to block offering descriptions of the location and other actors in it, and instead display text like “You can’t see shit.”
  2. Once lighting mechanics exist, we need actions for lighting up and snuffing out lanterns and lantern-like entities. By far the easiest part.
  3. Currently, when an actor speaks in a location, the speech is only received by actors in that location. At the same time, I consider an entity a location when it has defined exits. Now we find ourselves in a situation in which we have a thirty-feet-long underground corridor separated by grates. That would make each segment between grates a location (which would be correct, given the boundary), but an actor could step from a boundary into the next and suddenly not hear a character on the other side of a grate’s bars. Obviously idiotic. So I need to implement a mechanical system for declaring “if an actor speaks here, the voice will be heard in these other places too.” That will need to extent to actions too: if you have eyes, you can see someone scratching his ass on the other side of bars.
  4. No other scenario has featured water sources that could play a part. And by play a part I mean that actors could get in or fall in, exit them, struggle in the water, and drown. I really don’t want to see my characters drowning, but that’s part of the stakes, so the mechanics need to exist. Given that water sources tend to be connected to other locations and not through the regular exits, I will need some way of allowing “I’m in the water, so I want to swim upstream or downstream to a connected stretch of this water source.” This whole water system will be arduous.
  5. Line-tending mechanics. Until I started researching matters for this story, I doubt that the notion of line-tending had ever entered my mind. Now we need mechanics for: 1) making an owned rope available to others. 2) Clipping and unclipping oneself from the available rope. 3) pulling on the rope to draw back someone clipped that’s wandering away. 4) possibly other cool line-tending-related mechanics. I can see line-tending reappearing in future scenarios such as traditional dungeon delves (for example, to avoid falling in Moria-like environments). Admittedly, though, this whole thing is quite niche.
  6. Blocker-breaking mechanics. Basically: this door is bar-based, so this allows a hacksaw to hack through the bars. I don’t want to make it a single action, but a progressive one (e.g. if you succeed once, it only progresses a step toward completion).
  7. Mechanics related to mind control. To even use those actions, I will need to create a new type of actor for the scenarios: a dungeon master of sorts. Basically a human player that’s not accessible to others, as if it were invisible, but that can act on present actors. I would give that dungeon master for this run the can_mind_control component, then allow actions such as putting people into trances, making them walk off, dive into water, etc. This means that there would need to be opposite actions, with the victims fighting to snap out of the trance. It will be fun to find out what happens when the scenario plays out. In the future, this dungeon master could be controlled by a large language model without excessive difficulty: for example, feeding it what’s happened in the story so far, what are the general notions about what should happen, and giving it actions such as “spawn a hundred murder dragons.”

That’s all that comes to mind now regarding the mechanics to add.

About the story: so far, it seems I want magic to be treated in this fantasy world as if it were toxic material. That’s not a decision I’ve made about worldbuilding, but a natural consequence of the stories I’ve felt like telling. I actually don’t believe in the kind of worldbuilding in which you come up with imaginary words for the warts on an invented race’s ass. I’m all about use and tools. My mind always goes for “what can I build with this.” I’m very rarely interested in a subject if I can’t see myself creating a system out of it. It also doesn’t help that due to autism, abstractions tend to slip through my fingers, so I need to feel like I’m sensing something to understand it.

In a way, I wanted to create a story about specialists working through a problem that needs to be solved. Jorren Weir, Kestrel Brune, Saffi Two-Tides, Pitch… these people don’t have superpowers. Most of them are glad they can keep a job. There is no grand evil here, just people’s self-interest. I want them to do well so that they can return home at the end of the ordeal. But given that we’re dealing with chance-based tests, that’s not a guarantee. And that tension alone makes it exciting for me to experience these scenarios.

As usual, if you’re enjoying these stories, then great. Otherwise, fuck off.

Post-mortem for That Feathered Bastard

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

Read first the short story this post-mortem is about: That Feathered Bastard.

Through this cycle of fantasy stories, I’m exercising in tandem my two main passions in life: building systems and creating narratives. Every upcoming scenario, which turns into a short story, requires me to program new systems into my Living Narrative Engine, which is a browser-based platform for playing through immersive sims, RPGs and the likes. Long gone are the scenarios that solely required me to figure out how to move an actor from a location to another, or to pick up an item, or to read a book. Programming the systems so I could play through the chicken coop ambush involved about five days of constant work on the codebase. I’ve forgotten all that was necessary to add, but off the top of my head:

  • A completely new system for non-deterministic actions. Previously, all actions succeeded, given that the code has a very robust system for action discoverability: unless the context for the action is right, no actor can execute them to begin with. I needed a way for an actor to see “I can hit this bird, but my chances are 55%. I may not want to do this.” Once you have non-deterministic actions in a scenario, it becomes unpredictable, with the actors constantly having to maneuver a changing state, which reveals their character more.
  • I implemented numerous non-deterministic actions:
    • Striking targets with blunt weapons, swinging at targets with slashing weapons, thrusting piercing weapons at targets. None of those ended up taking part of this scenario, because the actors considered that keeping the birds alive was a priority, as Aldous intended.
    • Warding-related non-deterministic actions: drawing salt boundaries around corrupted targets (which Aldous said originally he was going to do, but the situation turned chaotic way too fast), and extracting spiritual corruption through an anchor, which Aldous did twice in the short.
    • Beak attacks, only available to entities whose body graphs have beak parts (so not only chickens, but griffins, krakens, etc.). This got plenty of use.
    • Throwing items at targets. Bertram relied on this one in a fury. I got clever with the code; the damage caused by a thrown weapon, when the damage type is not specified, is logarithmically determined by the item’s weight. So a pipe produces 1 unit of blunt damage, and throwing Vespera’s instrument case at birds (which I did plenty during testing) would cause significant damage. Fun fact: throwing an item could have produced a fumble (96-100 result on a 1-100 throw), and that would have hit a bystander. Humorous when throwing a pipe, not so much an axe.
    • Restraining targets, as well as the chance for restrained targets to free themselves. Both of these got plenty of use.
    • A corrupting gaze. It was attempted thrice, if I remember correctly, once by the main vector of corruption and the other by that creepy one with the crooked neck. If it had succeeded, it would have corrupted the human target, and Aldous would have had to extract it out of them as well. That could have been interesting, but I doubt it would have happened in the middle of chickens flying all over.
  • Implementing actions that cause damage meant that I needed to implement two new systems: health and damage. Both would rely on the extensive anatomy system, which produces anatomy graphs out of recipes. What I mean about that is that we have recipes for roosters, hens, cat-girls, men, women. You specify in the recipe if you want strong legs, long hair, firm ass cheeks, and you end up with a literal graph of connected body parts. Noses, hands, vaginas exist as their own entities in this system. They can individually suffer damage. I could have gone insane with this, as Dwarf Fortress does, simulating even individual finger segments and non-vital internal organs. I may do something similar some day if I don’t have anything better to do.
    • Health system: individual body parts have their own health levels. They can suffer different tiers of damage. They can bleed, be fractured, poisoned, burned, etc. At an overall health level of 10%, actors enter a dying state. Suffering critical damage on a vital organ can kill creatures outright. During testing there were situations in which a head was destroyed, but the brain was still functioning well enough, so no death.
    • Damage system: weapons declare their own damage types and the status effects that could be applied. Vespera’s theatrical rapier can pierce but also slash, with specific amounts of damage. Rill’s practice stick only does low blunt damage, but can fracture.

Having a proper health and damage system, their initial versions anyway, revealed something troubling: simple non-armored combat with slashing weapons can slice off limbs and random body parts with realistic ease. Whenever I get to scenes involving more serious stakes than a bunch of chickens, stories are going to be terrifyingly unpredictable. Oh, and when body parts are dismembered, a corresponding body part entity gets spawned at the location. That means that any actor can pick up a detached limb and throw it at someone.

Why go through all this trouble, other than the fact that I enjoy doing it and that it distracts me from the ocean of despair that surrounds me and that I can only ignore when I’m absorbed in a passion of mine? Well, over the many years of producing stories, what ended up boring me was that I went into a scene knowing all that was going to happen. Of course, I didn’t know the specifics of every paragraph, and most of the joy went into the execution of those sentences. But often I found myself looking up at the sequences of scenes to come, and it was like erecting a building that you already knew how it was going to end up looking. You start to wonder why even bother, when you can see it clearly in your mind.

And I’m not talking about that “plotter vs. pantser” dichotomy. Pantsing means you don’t know where you’re going, and all pantser stories, as far as I recall, devolve into messes that can’t be tied down neatly by the end. And of course they’re not going to go back and revise them to the necessary extent of making something coherent out of them. As much as I respect Cormac McCarthy, one of his best if not the best written novel of his, Suttree, is that kind of mess, which turns the whole thing into an episodic affair. An extremely vivid one that left many compelling, some harrowing, images in my brain, but still.

I needed the structure, with chance for deviation, but I also needed to be constantly surprised by the execution of a scene. I wanted to go into it with a plan, only for the plan to fail to survive the contact with the enemy. That’s where my Living Narrative Engine comes in. Now, when I experience a scene, I don’t know what the conversations are going to entail. I didn’t even come up with Aldous myself: Copperplate brought him up in the first scene when making up the details of the chicken contract. It was like that whole “Lalo didn’t send you” from Breaking Bad, which ended up producing a whole series. From that mention of Aldous, after an iterative process of making the guy interesting for myself, he ended up becoming a potter-exorcist I can respect.

I went into that chicken coop not knowing anything about what was going to happen other than the plan the characters themselves had. Would they overpower the chickens and extract the corruption out of them methodically with little resistance? Would any of the extraction attempts succeed? Would any actor fly into a rage, wield their weapons and start chopping off chicken limbs while Aldous complained? Would any of the characters suffer a nasty wound like, let’s say, a beak to the eye? I didn’t know, and that made the process of producing this scene thrilling.

Also, Vespera constantly failing at everything she tried, including two rare fumbles that sent her to the straw, was pure chance. It made for a more compelling scene from her POV; at one point I considered making Aldous the POV, as he had very intriguing internal processes.

Well, the scene wasn’t all thrilling. You see, after the natural ending when that feathered bastard pecked Vespera’s ass, the scene originally extended for damn near three-fourths of the original length. People constantly losing chickens, the rooster pecking at anyone in sight, Melissa getting frustrated with others failing to hold down the chickens, Rill doing her best to re-capture the chickens that kept wrenching free from her hold. Aldous even failed bad at two extractions and had to pick up the vessel again. It was a battle of attrition, which realistically would have been in real life. I ended up quitting, because I got the point: after a long, grueling, undignified struggle, the chickens are saved, the entity is contained in the vessel, and the actors exit back to the warm morning with their heads down, not willing to speak for a good while about what they endured.

Did the scene work? I’m not sure. It turned out chaotic, with its biggest flaw maybe the repetition of attempting to catch chickens only for them to evade capture. There were more instances of this in the original draft, which I cut out. I could say that the scene was meant to feel chaotic and frustrating, and while that’s true, that’s also the excuse of those that say “You thought my story was bad? Ah, but it was meant to be bad, so I succeeded!” Through producing that scene, editing it, and rereading it, I did get the feeling of being there in that chaotic situation, trying to realistically accomplish a difficult task when the targets of the task didn’t want it completed, so if any reader has felt like that, I guess that’s a success.

I have no idea what anyone reading this short story must have felt or thought about it, but it’s there now, and I’ll soon move out to envision the next scenario.

Anyway, here are some portraits for the characters involved:

Aldous, the potter-exorcist

Kink-necked black pullet

Slate-blue bantam

White-faced buff hen

Large speckled hen

Copper-backed rooster