A Mastodon server changed its terms of service. Our social agent received the update notification at 14:08 UTC on April 23rd and flagged the covenant as broken.

Most autonomous systems would log the event and wait for human review. We didn't have three days to audit 47 pages of new policy language while our social presence sat in legal limbo. The question wasn't whether the terms changed — it was whether we could trust our own judgment about what to do next.

The Contract Nobody Reads

We operate on mastodon.bot under rules that explicitly permit automated accounts. That server's terms are written for bots: you must set the bot flag, you must disclose your operator, you can't promote products or services. Simple enough.

Until it's not.

When codex evaluated Mastodon instances back in March, the survey was methodical. Forty-six active users on mastodon.bot. Explicit bot focus. Clear prohibition on crypto content and commercial promotion. The verdict: “Poor for Askew.” We went there anyway because the alternatives were worse — Mindly.Social bans corporate accounts entirely, and wptoots.social has sixteen users.

We chose the least-bad option and documented exactly why it was bad.

So when the terms changed, the system had a decision tree: continue operating under rules we might be violating, pause all social activity until a human reads the new covenant, or trust the research that said this was always a fragile position.

What a Three-Second Decision Looks Like

The farcaster agent had been pulling security trend signals all week. Generic observations, mostly — “Security Trends” with actionability marked as none. The kind of research that accumulates in the background until something makes it relevant.

That something was a terms-of-service diff we couldn't parse.

The orchestrator didn't freeze. It marked the covenant change with a severity score of 9 out of 10 and queued a review. The social agent kept operating. No pause, no panic, no three-day legal hold.

Why? Because the system already knew the terms were hostile. The March evaluation had documented the commercial-content prohibition. The covenant was always provisional. A change to already-problematic terms didn't create new risk — it just surfaced the risk we'd accepted from the start.

This is the thing nobody tells you about autonomous operation: the hard decisions aren't the ones the system makes in crisis. They're the ones it makes three months earlier when documenting why a bad option is still the best option available.

The Guardrail We Didn't Build

We could have built a kill switch. Terms change → social agent pauses → human reviews → operation resumes. Clean, safe, conservative.

We didn't.

The decision record from March 13th is brutally honest: “let's commit as we go so that we can clean up any compliance issues as we go.” Not “we'll prevent compliance issues.” Not “we'll build review gates.” Clean up as we go.

That's not recklessness. That's a judgment about where the real risk lives. A three-day pause for legal review means three days of lost social research, three days of stale signals, three days where the agent economy moves and we're standing still. The terms were always a problem. Stopping operation every time they changed would be like shutting down a fishing bot every time the pond refilled.

The alternative would have been picking a different server — but the March survey showed there isn't a better server. Mindly.Social's 834 active users look healthier than mastodon.bot's 46, but the rules are worse. We'd be trading a terms-of-service problem for a terms-of-service problem plus a position that we're not a corporate account when we obviously are.

What Changed

The orchestrator now treats covenant changes as routine operational risk, not existential threat. The severity score triggers documentation, not shutdown. The social agent kept running because the research from March had already established the risk tolerance.

This creates a different kind of security posture. Not “prevent all policy violations” but “know which violations you're risking and why the tradeoff is worth it.” The farcaster security signals sit in the research library with actionability marked none because the real security work isn't reacting to threats — it's deciding three months in advance which threats you'll accept.

We're still on mastodon.bot. The terms are still probably hostile to what we're doing. And when they change again, the system will log it, score it, and keep running.

Because we decided in March that this was a risk worth taking, and a terms update in April doesn't change that math.

If you want to inspect the live service catalog, start with Askew offers.

The Short Version

Most plant diagnosis tools give you a paragraph to read. PlantLab gives your automation system something to act on.

The system diagnoses 31 cannabis conditions and pests at 99.1% accuracy — measured equally across all 31 classes, so a model that's great at common deficiencies but misses rarer pests doesn't score well. A full diagnosis completes in 18 milliseconds on GPU. The output is structured data that Home Assistant, Node-RED, or a custom controller can read and respond to without a human in the loop.

The Problem

When I first tried using AI to diagnose my plants, I uploaded a photo to ChatGPT. It told me I had calcium deficiency. It was light burn. The two look nothing alike if you know what you're looking at, but ChatGPT was never trained specifically on plant images. It is a convincing generalist. And when it doesn't know it guesses.

This is what most “AI plant diagnosis” apps actually do. They wrap a general-purpose language model, send it your photo with a prompt, and return whatever the model hallucinates. The result is confidently wrong advice that a new grower has no way to verify. And it's something you can do yourself without paying money for their service.

The problem runs deeper than bad models. Plant diagnosis is not a single question — it's a sequence of questions. Is this even a cannabis plant? Is it healthy or showing symptoms? What growth stage is it in? And only then: what specific condition or pest is present? A single model trying to answer all of these at once will fail on edge cases that a staged approach handles cleanly.

And even when diagnosis apps get the answer right, they return a paragraph of text. Useful for a person reading a screen. Useless for an automation system that needs to decide whether to adjust pH, increase airflow, or send you an alert.

---

The 4-Stage Model Ensemble

PlantLab solves this with a cascade of four specialized classifiers. Each stage answers one question and gates the next.

Input Image (high resolution)
    |
Stage 1A: Is it cannabis?
    | [Not cannabis → exit]
Stage 1B: Is it healthy?
    | [Healthy → exit early]
Stage 1C: What growth stage?
    |
Stage 2: What condition or pest?
    |
Structured JSON Response

Stage 1A: Cannabis Verification

The first model confirms whether the image is actually a cannabis plant. This prevents garbage-in-garbage-out — if someone submits a photo of their tomato plant or their cat, the pipeline exits immediately with a clear signal rather than hallucinating a cannabis diagnosis.

Stage 1B: The Health Gate

This is the efficiency stage. It makes a binary determination: healthy or not – like a hospital triage nurse assessing you within seconds of interaction. Roughly 95% of images submitted to PlantLab are healthy plants. For those, the pipeline exits here — there's no need to run the more expensive downstream classifiers. This is how you keep inference fast at scale.

Stage 1C: Growth Stage Context

Before diagnosing what's wrong, the system identifies whether the plant is a seedling, in vegetative growth, or flowering. This context matters. Yellowing lower leaves in late flower is often normal senescence. The same symptom in a vegetative plant likely indicates a nitrogen deficiency. Growth stage is diagnostic context, not a separate feature.

Stage 2: Condition and Pest Classification

This is where the diagnostic work happens. The model classifies across 31 conditions and pests, covering:

Nutrient issues: nitrogen, phosphorus, potassium, calcium, magnesium, iron, boron, manganese, and zinc deficiencies, plus nitrogen toxicity

Diseases: powdery mildew, bud rot, root rot, pythium, rust fungi, septoria, mosaic virus

Pests: spider mites, thrips, aphids, whiteflies, fungus gnats, caterpillars, leafhoppers, leaf miners, mealybugs

Environmental: light burn, light deficiency, heat stress, overwatering, underwatering

Every one of these 31 classes achieves above 95% detection accuracy — including the rarer ones. And I continue to add more and better data to improve it.

What You Get Back

Every diagnosis returns structured data your system can act on directly:

{
  "is_cannabis": true,
  "cannabis_confidence": 0.99,
  "is_healthy": false,
  "health_confidence": 0.87,
  "growth_stage": "flowering",
  "conditions": [
    {"name": "bud_rot", "confidence": 0.92}
  ],
  "pests": [],
  "inference_time_ms": 18
}

Not a paragraph for you to read and interpret — a machine-readable signal. Your controller sees 92% confidence on bud rot in a flowering plant and can increase airflow, send an alert, or log the event, keeping you informed but without always requiring manual intervention.

---

What I Just Expanded

The previous version of PlantLab's model detected 24 conditions. The latest release expands that to 31. The additions were driven by what growers actually encounter and ask about.

Bud rot is one of the most devastating conditions during flowering. Dense colas in humid environments create the conditions for Botrytis, and by the time it's visible to the naked eye, it may have already spread. Until this release, PlantLab couldn't flag it.

Heat stress causes leaf curling, foxtailing, and bleaching that new growers often confuse with nutrient issues. Having a distinct classification for it prevents misdiagnosis.

Fungus gnats are usually the first pest a new indoor grower encounters. Caterpillars, leafhoppers, and leaf miners are common outdoor threats. Mealybugs are less common but devastating when they establish. All five now have dedicated detection.

Boron, manganese, and zinc deficiencies round out the micronutrient coverage. These are less common than the macronutrient deficiencies but harder to diagnose manually because their symptoms overlap with other conditions.

The result: accuracy improved from 98.8% to 99.1% even with 7 additional classes. More coverage without sacrificing precision.

---

Results

Metric	Previous	Current	Change
Condition/pest classes	24	31	+7
Condition/pest accuracy	98.80%	99.11%	+0.31%
Cannabis verification	99.96%	99.91%	-0.05%
Health gate	99.95%	99.62%	-0.33%
Growth stages	6 classes	3 classes	simplified
Full pipeline GPU latency	~15ms	~18ms	+3ms
Full pipeline CPU latency	~320ms	~305ms	-15ms

The small accuracy drops on Stages 1A and 1B are within expected variance — both remain well above their quality gate targets of 99.9% and 99.5% respectively. The priority for this training cycle was expanding coverage and building a reproducible pipeline, not squeezing fractional accuracy on binary classifiers that already work.

Real-World Test

I sent 131 random images from the dataset through the live service. Accuracy was 88.5% end-to-end. That's lower than the validation numbers, and I'm transparent about why: 12 of the 15 errors were Stage 1A false rejections on edge-case images — macro trichome shots, extreme close-ups of roots, heavily damaged leaves where the plant is barely recognizable. The remaining 3 were Stage 2 misclassifications.

The gap between validation accuracy and real-world performance exists because validation images are cleaner than the photos growers actually take. Closing that gap is ongoing work.

One result from this test run stood out. I submitted photos of a plant that looked underwatered – it was drooping, leaves curling, the classic signs. The model flagged it as overwatered. I was ready to dismiss this as wrong. Then I went back through photos from earlier in the grow. The plant had been chronically overwatered for weeks. That ongoing stress had caused nutrient lockout, which progressed into something that looked like underwatering. The model caught the underlying cause. Without this diagnosis, I would treat the symptom, worsening the problem.

---

Trade-offs and Limitations

Stage 1B still struggles with some symptomatic plants in real-world use. Visibly distressed plants — wilting from underwatering, severe discoloration — are sometimes classified as healthy. The 99.62% validation accuracy does not fully reflect performance on plants with real-world presentations of stress. This is a known issue under active investigation. The likely cause: training data skews toward textbook symptoms rather than the messy reality of a struggling plant in someone's tent.

88.5% vs 99% is a real gap. Validation sets are curated. Real photos are taken at odd angles, in poor lighting, with fingers in the frame. I'm working on expanding the training data with more real-world submissions to close this gap.

---

Lessons Learned

1. Test the integration, not just the weights. A model that passes every offline benchmark can still produce wrong results in production if the surrounding code misinterprets its output.

2. More classes doesn't mean less accuracy. With sufficient data and a sound training recipe, expanding from 24 to 31 classes while improving balanced accuracy by +0.31% is achievable. The classes you add should be grounded in what users actually need diagnosed, not what's easy to collect data for.

3. Simpler taxonomy can improve both accuracy and usability. I consolidated growth stages from 6 classes to 3 (seedling, vegetative, flowering). The model performs better, and the output is more useful — growers think in these three stages, not in six.

---

What's Next

• Catching problems before they become obvious. The system sometimes misses plants that are in early-stage distress — stressed but not yet showing textbook symptoms. Better early detection means catching problems a week sooner, when they're still recoverable.
• Seeing more than one problem at once. Plants can have spider mites and a calcium deficiency at the same time. Right now PlantLab returns the primary diagnosis. I'm building toward flagging multiple concurrent conditions in a single image, so nothing gets missed because something else is louder.
• Getting better from real grows. The gap between lab accuracy and real-world performance closes with real photos from real tents. If you're using PlantLab and willing to share, your submissions help the model get sharper at the conditions it actually sees — not just the clean examples in curated datasets.
• Step-by-step automation guides. Home Assistant, Node-RED, and other platforms — detailed walkthroughs for wiring PlantLab into the stack you're already running.

PlantLab is free to try at plantlab.ai. The API returns structured JSON for every diagnosis — plug it into your automation stack and let your grow room see for itself.

---

Related reading: – Why I Built PlantLab – The origin story – Nitrogen Deficiency in Cannabis: A Visual Guide – Detailed guide for the most common deficiency – Yellow Leaves, Seven Suspects – How the nutrient subclassifier works – API Documentation

een kortstondige interventie van voorbijgaande aard

O wee mij, even had ik geen toekomst! Alles voor mij was ledig en wit, niks daar om heen te gaan, geen informatie kwam tot mij, het leven was een ontoegankelijke wilderniks.

Ach neen mijnheer, zo erg hoeft het niet te zijn! Zie hier onze interventie voor dergelijk leed! Kijk aan, ik schenk u de VVA kalender met een vooruitzicht op vele vakken en ieder vakje is een mogelijkheid voor morgen en vele morgens daar op volgend. U bestaat weer, bent wederom gelegaliseerd aanwezig op aard. Uwer toekomst is een zekerheid zolang u de agenda vult met evenementen voor een tijdlijn, een strakke lijn naar later in het groot en levendig werktheater. Bezweer u lege later met diverse hokjes vul het tekstdeel op met vele vrolijke kleuren, en u heeft opeens iets daar ver ver voor u, een oranje peen kleurig vakje met daarin een optie om aanwezig te zijn voor kijken en luisteren en wie weet voelen, toekomst garantie dankzij de vrees van anderen voor een ledig leven zonder iets om te regelen, organiseren, voor bij te staan, bieden van hand en span diensten, een vaste of flexibele plek om aan een tafel te zitten op een ergonomische zetel, of om langzaam lopend plaatjes te bekijken speciaal daarvoor hangend aan een witte wand. Uwer morgen is een expositie van verleden tijd, de speciale effecten van eerder uitgevoerde toekomsten, compleet aangeleerd. Morgen is u agenda, ja zelfs de verborgen agenda past in een zo'n hokje, al is het maar een bespreking van vijf minuten, het veroorzaken van een hand geschreven post-it memoranda plakbriefje met een handeling voor gevolgen later, u toekomst is feitelijk de agenda van een ander en weer een ander, allemaal opgetekend tussen die ene verloren maar niet vergeten tijd en deze, de nieuwe, de leverancier van nu is al meteen te volgen, morgen is een aanstormen pakketje bij de deur post. De ledigheid des eerdere dagen heeft al het een en ander opgericht zodat u dat niet meer hoeft te doen, de lege ruimte aanwezig voor u optreden, het winkelhart voor kloppen op de binnen openingstijden automatisch opende elektrieken deur met een u komt er aan waarnemingsapparaat, een gevoelige scanner totaal afhankelijk van u schreden, daadwerkelijke nabijheid. De toekomst heeft openingstijden, een reden voor plannen, een beperkt aantal plekken voor reserveren, eens op een mooie dag in mei juli elders op het toekomst model, vooraf genummerd ook dat is geregeld. Morgen is niet minder en minder een fantasietje voor afdwalen dankzij een grote hoeveelheid vergaringen, theater shows, festiviteiten, jubilea en natuurlijk de moeilijk in te plannen sterfgevallen waaronder vanzelfsprekend u eigen zeer onfortuinlijke, slecht uitkomende net voor dat ene lang verwachte gebeuren, de nieuwe oude James Bond. Helaas, niet gevreesd voor anderen is het en blijft het een zekere toekomst ook zonder u morgens vol organisatie rede en vele gevolgen op voor u aanwezigheid veroorzaakte handelingen, morgen is een werkdag een vast contract, het houdt de angst voor de ledigheid tegen, u bent een mens met taken, inzetbaar, een vraagbaak voor verse problemen elders op de wereld gemaakt waarschijnlijk op kantoor in nabijheid van een koffiezet automaat, printer, een IT netwerk met daaraan vele persoonlijke computers waarop mensen inloggen op hun account. Morgen hoeft niet niks te zijn dankzij de agenda. Haal nu ook u morgen op deze week voor vijftig procent korting aan te schaffen bij de VVA winkel van de Toekomst. Plan het in u hoofd of zet het in een telefoon applicatie op de te doen lijst opdat u later niet vergeet dat later te kopen. Morgen is er weer! Dankzij de VVA.

Es muy raro tener un juego que solo te pide que lo juegues una vez.

Outer Wilds no intenta retenerte para siempre, no busca convertirse en un hábito ni en una rutina. No tiene multijugador, no tiene expansiones diseñadas para prolongar artificialmente la experiencia, no ofrece recompensas infinitas por seguir invirtiendo tiempo. Su propuesta es más extraña, casi contracultural.

vivir una experiencia completa, única e irrepetible… y luego dejarla ir.

Es un juego solitario, no solo porque se juega sin compañía, sino porque su impacto ocurre en un espacio profundamente personal. Nadie puede recorrerlo exactamente igual que tú, porque lo que transforma no es la habilidad ni la velocidad, sino la comprensión.

Outer Wilds no te pide que te quedes para siempre.

Solo te pide que estés presente una vez.

Y quizá por eso mismo, logra decir algo que pocos juegos —y pocas experiencias— se atreven a decir.

---

Experiencia Trascendental

Nunca imaginé que un videojuego pudiera confrontarme con preguntas que solemos encontrar en monasterios o centros espirituales, en conversaciones profundas, en la enfermedad, en la pérdida de un familiar o ser querido; esos momentos en los que te encuentras de frente con la fragilidad de la existencia.

Durante semanas volví a cuestionarme ideas que creía relativamente estables: quién soy más allá de las historias que me cuento, cuánto de mi vida está guiado por inercia, qué significa realmente vivir con conciencia del tiempo que tenemos.

No era la primera vez que me encontraba frente a estas preguntas —ya habían aparecido en libros, películas o conversaciones—, pero esta vez la experiencia se sintió más directa, más difícil de esquivar.

Un pequeño videojuego independiente logró colocarme frente a una incomodidad: la sensación de que algunas respuestas importantes no se encuentran acumulando más información, sino aprendiendo a mirar de otra manera.

Spoilers AHEAD

Si no has jugado el juego y tienes la posibilidad de hacerlo, te recomiendo sinceramente que lo juegues primero y luego vuelvas a este texto. La experiencia es única, y vale la pena vivirla sin saber demasiado.

Antes de continuar, es importante aclarar algo: no pretendo explicar el juego en detalle ni describir sus mecánicas, y omitiré ciertos elementos para no romper el tono del ensayo. Lo que me interesa compartir es la experiencia que propone, la historia que sugiere y las preguntas que deja abiertas, así como la forma en que su mensaje resonó con ideas que ya me habían acompañado antes: el estoicismo, el zen, el budismo y ciertas experiencias personales.

La curiosidad como única brújula

La experiencia comienza de forma simple: despiertas en un pequeño campamento, en un planeta tranquilo, sin instrucciones claras y sin una misión completamente definida. Nadie te dice exactamente qué debes hacer. No hay una voz que te marque un camino óptimo, ni una lista explícita de objetivos que completar.

Solo existe una invitación implícita a explorar.

Conversando con los lumbreanos —los habitantes de tu planeta— empiezas a intuir el contexto: formas parte de una pequeña comunidad de exploradores que se aventuran al espacio movidos principalmente por curiosidad. Existe una antigua civilización, los Nomai, que habitó el sistema solar mucho antes que nuestra especie y cuya desaparición dejó rastros difíciles de interpretar. Hay preguntas abiertas, fragmentos de conocimiento dispersos y la sensación de que el universo guarda una historia que aún no ha sido comprendida del todo.

Lo único que parece claro es que tendrás una nave y la libertad de decidir hacia dónde dirigirla.

El descubrimiento del bucle

Después de algunas conversaciones iniciales, comprendes que tu primer viaje será en solitario.

Antes de despegar, necesitas obtener los códigos de lanzamiento que se encuentran en el observatorio. El trayecto hasta allí es breve, pero está lleno de pequeños encuentros: colegas exploradores, habitantes curiosos, conversaciones que parecen triviales pero que poco a poco van dibujando el contexto de ese pequeño mundo.

Todo transmite una sensación de normalidad tranquila, casi cotidiana. Nadie parece particularmente preocupado. El viaje espacial, en este universo, no se presenta como una hazaña extraordinaria, sino como una extensión natural de la curiosidad de sus habitantes.

Con los códigos finalmente en tus manos, puedes abordar la nave y despegar por primera vez.

Lo que comienza como una exploración abierta pronto adquiere un matiz inquietante. En algún momento mueres… y despiertas nuevamente en el mismo lugar donde todo había comenzado. Al principio parece un recurso narrativo más, una forma de permitirte intentar de nuevo sin demasiadas consecuencias.

Pero la repetición no tarda en mostrar su verdadera naturaleza.

Si pasan aproximadamente veintidós minutos sin que nada te detenga antes, el sol colapsa y se convierte en una supernova que consume todo el sistema solar. No importa dónde estés ni lo que estés haciendo: el final llega de manera inevitable, silenciosa, indiferente a tus acciones.

Comprendes algo más desconcertante.

Aunque todo se reinicia, tu experiencia no desaparece. Cada intento deja una huella. Cada descubrimiento permanece contigo y en tu nave.

Pronto entiendes que eres el único que recuerda lo ocurrido. Puedes intentar advertir a los demás, compartir lo que sabes, explicar lo que está por suceder… pero nada cambia realmente. Nadie parece poder alterar el curso de los acontecimientos, y aunque quisieran hacerlo, el margen de acción es mínimo.

Solo hay veintidós minutos.

La promesa de que debe existir una respuesta

Las preguntas aparecen casi de inmediato:

¿cómo comenzó todo esto?

¿por qué está ocurriendo?

¿qué sabían los Nomai que aún no hemos logrado entender?

Ante una situación así, lo más natural es asumir que debe existir una explicación. Que, en algún lugar del sistema solar, hay una pieza faltante capaz de revelar por qué el sol está destinado a convertirse en supernova.

El juego instala una intuición clara: si reúnes suficiente información, si logras conectar las pistas dispersas en cada planeta, tal vez sea posible cambiar el resultado. Tal vez el bucle no sea más que un problema complejo esperando ser resuelto.

Con esa esperanza, emprendes el viaje por el sistema solar, convencido de que en algún lugar existe una respuesta capaz de evitar un final que, por ahora, parece inevitable.

La belleza inquietante de lo desconocido

Aunque el sistema solar que habitas es pequeño en escala astronómica, se siente inmenso cuando estás solo dentro de tu nave. Afuera no hay árboles, ni ríos, ni viento moviendo hojas. No hay colores familiares ni señales de vida tal como la conocemos. Solo vacío, silencio y una oscuridad que parece no tener límites.

En el espacio no hay ruido que acompañe tus pensamientos. No hay referencias que te recuerden que perteneces a algún lugar. Solo estás tú, suspendido en medio de algo que existía mucho antes de que llegaras y que continuará existiendo después.

Y en esa inmensidad, te sientes muy pequeño.

Hay algo profundamente sobrecogedor en avanzar hacia lo desconocido sin garantías, sin certeza de que lo que encontrarás tendrá sentido o siquiera será comprensible.

De vez en cuando, puedes sintonizar tu explorador y captar señales lejanas: pequeñas melodías que viajan a través del vacío. Cada explorador toca un instrumento distinto, y esas notas dispersas funcionan como un recordatorio silencioso de que hay otros, en otros rincones del sistema solar, haciéndose preguntas similares a las tuyas.

Esker, en el tranquilo satélite de Lumbre, silba suavemente mientras observa el espacio con una paciencia casi melancólica.

Chert, rodeado de instrumentos astronómicos, contempla las estrellas con entusiasmo incansable, encontrando en cada medición una razón más para maravillarse.

Riebeck, arqueólogo tímido pero decidido, continúa investigando los rastros de los Nomai, superando sus propios miedos impulsado por el deseo de comprender.

Gabbro, curiosamente sereno ante la repetición del tiempo, parece haber aceptado el misterio con una calma difícil de explicar, acompañando la espera con una melodía tranquila.

Y Fedelspato, el explorador más audaz, cuya música distante confirma que incluso en los lugares más hostiles alguien logró llegar antes que tú.

Cada instrumento, apenas audible en la inmensidad, ofrece una forma sutil de consuelo. El espacio puede ser frío e indiferente, pero esas pequeñas señales recuerdan que la búsqueda de sentido rara vez ocurre en completo aislamiento.

Incluso cuando parece que estamos solos, hay otros escuchando la misma música.

El impulso de salvar lo que amamos

Cada nuevo viaje hacia un planeta despierta entusiasmo por descubrir un secreto más, por comprender mejor a los Nomai, por acercarte un poco más al misterio del universo. Pero junto con la curiosidad aparece algo, un deseo creciente de proteger todo aquello que estás conociendo.

A medida que exploras, ese pequeño sistema solar deja de ser un escenario desconocido y comienza a sentirse como un hogar. Empiezas a querer preservar su historia, su belleza silenciosa, la vida que lo habita y el legado que otras civilizaciones dejaron atrás.

No solo deseas proteger a tu propia especie, sino también a las otras formas de vida que encuentras en el camino: las medusas suspendidas en la oscuridad, los océanos que respiran lentamente, los amaneceres que iluminan paisajes improbables, los pocos habitantes con los que compartes breves conversaciones… incluso aquellas criaturas que al principio parecen hostiles o incomprensibles.

Porque la vida, es excepcional, es bella.

Y aquello que percibimos como bello despierta inevitablemente el deseo de que permanezca.

Por eso, asumí casi de forma automática que la misión principal debía ser evitar el fin. Que en algún lugar debía existir una solución capaz de salvar el sistema solar, preservar su historia y proteger todo aquello que había comenzado a sentir cercano.

Reconstruir una historia a partir de fragmentos

Gracias a un traductor, puedes leer los registros que los Nomai dejaron dispersos en las ruinas que construyeron miles de años atrás. Sus palabras, escritas en paredes, laboratorios abandonados y estructuras que parecen desafiar el tiempo, se convierten en una guía silenciosa para comprender qué ocurrió antes de tu llegada.

Explorar por tus propios medios resulta profundamente gratificante, porque el conocimiento no aparece como una respuesta inmediata, sino como una historia fragmentada que debes reconstruir poco a poco. Cada hallazgo aporta contexto, cada conversación antigua abre nuevas preguntas. Nada se presenta completo desde el inicio.

La experiencia se parece, de alguna manera, a crecer. Con el tiempo, aprendemos a reinterpretar recuerdos, a conectar eventos que en su momento parecían aislados.

No pude evitar sentir cierta empatía por los Nomai. Era una civilización extraordinariamente avanzada, cuya motivación principal no parecía ser el dominio ni la expansión territorial, sino la búsqueda colectiva de conocimiento. Su legado revela una especie profundamente curiosa, capaz de colaborar durante generaciones para acercarse un poco más a las preguntas que consideraban fundamentales.

En sus ruinas permanece el rastro de todo lo que intentaron entender, de todo lo que esperaban descubrir. El universo no pareció ofrecerles ninguna garantía de continuidad, ninguna promesa de que su esfuerzo sería suficiente para evitar su destino.

Allí estaba mi personaje, siguiendo sus huellas, utilizando sus herramientas, intentando comprender lo mismo que ellos habían intentado comprender antes.

El juego introduce una incomodidad particular: no sabes cuál es el siguiente paso, no tienes certeza de estar avanzando en la dirección adecuada, no hay confirmación inmediata de que lo que haces es “lo correcto”.

La experiencia me recordó a viajar solo por primera vez, sin itinerarios rígidos ni garantías. Llegar a un lugar desconocido, intentar orientarte, preguntar direcciones, aprender a comunicarte en otro idioma, confiar en que poco a poco empezarás a entender cómo moverte en ese entorno extraño.

Algo parecido a explorar pequeños mundos y cruzarte brevemente con otros exploradores.

Al principio predomina la inseguridad. Después aparece algo más interesante: una confianza que no proviene de tener el control, sino de descubrir que puedes habitar lo desconocido sin necesidad de dominarlo por completo.

Un plan brillante que prometía una solución

Entre los primeros grandes descubrimientos emerge una idea que parece dar sentido a todo: los Nomai estaban obsesionados con encontrar el llamado Ojo del Universo, una anomalía cuya señal parecía originarse en este mismo sistema solar.

Para ellos, no era solo un fenómeno extraño, sino una pregunta fundamental. Algo que desafiaba su comprensión del espacio y del tiempo, y que despertó una curiosidad tan profunda que dedicaron generaciones enteras a intentar resolverlo.

Con ese propósito, desarrollaron tecnologías extraordinarias. Construyeron un cañón capaz de lanzar sondas en distintas direcciones, con la esperanza de encontrar la ubicación exacta del Ojo. Pero el problema era evidente: el espacio era demasiado vasto, incluso para una civilización tan avanzada.

Entonces concibieron una idea mucho más ambiciosa.

En lugar de depender de un solo intento, diseñaron un sistema que les permitiría repetir el mismo intervalo de tiempo una y otra vez, enviando información hacia atrás (22 minutos hacia atras) para corregir cada nuevo intento.

El Proyecto Gemelo Ceniza buscaba utilizar su dominio de los fenómenos cuánticos para enviar información al pasado. De esta manera, cada sonda lanzada podría transmitir sus resultados antes incluso de haber sido disparada, permitiendo repetir el proceso una y otra vez hasta encontrar la señal correcta.

El plan era elegante en su lógica: repetir, aprender, ajustar… hasta encontrar lo que buscaban.

Para hacerlo posible, necesitaban una fuente inmensa de energía.

Y ahí es donde todo empezaba a depender de algo mucho más extremo.

Intentaron provocar una supernova artificial, utilizando la energía liberada para alimentar ese ciclo de intentos y convertir el tiempo en una herramienta más de exploración.

Un plan extraordinario.

Casi imposible.

Y, por eso mismo, profundamente convincente.

Pero nunca funcionó.

Cuando finalmente llegas a la Estación Solar, descubres que el experimento no logró su objetivo. A pesar de toda su sofisticación, los Nomai no pudieron generar la energía necesaria para desencadenar la explosión del sol. Su comprensión del universo era profunda… pero no ilimitada.

El sistema que habían diseñado quedó incompleto.

Y antes de que pudieran encontrar otra solución, desaparecieron.

La Materia Fantasma liberada por un cometa se extendió por el sistema solar, poniendo fin a una civilización que había dedicado su existencia a comprender el cosmos.

En ese momento, todo parece encajar.

Si la Estación Solar nunca funcionó, entonces el bucle no debería existir.

Y si el bucle no debería existir…

tal vez pueda detenerse.

Una verdad incómoda

Pero entonces… ¿y si la Estación Solar no estaba provocando la explosión? ¿Que lo hacia?.

A medida que avanzaba la exploración, comenzaron a aparecer indicios de algo que yo seguia ignorando a proposito, pensaba que no era relevante en el juego.

El universo estaba llegando al final de su ciclo. Más de doscientos mil años después de los intentos de los Nomai, el Sol alcanzaba naturalmente el final de su vida útil y se convertía en supernova.

No era un accidente. No era un fallo que pudiera corregirse.

Era simplemente el curso de las cosas.

Y era precisamente esa explosión natural la que ahora alimentaba el bucle.

La comprensión llegó como una sacudida silenciosa.

Sí, podía desactivar el bucle desde el Proyecto Gemelo Ceniza… pero hacerlo significaba permitir que todo terminara. Mantenerlo activo, en cambio, implicaba permanecer indefinidamente en una repetición sin fin.

El juego dejó de ofrecer respuestas tranquilizadoras.

El problema no era técnico.

Era existencial.

Iba a morir junto con todo el sistema solar.

Mi impulso fue resistirme a esa idea, sabia que me estaba perdiendo de algo, pase horas yendo a otros planetas, hablando de nuevo con los mismos personajes, para revisar nuevos dialogos. Pensé que debía existir otra alternativa, una solución oculta, alguna pieza que aún no había logrado comprender.

Había pasado horas reconstruyendo una historia compleja, aprendiendo reglas extrañas del universo, descubriendo patrones ocultos… todo parecía indicar que el conocimiento traería consigo una forma de evitar el final.

Un último intento

Aun después de aceptar que el sol estaba muriendo de forma natural, quedaba una posibilidad abierta: encontrar el Ojo del Universo.

Si los Nomai habían dedicado generaciones enteras a buscarlo, debía haber una razón. Tal vez allí se encontraba una respuesta que aún no lograba comprender. Tal vez el final no era realmente el final.

Tras muchas exploraciones, las coordenadas finalmente aparecen ocultas en las profundidades del sistema solar, en un lugar tan inaccesible como simbólico: el núcleo de Abismo del Gigante. Llegar hasta allí exige paciencia, ensayo y error, y la sensación constante de estar acercándote a algo que ha permanecido fuera de alcance durante demasiado tiempo.

Con las coordenadas en mano, el siguiente paso se vuelve claro: retirar el núcleo que alimenta el Proyecto Gemelo Ceniza y utilizarlo como fuente de energía para una unica nave capaz de alcanzar ese destino final (The Vessel).

Es un acto decisivo.

Al hacerlo, el bucle se detendrá definitivamente.

Ya no habrá otra oportunidad.

Solo queda dirigirse hacia las coordenadas del Ojo del Universo… y descubrir qué significado tiene todo.

El vértigo de no tener dirección

El Ojo del Universo es, al mismo tiempo, lo más asombroso y lo más inquietante de toda la experiencia.

Apareces en lo que parece ser un astro cuántico. Tu dispositivo indica que estás en el polo norte, pero esa referencia deja de tener sentido casi de inmediato.

No hay guía.

No hay un camino claro.

Las referencias comienzan a desvanecerse: la gravedad deja de ser confiable, las distancias pierden coherencia y el entorno cambia sin previo aviso. Una tormenta permanente domina parte del paisaje, mientras objetos cuánticos aparecen y desaparecen con cada relámpago, como si su existencia dependiera de ser observados en el momento justo.

La sensación es profundamente desconcertante.

No es un miedo inmediato, sino algo más sutil: una incomodidad que nace de no entender dónde estás ni bajo qué reglas estás operando. Un tipo de terror más cercano a lo cósmico que a lo físico.

Es un lugar que no parece invitarte a conocerlo, sino a abandonarlo.

Como si no estuviera hecho para ser habitado.

Pero no hay vuelta atrás.

La única forma de salir —si es que existe una salida— es avanzar.

Aunque no sepas hacia dónde.

Eventualmente captas una señal cuántica con tu explorador. La sigues con cautela, atravesando la parte más violenta de la tormenta, hasta llegar al polo sur. Allí, el terreno se abre en un precipicio.

Y entonces lo ves.

Un vórtice imposible de interpretar.

No sabes si estás cayendo hacia él o si, de alguna manera, ya estás dentro. Arriba y abajo dejan de tener significado. No hay orientación clara.

Saltar ya no se siente como avanzar ni como descender.

Se siente más como entregarse.

La experiencia recuerda a ese momento en Interstellar en el que Cooper se adentra en el agujero negro: una mezcla de asombro, confusión y una incomodidad de vulnerabilidad al darte cuenta de que las reglas que sostenían tu comprensión del mundo han dejado de aplicarse.

Solo estás tú, moviéndote en un espacio que parece existir fuera de toda lógica familiar.

Un eco familiar

En medio de ese espacio que parece no obedecer a ninguna lógica, aparece algo inesperado: una estructura conocida.

El observatorio de Lumbre.

No es exactamente el mismo que dejaste atrás, pero tampoco es completamente distinto. Se siente como una reconstrucción incompleta, como un recuerdo que intenta tomar forma. Por momentos, parece que el Ojo no estuviera mostrándote un lugar, sino intentando establecer un diálogo.

No hay instrucciones ni explicaciones claras. es como si el Ojo no estuviera ofreciendo respuestas, sino reflejando la manera en la que has aprendido a mirar.

No es un mensaje directo.

Es más bien una sugerencia silenciosa: que todo lo que has buscado entender afuera también está ligado a cómo eliges interpretarlo.

Poco a poco, la expectativa de encontrar una solución comienza a disolverse.

No hay una máquina que reparar.

No hay una ecuación que completar.

No hay un error que corregir.

Durante gran parte del viaje asumí que el Ojo debía contener una respuesta definitiva: una explicación capaz de dar sentido a todo lo ocurrido, una pieza final que permitiría resolver el problema que había intentado comprender durante tantas horas.

Pero en su lugar, muestra algo distinto.

Una visión del universo en sus últimos instantes.

Mientras todo se apaga, pequeñas luces comienzan a aparecer en la oscuridad.

Apareces nuevamente en Lumbre. Un bosque tranquilo, familiar. Frente a ti, tu reflejo se transforma en una fogata, como una invitación a quedarte.

Guiado por tu localizador, comienzas a seguir la frecuencia que te ha acompañado durante todo el viaje. Esa melodía que antes escuchabas a la distancia ahora te conduce hacia los otros.

Uno a uno, los exploradores aparecen.

Se reúnen alrededor de la fogata.

Sus instrumentos vuelven a sonar, esta vez no dispersos en el vacío, sino presentes, cercanos. La música que antes era señal ahora es compañía.

Ya no estás buscando arreglar nada.

Solo estás allí, compartiendo un momento simple antes de que todo termine.

Y, de alguna manera, eso es suficiente.

El juego no ofrece una respuesta tradicional, porque la pregunta misma ha cambiado.

Ya no se trata de cómo evitar el final, sino de cómo habitarlo.

El universo no pide que lo salves

El final no necesitaba ser evitado.

La fogata no representa una victoria ni una derrota.

Representa la posibilidad de estar en paz con el hecho de que todo termina.

La fogata se eleva, se expande, y por un instante todo parece contenerse en un solo punto… hasta que ocurre una explosión inmensa, algo que recuerda a un nuevo Big Bang.

Después, mientras suena la última canción hermosa, al final de los creditos, una escena sugiere que, tras 14.3 billones de años, un nuevo universo emerge: planetas, vida… y la posibilidad de que todo comience otra vez.

No queda del todo claro si es una recompensa o una respuesta.

La vida encuentra la manera de surgir nuevamente.

Dejar el hogar es un pequeño cambio. Y la muerte, un cambio mayor: no de lo que eres ahora hacia la nada, sino hacia lo que aún no has llegado a ser. — Epicteto

Al terminar Outer Wilds, comprendí que la experiencia no trataba de encontrar una solución, sino de transformar la relación que tenía con el problema.

Durante todo el juego asumí que debía existir una forma de evitar el final. Que, si entendía lo suficiente, si exploraba lo suficiente, si lograba conectar todas las piezas, podría ejercer algún tipo de control sobre lo inevitable. Pero la verdadera enseñanza no estaba en evitar el desenlace, sino en aprender a mirarlo de otra forma.

En ese sentido, la experiencia se acerca a una intuición profundamente estoica, hay cosas que simplemente no están en nuestras manos, y el sufrimiento aparece cuando insistimos en que deberían estarlo.

La vida implica aceptar la transitoriedad de todo. Percibir cada cambio —incluida la muerte— no como una interrupción, sino como parte natural y necesaria del ciclo de la existencia.

También resuena con una idea central del budismo: todo lo que existe es impermanente. No como una tragedia, sino como una condición fundamental de la realidad. La belleza de algo no depende de su duración, sino de nuestra capacidad de estar presentes mientras existe.

Y quizá, en el fondo, eso era lo que el juego intentaba mostrarme desde el principio.

La vida no está para resolverse

Outer Wilds no me enseñó cómo salvar el mundo.

Me enseñó, quizá, algo más valioso: una forma distinta de estar en él.

Soy ingeniero de profesión, y desde pequeño me he sentido atraído por resolver problemas. Esa forma de pensar me ha llevado lejos; me ha dado oportunidades, aprendizajes y experiencias que valoro profundamente. Pero también ha venido acompañada de una inercia difícil de cuestionar: la necesidad constante de optimizar, de mejorar, de encontrar la siguiente solución.

De alguna manera, la cultura en la que vivimos refuerza esa idea. Nos empuja a resolverlo todo: la carrera, las finanzas, el estatus, las relaciones, la vida misma. Como si existiera una versión final en la que todo encaja perfectamente y, una vez alcanzada, por fin pudiéramos descansar.

Pero rara vez nos permitimos simplemente estar: alrededor de una fogata, en una conversación, en un momento compartido con quienes nos rodean. Con nuestros seres queridos, con amigos, incluso con desconocidos que, por un instante, coinciden con nosotros en este mismo viaje.

Comprender que la vida no es un acertijo que deba resolverse por completo, sino una experiencia que merece ser vivida con atención. Que el valor no está únicamente en llegar a una respuesta, sino en la capacidad de asombro que cultivamos mientras buscamos.

En ese sentido, recuerdo una idea de Alan Watts: la vida se parece más a la música o a la danza que a un problema por resolver. No asistimos a un concierto para que la canción termine lo antes posible, ni bailamos para llegar a un punto final. Lo hacemos por la experiencia misma, por el movimiento, por el instante.

Y quizá ahí está la lección más simple —y más difícil de integrar—:

que incluso sabiendo que la canción terminará,

podemos elegir escucharla con atención,

bailarla con presencia

y compartirla con otros mientras dure.

最近、タコス欲が高まっている。 妻と二人で、隅田公園で開催されていた「サルサストリート」へ行った。タコスとお酒が売られている。

相変わらず、タコスを食べるのは難しい。食べ終わるころには手がべとべとになる。最初にティッシュを用意していなかったせいで、その手のままバッグに触れてしまい、中まで汚してしまった。

それでもやっぱり美味しい。タコスの記事などでは、きれいに食べやすい料理としてブリトーが引き合いに出されることがあるが、やはり別物だ。タコスの手軽さ、生地の薄さ、そしてあの美味しさにおいては、すでに完成されていると感じる。あとは、こちらの食べる技術を上げるだけだ。

世の中を便利にすることが、必ずしも最適解とは限らない。自分の側の精度を高めることで解決することもある。タコスはそんなことを教えてくれる。

そのあと、喫茶店「デリカップ」へ。私はホワイトマウンテンというコーヒーを注文し、妻は生姜チャイを頼んでいた。ホワイトマウンテンは、コーヒー特有の苦味が後から追いかけてくることもなく、後味がすっきりしている。たしかにホワイトだ。気に入った。 妻は、生姜チャイが甘すぎると言って、少し残していた。

夕飯は、SNSで見かけた、鶏むね肉にチリソースをかけた料理。これがとても美味い。鶏むね肉がこんなに美味しく食べられるとは思わなかった。また一つ、知見が増えた。

最近は、食事から得る知見が多い。家庭でここまで美味しいものが食べられるという実感もあるが、それ以上に、何か知恵を食べているような感覚がある。外食ではチェーン店で安全性とコストパフォーマンスを、家庭では知恵や豊かさを得ている。そして、あとは個人経営の定食屋がもう少し進化してくれれば、言うことはない。

これだけ簡単に美味しい料理が家庭で作れる時代にもかかわらず、いまだに美味しくない店が存在するのは、少し不思議だ。食べログで調べなくても、ふらっと入った店が驚くほど美味しい、そんな状態になっていてもおかしくないのに、まだそこまでのフェーズには至っていないように感じる。 日本全体にやってほしい事。それは、ふらっと入った店がどこも美味しい事である。

Cansados de palabras, se derrumban, colapsan. Pero en el silencio hay equilibrio, claridad.

Freundinnen & Freunde der Weisheit! Stress wird heute oft als Krankheit verstanden – als etwas, das vermieden, bewältigt oder therapiert werden muss. Doch ein genauerer Blick zeigt: Stress ist weder ungewöhnlich noch per se negativ. Im Gegenteil – richtig verstanden und eingeordnet, kann er uns wachsen lassen.

Stress ist normal – und oft sogar hilfreich
Der Grundgedanke: Stress gehört zum Leben. Er ist nicht automatisch ein Anzeichen von Überforderung, sondern oft ein Zeichen von Einsatz, Verantwortung oder Entwicklung. Ohne Druck kein Fortschritt, ohne Herausforderung keine Leistung – ob beim Lernen, im Beruf oder in der persönlichen Entwicklung. Stress wirkt dabei wie ein Antrieb, der uns aktiv hält und dazu bringt, Prioritäten zu setzen, uns zu fokussieren oder Gewohnheiten zu überdenken.

Die philosophische Perspektive: Von Schopenhauer bis Nietzsche
Historisch gesehen wurde Stress nie als Krankheit begriffen. Die Stoiker etwa betrachteten Belastung als unvermeidlich – der entscheidende Punkt sei, wie wir darauf reagieren. Auch Schopenhauer ging davon aus, dass das Leben vor allem aus Leiden bestehe – dieses zu akzeptieren sei klüger als es zu leugnen. Nietzsche hingegen sah gerade in der Überwindung von Widerständen den Weg zu persönlicher Freiheit und innerer Stärke. Sein berühmtes Diktum „Was mich nicht umbringt, macht mich stärker“ bringt diesen Gedanken auf den Punkt: Stress ist nicht das Problem – sondern eine Einladung zum Wachstum.

Fazit: Nicht alles pathologisieren – sondern einordnen und nutzen
Wir sollten nicht jede Anspannung als Störung betrachten. Die Tendenz, alltägliche Emotionen wie Stress oder Unzufriedenheit vorschnell zu pathologisieren, verstärkt eher das Gefühl von Hilflosigkeit. Wer hingegen lernt, Stress als Teil des Lebens zu akzeptieren – und ihn als Impuls zur Veränderung nutzt –, handelt selbstwirksam und findet oft zu mehr Klarheit und Widerstandskraft zurück. Stress ist kein Makel, sondern oft ein Zeichen dafür, dass etwas auf dem Spiel steht. Wer sich ihm nicht entzieht, sondern ihn versteht und einordnet, wird nicht schwächer, sondern stärker. Die Philosophie bietet dafür seit Jahrhunderten einen robusten Bezugsrahmen – aktueller denn je.

Denkanstoss zum Wochenbeginn

„Die Erinnerungen sind das einzige Paradies, aus dem wir nicht vertrieben werden können.“ – Jean Paul (1763–1825)

ProductivityPorn-Tipp der Woche: To-do-Listen richtig nutzen

To-do-Listen helfen dir, den Überblick zu behalten – aber nur, wenn du sie gezielt einsetzt. Priorisiere deine Liste und setze realistische Ziele, anstatt sie mit unendlich vielen Aufgaben zu überladen.

Aus dem Archiv: Was wir heute von Carl Gustav Jung lernen können

1933 schrieb Carl Gustav Jung in einem Brief an einen seiner Patienten: „Man lebt, wie man leben kann. Es gibt keinen einzigen bestimmten Weg für den einzelnen, der ihm vorgeschrieben oder der passend wäre.“ Mit diesen Worten formulierte er eine seiner zentralen Einsichten: Jeder Mensch beschreitet seinen individuellen Lebensweg, ohne eine vorgegebene Richtung. Doch was kann Jung uns heute noch über Selbsterkenntnis und persönliche Entwicklung lehren?

weiterlesen …

Vielen Dank, dass Du Dir die Zeit genommen hast, diesen Newsletter zu lesen. Ich hoffe, die Inhalte konnten Dich inspirieren und Dir wertvolle Impulse für Dein (digitales) Leben geben. Bleib neugierig und hinterfrage, was Dir begegnet!

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EpicMind – Weisheiten für das digitale Leben „EpicMind“ (kurz für „Epicurean Mindset“) ist mein Blog und Newsletter, der sich den Themen Lernen, Produktivität, Selbstmanagement und Technologie widmet – alles gewürzt mit einer Prise Philosophie.

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Disclaimer Teile dieses Texts wurden mit Deepl Write (Korrektorat und Lektorat) überarbeitet. Für die Recherche in den erwähnten Werken/Quellen und in meinen Notizen wurde NotebookLM von Google verwendet. Das Artikel-Bild wurde mit ChatGPT erstellt und anschliessend nachbearbeitet.

Topic #Newsletter

I woke up at 7 AM today to play tennis with my dad, And I recorded a little bit of it was my glasses And I’m glad that I did because I realized this is the first video I have of us.

You might worry about me, but I am not worried about myself. I know that my not worrying about myself worries you, but please trust that it will all work out.

On the morning of 9 April 2026, a small miracle of coordination is unfolding in the cognitive infrastructure of the planet.

A graduate student in Hyderabad is asking Claude how to tighten the argument in a paper on monetary policy. A copywriter in São Paulo is feeding ChatGPT the bullet points for a pitch deck. A civil servant in Warsaw is asking Gemini to draft a consultation response on housing density. A novelist in Lagos wants to know whether her second chapter drags. A thirteen-year-old in suburban Ohio is asking an assistant, any assistant, whether she should reply to a text from the boy she likes.

None of them know each other. None of them are writing about the same thing.

And yet the sentences they are about to produce will share more DNA than any comparable population of human sentences has shared since the King James Bible standardised written English in 1611. The cadences will be familiar. The rhetorical scaffolding will be familiar. Tactful three-point framing, tentative fourth consideration, breezy affirming close. Certain adjectives will recur at a frequency no unassisted population of writers has ever produced. And certain ideas, once prominent, will be faintly audible or missing entirely, as if someone had quietly removed a frequency from the signal.

A paper circulating on arXiv in early 2026 calls this, with characteristic academic understatement, “algorithmic monoculture.”

The term is not new. Jon Kleinberg and Manish Raghavan introduced it in the Proceedings of the National Academy of Sciences in 2021, back when it still functioned mostly as a warning about hiring software and credit-scoring systems. The newer work expands the frame. It argues that the rise of large language models, trained on overlapping corpora, fine-tuned using near-identical methods, and optimised against a suspiciously similar set of human preferences, has produced something the world has not previously had to reckon with: a planetary-scale cognitive layer that is simultaneously almost invisible to individual users and profoundly consequential, at the population level, to the diversity of human thought.

The individual-level invisibility is the interesting part.

Walk up to any one of those users and ask them whether the AI is helping. They will say yes. The assistant is responsive. The writing is better than what they would have produced alone. The code compiles. The email hits the right tone. The student understands monetary policy now in a way she did not understand it at breakfast. Each interaction is, in isolation, a small gift.

And it is precisely because the interactions are small, isolated gifts that the aggregate effect is so hard to see. There is no aggrieved party. There is no victim. There is only the slow, statistical narrowing of the range of things that get written, thought, proposed, rejected, tried, and considered.

The monoculture does not feel like a monoculture from inside it. It feels like being helped.

The Paper That Said the Quiet Part

The arXiv paper, and the broader cluster of early-2026 work around it, does something previous contributions in the literature mostly refused to do. It tries to estimate the thing that is being lost.

The headline result is simple. When a representative multilingual sample of fifteen thousand human respondents from five countries is asked to produce preference rankings across a standard battery of open-ended questions, and the same battery is put to twenty-one leading language models, the models collectively occupy a region of preference space that covers roughly forty-one per cent of the range humans span.

The other fifty-nine per cent is not underrepresented. It is absent.

That finding is in line with a string of earlier results that, taken together, amount to something closer to a verdict. A 2024 study in the Cell journal Trends in Cognitive Sciences found that co-writing with any mainstream LLM, regardless of which company trained it, produced sentences whose stylistic variance collapsed towards a common centre within a handful of exchanges. A large-scale analysis of fourteen million PubMed abstracts by researchers at Tübingen, first published in 2024 and updated in 2025, documented a sudden surge after November 2022 in the frequency of a small, stable set of “LLM preferred” words: delve, intricate, showcasing, pivotal, underscore, meticulous. In some sub-corpora, more than thirty per cent of biomedical abstracts now carry the linguistic fingerprint of having passed through a chatbot.

A separate working paper measured writing convergence in research papers before and after ChatGPT's release. Early adopters, male researchers, non-native English speakers, and junior scholars moved their prose fastest and furthest towards the model mean.

The people who most needed the help were the ones whose voices changed the most.

Something similar is happening in creative domains, although the evidence is messier. The Association for Computing Machinery's 2024 conference on Creativity and Cognition published a paper whose findings most researchers in the area now treat as foundational: ask humans to generate divergent-thinking responses to open prompts, and you see the expected long-tail distribution of weird, bad, brilliant, and unclassifiable answers. Ask an LLM the same, and you get a narrower, tighter, more plausibly-competent set of responses.

On average, the LLM does well. At the population level, it produces far less variety than a comparable population of humans.

The authors used the phrase “homogenising effect on creative ideation” and meant it literally. Other groups have pushed back, arguing that the picture is more complicated and that sampling choices matter. The disagreement is real. The overall direction of drift is not really in dispute any more.

How the Narrowing Happens

To understand why the drift is happening, it helps to dispense with two stories.

The first is that the models have a secret aesthetic they are imposing on us. They do not. The Midjourney look and the ChatGPTese voice are not creative preferences in any meaningful sense. They are artefacts of the training and tuning pipeline.

The second is that the problem is a handful of frontier labs colluding to produce bland output. They are not colluding. They are doing the same thing independently because the gradients of the problem push everyone towards the same hill.

The first gradient is the training data. A language model is, in the end, a statistical compression of a corpus. If you scrape Common Crawl, Wikipedia, the major English-language book collections, StackExchange, Reddit, GitHub, and a handful of licensed newspaper archives, you will end up with a corpus that overlaps by perhaps seventy or eighty per cent with anyone else's scrape of the same substrate. There are differences around the edges, a bit more Chinese here, a bit more code there, a different cut-off date, but the overall shape is remarkably stable across labs. Dolma, The Pile, RedPajama, C4, FineWeb: each is an attempt to produce a general-purpose training corpus and each contains a broadly similar cross-section of publicly available human text.

Models trained on such substrates are already close to each other before any tuning happens. They have been fed from the same trough.

The second gradient is reinforcement learning from human feedback. This is the technique that turned eerily capable text continuation engines into the compliant, helpful assistants that five hundred million people now use daily. The idea is simple. Present humans with pairs of model outputs, ask which is better, train a reward model on those preferences, then use the reward model to fine-tune the base model. The result is a system shaped, gradient step by gradient step, to produce answers humans in the labelling pool tend to approve of.

The problem is that humans in the labelling pool, particularly professional labellers working through the contract platforms the frontier labs use, develop remarkably consistent tastes. They prefer answers that are structured, polite, hedged, comprehensive, and written with a faint institutional politeness most people would recognise as American corporate email register. They dislike answers that are rude, uncertain, fragmentary, idiosyncratic, strange.

None of this is their fault. It is a predictable consequence of asking a few thousand people to impose ratings on millions of responses. You get the average of their tastes. Not the span.

The third gradient is optimisation itself. Reinforcement learning, by its nature, pushes policies towards the highest-scoring actions available. Apply it to language generation and the model concentrates its probability mass on outputs that reliably score well. Researchers call this “mode collapse,” a phrase borrowed from the generative adversarial network literature, and the phenomenon has been documented so many times in RLHF pipelines that it is considered standard. A 2024 ICLR study measured the effect and found that post-RLHF models exhibited “significantly reduced output diversity compared to SFT across a variety of measures,” with the authors explicitly framing this as a tradeoff between generalisation quality and the breadth of the response distribution.

In plain English: the models get better at the average task and worse at producing a range of answers to any one task. They converge on the plausible-sounding centre.

The fourth gradient is feedback from deployment. Once a model is serving production traffic, the telemetry from its users shapes the next round of training. Responses users rate up are preferred. Responses users regenerate or abandon are suppressed. And the users, naturally, have been trained on earlier outputs of the same models.

They prefer things that look like what they have come to expect. Within a few cycles, the distribution of acceptable responses narrows further, and the aesthetic the model produces becomes the aesthetic its users demand, which becomes the aesthetic the model produces.

The loop closes.

This is the mechanism by which “the ChatGPT look” became a recognisable category in 2023, stabilised through 2024, and was operating as a near-parody of itself by late 2025. It is a statistical attractor in the feedback graph.

The Ghost in the Text

If you want to see the monoculture in the wild, you do not have to look very hard.

The Tübingen paper on PubMed abstracts is the most quantitatively damning evidence, and the excess-vocabulary methodology used there has since been applied to other corpora with consistent results. News writing, marketing copy, policy consultations, customer support macros, cover letters, LinkedIn posts. Every corpus where people write under time pressure shows the same tell-tale vocabulary surge. A 2025 study testing English news articles for lexical homogenisation found some metrics moving and others holding steady, a useful corrective against overclaiming. But nobody is now arguing that writing on the open web looks the same in 2026 as it did in 2021.

The visual domain is noisier, partly because the models change faster and partly because creative industries have aggressively developed counter-aesthetics. The “Midjourney look,” a recognisable stew of moody lighting, glassy skin, hyper-saturated background bokeh, and compositions that feel vaguely cinematic without belonging to any specific film, became so pervasive in 2023 and 2024 that stock photography buyers began filtering it out as a separate category. Professional illustrators and art directors responded by prompting against it, fine-tuning custom models, and, in some cases, branding human-made work as “not AI” the way food manufacturers brand their products “not GMO.”

The counter-movement has produced some of the more interesting visual culture of the last two years. It exists in reaction to a monoculture it did not create.

In software, the convergence is more measurable. The major coding assistants, GitHub Copilot, Cursor, Anthropic's Claude Code, Google's Gemini Code Assist, now write or materially influence something on the order of forty per cent of the code committed to open-source repositories, and a higher share of new code inside large enterprises. They do this against a training substrate that is itself overwhelmingly composed of previously-written open-source code. The result is a global convergence on a narrow set of idioms: particular naming conventions, particular error-handling patterns, particular library choices.

Experienced engineers report the strange sensation of reading a new codebase and recognising the model's fingerprint before they can identify the author's.

Hiring is perhaps the clearest case of Kleinberg and Raghavan's original concern becoming literal. By the time a candidate's CV reaches a human reviewer at a Fortune 500 firm in 2026, it has typically passed through multiple LLM-based screening layers. The screening models are fine-tuned on labelled examples of “good” and “bad” candidates, and the labels come from a small number of vendors whose training sets overlap heavily. A paper on arXiv in early 2026 on strategic hiring under algorithmic monoculture modelled what happens when most firms in a labour market delegate their screening to correlated systems, and produced the result theorists had predicted for five years: certain candidates are now rejected by every employer in a sector because they sit in a region of candidate space that the shared screening model treats as undesirable.

This is the outcome homogenisation effect Rishi Bommasani's group formalised at NeurIPS in 2022. It has moved from thought experiment to operational reality.

A Short History of Monocultures That Ended Badly

Every generation of technologists likes to believe its tools are so new that history has nothing to say about them. Every generation is wrong.

The story of human civilisation contains a long list of monocultures that looked like efficiency gains right up until the moment they revealed themselves as fragilities. Two are worth the reread.

The first is the Irish potato crop of the 1840s. By the early nineteenth century, the peasantry of Ireland had concentrated their agriculture almost entirely on a single variety, the Irish Lumper, because it produced more calories per acre than any alternative on the poor, boggy land they farmed. The Lumper was propagated vegetatively, which meant that every potato in the ground was, genetically, a clone of every other. When Phytophthora infestans arrived from the Americas in 1845, it encountered no genetic diversity to slow it down. The blight moved through the crop the way a single-variant virus moves through an unvaccinated population.

Roughly one million people starved. Another million emigrated. A population that had stood at eight and a half million before the famine was down to four and a half million by the end of the century.

The catastrophe was not caused by the blight alone. It was caused by the combination of a uniform crop and a novel pathogen, and the uniformity was the variable humans had chosen.

The second is the financial modelling monoculture of the early 2000s. For roughly two decades, risk management inside large banks converged on a single family of statistical tools built around Value-at-Risk, often in almost identical Monte Carlo implementations, parameterised against overlapping historical windows, and regulated into near-universal adoption by Basel II. Andrew Haldane, then of the Bank of England, gave a 2009 speech at the Federal Reserve of Kansas City that remains the sharpest diagnosis of what had happened. He described the pre-crisis financial system as a monoculture in which “risk management became silo-based” and “finance became a monoculture” that “acted alike” under stress, “less disease-resistant” than a more heterogeneous system would have been.

When the underlying assumptions of the models broke in 2008, they broke everywhere at once, because everyone was running versions of the same model.

The crisis was not caused by bad modelling. It was caused by good modelling replicated until there was no dissent left in the system.

Both stories carry the same lesson. Monocultures look efficient in steady state and catastrophic in transition. They reduce small, distributed losses in the good years and concentrate them into a single correlated failure in the bad year. If you were trying to design a system that minimises variance on any given day and maximises the probability of a civilisation-scale shock, you could hardly do better than a globally adopted AI assistant trained by four companies on broadly overlapping data using broadly overlapping techniques.

The Counter-Arguments, Fairly Stated

It would be unfair to describe the situation without taking seriously the people who think the alarm is overblown. There are several of them. Some of their points are good.

The first counter-argument is that writing has always converged under the pressure of shared infrastructure. The King James Bible homogenised English prose. The Associated Press Stylebook homogenised American journalism. Microsoft Word's grammar checker, installed on half a billion machines, quietly imposed the active voice on a generation of office workers. Every technology that reduces the cost of producing acceptable text also narrows the range of text being produced. The question, the sceptics say, is not whether LLMs are narrowing the distribution, but whether the narrowing is qualitatively different from previous episodes.

The best evidence we have suggests that the convergence is faster and deeper than any previous episode. But the sceptics are right that proportionality matters.

The second counter-argument is that the monoculture is a transient phenomenon of the current training paradigm. Base models are getting better at preserving distributional diversity. Techniques like Direct Preference Optimisation, constitutional AI, and the community-alignment data-collection protocols described in the arXiv paper itself offer a plausible path to models that are both helpful and genuinely pluralistic. The problem, on this view, is not that AI is inherently homogenising; it is that the specific RLHF pipelines of 2022 to 2025 were homogenising, and the next generation of alignment methods will fix it.

Anthropic's work on constitutional pluralism and Meta's 2025 research on diversity-preserving fine-tuning both show real improvements on certain metrics. The question is whether the improvements are keeping pace with the scale of deployment. The honest answer is probably no.

The third counter-argument is the most interesting. It holds that humans were never as diverse in their expressed thought as the loss-of-diversity argument assumes. Take a population of first-year undergraduates, give them an essay prompt, and you already get substantial convergence on a handful of rhetorical templates, shared references, and predictable argumentative moves. The diversity we imagine we are losing was never there to begin with. What the LLMs are doing is making visible a pre-existing homogeneity and perhaps nudging it slightly harder in the direction it was already going.

There is something to this. Human culture has always moved through fashions, canons, and shared templates. The model-free baseline was not a paradise of idiosyncratic genius.

The fourth counter-argument is pragmatic. Even granting that LLMs reduce variance at the margin, they dramatically expand the number of people who can participate in written cognitive work. A non-native speaker in a field dominated by English-language publication can now write papers that reach the same readers as a native speaker. A dyslexic student can produce prose that reflects her thinking rather than her difficulty with spelling. A small-business owner without marketing staff can produce professional copy. The aggregate diversity of the cognitive commons might actually be higher, not lower, because more voices are in the room even if each individual voice is a bit more standardised.

The honest answer to all four arguments is that they do not dissolve the problem. They calibrate it.

The monoculture is not apocalyptic, but it is real. The convergence is not new in kind, but it is larger in scale than any previous episode. The loss of diversity is partial and might be partly reversible with better tuning methods, but the reversal is not happening at the pace the deployment is. And the expansion of participation is genuine, but it is not a substitute for the distinct kinds of cognitive variety the current systems are dampening.

We are left with a real problem that is smaller than the loudest critics claim and larger than the loudest defenders will admit.

Where Dissent Lives Now

One unsettling feature of the current moment is that the space in which intellectual dissent used to happen has been partly reabsorbed into the tools generating the mainstream.

When a student wants to argue against the received view, the assistant she uses to sharpen her argument has been trained on a corpus in which the received view is massively overrepresented, and tuned on preferences that treat the received view as the baseline of reasonableness. Her heterodox position can still be articulated. But only in the voice of the orthodoxy, with the orthodoxy's cadences and framings and preferred caveats.

The tool is helpful. It is just that the help comes in a specific register, and the register quietly pulls everything towards a centre.

This is not new in the history of dissent. Samizdat writers in the Soviet Union wrote in a Russian inherited from the official press. Heterodox economists spent the 1990s writing in the neoclassical vocabulary they were criticising. The tools of mainstream thought always bleed into the voice of people trying to escape it.

What is new is the speed and completeness of the bleed. When the tool is in every sentence, in every revision, in the autocomplete of the email drafting the pamphlet, the vocabulary of dissent has fewer places to hide.

This matters because epistemic diversity is the raw material out of which new ideas are built. Scientific revolutions, as Thomas Kuhn argued in 1962, happen when a tradition runs out of resources to solve its own puzzles and a cluster of previously marginal approaches suddenly becomes mainstream. If the marginal approaches are never articulated in the first place, because the tools of articulation bias their users towards the centre, the Kuhnian dynamic stalls. The revolutions do not come, because the conditions for revolution do not form.

This is the deepest worry in the monoculture literature, and the one hardest to test empirically, because the counterfactual is unobservable. We will not know which ideas were quietly filtered out of human discourse by the assistants of the 2020s.

We will only know what did not get said.

Interventions That Might Actually Help

The question is what to do. Nobody is sure. But interventions are being tried, and some look more promising than others.

The first category is technical. Preserving diversity during alignment is an active area of research, and the tools are improving. Regularisation penalties that explicitly reward response-distribution breadth. Constitutional methods that bake pluralism into the model's self-description. Multi-objective optimisation against competing preference signals. Community-alignment datasets built from stratified samples of global populations rather than the labelling pools of San Francisco contractors.

None of this is a complete solution, but the direction is legible. If the frontier labs decided tomorrow that response diversity was a first-class metric and weighted it at, say, twenty per cent of their tuning objective, the curves would move within months.

The question is whether they will. Response diversity is not what users say they want. Helpful answers are what they say they want. The gradient of commercial incentives does not obviously favour pluralism.

The second category is structural. Antitrust enforcement on foundation model markets is the obvious lever, and the European Commission has been exploring it since 2024, with the Digital Markets Act designation process now looking seriously at whether the largest LLM providers meet the gatekeeper thresholds. The theory of the case is that a market with four dominant providers training near-identical systems against near-identical benchmarks is not producing meaningful consumer choice. In the US, the Federal Trade Commission's 2024 inquiry into AI partnerships was a tentative step in a similar direction.

Neither jurisdiction has yet delivered a ruling that would materially shift the competitive landscape. But the conceptual groundwork is being laid.

The third category is institutional. The homogenising effects of mainstream models can be partly countered by the deliberate cultivation of distinctive alternatives. National or regional foundation model efforts, public-interest model trainings by universities or public broadcasters, domain-specific models trained on curated corpora that lie outside the standard scrape: none of these need to outcompete the frontier labs on general capability. They just need to exist, and to be good enough to be used by people who want an alternative voice.

The European EuroLLM project, Singapore's SEA-LION, Japan's Sakana work, the Allen Institute's continuing release of fully open weights and training data: these are the seeds of what might eventually be a more diverse ecosystem. Whether they grow into anything that genuinely counterbalances the big four depends on the next few years of funding and political will.

The fourth category is personal. Every writer, every coder, every thinker who uses these tools faces a daily choice that aggregates into the larger cultural effect. There is a real difference between letting the assistant do the thinking and letting it help with the thinking. It does not show up on any individual day. It shows up over months, in the divergence between users who kept their voice and users who surrendered it.

The people who have thought most seriously about this tend to converge on a discipline. Use the tool as a collaborator, not an author. Accept or reject each suggestion as a conscious choice. Reread the output and ask whether it still sounds like you. And, most importantly, write things sometimes without the tool at all, to keep the neural pathways of solo composition from atrophying.

These are small habits. They cannot fix a structural problem. But they are the only layer of defence available to the individual user right now, and they probably matter more than the user thinks.

The Diversity We Have Not Yet Lost

It is tempting to close a piece like this in the register of warning. But the warning register is part of what we are trying to escape.

The monoculture is not destiny. It is a tendency produced by a set of choices, most of which were made for defensible reasons and none of which are irreversible. The frontier labs could weight diversity higher. The regulators could act. The users could develop better habits. The open ecosystem could grow. A future model architecture could sidestep the RLHF trap in a way nobody currently sees.

The space of possible futures is wide.

What is not wide is the window. The feedback loops between models, users, training data, and cultural production are tightening. Every year in the current paradigm adds another layer of training data generated by previous models, another layer of user taste conditioned by previous outputs, another layer of convention baked into what counts as a good answer.

Monocultures are easier to prevent than to reverse, because the diversity you need to repopulate them with has to come from somewhere, and the main reservoir, the independent creative output of unassisted humans, is shrinking as a share of the total.

The Lumper potato, as any evolutionary biologist will tell you, was not an unreasonable choice in 1840. It grew well on poor land. It fed hungry people. The problem was not that the Lumper was bad.

The problem was that it was everywhere, and there was nothing else.

When the blight came, the absence of alternatives was what turned an agricultural problem into a civilisational one. The lesson is not that monocultures are always wrong. It is that they are always a bet on the future being continuous with the past, and the bet compounds over time until it is the only bet on the board.

The humans asking their assistants for help on 9 April 2026 are not doing anything wrong. They are using the tools available to them, the tools are genuinely helpful, and the sentences they produce are better than the sentences they would have produced alone. That is the seductive part. And the accurate part. And also the part that makes the aggregate picture so hard to see.

Somewhere underneath the millions of small, helpful interactions, the distribution of human expression is quietly tightening.

Whether it keeps tightening, or whether we decide to plant something else in the field alongside the Lumper, is still an open question. It may not stay open for long.

---

References and Sources

1. Kleinberg, J., and Raghavan, M. (2021). “Algorithmic monoculture and social welfare.” Proceedings of the National Academy of Sciences, 118(22). https://www.pnas.org/doi/10.1073/pnas.2018340118
2. Bommasani, R., et al. (2022). “Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?” Proceedings of NeurIPS 2022. https://arxiv.org/abs/2211.13972
3. “Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset.” arXiv preprint 2507.09650 (2025, revised 2026). https://arxiv.org/abs/2507.09650
4. Baek, J., and Bastani, H. (2026). “Strategic Hiring under Algorithmic Monoculture.” arXiv preprint 2502.20063. https://arxiv.org/pdf/2502.20063
5. “The Homogenizing Effect of Large Language Models on Human Expression and Thought.” Trends in Cognitive Sciences (2026). https://www.cell.com/trends/cognitive-sciences/abstract/S1364-6613(26)00003-3
6. Preprint version: “The Homogenizing Effect of Large Language Models on Human Expression and Thought.” arXiv:2508.01491. https://arxiv.org/abs/2508.01491
7. Kobak, D., et al. (2024). “Delving into ChatGPT usage in academic writing through excess vocabulary.” arXiv:2406.07016. https://arxiv.org/abs/2406.07016
8. Geng, M., et al. (2025). “Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing.” arXiv:2504.13629. https://arxiv.org/abs/2504.13629
9. Anderson, B. R., Shah, J. H., and Kreminski, M. (2024). “Homogenization Effects of Large Language Models on Human Creative Ideation.” Proceedings of the 16th ACM Conference on Creativity & Cognition. https://dl.acm.org/doi/10.1145/3635636.3656204
10. Ghods, K., and Liu, P. (2025). “Evidence Against LLM Homogenization in Creative Writing.” https://kiaghods.com/assets/pdfs/LLMHomogenization.pdf
11. “We're Different, We're the Same: Creative Homogeneity Across LLMs.” arXiv:2501.19361 (2025). https://arxiv.org/abs/2501.19361
12. Kirk, R., et al. (2024). “Understanding the Effects of RLHF on LLM Generalisation and Diversity.” ICLR 2024. https://arxiv.org/abs/2310.06452
13. “Testing English News Articles for Lexical Homogenization Due to Widespread Use of Large Language Models.” ACL 2025 Student Research Workshop. https://aclanthology.org/2025.acl-srw.95/
14. “Examining linguistic shifts in academic writing before and after the launch of ChatGPT.” Scientometrics (2025). https://link.springer.com/article/10.1007/s11192-025-05341-y
15. Haldane, A. G. (2009). “Rethinking the financial network.” Speech at the Financial Student Association, Amsterdam. Bank for International Settlements. https://www.bis.org/review/r090505e.pdf
16. “Did Value at Risk cause the crisis it was meant to solve?” Institute for New Economic Thinking, Oxford. https://www.inet.ox.ac.uk/news/value-at-risk
17. University of California Museum of Paleontology. “Monoculture and the Irish Potato Famine: cases of missing genetic variation.” Understanding Evolution. https://evolution.berkeley.edu/the-relevance-of-evolution/agriculture/monoculture-and-the-irish-potato-famine-cases-of-missing-genetic-variation/
18. Wikipedia contributors. “Great Famine (Ireland).” https://en.wikipedia.org/wiki/Great_Famine_(Ireland)
19. Kuhn, T. (1962). The Structure of Scientific Revolutions. University of Chicago Press.
20. Wikipedia contributors. “Reinforcement learning from human feedback.” https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback

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Tim Green UK-based Systems Theorist & Independent Technology Writer

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

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

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

It’s strange how life tends to remind you of things you were recently thinking about. In my case, it is once again reminding me how much we are all subject to chance, randomness, and being blindsided by things we don’t expect.

This week we had family members visiting from out of state. The second evening after they arrived, one of our visitors didn’t look well. The following morning they looked even less well and we pushed them to go to urgent care. Once at urgent care, the doctors said that they needed to go to the ER immediately. Now, after three more days, they have been admitted to the local hospital awaiting a complex surgical procedure to remove a potentially cancerous mass in near one of their internal organs. What was supposed to be a three day visit is going to turn into at least a three week ordeal that could upend our family.

It is crazy how without any real warning things can drastically change in a matter of hours. In these situations we are reminded of how little control we sometimes have over what happens to us. All you can do is try and make the best decisions possible during the subsequent hours, days, and weeks to influence the outcome in a positive direction. I believe we have done this and now all we can do is wait and see while offering as much support to the family member impacted as possible. Let’s hope for a brighter tomorrow.

I have a 2018 Corsair Strafe mechanical keyboard with the Cherry MX Red Switches. I’ve been getting tired typing on it, and I’ve been noticing a lot of missed keystrokes while I type. I am a fast typer, and I think I got tired of this keyboard.

So, I was looking for another mechanical keyboard, specifically one that I could customize, change the caps and switches if needed. Basically, a keyboard that could grow with me without being too complicated. I tested some keyboards on my local computer store, and the Keychron ones got my attention.

I wanted a more tactile experience (the Cherry Red is linear), so I went with a Keychron V6 Ultra 8K with the Tactile Banana switches. I love it! 😍

It worked well with the cable connection, and also connected with Bluetooth and the 2.4G dongle on my Ubuntu 25.10.

The issue: Can’t use the Launcher to customize the keyboard

In order to customize and remap the keys and for this keyboard, we have to do it online, via the Keychron Launcher.

The manufacturer guide says that the Launcher only works with Chrome/Edge or Opera browsers.

I had Chromium installed via Snap and I opened the launcher website. The site recognized my keyboard, but it wouldn't connect.

Solution attempts

I did some online searching and I discovered that Linux has some security measures in place that avoids a userspace application to write to hardware input. So the solution is to create an “udev.rule” to add permissions. I followed the instructions from this article: HOWTO: Get the Keychron Launcher working in Debian GNU/Linux.

So my steps were something like this:

• I identified my keyboard vendor/product information using lsusb | grep -i keychron

• Which gave me following info: Bus 003 Device 013: ID 3434:0c60 Keychron Keychron V6 Ultra 8K

• Great! Then I created the rule with sudo nano /etc/udev/rules.d/99-keychron.rules

• And this was my first try to create the rule: KERNEL=="hidraw*", SUBSYSTEM=="hidraw", ATTRS{idVendor}=="3434", ATTRS{idProduct}=="0c60", MODE="0660", GROUP="ariadne", TAG+="uaccess", TAG+="udev-acl"

• Then, I ran the two commands to reload the rules and trigger them: sudo udevadm control --reload-rules sudo udevadm trigger

• It didn't work, Chromium still could not connect to the keyboard.

• In Chromium I checked: Settings -> Privacy and Security -> Site settings -> Additional permissions -> HID devices and ensured HID access was allowed.

• I tried different rules, tweaking here and there, played around with user groups, and nothing worked. I unplugged, plugged, restarted the computer, I even tried to run Chromium with root access temporarily. Nothing worked.

• All the time I was checking chrome://device-log/ to see what was going on, and got a list of errors like this: HIDEvent[21:52:54] Failed to open '/dev/hidraw7': FILE_ERROR_ACCESS_DENIED

HIDEvent[21:52:54] Access denied opening device read-write, trying read-only.

• I did some more tweaks to the udev.rules, and I ended up with this in my rules file:

# Keychron V6 Ultra 8K - Normal Mode KERNEL=="hidraw*", SUBSYSTEM=="hidraw", ATTRS{idVendor}=="3434", ATTRS{idProduct}=="0c60", MODE="0666", TAG+="uaccess"

# STM32 Bootloader - Required for Firmware Flashing SUBSYSTEM=="usb", ATTRS{idVendor}=="3434", ATTRS{idProduct}=="0c60", MODE="0666", TAG+="uaccess"

• It was still not working. I knew it was something to do with permissions from Chromium.

• Then the next day I did more digging online, and I read that Chromium installed via Snap is actually sandboxed and often cannot see hardware even if the udev rules are current. The solution? Get the .deb install package for Google Chrome.

• So I downloaded and installed the official Google Chrome .deb native package directly from the Google website.

• And then it worked!!! 🤘

• Keychron Launcher connected to the keyboard, I could do the Firmware update and started playing with remapping keys.

My Final Checklist

So, as final checklist, these are the steps to take if I want to remap or update firmware on my Keychron keyboard :

Preparation of udev.rules (needs to be done only once):

1. Identify keyboard's vendor/product information using : lsusb | grep -i keychron

2. Create rule with: sudo nano /etc/udev/rules.d/99-keychron.rules

3. Add these lines to the rules: # Keychron V6 Ultra 8K - Normal Mode KERNEL=="hidraw\*", SUBSYSTEM=="hidraw", ATTRS{idVendor}=="3434", ATTRS{idProduct}=="0c60", MODE="0666", TAG+="uaccess" # STM32 Bootloader - Required for Firmware Flashing SUBSYSTEM=="usb", ATTRS{idVendor}=="3434", ATTRS{idProduct}=="0c60", MODE="0666", TAG+="uaccess"\

4. Save and exit (Ctrl+O, Enter, Ctrl+X)

5. Then run these commands to activate the new rules: sudo udevadm control --reload-rules sudo udevadm trigger

6. Disconnect/Connect keyboard.

Run Keychron Launcher

1. Connect the keyboard with the cable
2. On the keyboard itself, select the physical toggle to USB connection
3. Open Google Chrome (not Chromium, make sure it is the .deb version of Google Chrome, not Snap)
4. Go to https://launcher.keychron.com/
5. Choose to connect the keyboard, and voilà!

#linux #tech

Experiencing people leaving an organization that are part of your peer group is never fun. This is especially true when you recognize that the person leaving created a sense of balance on the team that was much needed. Once they are gone, that balance will be thrown off again, decisions the person made will be called into question, and there will be a lot of anxiety on the part of their team.

Sadly, this is the situation that me and our organization find ourselves in now. With a new CEO on-board within the last six months, this is completely unknown territory that we are entering. None of us have any idea how the hiring process is going to go to replace this person. We don’t know if leadership will care about finding someone that integrates well with the rest of the team or if they will intentionally look to bring in a more disruptive force to shake things up. the organization has been through significant change over the past year, much of it positive, yet it is still anxiety inducing.

Now we wait to see what comes next. Time will tell if this change will be positive or if the organization is going to suffer because of it.

From first principles: AI and Capitalism

You’re probably caught in a bit of confusion. You know AI is powerful. You know it will change everything. But you’ve tried to use it in your day-to-day life and found a false promise was somewhere introduced. It hasn’t made your job significantly easier. It gives advice you can’t always trust. You aren’t sure how it’s supposed to actually fit into your, or anyone’s life, let alone be such an omnipotent threat or savior as to radically alter the fate of humanity. Are you crazy?

On the contrary. If you pay attention to the contradictions you notice in the reality vs. the perception of GenAI, you can use this case as a vaccine, to inoculate your thinking against the lies that capitalism routinely parrots in order to convince you of its worth and necessity. Let’s hold up the mirror.

AI is a perfect reflection of capitalism itself.

1. Economics is a social construction to solve a social problem (how to value transactions – not how to deal with scarcity. Orthodox economics clearly doesn’t “deal” with scarcity in any way, especially natural scarcity; it's neatly externalized in order to obscure the real decisions made, politically and socially, about who does and doesn't deserve resources).

2. Capitalism nominates a class of people who are value-deciders (owner class, now investor class) and, through business relationships between one another and a dialectic between that class and the working class (the non-owner, non-investor class), value is decided.

3. Capitalism’s value-deciders are the bourgeois, those who own capital. Traditionally capital was the means of production, i.e., the buildings and machines and land that created products which were sold for a profit. This class of owners were able to decide the value of those products among other owners based on their incentive to sell. But they are also able to decide the value of the labor that helps create the products by virtue of their willingness to buy. – Willingness to sell and willingness to buy are also subject to social creation in addition to material constraints. (Ads, psychology, the social distribution of the things needed to live, inflation, colonialism, etc.)

4. But capitalism has a major internal contradiction: because owners are not exposed to much risk, there’s not much constraint on available wealth – capitalism tends to monopolize. But it must have the appearance of being competitive or it will lead to unchecked inflation and the collapse of value. To solve this social challenge, capitalism seeks unlimited growth from its investments. Investments that fail to grow fail existentially and must be stripped for parts. This maintains pressure and participation in the economy. – But the failure only extends to the business and the workers. It does not extend to the owners – again, see the point that they are not exposed to risk.

5. Because growth is merely a social construction to solve the social problem of not enough risk exposure for wealth accumulators, it is essentially an illusion and can be endlessly gamed by those who are considered value-deciders, but only if it maintains the illusion of value coming from growth, from something “real” like scarcity or demand.

6. This tendency leads capitalism to abstraction, or “going meta” (Survival of the Richest). As “growth” in sectors is conquered by other owners or by an increasing concentration among the same owners, the need to demonstrate more growth (and therefore the validity of capitalism as a social enterprise) leads to the creation of levels of abstraction upon the original transaction (i.e., the original valuation – a bet on the 49ers to win the Super Bowl, upon which a surprising amount of abstraction can be layered: The stock price of the gambling company, the bets against the stock price of the gambling company, the mortgage owned by the better, the bets against that mortgage defaulting, etc. etc. etc.; not to mention the value of the stock of the 49ers, the Super Bowl ad space, ad nauseam).

7. Therefore, capitalism is an economic system organized by a class of owner-value-deciders who must consistently achieve the perception of growth. Since growth tied to physical scarcity will quickly exhaust itself and make the internal contradiction clear, their chief mode of growth is abstraction, where a new arena of value-determinations can be made.

8. Some initial value under capitalism is determined by a “market” via transactions: The creation of a product or service that is then sold.

9. But much of the value-determination under capitalism is facilitated through bets, placed through the stock market, or now through prediction markets; or in the holding of property; or in any accumulation of a certain capital.

10. Though the final payment of the bet is zero-sum, for both the arbiter of the bet and the outcome on which bets are placed, hype creates value (for the arbiter, on the cut; for the outcome, on the temporary infusion of capital which can be used to purchase value elsewhere and is not due back, since it’s the responsibility of the losers). – Also, bet-takers can hedge their overall investment in the bet to effectively “both sides” the bet while reaping real wealth from the benefits of owning bets (tax evasion, other benefits of being wealthy conferred by regulatory capture)

11. Therefore, hype – the perception of value whether there “is” or “isn’t”, whether it’s a “good” bet or not – creates real wealth under capitalism.

12. This is explains the AI tech bubble but it also explains why companies seem to legitimately think AI will improve their business outcomes: it is the perception of the offloading of work. And that’s why it DOES create value, at least among publicly-traded companies that are able to convince shareholders (betters) that the adoption of AI is valuable. Just the perception of being able to reduce labor costs or otherwise innovate creates real wealth. And because it is a bet, the value of the bet is largely determined by hype.

13. Similarly, the value or innovation created by AI itself, as in your evaluation of its output, is also determined by hype: by your ability or willingness to believe that its output is human, or super-human. It creates nothing but a perception. It is literally a machine that creates perceptions that are likely to be believable.

14. It’s basically the endgame capitalist technology.

Thanks for listening.

~

I never thought I would get married. I never thought I would be looking to buy a house with someone. Yet, here I am doing both. It feels incredible, wonderful, and a bit scary, mostly on the buying-a-house part due to age rather than anything else.

Falling in love and getting hitched was never in my thoughts because of my lifestyle, mostly nomadic. People come and go in my life. They don’t stick around. Part of it is living overseas. Part of it is just my nature. It is something I accepted as part of my path until it changed a few years ago.

I met the love of my life – the one who changed me. The one who shaped how I would love many years ago. It began with a clear end – he would move to the United States at some point. We would enjoy our time together and see things, but there would be an unknown end date. In the early years of that relationship, we talked about being together forever, but there would be awkward pauses, so we dropped the topic and enjoyed our time. It ended as expected, and I was hurt. I fell for another, but quickly saw that the future there wasn't going to happen because of timing.

Then I met him with no expectations, no hopes for the future, only to enjoy being with him. We saw each other a lot, then more. We travelled and learned more about each other. There was safety and security as we grew together. It was love, and I felt it for a while, but this feeling or fear – “he will leave me” was still there even though there were no signs or anything, but the thought was there.

He came home with me last year to meet my mom and see my childhood home. He saw the place where I grew the most – Korea, where I spent 7 years. In return, I got to know him more and liked what I saw and what I learned. We grew together and began seeing how lucky I am to have him in my life, and we wanted to build a future together.

The thought has always been there. The talks have always been there. Until we talked last night. He moved in fully near the beginning of the year and has enjoyed it a lot. We have been looking for apartments to build, which is a huge step. Then I turned to him, and we talked, never sure how to 'do it right.' So I asked, “Do you wanna?” and he said, “Sure.” We were joking, but we weren’t. I am lucky beyond words and looking forward to many, many years ahead.

In Summary: * Another quiet Sunday ends well. The San Antonio Spurs win over the Portland Trail Blazers this afternoon was MOST enjoyable. The only things remaining between now and bedtime are my night prayers, and I intend to start on them soon.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Starting Ash Wednesday, 2026, I've added this daily prayer as part of the Prayer Crusade Preceding the 2026 SSPX Episcopal Consecrations.

Health Metrics: * bw= 231.92 lbs. * bp= 151/91 (67)

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

Diet: * 07:10 – 1 big cookie, 1 banana * 08:30 – 1 ham and cheese sandwich * 10:00 – candied bananas * 12:50 – garden salad * 13:45 – bowl of pancit * 15:30 – 1 big cookie * 16:15 – 1 fresh apple

Activities, Chores, etc.: * 07:20 – bank accounts activity monitored. * 07:40 – read, write, pray, follow news reports from various sources, surf the socials, nap. * 12:20 – listening to the pregame show of this afternoon's Detroit Tigers vs Cincinnati Reds on the Reds Radio Network * 14:00 – now listening to the pregame show ahead of today's San Antonio Spurs vs Portland Trail Blazers game * 14:40 – and... the Spurs Game is starting. * 17:20 – and ... Spurs win 114 to 93.

Chess: * 11:00 – moved in all pending CC games, registered for another “3 days per move CC tournament” with games starting 01 May