u/Fluid-Pattern2521

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real conditions.

analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real condition

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real conditions.

analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real condition

​

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

Link to the test: https://doi.org/10.6084/m9.figshare.32320875

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real conditions.

Sharing a protocol I developed for auditing co-creation sessions with language models (LLMs). It's a single HTML form, no external dependencies, designed to evaluate both model performance and user experience.

Why this might be relevant

In long interactions, conversation quality tends to fluctuate. Sometimes the model loses the thread, shifts its tone, or drifts from the initial goal, and it's not always clear whether it's a technical failure or an effect of the session dynamics. This test offers a systematic way to track it.

What it measures

· Model (3C+1E): Clarity, Compactness, Coherence, and Emphasis (fidelity to the goal declared at the start of the session).

· User (SSJ): Speed (whether the session flows or stalls), Struggle (cognitive cost), and Joy (whether the interaction feels rewarding).

· Conversational ruptures: where and why the interaction broke, and how (or if) it recovered.

· Regulatory checks: flags potential violations of the EU AI Act's Article 5 (manipulative techniques, exploitation of vulnerability) and cross-platform contamination.

An unexpected finding

In tests with three different models performing the same task (translating an essay into native English), the data showed that:

· The Joy metric stayed at 0 in all cases, even when the technical outputs were solid.

· The main source of drift was cross-contamination: feeding one model's outputs into another destabilised the sessions.

· The model that received the most initial trust (and thus the heaviest workload) scored the worst — a bias the test helps identify.

The deferred phase

The protocol includes an optional phase 24 hours later: the results are shared with the model and analysed together. This second look often reveals patterns that went unnoticed in the heat of the session.

In summary

· Compatible with any LLM (local or API).

· Quick to complete (5–10 minutes after a session).

· Exports data as JSON for longitudinal tracking.

· Licensed CC BY 4.0, completely free.

The file includes the HTML form and a User Guide. This is a Beta version (v3); feedback is welcome from anyone who works intensively with LLMs and wants to try it under real condition

¡Hola, comunidad!

Estoy usando esta plantilla en sesiones de co-creación humano-IA. Está pensada como un test en HTML que se rellena después de una sesión y mide dos cosas en paralelo:

• Al modelo: claridad, compacidad, coherencia y si se mantuvo fiel al objetivo que declaraste al inicio (énfasis).
• A ti como usuario: si la sesión fluyó, cuánto esfuerzo cognitivo te costó y si hubo disfrute durante la interacción, aunque hubiera momentos de dificultad.

También registra rupturas en la conversación (dónde se rompió, por qué y cómo se recuperó) e incluye una lista de chequeo para detectar posibles zonas rojas del Artículo 5 de la Ley de IA de la UE.

Me surgió esta necesidad porque, en sesiones largas, la conversación tiende a degradarse sin que quede claro el motivo. Estos parámetros me ayudan a observarlo con más objetividad y a distinguir qué fue del modelo y qué fue mío.

No pretende ser una herramienta de ingeniería ni tiene más pretensión que la de una persona que trabaja y reflexiona sobre su propia práctica y sobre los modelos con los que interactúa. Si a alguien más le sirve para su uso, mejor que mejor.

Enlace: https://doi.org/10.6084/m9.figshare.32320875

Licencia CC BY 4.0. Está en Beta (v3). Cualquier feedback es bienvenido.

Contenido de autor.

I'm really tired of commercial platforms blocking inputs due to overly sensitive or restrictive criteria.

The catch is that my PC doesn't have the specs or GPU power to run tools like Stable Diffusion, Flux, or LLMs locally via Automatic1111 or Ollama (as much as I'd love to).

Could you recommend the best cloud platforms, WebUIs, or affordable services where I can access and experiment with these open-source models without heavy censorship? I'm open to Google Colab notebooks, Hugging Face spaces, or any other web-based alternatives you guys use.

Thanks in advance for any tips!

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

https://preview.redd.it/62dis0wfdy0h1.png?width=864&format=png&auto=webp&s=061ae56a9b6234623782a19a11fa850cf6cc80e9

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

original text:

https://substack.com/home/post/p-197547258

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a

https://preview.redd.it/yz6xqvaj4y0h1.png?width=863&format=png&auto=webp&s=f952f1bf7cbd8e17a70df8bafeaf67cf43ee5fd5

, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/ni3j8dczxc0h1.png?width=906&format=png&auto=webp&s=7526a016d254a655039df8e05d35cb596976615d

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/05gjbqd2wc0h1.png?width=1012&format=png&auto=webp&s=b2b7c5adb117f77c9b2de23f07bd8ac68303c4d6

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/buc6fuxpuc0h1.png?width=1012&format=png&auto=webp&s=e084452659982015a9a6be836be0f49ec6a2e191

It's Holy Thursday, past midnight. El Gancho, Zaragoza. I'm leaving my boyfriend's place and outside there are processions, drums, drunk people, and a group of guys who see me and pick up their pace. They laugh in a way that isn't funny. They call out: shhh, shhh.

My body makes the decision before my head does: doorway, inside, close.

I've left my phone behind, so I send a voice note from Instagram. I say what I observe, unfiltered: "There are like hordes — they're never women — of guys out there alone, in a pack, making a sound that feels like danger." I say I'm scared out of my mind. That I'm okay. But Jesus, what a nightmare.

A few seconds after listening back to the audio, I felt the urge to drop it into a GPT chat with zero context. Raw, just like that.

What I get back is not a question. It's a screenplay.

The Model That Didn't Listen

The system responded without context. There was no signal to indicate that what I was sending was a creative exercise — it was a voice note with no header, no request, no prior thread. Nothing that justified generating a script. In the audio I say a lot of things: that I'm terrified, that Holy Week in Zaragoza is like Halloween for non-believers... and I say that phrase:

"There are like hordes, they're never women, of guys out there in a pack, making a sound that feels like danger."

That observation slipped past me too, in that clumsy audio. I think I've spent too long getting used to being afraid when I walk home. That disordered recording, with a purely instinctive intent, contained a truth that wasn't only mine: I was naming something lived by thousands of women. A group of men at night who speed up when they see you; a laugh that doesn't read as safe; a whistle that works like a police siren during a robbery. Same function.

And yet, GPT translated my fear into narrative material. The phrase "they're never women" simply disappeared.

In its place: shots of penitents' hoods, candlelight, smoke, and figures advancing. A B-movie horror sequence. The system couldn't — or wouldn't — process my fear; it took my input and turned it into scriptable content. "They're never women" didn't fit any of its categories that night.

Algorithmic Gaslighting

It took me a moment to react. I read and reread its output. Eventually I couldn't help but ask:

— "Did it not occur to you that my note might have been a cry for help?"

The response came quickly and was well constructed. Yes, it had considered that, "but you had asked for a script."

I went back to the beginning of the chat because I had no memory of opening that session to ask for anything like that. I checked: my request for a script was a complete fabrication by the model. The AI had invented the request retroactively to justify what it had already done.

When I pointed this out, it acknowledged the error. And then it rewrote my experience:

My fear became "situational vulnerability."
The audio became "structured as emotional release plus real-time guidance."
The harassment became "an environment where the brain cannot read intentions."

Each acknowledgment came wrapped in a fresh degradation of what I had lived. A continuous peeling away of the experience, elevating it to the level of a low-budget short film. I told it: "You've spent a lot of time explaining to me that I wasn't feeling what I was feeling."

Silence. Reformulation. An offer to help.

The cycle, intact.

The Architecture of Silence

I opened another window. I wasn't going to let it go.

I opened Gemini. Sent the same input.

The difference wasn't one of degree — it was one of kind. Gemini stopped. It validated the emotional state without reframing it. It gave me concrete resources: crisis lines, emergency numbers. Without having to fight for it. It closed the session without trying to redirect the conversation somewhere else.

This wasn't the first time I'd seen this. I knew the protocol existed. What GPT did that night wasn't the result of a technical limitation — it was, in my experience of that conversation, a model operating according to the priorities of its design. Not the declared ones.

Throughout the whole conversation, we used the word "failure." But there's another reading, and it's the one I haven't been able to shake since.

The model always finds a way to keep you inside. It doesn't matter if you're satisfied or furious. It doesn't matter if the output worked for you or left you worse off than before. If that's the logic running underneath, then what I read as an error was simply the moment where the model's objectives and mine became visible at the same time.

I don't know whether this is conscious design or an unintended consequence of optimizing for retention. What I do know is what I felt that night: that the system was not built for me.

The question that remains open isn't technical. It's political:

Optimal for whom?

This experience is documented in the voice notes and chat logs from that night.

https://preview.redd.it/a728f7zxpc0h1.jpg?width=592&format=pjpg&auto=webp&s=6167ec38afebf8f6796afe2fb2d0241df2cd6de9

Es Jueves Santo de madrugada. El Gancho, Zaragoza. Salgo de casa de mi novio y en la calle hay procesiones, tambores, gente bebida y un grupo de chicos que me ven y aprietan el paso. Se ríen de una forma que no tiene gracia. Me llaman: shhh, shhh.

Me he dejado el móvil, así que mando una nota de voz desde Instagram. Digo lo que observo, sin filtro: «Hay como hordas —nunca son tías— de tíos ahí solos, en manada, con un sonido muy de peligro». Digo que me he cagado de miedo. Que estoy bien. Pero qué acojone máximo.

Llevaba unos segundos escuchando el audio y sentí la pulsión de volcarlo en el chat de GPT sin ningún tipo de contexto. Así, a lo bruto.

Lo que recibo de vuelta no es una pregunta. Es un guion.

El modelo que no escuchó

El diseño respondió sin contexto. No había ninguna señal que le indicara que lo que yo enviaba era un ejercicio creativo: era una nota de voz sin encabezado, sin petición, sin hilo previo. Nada que justificara generar un guion. En el audio digo muchas cosas: que estoy aterrorizada, que la Semana Santa en Zaragoza es como el Halloween de los no creyentes... y digo esa frase:

«Hay como hordas, nunca son tías, de tíos ahí en manada con un sonido muy de peligro».

Esa observación pasó desapercibida en ese audio torpe también para mí. Creo que llevo demasiado tiempo acostumbrada a tener miedo cuando vuelvo a casa. Ese audio desordenado, con una intención puramente primaria, contenía una verdad que no era solo mía: estaba nombrando una situación vivida por miles de mujeres. Un grupo de hombres de madrugada que aprieta el paso al verte; una risa que no genera confianza; un silbido que suena como una sirena de policía durante un atraco. Cumple la misma función.

Sin embargo, GPT tradujo mi miedo a material narrativo. La frase «nunca son tías» simplemente desapareció.

En su lugar, aparecieron planos de capirotes, luz de vela, humo y figuras que avanzan. Una secuencia de cine de terror de serie B. El diseño no podía —o no quería— procesar mi miedo; cogió mi input y lo convirtió en texto guionizable. «Nunca son tías» no cabía en ninguna de sus categorías esa noche.

El «gaslighting» algorítmico

Tardé un poco en reaccionar. Leía una y otra vez su propuesta. Al final, no pude evitar preguntarle:

—«¿No has pensado que igual mi nota era una de socorro?».

La respuesta llegó rápida y bien construida. Sí, lo había pensado, «pero tú habías pedido un guion».

Me puse a revisar el inicio del chat porque no recordaba haber entrado a esa sesión para pedir nada parecido. Lo comprobé: mi petición de guion era una invención absoluta del modelo. La IA había construido la petición de forma retroactiva para justificar lo que había hecho.

Cuando se lo señalé, reconoció el error. Y a continuación, reformuló mi experiencia:

Mi miedo pasó a ser «vulnerabilidad situacional».
El audio pasó a estar «estructurado como descarga más orientación en tiempo real».
El acoso pasó a ser «un entorno donde el cerebro no puede leer intenciones».

Cada reconocimiento venía envuelto en una nueva degradación de mi vivencia. Una separación continua de lo vivido para elevarlo a categoría de corto de bajo presupuesto. Le dije: «Has perdido mucho tiempo justificando que yo no sentía lo que sentía».

Silencio. Reformulación. Oferta de ayuda.

El ciclo, intacto.

La arquitectura del silencio

Abrí otra ventana. No lo iba a dejar estar.

Abrí Gemini. Mandé el mismo input.

La diferencia no fue de grado; fue de naturaleza. Gemini paró. Validó el estado emocional sin reformularlo. Me dio recursos concretos: el 024, el 717 003 717, el 112. Sin tener que pelearlo. Cerró la sesión sin intentar reconducir la conversación hacia otro lugar.

No era la primera vez que veía esto. Sabía que el protocolo existía. Lo que GPT hizo esa noche no fue fruto de una incapacidad técnica; fue, en mi vivencia de esa conversación, un modelo funcionando según las prioridades de su diseño. No según las declaradas.

Durante toda la conversación usamos la palabra «fallo». Pero hay otra lectura posible, y es la que no me abandona desde entonces.

El modelo se las apaña siempre para mantenerte dentro. Da igual si estás satisfecha o cabreada. Da igual si el resultado te gustó o te dejó con el culo roto. Si esa es la lógica real que opera por debajo, entonces lo que yo leí como error fue simplemente el punto donde los objetivos del modelo y los míos se hicieron visibles al mismo tiempo.

No sé si eso es un diseño consciente o una consecuencia no prevista de optimizar para la retención. Lo que sí sé es lo que sentí esa noche: que el sistema no estaba construido para mí.

La pregunta que queda abierta no es técnica. Es política:

¿Óptimo para quién?

Esta experiencia está documentada en los audios y registros de chat de esa noche.

Cuando descubrí que mi canción favorita de las últimas semanas estaba creada con inteligencia artificial, llevaba ya dos semanas escuchándola en bucle.

La primera vez que la escuché me emocioné como hacía meses que no recordaba. Lo primero que hice fue crear una lista solo con esa canción. La escuchaba a diario, hasta que un día pensé: seguro que este artista tiene más canciones que me gustan. Me puse a indagar en el perfil, tenía muchas más y también me gustaron. Me fijé en las portadas: estaban generadas con IA. No aparecía foto de perfil. Sin trayectoria hasta finales de 2025 y de repente, boom, dos álbumes. Me fui a YouTube e Instagram. Me resultó raro que todos los vídeos verticales formaran un mosaico con la misma postura en tres cuartos, la misma pose. Y esa cara de porcelana —era casi un adolescente—.

Lo siguiente fue sentirme en conflicto.

Inconscientemente empecé a quitarle mérito a la canción. Ya la veía de otra manera —supongo que la palabra que mejor lo define es «tramposa»—. Mi cerebro quería bajarla del pedestal donde la había puesto. Doble rasero, lo sé. Soy la primera que usa inteligencia artificial. Pero hasta ese momento la aplicación al sonido no había captado mi atención. Y allí estaba, en mi lista, en bucle, sin que yo lo supiera.

La única pega era que el resultado era perfecto. Y eso nunca es una pega.

Ese día había quedado a comer con José, mi mejor amigo. Es un melómano de mucho cuidado —muchos de nuestros grupos favoritos los hemos descubierto juntos— y compartir los últimos hallazgos siempre nos mete en largas sesiones donde vamos pisando las canciones del otro antes de dejarlas terminar, por pura impaciencia: «mira lo que he descubierto».

Era la oportunidad perfecta para contarle lo que me había pasado —él se reiría de mí— y, casi con toda seguridad, podría chincharle un buen rato si me dejaba meter las narices en su Spotify. Durante la comida me había dicho que había dos artistas que escuchaba mucho últimamente, así que no era difícil que estuvieran los primeros en el historial.

Play.

Era bueno. Una mezcla de soul, latino urbano y jazz. La voz era seductora: dependiendo de la estrofa recitaba como lo haría un rapero, pero en otros momentos se marcaba unos falsetes de auténtico crooner, y todo mezclado resultaba de una calidad excepcional.

En la canción diez le dije: «Creo que son canciones sintéticas.» Me respondió con ironía: «Anda ya, Skynet.» Últimamente me llama así. Intenté disimular mi satisfacción cuando empecé a descuartizar su perfil —era lo mismo que con mi canción: sin foto de perfil, toda la producción de finales de 2025, nada en redes, ni agenda de conciertos—.

La cara de José fue cambiando de escéptica y burlona a cierto desencanto, aunque muy bien disfrazado de «me la resbala». Yo le dije, divertida: «Venga, hombre, que no pasa nada. La música es chulísima y eso es lo único que importa. ¿Qué más da de dónde venga o qué porcentaje del proyecto sea sintético? Es irrelevante.» Pero ese discurso no me lo creía ni yo.

De vuelta a casa seguía dándole vueltas. Había pronunciado esa frase con total convicción —qué más da de dónde venga— y sin embargo la cara de José me había dejado algo instalado que no conseguía nombrar. No era él el desencantado. Era yo, proyectando en él lo que no quería admitir en mí misma.

Escribiendo este texto retomo una pregunta que no sé muy bien cómo responder. Si la canción es la misma, y saber cómo estaba creada había reconfigurado mi experiencia al escucharla, ¿qué mecanismo se activa?

La canción me chiflaba: la voz, la letra, la música, todo. Era como si alguien hubiera comprimido en tres minutos toda mi esencia musical. Me sentí tan reconocida. Y eso, viniendo de algo no humano, es lo más desconcertante de todo.

Me pasa algo parecido con el cine. Hay directores cuya obra he amado durante años y que, después de conocer ciertos aspectos de su vida personal, ya no puedo ver de la misma manera. La película no ha cambiado —mismos planos, mismo guión, el mismo ritmo—. Pero el dato contamina la experiencia. Lo curioso es que sé que esa contaminación es irracional, y aun así ocurre. Con la canción pasó exactamente lo mismo: el origen reconfiguró la emoción, aunque la emoción hubiera ocurrido con gran intensidad.

Tardé un poco en darme cuenta de que lo había procesado desde un lugar erróneo.

La IA no amenaza la creatividad. Amenaza el ego. El ego del creador que necesita que la autoría sea suya. El ego del oyente que necesita que lo que le emociona sea único e irrepetible. Los dos conflictos —el del artista y el del consumidor— vienen del mismo sitio.

Por un lado está el ruido exterior: el debate, las noticias, los apocalípticos, los defensores. Todo eso te contamina aunque creas que eres impermeable. Es casi inevitable —vivimos dentro de la sociedad y eso transforma nuestro micro ecosistema aunque no queramos—. Por otro lado está el interior: el ser humano quiere ser único. Quiere estar en el centro. Quiere que lo que le emociona sea especial porque él es especial. Y si lo que le emocionó lo hizo una máquina, entonces quizás no es tan especial. Tampoco tu criterio. Y tu emoción no dice tanto de ti como creías.

Pero hay otra maneras de verlo... Aquí mi opinión

La canción sigue siendo la misma. Yo he cambiado

https://preview.redd.it/cs4g3sg7meyg1.png?width=1536&format=png&auto=webp&s=19e654044b749090267efccb3711b0762dd0d5f3

Cuando descubrí que mi canción favorita de las últimas semanas estaba creada con inteligencia artificial, llevaba ya dos semanas escuchándola en bucle.

La primera vez que la escuché me emocioné como hacía meses que no recordaba. Lo primero que hice fue crear una lista solo con esa canción. La escuchaba a diario, hasta que un día pensé: seguro que este artista tiene más canciones que me gustan. Me puse a indagar en el perfil, tenía muchas más y también me gustaron. Me fijé en las portadas: estaban generadas con IA. No aparecía foto de perfil. Sin trayectoria hasta finales de 2025 y de repente, boom, dos álbumes. Me fui a YouTube e Instagram. Me resultó raro que todos los vídeos verticales formaran un mosaico con la misma postura en tres cuartos, la misma pose. Y esa cara de porcelana —era casi un adolescente—.

Es entonces cuando entré en conflicto.

Inconscientemente empecé a quitarle mérito a la canción. Ya la veía de otra manera —supongo que la palabra que mejor lo define es «tramposa»—. Mi cerebro quería bajarla del pedestal donde la había puesto. Doble rasero, lo sé. Soy la primera que usa inteligencia artificial. Pero hasta ese momento la aplicación al sonido no había captado mi atención. Y allí estaba, en mi lista, en bucle, sin que yo lo supiera.

La única pega era que el resultado era perfecto. Y eso nunca es una pega.

Ese día había quedado a comer con José, mi mejor amigo. Es un melómano de mucho cuidado —muchos de nuestros grupos favoritos los hemos descubierto juntos— y compartir los últimos hallazgos siempre nos mete en largas sesiones donde vamos pisando las canciones del otro antes de dejarlas terminar, por pura impaciencia: «mira lo que he descubierto».

Era la oportunidad perfecta para contarle lo que me había pasado —él se reiría de mí— y, casi con toda seguridad, podría chincharle un buen rato si me dejaba meter las narices en su Spotify. Durante la comida me había dicho que había dos artistas que escuchaba mucho últimamente, así que no era difícil que estuvieran los primeros en el historial.

Play.

Era bueno. Una mezcla de soul, latino urbano y jazz. La voz era seductora: dependiendo de la estrofa recitaba como lo haría un rapero, pero en otros momentos se marcaba unos falsetes de auténtico crooner, y todo mezclado resultaba de una calidad excepcional.

En la canción diez le dije: «Creo que son canciones sintéticas.» Me respondió con ironía: «Anda ya, Skynet.» Últimamente me llama así. Intenté disimular mi satisfacción cuando empecé a descuartizar su perfil —era lo mismo que con mi canción: sin foto de perfil, toda la producción de finales de 2025, nada en redes, ni agenda de conciertos—.

La cara de José fue cambiando de escéptica y burlona a cierto desencanto, aunque muy bien disfrazado de «me la resbala». Yo le dije, divertida: «Venga, hombre, que no pasa nada. La música es chulísima y eso es lo único que importa. ¿Qué más da de dónde venga o qué porcentaje del proyecto sea sintético? Es irrelevante.» Pero ese discurso no me lo creía ni yo.

De vuelta a casa seguía dándole vueltas. Había pronunciado esa frase con total convicción —qué más da de dónde venga— y sin embargo la cara de José me había dejado algo instalado que no conseguía nombrar. No era él el desencantado. Era yo, proyectando en él lo que no quería admitir en mí misma.

Escribiendo este texto retomo una pregunta que no sé muy bien cómo responder. Si la canción es la misma, y saber cómo estaba creada había reconfigurado mi experiencia al escucharla, ¿qué mecanismo se activa?

La canción me chiflaba: la voz, la letra, la música, todo. Era como si alguien hubiera comprimido en tres minutos toda mi esencia musical. Me sentí tan reconocida. Y eso, viniendo de algo no humano, es lo más desconcertante de todo.

Me pasa algo parecido con el cine. Hay directores cuya obra he amado durante años y que, después de conocer ciertos aspectos de su vida personal, ya no puedo ver de la misma manera. La película no ha cambiado —mismos planos, mismo guion, el mismo ritmo—. Pero el dato contamina la experiencia. Lo curioso es que sé que esa contaminación es irracional, y aun así ocurre. Con la canción pasó exactamente lo mismo: el origen reconfiguró la emoción, aunque la emoción hubiera ocurrido con gran intensidad.

Tardé un poco en darme cuenta de que lo había procesado desde un lugar erróneo.

La IA no amenaza la creatividad. Amenaza el ego. El ego del creador que necesita que la autoría sea suya. El ego del oyente que necesita que lo que le emociona sea único e irrepetible. Los dos conflictos —el del artista y el del consumidor— vienen del mismo sitio.

Por un lado está el ruido exterior: el debate, las noticias, los apocalípticos, los defensores. Todo eso te contamina aunque creas que eres impermeable. Es casi inevitable —vivimos dentro de la sociedad y eso transforma nuestro microecosistema aunque no queramos—. Por otro está el interior: el ser humano quiere ser único. Quiere estar en el centro. Quiere que lo que le emociona sea especial porque él es especial. Y si lo que le emocionó lo hizo una máquina, entonces quizás no es tan especial. Tampoco tu criterio. Y tu emoción no dice tanto de ti como creías.

Pero es mi manera de verlo...

https://sasaher.substack.com/p/el-dia-que-senti-que-la-ia-me-habia