Cómo usar ChatGPT para detectar errores ocultos en Windows
Cuando Windows se congela, se reinicia solo o una aplicación se cierra sin avisar, casi todos acudimos a Google para conocer el motivo. Lo que no sabías es que tu ordenador lleva un registro detallado de cada fallo o advertencia y lo guarda en una herramienta que viene instalada por defecto en el sistema operativo.
Cuando Windows se congela, se reinicia solo o una aplicación se cierra sin avisar, casi todos acudimos a Google para conocer el motivo. Lo que no sabías es que tu ordenador lleva un registro detallado de cada fallo o advertencia y lo guarda en una herramienta que viene instalada por defecto en el sistema operativo.
Seguir leyendo: Cómo usar ChatGPT para detectar errores ocultos en Windows
En contexto
- Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Fuente: Hipertextual
- Publicado: 12/10/2026
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Why it matters
Every week there is an announcement that promises to change everything, and every week most organisations are still fighting the same battle: messy data, processes nobody documented, and expectations running faster than capability. This story reads better with that in the background.
I always separate two things that get mixed up: the demo and the operation. A demo needs to work once with someone watching. An operation needs to work a thousand times with nobody watching, with odd cases, dirty data, at two in the morning. The gap between the two is where the budget goes.
What usually goes wrong
The mistake I have seen repeat most is measuring nothing before starting. It gets deployed, everybody feels things improved, and when somebody asks by how much, there is no answer. Without a baseline there is no way to defend next year's budget, and that is where good projects die.
What to watch
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
- Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
- Where the data ends up and under what contract, especially when customer information is involved.
How I read this entry
If I had to bring this into an organisation, I would start with a boring, measurable process — reconciliation, first-line support, document review — and get it running with a metric before touching anything flashy. The credibility for big projects is bought with a small result nobody can argue with.
The original story is published in another language; the excerpt is quoted as the publisher delivers it and the commentary is written in English.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
