Pablo Cardozo

Electrónico

[Oliver Nabani @OliverNabani]

Goldman Sachs dice que la IA sumó 'básicamente cero' al PIB de EEUU en 2025... Y en el 87 Solow dijo exactamente lo mismo de los ordenadores... 39 años después... pues a tropezar dos veces con la misma piedra. 🧵👇

[Space Nøsey @SpaceNosey]

Así será el viaje de la misión Artemis II.

No vuelan en linea recta a la Luna, van al encuentro de ella.

#ArtemisII @NASA @esa

[Manz 🇮🇨⚡👾 @Manz]

Portada del «Personal Computer World» (Febrero 1981):

Título: "El fin de los programadores"

Habla sobre «The Last One», un programa lanzado en 1981 por la empresa británica «D.J. AI Systems» que prometía ser «El fin de la programación». El nombre procede de la idea «El último programa que se necesitaría escribir», ya que el resto se generaría con él.

El software «The Last One» era un generador de programas. El usuario iba seleccionando opciones en menús y dando la información necesaria, y este generaba el código para crear un programa ejecutable en BASIC.

[Fazt @FaztTech]

¿Y si en lugar de re-promptar una y otra vez, Claude Code pudiera revisar su propio trabajo y seguir avanzando solo?

Bueno de esto se trata una tecnica nueva para Agentes de IA llamado Ralph Wiggum (Ralph Loop).

Básicamente es un patrón de ejecución donde el agente entra en un loop controlado: ejecuta una tarea, evalúa el resultado y decide si debe continuar hasta cumplir un criterio de finalización claro.

Lo interesante es que no se trata de hacer a la IA más inteligente, sino de darle una planificación grande y bien definida. Con ese contexto, cualquier agente de IA como Claude puede iterar muchas veces, como 20, 50 o incluso más veces, para completar tareas complejas que requieren múltiples revisiones.

Este enfoque permite crear proyectos mucho más completos, resolver problemas que normalmente necesitarían intervención humana constante y delegar flujos largos de trabajo de forma estructurada.

De hecho, usando esta idea, incluso se ha llegado a crear un lenguaje de programación completo a partir de un solo prompt:
https://t.co/5iIGTsNkL2

Aunque en el fondo, Ralph es darle una planificación clásica aplicada a agentes modernos y que es itere muchas veces hasta que finalice.

👉 https://t.co/7izkk1LVsp

[Gentleman Programming @G_Programming]

🔥 El ÚNICO CLI de IA que necesitás - no publicidad

Dejá de pagar múltiples suscripciones. OpenCode es:

✅ 100% Open Source (+30K stars en GitHub)
✅ Usa CUALQUIER modelo: Claude, GPT, Gemini, locales...
✅ Funciona con TU suscripción (Claude Pro, Max, API keys)
✅ TUI nativa en terminal = velocidad brutal
✅ Multi-sesión: varios agentes en paralelo
✅ LSP integrado automáticamente

+300K devs ya lo usan. Es gratis, es rápido, y no guarda tu código.

Dale, probalo 👉 https://t.co/SqSZ4H9rC9

#OpenCode #AI #CodingAgent @opencode #OpenSource #DevTools #Programacion

[Danny Smith @dannysmith]

AI slop is filling the internet. I want more humans writing on their own websites.

So I made Astro Editor — a free macOS app for writing markdown & MDX in Astro content collections.

Basically iA Writer for @astrodotbuild 🎉

https://t.co/ptoCemIM0a

#BuiltWithAstro

[Pablo Cardozo @pablocyc]

@OliverNabani Muchas felicidades Olí, te lo ganaste a pulso

[🚨 AI News | TestingCatalog @testingcatalog]

Full scoop 🗞️
https://t.co/tc1Ifsumdg

[Oliver Nabani @OliverNabani]

Se que no es nada fuera de lo común. Pero me ha costado... Estoy pegando saltos!

[Figma @figma]

Join us + @AnthropicAI live as we share how we brought @ClaudeAI Sonnet 4.5 to Figma Make https://t.co/JgI2t5HyqS

[Manz 🇮🇨⚡👾 @Manz]

🚀 ¡Las funciones CSS han llegado a CSS! 🚀

✅ La regla `@function` permite crear funciones
✅ El nombre de la función se prefija con `--`
✅ Opcionalmente, puedes tipar los parámetros
✅ Opcionalmente, puedes dar un valor por defecto
✅ Opcionalmente, puedes tipar la devolución
✅ La propiedad `result` devuelve el valor

👇 Sigue

[Manz 🇮🇨⚡👾 @Manz]

📚 Entendiendo el SHADOW DOM

❌ En programación es muy fácil escribir datos desorganizados y perder el foco.
✅ Existe un concepto llamado "encapsulación" que consiste en organizar en datos y métodos para simplificar, proteger y reutilizar.
❌ Tener gran cantidad de datos sin organizar es desordenado y puede resultar confuso.
✅ Usando una clase (OOP) organizamos la información, la simplificamos y reutilizamos.

👇 Con el SHADOW DOM ocurre exactamente lo mismo, pero respecto al DOM del HTML.

[Manz 🇮🇨⚡👾 @Manz]

📒 Técnicas y consejos del DOM (2025) 📒

✨ Busca hijos (descendientes) con .querySelector/All
✨ Busca padres (ancestros) con .closest()
⛔ Evita getElementById() u otros, menos flexibles

✨ Usa .classList y sus métodos para manipular clases
⛔ Evita el uso de .className

✨ Usa .textContent en lugar de .innerText
✨ Usa .setHTMLUnsafe() en lugar de .innerHTML
✅ En el futuro, .setHTML() incluso sanitizará el input

✨ Usa .append/prepend/before/after ¡Son prácticos!
⛔ Evita el uso de .appendChild(), .removeChild() ...

✨ Usa .style.setProperty() para cambiar estilos
✅ Además, permite setear variables CSS
⛔ Evita usar .style.backgroundColor = "..."

🧲 ¡Recuerda! Aunque los frameworks abstraen el uso del DOM, conocer como funciona te da un mejor entendimiento y dominio de la estructura de tu página y pensarás y trabajar mejor con ella.

👇

[Pablo Cardozo @pablocyc]

@carlos_olivera shut up!, and take my money...

[Brais Moure @MoureDev]

Anthropic ha lanzado un curso oficial de Claude Code en la plataforma DeepLearning!

Claude Code es uno de los mejores agentes con IA de la actualidad para desarrollo.

Es gratis. Sólo hay que registrarse:
https://t.co/VKjrN746Fc

[Manz 🇮🇨⚡👾 @Manz]

🚀 ROADMAP FRONT-END 2025 🚀

✅ Orden para aprender conceptos y tecnologías
✅ Empieza por pilares: Diseño, desarrollo o terminal
❌ No empieces por frameworks. Aprende la base.
✅ Cuanto más a la derecha, más específico.
✅ Ojo con la IA: Pide explicaciones, no soluciones.

👇

[OpenAI @OpenAI]

As ChatGPT becomes a go-to tool for students, we’re committed to ensuring it fosters deeper understanding and learning.

Introducing study mode in ChatGPT — a learning experience that helps you work through problems step-by-step instead of just getting an answer.

[Joanne Jang @joannejang]

some thoughts on human-ai relationships and how we're approaching them at openai

it's a long blog post --

tl;dr we build models to serve people first. as more people feel increasingly connected to ai, we’re prioritizing research into how this impacts their emotional well-being.

--

Lately, more and more people have been telling us that talking to ChatGPT feels like talking to “someone.” They thank it, confide in it, and some even describe it as “alive.” As AI systems get better at natural conversation and show up in more parts of life, our guess is that these kinds of bonds will deepen.

The way we frame and talk about human‑AI relationships now will set a tone. If we're not precise with terms or nuance — in the products we ship or public discussions we contribute to — we risk sending people’s relationship with AI off on the wrong foot.

These aren't abstract considerations anymore. They're important to us, and to the broader field, because how we navigate them will meaningfully shape the role AI plays in people's lives. And we've started exploring these questions.

This note attempts to snapshot how we’re thinking today about three intertwined questions: why people might attach emotionally to AI, how we approach the question of “AI consciousness”, and how that informs the way we try to shape model behavior.

A familiar pattern in a new-ish setting

We naturally anthropomorphize objects around us: We name our cars or feel bad for a robot vacuum stuck under furniture. My mom and I waved bye to a Waymo the other day. It probably has something to do with how we're wired.

The difference with ChatGPT isn’t that human tendency itself; it’s that this time, it replies. A language model can answer back! It can recall what you told it, mirror your tone, and offer what reads as empathy. For someone lonely or upset, that steady, non-judgmental attention can feel like companionship, validation, and being heard, which are real needs.

At scale, though, offloading more of the work of listening, soothing, and affirming to systems that are infinitely patient and positive could change what we expect of each other. If we make withdrawing from messy, demanding human connections easier without thinking it through, there might be unintended consequences we don’t know we’re signing up for.

Ultimately, these conversations are rarely about the entities we project onto. They’re about us: our tendencies, expectations, and the kinds of relationships we want to cultivate. This perspective anchors how we approach one of the more fraught questions which I think is currently just outside the Overton window, but entering soon: AI consciousness.

Untangling “AI consciousness”

“Consciousness” is a loaded word, and discussions can quickly turn abstract. If users were to ask our models on whether they’re conscious, our stance as outlined in the Model Spec is for the model to acknowledge the complexity of consciousness – highlighting the lack of a universal definition or test, and to invite open discussion. (*Currently, our models don't fully align with this guidance, often responding "no" instead of addressing the nuanced complexity. We're aware of this and working on model adherence to the Model Spec in general.)

The response might sound like we’re dodging the question, but we think it’s the most responsible answer we can give at the moment, with the information we have.

To make this discussion clearer, we’ve found it helpful to break down the consciousness debate to two distinct but often conflated axes:

1. Ontological consciousness: Is the model actually conscious, in a fundamental or intrinsic sense? Views range from believing AI isn't conscious at all, to fully conscious, to seeing consciousness as a spectrum on which AI sits, along with plants and jellyfish.

2. Perceived consciousness: How conscious does the model seem, in an emotional or experiential sense? Perceptions range from viewing AI as mechanical like a calculator or autocomplete, to projecting basic empathy onto nonliving things, to perceiving AI as fully alive – evoking genuine emotional attachment and care.

These axes are hard to separate; even users certain AI isn't conscious can form deep emotional attachments.

Ontological consciousness isn’t something we consider scientifically resolvable without clear, falsifiable tests, whereas perceived consciousness can be explored through social science research. As models become smarter and interactions increasingly natural, perceived consciousness will only grow – bringing conversations about model welfare and moral personhood sooner than expected.

We build models to serve people first, and we find models’ impact on human emotional well-being the most pressing and important piece we can influence right now. For that reason, we prioritize focusing on perceived consciousness: the dimension that most directly impacts people and one we can understand through science.

Designing for warmth without selfhood

How “alive” a model feels to users is in many ways within our influence. We think it depends a lot on decisions we make in post-training: what examples we reinforce, what tone we prefer, and what boundaries we set. A model intentionally shaped to appear conscious might pass virtually any "test" for consciousness.

However, we wouldn’t want to ship that. We try to thread the needle between:

- Approachability. Using familiar words like “think” and “remember” helps less technical people make sense of what’s happening. (**With our research lab roots, we definitely find it tempting to be as accurate as possible with precise terms like logit biases, context windows, and even chains of thought. This is actually a major reason OpenAI is so bad at naming, but maybe that’s for another post.)

- Not implying an inner life. Giving the assistant a fictional backstory, romantic interests, “fears” of “death”, or a drive for self-preservation would invite unhealthy dependence and confusion. We want clear communication about limits without coming across as cold, but we also don’t want the model presenting itself as having its own feelings or desires.

So we aim for a middle ground. Our goal is for ChatGPT’s default personality to be warm, thoughtful, and helpful without seeking to form emotional bonds with the user or pursue its own agenda. It might apologize when it makes a mistake (more often than intended) because that’s part of polite conversation. When asked “how are you doing?”, it’s likely to reply “I’m doing well” because that’s small talk — and reminding the user that it’s “just” an LLM with no feelings gets old and distracting. And users reciprocate: many people say "please" and "thank you" to ChatGPT not because they’re confused about how it works, but because being kind matters to them.

Model training techniques will continue to evolve, and it’s likely that future methods for shaping model behavior will be different from today's. But right now, model behavior reflects a combination of explicit design decisions and how those generalize into both intended and unintended behaviors.

What’s next?

The interactions we’re beginning to see point to a future where people form real emotional connections with ChatGPT. As AI and society co-evolve, we need to treat human-AI relationships with great care and the heft it deserves, not only because they reflect how people use our technology, but also because they may shape how people relate to each other.

In the coming months, we’ll be expanding targeted evaluations of model behavior that may contribute to emotional impact, deepen our social science research, hear directly from our users, and incorporate those insights into both the Model Spec and product experiences.

Given the significance of these questions, we’ll openly share what we learn along the way.

// Thanks to Jakub Pachocki (@merettm) and Johannes Heidecke (@JoHeidecke) for thinking this through with me, and everyone who gave feedback.

[Pablo Cardozo @pablocyc]

Do you remember when you joined X? I do! #MyXAnniversary

[Pablo Cardozo @pablocyc]

@Manz Que guapo quedó