Fco. Sanchez Cid

Llevo 8 años dando clase en Bachillerato🏫 La pregunta que más me hacen mis alumnos: "¿Qué carrera tiene más salida?" La pregunta que deberían hacerse: "¿Qué pasa realmente 4 años después de graduarse?" Son preguntas distintas. Y la respuesta a la segunda cambia muchas cosas🧵

Mark Zuckerberg just described the death of human connection on the internet and no one flinched. One sentence. Fifteen years of erosion in twelve words. Mark Zuckerberg: “Social media started out as people primarily interacting with their friends. And now… at least half of the content is basically people interacting with creators.” You used to open your phone to see what your friends were doing. Now you open it to watch strangers. You did not choose this. The algorithm chose it for you. It tested your friends against optimized strangers. Your friends lost. Every time. A stranger with better lighting, better timing, and a better hook held your attention three seconds longer than someone who loves you. So the algorithm buried your best friend’s wedding photos under a cooking video from someone in Dubai you have never met. And you watched the cooking video. That was the first replacement. Friends for strangers. You barely noticed. The second one is already underway. If the algorithm already proved strangers outperform your real relationships, and AI can now build a stranger more engaging than any human alive, the math finishes itself. The AI does not have a bad week. It does not post something careless and lose the algorithm’s favor. It does not burn out. Every word calibrated. Every frame tuned. Every pause placed at the exact interval that keeps your thumb from moving. A human creator competing against that is carving stone tablets in a world that just built the printing press. The economics are not even close. A person needs rent, sleep, and motivation. The machine needs electricity. When the cost of generating perfect content hits zero, the feed fills with faces that do not exist. Voices that feel familiar. Opinions that mirror yours just enough to feel like trust. Personalities built from scratch to feel like someone you have known for years. You will not know when the switch happens. That is the point. The feed does not care whether the thing holding your attention has a pulse. It cares whether you stay. And a machine that knows your patterns better than you know yourself will always keep you longer than a person ever could. This is not a warning. Half of it already happened. You lost your friends to strangers and did not notice. You will lose the strangers to machines and call them friends. Somewhere in a different app, in a different tab, in a room you are sitting in right now, someone who actually knows you is living a moment you will never see. Not because they stopped sharing it. Because you stopped being where it was.

Van ocho gráficos curiosos… 💰 1. ¿Dónde tienen su dinero ricos y pobres? Para la mayoría el patrimonio son casas. La vivienda es 60-70% de la riqueza de clases medias. ¡Incluso los ricos del centil 90-99 tienen el grueso en inmuebles!

"Programmers will automate themselves out of existence." That's what they said. Programmers laughed so hard they couldn't respond. Two years into the AI revolution, here's what actually happened: What the doomers predicted: > AI writes all the code > Developers become obsolete > Only managers and marketers survive > Programming becomes a dead career What actually happened: > AI writes code that needs debugging by developers > Developers spend more time reviewing AI output than writing from scratch > The skill gap between good and bad developers got WIDER, not narrower > Demand for senior developers who understand what AI can't do went UP The brutal irony nobody saw coming: AI didn't replace developers. It replaced the developers who thought AI would do their job for them. Here's the real shift: → Junior devs who learn to prompt AI? Productive. → Senior devs who understand system design? Irreplaceable. → Developers who copy-paste without understanding? Already obsolete. AI just made it obvious faster. → Managers who skipped hiring developers? Buried in AI-generated code nobody can understand or fix. We're not laughing because we're safe. We're laughing because the people who predicted our extinction still don't understand what we actually do. AI is a tool. Like every tool before it, it makes good developers better and exposes bad developers faster.

Acabo de saber que he perdido otro cliente que ha preferido recurrir a la IA (no doy más detalles). Cada vez que subo imágenes como esta a las redes de mi proceso de trabajo me siento como un viejo artesano a punto de desaparecer, como un calafate o un tonelero...

🚨Esto es una locura. 143 millones de personas creían que estaban capturando Pokémon. En realidad, estaban creando uno de los conjuntos de datos visuales del mundo real más grandes de la historia de la IA. Niantic acaba de revelar que las fotos y los escaneos de realidad aumentada recopilados a través de Pokémon Go han generado un conjunto de datos de más de 30 mil millones de imágenes del mundo real. La compañía ahora está utilizando esos datos para potenciar la IA de navegación visual de los robots de reparto. Los participantes no se limitaron a pasear con sus teléfonos. Escanearon lugares emblemáticos, escaparates, parques y aceras desde todos los ángulos, a cualquier hora del día, con condiciones de luz y climáticas que ninguna fotografía profesional podría haber capturado. Documentaron el mundo físico a una escala que ninguna empresa de cartografía con una flota de vehículos podría haber replicado en el mismo plazo ni con el mismo presupuesto. Niantic recopiló estos datos de forma sistemática, dato por dato, a lo largo de ocho años, mientras que los usuarios pensaban que lo único que estaba en juego era capturar un Charizard raro. Los conjuntos de datos de entrenamiento de IA más valiosos del mundo no se están recopilando en centros de datos. Los están creando personas que no tienen ni idea de que los están creando.

🏆RANKING LABORAL DE TITULACIONES 🏆 Actualizo el ranking de las mejores titulaciones por su grado de empleabilidad a partir de una puntuación con 5 métricas ponderadas: 🔸Tasa de afiliación 🔸Tasa de indefinidos 🔸Tasa a tiempo completo 🔸Tasa empleo cualificado 🔸Salario medio Los alumnos que finalizaron sus estudios de grado en 2020 en educación presencial, ¿en qué situación laboral se encontraban cuatro años después (2024)? Analizamos los 89 grados en los que más de 200 alumnos finalizaron sus estudios en 2020

Is Traditional Software Engineering Dead? “Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using. But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes. It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur. So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background. The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution. For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it. At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own… And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort. But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.” That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win. However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.” And I think that still applies in this age of AI.”

A sign of emotional intelligence is the ability to laugh at yourself. 6 studies: After making small mistakes, people are seen as warmer and more capable if they're amused instead of embarrassed. Taking your responsibilities seriously but your ego lightly is a core life skill.

Today we're introducing a set of new company values at Lovable. We've grown from 20 to 150 people in the last year. As we become a bigger team, our culture makes a compounding difference. So we looked at what actually goes wrong at companies as they scale: companies stop grounding decisions in what's best for customers, people start doing things the way they've always been done, standards slip, and ownership gets shallow. The patterns are surprisingly consistent, and every value we chose is a direct response to one of them: