Luciano Esteban

Solo quedan cupos virtuales! gracias por todo el apoyo, increíble la gente que se anotó! luma.com/the-ai-collect…

En 2014, Peter Thiel impartió una clase de 1h sobre cómo crear un monopolio partiendo de 0. Explicó cómo: · Google se convirtió en intocable · PayPal superó a todos · Facebook arraso a la competencia Estas son las 11 lecciones de su clase: 1. Crea valor y, después, captúralo

A Apple se le ha colado por accidente un CLAUDE.md. Ha pasado en su app de soporte. Además de lo curioso del contenido... Se confirma que están usando Claude Code para desarrollar su software.

UN DESARROLLADOR JAPONÉS CREÓ UNA APP QUE MUESTRA UN GATO GORDO EN LA PANTALLA Y TE OBLIGA A TOMAR UN DESCANSO.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

GPT Image 2 is insanely good...I generated a 360° equirectangular panorama in Happycapy with just a skill + prompt. Step 1: Select the generate-image skill Step 2: Enter a prompt like: “Use a frontend 360 viewer to display an equirectangular image of […] using the GPT-Image-2 model.” Wanna see how you all get creative with this

En 2019, el profesor del MIT, Patrick Winston dió una conferencia magistral de 1 hora llamada «Cómo hablar». Tiene más de 18 millones de vistas por una razón. Sus conceptos clave: - Tus ideas son como tus hijos - La regla de los 5 minutos para conferencias de trabajo - Por qué los chistes fallan al principio En vez de ver Netflix hoy, deberías ver este video. 15 lecciones sobre comunicación: 🧵 1. Tu éxito está determinado por tu habilidad para hablar, habilidad para escribir y la calidad de tus ideas. En ese orden.

Sam Altman literally gave a 43-minute masterclass on turning ideas into billion-dollar companies:

Brillante lo de Anthropic. ① Sacas Opus 4.6 ② La gente alucina con lo bueno que es ③ Lo nerfeas durante semanas por costes ④ Lo relanzas como Opus 4.7 ⑤ Y de paso cambias el tokenizador para cobrar más ⑥ A facturar

Sam Altman just confirmed a world-shaking cyberattack is coming this year and nothing companies can do will stop it. The Axios co-founder asked him directly if a catastrophic cyber event was realistic in the next 12 months. Altman's answer: "I think that's totally possible. Yes." The CEO of the most powerful AI company on Earth just told the world to brace for impact. And his solution is NOT prevention. It's "resilience." That's the language governments use when they've accepted something bad is going to happen and they're now focused on surviving it instead of stopping it. Because the same AI that writes code for startups is about to write exploits for adversaries. Altman admitted the frontier models are already dangerously capable at cybersecurity. The next generation will be significantly more so. And once those capabilities leak into open source, the game changes permanently. But cyber isn't even the scariest thing he said: The real bomb came a few minutes later when he was talking about biosecurity. He said the models are getting extremely good at advanced biology and wonderful things will happen, like curing diseases that have killed people for centuries. Then he said this: "Someone is going to try to misuse those." And right now, the frontier models are still locked inside responsible companies with safety layers and classifiers. OpenAI can mitigate a lot of the risk because they control the stack. But open source is catching up FAST. And when it does, any group with an internet connection and enough compute can ask an AI to help them engineer a novel pathogen. Altman's exact words: "The needs for society to be resilient to terrorist groups using these models to try to create novel pathogens is no longer a theoretical thing, or it's not going to be for much longer." Let that sink in. And this is where the story gets completely insane... Because Altman's response to all of this isn't just "build better safety classifiers." His response is a policy blueprint that Axios editors called a "Bernie Sanders fever dream." He's quietly pitching it to Washington right now: It calls for rebuilding the social contract, redistributing the gains from AI, new tax structures, and fundamentally rethinking the relationship between labor and capital in an economy where a single person with AI can replace an entire team. The part nobody saw coming? Republican senators and a senior Trump cabinet secretary told him they agree. One of them told Altman directly that capitalism needs to be reimagined because "way too much leverage is going to be with capital and not with labor." The CEO of OpenAI is now selling Bernie Sanders economics to Republican administration officials and they're listening. Step back and look at the full picture now: The man building the most powerful technology in human history just admitted 3 things in one interview. 1. A catastrophic cyberattack is likely within 12 months 2. AI-enabled bioterrorism is about to become a real threat 3. The only solution he sees is a radical restructuring of capitalism that nobody in Washington is politically ready for He's not saying AI will change the world someday. He's saying the change is already here, the risks are already landing, and the institutions designed to protect us are years behind. Most people are still debating whether ChatGPT will replace their jobs. Altman is quietly telling Washington the real question is whether society can HOLD TOGETHER through what's coming next.

Seedance 2.0 - Advanced Workflow Series 4. VFX & AAA Studio Quality Look Upgrade your visuals to match AAA studio standards. Reconstruct specific shots with stylized VFX and lighting FX to elevate the final quality. Workflow 👇

NASA Artemis passing close to the Moon

🚨 In 1992, a MIT lecture quietly revealed more about product and sales than most 2-year MBAs ever will. Most people have never seen it. It came from Steve Jobs and instead of teaching theory, he broke down how great products actually win. Watching it today feels unreal. He explained that people don’t buy products they buy meaning. The best products aren’t just functional, they connect with how people see themselves. That’s why some ideas spread effortlessly while others die, even if they’re technically better. He also made it clear that marketing isn’t about features. It’s about clarity. If you can’t explain why your product matters in simple terms, it won’t matter at all. Complexity doesn’t impress it confuses. And his biggest edge? Obsession with experience. Not just what the product does, but how it feels. The small details, the simplicity, the story that’s what separates good from unforgettable. That’s why this MIT lecture still hits hard. Because while most people are building products… Very few understand why people actually buy them.

RIP Sora, you gave us the greatest ai video of all time

Este sencillo juguete demuestra visualmente cómo levantar peso afecta directamente a nuestra postura.

Por si te lo preguntas: Marco Grossi, un estudiante de diseño en Barcelona, creó la herramienta en 2010 simplemente porque necesitaba unir dos archivos PDF y no encontraba una forma rápida y gratuita de hacerlo. Aunque la mayoría de sus herramientas son gratuitas, el negocio se sostiene principalmente (entre un 80% y 90%) gracias a las suscripciones premium de empresas. Debido al éxito de iLovePDF, Marco lanzó iLoveIMG, una herramienta similar dedicada exclusivamente a la edición y conversión de imágenes.

Mi lista de modelos de IA favoritos a día de hoy Código → Opus 4.6 Escritura → GPT-5.4 Imagen → Nano Banana Pro Música → Suno 5 Video → Seedance 2.0 Razonamiento → Gemini 3.1 Pro Noticias → Perplexity OCR → GLM-OCR Voz → ElevenLabs Transcripción → Whisper IA Local → Qwen 3.5 9B

"Google Maps" Porque lanzó una mega actualización de sus mapas.

"Warren Buffett" Por sus dichos sobre la confianza en los negocios: “Me gusta tratar con gente con la que creo que un contrato de una sola página es suficiente. Si necesito 50 páginas para protegerme... siempre me preguntaré si necesitaba 51”.