Federico Diaz Sparta

My kind of Spirit Animal

@lunatika_shd En rappi 1 sola tanda de 10 nos salió Mbappe, CR7 y el Dibu

Panini siempre funcionó así: por lotes. No es distribución random posta. No solo pasa con las del mundial. Me acuerdo cuando era piba, para llenar el álbum de Los Simpson, teníamos que comprar en distintos lugares porque en los lotes que llegaban al kiosco cercano tocaba siempre lo mismo. Hay que comprar un poco en ML, un poco en carrefour, un poco en Rappi. Y así.

@renapiermarini Gran idea!

Hoy tuvimos la primera reunión con una empresa de venta de maquinaria agrícola para armar un sistema similar a este. Tienen todo en papel, no tienen procesos escritos y es todo bastante ineficiente. Vamos a documentar toda la operatoria y luego arrancar a programar!

Prince - With This Tear youtu.be/OVvEfey2bmg?si… a través de @YouTube

BREAKING: Vibe-coding platform Lovable reportedly suffered a breach that exposed users’ AI chat histories, source code, & database credentials.

👀"I published a completely blank website. — white page, zero visible content. But seven layers of structured data underneath: JSON-LD, llms.txt, Ed25519-signed entity claims. Within 36 hours it became the no. 1 cited source in Perplexity. ChatGPT cited it independently - > No human ever saw content on that page."

@ArtuGrande @RaidColumbia Bien ahi grande artu!!

Este finde corro 21km. En la semana sentí una sobrecarga muscular en los gemelos. Toco volver a fisio y parar unos dias. Se viene el desafío deportivo más intenso de mi vida @RaidColumbia ⛰️🏃‍♂️ Como se ve / como me siento (?

@frangss_ Muy bueno!

Hace días nos veníamos guardando esta noticia, por las dudas, para que todo salga bien SANOA AHORA ESTÁ DISPONIBLE EN AXION 🟣🤩 (La dirección exacta en la respuesta a este tweet) Sanoa no tiene techo, lo que creció en estos 3 meses era impensado para nosotros 🫡🫶

🇦🇷 @rauchg (CEO de @Vercel) anticipa posible IPO para 2027, buscando el “timing perfecto”. 💰 Vercel está valuada en ~US$9.300 millones (2025). 📈 Facturación anual de US$300M y run rate de US$340M a febrero 2026. 🤖 Grandes players de IA usan Vercel: @Meta AI @claudeai (@Anthropic) 🧠 Sobre IA: Rauch afirma que los modelos ya superan al humano en la mayoría de tests. ⚡ En Vercel hablan más de “superinteligencia” que de AGI. 🏢 Impacto en empleo: CEOs están rediseñando empresas con equipos mucho más chicos gracias a IA. 👨‍💻 Caso extremo: CTOs que antes tenía miles de ingenieros ahora podría operar con ~100. 🇦🇷 Oportunidad para Argentina: - Potencial clave en infraestructura global de IA. - Ventaja en energía para data centers - Rauch busca conectar CEOs de EE.UU. con sector energético argentino Cada día que pasa es mas surrealista este episodio de podcast, en unos años diran que fue IA 👇🇦🇷🧉

Building a Brick House (1946) — Postwar Home Construction Guide youtu.be/oEL3K45yWUw?si… a través de @YouTube

@renapiermarini Y bueno...mejor ni doy mi opinion sobre lo que es peugeot.

Se acuerdan de esto? tengo más lore 🍿 Peugeot le entregó un auto nuevo y esta vez no tiene detalles estéticos PERO... suspenso... NO LE ANDA NADA, ESTA LLENO DE PROBLEMAS ELECTRICOS. Como puede ser que entreguen los 0km así? y encima reponen con otro PEOR!!!!

Orcas eat great white sharks. They hunt seals, dolphins, and baby whales. They have never killed a single human in the open ocean. Not once, in all of recorded history. An orca's brain weighs up to 15 pounds. Yours weighs about 3. They have roughly double the brain cells we do in the regions that handle complex thought. A neuroscientist at Emory named Lori Marino put an orca brain in an MRI and found these animals can tell different species apart underwater. They do it by sending out clicks that bounce off everything around them and come back as a kind of 3D sound map (this is called echolocation). From 500 feet away, an orca knows you're a human and not a seal. It skips you on purpose. The answer is culture. Orcas around the world are divided into at least 10 separate populations, each with its own food rules, its own language, and its own way of hunting. All of it learned from their mothers. One population eats only fish. Another eats only marine mammals like seals and sea lions. These two populations can live in the exact same water and never swap a single meal. A baby orca learns what food is from its mother, and that list stays the same for life. In the Pacific Northwest, one population called the Southern Residents eats almost nothing but Chinook salmon. Scientists have documented them killing harbor porpoises 78 times over six decades, carrying the dead porpoises in their mouths, and never once eating them. Even when the group was starving. A 2023 study in Marine Mammal Science looked at all 78 cases and concluded it was play. These orcas would rather go hungry than eat something their culture says isn't food. Researchers studying whale behavior in 2001 found that orca cultural traditions "appear to have no parallel outside humans." Each family group has its own dialect, its own version of the language. Calves spend about two years just learning how to make all the sounds their family uses. Mothers will slow down a hunt on purpose so their young can watch. In 2005, a 12-year-old kid was swimming in Helm Bay, Alaska when an orca came at him full speed. At the very last second, the orca seemed to realize it was charging a human. It bent its entire body in half and turned back to open water. In captivity, it goes differently. SeaWorld's Tilikum killed three people during his life in a concrete tank. Research from 2016, published in the journal Animals, traced it to psychological collapse from being locked away from the family bonds orcas need to stay stable. I think calling this a "mystery" undersells the science. Orcas decide what to eat based on culture, not instinct. No orca mother has ever taught her calf to hunt humans, so no orca hunts humans. Only about 75 of those salmon-eating Southern Residents are still alive. Their pregnancy failure rate is 69% because we've destroyed their salmon runs. They won't break their food culture to survive. Whether we care enough to protect theirs is the part that actually matters.

Sweet Thang youtu.be/xJwihdYfyN4?si… a través de @YouTube

Amazonia has a new logo, and it’s a masterpiece. The letters were extracted from satellite images of the river itself

Willie Nelson - Blue Eyes Crying In The Rain youtu.be/JA644rSZX1A?si… a través de @YouTube

@lautirshaid Armate un telegram y nos sumamos

En Buenos Aires la comunidad de coding está re armada, pero la de data no existe — o no se habla, no sé Alguien tiene recomendaciones para meterse en esa burbuja? Si no hay, estoy para armarla

The conspiracy version of this is wrong. The real version is worse. Anthropic published a postmortem last September documenting three separate infrastructure bugs that degraded Claude's quality for weeks. Routing errors sent requests to wrong server pools. A compiler bug corrupted token selection. An adaptive thinking system started under-allocating reasoning on complex turns. 30% of Claude Code users got misrouted during the affected period. None of that was intentional. All of it produced exactly the pattern in this chart. Here's what actually drives the decline. Every AI company faces the same constraint: inference costs scale linearly with users but revenue doesn't. Quantization (compressing model weights from 16-bit to 8-bit or 4-bit) cuts GPU memory by 2-4x. Adaptive thinking allocation reduces compute per request. Batching groups requests to maximize throughput. Each optimization is individually rational. Each one shaves quality by a few percent. Stack five of them under peak load and users feel it. The timing matches launches perfectly because launch day has minimum users on the new model and maximum GPU allocation per request. Three months later you have 10x the users on the same infrastructure. The quality delta between "launch day inference budget" and "Tuesday afternoon at peak load inference budget" is the entire gap in that chart. Benchmarks miss this because benchmarks run on dedicated hardware with no load balancing, no quantization, no request batching. The model that scores 92% on MMLU in a lab scores 92% on MMLU in production too. But the user experience of interacting with that model through six layers of inference optimization at 4pm EST? That's a different product. The real problem is that "intentional nerfing" gives companies too much credit. Intentional nerfing implies control. What's actually happening is that nobody fully understands how inference optimization degrades the long tail of capabilities until users report it weeks later.