Francisco Balmaceda

@viantirreau Al menos dicen 'Longest line of' y no 'Longest cheesestake'

Buena po Talca

No entiendo el proyecto de la tarjeta MetroMuv. Me da la impresión de que es solo para expandir la operación de metro y generar nuevos ingresos al mismo tiempo de estar presentes en el bolsillo de las personas (como copecpay). Espero impulse mejoras reales del servicio algún día

El tinydesk de 31 minutos me dió un muy feliz inicio de semana :)

Jamiroquai

Yes

@pqoqubbw @Atlassian @browsercompany @diabrowser @arcinternet Please do but only enough to improve the experience without changing the functionalities. Im hopping we won't need an atlassian account tho

@Atlassian @browsercompany @diabrowser @arcinternet do not touch arc

It’s time for a browser that’s actually built for work - a browser that helps you do, not just browse. We’ve entered into an agreement to acquire @browsercompany, the team behind @diabrowser and @arcinternet, to transform how work gets done in the AI era. Stay tuned 🔗 go.atlss.in/z7xbm4

@browsercompany Please @Atlassian, give arc proper love and don't fuck up the product like every other shit you've touched.

Today, The Browser Company of New York is entering into an agreement to be acquired by Atlassian for $610M in an all-cash transaction. We will operate independently, with Dia as our focus. Our objective is to bring Dia to the masses. 🔗 More details from our team below

El mini del BostonTimes no es lo mismo (por pura UI, creo que usan el mismo que el nyt)

@RhysSullivan 403 should be higher

http status code tier list based on vibes

She dumped me last night. Not because I don't listen. Not because I'm always on my phone. Not even because I forgot our anniversary (twice). But because, in her exact words: "You only pay attention to the parts of what I say that you think are important." I stared at her for a moment and realized... She just perfectly described the attention mechanism in transformers. Turns out I wasn't being a bad boyfriend. I was being mathematically optimal. See, in conversations (and transformers), you don't give equal weight to every word. Some words matter more for understanding context. Attention figures out exactly HOW important each word should be. Here's the beautiful math: Attention(Q, K, V) = softmax(QK^T / √d_k)V Breaking it down: Q (Query): "What am I looking for?" K (Key): "What info is available?" V (Value): "What is that info?" d_k: Key dimension (for scaling) Think library analogy: You have a question (Query). Books have titles (Keys) and content (Values). Attention finds which books are most relevant. Step-by-step with "The cat sat on the mat": Step 1: Create Q, K, VEach word → three vectors via learned matrices W_Q, W_K, W_V For "cat": Query: "What should I attend to when processing 'cat'?" Key: "I am 'cat'" Value: "Here's cat info" Step 2: Calculate scoresQK^T = how much each word should attend to others Processing "sat"? High similarity with "cat" (cats sit) and "mat" (where sitting happens). Step 3: Scale by √d_kPrevents dot products from getting too large, keeps softmax balanced. Step 4: SoftmaxConverts scores to probabilities: "cat": 0.4 (subject) "sat": 0.3 (action) "mat": 0.2 (location) "on": 0.1 (preposition) "the": 0.1 (article) Step 5: Weight valuesMultiply each word's value by attention weight, sum up. Now "sat" knows it's most related to "cat" and "mat". Multi-Head Magic:Transformers do this multiple times in parallel: Head 1: Subject-verb relationships Head 2: Spatial ("on", "in", "under") Head 3: Temporal ("before", "after") Head 4: Semantic similarity Each head learns different relationship types. Why This Changed Everything: Before: RNNs = reading with flashlight (one word at a time, forget the beginning) After: Attention = floodlights on entire sentence with dimmer switches This is why ChatGPT can: Remember 50 messages ago Know "it" refers to something specific Understand "bank" = money vs river based on context The Kicker:Models learn these patterns from data alone. Nobody programmed grammar rules. It figured out language structure just by predicting next words. Attention is how AI learned to read between the lines. Just like my therapist helped me understand my focus patterns, maybe understanding transformers helps us see how we decide what matters. Now if only I could implement multi-head attention in dating... 🤖 Still waiting for "scaled dot-product listening" to be invented.

Es la peor noticia que me ha llegado en las últimas semanas, mal día para el perno

nyt puso el mini tras la suscripción :c

Fui a la oficina y llegando me avisaron que teniamos bloqueado el acceso desde el lunes. Tocó homeoffice

avisen cuando el mut tenga un doggis

@jdwmco Denuevo

1 día sin coca light

Wordle 1,528 4/6 ⬛⬛⬛🟨⬛ ⬛🟨🟨🟨⬛ ⬛🟨🟩🟨🟨 🟩🟩🟩🟩🟩

Y ahora excelente desempeño en el mini? gracias nyt por un día facilito

Connections Puzzle #801 🟨🟨🟨🟨 🟦🟦🟦🟦 🟩🟩🟩🟩 🟪🟪🟪🟪 Mejor connections en un buen rato

Wordle 1,523 6/6 ⬛⬛🟩⬛⬛ ⬛🟩🟩🟩⬛ ⬛🟩🟩🟩⬛ 🟨🟩🟩🟩⬛ ⬛🟩🟩🟩🟩 🟩🟩🟩🟩🟩

@jejesup Paso

Siento que podría funcionar una adopción nacional de Jojo Siwa. Onda que venga a algún festival medio irónicamente pero que igual se llene y la protejamos y ella ame Chile y así