Ayash

Evanescent Ganyu

india when ?

@AvalancheFDN Grotto ftw

New Retro9000 Grantees Selected 🔺 6 projects have been awarded retroactive grants totalling $160K from the $40M funding pool, recognising teams shipping L1s and infrastructure tooling live on Avalanche. Build. Deliver impact. Get rewarded.

Just realized Noelle not generating particles on her skill is because she's not a real knight of favonius. Can't wait for her to be promoted so she can generate some energy soon

Introducing layer1.wtf A live performance monitor for Avalanche L1s. Direct RPC data. Updated every 5 seconds. See what’s actually happening on-chain. layer1.wtf

If you're a crypto builder, read this. Applications for the Build Games, a $1,000,000 builder competition on Avalanche just opened. - all star judges - designed for founders - any idea on avalanche is eligible Read more and apply here: build.avax.network/build-games?re…

Avacado Docs just dropped avacado.app/docs Check the docs and tell us what you think.

I am becoming progressively more worried for our humanity due to extensively terrifying improvements to AI generated content media. From blatant misinformation to even the recent commercial adaptations of AI (acquisition of Suno into the music industry) WHICH NOBODY NEEDS. We need artists to thrive - not feel discouraged. Please be weary with what you see or hear online, contemporary media feels like nothing is real anymore. AI sucks and we need to stand up against it.

this is my strimmer bro am i cooked 😭

@se_ya336 dangerous

and now, it’s a sign.

開眼 #原神LunaIV #コロンビーナ #GenshinImpact

What should I tell him ?

@se_ya336 @letterboxd dee dekh ke jumpscare ho gya phirse

MERRY CHRISTMAS!! to everyone from the @l1beat_io team!! @avax @Avalanche_In

@maheaa_07 tips on never waking up ?

Sleeping for 17h-18h on weekends Is it normal?

My son called me crying “Dad, school ended 4 hours ago. I’m still waiting for you to pick me up!” Click. I hung up. Weak pitch. No clear call to action. Didn’t give me a reason to care. I went back to cold emailing billionaires. He’ll have to learn a better way to get my attention if he wants my help.

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.

is it time chat

the r/bald subreddit is an example of wholesome masculine body-positivity every post is a guy showing his clearly-past-the-point-of-no-return thinning hair & asking "is it time?" and the top reply is always this image