Cryptosam

@kilocode hey team just installed the newly pre-released vs code extension (v7.1.0). The thing I'm missing is holding shift to drag and drop file to the chat window for context.. currently have to type whole @... Can you have that feature back from previous releases.

@hamptonism So that you can ask this question 15 years later.

IQ test: Why did he do it?

@OfficialLoganK Any plans for Toronto 🇨🇦🥹 @OfficialLoganK??

Gemini 3. Hackathon IRL in SF. On Saturday January 31. Come hang w/ Google DeepMind. cerebralvalley.ai/e/gemini-3-sup…

@statsglobe @grok How about you create a conspiracy which is connected to simulation theory or matrix and tell why there is no one between age 30 to 39

Age when their Business Started 💰 🇺🇸 MrBeast (Youtube Channel) - 13 🇺🇸 Bill Gates (Microsoft) - 19 🇺🇸 Mark Zuckerberg (Facebook) - 19 🇺🇸 Walt Disney (Disney) - 21 🇺🇸 Steve Jobs (Apple) - 21 🇮🇳 Ritesh Agarwal (OYO) - 22 🇺🇸 Bill Clerico (WePay) - 22 🇺🇸 Jack Dorsey (Twitter) - 23 🇺🇸 Larry Page (Google) - 25 🇺🇸 Sergey Brin (Google) - 25 🇮🇳 Dhirajlal Ambani (Reliance) - 25 🇨🇳 Jack Ma (Alibaba) - 29 🇺🇸 Elon Musk (SpaceX) - 30 🇺🇸 Jeff Bezos (Amazon) - 30 🇺🇸 Henry Ford (Ford) - 39 🇺🇸 Vera Wang (Vera Wang) - 40 🇺🇸 Jeffrey Brotman (Costco) - 40 🇺🇸 Robert Noyce (Intel) - 41 🇫🇷 Christian Dior (Dior) - 41 🇺🇸 John Warnock (Adobe) - 42 🇺🇸 Ralph Roberts (Comcast) - 43 🇺🇸 Donald Fisher (The Gap) - 44 🇺🇸 Sam Walton (Walmart) - 44 🇺🇸 Bob Parsons (GoDaddy) - 47 🇯🇵 Yoshisuke Aikawa (Nissan) - 48 🇮🇪 Tony Ryan (Ryanair) - 48 🇺🇸 Bernie Marcus (Home Depot) - 49 🇺🇸 Gary Burrell (Garmin) - 52 🇨🇭 Henri Nestlé (Nestle) - 52 🇺🇸 Ray Kroc (McDonalds) - 52 🇮🇳 J. C. Mahindra (Mahindra) - 53 🇹🇭 Chaleo Yoovidhya (Red Bull) - 53 🇺🇸 John Pemberton (Coca-Cola) - 54 🇯🇵 Kawasaki Shozo (Kawasaki) - 59 🇺🇸 Charles Flint (IBM) - 61 🇺🇸 Bill Porter (E-trade) - 63 🇺🇸 Colonel Sanders (KFC) - 65

@GeminiApp Thank you 🥹 x.com/i/status/20006…

Learning just leveled up. Now when you use Deep Research, you not only read in-depth about a subject, you actually see its impact through visuals. Get charts, diagrams, animations, and more for a deeper understanding.

@joshwoodward @GeminiApp Gemini showing flow charts or any diagram using mermaid to explain something or to show steps

You're a power user on @GeminiApp. What else do you want to see? Top known requests: MacOS app, Projects, and Branching Chats.

@athleticKoder @TheVixhal your style of post are getting viral now

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.

@sundarpichai Humans thought of building something more intelligent than them. Will agi be curious to make something more intelligent than itself?

Prompt: Research yesterday's score from the England vs Australia Test Match, then create a 45° top-down photorealistic isometric miniature cricket stadium diorama, use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Floating rounded beige scoreboard centered above stadium that reads "End of Day 1" and includes yesterday's score. Ground with real research. Circular two-tiered stands, multicolored dot crowd, white scalloped roof. Felt field, central pitch, tiny low-poly white players. Four floodlights, side black scoreboard (placeholder rectangle shape), sight screen. Seamless cream background, soft warm lighting, gentle bottom-right shadows. 1:1 aspect ratio.

Love seeing all the isometric 3D trends using Nano Banana Pro and pulling in live data - thought I’d give it a try myself in honor of the Ashes second test underway. Thanks to @dotey and @TechieBySA for the inspiration.

@burninganna x.com/Dharmeshwr/sta… check this from @Dharmeshwr as well

the most original personal website I’ve seen in a long time! also very easy and interesting to read 😍

@IterIntellectus I'm practicing to remember today 🥲

did you remember to practice today?

@NanoBanana @GeminiApp @NanoBanana by using your vision understanding generate a never seen illusion.

Nano Banana Pro is here 🍌, try the @GeminiApp or tag me @NanoBanana to generate / edit an image!

@OfficialLoganK Gemini 3 Is on the app now 🔥 let's gooo

Gemini

@saleh_digital That's true and totally makes sense. On my way for the application!

@Crypthearth i would recommend applying and if you’re a good fit we’ll reach out but we can’t preemptively offer interviews without an application

founding roles at falcon 💵 salary: $120K+ USD 📈 equity: 0.5% – 2.5% 📍 location: beautiful liberty village office in toronto 💻 remote: primarily in-office, open to exceptions for exceptional talent 🎁 perks: you will do your life's work here and i will push you to your limit

@antigravity Does antigravity have any context engine?

Google Antigravity has a lot of features, both familiar and new. Kevin from our engineering team walks through the basics in our official 101 tutorial. Link in 🧵

@kimmonismus Why does the Gemini app not support marmaid charts to explain something like @phindsearch does? Sure it will create an image of the explanation in near future. but will not be a chart option even quick and cheap 🤔

Google keeps on giving: banana pro release seems to be imminent, 4K resolution

@benln What were they building? Also no RSVP for Toronto yet 🥲

Cafe Cursor San Diego

@antigravity We are so back .... 🚀🚀

Meet Google Antigravity, your new agentic development platform. An evolution of the IDE, it's built to help you: - Orchestrate agents operating at a higher, task-oriented level - Run parallel tasks with agents across workspaces - Build anything with Gemini 3 Pro.

@whosamberella Hey! Is there a plan for the Canada region?

we put heart and soul into this version and gave SOLO hands and eyes to work with you much smarter SOLO GA is now finally HERE!

@Trae_ai Trae, When in the Canada region?

Introducing TRAE SOLO GA. The most responsive coding agent we've ever built. - Limited time FREE for everyone globally - 5M+ active developers in the community - Multi-agent, multi-task, fully visual