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／ #AveMujica# アモーリス シグネチャードラムスティック #LERNI# より 4月26日 発売‼️ ＼ お求めは全国の楽器店にて。 4月26日、27日にKアリーナで行われる MyGO!!!!!×Ave Mujica 合同ライブ「わかれ道の、その先へ」 の物販ブースでも販売いたします❗️ 是非お手に取ってください🌙 #バンドリ#

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at:

Yes, the rumors are true: The more powerful Notebook experience is now 100% rolled out to ALL Pro users. You might be asking... How exactly do I use it? Here are a few queries to try (ofc dependent on your sources). Hot Tip: bookmark this post! Free users too 😉 For Learning: "Create 5 quizzes of ascending difficulty focusing on a different topic from my sources" "Turn my raw lecture notes into a formatted PDF study guide complete with a 5-question practice quiz." "Add all of the URLs from this class syllabus as sources" For Business: "Calculate revenue growth across each product line and plot the trends" "Read our marketing brief, search the web for competitors, and recommend how we should tune our messaging" For Personal: "I uploaded a bunch of receipts from our kitchen renovation, make a spreadsheet to track all the work and costs" "Help me analyze this property purchase. Look up recent market performance in the neighborhood, predict trends, and make a spreadsheet to model all the data you find"

For students majoring in biology/medicine, or anyone who likes biology, you can take a look. It feels very professional and really makes you feel like you're learning something.

📢Meet Qwen3.8-Max — our most capable model to date. Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉 Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters: - Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub： - Real work, real results: Production-quality deliverables across hundreds of professions. - Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy. - Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction. 💰Pricing: Input: $2.0 / M tokens Output: $6.0 / M tokens Implicit Caching: $0.25 / M tokens Start building with Qwen3.8-Max! 🚀 📖 Blog: ✅ Qwen Studio: ⚡ API:

Only if education could be this interactive ❤️‍🔥 I've had a looong wish to build something genuinely useful through vibe coding, and I finally did it. A 3D human anatomy application built with @threejs using GPT 5.6 Sol. It all started with a single design image that I created using GPT Image 2.0. I then used it to generate every 3D organ image, one by one. Next, I converted each of those images into 3D models using @tripoai (and no, they didn't sponsor this 😄). After that, I opened Codex, wrote a master prompt based on the design, and gave it the prompt, the design image, and all the 3D models. Codex built the first version beautifully, but there was one big problem. Every single 3D model was nearly 120-150 MB. That obviously wasn't practical for the web and was giving a performance of 16fps. After a few iterations, Codex optimized each model down to roughly 2–5.5 MB while preserving the visual quality, reducing the total asset size from ~900 MB to just 28.6 MB. And each model loads on demand. Along the way, Codex also generated those anatomical illustrations showing where each organ sits in the human body, and even created the interactive hotspot markers that explain different parts of every organ. It handled all of that. The process wasn't exactly one shot, but it also wasn't difficult. You just have to do it step by step. It genuinely felt like building something that could make learning anatomy much more engaging. The inspiration came from @DilumSanjaya's 3D animal plant cell project. I remember seeing it and thinking, "I want to build something like this one day." And I did it :D Live: Code:

A humanoid robot is fundamentally an embodied AI system. While motors, actuators, batteries, and sensors provide the body, neural intelligence provides the mind. Without neural computation, a humanoid is simply an advanced machine. With it, the robot becomes capable of adapting to unpredictable environments, understanding language, recognizing objects, planning tasks, and continuously improving through experience. This shift positions "Humanoid Neural" as a foundational concept within the robotics ecosystem. Neural Intelligence is the Core of Future Humanoids. Modern humanoids rely on neural-network-based systems to perform nearly every cognitive function. These include: Visual perception Speech recognition Language understanding Object identification Human pose estimation Motion planning Reinforcement learning Dexterous manipulation Long-term memory Decision making Navigation Emotional recognition Social interaction As robots become more capable, nearly every subsystem transitions from traditional programming toward learned neural models. This makes "neural" less of a niche AI term and more of an umbrella for robot intelligence. A Broad and Scalable Brand One of the strongest characteristics of is that it is not confined to a single product category. #Languageunderstanding# #neuralnetwork# #socialinteraction# #neuralntelligence# #smarthumanoids# #iq#