Chetaslua
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Chetaslua
@chetaslua
AI News | AI Prompting and Comparison| Breaking AI news before it’s famous


MiniMax M2.7 is real deal man I have made a complete website with proper fan physics and fibre physics this looks so good @MiniMax_AI i am waiting for the next update man , i want a big model next cook it for us Prompt is in comment for normal paper receipt physics sim







We’re launching a brand new, full-stack vibe coding experience in @GoogleAIStudio, made possible by integrations with the @Antigravity coding agent and @Firebase backends. This unlocks: — Full-stack multiplayer experiences: Create complex, multiplayer apps with fully-featured UIs and backends directly within AI Studio — Connection to real-world services: Build applications that connect to live data sources, databases, or payment processors and the Antigravity agent will securely store your API credentials for you — A smarter agent that works even when you don't: By maintaining a deeper understanding of your project structure and chat history, the agent can execute multi-step code edits from simpler prompts. It also remembers where you left off and completes your tasks while you’re away, so you can seamlessly resume your builds from anywhere — Configuration of database connections and authentication flows: Add Firebase integration to provision Cloud Firestore for databases and Firebase authentication for secure sign-in This demo displays what can be built in the new vibe coding experience in AI Studio. Geoseeker is a full-stack application that manages real-time multiplayer states, compass-based logic, and an external API integration with @GoogleMaps 🕹️


New release: Multi-agent part 1

My first test of MiniMax M2.7 this made 3d fibre physics with all the specs of m2.7 is written



Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Att…




MiniMax M2.7 is real deal man I have made a complete website with proper fan physics and fibre physics this looks so good @MiniMax_AI i am waiting for the next update man , i want a big model next cook it for us Prompt is in comment for normal paper receipt physics sim


wait this is hilarious i just realized grok-4.20's base model scores the same as MiMo-V2-Flash imagine this Grok model being hyped up and waited for for like 6 months straight is not even at its core smarter than a 200B Xiaomi model lololol its also less price and token efficient


We found this line in the Grok config, : “Grok 4.20 AGI (beta)” Artificial Grok Intelligence Probably just an easter egg… or maybe not 👀 @elonmusk can you confirm 🤯






