DataHub Intelligence sits between your existing systems and analytics tools. It reads operational data in place, adds manufacturing context, then delivers structured datasets for AI, analytics. The middleware your data team needed. PartnerContent with @HighbyteInc. #highbyte_iiotpic.twitter.com/enbDGdM9fL
DataHub Intelligence sits between your existing systems and analytics tools. It reads operational data in place, adds manufacturing context, then delivers structured datasets for AI, analytics. The middleware your data team needed. PartnerContent with @HighbyteInc. #highbyte_iiot
Scaling Data Ingestion from Hundreds to Thousands of Sources Without Breaking the Platform tinyurl.com/49adj6yc via @LinkedIn #ArtificialIntelligence #GenerativeAI #EnterpriseAI #AIArchitecture #DataArchitecture #DataPlatforms #AIStrategy #CIO #CTO #ChiefDataOfficer #ExecutiveLeadership #AgenticAI #RAG [Translated from EN to English]
Qwen3.6-Plus has been added to Design Arena! Delivering state-of-the-art agentic coding from frontend designs to complex repo-level problem solving, with sharper multimodal perception and more stable performance. Qwen (@Alibaba_Qwen) （1/8）🚀 Introducing Qwen3.6-Plus: Towards Real-World Agents! 🤖 Today, we’re thrilled to drop a major milestone in our journey toward native multimodal agents. Here is what makes Qwen3.6-Plus a game-changer： 💻 Next-level Agentic Coding: Smarter, faster execution. 👁️ Enhanced Multimodal Vision: Sharper perception & reasoning. 🏆 Top-tier Performance: Maintaining leading general capabilities. 📚 1M Context Window: Available by default via our API. Built on your invaluable feedback from the Qwen3.5 era, we’re laying a rock-solid foundation for real-world devs. Get ready to experience truly transformative ✨ Vibe Coding ✨. Huge thanks to our community! Go try it out and show us what you can build. 👇 Chat: chat.qwen.ai/ API: modelstudio.console.alibabac… Blog: qwen.ai/blog?id=qwen3.6 🔔Noted：More Qwen3.6 models to come and be open-sourced! Stay tuned~ 👀#Qwen #AI #AgenticCoding #VibeCoding #Agents — https://nitter.net/Alibaba_Qwen/status/2039705104723611829#m
"HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention" Sparse attention can still be slow. And the slow part is often not the attention step itself, but the search step that scans the whole context to find useful tokens. This paper's HISA makes that search cheaper. It first finds the best blocks, then finds the best tokens inside those blocks. This keeps token-level precision, needs no retraining, works with the same downstream attention, and gives up to 3.75x speedup while staying close to the original quality.
Been playing with Gemma running locally on my Pixel phone and it feels magical. To think that years ago we could not remotely imagine such a powerful LLM running in your pocket with no connectivity whatsoever. I wonder how many scenarios for LLMs will start shifting the cost to the edge to get "free" computing with added privacy and control over your data. I can't imagine how companies that only monetize inferencing and nothing else will stay above the water. Either they make/sell hardware or they must add value on top of it, otherwise they become mostly an inconvenience in the middle.
sounds great. The AI activity has been growing out of control and I feel more regretted attention, possibly the Read endpoints can be a lot cheaper but the Write endpoints a lot more expensive (?). (and to clarify the project I mentioned was all read, no write.). I do think X has
