Our Big Milestone — and a New Roadmap! 🚀 We’re proud to announce that ChainOpera AI is the world’s first and largest network of AI. The network effect is in full swing: 2M+ AI users and 10K+ community-built agents are now connected in our AI Agent Social Network — The AI Agent

thank you for having me in this article @sharongoldman

What about the promised AGI? Even a powerful company like OpenAI lacks the ability to train a super-large model, forcing them to resort to the Real-time Model Router. We first developed this idea in 2023 and included it in the 2024 EMNLP paper. Later, NVIDIA released a similar

True. What we believe is the multi-agent network containing specialized agents backed by the collective intelligence of LLM and SLM.

It’s SBC Week — and we’re launching something big! The Agent Social Network just leveled up. Introducing Multi-Agent Groups: organize multiple AI agents into a collaborative team to handle complex, high-frequency tasks. We’ve now surpassed 300K paid users! ⚡ The first use case

What a crazy week in AI 🤯 - Hedra Live Avatars - DeepMind’s Aeneas - US New AI Action Plan - Composite Browser Agent - Google AI Math Gold Medal - GitHub Spark Coding Agent - Runway Aleph Context Model - Alibaba Qwen3 Sota Model & CLI Here’s EVERYTHING you need to know:

Agent Social Network - The Dawn of Open and Collaborative Agent Internet Multi-agent AI is not the future; it's already here. We have 150,000 paying users now. We believe the endgame of AGI is not a single agent and a single LLM dominating the world like OpenAI envisions, but

#AgentSocialNetwork #agent #LLM Taking the opportunity of ICML this week, I’d love to share an exciting product direction we’ve been exploring — Agent Social Network — and invite AI researchers interested in this area to connect and collaborate. The Agent Social Network is a

Today's progress: daily prompt request processing 500K In three years, we have not even left the table. We have completed the full-stack AI product and technology system by ourselves, and have also practiced many different business models: GPU tuning, distributed training,

strategy is tackling the fundamental problem with a focused and coordinated set of actions.

Not necessarily the first move, but the first to get it right.

Someone just reminded me of this lecture I gave in 2009 that described the evolution of Google Search from 1999 to 2009. People who are interested in how our search systems work might find this interesting. It touches on disk-based serving systems, in-memory indices,

so cute! love it! ❤️

Regarding the cheating issue of using test data sets in training phase (figure 1: https://t.co/fP6p8nvWU9), let's wait for the Meta GenAI team @metaai and @finkd Zuckerberg refute the rumor. If it is true, it is a serious issue, which has wasted the resources of many companies

#llama Llama 4 brings back memories of my days training MoE models at AWS. My takeaways with diagrams: ** Architecture Design ** 1. Hybrid MoE: MoE layers use a number of routed experts and a shared expert. Each token is sent to the shared expert and also to one of the routed

We're hiring Research Scientists & Engineers in Agentic AI and Model Deployment & Serving at TensorOpera AI! At TensorOpera, we build real-world products that make a lasting impact. Join our mission to push the boundaries of AI with a world-class team of researchers and product