【#OpenDataSpaces】# Interview Part 3 is live! Break down silos across organizations and borders and turn data into capital. Part 3 explores how Open Data Spaces serves as #ContextLayer# for AI, including #DynamicOntology#. Watch the full version on🔗

【#OpenDataSpaces】# Interview Part 2 is live! AI adoption is accelerating — but why does agentic AI fail in enterprise? Part 2 gets to the root cause: the #context# scarcity. And distributed data management is the missing piece. Watch the full version on🔗

At @NVIDIAAI we continue to push open data, techniques and models forward because we know that every organization needs the freedom to build and deploy AI in their own way. We're now the biggest institutional contributor on HuggingFace and we expect to continue publishing. It's not charity or a science project - we know that when AI grows, NVIDIA's opportunities also grow. More analysis on the state of open source AI here:

At Flink Forward Asia Shenzhen 2026, Feng Wang, Researcher and Head of Open Data Platform at Alibaba Cloud & Vice Chair of the Alibaba Open Source Committee, highlighted the evolving data foundation for AI: "In the AI era, models and data together determine the quality and efficiency of Agents. Apache Flink evolves into Agentic Streaming for AI, working alongside Agentic Lake to build AI-native data platform." The next generation of intelligent agents is built on a unified, AI-native data infrastructure designed for real-time agentic workflows. #AlibabaCloud# #ApacheFlink# #ApachePaimon# #ApacheFluss# #DataAI# #AI# #Agent# #RealTimeData#

Countries with the highest % of female population 👩 1. 🇭🇰 Hong Kong — 54.9% 2. 🇲🇩 Moldova — 54% 3. 🇲🇴 Macao — 53.9% 4. 🇱🇻 Latvia — 53.7% 5. 🇦🇲 Armenia — 53.6% 6. 🇷🇺 Russia — 53.6% 7. 🇺🇦 Ukraine — 53.5% 8. 🇬🇪 Georgia — 53.4% 9. 🇧🇾 Belarus — 53.4% 10. 🇵🇷 Puerto Rico — 52.9% 11. 🇱🇹 Lithuania — 52.8% 12. 🇦🇼 Aruba — 52.8% 13. 🇹🇴 Tonga — 52.6% 14. 🇷🇸 Serbia — 52.5% 15. 🇪🇪 Estonia — 52.5% 📌 Source: World Bank Open Data (2023)

🤖 Mobile GUI agents are fine on short tasks but fall apart on long, multi-step ones. The reason is simple: just piling up action and screenshot history blows up the context and dilutes the cross-app information that actually matters. This work's idea is to promote context management from "passively accumulating history" to "an action the agent deliberately chooses." Just as it picks a UI operation, the agent can also choose to fold its history and UI state. It keeps three structured fields — folded action history, folded UI state, and a recent step record — balancing the overall arc with the immediate details. That's the Context-as-Action framework, MemGUI-Agent. 🎯 It's trained on MemGUI-3K (2,956 annotated trajectories), producing the 8B MemGUI-8B-SFT via supervised fine-tuning. The result reaches best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark. Letting the agent itself decide what to keep and what to fold feels broadly useful for any production agent fighting context bloat. MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management #GUIAgents# #LLM#