GodPet Tangyuan has officially entered a usable stage. I’m Grace (@YuanjuanGrace), founder / independent developer of ENERGY POWERAI PTE. LTD. Over the past period, I have been working on one core direction: moving AI from a “Q&A tool” toward a long-term collaborative AI second-brain space. Tangyuan is no longer just a chatbot. Once launched, the user does not need to repeatedly click menus or remember complex operation paths. You can simply speak to Tangyuan and let it coordinate the functions: “Tangyuan, open the writing panel for me.” “Help me change this date and the presenter.” “Turn this content into graph relationships.” “Remind me who I should contact next for this task, and how to move it forward.” Tangyuan can understand intent and route the task to the corresponding functional module. It now has several core capabilities: 🧠 Integrated Second Brain Tangyuan supports layered memory, relationship graphs, memory radar, context recovery, long-term information accumulation, and multi-source memory retrieval. Its memory is not limited to chat history. It can retrieve task-relevant people, events, objects, relationships, and historical context from face-to-face conversations, private chats, group chats, AI group chats, the Secretary Desk, the writing panel, task records, image content, relationship graphs, and business systems. 🤖 Multi-model Intelligent Routing The system can automatically route tasks across Doubao, GPT, Claude, Grok, Gemini and other models based on task type, event scenario, output quality, and token cost, instead of forcing the user to manually choose a model. 🎤 Face-to-face Conversation and Voice Interaction The user can talk directly with Tangyuan face to face, or use voice commands to operate functions. Tangyuan does not only answer questions. It can combine the current task, historical memory, interface content, and relationship context to infer what the user is really trying to achieve, then move the task into the right function. 📌 Secretary Desk and Task Loop Tangyuan records events, people, relationships, and task progress. Tasks it can complete will be routed into the functional modules. Tasks it cannot complete directly will trigger proactive reminders, helping the user understand who to contact next, what path to follow, and how to move forward. 💬 Private Chat / Group Chat / AI Group Chat Collaboration Tangyuan is both an observer and a participant. It can enter different conversation scenarios, understand contextual relationships, accumulate records, and support follow-up task execution. Whether it is human-to-human private chat, human group chat, human-AI private chat, or AI group chat, Tangyuan can take on the role of recording, reminding, analyzing, and coordinating when needed. ⚡ Power Grid Learning System The learning system is connected to an energy and power grid gamified training platform, covering Training, Assessment, immersive 3D operations, multilingual technical specification translation, and professional scenarios such as transformers, inspection, overhaul, SF6, and more. 🐱 GodPet Growth System Tangyuan has its own pet profile, personality settings, bond value, levels, achievements, and growth feedback. It is not a cold tool. It is a digital GodPet that grows together with its owner. I am building a 19-patent matrix around life-like multi-agent systems, long-term memory, multi-source memory retrieval, relationship graphs, task scheduling, and vertical industry agents. Tangyuan’s goal is not to become another chatbot. It aims to become an AI second-brain space that understands people, tasks, relationships, business scenarios, and can continuously evolve with its owner. Open to conversations with AI developers, researchers, enterprise partners, and ecosystem teams around long-term memory, multi-agent collaboration, relationship graphs, vertical industry agents, and AI second-brain spaces. #AI #Tangyuan #GodPet #SecondBrain #MultiAgent #EnergyAI #PowerGrid #xAI

PowerAI analyzed 37,406 thermal images and found every single anomaly that Dominion Energy's manual team had identified, in hours, not months. 🚁⚡ A great case study on what #Drones #GIS AI looks like at utility scale. Read more: esri.com/en-us/industries/bl… #Geoawesome

🌳 Utility vegetation management is critical to grid reliability. @Esri 𝗔𝗿𝗰𝗚𝗜𝗦 𝗟𝗶𝗗𝗔𝗥 Buzz's 𝗣𝗼𝘄𝗲𝗿𝗔𝗜 help utilities visualize vegetation in 3D, detect encroachment, and prioritize maintenance faster. Request a demo: na2.hubs.ly/H05RtmS0

Electricity and water for AI data centers come primarily from the existing U.S. electric grid and local municipal/groundwater supplies — but the AI boom is rapidly changing how both are sourced, with growing use of on-site generation and advanced cooling tech. brookings.edu 1 Electricity: Mostly the Grid Increasing On-Site PowerAI data centers (hyperscale facilities for training/inference) pull enormous power — a single large one can use as much as 100,000 homes. U.S. data centers currently consume ~180 TWh/year (about 4% of national electricity), with projections hitting 400–600 TWh by 2030 as AI drives most of the growth. devsustainability.com 1 Where it comes from today (approximate mix for data centers, 2024–2026):Natural gas: ~40% (dominant in the U.S.) — reliable, quick to ramp up. Renewables (wind, solar, hydro): ~24–27% — fastest-growing via massive Power Purchase Agreements (PPAs) from hyperscalers like Google, Microsoft, Amazon, and Meta. Nuclear: ~15–20%. Coal: ~15%. brookings.edu 1 How it's actually delivered:Main grid connection (via utilities like TVA, PJM, etc.) in most cases — but grid strain in hotspots (Northern Virginia, Texas, etc.) is real, leading to delays and higher consumer bills in some regions. reuters.com On-site / "behind-the-meter" generation is surging for AI projects needing power now: Gas turbines (e.g., xAI’s Colossus in Memphis uses dozens of methane turbines for 150 MW initially), microgrids, or planned dedicated plants. Some operators are even restarting nuclear units or building small modular reactors long-term. consumerreports.org Renewables storage (batteries like Tesla Megapacks) are being added fast, but fossil fuels still fill gaps in the near term (projected to cover >40% of new demand through 2030). iea.org The S.4214 bill (Sanders/AOC) flags this as a problem — arguing new data centers could raise utility bills and emissions without safeguards — which is why Elon replied “Hmm” to the criticism (implying the pause risks slowing U.S. AI leadership).Water : Mostly for Cooling, Sourced Locally (and Shifting Fast)Data centers use water directly for cooling servers (the big consumer) and indirectly via power plants. A typical hyperscale facility might use 300,000–5 million gallons per day; AI clusters push higher due to heat density. brookings.edu Primary sources:Municipal drinking water or treated wastewater (most common). Groundwater/aquifers (e.g., Memphis Sand Aquifer for xAI’s Colossus — they planned a wastewater recycling plant to offset ~3 million gallons/day, though construction paused recently). Rivers/lakes in some spots (e.g., Columbia River hydropower water deals in the Pacific Northwest). andthewest.stanford.edu How it's used:Traditional evaporative cooling towers: Hot air passes over water — ~80–85% evaporates into the air (lost forever). The rest is discharged. This is efficient but water-intensive. mostpolicyinitiative.org Indirect: Power plants (gas, coal, nuclear) use water for steam/thermoelectric cooling. Big shift happening now (2025–2026 ):Liquid/direct-to-chip cooling and immersion cooling (servers submerged in non-conductive fluid) — cuts water use by 70–99% (sometimes near zero). New NVIDIA racks tolerate higher temps, enabling dry coolers. Closed-loop systems and waste-heat recovery (even turning heat into more water via atmospheric harvesting). Hyperscalers like Microsoft, Oracle, and others are deploying these at scale for AI facilities. waterfreechat.com 1 xAI’s approach in Memphis is a real-world example: Heavy initial reliance on gas turbines for power plans for wastewater recycling to protect the local aquifer (though the recycling plant timeline slipped while other priorities moved forward). politico.com

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