WorldGrow’s pipeline works like a growing brain: – Scene-friendly SLAT encodes 3D context
– 3D block inpainting ensures spatial continuity
– Coarse-to-fine refinement keeps global layout + fine detail Each module adds realism while keeping the world endless.
@godofprompt
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Technical Overview of WorldGrow’s Generative AI Pipeline
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WorldGrow: A New AI System for Infinite 3D Environment Generation
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Holy shit… this might be the first AI that can literally grow worlds 😳
— God of Prompt (@godofprompt) 28 octobre 2025
It’s called WorldGrow a new system from Huawei & SJTU that generates infinite 3D environments block by block.
No loops. No stitching. Just a single seed expanding into a seamless, photorealistic world.
→… pic.twitter.com/tiGKRncJWGHoly shit… this might be the first AI that can literally grow worlds It’s called WorldGrow a new system from Huawei & SJTU that generates infinite 3D environments block by block. No loops. No stitching. Just a single seed expanding into a seamless, photorealistic world. →
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Using AI Agents and Knowledge Graphs for Customer Intelligence
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The design behind Enterpret is genius.
— God of Prompt (@godofprompt) 27 octobre 2025
→ Real-time Knowledge Graph
→ Adaptive Taxonomy that evolves as feedback changes
→ Agent alerts that close the loop automatically
It’s not just analytics.
It’s Customer Intelligence in motion. pic.twitter.com/H88QdCLjvMThe design behind Enterpret is genius. → Real-time Knowledge Graph
→ Adaptive Taxonomy that evolves as feedback changes
→ Agent alerts that close the loop automatically It’s not just analytics. It’s Customer Intelligence in motion. -
Using AI to analyze enterprise customer feedback patterns
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Here’s what it looks like in action 👇
— God of Prompt (@godofprompt) 27 octobre 2025
Ask:
“Why are enterprise users churning in Q3?”
Enterpret instantly surfaces every pattern, complaint & correlation.
You don’t analyze feedback anymore you converse with it. pic.twitter.com/oQ9ZdTMUpSHere’s what it looks like in action Ask: “Why are enterprise users churning in Q3?” Enterpret instantly surfaces every pattern, complaint & correlation. You don’t analyze feedback anymore you converse with it.
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Enterpret uses AI to automate customer feedback analysis and alerting
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Let’s say your app keeps crashing after login. Enterpret spots 500 complaints about it across: – Support tickets
– App store reviews
– Slack messages
– Reddit threads Then it automatically alerts your PMs with context. -
AI Agents for Real-Time Feedback and Insight Automation
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Here’s what happens behind the scenes:
— God of Prompt (@godofprompt) 27 octobre 2025
1. Captures every conversation in real time
2. Builds a Knowledge Graph that links feedback → features → users
3. Uses Adaptive Taxonomy to group insights dynamically
4. Deploys AI Agents to detect bugs or churn signals instantly pic.twitter.com/sg8JsQRELMHere’s what happens behind the scenes: 1. Captures every conversation in real time
2. Builds a Knowledge Graph that links feedback → features → users
3. Uses Adaptive Taxonomy to group insights dynamically
4. Deploys AI Agents to detect bugs or churn signals instantly -
Enterpret uses AI to organize and analyze customer feedback at scale
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Every company says “we care about customer feedback.”
— God of Prompt (@godofprompt) 27 octobre 2025
But when you scale to millions of users, feedback turns into chaos — thousands of Slack messages, Zendesk tickets, Reddit rants & app reviews.
Enterpret fixes that.
It makes all your feedback talk to each other. pic.twitter.com/5dbkAmBSJKEvery company says “we care about customer feedback.” But when you scale to millions of users, feedback turns into chaos — thousands of Slack messages, Zendesk tickets, Reddit rants & app reviews. Enterpret fixes that. It makes all your feedback talk to each other.
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New Research Proposes Unified Framework for Language, Robotics, and AGI
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The paper ends with a big-picture figure a roadmap showing how this approach connects language, robotics, and symbolic reasoning into one unified framework. It’s literally titled “The Path to Real Deep Research.” If they’re right, this is the bridge to AGI.
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Multi-Agent AI Systems Demonstrate Improved Reasoning Performance
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They even connected multiple “deep researchers” together. Each agent worked on a subproblem and merged insights. Result: +22% faster convergence on shared reasoning benchmarks. It’s literally a scientific community made of AIs. This one’s unreal. The model autonomously
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Analysis of AI Model Memory Retention via Reflection
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Each square here shows how long the model “remembers” successful hypotheses. Retention stabilizes at ~74% after 10 research cycles. That’s memory through reflection, not parameter updates. It’s how the system learns what’s worth keeping.