“Latent Spatial Memory for World Models” world models need a 3D memory to avoid drift, but RGB point cloud caches are slow because each step renders pixels and re-encodes them into latents via a VAE. The Mirage approach
MULTIMODAL AI
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Using ELI5 skill to simplify learning with GPT-image infographics
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personally a huge fan of asking codex to simplify things I have a small skill “eli5” which breaks down things I’m learning and creates cute little infographics about it using in-built gpt-image 2.0
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Claude Fable’s Pixar-quality video previews Alpha School AI education
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Claude Fable made this Pixar-quality educational video in one shot.
— Matt Shumer (@mattshumer_) 15 juin 2026
It's a preview of what I'm doing next:
I've joined Alpha School to push the limits of what AI can do for learning.
We're going to transform education for 1B kids. pic.twitter.com/b936wcQQqMClaude Fable made this Pixar-quality educational video in one shot. It's a preview of what I'm doing next: I've joined Alpha School to push the limits of what AI can do for learning. We're going to transform education for 1B kids.
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Models weak on vision cause error accumulation in visual steps
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Very clever. And matches what I would expect: models are weak on vision relative to everything else, so visual steps are where errors accumulate most in workflows.
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Stanford HAI director Fei-Fei Li featured on FastCompany cover explaining world models
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HAI Founding Director @drfeifei is featured on @FastCompany
's cover, explaining "world models" – AI that understand physical space and real-world dynamics. Rooted in human-centered philosophy, she explains what makes it different and what's at stake: -
ElevenLabs Music v2 SDK: Text-to-Music, Reference Matching, Multilingual
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With Music v2 SDK, developers can: – Generate tracks from text prompts with improved vocals, instrumentation, and arrangement. – Reference-match existing tracks. – Generate across languages with improved multilingual output.
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PyMuPDF: Analyze PDFs for RAG with Azure Layout
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PyMuPDF: Parse PDFs for RAG with Azure Layout! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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Single-image to multi-view enhances dragon head model
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Then we used single-image to multi-view on the same dragon head.
— AI Highlight (@AIHighlight) 15 juin 2026
This gave the model more angles to work from instead of relying on one flat front view.
For a shape like this, the side profile, horn direction, jawline, and scale structure all matter. Multi-view made the asset… pic.twitter.com/IeNLucluWkThen we used single-image to multi-view on the same dragon head. This gave the model more angles to work from instead of relying on one flat front view. For a shape like this, the side profile, horn direction, jawline, and scale structure all matter. Multi-view made the asset
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Using Image Generation for Clean 3D Dragon Head Reference
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The first asset was a dragon head bust.
— AI Highlight (@AIHighlight) 15 juin 2026
We started with Image Generation to create a clean reference image first: single object, centered, full silhouette, clear horns, scales, and facial structure.
That clean starting point matters because 3D generation works better when the… pic.twitter.com/8tcHnNL0V8The first asset was a dragon head bust. We started with Image Generation to create a clean reference image first: single object, centered, full silhouette, clear horns, scales, and facial structure. That clean starting point matters because 3D generation works better when the
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3D AI creates dragon head and NPC from an image
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This AI 3D tool is on another level.
— AI Highlight (@AIHighlight) 15 juin 2026
A dragon head and a dungeon NPC. Both from a single image.
Here is how: pic.twitter.com/XUXvuj1jPEThis 3D AI tool is on another level. A dragon head and a dungeon NPC. Both from a single image. Here's how: