FlowBlending Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Generation
GENERATIVE AI
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Dream2Flow: Video Generation with 3D Object Flow for Manipulation
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Dream2Flow
— AK (@_akhaliq) 2 janvier 2026
Bridging Video Generation and Open-World Manipulation with 3D Object Flow pic.twitter.com/5m09xRzjG0Dream2Flow Bridging Generation and Open-World Manipulation with 3D Object Flow
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DiffThinker: Generative Multimodal Reasoning with Diffusion Models
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DiffThinker Towards Generative Multimodal Reasoning with Diffusion Models
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JavisGPT: Multi-modal LLM for Video Understanding and Generation
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JavisGPT A Unified Multi-modal LLM for Sounding-Video Comprehension and Generation
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AI 2025 Advances: Reviewing Predictions and Accuracy
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¡DIRECTO ESPECIAL ESTA TARDE! Hoy estaremos analizando los grandes avances en IA del 2025 corrigiendo las predicciones que hice a comienzos del año pasado! ¿Cuántas habré acertado? Lo votaréis vosotros a partir de las 18:30h española. Link a continuación!
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Instagram evolves for AI as DeepSeek hints at next-gen architecture
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Top stories in AI today: – IG head platform must “evolve fast” for AI
– DeepSeek hints at next-gen AI architecture
– Use Codex to write code on the web
– OAI overhauling audio for upcoming device
– 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/instagrams-a
i-driven-identity-crisis
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OpenAI Create-Plan Skill: Automated Planning Operations
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if you want to see how the create-plan skill operates: https://
github.com/openai/skills/
blob/main/skills/.experimental/create-plan/SKILL.md
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The Efficiency Era of AI Models
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The lottery ticket hypothesis wasn't wrong. We just weren't ready for it. In 2025, sparse models are no longer academic curiosities. They're production infrastructure. The future isn't bigger models. It's smarter pruning. Welcome to the efficiency era. Read it here if you
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Sparse Models Deliver Real-World Gains
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Real-world results from companies already deploying sparse models: OpenAI: 40% cost reduction on GPT-4 API
Meta: 3x throughput increase for Llama inference
Google: 60% memory savings for production transformers The early adopters are already winning. -
Transformational AI Model Efficiency Gains
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The deployment implications are massive: – GPT-3 scale models (175B params) → 17.5B params at same accuracy
– Monthly inference costs: $500K → $50K
– Latency: 2 seconds → 200ms
– Memory requirements: 350GB → 35GB This isn't incremental. It's transformational.