LLaVA-3D revolutionises 3D vision by extending 2D large multimodal models with spatial awareness, enabling 3.5x faster training and state-of-the-art 3D understanding. —  @meisshaily #ArtificialIntelligence# #AI# #TechNews# #TechNology# #VisionModels# #Tech#

How can I fine-tune vision models efficiently for multimodal #AI# applications? I’ve been on this journey myself, navigating the complex landscape of model adaptation, and I want to share what I’ve learned.  — @meisshaily #ArtificialIntelligence# #TechNews# #Tech# #Technology# #VisionModels# #Gemini# #Claude#

Curious about generalist vision models? Check out "Vision Banana: Generalist Vision Model from Nano Banana" presented by Nithish Kannen from @GoogleDeepMind at #ICLR2026#! Stop by the Google booth (#411#) at 10AM to learn more.

$NVDA is bringing more AI inference directly onto robots and drones with Jetson Orin Nano 2 delivering 2x the performance at lower power. That means more language and vision models can run locally at the edge without relying on the cloud.

Thoughts on mlx-lm: top-priority is making it the central registry of MLX model implementations, with tools for evaluation and profiling, and we should add vision models too. Inference engines can have their own schedulers and custom kernels, and do whatever hack to make inference ultra fast, while importing mlx-lm as a library of models. We can rely on community to contribute model implementations, but there would be a fixed procedure to verify correctness of the implementation, ideally automatically. Everything else except for critical bugs, should be irrelevant at the moment and I'm closing PRs and issues aggressively. Many people will be mad at this, and certainly I would be making mistakes closing legitimate things, but for the project to survive, and for the community to grow healthy, I don't see another way.

This is how you build the next generation of AI applications. You need an idea, a good model architecture, and a great harness. That's what MUZIM did. It's an app that runs local multimodal and vision models offline to parse and index your images, videos, and documents frame by frame. • Super fast on-device processing • On-device video & image understanding • You can also bring your API keys for heavy reasoning • Your data stays where it is / nothing goes to the cloud You can do a couple of things with this: 1. Vibe searching for whatever you remember in photos or raw video clips. The app will jump straight to the exact frame and timestamp of the video. This is search as it should be. 2. Use local-running agents to perform tasks on your files. I show a couple of examples in my video. You can check it out here: Thanks to the @muzim_opensoul team for showing me the tool and partnering with me on this post.

Interactive video world models that generate footage as you control them — here's a unified benchmark that finally measures them fairly 🎮 Title: WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation URL: 🎮 Overview WBench is a unified framework for comprehensively evaluating interactive video world models. With 289 test cases and 1,058 interaction turns, it unifies text, 6-DoF pose, and discrete-action control so models with different native inputs can be compared on equal footing. ❓ Challenges Solved Interactive world models are advancing fast, but there was no comprehensive standard to assess them. Existing benchmarks only partially covered the needed competencies, and differing input interfaces made apples-to-apples comparison hard. 💡 Methodology & Proposed Approach Evaluation spans five core dimensions. ・Video quality ・Setting adherence ・Interaction adherence ・Consistency ・Physics compliance Tasks cover navigation, subject action, event editing, and perspective switching. It uses 22 automatic sub-metrics combining specialist vision models with large multimodal models, all validated against human judgments. 📊 Experimental Results Analyzing 20 state-of-the-art models revealed that no single model performs strongly across all dimensions, exposing characteristic strengths, weaknesses, and persistent challenges across approaches. #WorldModels# #Benchmark#

Why do AI-generated UIs all look so generic? It's the workflow, not the prompt 🎨 A practical playbook for producing genuinely beautiful UIs. Title: Generating Beautiful UIs URL: 🎨 Overview This post lays out a practical methodology for generating beautiful UIs with AI. The thesis is that there's no single magic technique — what works is a disciplined workflow built on pre-defined design systems and fast iteration loops. ❓ Challenges Solved AI-generated UIs tend to come out generic and predictable. The post names the common failure modes. ・Dashboard-ification: turning everything into a dashboard ・Nested cards: redundant cards inside cards ・Instruction leakage: prompt instructions bleeding into the on-screen copy ・Weak compositional logic: layouts that break down and lack beauty or resonance 💡 Methodology & Proposed Approach The post recommends a methodical workflow built from these steps. ・Use component libraries: shadcn/ui via MCP integration ・Pre-define the design system: keep design tokens as readable files to prevent hallucination ・Enforce constraints: use Tailwind config to block drift ・Iterate with vision models: feed screenshots to run a visual improvement loop ・Generate multiple options before committing ・Test with hostile, realistic data during development 🌍 Use Cases / Experimental Results Combining fast inference with a disciplined workflow turns AI from a gimmick into a real prototyping accelerator. ・Codex-Spark runs at ~1,200 tokens/sec on Cerebras, generating several design options in minutes ・With proper tooling, components compile on the first attempt ・Tighter feedback loops reduce wasted tokens The conclusion: AI is a fast, overconfident junior designer that still needs human art direction, not an autonomous replacement. #UIDesign# #GenerativeAI#