#多模态

Gemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.

Beyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench🏆 #1 in parsing speed among open-source models⚡ RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! 🌟

Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

Computer built Big Boy No. 4014, the world's largest operating steam locomotive, as a live isometric SVG with its own renderer and a 7-step operating sequence from cold boiler to 60 mph. The live model has 1,324 parts and 15,312 convex faces, built from @UnionPacific's published figures. Try it: http://big-boy-4014.pplx.app

Spent some time experimenting with MiniCPM-V4.7 35B-A3B and built this Neo-Brutalist portfolio UI, featuring bold colors, chunky layouts, and a slightly unconventional visual style. What makes this experiment interesting is the model behind it. OpenBMB’s MiniCPM-V 4.7 is pushing multimodal AI toward more practical deployment on everyday hardware. With 35B total parameters but only 3B activated at a time, MiniCPM-V 4.7 35B-A3B delivers 17+ tokens/s on iPad Pro M4 (16GB) and 13+ tokens/s on iPhone 17 Pro Max, using just ~3GB of memory in its optimized mobile setup. 🤯 There’s also a compact 1B variant designed for tasks like OCR, chart, math, and general understanding. Both variants support a 256K context window, and the model weights, inference code, and fine-tuning code are open source. For me, the interesting part of multimodal AI is its potential to work with visual information beyond text, and to make those capabilities more accessible through efficient, flexible deployment. Explore MiniCPM-V 4.7 from @OpenBMB: GitHub：http://github.com/OpenBMB/MiniCPM-V Hugging Face：http://huggingface.co/collections/openbmb/minicpm-v-47

I built an automated report generator that runs entirely on my own machine. Input: structured data + charts. Output: a full long-form report — earnings interpretation, weekly summaries, whatever you need. No API. No cloud. No monthly bill. -> Here's MiniCPM-V 4.7 35B-A3B doing exactly that 👇 It read the charts AND the dense tables in one pass, then streamed out analyst-grade markdown: scorecard, segment breakdown, margin drivers, cash flow, even a bull/bear case. The part that got me: it didn't just OCR numbers. It connected them — input costs → margin pressure → guidance risk. That's the jump from "text extraction" to "actual analysis." Why this works locally: → 35B total params, only ~3B active (MoE) → Efficient visual encoding keeps multi-chart inputs cheap → Structured markdown output — goes straight into your doc pipeline Try it GitHub: http://github.com/OpenBMB/MiniCPM-V Hugging Face: https://huggingface.co/collections/openbmb/minicpm-v-47

Our next generation of open weights multimodal decision models comes in two sizes, pplx-decider-v1.2 and pplx-decider-flash-v1.2. Text and vision scores improve. Inference is up to 5x faster: all questions in a request run in one pass. Flash is the top 4B on the Decision Index.

Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.

Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.