MCP Client2026
Fetches multi-source content (webpages, YouTube, PDFs, WeChat, paywalled articles, podcasts), uploads it to Google NotebookLM, and generates outputs such as podcasts, PPTs, mind maps, or quizzes. Differentiators: automatic paywall-bypass pipeline, Claude Code Skill integration, and CLI + MCP components for WeChat and document scraping.
AI Dataset2026
Provides a physical reconstruction benchmark of OmniDocBench v1.5 by producing five real-world photographic variants (Scanning, Warping, Screen‑Photography, Illumination, Skew) for each of 1,355 pages, inheriting original ground-truth to enable controlled, scenario-wise evaluation of document parsing robustness.
AI Dataset2026
Paired brain MRI scans and radiology text annotations for multimodal vision–language research. Provides image-level labels and image–text pairs suited for VQA, classification, and image-to-text tasks; CC BY-NC-SA 4.0 and ~10K–100K samples — research/non-commercial use.
AI Deploy2026
Runs local AI models on Apple Silicon as an OpenAI‑compatible server, emphasizing low latency, prompt caching, and reliable tool-calling. Optimized for M1–M4 Macs with multimodal support and drop‑in compatibility for IDEs and agent frameworks.
AI Video2026
Converts scene intent into production-ready Seedance 2.0 prompts, reference-role mappings, and IP-safe rewrites for multimodal (text/image/audio/video) video generation. Ships as a modular agent-skill OS with multilingual examples, troubleshooting tools, and pro filmmaker handoff artifacts.
AI Model2026
An instruction‑tuned Gemma 4 E4B multimodal model on Hugging Face that accepts text, images and audio and generates text; notable for 128K long context support, built-in thinking mode, and an on‑device‑friendly E4B architecture under an Apache‑2.0 license.
AI Model2026
Community fine-tuned multimodal Qwen3.5-9B using Claude 4.6 distilled data to change the model's 'thinking' behavior; offers an uncensored 'heretic' flavor with image-text-to-text I/O, benchmark comparisons, and deployment notes for inference frameworks.
AI Dataset2026
Provides an annotated multimodal human-motion dataset for language-to-action and robotics research, with BVH and MuJoCo files plus recordings targeted at Unitree-G1 and NVIDIA-SOMA platforms. Covers locomotion, gestures, dance and object interaction with English annotations and 100K–1M samples.
AI Model2026
Generate text, images, video, audio and action/robot trajectories from combined text, image, video, audio and action inputs. A Mixture-of-Transformers omnimodal foundation model (Cosmos3‑Nano, 16B params) focused on Physical AI (robotics, AV, simulation) and optimized for NVIDIA GPU runtimes.
AI Dataset2026
Provides 10 million synchronized egocentric experience episodes with structured 3D/4D multimodal annotations — 2.88B RGB frames, 720M depth frames, 576M pose/mocap frames and ~1PB total. Designed for embodied AI, robotics, and multimodal pretraining; research-only, gated access.
AI Model2026
Instruction-tuned Mixture-of-Experts multimodal model that generates text from text+image inputs while activating a 4B subset of parameters for faster inference; supports a 256K context window, multilingual vision-language tasks, and is available under Apache-2.0.
AI Dataset2026
Large-scale mid-training corpora for multimodal models: 10,809 ~60s video shards, caption splits (30s/60s/180s/>10min), 84 spatial-reasoning shards, and CSV mappings to source YouTube IDs. Small Parquet preview configs are provided for schema inspection.