Discover the Best AI Resources

Lets AI agents use a user's logged-in Chromium browser to read pages, fill forms, capture screenshots, and debug web workflows in an isolated Agent Window so the user's normal browsing is not interrupted.

Qwen-AgentWorld-35B-A3B

Simulates agentic environments and predicts next environment states from actions and interaction history using a language-based world model across seven domains. Trained via a CPT→SFT→RL pipeline with an MoE architecture and very long context; intended for environment simulation and agent research.

AgentWorldBench

Provides 2,170 reference-grounded evaluation samples across seven agent domains (MCP, Search, Terminal, SWE, Android, Web, OS) to score language world models on Format, Factuality, Consistency, Realism and Quality. Includes per-domain JSONL files, judge prompts and an evaluation script for reproducible scoring.

bigfacing/GOKU-2M

Provides ~2 million instruction-aligned video-edit pairs for training and evaluating instruction-based video editing and generation models. Covers multi-task and structural edits (e.g., camera/subject movement), produced via a synthesis pipeline with progressive filtering; licensed CC BY-NC-4.0.

God's Eye View

Browser-based photorealistic 3D globe that visualizes live public signals — flights, ships, satellites, earthquakes, traffic and public cameras — paired with a realtime AI voice agent for entity Q&A, scene summaries and hands‑free navigation.

GOKU-2M

Multimodal video dataset for text-to-video and video-to-video research: about 2 million short English videos and extracted frames for instruction-based video editing and generation. Hosted on Hugging Face and licensed CC BY‑NC 4.0 (non-commercial).

NVIDIA GLM-5.2 NVFP4

Provides a pre-quantized NVFP4 checkpoint of GLM-5.2 for long-context reasoning and coding; reduces model footprint so GLM-5.2 can run on multi‑GPU Blackwell nodes and is ready for inference with SGLang and vLLM.

nvidia/Qwen3.6-27B-NVFP4

NVFP4-quantized variant of Qwen3.6-27B that reduces parameter bits from 16 to 4, cutting disk and GPU memory requirements by ~2.5× while keeping comparable benchmark accuracy; ready for vLLM-based inference on NVIDIA hardware and supports long, multimodal contexts.

SVG Generation Benchmark (Static)

Compares 30 frontier LLMs generating static SVG markup from 500 prompts using 1,355,161 human votes across three leaderboards (Preference, Coherence, Alignment); provides raw SVGs, 768×768 rasterized PNGs, and per-comparison human vote records under a CC-BY-4.0 prompt license.

Ornith-1.0-397B

Provides an open-source Mixture-of-Experts coding LLM (397B) optimized for agentic, tool-enabled coding workflows with a 262,144-token context window, OpenAI-compatible API, serving recipes (vLLM/SGLang), and published coding-benchmark results.

LFM2.5-230M

230M-parameter multilingual instruction-tuned text-only LLM for on-device agentic pipelines and data extraction; 32K context, 19T-token pretraining, optimized for fast CPU/edge inference (e.g., 213 tok/s on Galaxy S25 Ultra, 42 tok/s on Raspberry Pi 5); not for heavy reasoning or complex code generation.

EdgeBench

Measures how autonomous AI agents learn via long-horizon, feedback-rich executable tasks; publishes 51 public tasks from a 134-task suite and provides SForge, a two-container evaluation harness for iterative 12+ hour runs to track learning trajectories.