Discover the Best AI Resources
Curated essentials, no noise — just what matters
Turns a codebase into a live structural knowledge graph that coding agents can query in milliseconds. Bi-temporal, replay-aware indexing of symbols and relationships performed locally with zero LLM API calls; Rust-native, MCP-native integrations and fast incremental updates.
Provides a ~9.2M-instance Japanese multimodal post-training dataset for vision–language models, combining image–text pairs, PDF corpora and generated VQA to improve Japanese VLM performance; access is restricted by Japanese copyright (download via llm-jp GitLab).
Generates text by iteratively denoising blocks of tokens with a two-tower design: a frozen autoregressive context tower and a trainable diffusion denoiser tower, trading minimal quality loss for higher wall-clock throughput.
Provides one million executable, human-readable CadQuery construction sequences synthesized by an LLM-in-the-loop—each sample includes renders, STL/STEP exports, precomputed DINOv3 embeddings and a FAISS index. Designed for training and benchmarking text/image→3D and CAD-program generation models (Apache-2.0).
Automates video editing driven by LLM agents: reads word-level transcripts to propose and execute cuts, remove filler words, auto grade color, burn subtitles, and generate animation overlays. Self-evaluates every cut before showing a preview; aimed at talking-heads, tutorials and interviews.
A Mixture-of-Experts instruct-capable LLM (295B total, 21B active) designed for long-context reasoning, code/agent workflows and instruction-following; released by Tencent Hy Team with safetensors weights on Hugging Face.
Provides 1,003,589 full chain-of-thought reasoning traces and final answers generated by GLM-5.1, split into main/Math/PHD-Science/Multilingual-STEM subsets. Useful for instruction-tuning, supervised fine-tuning, and reasoning experiments; released under Apache-2.0.
Delivers an ultra-efficient, edge-friendly multimodal image-and-video-to-text model optimized for on-device deployment. Uses mixed 4x/16x visual token compression, a low-FLOPs visual encoder, and multiple quantized variants for mobile and embedded inference.
Multimodal agent model for long-horizon coding, image-text understanding, and autonomous task orchestration. Built as a 1T-parameter Mixture-of-Experts with 256K context and native int4 quantization — intended for coding-driven design, persistent background agents, and swarm-style sub-agent workflows.
Turns plain-English system or process descriptions into polished, themeable architecture, workflow, sequence, data-flow and lifecycle diagrams as a self-contained HTML file, with one-click theme toggle, copy-to-clipboard and export to PNG/JPEG/WebP/SVG (native up-to-4× rasterization).
Provides 336,146 Turkish instruction-following chat examples (system→user→assistant) for supervised fine-tuning; single train split (no validation/test), reported MIT license, diverse tasks (rewrites, summarization, QA) and a uniform system prompt that may bias model behavior.
Provides paired before/after satellite images with question–answer annotations for semantic change understanding. Includes Yes/No and multiple-choice formats, delivered in Hugging Face datasets (streaming-friendly), suited for remote-sensing multimodal VQA and semantic change captioning research.