AI Dataset2026
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).
MCP Server2026
Provides a local-first web-intelligence layer for AI agents: search, fetch, crawl, extract, cache, find-similar and agent-style research without API keys or per-query billing, running as an MCP server, REST daemon, or SDK.
AI Model2026
Produces 384‑dim multilingual (and code) embeddings with up to 32,768 token context, optimized for low‑latency production retrieval. Compact 97M model with ONNX/OpenVINO and vLLM/GGUF deployment options for edge and high‑throughput use.
AI API2026
Provides a single OpenAI-compatible /v1 API that aggregates the free tiers of 16 LLM providers into one unified endpoint. Features smart routing and automatic failover, per-key free-tier tracking, encrypted key storage, embeddings/media routing, and a Docker one-liner for local use.
AI Dataset2026
Provides a 30K+ problem multimodal, multilingual dataset of Olympiad-level math problems with expert solutions and a math-aware retrieval benchmark—includes images, hierarchical topics, provenance from official booklets, and LLM-assisted metadata (v0, CC BY 4.0).
AI Dataset2026
Provides 104.9M curated image–text pairs with precomputed embeddings, structured annotations and pre-encoded VAE latents for text-to-image pretraining and retrieval. Combines filtered web sources and synthetic samples with multi-model re-captioning, deduplication and safety filters; Apache-2.0.
AI Infra2026
Provides tools and samples to build context management, enrichment, and retrieval solutions on Google Cloud Knowledge Catalog — an AI-oriented data catalog that builds a dynamic knowledge graph for structured and unstructured data, suitable for RAG and agent workflows.
AI Dataset2026
A retrieval benchmark suite focused on “oblique queries,” where relevance depends on latent attributes rather than surface keywords. Includes five tasks with large corpora, qrels (and pooled judgments), and task-specific constraints for evaluating embedding-based retrievers and reasoning-augmented retrieval.
MCP Server2026
Provides long-term memory for AI coding agents by compiling sanitized lifecycle observations into a git-versioned Markdown wiki that enables cross-agent handoffs, per-project isolation, and optional vector-backed retrieval.
Analyzes spatial representations in vision–language models and reveals a consistent vertical-position ↔ distance entanglement; introduces SpatialTunnel, a synthetic benchmark that exposes this perspective-driven shortcut, and provides code and a project page.
AI Agent Papers2026
Analyzes when masking stale observations improves long-horizon search agents and why, identifying an asymmetric inverted-U relationship between masking benefit, retriever quality, and model capacity; explains a token-for-turn trade-off and releases evaluation scaffolds and trajectories.