AI Infra2025
Keeps codebases, PDFs, Slack, and docs continuously indexed for RAG and knowledge graphs by recomputing only what changed, not the whole dataset. You declare target state in Python; a Rust engine maintains it with per-row lineage back to the source.
AI Infra2025
Transforms unstructured financial content—papers, news, blogs, and filings—into a queryable semantic knowledge graph for retrieval-augmented research. Combines domain-tuned LLMs, embedding-based search, and modular ingestion pipelines; aimed at quant research teams and institutional workflows.
AI Agent2025
Autonomously executes diverse biomedical research tasks by combining LLM reasoning, retrieval-augmented planning, and code-based execution. Includes a web UI and Gradio demo, a curated Know‑How library, MCP integration, and a biology-tailored reasoning model (Biomni‑R0).
AI Video2025
Turns a raw idea, novel, or screenplay into a complete multi-shot video through a multi-agent pipeline that scripts, storyboards, and renders shots while a vision model checks character and scene consistency across the whole story.
AI Agent2025
Builds a table-of-contents tree index over long PDFs and uses LLM tree search to fetch relevant sections — no embeddings, chunking, or vector database. Hits 98.7% on FinanceBench, for financial, legal, and technical docs where relevance needs reasoning.
AI Agent2025
Provides ready-to-use sample agents for Google’s Agent Development Kit across Python, TypeScript, Go, Java, Kotlin, and Android, from simple assistants to multi-agent workflows.
AI Infra2025
Converts PDFs into AI-ready structured outputs (Markdown, JSON with bounding boxes, HTML) for RAG and accessibility workflows; offers deterministic local parsing plus a hybrid AI mode for complex tables, OCR, formulas, and auto-tagging previews.
AI Agent2025
Transforms research papers, natural-language specs, and technical descriptions into runnable code via a multi-agent system. Covers Paper2Code, Text2Web, and Text2Backend; scores 75.9% on OpenAI's PaperBench, ahead of top ML PhDs.
AI Agent2025
Packages an AI agent's memory — data, embeddings, search indexes, and metadata — into one portable .mv2 file, replacing multi-service RAG stacks. Combines BM25 and HNSW search with temporal queries and sub-millisecond local reads, fully offline.
AI Agent2025
Wraps Claude Code as an MCP server that orchestrates 100+ specialized agents into self-organizing swarms — hierarchical, mesh, or adaptive consensus — backed by persistent vector memory, coordination hooks, and secure cross-machine federation.
AI Agent2025
Wraps Claude Code and Codex with an execution harness that turns one coding agent into coordinated swarms. A single init command adds ~98 agents, an MCP tool server, cross-session vector memory, and cross-machine federation.
Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.