Lets AI agents describe interactive UIs as declarative JSON instead of executable code; client apps render the components with native widgets from a pre-approved catalog, keeping agent-generated UI safe across trust boundaries.
A library of 232 ready-made AI agent personas across 16 divisions — engineering, design, marketing, sales, security, finance, and more. Each defines a role, workflow, and concrete deliverables rather than just a prompt template.
Generates production-grade synthetic datasets from scratch or from seed data using dependency-aware samplers, LLM-backed text columns, built-in validators, previewing, and LLM-as-judge scoring.
Turns documentation sites, GitHub repos, PDFs, videos and other sources into ready-to-use skill packs for Claude, Gemini, OpenAI and RAG frameworks like LangChain. Detects conflicts across sources, transcribes video, and exports to 21 formats.
Detects motion from Wi‑Fi channel state information (CSI) on cheap ESP32 boards and integrates natively with Home Assistant; offers an optional on‑device ML detector that requires no calibration.
Provides mined hard negatives and relevance scores for 1.88M queries across seven retrieval datasets, enabling contrastive fine-tuning and nv-retrieve filtering; includes full 2048 mined negatives per query, paired query/document splits, and parquet-formatted files for large-scale training.
Drives an LLM-powered agent to autonomously research, write, and ship ML code by accessing Hugging Face docs, datasets, repos, and cloud compute. Provides interactive CLI and headless modes, approval gates, tool routing, and integrations for HF, GitHub, and Anthropic models.
Aggregates SEC EDGAR filings into raw files, parsed plaintext, and rich filing metadata for LLM training and retrieval. Includes ~8.05M filings (~590 GB, ~43B tokens), per-filing token counts, and parsed outputs; Apache-2.0.
Runs recurring workplace tasks across 100+ tools (Slack, GitHub, Gmail, Notion, Linear) as scheduled sub-agents that triage errors, draft outreach, and compile daily briefs. Each run executes in an isolated Firecracker microVM with scoped permissions.
Turn plain-English requests into editable draw.io diagrams: the model writes the underlying draw.io XML, which renders live in an embedded canvas. Upload images, PDFs, or text to replicate, refine through chat, and roll back via version history.
Provides a frontend-design skill plus 20 steering commands and curated anti-patterns to steer LLMs toward clearer, accessible UI designs. Designed to plug into AI harnesses (Cursor, Claude/Gemini CLI, code agents) for auditing, critiquing, and polishing interfaces.
Extracts local chat logs, code context, diffs, and tool outputs from AI coding assistants and exports them as ML-ready JSONL. Auto-discovers common storage locations and handles SQLite/JSONL formats; scan extracted files for secrets before sharing.