Real-time 3D Gaussian Splatting renderer for web apps using THREE.js. Integrates splat and mesh rendering with a Rust + Wasm component, supports major splat formats (.PLY, .SPZ, .SOG) and targets broad WebGL2 support for mobile-friendly dynamic scenes.
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.
Parses the local JSONL logs that coding-agent CLIs write and turns them into token and cost reports, no API keys or telemetry. Breaks spend down by day, month, session, and Claude's 5-hour billing windows across Claude Code, Codex, Gemini CLI and more.
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.
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.
Provides a community-curated database of AI model metadata—specs, pricing, and capabilities—and exposes it via a JSON API and a TOML-based contributor workflow for programmatic lookup and integration.
Extends RAG beyond text: parses PDFs and Office files containing images, tables, equations, and charts, then queries them through one multimodal knowledge graph. Built on LightRAG, it replaces separate parsing and retrieval tools.
Gives AI coding assistants a queryable index of n8n's 2,000+ workflow nodes — their real properties, operations, and 2,300+ templates — so generated workflow JSON validates instead of hallucinating node names and connections.
Turns commodity WiFi Channel State Information into spatial sensing: 17-keypoint pose estimation, presence detection, and contactless breathing/heart-rate monitoring through walls, with no camera. Runs on a mesh of ESP32-S3 nodes (~$9 each).
Reimplements the vLLM inference engine from scratch in ~1,200 lines of readable Python, matching its offline throughput on small models. Prefix caching, tensor parallelism, torch.compile, and CUDA graphs are all kept legible.
Run Claude as a programmatic agent in Python: one-shot query() calls or a stateful ClaudeSDKClient for multi-turn loops. Define in-process tools, lifecycle hooks, and per-tool permissions; it bundles the Claude Code CLI and exposes its full toolset.