Indexes any codebase into a knowledge graph — dependencies, call chains, clusters and execution flows precomputed — then serves it to coding agents through 17 smart MCP tools so Cursor, Claude Code and Codex never miss code.
Genesis AI's open simulation platform for physical AI: one Pythonic API over a multi-physics engine (rigid, FEM, MPM, PBD, SPH, Stable Fluid, IPC), the Nyx path tracer, and Quadrants, a cross-platform Python-to-GPU compiler. It is built to be the evaluation engine for robot foundation models rather than a data factory: 30,000 parallel environments at 43M FPS, and under a zero-shot real-to-sim protocol, sim-vs-real evaluation agreement of Pearson 0.8996 with mean maximum rank violation 0.0166. Apache 2.0, 29.9k stars.
A from-scratch C++/CUDA inference engine for five explicitly registered Qwen checkpoints on one NVIDIA GeForce RTX 5090. Startup-frozen residency picks MTP or DFlash speculative decoding, Vision, and one of five KV storage formats; a shared Device KV pool plus pinned Host State/KV checkpoints reuse exact prompt prefixes across 240k-token contexts. Measured aggregate decode reaches 1,146.9 tok/s at concurrency 8 and 15,544.3 tok/s on a 7,680-token prefill.
A one-stop VLA toolbox from Dexmal: the unified DexData format spans pretraining, fine-tuning (full/LoRA/RL), inference, and evaluation, supporting mainstream models (π0, CogACT, OFT, MemVLA, GR00T N1) plus the in-house dual-expert DM0; DB-pretraining brings consistent gains across five simulation benchmarks, with 62% average success on RoboChallenge Table30 real-robot evaluation.
A fixed-protocol harness for comparing image-to-3D models. One set of AI renders of a fictional submersible goes into TRELLIS V1/V2 and TripoSR; each mesh is inspected through an identical Blender 4.3 EEVEE orbit and scored on a 0-2 five-axis rubric, then the winner ships into a Three.js + three-mesh-bvh explorer with real collision. TRELLIS V2 stochastic ranks first at 1.56 MB / 11,866 faces.
Bimo is a 45 cm, roughly 1.6 kg hip-head biped kit: eight STS-3215 bus servos, a BNO08x 9-DoF IMU, four VL53L0X rangefinders, two 180-degree cameras, and a custom RP2040 board closing a 20 Hz control loop. The repository publishes the Python control API (1023 lines), three MCU firmware builds (1349 lines), a ROS2 wrapper (1431 lines) and an Isaac Lab training environment (874 lines: six reward terms, dense domain randomization, and a system-identified STS3215Actuator with 3.113 Hz bandwidth, directional gear backlash and a 5 ms bus delay), plus a 26MB Bimo.usd model. The CPG gait's 104 Fourier coefficients and per-joint amplitude gains are hardcoded in both Python and C, and a [256,128,64] PPO teacher distills into a [64,32] student that goes through onnx2c into the firmware, so the robot walks untethered on the MCU. All code is Apache-2.0, but the CAD and electronics the README promises are still marked coming soon: no STL, STEP, schematic, gerbers or BOM exist in the working tree or in git history, and neither release ships pretrained weights (zero assets) or a DIY assembly manual. Currently v1.1.0 with 198 stars, in pre-order status.
A compact generalist navigation model from Light Origins: Qwen3-VL-4B backbone + dual-channel pointing + RVQ action tokens. One checkpoint covers instruction following, object navigation and visual tracking, transferring zero-shot across humanoid/quadruped/wheeled/aerial robots. Trained entirely in simulation, Apache-2.0.
ElectroPup is a 4.5 kg, 12-DoF DIY quadruped: twelve MG4010E-i10v3 CAN BLDC actuators split into four per-leg CAN segments, a Raspberry Pi 5 for compute, and a single ~7,700-line pure-Python service with no ROS in the stack. The repository publishes OnShape CAD, two KiCad v8 boards, a bill of materials, measured weights and power draw (21.6 W suspended, 33.8 W standing), plus MuJoCo simulation, a React Native/Expo browser UI, four Bezier-foot gaits, and a full build guide. The author also documents known gaps: no license file, no contact sensing, and a simulation model without approximate masses or inertias.
A small Python + C++ library from Kevin Zakka that steps thousands of MuJoCo simulations in parallel on the CPU through a single Batch object: C++ thread pool execution with the GIL released, bind() giving live array views across the whole batch, and expand() for per-simulation model parameters. Memory scales with thread count rather than simulation count, so 4096 simulations stay under 256MB; on a 24-thread machine with a Unitree G1 scene it measured 10.3x over a serial mj_step loop, reaching 676,019 sim-substeps per second with ten substeps batched per call. The repo ships six self-contained solver examples spanning iLQR, predictive-sampling MPC, PPO reinforcement learning, CEM hardware co-design and damped Gauss-Newton system identification, including a Go1 joystick controller that learns to walk in under a minute on a five-year-old M1 laptop.
Open-source hand exoskeleton: 12 joint encoders, 8 tendon-driven motors, on-device Teensy control, and synchronized capture/EMG for motion capture, teleoperation and haptics research.