Add GitHub links to dataset card

README.md

@@ -69,7 +69,7 @@ homepage: "https://www.zerotrustphysics.com"
 
 **First-principles physical ground truth trajectory datasets for autonomous systems and Sim-to-Real calibration. Generated in pure Rust, sealed with SHA-256 hash chains, and verified on-chain.**
 
-Training robot brains in cartoon physics environments (like MuJoCo or NVIDIA Isaac Sim) leads to the **Sim-to-Real Cliff**. When your policy transitions from smooth simulators to physical hardware, it encounters non-deterministic slippage, thermal degradation, control loop jitter, sensor latency, and joint wear that linear models ignore. 
+Training robot brains in visual simulation environments (like MuJoCo or NVIDIA Isaac Sim) leads to the **Sim-to-Real Cliff** when physical limits are uncalibrated. When your policy transitions from standard simulator environments to real hardware, it encounters non-deterministic slippage, thermal degradation, control loop jitter, sensor latency, and mechanical wear that simplified dynamics models can ignore. 
 
 G^G provides sovereign physical ground truth datasets modeling raw physics failures at 1000Hz (1ms step latency) to close the Sim-to-Real gap.
 
@@ -202,4 +202,5 @@ John Kruze builds physics ground truth for autonomous systems. 13 physics domain
 The core thesis: autonomous systems operating without human-in-the-loop require deep physical reasoning, not visual game engine approximations. When GPS is denied, when communication is jammed, when the wind shear hits — your autopilot must reason from physics priors alone.
 
 **Website:** [www.zerotrustphysics.com](https://www.zerotrustphysics.com)  
+**GitHub:** [github.com/johnkruze/genesis-core](https://github.com/johnkruze/genesis-core) (engine source) · [github.com/johnkruze/gg-physics](https://github.com/johnkruze/gg-physics) (framework docs)  
 **Author:** John Kruze ([LinkedIn](https://www.linkedin.com/in/john-kruze-34a6683a5/))