StarBound

Dyson sphere videos make the scale look spectacular. I wanted to explore what makes the engineering fail. StarBound follows a Mercury industrial seed through mining, manufacturing, launch, solar collection and power delivery to Earth. A deterministic, simplified physics engine tracks mass and energy conservation, thermal limits, beam diffraction and receiver caps. Change a mission parameter and it recalculates the scenario. Move collectors too close to the Sun without enough cooling, and the model disables electrical output. Under the default assumptions, switching from an optical relay to direct microwave drops Earth power from 13.23 MW to 127 W. Then I put GPT-6 Astra in the loop. Astra proposes a mission policy, my engine grades it, and Astra revises from the measured result. Its proposals cannot change the physics or scoring rules. I ran GPT-5.6 Sol through the same protocol and published the proposals, measured results and comparison as JSON. Their best mining scores tied. The point is an AI engineering decision you can inspect, reproduce and challenge. https://github.com/vnmoorthy/starbound/blob/main/deliverables/StarBound-Mission-Briefing.pptx