@BodaSatvikChandra

“Spent these hours doing a massive architectural refactor of CopterCode to eradicate all hardcoded ‘magic numbers’ and implement strict Pydantic schemas. I also properly structured the GitHub repository into modular directories (config/, schemas/, docs/) and debugged a Plotly deployment issue on Streamlit Cloud by setting up a strict requirements.txt. The live app is now fully operational and shipped!”

Just locked in a massive refactor on my Astro-Forge drone generator, finally tearing out all the hardcoded “ghost variables” to wire up a mathematically honest, end-to-end physics solver. Now, when you prompt it with something like “high speed racing drone,” the system automatically provisions actual hardware from a JSON database (like Emax motors and F7 stacks), iteratively calculates the exact volumetric frame mass instead of guessing, and runs a structural solver that sweeps arm dimensions until it guarantees a 1.5+ safety factor against real motor thrust. From there, it headlessly auto-generates the OpenSCAD geometry and spits out a full Markdown Assembly Manual complete with an itemized BoM, material cost, and print time. The whole pipeline is now wrapped in a clean CLI and fully protected by automated pytest invariants, making it incredibly satisfying to watch the system catch AI hallucinations and cleanly route to the correct aerodynamic topology!

The Astro-Forge v5.0 pipeline is complete! I upgraded the Python command center to feature an interactive CLI. The system now interviews the user about their mission profile and target environment, dynamically piping those inputs into the local Phi-3 LLM.I successfully tested it by requesting a “Supersonic Drone” for Earth’s atmosphere. The script generated the physics-audited OpenSCAD geometry (with a new minimum-size failsafe for the center chassis), automatically drafted a Markdown assembly manual with fabrication stats, and rendered a brand-new 3D aerodynamic snap-on canopy to protect the flight controller

I upgraded the AI-to-Hardware pipeline by injecting a custom Python physics module and a CLI dashboard (using the Rich library). To test the fail-safes, I intentionally prompted the local Phi-3 model to design a massive 387mm drone for a Martian environment, requesting paper-thin 2mm arms.The AI extracted the JSON, but my physics engine intercepted the data before generation. It calculated the aerodynamic drag forces, flagged the 2mm arms as a structural failure, and automatically overrode the dimension to a safe 6.0mm minimum. The script then printed the telemetry to my custom dashboard and automatically launched the corrected 3D blueprint in OpenSCAD.

The AI-to-Hardware pipeline is officially live! I built a Python command center that connects to a local Phi-3 LLM. Instead of manually coding dimensions, I can now use natural language prompts to design a frame. I asked the AI for a compact 120mm chassis inspired by Ejen Ali, accommodating a custom 25x25mm PCB. The AI successfully parsed the prompt, extracted the structural specs into JSON, and instantly triggered my backend generator to create the 3D