🏆 aeroForge-G3: The Autonomous Generative Engineering Platform
Tagline: Moving from Napkin Sketches to Physics-Validated Hardware in Seconds.
💡 The Problem
Traditional hardware engineering is bottlenecked by a "serial" workflow: manual CAD drafting, followed by siloed simulation, followed by human-led redesign. This loop takes weeks. aeroForge-G3 collapses this entire lifecycle into a single, autonomous "parallel" process. By treating physical geometry as executable code, I am removing the human bottleneck from the design-test-fail-fix cycle, potentially saving aerospace and robotics firms millions in R&D hours.
🚀 The Solution
aeroForge-G3 is a Multi-Agent Engineering Swarm. It doesn't just "imagine" a drone; it engineers one. The "Wow Factor" lies in its Self-Healing Design Loop:
- The AI designs a system based on a prompt.
- The AI tests that system in a high-fidelity physics environment.
- The AI witnesses the failure (e.g., a crash due to center-of-mass issues).
- The AI autonomously rewrites its own CAD code to fix the flaw and re-simulates until the mission is a success.
🛠 Technical Execution
As a solo developer, I built a production-grade distributed system to handle the heavy computational demands of real-time physics and LLM reasoning.
The Brain: Google Gemini 3 Pro
I leveraged Gemini 3 Pro as the central reasoning engine. Unlike standard LLMs, I utilized Gemini’s long-context window and advanced spatial reasoning to:
- Generate
build123dPython scripts: Directly translating natural language mission specs into mathematical, manifold-correct 3D geometry. - Analyze Telemetry: Gemini acts as the "Chief Engineer," interpreting raw physics data (drag, lift, torque) from the simulation and identifying specific geometric points of failure.
The Architecture: Distributed & Scalable
- Agent Orchestration: I used LangGraph to manage the state machine between the Designer, Simulator, and Supervisor agents, ensuring the system never enters an infinite "hallucination loop."
- Real-Time Central Nervous System: I implemented Apache Kafka to handle the asynchronous communication between the AI agents and the physics engine.
- Compute Layer: High-fidelity simulations require immense power. I built a layer to shard simulation workloads across Vultr NVIDIA H100 instances, achieving >60Hz real-time telemetry.
- Physics Engine: Integrated the NVIDIA Genesis engine to provide the ground-truth data the AI needs to validate its designs.
🏗 System Architecture
Prompt → Gemini 3 (Architect) → Python/build123d → Kafka → Genesis Physics (H100s) → Telemetry Feedback → Gemini 3 (Reviewer) → Loop/Correction → Validated STL/G-Code.
🚧 Challenges I Overcame
- Geometric Determinism: LLMs often struggle with "watertight" CAD. I developed a validation kernel that acts as a compiler for the AI's code, ensuring every generated drone is 3D-printable and structurally sound before simulation begins.
- Physics Latency: Sharding the simulation across multiple H100s was essential to prevent the LLM from timing out while waiting for "test flight" results.
🏅 Accomplishments I'm Proud Of
- Zero-Human Design: Watching the system autonomously identify that a drone’s arms were too thin to support the battery weight, and then seeing it live-code a reinforced frame without any input from me.
- Full-Stack Mastery: Successfully integrating a cutting-edge LLM (Gemini 3) with industrial-grade tools like Kafka and Genesis.
🧠 Lessons Learned
- Agents need a "Skeptic": The system only became reliable once I added the "Supervisor Agent." Giving an AI the permission to "reject" its own work is the key to autonomous engineering.
- Hardware as Software: This project proved that when you treat CAD as code, the speed of hardware innovation can finally match the speed of software development.
aeroForge-G3 isn't just a tool; it's an engineer that never sleeps.
Log in or sign up for Devpost to join the conversation.