I successfully refactored a manufacturing engine from building drones to building robot dogs in 88k tokens. Then I tried to run the code. I successfully refactored a manufacturing engine from building drones to building robot dogs in 88k tokens. Then I tried to run the code. Yesterday, I felt like I had unlocked a cheat code for software engineering. I took OpenForge my Neuro-Symbolic Manufacturing Engine originally built to design and simulate drones and challenged Google’s Gemini 3.0 to refactor it. The prompt was simple in theory: stop building things that fly, start building things that walk. OpenForge OpenForge The result seemed miraculous. In a single context window of roughly 88,000 tokens, the AI seemingly swapped the "organs" of the system while keeping the skeleton intact. It rewrote the physics service, pivoted the sourcing agents, and generated a new simulation. It was fast, it was confident, and the code looked pristine. I wrote an article celebrating the flexibility of Neuro-Symbolic architecture. I spoke too soon. I spoke too soon. Today, I am deep in the trenches of debugging, and I’ve realized that I fell into the Trap of Competence. Here is what I learned about the dangerous difference between Gemini 2.5 and 3.0, and why one-shot refactoring is a myth. Trap of Competence. The Mirage of the One-Shot Pivot The premise of OpenForge is simple: It translates user intent ("I need a surveillance bot") into flight-proven hardware designs and physics-based simulations. When I asked Gemini to pivot this from drones to quadrupeds, I fed it the entire codebase as architectural context. Gemini 3.0 ingested it and confidently spit out new code for every .py module. On the surface, the code was syntactically perfect. It imported the right libraries, it named functions correctly, and it followed the general structure of the original files. But when I actually tried to execute the build, the system collapsed. The Flattenin Effect The original OpenForge drone code wasn't just a collection of functions; it was a linear narrative of distinct steps, filled with Battle Scars. Battle Scars. There were remarks, specific error-handling gates, and logical detours from previous failures. The architecture was designed to handle the messy reality of engineering where specific steps need to finish before others begin. Gemini 3.0, in its attempt to be efficient, flattened the architecture. flattened the architecture. It lumped distinct, linear steps into singular, monolithic processes. It looked right on the surface, but it glossed over the nuance and depth of the original code. It treated the refactor as a stylistic rewrite rather than a logical translation. The result was cascading failures. Because the scaffolding (the step-by-step separation) was removed, troubleshooting became a nightmare. Fixing a torque calculation in the physics module would inexplicably break the inventory sourcing logic, because the AI had subtly entangled them in its attempt to optimize the code flow. cascading failures The Paradox: Why Gemini 2.5 Was Better (By Being Worse) This failure highlighted a counter-intuitive reality about the progression of LLMs. Gemini 2.5 (The Junior Dev): In previous versions, the model had strict limitations. It would truncate code or fail to hold the whole context. This was frustrating, but it was a safety feature. It forced me to act as the Architect. I had to break the project down into small, distinct tasks. I had to hold the model’s hand. The friction ensured the modularity of the code remained intact. Gemini 3.0 (The Overconfident Senior): Gemini 3.0 has the speed and the reasoning capabilities to ingest the whole project and output a full result. But its confidence is deceptive. It skips the show your work phase. It assumes it understands the why behind your architecture, when it really only understands the what. It builds a beautiful house, but it forgets to pour the foundation. The Lesson: Architecture is Still King My hypothesis was that with a big enough context window and a smart enough model (Gemini 3.0), I could treat a complex codebase like a fluid document something to be rewritten rather than engineered. I was wrong. If you expect an AI to one-shot pivot a codebase based solely on the original files, you are going to be disappointed. The improved reasoning of Gemini 3.0 does not replace the need for foundational architecture. The Fix: I am now restarting the refactor, but differently. Instead of giving Gemini the code and saying change this, I am walking it through the logic flow first. logic flow Define the step-by-step process.
Explain why the code is structured this way.
Force the AI to generate the scaffolding before it generates the implementation. Define the step-by-step process. Explain why the code is structured this way. Force the AI to generate the scaffolding before it generates the implementation. We are moving from an era of Code Generation to System Generation. But until the AI can understand the history of why a system was built a certain way, the human architect is the only thing standing between a working product and 88,000 tokens of spaghetti code. Code Generation System Generation

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From Drones to Robot Dogs: How I Refactored a Manufacturing Engine in 88k Tokens

The Trap of Competence: How I Fell for the Mirage of the One-Shot Pivot with Gemini 3.0

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