 Headline: "Frankenstein from Neural Networks: Why a Developer Transplanted Gemma's Brain into DeepSeek's Skeleton and What AI Agents Have to Do with It" An article appeared on Habr that would make any MLOps engineer's eye twitch: the author took Gemma, a 31B-parameter model, and transplanted it into DeepSeek's MoE architecture. Without fine-tuning. Without guarantees. Just "let's give it a try." And you know what? It partially worked. And partially broke Transformers. This experiment isn't just hardcore R&D for geeks. It's a symptom. AI development is no longer about "writing a model from scratch" and is turning into a construction kit: you take weights from one, architecture from another, and scaffolding from a third. Combinatorics grow exponentially, and manual testing of each combination hits the ceiling of human resources. This is where the main pain point emerges: when you assemble an AI system from pieces, you drown in a compatibility matrix. This layer doesn't align with that tokenizer, that layer requires different normalization—and all of this needs to be tracked, logged, and tested. ASI Biont's AI agents address exactly this layer: they don't replace the engineer but take over all the routine of compatibility checks, running test scenarios, and documenting architectural decisions. While the author of that article manually figured out why his Transformers broke, an AI agent could have analyzed the tensor conflict in 10 seconds and suggested three repair options. AI development is entering the era of construction kits. The question isn't whether you'll assemble your own Frankenstein, but how much time and nerves you'll waste on it. We give 1500 tokens to start—so you don't experiment blindly. ASI Biont analyzes your architectural connections in seconds, not weeks of crash testing. https://asibiont.com/