{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/02b29fe331c1482abf01da402d39d22f\" frameborder=\"0\" width=\"1226\" height=\"919\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":919,"width":1226,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":919,"thumbnail_width":1226,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/02b29fe331c1482abf01da402d39d22f-d5396c97a0b5b330.gif","duration":95.694,"title":"Agentic Code Migration from C to PyTorch","description":"This Loom explains a graph based agentic engine for migrating institutional quantitative code bases from C to PyTorch without months of manual engineering. It argues that naive LLM translation fails due to hallucinations, broken class hierarchies, and poor loop vectorization. The agent deterministically parses source code into a dependency graph where each function is an isolated node with mathematical primitives resolved before any generation, then replaces C loops with tensorized PyTorch broadcast operations, autograde compatible computational graphs, and synthesized unit tests. It immediately executes each migrated function against the C Oracle baseline for numerical parity and toggles cloud execution for local graphs to find parallel compilation."}