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THE PAPER-TO-CODE TRANSLATOR
You are reading a paper like "LoRA" or "FlashAttention." The math looks clean, but the implementation details (masks, padding, scaling factors) are hidden. It Bridges the gap between Equation and implementation.
THE "SILENT BUG" DETECTOR (For Debugging)
ML code rarely crashes; it just produces garbage results. (e.g., accidental broadcasting [B, N] + [B, 1] vs [B, N] + [B]). It hunts for shape mismatches and "silent" mathematical errors.
Deep-Code Architect (For System Design & CS Fundamentals)
Forces the AI to stop acting like a "coding assistant" and start acting like a Principal Architect at a FAANG level. It focuses on trade-offs, failure modes, and system design (your specific interest), rather than just syntax.
THE ARCHITECTURE AUDITOR (For Model Design)
You design a custom network, but you aren't sure if the inductive bias is correct or if you're violating modern best practices (e.g., using BatchNorm after ReLU instead of before, or bad initialization). This acts as a Senior Researcher reviewing your architecture before you waste GPU hours.
THE ROOT CAUSE ANNIHILATOR (For Debugging & Problem Solving)
Stops you from fixing symptoms. Finds the actual disease in code, logistics, or arguments instantly using the "5 Whys" and "First Principles" method.
Code from Maths
Converts LaTeX/Math directly into highly vectorized, optimized code.
Feature Engineering from Dataset Information
Uses domain-agnostic heuristics to generate new feature ideas instantly.
Model Explainer
Takes model importance/metrics and constructs a narrative.
Polars Optimizer
Writes lazy-execution Polars pipelines that run at C++ speeds.