Developers Built a Neural Net and SQL Analytics Tool Using Only Python's Standard Library

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Two developers building DataLens, a data analytics tool, imposed a strict no-third-party-package rule, forcing them to replace NumPy entirely with hand-written Python code. They manually implemented matrix multiplication, activation functions, and backpropagation using nested loops and Python's lesser-known array module, which stores numeric data contiguously for better performance. For the SQL analytics layer, they leveraged Python's built-in sqlite3 module in in-memory mode, discovering it could handle joins, aggregates, and indexes without any external database setup. A key challenge was representing 2D matrices using the array module, which only supports primitive types, requiring them to flatten matrices to 1D and manage index arithmetic manually. The project forced both developers to deeply understand foundational machine learning mechanics they had previously taken for granted through high-level libraries.

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