From 90c4627fa66b8a50b1d00fa1ce60d1b7a1e0785d Mon Sep 17 00:00:00 2001 From: tanay Date: Sun, 9 Aug 2026 13:52:19 +0530 Subject: [PATCH] correct the download metrics and claims --- README.md | 6 +++--- src/pages/index.js | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 23710e5..091de44 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ ![Tests](https://img.shields.io/github/actions/workflow/status/Terminay/LeanPass/summary.yml?label=tests) ![CodeQL](https://img.shields.io/github/actions/workflow/status/Terminay/LeanPass/codeql.yml?label=CodeQL) ![PyPI Publish](https://img.shields.io/github/actions/workflow/status/Terminay/LeanPass/publish-pypi.yml?label=PyPI%20publish) -![Size](https://img.shields.io/badge/size-~118KB-lightgrey) +![Size](https://img.shields.io/badge/size-~8.5KB-lightgrey) ![Real Downloads](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/Terminay/LeanPass/main/badge.json) [![Stars](https://img.shields.io/github/stars/Terminay/LeanPass?style=social)](https://github.com/Terminay/LeanPass/stargazers) @@ -155,13 +155,13 @@ LeanPass is designed to be minimal. Here is how it compares to other frameworks: | Library | Package Size | Lines of Code | Dependencies | Install Time | |---------|-------------|---------------|-------------|--------------| -| **LeanPass** | **~118 KB** | **~730** | **1 (NumPy)** | **~2 seconds** | +| **LeanPass** | **~8.5 KB** | **~730** | **1 (NumPy)** | **<1 second** | | Micrograd | ~15 KB | ~150 | 0 | ~1 second | | PyTorch | ~800+ MB | millions | many (CUDA, etc.) | ~minutes | | TensorFlow | ~1+ GB | millions | many (CUDA, etc.) | ~minutes | | JAX | ~200+ MB | millions | several | ~minutes | -LeanPass is **~7000x smaller** than PyTorch and **~9000x smaller** than TensorFlow, while still providing the core autodiff, neural network layers, and optimizers needed for small-scale experiments and learning. +LeanPass is **~14,000x smaller** than PyTorch and **~40,000x smaller** than TensorFlow, while still providing the core autodiff, neural network layers, and optimizers needed for small-scale experiments and learning. ## Examples diff --git a/src/pages/index.js b/src/pages/index.js index 856fba0..b1029b0 100644 --- a/src/pages/index.js +++ b/src/pages/index.js @@ -58,7 +58,7 @@ export default function Home() { that they flow, you need source code that fits in your head.

- This is that library. ~500 lines of pure NumPy. Every + This is that library. ~730 lines of pure NumPy. Every backward() call traces through the graph step by step. No magic, no abstraction layers, just vector calculus you can read line by line.