AI · alternatives · 2026

24 alternatives to Autograd.c – A tiny ML framework built from scratch

built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/ perfect for learning how ml frameworks work under the hood :)

Autograd.c is a minimal machine learning framework written in C that replicates core PyTorch functionality. Built as a learning resource following Professor Vijay Janapa Reddi's ML systems textbook, it is designed for… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Microcrad – Micrograd Reimplemented in C▲79
A re-implementation of Karpathy's micrograd in C. Contribute to oraziorillo/microcrad development by creating an account on GitHub.
Jun 2026 · github.com · its alternatives →
2
Continuous Machine Learning – CI/CD for Machine Learning Projects▲171
Bring DevOps practices to your projects for automatic, reproducible, and fast machine learning.
2020 · cml.dev · its alternatives →
3
I built an AI that turns GitHub codebases into easy tutorials▲923
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
2025 · github.com · its alternatives →
4
Boldly go where Gradient Descent has never gone before with DiscoGrad▲232
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
2024 · github.com · its alternatives →
5LC
Learn C and its lower levels interactively, in the browser▲356
I made a simple virtual machine that runs C in the browser.This project is made as an experiment to see if C can be learned easier if the lower level is covered in paralel. Sandbox: https://vasyop.github.io/miniC-hosting Tutorial part 1: https://vasyop.github.io/miniC-hosting/?0 More info: https://github.com/vasyop/miniC-hosting/blob/master/README.md Please support this project: https://github.com/vasyop/miniC-hosting/blob/master/support.md
2019 · its alternatives →
6
AutoAI – A framework to find the best performing AI/ML model▲41
Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation. - blobcity/autoai
2021 · github.com · its alternatives →
7HP
High-performance ahead-of-time compiler for Machine Learning▲83
2018 · scss.tcd.ie · its alternatives →
8
MindsDB▲235
In-database machine learning
2022 · its alternatives →
9
Provide a CSV and a target field, generate a model and code to run it▲172
Provide an input CSV and a target field to predict, generate a model + code to run it. - minimaxir/automl-gs
2019 · github.com · its alternatives →
10
Machine learning automation from creating to using models in production▲72
a delightful machine learning tool that allows you to train, test, and use models without writing code - GitHub - nidhaloff/igel at v0.4.0
2021 · github.com · its alternatives →
11
Fullstack ML – From Notebooks to Deployment▲198
Contribute to xadrianzetx/fullstack.ai development by creating an account on GitHub.
2019 · github.com · its alternatives →
12
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
13
Apple MLX▲138
An array framework for machine learning on Apple silicon
2023 · pypi.org · its alternatives →
14
I wrote an autodiff in C++ and implemented LeNet with it▲36
2024 · gitlab.com · its alternatives →
15
Minimal DL library in C – 24 NAIVE CUDA/CPU ops, autodiff, Python API▲13
minimal DL library in C: 24 NAIVE cuda/cpu ops, autodiff engine, python API (ops bindings/layers/models), tensor abstraction, strides, complex indexing (multi-dim slices like numpy), computation-graph visualizer, saving/loading params, primitive garbage collection, autogenerated tests, optimizers, w
Dec 2025 · github.com · its alternatives →
16
I wrote a minimal deep learning library from scratch in Python▲25
A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
17
minigrad – a minimal, educational autograd library (~100 loc in Python)▲5
A minimal implementation of autograd (in pure Python) 🍰 - kennysong/minigrad
2020 · github.com · its alternatives →
18
agor▲102
The command center for AI enablement. Empower your team with AI teammates — Claude Code, Codex, Gemini — on a shared canvas anchored on git branches, with real-time multiplayer and an MCP surface agents drive themselves.
Nov 2025 · agor.live · its alternatives →
19
Xcessiv – Fully managed web application for automated machine learning▲108
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. - reiinakano/xcessiv
2017 · github.com · its alternatives →
20
Minigrad – a small neural network lib in Golang▲9
I built a neural network library in golang on an autograd engine. Faster and easy to use like PyTorch. Please give it a try and share your feedback. if you like it, a github star will be appreciated.
2024 · github.com · its alternatives →
21
Deep Learning for Program Synthesis▲93
By Rishabh Singh, Jacob Devlin, Abdelrahman Mohamed, and Pushmeet Kohli, Microsoft Research Despite the many advances in computing over the past decades, the actual process of writing computer software has not fundamentally changed — a programmer must manually code the exact algorithmic logic of a p
2017 · microsoft.com · its alternatives →
22
Cortex – machine learning infrastructure for developers▲86
Production infrastructure for machine learning at scale - cortexlabs/cortex
2019 · github.com · its alternatives →
23
Mljar Studio – local AI data analyst that saves analysis as notebooks▲73
Hi HN, I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio. The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun. What MLJAR Studio does: - Sets up a local Python environment automatically, runs on Mac, Windows, and Linux - Installs missing packages during the…
May 2026 · mljar.com · its alternatives →
24SF
Smart Fruit – A Python schema-based machine learning library▲44
I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018 · its alternatives →

Also compare

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →