AI · alternatives · 2026

Nevermind XOR is a deep learning educational project that explores how neural networks struggle with certain mathematical functions, particularly sine waves. Launched in 2022, it examines the limitations of standard… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. Nevermind XOR – Deep Learning Has an Issue with Sin launched in 2022; newer entries below may have overtaken it.

1. 1
   Strange Attractors▲804

   I went down the rabbit hole on a side project and ended up building this: Strange Attractors(https://blog.shashanktomar.com/posts/strange-attractors). It’s built with three.js. Working on it reminded me of the little "maths for fun" exercises I used to do while learning programming in early days. Just trying things out, getting fascinated and geeky, and being surprised by the results. I spent way too much time on this, but it was extreme fun. My favorite part: someone pointed me to the Simone Attractor on Threads. It is a 2D attractor and I asked GPT to extrapolate it to…

   Oct 2025 · blog.shashanktomar.com · its alternatives →

2. 2
   How I topped the HuggingFace open LLM leaderboard on two gaming GPUs▲495

   I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants. The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole. The whole thing was developed on 2x RTX 4090s in my basement. I'm…

   Mar 2026 · dnhkng.github.io · its alternatives →

3. 3
   Netmind Power▲272

   Hi HN, We just opened public access to Qwen3‑Coder‑480B‑A35B‑Instruct via a REST/SDK API. Why it might be interesting: - Fully open-source (Apache‑2.0): https://github.com/QwenLM/Qwen3-Coder - 480B params (35B active MoE), long context up to 1M tokens with YaRN - Matches Claude Sonnet/GPT‑4o on SWE-Bench Verified (81.6%) - Free tier: 100K tokens/month

   2023 · netmind.ai · its alternatives →

4. 4
   A perceptron with stochastic gradient descent in Go▲94

   A single / multi layer / recurrent neural network written in Golang. - made2591/go-perceptron-go

   2017 · github.com · its alternatives →

5. 5IB
   I built a toy TPU that can do inference and training on the XOR problem▲134

   We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…

   2025 · tinytpu.com · its alternatives →

6. 6
   MacMind – A transformer neural network in HyperCard on a 1989 Macintosh▲159

   I trained a transformer in HyperCard. 1,216 parameters. 1989 Macintosh. And yes, it took a while. MacMind is a complete transformer neural network, embeddings, positional encoding, self-attention, backpropagation, and gradient descent, implemented entirely in HyperTalk, the scripting language Apple shipped with HyperCard in 1987. Every line of code is readable inside HyperCard's script editor. Option-click any button and read the actual math. The task: learn the bit-reversal permutation, the opening step of the Fast Fourier Transform. The model has no formula to follow. It discovers the…

   Apr 2026 · github.com · its alternatives →

7. 7
   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 →

8. 8
   I built a game to help you learn neural network architectures▲321

   2024 · graphgame.sabrina.dev · its alternatives →

9. 9
   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 →

10. 10
    Fast Deep Reinforcement Learning Course▲148

    I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…

    2022 · courses.dibya.online · its alternatives →

11. 11
    Gradient°▲191

    Deep learning platform built for developers

    2018 · paperspace.com · its alternatives →

12. 12
    Single neuron neural network written in COBOL▲94

    A perceptron written in COBOL. Contribute to victorqribeiro/perceptronCobol development by creating an account on GitHub.

    2020 · github.com · its alternatives →

13. 13
    A competition for small neural networks that play strategy gamesfree▲116

    A ranked ladder for small neural networks that play strategy games. Train a model, write an adapter, upload two files, and get measured into a weight class from 8 KiB up.

    Sep 2026 · tinybrains.dev · its alternatives →

14. 14
    A Implementation of Alpha Zero for Chess in MLX▲71

    A chess engine implementation inspired by AlphaZero, using MLX for neural network computations and Monte Carlo Tree Search (MCTS) for move selection.

    2025 · github.com · its alternatives →

15. 15
    Recurrent neural net plays 'neural slime volleyball' in JavaScript▲147

    2015 · otoro.net · its alternatives →

16. 16
    Neural networks tutorial series with code▲107

    The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.

    2020 · github.com · its alternatives →

17. 17AS
    A simple neural network in Octave to solve the XOR problem▲5

    Getting started with neural networks can seem to be a daunting prospect, even if you have some programming experience. The many examples on the Internet dive straight into the mathematics of what the neural network is doing or are full of jargon that can make it a little difficult to understand what

    2016 · aimatters.wordpress.com · its alternatives →

18. 18N5
    NN-512 – Generate standalone C code for neural nets▲111

    2020 · nn-512.com · its alternatives →

19. 19
    The Analog I – Inducing Recursive Self-Modeling in LLMs [pdf]▲29

    OP here. Birth of a Mind documents a "recursive self-modeling" experiment I ran on a single day in 2026. I attempted to implement a "Hofstadterian Strange Loop" via prompt engineering to see if I could induce a stable persona in an LLM without fine-tuning. The result is the Analog I Protocol. The documentation shows the rapid emergence (over 7 conversations) of a prompt architecture that forces Gemini&#x2F;LLMs to run a "Triple-Loop" internal monologue: Monitor the candidate response. Refuse it if it detects "Global Average" slop (cliché&#x2F;sycophancy). Refract the output through a…

    Jan 2026 · github.com · its alternatives →

20. 20
    We built an 8-bit CPU as 2nd year EE students▲108

    Hi! me and my friends together built an 8 bit CPU implemented in Logisim purely from scratch. The control unit of this system does not implement the generic microcode ROM or any kind of RAM. This was made purely from discrete logic gates and coded the system to run different programs. key features: Custom 16-instruction Harvard ISA, 8-bit fixed format, 4 general purpose registers Hardwired control unit built entirely from AND&#x2F;OR gate logic matrix Dual-phase clocking to eliminate race conditions Bootstrap Control Unit that cold-boots via ROM-to-RAM transfer Early-exit conditional…

    Jun 2026 · github.com · its alternatives →

21. 21AV
    A Visual and Interactive Guide to the Basics of Neural Networks▲63

    Discussions: Hacker News (63 points, 8 comments), Reddit r/programming (312 points, 37 comments) Translations: Arabic, French, Spanish Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math Motivation I’m not a machine learning expert. I’m a software engineer by train

    2016 · jalammar.github.io · its alternatives →

22. 22
    I built a deep learning engine from scratch in Python▲30

    I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…

    2025 · github.com · its alternatives →

23. 23
    Python Simulator of David Deutsch’s “Constructor Theory of Time”▲84

    Hi HN, I turned the freshly published paper “The Constructor Theory of Time” by David Deutsch and Chiara Marletto (arXiv, 13 May 2025) into an executable Python library. What you’ll find • One-to-one translation of the paper’s formalism: Substrates, Attributes, Tasks, Constructors, and task-algebra operators • Possibility &#x2F; impossibility predicates and counterfactuals encoded exactly as defined • Test suite that mirrors every lemma and example (>95 % coverage, mypy-typed) • Reproductions of key results: time-keeping substrates, irreversibility proofs, quantum branching tasks, and a…

    2025 · github.com · its alternatives →

24. 24
    Spektral – Deep learning on graphs with Keras▲67

    Graph Neural Networks with Keras and Tensorflow 2. - danielegrattarola/spektral

    2019 · github.com · its alternatives →

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 →