AI · alternatives · 2026

24 alternatives to MacMind – A transformer neural network in HyperCard on a 1989 Macintosh

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…

MacMind is a complete transformer neural network built in HyperTalk, the scripting language for Apple's 1987 HyperCard software, running on a 1989 Macintosh. It includes embeddings, positional encoding, self-attention… 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.

1
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 →
2
MakeML▲274
Train Neural Networks without a line of code
2019 · makeml.app · its alternatives →
3
Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training▲265
I replicated David Ng's RYS method (https://dnhkng.github.io/posts/rys/) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76…
Mar 2026 · github.com · its alternatives →
4
Apple MLX▲138
An array framework for machine learning on Apple silicon
2023 · pypi.org · its alternatives →
5
LambdaNet – A functional neural network library written in Haskell▲56
Purely functional artificial neural network library implemented in Haskell. - jbarrow/LambdaNet
2014 · github.com · its alternatives →
6
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 →
7
I built a game to help you learn neural network architectures▲321
2024 · graphgame.sabrina.dev · its alternatives →
8
Hyper: Self-driving Company Brain▲121
Hey HN -- I'm Shalin, one of the cofounders of Hyper. My cofounder Kanyes and I have been power users of a lot of second brain type software like Notion and Obsidian for years, and tried fine-tuning GPT-2 back in 2020 to solve this exact problem of giving AI access to all context of my life so it can do work for me more intelligently. We were too early then, and spent the next 4 years building robots at Matic. Earlier this year, we realized this was perhaps the right time to take a second crack at this problem, so we quit our jobs and built Hyper. It's super exciting that we finally shipped…
May 2026 · heyhyper.ai · its alternatives →
9
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 →
10
Sonnet▲134
A new library for constructing neural networks from DeepMind
2017 · deepmind.google · its alternatives →
11
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 →
12
Neural Network Evolution Playground with Backprop NEAT▲73
2016 · blog.otoro.net · its alternatives →
13N5
NN-512 – Generate standalone C code for neural nets▲111
2020 · nn-512.com · its alternatives →
14
Recurrent neural net plays 'neural slime volleyball' in JavaScript▲147
2015 · otoro.net · its alternatives →
15
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 →
16
We made our own inference engine for Apple Silicon▲186
We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com · its alternatives →
17
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 →
18IB
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 →
19
Entropy-Guided Loop – How to make small models reason▲33
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave…
2025 · github.com · its alternatives →
20
Layered – Neural Networks in Python 3▲56
Clean implementation of feed forward neural networks - danijar/layered
2015 · github.com · its alternatives →
21MA
Mind, a flexible neural network in JavaScript▲20
2015 · mindjs.net · its alternatives →
22
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 →
23
Bender – a Deep Learning framework for iOS▲81
Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood. - xmartlabs/Bender
2017 · github.com · its alternatives →
24AV
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 →

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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 →