AI · alternatives · 2026

This project demonstrates tiny neural networks trained to navigate 2D mazes using only local sensory input. The creator built 14-byte AI models across 46 experimental phases, achieving a 96.5% solve rate on unseen… 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. 1
   Needle2: 14MB agentic LLM for phones, wearables, smart home and robots▲537

   Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…

   Aug 2026 · cactuscompute.com · its alternatives →

2. 2
   I built a tiny LLM to demystify how language models work▲915

   Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.

   Apr 2026 · github.com · its alternatives →

3. 3
   100 Vibe Coding▲351

   Hi HN, I have been making side projects since I was 14, and my team mate (this is his very first side project! Congrats!) designed and implemented the UI. Over time, people kept asking me how they could start coding and turn ideas into reality. So we had an idea. Coding is changing. With AI, the bottleneck is no longer writing every line by hand but knowing how to prompt effectively to reach a working outcome. Our objective is not to make people code better, or even learn to be a proficient software engineer, but just to keep it simple and start building. We want to remove fear and barriers…

   2025 · 100vibecoding.com · its alternatives →

4. 4
   I built a game to help you learn neural network architectures▲321

   2024 · graphgame.sabrina.dev · its alternatives →

5. 5
   Second Brain for AI▲286

   Persistent memory for Claude, ChatGPT & Cursor. Free.

   May 2026 · github.com · its alternatives →

6. 6
   Second Brain▲281

   Hi HN, I'm Dom, a solo founder. I built Second Brain to solve a common issue I had with AI chats: the knowledge disappears. There’s no structure, no memory, and no way to reuse or build on them. So I built a tool that combines AI chat with a visual, interconnected knowledge system. Key features: - Save AI responses into editable docs (Notion-style Pages). - AI Visual board with multiple content & chat nodes (like Miro meets ChatGPT). - Add and chat with YouTubes, TikToks, Audio, PDFs, GitHub repos, Looms, websites, etc. - Organize using Projects and Smart Tags — then chat with full contexts.…

   2025 · second-brain-lac-ten.vercel.app · its alternatives →

7. 7
   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 →

8. 8
   Visual A* pathfinding and maze generation in Python▲135

   I was fascinated reading through another recent HN submission about a highly efficient implementation of A* in Lisp, which got me thinking about how I could do something similar in Python. However, these kinds of pathfinding algorithms really need complex terrain/mazes with interesting obstructions to showcase what they can do and how they work. So, I started thinking about how I could generate cool and diverse random "mazes" (they aren't really mazes, but I'm not sure what the best term is). I got a bit carried away thinking of lots of different cool ways to generate these mazes, such…

   2024 · github.com · its alternatives →

9. 9AN
   A new A.I. Algorithm for Polygonal Mesh Mazes▲101

   2012 · petercottle.com · its alternatives →

10. 10IU
    I used AI to recreate a $4000 piece of audio hardware as a plugin▲160

    Hi Hacker News, This is definitely out of my comfort zone. I just wanted to show you guys because I'm super proud of it. It's a 100% faithful recreation based off of the schematics, patents, and ROMs that were found online. So please watch the video and tell me what you think https://youtu.be/auOlZXI1VxA The reason why I think this is relevant is because I've been a programmer for 25 years and AI scares the shit out of me. I'm not a programmer anymore. I'm something else now. I don't know what it is but it's multi-disciplinary, and it doesn't involve writing code myself--for…

    Jan 2026 · its alternatives →

11. 11IB
    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 →

12. 12MF
    My friend's project to simulate an entire C. Elegans▲147

    2013 · nemaload.davidad.org · its alternatives →

13. 13
    Explore large language models with 512MB of RAM▲138

    Explore large language models in 512MB of RAM. Contribute to jncraton/languagemodels development by creating an account on GitHub.

    2023 · github.com · its alternatives →

14. 14
    Lexie▲102

    Built this because kids 10-15 can't prompt AI effectively. They'd type "make quiz plz" and get confused when ChatGPT asks for context. Lexie takes 46 steps of AI prompting, context explanation, and output formatting and makes it one step: point camera, get quiz. The photo constraint isn't just better UX, it's the only way to get usable output from that age group. This demographic gets the worst deal in edtech. Too advanced for baby apps, too young for adult tools. They get gamification theater instead of real learning tools. I stripped all that out. Photo your geology notes, get instant…

    Apr 2026 · lexielearn.com · its alternatives →

15. 15
    NeuroBlock▲120

    No-code AI Lab: Train models, access datasets, run inference

    Feb 2026 · neuro-block.com · its alternatives →

16. 16AV
    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 →

17. 17IC
    I created a Neural Network from scratch, in scratch▲7

    I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.

    2024 · bell-boy.github.io · its alternatives →

18. 18
    I ran a language model on a PS2▲46

    The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc. Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this. And it works. On real hardware.

    Mar 2026 · github.com · its alternatives →

19. 19
    I'm 15 and built a neural network from scratch in C++,just math▲8

    I’m 15 and self-taught. I'm learning ML from scratch because I want to really understand how things work. I’m not into frameworks. I prefer math, logic, and C++. I implemented a basic MLP that supports different activation and loss functions. It was trained via mini-batch gradient descent. I wrote it from scratch, using no external libraries except Eigen (for linear algebra). I learned how a Neural Network learns (all the math) -- how the forward pass works, and how learning via backpropagation works. How to convert all that math into code. I’ll write a blog soon explaining how MLPs work in…

    2025 · github.com · its alternatives →

20. 20
    Moondream, a small vision language model that runs on 8GB of RAM▲9

    I've been working on training this small vision language model for the last month - excited to release the first prototype today! It is based on SigLIP (image encoder), Phi-1.5 (text model) and trained using the LLaVa-1.5 training dataset. It runs reasonably fast on CPU with ~8GB of RAM in full 32-bit precision. There's plenty of room to speed it up and reduce memory consumption by quantizing the model. I posted a video of it running on my M2 Macbook Air (on CPU not MPS, so performance should be comparable on other hardware) on Twitter to demonstrate inference speed:…

    2023 · github.com · its alternatives →

21. 21
    I built an AI that uses a metacognitive loop 2 solve invention problems▲9

    Hi HN, I'm the creator of this project. For the past months, I've been working on building an AI agent that could move beyond simple generation and tackle inventive challenges autonomously. The core idea was to create a system with a "metacognitive loop"—the ability to recognize when it's stuck on a fundamental problem and then launch a sub-mission to solve that specific bottleneck before continuing. The linked article is a deeper introduction to the system's architecture and a snapshot from a recent run. I tried to design it to be evidence-grounded and self-critical to avoid the pitfalls of…

    2025 · robw1se.substack.com · its alternatives →

22. 22
    Neural Net Flies Navigate through a maze▲14

    In this simulation, flies are controlled by a neural network. You can adjust the hyperparameters. There's a maze mode, in which flies have to avoid some obstacles. I think the effect is pretty cool. What do you think? updated link: https://claude.ai/public/artifacts/5f7017c8-98fb-4d89-a32c-1...

    Dec 2025 · claude.ai · its alternatives →

23. 23IV
    I've trained my neural network to play Tinder▲10

    The video is here: https://www.youtube.com/watch?v=Ohs6sgmVNYI This video is a prof of concept. I always wanted to see if I could make my Neural Network play tinder for me. So I gather some pictures, created a fake tinder app (so I don't expose real people) and began the training. For this experiment I'm using scrcpy to control my cellphone and pyautogui alongside my neural network. The script is very simple: Get the image, run it trough a neural network, move the mouse to the output (yes or no) and click. There's a lot of hard coded things in my code, but as I said earlier,…

    2019 · its alternatives →

24. 24BH
    Beating Hinton et al.'s capsule net with fewer params and less training▲14

    Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…

    2019 · 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 →