Dev tools · alternatives · 2026

This is an open-source hardware design for a specialized machine learning chip capable of both inference and training, demonstrated on the XOR problem. Built by developers without professional hardware experience, it… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1. 1
   Open source machine learning inference accelerators on FPGA▲57

   Tom from Tensil here - happy to answer questions! We developed Tensil to bring custom ML accelerators to people who don't have the resources of companies like Google, Facebook and Tesla. Currently, we're focused on supporting convolutional neural network inference on edge FPGA (field programmable gate array) platforms, but we aim to support all model architectures on a wide variety of fabrics for both training and inference. Tensil is different from other ML accelerators in that it is open source and really easy to use. For example, you can generate a custom accelerator with one command: $…

   2022 · tensil.ai · its alternatives →

2. 2
   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 →

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. 4AT
   A tiny LLM running at 21,000 tok/s on a $250 FPGA (Live Demo)▲79

   A 3.16M-parameter INT4 transformer running entirely in the on-chip memory of a Xilinx Kria KV260. Zero DRAM in the token loop, 59,965 tok/s on the fabric, bit-exact. Chat with it live.

   Aug 2026 · mikeayles.com · its alternatives →

5. 5RX
   Razer x Lambda Tensorbook▲64

   Hi all, long time lurker, first time poster. I want to share with you all something we've been working on for a while at Lambda: the Razer x Lambda Tensorbook: https://www.youtube.com/watch?v=wMh6Dhq7P_Q But before I tell you about it, I want to make this all about me, because I built this for me. See, while I'm genuinely interested in hearing from the community what you think as this is the culmination of a lot of effort from a lot of people across so many different fields (seriously, the number of folks across manufacturing, engineering, design, logistics, and marketing who…

   2022 · its alternatives →

6. 6
   Neural Network Evolution Playground with Backprop NEAT▲73

   2016 · blog.otoro.net · its alternatives →

7. 7AB
   A big tech dev experience for an open source CMS▲58

   Hey HN! We're building an open-source CMS designed to help creators with every part of the content production pipeline. We're showing our tiny first step: A tool designed to take in a Twitter username and produce an "identity card" based on it. We expect to use an approach similar to [Constitutional AI] with an explicit focus on repeatability, testability, and verification of an "identity card." We think this approach could be used to create finetuning examples for training changes, or serve as inference time insight for LLMs, or most likely a combination of the two. The tooling we're…

   2025 · contentfoundry.com · its alternatives →

8. 8
   Token Economics Calculator for AI inference hardware▲13

   Hi HN, I'm Paul from Tensordyne. We build AI inference systems and chips on logarithmic math. We've put together an interactive Token Economics Calculator to help make apples-to-apples comparisons of inference hardware across vendors: We're interested in how closely it lines up with the community's view of the market. Why we built this Investors and customers kept asking how our system compares to others (NVIDIA and a growing list of startups). Plenty of publicly available data exists, but it's scattered and inconsistent. News articles, provider sites, Artificial Analysis, MLCommons, and now…

   Nov 2025 · tensordyne.ai · its alternatives →

9. 9
   Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21

   Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining

   Aug 2026 · github.com · its alternatives →

10. 10IB
    I built a lite LPU that can do inference on Karpathy's MicroGPT▲18

    We had no guide or course that teaches chip design at our university. We had taken a digital logic course, but were disappointed with the fact that the most complex project we did was building a full adder in Quartus using logic blocks, not even in RTL!5 Therefore, we decided to challenge ourselves to dive deep into machine learning (ML) hardware and learn as much as we could on our own. We wanted to prove that basic math (like y = mx + b) and basic logic circuits are enough to help anyone understand how modern AI hardware works. Our goal was to design our own version of the LPU from scratch…

    Aug 2026 · lpulite.com · its alternatives →

11. 11
    New approach for CI/CD/compute system▲22

    I've been tired with the current options on the market for awhile and decided to do something about it after the running into the disaster that is MLOps at my last two startups and having to manage a multiple operation platforms both for my fellow ML engineers, the general application CI/CD and orchestration layers while simultaneously building the application itself. Its still extremely early for the product but its functioning and is well on its way. I'd love feedback on the approach and peoples thoughts on the problem space. Personally my irritations have been in the poor tooling,…

    2025 · egdaemon.com · its alternatives →

12. 12
    Serve 100 Large AI models on a single GPU with low impact to TTFT▲7

    I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…

    Nov 2025 · github.com · its alternatives →

13. 13
    We Beat MLPerf: Modern Storage for KV Offload and LLM Training▲35

    High performance storage engine for efficient LLM inference and GPU Training.

    Sep 2026 · theopenlake.com · its alternatives →

14. 14
    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 →

15. 15AL
    Assemble Labs – Hardware Brain for LLMs/IDEs▲19

    Hi HN friends, we're Nima (nimabanai) and Craig (cbschind) from Assemble Labs (https://assemblelabs.co) building the hardware context layer for AI to help you write better firmware faster. We’ve built an MCP server that plugs into any AI tool you’re using (Cursor, Claude Code, Gemini, etc.) and brings complete hardware context (schematics, datasheets, etc.) to your existing environment (new app fatigue is real...) with accuracy and in real time. Our goal is to make writing and debugging firmware on custom hardware faster and easier. Try out the free beta release:…

    2025 · its alternatives →

16. 16
    SOTA long memory eval with open source models▲5

    Cursor published a great post last week on scaling long-running autonomous coding agents to build a browser. We’ve been exploring a similar idea, but applied to math proofs We show how a swarm of agents, coordinated via shared memory, can generate a Lean proof for a non-trivial multi-step math problem-Putnan A2-that a single agent struggled with. We were feeling fancy so we wrote a full post and captured the runtime. Harness will be public soon(tm) for you to run it yourself.

    Mar 2026 · ensue.dev · its alternatives →

17. 17
    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 →

18. 18
    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 →

19. 19
    OpenGraviton – Run 500B+ parameter models on a consumer Mac Mini▲13

    Hi HN, I built OpenGraviton, an open-source AI inference engine designed to push the limits of running extremely large models on consumer hardware. The system combines several techniques to drastically reduce memory and compute requirements: • 1.58-bit ternary quantization ({-1, 0, +1}) for ~10x compression • dynamic sparsity with Top-K pruning and MoE routing • mmap-based layer streaming to load weights directly from NVMe SSDs • speculative decoding to improve generation throughput These allow models far larger than system RAM to run locally. In early benchmarks, OpenGraviton reduced…

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

20. 20
    We Built an Autonomous RC Car Using Neural Networks and Raspberry Pi▲23

    Autonomous RC car using Raspberry Pi and ANN. Contribute to multunus/autonomous-rc-car development by creating an account on GitHub.

    2016 · github.com · its alternatives →

21. 21SD
    Self-driving Lego Robot using Python, Neural Networks▲17

    Hey folks, Check out a project I built over the past few days .. a lego mindstorms robot that autonomously drives around a track using vision/neural networks. It was coded entirely in Python. I did a short write-up and would love to get feedback. link: http://slowping.com/2012/self-driving-lego-mindstorms-robot/

    2012 · slowping.com · its alternatives →

22. 22
    MLGarden, a tool/toy to build and train simple neural networks visually▲11

    I made this tool to get some better intuition on how neural networks/backpropagation worked, but I'm really unsure what to do with it now, so I've put it up on github, and I wrote a little primer on backprop and neural networks to showcase it. Really curious to hear any thoughts you might have, or anything I got wrong in the write up!

    2024 · github.com · its alternatives →

23. 23
    Banana.dev Serverless GPUs for Machine Learning Inference▲16

    Inference hosting for AI teams who ship fast and scale faster.

    2022 · banana.dev · its alternatives →

24. 24
    We made a tool that lets you research electronic components faster▲8

    Hi HN, I’ve spent the last decade building hardware products like humanoid robots, 3D printers, and self-driving tractors. I needed a tool to navigate technical documents faster, so I created one with friends. This tool helps with component search, cross-referencing, comparison, and debugging. We’d love your feedback, whether you find it useful or not. Thank you! Try it here: www.convergelab.ai

    2024 · convergelab.ai · 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 →