Alternatives
Products that do what Jixp, a Lisp DSL for describing Jax neural nets does
This is a side project I've been working on while learning Jax. I noticed that a bunch of the neural net math looked like it would work well in a lisp syntax because most of the data flows through layers in a "functional" manner. Data is threaded through one layer at a time, each layer composing with the previous layer. Jixp is designed as a learning tool for myself while learning Jax to toy around with different shapes/styles of models. I used this to train a 4m parameter model on my Obsidian vault to see if I could use it for recall which partially worked. ```lisp (let-dim (d 256)…
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2021 · github.com
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2021 · apress.com
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- 5JE
2020 · github.com
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2017 · github.com
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2011 · github.com
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2015 · github.com
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2018 · github.com
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2018 · github.com
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This is a new and very preliminary Lisp implementation that is based on Common Lisp. The main goals are to minimize external dependencies. Currently only sbcl is supported. The only other dependency is on Quicklisp, which should strictly be necessary only for testing.
May 2026 · github.com
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Hi HN! I kept hearing about Lisp and why it was special but didn't have a great place to start. I wanted something self-contained that helped me develop intuition rather than fluency. My hope is this is helpful for casual programmers, professionals from other fields who interact with code, or engineers who haven't had much exposure to interpreters or compilers. Hope this helps anyone else who's curious about getting a sense of Lisp and what makes it special with a limited time investment! Feedback or other cool Lisp things welcome :)
2022 · github.com
- 15JA
2014 · jixee.me
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2012 · patient0.github.com
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I started this project back in 2015, to translate the TeX original specification into an easily parsed format (s-doc), and to create an HTML rendering of that format as a proof of concept. The project is homed here: https://codeberg.org/dlowe/metaspectre/ Differences from the Hyperspec (from the README): - Most importantly, it is free to modify and distribute. - The original TeX is very hard to parse and use for things other than generating a printed copy. The Hyperspec is an HTML rendering which can be parsed as HTML, but loses a lot of information. The Metaspec has…
Jun 2026 · metaspec.dev
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The point of this seemingly useless exercise was to stress-test my elisp-eval MCP https://github.com/agzam/death-contraptions/tree/main/tools/... AI hands it a string of Lisp, it runs inside live Emacs session, poking into it via emacsclient. Behaves like a REPL the LLM can drive - state persists between the calls. Emacs is a Lisp machine. Buffers, files, network, subprocesses, games, major modes, the UI itself: every feature is already an elisp function. Hand an LLM a way to run arbitrary elisp and it inherits the entire Emacs API for free. No…
Apr 2026 · imgur.com
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Rune▲11Markdown that renders as you type — code, diagrams, math
Jun 2026 · rune-landing-omega.vercel.app
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Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
Dec 2025 · github.com
- 21IM
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
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2019 · github.com
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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
- 24IM
Hi all, powerful open-source Lisp implementations are a dime a dozen, why not try JMurmel instead? The language is inspired by Common Lisp, except it's a Lisp-1, and it is mostly a subset of CLtL-1. Jmurmel can be used standalone with or without the Repl, for "#!"-style hashbang scripts or embedded in a Java program. It features [documentation for the core language](https://jmurmel.github.io/murmel-langref.html) as well as [documentation for the default library](https://jmurmel.github.io/mlib.html), a REPL, an interpreter, a compiler, macros, backquotes,…
2024 · github.com
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