Rome wasn't built in a day. I'll get there with optimisations im just going for "correctness" first. I've had some amazing resources be sent from me from academics around the world so once I get this to a "point" I'll begin optimising it.
72 karma · joined December 7, 2025
Current bank teller and amateur archivist. Love to discuss tech.
zanehambly@gmail.com
Rome wasn't built in a day. I'll get there with optimisations im just going for "correctness" first. I've had some amazing resources be sent from me from academics around the world so once I get this to a "point" I'll begin optimising it.
The original test implementation of this for instance was written in OCaml before I landed on C being better for me.
I’m self taught in this field. I was posting on R/compilers and shared this around with some friends who work within this space for genuine critique. I’ve been very upfront with people on where I use LLMs. It’s actually getting a bit “too much” with the overwhelming attention.
I'm not the one who posted to HN but I am the project author. I'm working my way into doing multiple architectures as well as more modern GPUs too. I only did this because I used LLVM to check my work and I have an AMD GFX 11 card on my partners desktop (Which I use to test on sometimes when its free).
If you do have access to this kind of hardware and you're willing to test my implementations on it then I'm all ears! (You don't have too obviously :-) )
I mean this with respect to the other person though please don't vibe code this if you want to contribute or keep the compiler for yourself. This isn't because I'm against using AI assistance when it makes sense it's because LLMs will really fail in this space. Theres's things in the specs you won't find until you try it and LLMs find it really hard to get things right when literal bits matter.
This is also just what I intentionally avoided when making this by the way. I don't really know how else to phrase this because LLVM and HIP are quite prolific in the compiler/GPU world it seems.
Didn't realise this was posted here (again lol) but where I originally posted, on the R/Compilers subreddit I do mention I used chatgpt to generate some ascii art for me. I was tired and it was 12am and I then had to spend another few minutes deleting all the Emojis it threw in there.
I've also been open about how I use AI use to people who know me, and I work with in the OSS space. I have a lil Ollama model that helps me from time to time, especially with test result summaries (if you've ever seen what happens when a Mainframe emulator explodes on a NIST test you'd want AI too lol, 10k lines of individual errors aint fun to walk through) and you can even see some Chatgpt generated Cuda in notgpt.cu which I mixed and mashed a little bit. All in all, I'm of the opinion that this is perfectly acceptable use of AI.
I do use LLM's (specifically Ollama) particularly for test summarisation, writing up some boilerplate and also I've used Claude/Chatgpt on the web when my free tier allows. It's good for when I hit problems such as AMD SOP prefixes being different than I expected.
Some interesting results:
93.8% energy reduction per inference, 16x memory compression (7B model: 28GB → 1.75GB), Zero floating-point multiplication, Runs on CPUs, no GPU required and Architectural epistemic uncertainty (it won't hallucinate what it doesn't know)
Repo: https://github.com/Zaneham/Ternary_inference
Happy to answer questions :-) Happy holidays and merry christmas!