What AI did:
- Generated README templates (boilerplate markdown)
- Suggested commit messages (I didn't always edit them)
- Helped with documentation structure
What I wrote:
- All Python training logic (train.py, trainer.py, callbacks)
- All model architectures (gpt.py, tiny.py, small.py, etc.)
- Tokenizer integration
- Data pipeline
- CLI scaffolding (
Don’t feed the trolls. This was your idea and you made something that works. Who cares if its (partially) done by AI. Whomever is taking offense by people using AI for coding, is just having a hard time adapting to the current state of affairs.
It’s here, it’s happening. Try the project, if you like it thats great, if you don’t then move on.
And if you don’t intent to try it for whatever reason that’s fine as well but don’t be salty to the OP for sharing their passion project.
It isn't trolling if there is a genuine concern. It isn't about the fact it is all AI generated, I don't care about that personally. I do however care if someone lies about provenance.
I find that hard to believe. You chose to put emojis into comments returned during script executions? You chose to store python scripts as strings in a TypeScript file rather than as python script files? You're even aware of the fact there are no python files in the project, rather it is strings in typescript files that get interpreted as python files, and you still refer to python files by filename in your comment like you expect it to exist in "your" codebase? You're competent enough to put together a project like this but then choose to use if-else for something solved better with match case? (LLM's do that since it's a recent addition to python and so LLM's avoid using match case, but humans rarely fail to use it)
Your medium article, your *.md and most of your code ALL looks LLM generated, which isn't as much a problem in my books, but lying about it is a huge problem.
Fair points and I get where you’re coming from.
I’ve been very open that AI helped with repetitive parts (docs, boilerplate, commit messages). The functional code training logic, model architecture, CLI was written and tested by me.
Some design choices (like storing scripts as strings or using if-else) were just pragmatic decisions made while iterating fast, not signs of AI authorship.
Either way, the project is open source — you can inspect, critique, or even improve any part of it. I’m happy to take constructive feedback. My goal’s just to make LLM training more accessible.
If you're iterating fast, then working on python files where you get the benefits of linting, syntax highlighting etc. and then reading the files as strings or bundled as required is MUCH faster than blindly mucking around in strings. As a side note, how fast do you type? Some of your multi-hundred line addition commits happen rather rapidly back to back.
The only time it would be faster to iterate with your scripts hard-coded into your TS files would be if an LLM is doing your iterating for you.
Why would anyone invest time and effort in a project where the author lies through their teeth about provenance? Why use your project that contributes, as it appears, nothing a LLM can't just give me? Why use this when I could just use an LLM to get the same directly in python without dicking around with npm?
You were called a troll in another reply but the comment you are replying to feels very AI. —'s and explaining to you what open you can do with open source?
I miss when trolling just meant rage baiting, but yes almost certainly we're all being deceived by OP, if that carries an ulterior motive or stems from a simple lack of moral values is up for debtate but at this point it isnt worth the sustained energy.
This project being upvoted as much as it is reflects more on the community than it does OP at the end of the day.
What AI did: - Generated README templates (boilerplate markdown) - Suggested commit messages (I didn't always edit them) - Helped with documentation structure
What I wrote: - All Python training logic (train.py, trainer.py, callbacks) - All model architectures (gpt.py, tiny.py, small.py, etc.) - Tokenizer integration - Data pipeline - CLI scaffolding (