Show HN: Lathe – Use LLMs to learn a new domain, not skip past it (github.com)
Lathe is an experiment in using LLMs to teach me something new, instead of doing the work for me. It generates a hands-on, source-backed tutorial for any technical topic you want to learn. Then you work through it yourself by reading and typing the code by hand (gasp) in a local UI built for exactly that.
It's a Go CLI plus LLM agent skills (Claude Code / Cursor / Codex). You prompt something like "/lathe build a 3D slicer in Erlang", run `lathe serve` to spin up a local webapp, and read it in your browser. Every tutorial comes with the things that have made self-learning a pleasant experience for me in the past:
- table of contents that follows along as you scroll - side-notes that nudge you to think - exercises for the reader - sources backing up the content that you can use to take you deeper
To help make up for the lack of human brainpower behind the tutorial, you can also ask questions about the content, have another LLM verify the tutorial actually compiles and runs, or extend it with another part (no more "Part 4 of 6" that hasn't seen an update since 2021).
I didn't build lathe to replace human-written tutorials. I built lathe because I _love_ human-written tutorials, but wanted to learn technical domains where no good human-written tutorial exists yet (building a 3D slicer from scratch, making embedded Zig approachable, etc). There's a longer story in the README about how I got started with programming through PSP homebrew tutorials, and why losing that to LLMs bugged me enough to build this.
I'm not here to sell you anything (there's nothing close to a VC-backed startup here :D). It's an LLM, and its output is usually good but not perfect by any means. So far, my experience is that because you're the one typing and actually engaged, you catch the weird stuff (and I'm finding that pushing back on it is its own kind of learning). And yes, it's vibecoded, because it's low scope, low risk, and scratching a personal itch. I run it on Claude Code + macOS personally, other setups should work but I haven't been able to verify them yet.
If you can find resources to learn something that was written by a human, read that first. But Lathe is here to fill in the gaps when that isn't the case, and I hope it serves as an example where LLMs can help us think better, rather than less.
Repo: https://github.com/devenjarvis/lathe
Would love your feedback if you decide to check it out!
56 comments
[ 4.8 ms ] story [ 87.7 ms ] threadAlso, I wouldn't say "have another model test the tutorial compiles" a feature, but also I do not expect a fool-proof tutorial from a one-shot, I guess.
Not sure why I would try this over a hand-written promot. Also wondering why ChatGPT Study mode failed, it seemed interesting.
But at the same time, I'm afraid getting everything laid out for you in exactly the way you want will erode some of the understanding you build by going through a primary source directly and figuring things out the hard way. So this having more focus on actually doing stuff by yourself seems right up my alley (while still tending to the LLM induced intellecutal laziness... ) .
If it does find some, maybe it could supplement them instead of just from scratch
I don’t write my own - I can’t optimize for the models understanding, and so I just give the skill-creator skill an outline and then have it refine until the output is what I want.
https://pchalasani.github.io/claude-code-tools/plugins-detai...
For example I’ve used this to better understand counter-intuitive things about diabetes/insulin, dopamine and motivation, Claude’s implementations, etc (to combat so-called cognitive debt).
Strong LLMs are surprisingly good at this type of quizzing, they display a semblance of “theory of mind”.
Traditional method of looking up stuff, going through guided lessons etc are just more streamlined and faster than this method.
I'll definitely check out your other skills.
Well, but it will still serve you content from humans, but without any attribution.
Even now, LLMs are terrible educators. They do not make coherent progressive curriculums. They hallucinate details which the student will not have the knowledge to challenge.
If you use an LLM to make a tutorial you will get some benefit for sure, especially if you use it for Socratic sessions based on a corpus of data you provide (like a blog post or documentation).
Don’t expect it to teach you reliably though. It feels good to ask the LLM whatever you want, but if you’re learning a topic you don’t have the instinct to realize when it’s giving you a poorly chosen progression of information or teaching you something flat out untrue.
Still, it took a lot more effort than just delivering the initial request. AI makes everyone produce something average but you still need taste to produce something good - I guess this applies to courses too.
I was telling my friend the other day. The way you learn programming is by typing code out by hand. And I suggested using LLMs to generate minimal educational examples aligned with his interests and needs.
I've tried the Zed Shaw method to learning programming (just typing out code examples by hand -- doing "studies", the same way you would with music or art). I tested it on a programming language I had been learning for a while and was struggling with. After just a few hours of typing my fluency had skyrocketed.
I realized that in several hours of typing I had written more code than in weeks of study. Because when you don't know a language yet, producing code is extremely slow and error prone. But typing out correct code is relatively straightforward.
So due to changing my approach to "just blindly typing", I got more practice (at least as far as reading and muscle memory goes) in a few hours than the previous few weeks.
Now of course understanding is important too, but it's a separate dimension, and largely comes after memory and fluency in my experience. (Understanding something theoretically and being able to use it are two very different things!)
The general principle here is Stephen Krashen's Input Hypothesis of language acquisition (https://en.wikipedia.org/wiki/Input_hypothesis) which says a baby learns language by just hearing stuff -- just being exposed to inputs -- and that adults can learn the same way too.
And I heard it on the excellent website (now defunct?) All Japanese All The Time, where the author tested the hypothesis on himself by mostly listening to a lot of Japanese and gained fluency in a year.
https://web.archive.org/web/20080705194055/http://www.alljap...
90% of my Claude usage is getting it to write me guides, that I can then spend most of my time following to build the end results.
Keeps the brain healthy and also provides bespoke learning, rather than a generic course off the internet. Definitely a great use of AI.