I learned some of this the hard way. I did the thinking and the distillation, but I had AI write the prose and that's all a lot of people saw, the AI tell-tales.
It's not a trick bud. The github page shows my user name and Claude. The content is intended to be read by an AI agent and explored through a text interface. That is explicit in the readme and the primer itself.
If you think you can generate this artifact with a prompt then show me. This was 2 days of exploration and research.
If you think that "2 days" makes it sound a lot... You'd be surprised how long it takes to actually make learning materials. I don't want to be too harsh, in case you're a high school student etc. I see it's good faith, but do note the reaction here.
I read a couple of good analogies to predict how you and others will feel about your AI content: 1) telling people at the breakfast table about the dream you just had, 2) showing all your loose acquaintances the photos of your newborn baby.
That is, it's very precious and interesting to you, but it really isn't to anyone else. This is true about generated text, images and songs. I've generated a lot of what I think of as bangers with Suno but learned quickly that they have zero value to anyone else. Part of the value to me is the thrill and dopamine hits of having generated it. This simply doesn't translate to anyone else. It will take a while until society internalizes this.
This is not to say that AI can't have any role in the creative process. But the effort will be still high and original human thinking and intent and input is still very important.
it's a worthwhile lesson. thank you. There was a great deal of effort on my part, but not in the prose. You've taught me something and I appreciate it.
it's not that you're teaching the AI, it's that you're framing the conversation on a reference material and having a conversation around it. Exploring a problem with referential framing, like a white paper or a dense blog post is a useful cognitive hack. You just have to be careful to pin extraordinary claims to extraordinary evidence.
my apologies it wasn't up to your standards. In fairness to me, that line is exactly what my effort was. I wasn't trying to "learn ML" I am trying to build a mental model that let's me decompose real problem into ML primitives.
It's unclear to me if you think the resource has no value or if it bother you that I wrote it using a coding agent.
I wrote the syllabus and worked through each section. Where my understanding was weak I explored the space, pulled in research, referred the model to other sources, and just generally tried to ground the topic in something I understood.
What resulted was something that helped a lot of subjects click together for me. Especially when to reach for a particular activation function and the section on gating.
This enter survey was motivated by an ML expirent I ran with assosicative memories that just failed horribly. So rather than post mortem that I set about understanding why it failed.
Anyhow, thank you for the feedback. I submitted this in good faith that it may help others.
I'll give a software example because I'm just better at it, but if you describe Trello and tell me to decompose it on a white board I start thinking in terms of queues and write|edit|save tuples. I don't have to invent queues in my head and I have to wonder if it is possible to proactively assign a series of tranforms or schedules on an fresh input.
I know how to do it and it's all internalized. Even if I've never needed to do it.
that's the toolbox I'm trying to develop in ML. For example. I've studies LSTM and implemented one. What I didn't know was if gating was turing complete and essentially unbound. I didn't know if gates could be arbitrarily complex. Importantly I had no idea how to translate "I need a switch here" to a gate, or if a switch was even possible given the need to be differentiable for backprop.
I've bounced off of many good textbooks. Even Karpathy's YouTube series was too dense for me. I'm trying to come in at a more palatable level.
This was a two day exploration where I provided the syllabus and ran through it with Claude Code, asking questions, trying to anchor it to stuff I understand well. I feel like the artifact has value.
I think chatting with an llm alongside a textbook can be helpful but producing learning material when you yourself are a novice is not really that valuable.
I'm just trying to develop the lens where I can see a problem and know what properties of it are meaningful from an ML standpoint.
Coming from a specific domain where I have a sharpened instinct for how things are haven't really given me the ability to decompose the problem using ML primitives. That's what I'm working on.
This is my weekend project. I am building up my pattern recognition in machine learning. By that I mean see X problem, instantly think of Y solution. The primer markdown file is the artifact of that exploration.
read it from top to bottom or better have your favorite language model read it and then explore the space with a strong guided syllabus.
Framing a business problem in terms of ML is indeed important. Where does classification come in, where does regression come in, when to use retrieval, when to use generative solutions. Would be a good section to add imo.
For me getting the AI harness to solve the puzzle has become the puzzle. Just one more level of abstraction. Just like I don't code in assembly language anymore, now I don't name every variable.
AI coding is just a non-deterministic programming language and compiler duo
I find this so hard to get my head around. I am wildly more prolific with agentic coding. It's at minimum a 10x for the first several iterations and when you get into the heavy detail part I am still the choke point.
reply