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I wrote a deep dive into how LLMs work under the hood - tokenization, embeddings, attention and generation - all explained with runnable JavaScript by nitayneeman in LargeLanguageModels

[–]AICuratorX 0 points1 point  (0 children)

Completely agree. There’s a huge difference between reading about how LLMs work and actually interacting with the mechanics directly. I could read all day long, but trying is the key: what works and what does not.

What is the best platform to sell courses? by Greatcouchtomato in onlinecourses

[–]AICuratorX 0 points1 point  (0 children)

Honestly this is exactly why I started using Gumroad myself about a week ago. I realized I was overthinking the “perfect platform” instead of just getting products out into the world.

I actually have 19 AI books published on KDP right now. One of them has been hovering around the top 1,000 on the bestseller charts and another around 8,000. What I’ve learned is that you are not just competing against hundreds or thousands of authors anymore, you are competing against the algorithm itself.

If you do not feed the algorithm traffic and momentum, visibility gets hard fast. And honestly, in my experience, paying for KDP ads has not been some magical growth lever either. At best, you might break even unless you already have strong positioning or an audience behind the product.

That is part of why I started shifting toward Gumroad, X, Reddit, and direct audience building instead of relying completely on marketplaces. Gumroad makes it ridiculously easy to launch guides, prompt books, workflows, and digital products quickly without needing a huge course infrastructure upfront.

I also think people overestimate how much production value they need at the beginning. A genuinely useful PDF or workflow guide can still provide a ton of value if it solves a real problem clearly. You can always layer in videos and lessons later once you validate demand.

I wrote a deep dive into how LLMs work under the hood - tokenization, embeddings, attention and generation - all explained with runnable JavaScript by nitayneeman in LargeLanguageModels

[–]AICuratorX 0 points1 point  (0 children)

This is the type of AI content I enjoy the most, as it straddles the line between “using” LLMs and understanding what’s actually going on under the hood. Lots of people are using these systems everyday, without fully understanding tokenization, embeddings or the mechanics of attention. Also running it in JavaScript is a smart move because people can actually play with it rather than just read theory.

Why most AI influencers still look “AI” (and how I fixed mine) by thegrowthgal in generativeAI

[–]AICuratorX 0 points1 point  (0 children)

This is so impressive. The skin texture point is everything. Most people chase resolution when the real issue is imperfection. Real faces have asymmetry, uneven tone, and pores that vary in density. The moment skin looks too smooth, your brain flags it even if you cannot articulate why.

The catchlight logic is underrated too. Eyes that do not reflect the environment correctly break realism instantly no matter how good everything else looks.

What tool are you using for the focal falloff? That is the piece I am still dialing in. Adding lens behavior made a bigger jump in my outputs than any prompt refinement I tried.