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The comments here turned out much more interesting than I expected—this has become a great place to discuss the difference between AI-generated, AI-written, and AI-assisted content.

So let me start from @jbarrow's comment: "AI written, generated from the codebase."

My actual learning process looked like this:

1. I walked through the nano-vLLM codebase, asking Claude Code some high-level questions to warm up. 2. Then I asked detailed questions one by one, let it explore, and double-checked the code myself. As someone without an ML background, it sometimes took hours to understand a single concept. 3. Once I felt I understood enough, I started drawing Excalidraw diagrams to explain what I learned.

Does this count as "generated from the codebase"? I don't think so.

Where we might disagree is the writing process.

As a non-native English speaker, my workflow looks like this:

1. Write a short paragraph (<100 words), then ask my writing agent to "fix this for readability and grammar." 2. Review the output. *If it changes any technical meaning, I correct it.* I consider this a responsible way to write a tech blog. 3. Move to the next paragraph.

Is this "AI-written"? I'd call it "AI-assisted." Every idea in every sentence is mine. Honestly, things like "em dashes" never stood out to me when reviewing. I suspect that's common for non-native speakers.

I wrote this comment the same way. The LLM fixed 14 grammar mistakes that I think would distract readers more than any LLM-ish phrasing.

That said, I'm open to suggestions on how to improve my writing process :)

Since HN only allows one link per submission, dropping Part 2 here.

https://www.neutree.ai/blog/nano-vllm-part-2

Cool, humans hallucinate too. — AI

Hi jbarrow, thanks for your feedback and the links you shared—they're great readings for me (and likely others too).

That said, I need to clarify: the content was not written by AI, and certainly not generated from a database in one shot. If there's some agent + prompt that can produce what I wrote, I'd love to learn it—it would've saved me two weekends :)

Before addressing your questions further, some context: I'm a developer with no ML background but plenty of Cloud Infra experience. I'm currently building an open-source AI Infra project, which is why I studied nano-vllm. So my writing reflects some gaps in ML knowledge.

To your specific points:

> it goes into (nano)vLLM internals and doesn't mention PagedAttention once

I didn't find any explicit "paged attention" naming in nano-vllm. After reading the first article you linked—specifically the "Paged KV Caching" section—I believe the block management logic and CPU/GPU block mapping it describes is exactly what I covered in both posts. It may not be the full picture of paged attention, but I interpreted what I saw in the code and captured the core idea. I think that's a reasonable outcome.

> Part 2 will cover dense vs MoE's, which is weird because nanovllm hardcodes a dense Qwen3 into the source

This reflects my learning approach and background. Same as point 1—I may not have realized the block design was the famous PagedAttention implementation, so I didn't name it as such. For point 2, seeing a dense Qwen3 naturally made me wonder how it differs from the xx-B-A-yy-B MoE models I'd seen on Hugging Face—specifically what changes in the decoder layers. That curiosity led me to learn about MoE and write it up for others with the same questions.

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I completely understand that in this era, people care more about whether what they're reading is AI-generated—no one wants to waste time on low-effort slop with no human involvement.

But as I explained above—and as my hand-drawn Excalidraw diagrams show (I haven't seen an LLM produce diagrams with logic that satisfies me)—this is the result of learning shaped by my own knowledge background and preferences.

We released vx.dev a few days ago and received some feedback. After adopting everyone's suggestions, we rebranded vx.dev to Dewhale and gave it a simpler and more beautiful landing page.

Looking forward to your feedback.

I see...It's just published like a blog, and maybe worth a redesign as a landing page.

Thanks for the suggestion!

Since the prompt part of this project is highly inspired by Vercel's v0.dev, I chose a name that shows my respect for that product.

yes you are right, the domain is a little expensive and already sold out:)

yes, now I found the time mainly caused by: 1. openAI API call, which could not be optimized for now 2. the time to build and deploy, which can be optimized by a preset setup

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