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Skim a treatise on the history of centering content
Skim over the “this question is off topic / duplicate” noise if Stack Overflow
Find some code on the page
Try to map how that code will work in the context of your other layout
Realize it’s plain CSS and you’re looking for Tailwind
Keep searching
Try some stuff until it works
Or…
Ask LLM. Wait 20-30 seconds. Move on to the next thing.
I did that a lot initially, it’s really only with the advent of Claude Code integrated with VS Code that I’m learning more like I would learn from a code review.
It also depends on the project. Work code gets a lot more scrutiny than side projects, for example.
Aaand done. Very first result was a blog post showing all the different ways to do it, old and new, without any preamble.
Given how often people acknowledge that ai slop needs to be verified, it seems like a shitty way to achieve something like this vs just checking it yourself with well known good reference material.
If you haven’t been convinced by pure argument in 2026 then you probably won’t be. But the great thing is you don’t have to take anyone’s word for it.
This isn’t crypto, where everyone using it has a stake in its success. You can just try it, or not.
But there is an interesting looking-glass effect at play, where the truth seems obvious and opposite on either side.
You: Hey ChatGPT, help me center a div.
ChatGPT: Certainly, I'd be glad to help! But first you must drink a verification can to proceed.
Or:
ChatGPT: I'm sorry, you appear to be asking a development-related question, which your current plan does not support. Would you like me to enable "Dev Mode" for an additional $200/month? Drink a verification can to accept charges.
Maybe in 3-5 years this will work on consumer hardware at speed, but not in the immediate term.
DeepSeek-V3.2-Exp has 37B active parameters, GLM-4.7 and Kimi K2 have 32B active parameters.
Lets say we are dealing with Q4_K_S quantization for roughly half the size, we still need to move 16 GB 30 times per second, which requires a memory bandwidth of 480 GB/s, or maybe half that if speculative decoding works really well.
Anything GPU-based won't work for that speed, because PCIe 5 provides only 64 GB/s and $2000 can not afford enough VRAM (~256GB) for a full model.
That leaves CPU-based systems with high memory bandwidth. DDR5 would work (somewhere around 300 GB/s with 8x 4800MHz modules), but that would cost about twice as much for just the RAM alone, disregarding the rest of the system.
Can you get enough memory bandwidth out of DDR4 somehow?
An article about the best open weight models, including Qwen and Kimi K2 [3].
Microsoft’s dedication to infusing OpenAI tech into everything seems like a play to cut even those tepid brains out of the loop and capture the vehicles of planning and production. Training your workforce to be dependent on third-party thinking, planning, and advice is an interesting strategy.
Come on!