philipportner

Wasn't aware of all of those, thanks for sharing.

> Are Rubber Ducks offloading thinking?

Just that an actual rubber duck doesn’t do anything. You solve the problem you have by talking, and in doing so, thinking, to come up with a solution, an idea, or gain better understanding.

After that you either implement something yourself or have learned something.

With an LLM you offload all of that, the only thing you still do is tell it what the problem is. The agentic duck does the rest and you look at the output.

Even if you have to argue, you argue without having gone through the steps to gain anything yourself.

How do you keep up with such information? Any sources you could recommend? Closest I know would be SemiAnalysis

Good point, thanks! I haven't been keeping up with most of the new model internals.

Congrats on the great job with the Altar II. If I didn't already have too many keyboards, I'd hop on the Kickstarter! FWIW, I fully agree with your opinion on including a `half working` fingerprint sensor.

if you assume that training requires about 3x the compute of inference (one forward pass, one backward pass, parameter updates), and we take DeepSeek-V3 since their numbers are public.

they used ~14.8 trillion tokens with about 2.66 million GPU hours. 14.8 * 3 = 44.4 t inference tokens.

obviously, this is back of the envelope math, but at 100t/s you would need like ~14k years. scale this to >100k GPUs and your in the hours to a couple days range.

Hasn't changed at all since AI agents became a thing. tmux, nvim with a few plugins, mainly fzf and LSP support. If I do use an AI agent, I just run it in another tmux window.

> My favorite side effect is that I now love all foods. Prior to this, I was a rather picky eater. Now I love everything!

I feel like there's a burntsushi joke hiding in there somewhere.

All the best Andrew.