blintz

Are frontier models actually astronomically expensive to train? GLM 5.2 was trained on ~30T tokens, so ~10^25 FLOPs. Say you get B300's for $5/h (pretty high), and you get 50% MFU; that's ~$15M. Of course there's also a bunch of risk that the training itself goes badly, post-training, etc. But still, compared to the inference spend after, it's not that crazy.

This is cool. Something about dropping everything to go see a movie in an empty theater is sort of tempting.

I really want automated QA to work better! It's a great thing to work on.

Some feedback:

- I definitely don't want three long new messages on every PR. Max 1, ideally none? Codex does a great job just using emoji.

- The replay is cool. I don't make a website, so maybe I'm not the target market, but I'd like QA for our backend.

- Honestly, I'd rather just run a massive QA run every day, and then have any failures bisected, rather than per-PR.

- I am worried that there's not a lot of value beyond the intelligence of the foundation models here.

They are playing a bit fast and loose with the word "banned".

> Your smartphone contains materials processed through semiconductor fabrication, chemical etching, metal anodizing, glass tempering, and electroplating — none of which you could start a new facility for in California without years of litigation.

I agree that we should make it easier to do things, specifically by decreasing the amount of litigation involved in doing stuff. But the risk of a bunch of litigation isn't a ban, right? I get that it's trying to be attention-grabbing, but calling it a ban when it's not just sort of confuses the issue.

I have tried many times to do this, but lack even the minor discipline required. I inevitably make changes to the commands I want to run at the command line, rather than in the script, and then later forget to edit them in the script.

Instead, I now swear by atuin.sh, which just remembers every command I've typed. It's sort of bad, since I never actually get nice scripts, just really long commands, but it gets you 50% of the way there with 0 effort. When leaving my last job, I even donated my (very long) atuin history to my successor, which I suspect was more useful than any document I wrote.

My only hot tip: atuin overrides the up-arrow by default, which is really annoying, so do `atuin init zsh --disable-up-arrow` to make it only run on Ctrl-R.

I see, yeah, I can see how if it's like 100% matching some parts of the style, but then failing completely on other parts, it's a huge pain to deal with. I wonder if a bigger model could loop here - like, have GPT 5.2 compare the fine-tune output and the Nano Banana output, notice that trees + water are bad, select more examples to fine-tune on, and the retry. Perhaps noticing that the trees and water are missing or bad is a more human judgement, though.

I was most surprised by the fact that it only took 40 examples for a Qwen finetune to match the style and quality of (interactively tuned) Nano Banana. Certainly the end result does not look like the stock output of open-source image generation models.

I wonder if for almost any bulk inference / generation task, it will generally be dramatically cheaper to (use fancy expensive model to generate examples, perhaps interactively with refinements) -> (fine tune smaller open-source model) -> (run bulk task).

I reset my network settings and updated before realizing this is just an outage. I kept searching “<MY LOCATION> Verizon outage” but did not even consider it could be nationwide. I guess it shows how rare nationwide outages are.

Google (and Vercel) are great for doing this! I would like to see Anthropic and OpenAI do something similar, since they too greatly benefit from Tailwind CSS.