brilee

"It's young, and it shows"

"Holds up"

"not a toy, but not a sprawling project either, and ideally.."

"And that’s the trap"

ctrl F "real" -> 6 usages

ctrl F "genuine" -> 4 usages

(author here)

As a serial successful field-hopper, I agree that I'm not the right person to be making these estimates.

But the external view is that college courses roughly expect you to do what I'm claiming, in roughly the time investment that I'm claiming -- and undergrads are typically in 4+ classes at a time. So is it that the whole educational system is delusional? (I fully acknowledge it might be so!)

I could have sworn that sickly sweet smell was the smell of various phosphine reagents? Just my vague recollection of my time in lab from 15 years ago.

Does this handle, e.g., water of hydration CaSO4 . 2H2O? states of matter H2O(g)? does it preserve subunit information, as in (C6H5)CH2COOH? Writing a parser for basic formulae is such a tiny tiny part of the actual problem... deciding the scope of what you want to handle and how is the real problem

Great write-up, but I admit that I found the interweaving of human and AI-written content/headlines/summaries pretty distracting. I kept on wanting to scroll past, but had to keep on backtracking to find the human thread again.

I think if you want to give your reader a quick intro to, e.g., what is the Adam optimizer, a simple link to Wikipedia is fine. No need to copy-paste an AI tutorial on Adam into the blog post.

AI slop

Taste is the field behind the goalposts, and we as humans are constantly expanding that field by "having taste" and developing taste as a community.

https://www.moderndescartes.com/essays/taste/

For those commenting on cost per token:

This throughput assumes 100% utilizations. A bunch of things raise the cost at scale:

- There are no on-demand GPUs at this scale. You have to rent them for multi-year contracts. So you have to lock in some number of GPUs for your maximum throughput (or some sufficiently high percentile), not your average throughput. Your peak throughput at west coast business hours is probably 2-3x higher than the throughput at tail hours (east coast morning, west coast evenings) - GPUs are often regionally locked due to data processing issues + latency issues. Thus, it's difficult to utilize these GPUs overnight because Asia doesn't want their data sent to the US and the US doesn't want their data sent to Asia.

These two factors mean that GPU utilization comes in at 10-20%. Now, if you're a massive company that spends a lot of money on training new models, you could conceivably slot in RL inference or model training to happen in these off-peak hours, maximizing utilization.

But for those companies purely specializing in inference, I would _not_ assume that these 90% margins are real. I would guess that even when it seems "10x cheaper", you're only seeing margins of 50%.

Desalinated water is also less dense than normal seawater, so the water column inside the output pipe would create a pressure imbalance with the water column outside the pipe, assisting in the outflow? I'm having trouble figuring out how to resolve this seeming perpetual motion machine

I'm working on a startup in this space and wrote up my thoughts here: https://www.moderndescartes.com/essays/study_mode/

As a former big tech engineer, I can't help but come up with a gazillion ways to work around these sorts of seemingly straightforward policies.

Here's one way they could get around their own privacy policy: keep track of what % of Claude-generated code is retained in the codebase over time (as an indicator of how high-quality / bug-free the code was); A/B test variations of Claude Code to see which variations have higher retention percentages.

No usage data is retained, no code is retained, no data is used (other than a single floating point number) and yet they get to improve their product atop your usage patterns.

Here's another idea: use a summarization model to transform your session transcript into a set of bits saying "user was satisfied/dissatisfied with this conversation", "user indicated that claude was doing something dangerous", "user indicated that claude was doing something overly complicated / too simple", "user interrupted claude", "user indicated claude should remember something in CLAUDE.md", etc. etc. and then train on these auxiliary signals, without ever seeing the original code or usage data.

Back when Claude Code had per-token pricing, almost nobody used it because it was clearly much more expensive than the Cursor pricing - $20 a month flat for Cursor vs $5-10 a day for per-token Claude. The incentives manifested in the way both products used tokens - Claude Code has no particular qualms about submitting a gigantic number of tokens and letting Sonnet figure it all out, whereas Cursor puts in a lot of traditional software engineering to figure out the correct minimal context to put in. Now that Claude Code is on a fixed price plan, it strangely doesn't seem like Anthropic is doing anything to optimize the number of tokens consumed by Claude.

I think it's quite plausible that Anthropic is bleeding out ~100/month on token costs per $20/month user, and even at 80% margin, this is just merely breakeven. Their limited capacity also means that they are _losing_ the opportunity to sell the same capacity at a per-token marginal profit. I think the only plausible endgame here is that Anthropic uses the usage data to RL-finetune Claude Code to the point where it is actually worth a $200/month subscription.

Enjoy the $20/month Claude Pro plan while it lasts; I don't really see it sticking around for more than a year at best.

I'm a second-gen Korean-American; my korean is weak but conversational. I am intrigued by the reasoning model that analyzes my speech and points out various mistakes I'm making. It's a good first attempt at separating the 2 tracks of actual conversation vs mistake-correcting.

I think showing the raw reasoning text is not quite the right UI; maybe highlighting the specific text in red and showing a suggested correction would work better?

It's also a little awkward that the conversation is live; I don't really have any breathing room to read the reasoning traces on what mistakes I made / could have done better. I hung up the first time I tried to figure out how to pause.

For those of you suggesting we use the extra energy for $pet_topic I suggest reading https://www.moderndescartes.com/essays/factobattery/

Tldr: most applications of free energy have capital costs that far outweigh the free energy harvest potential.

The loop can be anywhere, not just the north pole :)