13,551 karma · joined May 15, 2013
You can tell people to just do something else, there's probably a separate natural solution, etc. but sometimes you're willing to sacrifice some peak performance just have that uniformity of operations and control.
https://www.pi.website/blog/pistar06 has some reasonable footage of making espresso drinks, folding cardboard boxes, etc.
I agree that context matters, and I had the same thought as you. But does that mean that anything he writes on the topic of "who was first" is inherently tainted?
Nice write up and very clever work. I'm surprised by the AWS response that you linked to though (https://aws.amazon.com/blogs/security/ec2-defenses-against-l...).
While I was sure they'd note that Nitro doesn't have this vulnerability due to its design, it seems weird not to talk about Firecracker and Lambda and so on. Maybe those are always on Cascadelake+ hardware? (I also haven't followed this space for 5 years, so maybe I'm asking the wrong question)
Python has some semantics and behaviors that are particularly hostile to optimization, but as the Faster Python and related efforts have suggested, the main challenge is full compatibility including extensions plus the historical desire for a simple implementation within CPython.
There are limits to retrofitting truly high performance to any of these languages. You want enough static, optional, or gradual typing to make it fast enough in the common case. That's why you also saw the V8 folks give up and make Dart, the Facebook ones made Hack, etc. It's telling that none of those gained truly broad adoption though. Performance isn't all that matters, especially once you have an established codebase and ecosystem.
I never took 193p, but I always found 148 to be hands on, and I made it very hands on for the year I contributed: https://web.archive.org/web/20130522184434/https://graphics.... .
I regret that we put my subdivision assignment as the last one, and we allowed students to skip one assignment. Most students skipped it, but those that did the work thought it was super cool to have their own subdivision tool for making smooth meshes.
> The following code for example, simply returns an uninitialized value:
#include <optional>
int f() {
std::optional<int> x(std::nullopt);
return *x;
}> I have an updated, I found out that T2 4K is an HDR movie that needs to be played with MadVR and enable HDR on the TV itself, now the colors are correct and I took a new screenshot: https://i.imgur.com/KTOn3Bw.jpg
> However when the TV is in HDR mode the 4K looks 100% correct, but when seeing the screenshot with HDR off then the screenshot looks still a bit wrong, here is a screenshot with correct colors: https://i.imgur.com/KTOn3Bw.jpg
> Editing of ANGPTL3 was associated with few adverse events and resulted in reductions from baseline in ANGPTL3 levels.
Two out of 15 having adverse advents seems pretty high, especially since one died. The supplemental material rules this as an unrelated death (https://www.nejm.org/doi/suppl/10.1056/NEJMoa2511778/suppl_f...).
Definitely seems promising for a larger-scale trial, but it seems weird to make such a strong conclusion about adverse reactions.
https://podcasts.apple.com/us/podcast/conversations-with-tyl...
It was a bit towards the end, I think.
> Those expenditures may be approaching $1 trillion for 2025, while AI revenue—which would be used to pay for the use of AI infrastructure to run the software—will not exceed $30 billion this year
While it's clear that the author is summing up the spending from the big players, it's not clear to me that their math is right for revenue. Yes, OpenAI, Anthropic, Thinking Machines, SSI, etc. have pretty limited direct revenue (including zero!).
But this comparison assumes no revenue growth for other top computing users. Some companies are certainly saving money on some tasks and increasing revenue, particularly in fields like customer support. See the confusing figure in section 5 of https://hai.stanford.edu/ai-index/2025-ai-index-report/econo... .
That chart is by number of respondents and not weighted by revenue. Like the MIT study, it would not be surprising that "just pipe this to an LLM" isn't enough for most fields or companies. But a few have likely made material improvements.
10% of respondents saying they've seen a >10% revenue gain could be substantial, if they're bigger firms with high leverage in computing.
Edit to add: the comparison also makes a classic "GDP vs market cap" style mistake. Capital expenditure has multiple years of useful life. Revenue is annual. You'd want to compare depreciation vs revenue.
> Blue Owl Capital Inc. and Meta will split ownership of the Hyperion data center site in Richland Parish, Louisiana, with the tech giant retaining just 20% of it, according to people with knowledge of the matter. To finance the build-out, Morgan Stanley arranged over $27 billion of debt and about $2.5 billion of equity into a special purpose vehicle
At 225 over (current?) treasuries:
> The bonds priced at about 225 basis points over Treasuries,