dgacmu

I'm giving context for why a study of higher intensity cardio -in older people- might be interesting to people, and why it's not just "exercise good"

Mostly because functional strength is useful and keeps you alive. major goals as you age are avoiding falls and being able to continue doing things for yourself. Strength fits that bill pretty well (and it also improves fat free mass).

And on a slightly more technical note, recovering from higher volume becomes harder as you age, so focusing on a smaller number (5ish) of reps at higher weight gives you adaptation without quite as much stress.

But I should be clear, when I said real lifting, I don't mean to exclude any form of well calibrated progressive overload, whether that's strength focused or hypertrophy focused. I do mean to exclude the "go to the gym and lift a 10 lb weight the same number of reps each time" BS

No, but past recommendations for older adults (note that the average age in the study was 72 years old) were towards "gentle" or moderate exercise. We're seeing a shift now towards recommending real weight lifting and higher intensity as we age. ("Real" -> closer to powerlifting in terms of goals and methods)

That's incorrect, I have one on my desk right now. They've stopped selling it now, but I got one a year and a half ago:

> Apple M4 Pro chip with 14‑core CPU, 20‑core GPU, 16-core Neural Engine 64GB unified memory 2TB SSD storage 10 Gigabit Ethernet Three Thunderbolt 5 ports, HDMI port, two USB‑C ports, headphone jack Accessory Kit $2,649.00

That's not nearly as true when you look at AI training clusters. They're basically supercomputers but without an FP64 focus.

(These are the systems to which GP was referring at Google.)

Sorry! Power Usage Effectiveness. It's a measure of the overhead of delivering power to the servers (or motherboards, depending on how you measure it), taken to include all other datacenter power overheads: AC/cooling, UPS losses, and sometimes transformer/rectifier losses.

A PUE of 1.2 means that for every watt of power you're using in a server, you're using 1.2 watts drawn from the grid (approximately). Those extra 0.2 watts are being spent on cooling and other things.

As someone else noted - it's effectively zero compared to the water evaporated in evaporative chillers.

But it's still misleading. The major source of water use in datacenters, by _far_, is the water used in power generation. This improves PUE, which reduces power draw, but the savings are almost certainly under about 20% given that many modern datacenters already operate at a PUE of under 1.2. So if you're running on coal or gas, you're still consuming quite a bit of water indirectly.

Now that said again - the water consumption part of this equation is generally overhyped. The power draw is the problem, as are the really bad temporary hacks to the power problem (e.g., what x.ai is doing with "temporary" gas turbines).

You can do a perfectly good, very efficient mini split for USD $5k. Avoids the leaks of window of portable units. And if you're feeling fancy you can get it as a cold climate heat pump. They're great options for retrofitting - can do multiple indoor air handlers, etc., for far less than $50k

Speaking as a professor: the filter is really helpful. Having students struggle for two years in a program they're going to fail out of is terrible. I've seen it happen - and I saw more of it happen for the years CMU also stopped requiring the SAT.

The SAT is a very imperfect measure but it turns out a lot of the others are even worse.

And so useful when you don't feel like scraping the dishes. ;-)

Both VRBPAC and CDC's ACIP did remarkably good work during the pandemic under enormous pressure - what specific complaints do you have? They got safe and effective vaccines out there fast, did a really reasonable job of balancing the cost benefit w.r.t. side effects of both j&j's and the somewhat elevated myo/pericarditis risk from moderna's vaccine in young men, etc.

Be specific?

(I have complaints - I thought Dr. Meissner was wrongheaded about opposing allowing EUA to extend to pediatric populations. But the committee as a whole functioned well and balanced the urgency of the situation with the need for voluminous data.)

I think you mean it's lognormal, at least if we're discussing native English speakers or comparing those with similar amounts of exposure to the language.

(The median English speaker almost certainly knows several thousand words, or word stems to avoid duplication. But the number who know all words in the tail is exceptionally small.)

From one person who's completely not-anonymous on HN to another: Why? Do you worry about the association of your IP address with your real name by yet another site other than any place you've made purchases from or signed up for an account from?

(I'm asking seriously, as I can see some risk to having that linkage more public, but given the rate with which services holding PII are compromised and my own personal rate of receiving notices of "oops, we kinda sorta leaked everything about you, here, have more free credit monitoring", I assume almost all of this is available already.)

The problem for people like that is that they're working in a system that rewards and mandates bad behavior. You want your traffic tickets to stick so you can make quota? Ticket more black and poor people. They won't contest the tickets as much. Drug arrest and conviction quota? Find people who can't afford an attorney, they'll get an overworked PD and likely take a fast plea bargain.

Good people are responsive to the incentives we've created for them also.

He's not a loser; he's done some really fun work that many people use daily. I've used his range mapping trick in multiple projects/papers. It's elegant.

It sounds like he's gotten bad advise about how to market himself /or/ this is being marketed to people who have bigger checks to write and whom he believes will be responsive to this kind of marketing. As an academic, it rubs me very wrong - I think it's detrimental to the field when we get into h-index stacking contests or citation count comparisons. But I don't know what incentives he's responding to, which seems important for putting this stuff in context.

(as an aside, it turns out that polars + fastexcel is about 10x faster than pandas + openpyxl for searching that dataset, if anyone else is curious what he was actually talking about. :)

I suggested to a masters' student that a problem we were working on would benefit from analyzing it mathematically. He brought an incorrect solution the next time we met, and on a whim, I asked Gemini to do it. Gemini got it right. I started looking for more ways to use it after that.

Ah! Good question: Google's non-open-weights models (Gemini, etc) have almost always outperformed on image recognition tasks compared to any other models. I use a mix of in-house and Gemini for image classification tasks for $startup. No other models have done as well, and I had hoped that some of that would spill over into their open source models. It does to a degree - bigger Gemma models are okay.

I was excited about this until I fed it one of my local test problems: coin identification. I then spent 10 minutes arguing with it that a photo of a 1998 washington quarter was not, in fact, a Morgan Silver Dollar. I mean, I wish it was.

It went into a crash loop on a british columbia 1 dollar coin. This happened with both Q4_1 and Q8. Maybe I'm holding it wrong or it's just really bad for this task.

In contrast, gemma4 gets the british columbia coin right though it also mis-identifies the quarter. gemini 3.1-flash-lite nails them both.

Was getting about 50 t/s output on a 3090 with Q8 which seems ok.

But it does, at least to some degree: For the cpu (or brain) you want high density to minimize the latency between components. For the heat sink, you want high surface area, which you can actually do to some degree three-dimensionally, particularly when you have active cooling. Look at a typical heat sink with a fan attached to it -- it has some depth, because that depth allows more heat to be transferred to the air to it by increasing the surface area exposed to the air. Lungs do the same thing, and they do function as part of our cooling system. So if you have a way that the flow of the exchange medium is not limited by the external surface area of your heat exchanger (a fan, a pump, a diaphragm), you can go pretty far.

I'm probably out of date, but Google's advanced protection at one point did account recovery via postcard to your home address. High latency but pretty good as a fallback.

One of the explicit goals of the NSF is to train the next generation of scientists. Part of that is making sure that you're creating a rich pipeline of people who are going to do innovative things. Broadening participation is much more about things like getting more (usually younger) people from all walks of life interested in joining your field. Which is basically an unmitigated good -- first, the obvious advantage that having more people who want to be in a field is good for it from the perspective of choosing the best folks. And second, the less obvious but perhaps more important thing that people with different perspectives often end up thinking about problems differently. It's not nearly as helpful to have 1000 people all focused on chasing the same problems with the same toolbox of solutions as it is to have 1000 people focused on different problems with different ideas of how to approach them.

I say this as a professor at a top computer science department. I have _never_ felt limited in my ability to collaborate with the best folks in my area. Ever. I do! And it's great! And I also believe strongly it's important to make sure we are growing those next generations of amazing people, because the thing that makes research awesome is working with them.

But then you'd have to keep stealing more gold bars for the upkeep, and who has time for that?

Large company again makes local decision without considering the effects outside that single product line.

I wonder how many Linux GPU sales their decision to penalize Linux on their FPGA line will cost them.

I don't think this is correct. Heidi's work made a different observation: That you can smear quorum intersection across phases of paxos, whereas the blog post in this submission is observing that you can do bog-standard quorum intersection in a way other than just thinking about majority intersection, via algebraic/geometric structures. I believe these are generally orthogonal observations.

(Heidi's work is both deeper and more practical; this post is just a really cute observation that there's something mathematically deeper underlying the idea of intersecting quora.)

Chinese EV policy in the US is about propping up our auto industry despite its best efforts to lose the EV battle. This has nothing to do with "communism", it's a purely economic thing that ties into internal US voting blocs.

Kenji's stick blender mayo recipe is also really good and really easy.

I woke in the middle of my first one due to inadequate sedation and it felt like someone was pushing their fist into my stomach too hard and/or having cramps. Tolerable but unpleasant. I elected for propofol on my second and was happier (though both midazolam/fentanyl and propofol basically make you kinda useless for the rest of the day).

It is sometimes said that reality has a liberal bias. But it is literally the case that historians rank these two presidents at nearly opposite ends of the spectrum, and the article's tone seems to reflect that. Which isn't really an example of bias in Wikipedia - it is supposed to reflect what reliable sources say.

I gently disagree. I think that having provenance information logged is valuable - both to the project ("please ban dga because he's submitting ai slop") and to people who might want to study all of this stuff ("interesting, ai coauthored PRs were rejected at a rate X times that of non-attributed PRs"). I think a non-advertising header of some sort that included more specific information about the LLM would be even better, of course.

Yup. Not just you.