mNovak

Everyone's focusing on the cold gas thrusters, which of course is very cool, but not actually shocking that it technically could be done (economically is another question).

But can we talk about the just casually driving straight vertical or sticking to the ceiling? I didn't think that would be technically possible for a 1000kg+ car with probably at least 2" of ground clearance.

There's presumably very limited stores of compressed gas, so you might get one hop or a couple 0-60 boosts per charge. But with electric fans, suction is a feature with much longer duration (minutes?), so you could feasibly do the batman driving on the tunnel ceiling past traffic trick.

Google very very highly weights recent events. Makes it a pain in the ass to try to dig up historic information.

Appears to be back online.

I mean, not auto-purchasing with the card, no. But my wife has definitely used chatGPT to plan activities on the trips we've already booked.

So the goalposts have moved to include continual learning.

In a sense I think no one will agree on a definition of AGI until it becomes impossible to construct any benchmark under which an AI underperforms "average" humans. That or it's defined retrospectively, after it's overwhelmingly obvious it met any such definition.

I'm confused, is this a research device, hobby/tinker item, or consumer product?

I don't mean to be negative, but making a sustainable business on a hardware race to the bottom sounds challenging.

I'd be curious to to know from others here, what in-home tasks do people think they'll really be able to automate with any of the various bimanual or humanoid robot platforms that seem to be going around?

Cynically, I suspect oAI is subsidizing OR tokens to manipulate perceived traction, since OR usage stats get widely publicized

ASIC makes it sound like it's a single chip, but in reality serving trillion-param models on Cerebras requires a full cluster (as in multiple racks, MW of power).

Some interesting twitter analysis here:

https://x.com/bleysg/status/2073937651150029084

Major tenants like OAI have near total control. These GW-class datacenters are built to spec for the tenant. Construction doesn't start until all the details are signed off.

What I like about this, is that it significantly increases the probability of a sci-fi scenario where you're picking up a hot chip on the black market; rumor has it, Mythos 9 weights baked in...

> The only argument they have here is that they use GB300 GPU's which for some reason should not be available to chinese citizens

Note that Chinese companies are free to rent from GB300 clouds internationally. There are large datacenter hubs in Singapore and Malaysia serving chinese and other customers.

Though there is also reported [1] significant smuggling of Nvidia chips into China as well.

[1] https://epoch.ai/publications/chip-smuggling

On the one hand, these debts may be off the balance sheet, but institutional investors certainly know about them and can reason about the company's valuation. Retail investors may be caught out slightly more.

But on the other hand, these companies are essentially paying for the service of taking the debt off books (by paying the leasing premium to the SPV partners). I guess I'm wondering what they really gain from doing so, if again sophisticated investors can see through the games?

Archive link:

https://archive.ph/20260720174223/https://asia.nikkei.com/bu...

That's the government's problem

Noticed this as well. And the additional definitions under "explore this word" are all relating to the root word, not the actual form of the answer

Definitely agree, I was very confused to see the latest guess (and with the most narrow letter ranges) at the bottom of the list.

Though, by the time we've replicated a complete industrial hinterland (up to and including a semiconductor supply chain) which the author describes, it seems like generating fuel and engines wouldn't be impossible.

Also to the authors last point (extremely long time scales causing degradation), it seems like we'd want high thrust capabilities regardless. i.e. maybe a small gravity well doesn't gain us anything, since we'd need big engines to get up to speed anyway.

I suppose it depends if you're assuming the probe is a complete factory, just taking in regolith and spitting out new probes, vs if the probe deploys and builds up the factory on the surface of an asteroid.

In the latter case spinning doesn't get you far.

> So the question is can they keep the pricing up on the older ones a few years down the line

They don't expect to keep the prices flat over time, and everyone involved will have planned for this. Prices are highest when they're the newest and greatest (part of why it's valuable for neoclouds to be first in line for new models), and drop year by year as newer GPU models can do equivalent work at lower cost.

You can see a pretty cool dataset of this at [1]; H100 prices where $3/hr in 2023, and dropped linear-ish to $1.75/hr by 2025. And also the notable exception that prices are up this year due to shortage.

[1] https://semianalysis.com/gpu-pricing-index/

Unrelated to the accomplishment or proof itself, but it's interesting how much of the prompt, even in this latest-and-greatest model, is spent essentially telling the model to actually solve the problem. Things like "Reject status reports, vague optimism, and claims that an unproved global compatibility statement is 'routine'."

Also a lot prompt spent feeding it strategies, which feel like they should/will eventually be deduced by the model itself, not explicitly stated. That's not to take away from the outcome in any way; rather, it feels sort of like when you would prompt GPT 4, "think through your answer step by step," as a sort of proto-chain of thought.

>> approximately 700,000 A100e GPU hours of black-box automated red teaming

Amusing that they use A100e as the reference point to sound impressive. Different ways you could make that conversion, but based on FP4 FLOPs (yes it's disadvantageous to A100, that's the point), that's something like 200hr on a GB300 NVL72 rack.

Not nothing either, but far less astounding sounding than 700k hrs.

If I recall correctly, Gibson had never even used a computer at the time of writing Neuromancer, so that's perhaps not shocking.

Uhm.. Super High Frequency (SHF) and Extremely High Frequency (EHF) would like to have a word.

Though yes in my experience, at those frequencies people stop using ITU designations, and switch to IEEE (S,C,X-band etc).

Logic gates and memory bits have very different fabrication processes (mostly because DRAM is optimized for a high density of big capacitors doing the storage).

You can put some memory on the logic wafer (SRAM) but it's area inefficient, which is wasteful on your expensive N2 wafer. So a dedicated DRAM process is vastly cheaper per bit, even at current elevated prices.

I enjoyed that book. But which part are you referring to?

Someone please give Nemotron a thesaurus. This reminds me of the early days of SEO, where you try to hit 1% keyword density.

The new game is finding a single sentence with the most instances of "safe" or "safety". My current high score is 4..

Interesting writeup. Would be nice to have a couple images of Linear A/B scripts to visualize. Looking on google, they're very daunting!

The water is a clever impedance matching trick. The contrast in density between air and human flesh is high, so the waves all reflect off the surface rather than penetrating and reflecting off the internal structures we care about.

That's why normally you're concerned with really good transducer contact (squeezing out any air) or use a gel to match impedance.

I'm a bit rusty on CT, but I'd guess the resolution is proportional to the total number of transducers in the array (e.g. larger sensing surface equals tighter resolution) since you're basically taking a Fourier transform of the incident wave.

This is really interesting! And perhaps surprisingly doesn't trigger any immediate major technical red flags (as someone who has worked with MRI and phased array beamforming), as many HN HW articles do.

My only criticism from the tech video would be that they spend some time lauding the nanometer deflection sensitivity, which might lead some to believe that's indicative of the image resolution. It's not, and it's somewhat of a distraction -- that's just giving us amplitude information, which is comparatively less important than correlated time/phase across the 100k sensors. They do later on state ~mm resolution, which is still great!

Doppler and motion blur may be an issue (e.g. heart beating), as one slice requires a full ring of sequential exposures. But still way faster than MRI, so probably fine.

On a lighter note, it could seriously change the meaning of get FUCT (Full body Ultrasound Computational Tomography)!

We already ultrasound babies in the womb, so one would hope this has been studied.