gateorade

I'm not sure this is obvious. For the consumer market, maybe. But at the end of the day, there are physical limits on how much compute you can squeeze into any given area. If your job requires more parallel compute than can reasonably be squeezed into a package for power/thermal reasons, then distributing the workload off-package is a requirement.

Maybe hardware will morph into a more heterogenous mix than we have now, with many single-package cpu-gpu nodes working in parallel, instead of a few cpus orchestrating a gigantic sea of GPUs, but maybe not.

I'm more convinced that this is just one stage of the cycle. GPUs in consumer hardware weren't really a thing before the 90s. From the 90s to like 2013 graphics software (and it's ability to load hardware) improved rapidly. Since then it's kind of stagnated and a lot more of the focus has shifted to doing the same amount of rendering with less power/less heat/less space. Even if we do see a shift toward SoCs/APUs/whatever in consumer hardware over the next few years, I'd bet on some sort of paradigm shift to come along (fully raytraced rendering?) and swing the pendulum pack toward big discrete GPUs at some point again.

This is a nice anecdote, but it doesn’t mean anything.

In the US at least, being raise by a single parent is a massive statistical indicator for basically every negative social outcome. Poverty, criminality, future generational single parenthood, etc.

This is basically the case for any digitally controlled effector/sensor. Need a serial bus to get data from some ADCs? An FPGA can do N buses in parallel. Need a PWM to control a motor? An FPGA can do N PWMs in parallel.

The problem of course is that the effector/sensor is usually still a physical object that has a cost to replicate, even though the control circuitry in the FPGA is free to replicate. As is the case with ultrasonic transducers.

Just spitballing here but it might have to do with the fact that materials that absorb light are more heat conductive than materials that are reflective.

The SR71 was famously painted black as a mitigation for surface heating issues because the black paint conducted heat away from the areas the were really susceptible to ram-heating.

Perhaps reflective surfaces reflect some percentage of the incoming energy away, but thermally conductive surfaces conduct a larger percentage of that energy away and are able to safely sink it into some thermal mass.

I’m generally skeptical of pharmaceutical weight loss solutions as a bandaid over the underlying problem, but for this statement to mean anything you’d have to compare it to traditional methods of weight loss.

If you simply eat at a calorie deficit and aren’t taking specific steps to preserve muscle (resistance training and high protein), a lot of the weight you lose will be muscle mass.

This is kind of weird because there’s a lot more data to suggest now that dietary fats are almost never the problem when it comes to obesity, and in fact dietary fats are really important for a lot of critical processes (brain function, neuroprotection, hormone synthesis). I get it for caloric restriction though since fat is denser than protein or carbs.

It's complicated. Reducing calorie intake is absolutely a path to weight loss, but we tend to ignore the adaptability of the body. If you suddenly reduce your caloric intake, the body thinks it's entering into a time of scarcity and will lower its metabolic rate through hormone modulation and by shedding muscle.

This is one reason why people struggle so much with dieting. They lose lot's of weight at first, but then plateau when the body adapts. When they get discouraged and fall off the diet, they put the weight back on quickly because they now are burning less calories than when they started.

The most effective way of losing weight long term is likely some combination of a slight caloric deficit paired with resistance training (to discourage the body from shedding metabolically expensive tissue) and short bouts of reverse dieting . (ie. slight deficit for 3 weeks, then 1 week of a slight surplus, etc.)

This is largely incorrect, and also not what the person you're replying too is saying. Calorie numbers in food are just a weighted sum of the grams of carbs, fats, and proteins in the food. Each of those groups are assigned a fixed weight based on an estimate of how many usable calories are in a gram of each.

The commenter above is talking about how we often ignore the adaptability of the body, and that if you suddenly cut caloric intake by say 300 a day, the body will surprisingly quickly adapt by lowering metabolic rate to compensate. This can be done through the simple modulation of hormones and may also involve the shedding of metabolically expensive tissue, like muscle.

compute in memory

I agree with this and would even go as far as to say color E-INK screens are basically ready to push out greyscale ones right now. The problem is that no one is making a device with them that's any good. I owned one of the boox color e-ink devices for like a week and the experience was awful because it's a full android os shoehorned into something that kind of works on an e-ink display.

Please stop. Doing this the first week ChatGPT was released was cute. Now it's so so tired.

Yeah that’s basically my point. The hype on HN/Twitter/etc. forget this.

This has been my experience. I’m really impressed by how well GPT-4 seems to be able to interpolate between problems heavily represented in the training data to create what feels like novelty, eg. Creating a combination of pong and conway’s game of life, but it doesn’t seem to be good at extrapolation.

The type of work I do is highly niche. I’ve recently been working on a specific problem for which there are probably only a hundred at most implementations running on production systems, all of them highly proprietary. I would be surprised if there were any implementations in GPTs training set. With that said, this problem is not actually that complicated. A rudimentary implementation can be done in ~100 lines of code.

I asked GPT-4 to write me an implementation. It knew a decent amount about the problem (probably from Wikipedia). If it was actually capable of something close to reasoning it should have been able to write an implementation, but when it actually started writing code it was reluctant to write more than a skeleton. When I pushed it to implement specific details it completely fell apart and started hallucinating. When I gave it specific information about what it was doing wrong it acknowledged that it made a mistake and simply gave me a new equally wrong hallucination.

The experience calmed my existential fears about my job being taken by AI.

Why would you start a company that is entirely dependent on a third party’s closed product, over which you have zero control? This just feels immensely risky.

Weird question, but if you encourage bees to come through these methods, how prevalent are they? I like the idea of doing this, but I also have a young child and am somewhat allergic to bee stings myself and wouldn't want my yard to become overrun with bees.

The f-22 is also the US fighter with the highest combat ceiling. I believe the actual number is still classified. The f22 took the shot on the balloon last week at 58 thousand feet, which is higher than many fighters can fly.

This one was considerably lower. But IIRC Alaska is a common station location for f-22s so may have just been coincidence.

International law’s got nothing to do with it. Basically Every spacefaring has made it their policy that an attack on one of their satellites would be treated as an act of war and everything would be on the table in terms of retaliation

I guess I misunderstood your point. I was thinking you were saying that you thought most people walking into a dealership and getting loans a on 30k+ new car could afford to pay cash instead. For the same car.

The numbers suggest this is false, for the US at least. The oft-repeated study comes into mind that 47% of Americans can't cover a surprise 500 dollar expense without it being a major worry. See also the fact that the average new car purchased in the US today is 50 grand. I know I'm comparing apples and oranges here but if both those stats are true then it's clear there's likely a large overlap between people who are broke and people who are driving expensive cars.

Aside from that, there's the fact that if you can actually afford it, then it's likely better to finance than to pay cash because you can invest the full purchase price of the car for a higher return than the interest you would pay.

The primary benefit of FPGAs is not to be an alternative type of computing hardware to cpus or gpus but to do things that neither of them are particularly good at. Mainly producing some output for some input or series of inputs with extremely low latency (like in the single digit nanoseconds). This is what ASICS are usually used for but FPGAs come in handy when it doesn’t make sense to spend a million plus dollars spinning your own asic. FPGAs are good for things like building digital transceivers, extremely precise synchronization systems, or industrial/scientific/medical applications where you need to sequence various effectors/sensors/etc. with extremely tight timing.

Since they are just general digital logic machines they can be obviously implement a cpu and are great are parallel computation, but in most cases cpus and gpus are much better at general computation because they’re specifically designed for that task.

Anecdotal obviously but I’m in the industry (I’m assuming you are as well from the nature of your comment) and I work with many current and former pilots and I think this characterization is a little unfair. Many military pilots do come from engineering backgrounds since a degree is generally a requirement for pilot training, and an engineering degree is often viewed as a good qualifier for becoming a pilot. Military pilot training is also highly technical in nature and involves rigorous classroom learning on the underlying physics and systems of tactical aircraft. But aside from this, my experience is that technically competent (not necessarily a given) pilots can have a better practical understanding of these sensor systems than the engineers that designed them because the designers very rarely get a comprehensive picture about what it’s actually like to use them in combat scenarios, simulated or otherwise. The problem in my mind with simply writing off the TICTAC and GIMBLE incidents as a bad reading of noise on the scope is that, from multiple pilot testimonies, dozens of tracks were observed via radar, ir/eo, and visually (like just the eyeballs). I’m not saying that these are craft that are violating the known laws of physics, I personally have other theories, but I’m not sure it’s just noisy sensors either.

Even if you could convince your management to downlevel you, you're not going to be able to stop juniors coming to you for help or product owners/managers from giving you tasking of a similar level to what they've come to expect.

In my mind there's really only one way to achieve what you're after here. If you're truly ready to exit in pursuit of this new career then go to your management and inform them that you'll be leaving, but offer to continue working for them on a 1099/consultant basis for some number of predetermined hours per week or on the completion of some feature(s) by some date (as long as the time required to deliver by that date aligns with how much you want to work).

If they're receptive make sure you make it clear that they will have no bearing on when/where you perform the work.

Otherwise, try to find another employer to make a similar arrangement with, or apply for very low-efforts jobs (probably at non-tech companies) where you can come in fresh without a reputation of being a high-performer and phone it in.

The performance of matlab/Python the traditional engineering world isn’t relevant though, its just for modeling/simulation, it never runs in live systems. It’s way cheaper to just buy a faster machine then to try and retrain a mechanical engineer who learned matlab in college on Julia, at least for now.

MATLAB is ubiquitous in the aerospace industry. There probably isn’t an aircraft or spcecraft built in the west in the last twenty years where matlab wasn’t used.

The two big reasons are simulink and the availability of extremely domain-specific modules.

Simulink is a graphical modeling lanaguage for developing/simulating closed-loop control systems among other things. Flight controls people build aircraft controls in simulink and then the software engineers take it and use it to generate code that can be integrated into the rest of the flight software.

Then Mathworks builds and maintains a specific tool box for every niche engineering domain under the Sun. Need a set of simulink blocks and/or matlab functions for simulating phased-array radar systems? Mathworks has you covered, for an expensive license fee.

Both these things, IMO are ripe for replacement by open-sourced Python-based modules that do the same things, but it would take the right people with the right domain knowledge to have the incentive to do the work.

As a counterfactual to this, go on linkedin and type "sandia national labs" into the search bar and see how many results come up with people freely listing them as their employer.

This isn't the movies, you're allowed to tell people if you work at a national lab. You just may not be able to go into detail on what your work entails.

I work in a classified environment, but if I didn't intend to remain anonymous on HN I'd have no problem telling you who my employer is.

The story about how data had to be manually transferred between the unclassified and classified networks might seem odd to people not used to how this world works, but it's extremely common to this day and absolutely necessary in certain scenarios. If you have a network that must be absolutely secure, both to data extraction and the injection of malicious software, airgapping the network and tightly controlling writable media is the only option. See STUXNET [1].

These days there are some solutions where the high-side and low-side networks are only pseudo airgapped with things like data-diodes or cross-domain guards [2] but these are really only implemented in scenarios where the frequency of high-side to low-side transfers (and vice versa) necessitates it and/or in relatively low-security (but still classified) networks.

1. https://en.wikipedia.org/wiki/Stuxnet 2. https://en.wikipedia.org/wiki/Cross-domain_solution

I mean, that might be true if someone confesses to heinous/violent crimes (and shouldn't they?) but its definitely not true for things like minor drug use/sale etc.

I’m only an aspiring woodworker myself and don’t have any book recommendations but I really enjoy and am really informed by Bourbon Moth Woodworking on YouTube.

The SSDs in the apple silicon machines are likely much much faster than whatever you have in your macbook pro so when they do spill over into swap it would be much less noticeable. That being said, I'm pretty pro apple silicon (purchased an M2 Pro this morning) and I would never recommend anyone buy a new computer with less than 16GB of ram unless the usecase is strictly web browsing/streaming/ms word type stuff.

I’m not an Elon basher but I’m genuinely confused by what you’re saying. You’re saying the fact he has a BA implies it’s something in STEM rather than Econ? Maybe I’m wrong but isn’t a BA the degree you would expect to get an basically any school when studying Econ?