chunky1994

This difference in emotional reaction is because of the effort involved in the process. Functionally, we see YouTube video creation as a fundamentally difficult exercise (to do well) and results in a singular product (one video). Any additional content would need an ongoing investment of time and money from the creator. The LLMs though would not require an ongoing investment beyond the first training run, that is probably why you have a problem with it, they're an extremely high leverage way of taking advantage of content.

The difference is though there isn't a whole lot of "whole cloth novel solutions" being written in software today so much as a "write me this CRUD app to do ABC" which current generations are exceedingly good at.

There are probably 10% of truly novel problems out there, the rest are just already solved problems with slightly different constraints of resources ($), quality (read: reliability) and time. If LLMs get good enough at generating a field of solutions that minimize those three for any given problem, it will naturally tend to change the nature of most software being written today.

I'd like to see the diets in the study that are specified as the "calorie-reduced diets". (Can't seem to find the paper). If it's the same as the Standard American Diet, this muscle loss is quite explainable. I think the mitigation is relatively easy though, if you want to shift the p-ratio, recommending a daily high protein shake would do a lot to stave off muscle loss (and even more if resistance training is applied of course). The exercise addition is probably the hardest to adhere to.

I'd suggest investing your time in the product first and converting the waitlist to real users before trying to take on external investment. It will either make it much easier or you won't need to raise depending on what happens when your product goes out to the 1000 user list.

If you train one of the larger models on these specific problems (i.e DM for D&D problems) it probably will surprise you. The larger models are great at generic text production but when fine-tuned for specific people/task emulation they're quite surprisingly good.

I'm a bit skeptical to give up conservation of energy in a system with friction. Isn't it more accurate to say that if we were to calculate every specific interaction we'd still end up having conservation of energy. Now whether or not we're dealing with a closed system etc becomes important but if we were to able to truly model the entire physical system with friction, we'd still adhere to our conservation laws.

So they are not approximations, but are just terribly difficult calculations, no?

Maybe I'm misunderstanding your point, but this should be true regardless of our philosophy of physics correct?

The conserved quantity is derived from Noether's theorem itself. One thing that is a bit hairy is that Noether's theorem only applies to a continuous, smooth (physical -> there is some wiggle room here) space.

When deriving the conservation of energy from Noether's theorem you basically say that your Lagrangian (which is just a set of equations that describes a physical system) is invariant over time. When you do that you automatically get that energy is conserved. Each invariant produces a conserved quantity as explained in parent comment when you apple a specific transformation that is supposed to not change the system (i.e remain invariant).

Now in doing this you're also invoking the principle of least action (by using Lagrangians to describe the state of a physical system) but that is a separate topic.

Right, the rephrasing of the sentence is a tad more accurate. Your three entities are [invariant -> conserved quantity]: (translation -> momentum), (rotation -> angular momentum) and (time -> energy).

This is exactly why using GPT to supplement but not supplant writing code is very good. Code generation is generally more helpful as you go to higher layers of abstraction as long as you can stay at that layer of abstraction. Once you need to jump a level lower because your abstraction fails in a specific context you need pre-existing knowledge of how the abstraction works in order to ask GPT the right questions.

Hence, depending on the domain knowledge of the piece of code you are dealing with, GPT can be very helpful in generating a good scaffolding to get off the ground quickly (i.e you want to write a web app but don't want to deal with having to learn how to write a whole react app etc.), but asking GPT to...write an optimizer for a C runtime would end up with poor results as its heavily bent on the specifics of that task where a specialists' knowledge would outweigh any abstractive advantages.

One very useful experiment I did early on was try to solve a problem with GPT where I had deep domain expertise and see where the cracks are vs in one in which I had very poor expertise. This led me to make my abstraction based statement above, and so far I've seen it remain true with every successive version.

So the way it works is that if you have a setup like so: Your client <-- bad actor --> Public Webserver.

Your first layer of defense is assuming its hard for a bad actor to get to that spot (which this attack is showing is possible).

Now the next thing the bad actor needs to do to compromise your traffic is deal with HTTPS/VPN. The only real way to do that would have to be to convince your client that the certificate from the CA that's signing the https traffic received from the webserver/VPN provider is trusted/ (i.e a classic MITM with a compromised client and a local CA).

Most clients will warn you that this cert is unrecognized, so with an untampered client unless you just clickthrough the cert warnings you will be protected. However, people often ignore cert warnings and that means that all your traffic is now cleartext for the bad actor.

I'd argue the number of startups that have trust all local network connections is not far behind. I doubt a company seriously considers their wifi connectivity as a threat vector until they get to a series B size.

Hypothetically I'd imagine if you take early startup infrastructure snooping + <insert criminal act here> you probably have a criminal enterprise that's cashflow positive.

I am assuming this is a bug, otherwise it would be a very weird move on apple's part. While they're usually pushy about updates etc. for anyone who uses defaults they usually respect user decisions.

I've dealt with the apple auth chain quite a bit and from the reports it looks like the earliest this started happening was Jan 10th with this notification "macOSInstallerNotification_RC" getting pushed to everyone on Ventura. Very likely the bug then make this appear as a required security update and auto installs it despite user input (I am not sure how many people without the install security updates toggle have also been upgraded).

Also if you have a firmware password on that is still needed for security upgrades so that's probably why there's a difference between password less updates and non-passwordless upgrades but that's just a guess.

I used to be an LLM until I took an arrow to the knee. Aside from the joke, I think the barrier would definitely reduce and in-game characters would be contextually far more aware but how do you enforce plot progression in such a truly open world? Can you control the boundary of LLM expression?

Correlation =/= causation. This is most likely coincidental. I highly doubt Dustin's differing views caused a (near) unanimous ousting of a completely different company's CEO that had nothing to do with Dustin's primary business.

It's 300k cash, rest in "profit participation". MSFT has enough RSUs to make that happen without it being a problem

These are all also illegal. This doesn't take way from the parent.

More like:

- It should be illegal to defraud unsuspecting individuals by hiding information that would necessarily clarify the riskyness of the contracts they are engaging in.

- Businesses have way more information asymmetry advantage and we want to help limit this.

- Financial responsibilities of business and individuals is something that should be looked at by a third party because this can be financing bad things and/or be unfair to the individual due to the aforementioned information asymmetry.

- Various other reasonable points, while not perfect can certainly provide conscientious arguments for the them.

The drug trade is definitely culpable for violence and human misery. Unless you're perhaps talking exclusively about psychedelics (and even that isn't squeaky clean, but production and distribution is less violent due to the origin and effort required in distribution), the users while not directly responsible, are absolutely funding some grotesque stuff behind the scenes that they usually brush under the rug.

None of this means that the war on drugs etc was justified or that its implementation did not have serious flaws, just that the drug trade isn't a manufacturer of purely victimless crimes.

I'm not being facetious at all when I say, if you have more stories like this, please consider making a podcast.

Electrostatic shielding is actually about shielding the inside of a closed (hollow) conductor from the outside (the standard deriviation from Gauss's law).

If the conductor is non-grounded then a charge outside the conductor is fully affected by charges inside the conductor but not the position of all the charges (since the divergence of the field through a closed surface will produce a scalar field that varies with distance to the point at which we are measuring the electric potential so the only thing that matters is how far from the closed surface we are (and assuming all charges are enclosed within the closed surface we can represent them as a net charge at the center of the closed conductor).

If the conductor is grounded you are then basically changing the flux with which we can calculate the electrostatic potential at a point outside the sphere (and therefore changing how an external charge will be affected by an internal charge)

I think the difference in [0] is your input variable space is much smaller for "traditional" engineering disciplines. To grossly simplify: ME? You need tolerances for a specific amount of force (in different directions.) EE? You need tolerances for specific amounts of current passed through a circuit.

With software your inputs are tremendously varied, because user input is not as well-formed as in any other engineering disciplines. In fact we deliberately try to find ways to input things that are not supposed to be inputs by designers of systems (i.e hacking things). If "hacking" bridges was a thing, it would be something like people trying to see if they can diffuse a thermite solution into the rock-face instead of driving a car over it, and I'm not sure any structural engineer has planned for that scenario.

While it does seem like a matter of semantics, in the end, whether something is classified as an addiction or a dependance doesn't seem to be as binary as "has a biological response to removal of said substance".

I'd argue that since we do have dopamine/seretonin driven reward systems, anything that modifies a physiological expression of the neuro-chemical lifecycle from pre-exposure to post-exposure (whether that be density of receptors, abundance of neurotransmitter, some yet unknown neurochemical expression), then that substance can cause addiction.

Video games could do this, just not as dramatically as heroin, but given that modern day life is basically just a hamster wheel of our neurochemical systems, anything has the potential to be addictive as defined above, it all depends on where on the spectrum of addiction said substance lies.

It really can't happen this way. Between 3 and 4, you'd tank bitcoin's value significantly (assuming $1B is able to triple bitcoin's market value). Any sizeable liquidation of bitcoin at an inflated price is going to affect the price heavily and so now you no longer have 100% backing. (This of course depends on how elastic BTC/USDT price is)

It would be embezzlement (and not fraud) if the money had first gone to Netflix and then been redirected to Kail (without their knowledge).It's only fraud because the funds never went to Netflix in the first place

Embezzlement: Misappropriation of funds Fraud (in the inducement): (Specifically wire/mail fraud when talking about contracts): Misrepresentation of contractual terms to induce entering into a contract. (Here the misrepresentation is the amount of money that the vendor was going to charge netflix since technically his kickback would've reduced the expenses to Netflix)

Its definitely against standard contractual terms (i.e conflicts of interest terms) and here they would all probably come under fraud in the inducement; i.e Kail induced Netflix to enter into contractual terms based on misrepresentations of what Netflix was gaining when it signed the contract (leaving out his personal gain which absolutely would be a material benefit that netflix itself could have gained had it known those terms existed).

Pretty nice overview, I'd highly recommend Hartl's gravity for a physics focused introduction to tensor calculus along with an actual application centric usage of the same: https://www.amazon.com/Gravity-Introduction-Einsteins-Genera...

Thank you!

I’m not trying to be needlessly contrarian and am genuinely trying to learn here as I don’t believe I have adequate knowledge on these issues; Having said that could you please cite sources for both (1) and (3)?

I can see (2) being acceptable at face value, however (1) seems to be rather dubious given the standard inflation of lifestyle where more affluence leads to moving to areas of more affluence, does it not?

For (3) I’d imagine this has more to do with rates of adverse health factors amongst impoverished communities, which I’d be given to believe is relatively evenly distributed if controlling for wealth?

They don't need to do that. The majority of (monetary) value add in the supply chain is large-scale refinement and transportation of the drug product from a region of lower economic value to a region of higher economic value (i.e smuggling). Both the production and last-mile distribution are very decentralized and limited in terms of monetary compensation.

If you've already made 95% of your profit in the central steps of the value chain, taking an increased risk to increase efficiency of last mile distribution is not worth it. Especially when centralization in an uber for drug dealers type of application heavily increases the risk of being neutralized and prosecuted by law enforcement rather than decentralized street level dealers who can never effectively compromise those higher up in the supply chain.

Firstly, this fine was issued by FINRA, not the SEC. Secondly, which laws did they break exactly?

This is a substantial fine for the scope of violations that were being investigated, as this has nothing to do with any of the gamestop restrictions earlier this year but their messaging, outages and vetting of customer expertise during 2018-2020.

It boils down to a $70M fine for not having a high avilability system, being bad at explaining trading concepts and allowing people to take risks that other banks would not. That's quite a reasonable amount.