skysight-inc/HackerNews huggingface dataset:
I think this view is best established in The Remains of the Day by Kazuo Ishiguro. It's a fairly traditional view but well described there.

For what it's worth, I think it depends on the shape of the thing. "What actually" is not a fixed concept like that. For instance, "what makes a good vacation?". One could say "not having faeces on your nose for the whole time" since, after all, it is unlikely that having faeces on your nose the whole time is likely to make a good vacation even if you're on the most lovely beach surrounded by all you live. But that's a baseline that's easy to meet. So what _actually_ makes a good vacation? The truth is: ∄ an activity or characteristic X such that ∀ vacations V that have X, V is a good vacation.

So what "actually makes people happy"? No one knows. We know things that can make you unhappy (faeces on the nose, not having any relationships) and we know that there exists a baseline happiness we have (the Hedonic Treadmill, etc.). But really, not much more.
I disagree on both points. The US government has deemed US dollars to be the only thing that can be used to pay taxes, so therefore it has some root value.

On commodities, if there is strong demand for gold, uranium, corn, etc. the market can respond and invest more capital into creating them. Bitcoin - not so. No matter how much more capital is invested in mining, the rate of inflation is fixed, and the pie is just split differently. But there cannot be an influx of new Bitcoin above what is specified in the protocol.
Heh, soon you'll be able to do that too when the vaccine comes out. What a great end to the year.
I'd really like to see more information on when you should become a C-Corp vs LLC, and how big of a pain in the ass it is to change. Right now, a C-Corp seems like a lot of paperwork and taxes without a lot of benefit.
>SV thing, to tilt towards 100% work in the work-life-balance.

I think it's a SV (and some other industries) thing. Even more specific, it's a thing for a certain type of product. For a product like the one in the OP it's a natural monopoly. To be really successful you need the designers of a building and the builders using the same software. The plumbing contractor can't say that they don't use PlanGrid and expect to get the bid if that's what everyone else uses. So there's enormous pressure to be the winner since there's only going to be one while everyone else loses.

And the rewards for winning are tremendous. She spent ~7 years on PlanGrid and now is (presumably) extremely wealthy. She never has to work a day again in her life. In terms of balance that is pretty great, no?
Starter – Website for Your Business
Well, same issue and same solution (see other answers), except that in Rust, instead of `RuntimeException` it's named `panic`.
The way I frame error handling is there are three kinds of errors:

The first kind of error is the "programming oops" error--division by 0, null pointer dereference are obvious examples. These kinds of errors potentially arise from almost everywhere, but you're never expecting to handle them except at the very highest catch-all level. Consequently, you generally want to have maximum-detail error logging of the entire stack trace and perhaps the environment around the cause of the failed exception to understand what wrong.

The second case of error is the expected error class--I/O exceptions and file-not-found are the prototypical examples here. Generally, this will be handled immediately, and you may be unwilling to pay the high performance penalty of constructing the exception context if you are going to throw that context away on the very next line. Instead, your want your exception context to be no more expensive than an error code.

The third and final class of error is application errors that combine both properties. You want contextual information for errors, explaining not just what went wrong but also the sequence of processing decisions that led you to do that wrong step (e.g., pointing to configuration file entries). A stack trace isn't necessarily the worst default for context, but it's usually far from the best information. The application, when handling the error, may well want to poke heavily at the error details to display it in more user-friendly ways.

As it exists at present, most error handling systems focus on either the first or second class of errors. More rarely, they try to handle both of these classes (Rust's Result/panic divide is an example here). But there is very little support for the third class, with the closest alternative being providing context via chaining of exceptions.
chesstempo.com has the best library of free tactics puzzles IMO. They also support alternate puzzle modes like blitz (time to solve affects your rating) and mixed (some positions require finding the only defensive move which doesn't lose).
I would suggest the opposite. IRS realizes the only revenue they're likely to extract correctly are small business owners, and middle to upper class who don't have the time nor legal fee tolerance to fight it. They already extract so little on an |absolute value| basis from the lower 70% of Americans, that it only makes sense to focus on the top x%
If anyone hasn't seen it, now is a good time to look at https://python-poetry.org/ It is rapidly becoming _the_ package manager to use. I've used it in a bunch of personal and professional projects with zero issues. It's been rock solid so far, and I'm definitely a massive fan.
> What value can Octave give me, relative to python?

For me the main thing is that math is "native", I don't need to import anything to start doing linear algebra. In python, doing math seems like an afterthought. For example, python offers you strings and dictionaries out of the box, but not ndarrays. In Octave, it is exactly the opposite, and it just feels right.
Ask HN: Generating 10%+ Annual Returns
I understand its no where near a certainty but what's the least risky method - how would you do if you needed to?

I guess the principal amount matters - I have $400k.
That is a fantastic comment. I also recommend the BEAM book to those who want to go deeper: https://blog.stenmans.org/theBeamBook/

I haven’t finished it yet but the chapters on scheduling are great.
> How many registers does an emulator (such as Rosetta 2) have to implement and test? (Subregisters should be counted.)

it still shouldn't because they can't be distinct in the emulator as writing on a subregister affects the larger register and viceversa.
Can anyone explain why vesting acceleration is considered "undesirable" by acquirers and VCs?

https://handbook.clerky.com/hiring/vesting-acceleration

Does it mess with the company valuation or who gets paid out? Isn't this a bit unfriendly to early employees?
> Can deep learning find the cracks in P vs NP?

No. It really is just heuristic building. A core problem with using ML in this sort of use case is that it is often brittle. Once it gets outside of the context it was trained in it may or may not be able to generalize it's training to new contexts. We may have difficulty knowing when it is very wrong.

I think ML in research science could be viewed as a very good intuitive oracle. Even if they are right 95% of the time, you have to do this work prove the long way every time because that 5% matters. The real utility is in "scanning the field" to better focus research on things likely to bear fruit.
Fascinating work. I wonder if this approach works to model interactions (no reason it shouldn’t).
The interactions of proteins with other proteins and well as as molecules like lipids, water and electrolytes form the basis for cellular processes. If that can be inferred correctly, you are looking at the building blocks of a “human simulator”.
That usually (right?) doesn't lead to staying in the host's DNA, but I understand that some of our DNA is virus DNA so it DOES happen. Viruses per se aren't exactly rare, so maybe he specifically meant this phenomenon.

But also - that's reverse transcription, not reverse translation. Maybe Lupe made this point a little too late in his description.

EDIT: Sorry, moved around the above after rethinking
/e/ is mud among the LineageOS community (which is upstream of it), due to several reasons. Sadly I can't find the relevant /r/lineageOS posts .

Here's some reading that covers the basic points though:

https://intangiblesheep.neocities.org/rants/eelo.html

https://web.archive.org/web/20191224031946/https://ewwlo.xyz...
At Sun back in the day our workstations tended to have fairly promiscuous login settings, so one of my coworkers took the liberty to launch folding@home on every machine in the org. Listing running processes one day, I saw this thing pegging my CPU; asked around and others had it too. A virus!?! Then he fessed up. Kinda miffed at first but ultimately really cool, so we let the thing keep running. That was my introduction to the whole protein folding problem, and it's really great to see this milestone!
I think I'm almost as uninformed as you, but I believe it comes down to the difference between perfect solutions and close enough solutions. Consider the classic NP problem of the traveling salesman problem.

"[Modern heuristic and approximation algorithms] can find solutions for extremely large problems (millions of cities) within a reasonable time which are with a high probability just 2–3% away from the optimal solution." [0]

When close enough is enough, NP problems can often be solved in P time, and I suspect this is one of those cases. For crypto however, close enough is not enough.

[0] https://en.wikipedia.org/wiki/Travelling_salesman_problem#He...
I don't think it is. Look at the graph.
Folding@home mostly tries to calculate protein dynamics using already solved structures, so their work is still critical.
Out of interest, does anyone use SymPy outside the scientific/ academic community?
This is a lot bigger than people are assuming if protein folding can be done quickly and cheaply it will trickle down to a lot more than medicine. It is going to advance bio fuels, food production and a lot more.
1. The median age of death at 79, and 50% of people have 3+ diseases doesn't imply that "Nearly all of the people who die from COVID had a life expectancy of < 1 year" as you say. Those 3+ diseases you mention include diabetes, high blood pressure, and obesity etc... Diseases that people commonly live with for decades. The median remaining lifespan of a person who dies of COVID has been estimated to be around 12 years [1,2].

2. Lets say you are correct and 90% of people who die of COVID would have died within 1 year. And let's assume that you have some magic PR method that will convince 100% of the population that this is true.

That means that COVID is still more deadly than the Flu, and some large percentage of people will still choose to remain home, stop traveling, stop eating out etc...

These people will continue to damage the economy until an effective vaccine is available. And even in your scenario a vaccine that is 95% effective will still save millions of lives.

An effective vaccine will save lives, and it will improve the economy. There is no logical reason to deny that fact.

[1] https://wellcomeopenresearch.org/articles/5-75
[2] https://www.medrxiv.org/content/10.1101/2020.06.08.20050559v...
First time doing IT Software Consultancy, think I'm being phased out
Hello HN,

I'm an experienced IT professional in Software (for the sake of anonymity), it's my first time doing consultancy work, always been inhouse so far, so this is why I'm working through one of the popular platforms. The project was supposed to be for a couple of months, it's been 3 times as much and there looks like it could be enough work next year as well.

Correct me if I'm wrong, but I think all consultants get to a point where they see more and more people being onboarded to the project they work for, but this time all are inhouse, so I think I'm being phased out slowly, although my role isn't yet redundant but I do expect it at this rate.

I communicate daily with the client, don't know if this is the kind of thing you wanna bring up or if this is usual stuff if you're a consultant, that they are seen as a kick starter and the client hires inhouse as soon as they get the chance and have gone past that tight spot that required a highly paid experienced consultants in the first place.

Any thoughts or strategies to deal with this (maybe avoiding being phased out) or is this unavoidable and you should always being on the lookout for the next project?

Thank you
> I think this misses the parent’s point.

Just because you disagree with me, it doesn’t mean that I misunderstood the viewpoint I’m responding to.

> The point is to not let the algorithm make decisions.

And my response is—that’s not enough. It sounds like the algorithm, because it is biased, has the effect of increasing the bias in the whole system. If your response is that humans should work harder to counteract biases in machine systems, well, I think that’s just a way to CYA and assign blame but not a way to solve the problem—humans will remain biased, and they will trust automated systems even when that trust is misplaced.

As an analogy, it’s like a driver in a partially autonomous car. As soon as the automation takes over, the driver stops paying attention to the road. We can make a big fuss and production and talk about how it’s the driver’s fault, and the driver should pay attention, but we’ve placed them in a system where they are discouraged from paying attention, and the system is more dangerous as a consequence.

> Also guns don’t kill people, people do. Otherwise explain to me why it would be okay for certain institutions to be armed but not individuals. If guns are the problem, then no one should have them (including the military/police).

This is a false dilemma / false dichotomy. This argument assumes that EITHER access to guns is to blame OR people are to blame, but not both, but there are obviously other ways to think about the problem.

Any rational way to look at problems will look at multiple contributing factors.
So the median accuracy went from ~58% (2018) to 84% (2020) in 2 years?

Does 84% == solved?

Also, any low hanging frut implications for longevity tech?
@dang, please combine the thread with https://news.ycombinator.com/item?id=25253488
Yeah, new research seems to show its the opposite... animal fats are extremely good for you, and its plant fats - specifically linoleic acid - that promote metabolic dysfunction.
me too... should have done bitcoins :)
For that final batch of 5-10% of people, today's headlines from the Moderna data dump shows that its vaccine is 100% effective (so far) in preventing serious disease to those that have been vaccinated. And 100% effective in preventing death (there were deaths in the placebo group).
Earlier post on this with direct results: https://news.ycombinator.com/item?id=25253488
Also why is vesting acceleration given to advisors, rather than employees?
> The only reason anything has value is if people value it...value itself is a social phenomena

That's not totally true, certain things have a more fundamental value in that if you don't get them you die, such as food and shelter. If we were ethereal economic agents living on a free and infinite compute substrate you could say that, in the meantime the Maslow hierarchy makes certains things have intrasical value that other things don't have.
It is all about rates, not binary can/cannot statements. An non-symptomatic person can indeed spread the virus, but someone with a fever who is coughing constantly (ie a symptomatic case) will spread exponentially more virus. So a vaccine that prevents such symptoms can in practice reduce infection probabilities. Bring those probabilities low enough and spread stops.
Tony's a genius, but it's important not to cargo cult this.

"Culture-first" worked at Zappos because of the specific culture they chose to pursue, namely an intensely customer-centric one. At that point, their culture is not merely culture but also strategy: we will win in this market by rejecting short-term optimization (charging for returns, spending less on customer support agents) in favor of long-term thinking that will produce customer loyalty and thus long-term success.

I know, I know. Culture eats strategy for breakfast. But the reality is that if you want to build a successful business that isn't a daily soul-sucking grind (strategy only) or a fluffy wannabe 'startup' flapping in the wind (culture only), you need both.
maybe you don't want the two distinct customer sets to know about possibly similar products.
This drive train simply can not handle high torque as the chainrings will bend under load. They admit it themselves. Increasing their thickness will cause a whole lot of compatibility problems and also turn the whole thing into a monster.

This expensive ceramic bearings are exposed and will experience significant wear. Properly insulating them would wipe out whatever miniscule gain in efficiency they claim as the insulation increases losses.

Finally reducing chain friction (which is already very low on a tuned drivetrain) to some fraction (that has yet to be proven for real conditions) is tantamount to snake oil in my opinion.

These are just few. I could go on but it is not worthy the typing and mental efforts.
The Larry Gonick Catalogue
love this, I can already see how something like this could add keyboard shortcuts to apps that don't have them (or not the ones I want)

thanks for making this open source!
This (individual non-commercial license) is my current approach, too. I wonder though, why do we have such a lack of fast scientific plotting, as you wrote?

Is it due to the fact that few people really care or is there some inherent set of hard problems that Mathworks figured out how to handle?
> Go just seems to be a faster Elixir in a lot of ways

If you haven't worked with both, you just don't know. The developer experience in go is nowhere near the developer experience in Elixir. It's just so much easier to write scalable code in Elixir than Go. Sure it's not as fast, but it's not god-awful slow (speedwise, you're doing better than python django, for example), and for 90% of people shopping for what elixir and go offer, the network is the bottleneck.
I ran Folding@Home at Google on hundreds of thousands of fast Xeon cores for over a year. I concluded at the end that unbiased MD simulations are not an effective use of computer time.
>in the context of machine learning, but I am still not really clear on why such a property is necessary or even desirable in the context of high dimensional statistical learning algorithms.

Depending on the context of where the Algo is used interpretability may in fact be necessary. (Try getting Deep Learning Models deployed in a Consumer Banking company.) Generally outside of hardcore tech companies (FAANG, etc.) you are usually building things in conjunction with Business partners within your org and good luck explaining to them that you want to deploy a completely opaque algorithm that will somehow solve their problems.
Pretty interesting that they only used about $15k worth of resources (retail price) to achieve this. It's not a technique that would have been out of reach for other organizations based only on not being able to afford the compute.
Do in-app purchases work or is there a workaround for that?
Future returns could certainly be less. But all we have to go on is what's happened so far. In the past even the worst entry+exit points are acceptable for retirement if not astounding, if you've saved something like 15% of your income for age 30-65 (I forget the exact numbers but that's more-or-less close to it). Yes, some people have to choose between cutting their standard of living and retiring on time, and retiring later for the expected standard of living. I'm not sure what we can do to change that? Like you said, we have limited time and everyone's situation is different. Go onto any of those retirement income calculators and compare the person who starts saving in their mid-30's but maxes everything out to the person who starts saving with their first job out of school and gradually increases from $100 or $200/mo.

Your comment was that equity investing can be disastrous if you hit the timeline wrong, and my only point was that unless you're speculating or are only in the market for 5-10 years, that has never happened. We have no way of knowing if that will happen in the future or not, but there's no reason to believe that we're going to see decades of middling or negative growth. There's really no other option if you want to have a real retirement unless you plan to just rely on whatever government assistance you can get.
Comma.ai Ranked Above Tesla Autopilot by Consumer Reports
100% accuracy is "solved".
London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery
so you're describing diffused learning? is that the default mode? havent taken the course so idk what the take on it is
This is amazing, if we can simulate multi-protein interactions, you could imagine in our lifetimes being able to see a fully computation driven simulation of a human blood cell. That would be a huge breakthrough.
My favorite proposal is to require that all companies take back their products at EOL and recycle them to an extremely high standard. This pushes the externalities of low lifespan products directly back onto the manufacturer, encouraging them to design for both lifespan and easy repair/recycling.

Unfortunately we’ll also have to deal with IKEA style tactics of converting a single company into a complex web of disposable sub companies in order to dodge their tax or regulatory obligations.
> But wasn't protein folding supposed to be NP-hard?

Yeah, at least some variations of it are NP-hard. SAT is THE NP-complete problem, but there are some really good SAT solvers around. This basically means: They have a solution that mostly does very well on most instances. But because (probably) P != NP, you will never have a polynomial time algorithm for this.
> Fortum, a Finnish startup

Uhuh, Fortum[1] is not exactly a startup. It is a state-owned corporation with annual revenue of about 5 billion and one of the largest players in the Nordic energy sector.

[1] https://en.wikipedia.org/wiki/Fortum
Maybe you can apply at https://prototypefund.de/en/
That forbes article is quite weird.
The trials have obviously been designed to find out if the vaccine helps prevent serious illness, as fast as possible.
Questions like 'can vaccinated people still spread covid' are much harder to answer in a trial. Perhaps they'll look at that later.
They aren't penalized, they can still do the X alternative that relies only on understanding the material. This is merely a method for determining who to take a closer look at, not what that closer look will reveal.
there are also a variety of problems that are hard to approximate.
I'm not trying to argue that it's a "good" thing, simply that tons of things hack our DNA (there are even some studies that emotional trauma can have repercussions imprinted on our DNA) My major point is that DNA hacking/damage isn't always a life-ender. Even skin cancer is often removed without issue.
> till oponenent eventually makes a mistake.

Any person(including world champ) makes lot of mistakes and that's why they get badly beat by computers. The thing to know is when the opponent made a mistake and knowing how to attack it to advantage(that is basically called tactic). I wouldn't recognize the mistake of players rated 500+ point above me but a GM could and would punish them.
Sherwin Williams Fires Employee with 1.5M Followers on Paint-Mixing Channel
2.7 was essentially a bridge to Python 3. All features it has were backported from 3. This is why so many people before 2015 were saying that Python 3 doesn't provide any new features.