skysight-inc/HackerNews huggingface dataset:
When the goal is to have a roof over your head, between renting or buying, the better option is to buy. If the goal is to invest wisely, of course buying a house is worse than say something like an index fund. But the problem is: I can't sleep in an index fund. A house isn't a depreciating asset. Renting is not an asset at all. Mortgages are fixed. Rent tends to frequently increase, skyrocketing at worse.

This article takes the very common scenario: 30 yr mortgage at market interest rate to compare to renting. Even on these terms its still lopsided. What about the people who do 15 year mortgage? What about those with large down payments? What about those who pay a little extra to their mortgage each month?

The overall home market keeps with inflation, but in markets where the land is trending towards scarcity, you are poised to make money as what happened in my first home. I like to think those who were lucky to purchase a house in the valley area before the extreme scarcity made a nice profit if they decided to sell.

The author fails to find a third point: in many cases, owning is cheaper than renting, especially in my city and cities like it. Rent here is around 1300 for a 1 bedroom 500 - 900 sqft apartment, depending on where you live. If you want multiple bedrooms, well now you are in 1800-2000 territory for 1300 sqft. You can own a 1800 sqft house for about 850 a month. You can put away half of your savings from rent for unexpected expenses, and use the other half to pay the mortgage off sooner, or you can spend it, either way it's roughly half the cost.
There is an old story about a financial economist and passionate defender of the efficient markets hypothesis (EMH) who was walking down the street with a friend.

The friend stops and says, "Look, there is a $20 bill on the ground!"

The economist turns and coolly replies, "Can't be. If there was a $20 bill on the ground, somebody would have already picked it up."
Sure everyone should take into account their own situation, but assuming that most situation are going to pay the minimum each month...the author's math is very misleading.
This is specifically called out in the article:

> while yes, the headaches of real estate in the Bay Area—where The Melt first set roots—are well established, fingering real estate is a bit like pointing out that it gets cold every winter
Nobody knows what AI is though.

Even Data Scientists couldn't tell you if it just means neutral networks or if it include ML techniques. There are technologies like AutoML which automate feature engineering but is that ML or AI. Not sure.

So I am not concerned whether people know AI/ML or not. What I have issue with is people thinking that you can 90% of AI/ML using SQL. Which makes no sense.
Collaborating on next generation tools for open source data science
I almost took a "big data" data scientist job about a year ago with a local company.

After talking to a number of their engineers, it became quite clear to me that instead of a data scientist, they just badly needed a DBA / someone with ownership and a complete vision of the data structure.

They had no foreign keys, poorly 'designed' indexes, and tons of redundant tables with no rhyme or reason to them.

They'd organically grown their database with hardly any review. They did not have big data, they just had a big mess. And wanted someone else to clean it up.
I'm sorry, but the numbers you're putting out there indicate that you might be a person who does not understand how to value their investment property. The typical baseline number to have is one of the expected or historical cap rate. The terms of your mortgage and down payment are almost wholly irrelevant. And leverage is often considered good in real estate investing.
Plus the property taxes, insurance, and cost of capital for the downpayment.
You mean like XBM, XPM, SVG, and EPS?
OP complains about flash wait states but doesn't know you can move your handler code to ram to avoid them?
Reminds me of a Harvard Daily Stat from 2012, inspired by Zillow data, which comes to a different conclusion. The Zillow data was based on a larger sample size as compared to the article above, which I felt was too qualitative.

The Daily Stat: After Just a Few Years, Home Ownership Beats Renting[1]

In three-quarters of American towns and cities, it takes 3 years or less for a homebuyer to begin seeing savings over the cost of renting, according to a CNN report on data from Zillow[2].

Factoring in such costs as mortgages, rents, down payments,
commissions, taxes, and maintenance, Zillow calculates that the "breakeven horizon" is as low as two years in some areas. But in New York City, which has some of the nation's highest rents, it still takes more than a decade before ownership makes more financial sense than renting.

[1] Harvard Daily Stat, 9/12/2012
[2] http://money.cnn.com/gallery/real_estate/2012/09/06/buy-rent...
This is such a line of bullshit they’re feeding the millennials. They want you to work without benefits in the “gig economy” and rent forever. Don’t fall for it.
None of this should have much of anything to do with investments. I don't "invest" in crypto in the same way that I don't "invest" in the Euro, or even in the US dollar.

I put the vast majority of my money in the stock market. And I have some money in other currencies, centralized services like venmo/paypal/ect, and yes, a bit in crypto. Currencies which I then use to spend on things.

The point of a currency, yes even cryptocurrencies, is to SPEND it, not hold it.
100%. What this all boils down to in 2018 is data. Data acquisition, data cleansing, data filing, etc. But what else is there, now? You're going to create a new, novel ANN network? Cool, can't wait to peruse your paper. Maybe someday it's considered groundbreaking. Right now it's not solving immediate problems. Immediate problems were solved with 1970s ideas + 2018's infrastructure.
> owning is cheaper than renting

This is the key thing! Obviously owning is cheaper than renting, as renters have to cover the costs of their landlord owning, and then some profit for them on top of that.
Juicero was hilarious and easy to make fun of, but if you compare it to Nespresso, it's not really such a ridiculous concept.
And the cost of risk, which is partly covered by insurance, which costs money.
The sad part is that, after so many California exodus arrived, they will turn Boise to yet another CA city that they just left, as seen in other cities, by then they will move elsewhere I guess.
I might be missing something, but why wouldn't the passengers step in and override the car? Is this assuming the car is unmanned?
I'm a flaming flaggot around here (I truly thought I just coined that expression, but [1]). I almost flagged this one, but a black friend of mine recently had Walking While Black story of his own, so I read it. Lo and behold, the story covered his scenario, waving to a cop, first thing. I hear about things like this all the time online; from rappers and comedians; occasional news story; once in a great while, a first-person story from a colored friend. I usually rationalize it away thinking: "That's really only a problem in backwoods towns, far, far from here. Here you get the obligatory asshole cop every now and then, of course, but it's not the norm." This story made me realize A) it doesn't have to be the norm, it just has to be enough to cause someone to ALWAYS be on alert, to be a life altering problem B) No matter how quick I get at dismissing the instinctual thought pattern "different person is scary", the fact remains, I have those thoughts and they likely affect my actions in a way noticeable to other humans. Neither of those realizations are a surprise to me, but this story convinced me I haven't thought critically enough about them. Getting this stubborn old mind to reconsider it's position is an epic hack.

[1] https://www.urbandictionary.com/define.php?term=flaggot
Feature extraction is the most obvious.

I give you a problem e.g. "tell me what factors are influencing a customer's likelihood to leave a bad review". And with ML you can actually produce a list of sorted factors with weighted percentages. You simply can't do that with SQL.

Also remember that SQL is for databases so you often can't do anything algorithmic e.g. K-means clustering, linear regression, random forest, decision trees etc. Where as ML encompasses non-databases as well e.g. from a Kafka stream of numbers predict the next number.
I think the problems the fossil fuel industry are having, such as they are, have little to do with climate change and more to do with either economics being against them, in the case of coal, or fraking in the case of natural gas. The "environment concerns" around fraking include increased earthquakes and contamination of groundwater and are a pretty big deal. The tradeoff is that by allowing fraking, the US is no longer dependent on the middle east for oil and doesn't need to worry about having to import natural gas for the foreseeable future, so it's not as clear cut as it would otherwise be.

I don't think anything that the Trump administration has done has changed any of the core issues. All of the changes they have made have been pretty blatent givaways to their donors, which also happen to be alot of fossil fuel companies, but they're still rearranging deckchairs on the Titanic as far as actual effects on coal. As far as Oil and Natural Gas, there's still the problem of regulatory uncertainty and the dropping cost of alternative energy, which means companies are still more likely to keep fraking going on existing wells until they're dry rather than try to actually drill in the new areas that have been opened up. They still want the option to drill later on if conditions change, but they're moving very slowly in most cases.
The best is when you ask someone why they want an AI/ML masterwork, they just say it's the future and we don't want to be left behind.

It's interesting because this article shows the overlap of what a non-tech thinks is AI and what is common fodder for any decent programmer. So many things get lost in buzzword to English translation, it's easy to forget that most people correlate the plastic box sitting in front of them with an intelligent Magic 8 Ball.
Great technology is not a product.

Let me repeat that: Great technology is not a product!

Founders would do well to remember. If you fail to find product-market fit, if you're thinking too far ahead, or if your product has too many compromises: you can invent ground-breaking technology that completely revolutionizes the industry then promptly file for bankruptcy and walk away with nothing.

Again let me repeat that: You can invent ground-breaking technology, be 100% correct about the future, and be 5-10 years ahead of everyone else... then watch your dreams go up in smoke as you walk away with zero dollars and without even a wikipedia blurb mentioning your accomplishments.

Great ideas without execution are worthless.

Great ideas plus poor execution are a deadweight loss on humanity.
I'm not sure what's simple or understandable about a basic staple of life whose real value is skyrocketing. It's not at all clear whether the next generation will actually be able to afford housing at anywhere near current valuations. Maybe future Americans can afford to allocate a greater share of their incomes to housing, but the economic effects of the disappearance of all non-housing consumer spending (as mortgages approach 100% of paychecks) might not be great for home values. Who knows? Anyway, not simple.
Ah, so he's trying to reduce the competition :)
Not exactly. CS in tier 2 IIT's also gets you those headlines. Basically the numbers decrease (1 from IIT Hyderabad vs 5+ from IIT B IMO)
Home buyers often overlook the cost of selling their home when considering if renting is cheaper. That is 6% in realtors fees and another 2-3% in closing costs. Renting is a great deal if you are not going to live somewhere for 5+ years before moving
The biggest issue I notice is that the rental inventory is _much_ lower quality than the for sale inventory. Might be a Bay Area thing.
We can't fix every problem. And fixing every problem shouldn't be a gating function for fixing some problems. Web servers generally want to send a complete response. And sometimes some middle box chops off the response halfway through. And if that origin server set a correct Content-Length header and the response isn't that long, we can be fairly certain that the response is probably incomplete. And I really can't come up with a non-contrived use-case where we want those incomplete responses to be delivered silently to client code.

It seems like what you are saying is that because a file might be corrupt we shouldn't worry about a completely unrelated case where the file is fine but the transfer is incomplete. By that logic, why do we worry about trying to report errors at all?
I don't find this IAT stuff very convincing. As a counterpoint:

https://www.wsj.com/articles/the-false-science-of-implicit-b...
It's not so much the registers themselves, it's the plumbing required if you're going to let any register supply any input or output of the ALU. Hardwiring a register to one of the ALU's ports means you avoid a multiplexer (and the same applies to hardwiring a register to the AGU's ports, which is how you get 'base' and 'index' type registers).
The catch it the “and looking at our data” part. ML is basically a collection of thorough ways to look at your data, understand the patterns and infer what that means for the future.

In your example, you should absolutely start by cleaning your data up and run some basic SQL aggregations and plotting volume over time. So you look that that and notice (1) volume is increasing over time, (2) some holidays bump a few days ahead but drive very low volume day of (3) weekends are higher, but the effect isn’t pronounced the whole year and (4) summer is better for you than winter except for the Christmas season. Now: it’s two days before Halloween, what’s our anticipated sales volume?

If you baked all those observations into an ARIMA model, it’s trivial to crank out a forecast with quantifiable accuracy. If you just have lines on a graph, it’s hard to pin down all the independent effects and recombine them for arbitrary scenarios.
What do you use to find these areas?

Is there any particular neighborhoods you're willing to share?
> I just wanted to say that GitLab is a great project to contribute to, with a very friendly and professional community!

I'd like to second this. My experience getting a (minor, admittedly) contribution merged was painless, prompt, efficient, and dignified. If I ever run into something funny with GitLab again, it'll comfort me to know that a patch can be running on the live global site in less than a day.
I started programming objc on NeXT and still remember the book by Brad Cox. No, I don't want to puke when I see objc.
Well, we are in the peak of a wave of hype about AI/ML, maybe even just past that peak. Many fundamental technological advancements in the field of AI/ML have sort of coalesced together at the current time to form a strong feature set that can be more broadly applied by a wider audience, not just those hardcore computer scientists who invented the technology.

I've been in the thick of this previously, facing a complex rules-based engine that did most of its incredible feats in the fraud detection domain using a number of really complicated SQL queries. At the same time, I've used the results of such queries combined together with machine learning and predictive analytics, giving you the best of both worlds. Both have strengths and weaknesses.

These are tools in the toolbox, and I think the adage "try to use the best tool for the job" still applies. Sometimes, you use the tool you have and you know, and all the more power to you if you can get the job done using that tool. If you are a master of that tool (i.e. SQL in this case), you can often push its capabilities very, very far.

That said, I think the best thing to do right now is try to separate the signal from the noise regarding AI/ML and find what really works and what does not. Then find how these new tools can either complement or replace previous approaches. I think they work together quite nicely - and we see that sometimes, for example, with AI/ML tools integrated close to SQL engines.

AI/ML has a place, and so does SQL. I will say, though, that I for one don't want to be caught on the side of the discussion where I don't learn enough about what is possible with AI/ML, and then get left behind. I think many of my colleagues and professionals in the field and here on YC feel similarly.

Actually, I think even non-technical people feel the same way - the fear of being replaced by AI/ML is higher than ever.

So, keep applying SQL and get that low-hanging fruit. But make sure to learn the new stuff too, and add it to your toolbox.
Edit and Continue also works in VS C++ and has been a godsend for me when tweaking DSP code. This particular feature has remained unmatched by other IDEs/toolchains for what, 15 years? That's pretty impressive stuff you've worked on.

It's certainly not adapted to all domains, but in DSP applications where you're looking at immediate audio/video feedback (in my case, audio synths and effects) it makes a lot of sense to regularly switch into "tweaking mode" - not only in order to refine the code, but also to get a better understanding of how small variations in each part of the code can affect the live output, which can be a large portion of the work you'll do in DSP applications. It's just awesome, you're working on a full-fledged application and you can turn it into a live editor at any moment.
> not just information, but useful information.

all information is useful, as utility is dependent on the context for which the information is needed.
Wholesale housing policy reform is what's going to happen. It might take a decade or so, but it's gonna happen.
There are two different kinds of fraud. One is where there is fraudulent payment like from account being taken over. Venmo and Zelle can reverse those transactions. The other kind is where purchase is fraudulent. Venmo and Zelle don't offer any protection for purchases or sales. It is only credit cards and equivalent that offer purchase protection.
Anecdotally[1] speaking, you're correct but you're missing the message. You're correct that no one should hire expensive data scientists for this. But what happens is that there's no marketing against these sorts of pragmatic best practices, so it never comes on the radar of business executives making decisions. Instead they're inundated with ML/AI/Data Science pitches and mentions everywhere. And so when they go to invest in improvements like this, they reach towards the buzzwords they know instead of the solutions they're not aware of.

What ends up happening is effectively the Data Science engagement becomes 90% data cleaning, a handful of SQL statements that should have existed beforehand but never did because the data infrastructure wasn't there, and possibly a veneer of ML/AI just to say it was used. Clients come out happy (sometimes), despite overpaying for what was a much more basic engagement than they think it was, and they go on preaching to their business exec friends the virtue of ML/AI and the cycle continues.

[1] I built up a Business Intelligence/Analytics team at my last job, and currently work for a marketing agency managing digital analytics for Fortune 50 clients. Lots of exposure to analytics in lots of varying environments, and I've seen firsthand how ML engagements get pitched and results get presented.

I also own a consultancy that's the anti-version of this phenomenon, offering digital analytics management and support services. 50% of my work involves being a knowledgeable resource for marketing and business execs to lean on to cut through the bullshit. With most of the rest being basic Google Analytics/Google Tag Manager management, CrazyEgg, and drip marketing campaigns. All of which seems like AI-level magic for clients when done correctly.
I think the main point of the article is that 99% of the value in that is in a.) sending the follow-up email at all (which just takes a cronjob) and b.) identifying which customers to send that follow-up e-mail to (which just takes a SQL query). While it's probably nice to try and predict the ideal time to send it, the gains you get from that are marginal compared to steps a & b, which many companies aren't even doing today.
Because PoW doesn't have an answer to selfish mining attacks?
I own because I like knowing I can modify my living space however I like. I took out a bedroom to make a home theater, could never do that renting. There are definitely advantages to home ownership psychologically that can’t be defined in a dollar figure.

His main point though was that renting isn’t throwing money away. I say if renting is the better option for you then who cares?
It should be possible to check for autocorrelation of block arrival times, and correlation with forks. I don't know whether anyone's gone looking for that, but Eyal and Sirer have a strong incentive to monitor for it, and obviously have the chops to do so.
Point of Vue: Angular is doomed, React is OK – We deserve better
The article takes tax deduction into account in the calculations. It also discusses most of the other things you mention as well
Companies with budgets are willing to think in terms of quarters or years. You’ve laid out the plan for them and they’ve agreed to it. The money is allocated in an annual budget or obtained with a purchase order, so it’s not going to be suddenly reallocated unless there’s a big problem. Experienced clients wouldn’t move to another agency to do analytics “faster” because they would know they’d just have to ramp up the relationship and the systems again.
You would still have supply and demand dynamics in an arbitrage free economy. If there is too much supply, no one's going to care what your mortgage is.
I'm your age living in the NYC metro area and had the same feelings. I opted for a renovated co-op which only cost a few hundred K and has a maintenance charge under $1000/mo. that includes all utilities. By getting a co-op or condo you limit your responsibility to what's within the walls. Sure you'll have the occasional plumbing/electrical issue but for that you can hire a maintenance guy or neighbor. No need to worry about the big issues.
MIUI 9.5 arrives xiaomi Redmi 4
While this is mostly true, it’s important to understand that rents are not just “cost + profit.” They are market based. Sure, the market often pushes it towards something close to that formula, but it is not a guarantee, and many of the subtleties of buying vs renting are lost on those who reduce it to that formula.
Perhaps not that specific option which controls some smaller facet of sharing and recommendations, however but it's definitely the case when you turn off syncing "Web & App Activity" with the Google panopticon.

Where the history would usually be, there's just this nagging message:

> To see your home and work, past searches, and other suggestions tailed for you, turn on Web & App Activity.

...Which, as mentioned already, misleadingly implies that local-history features are difficult or nonstandard.
Man, I am really curious what position you hold that you know the AI strategy for 99% of Fortune 500 companies. Those same Fortune 500 companies would pay you a lot of money for this level of insight into their competitors.
Financial organizations will be able to provide zero-knowledge proofs of their aggregate resource commitments, including distributions of credit score attestations. Organizations can lend to each other on the basis of those.