skysight-inc/HackerNews huggingface dataset:
> On my memory, large projects on PHP and Python falling into that category are all around the place

You need at least a package manager in a large project. On PHP you will have composer, on Python you will have PIP. If your PHP project is web-based, you will likely have some tooling to setup your environment (SQL migrations, static pages...), that's also tooling.

> Gtk+ for examples has huge list of dependencies, yet, not as single time my code failed to run properly because Gtk's own dependencies somehow influenced my code

I've also been a GTK developer and I did had lots of issues with GTK incompatibilities between version, it's not a coincidence a lot of people moved to Qt recently, they did experience the same thing. You probably picked the worst library for backward compatibility in your example. Also GTK is not a language but a library so I don't see your point.

> No, the figure that you are giving is for synthetic benchmarks that do nothing more than arithmetics with primitive data types. I would like to see performance figures a tree sort or merge with complex data structures. The performance will suck equally.

I've never seen any test where Python has the same speed as Javascript, arithmetics, sorting or whatever else, it generally lags behind. All of that is due to the massive competition between JS engines and the massive amount of money people are throwing at it, Python can't match any of that, it's not 2003 anymore.

> When you forego backward compatibility for the benefit of this approach, you do not resort to perversions with tooling to bring it back, resulting in a worse result.

I don't really understand your point. In Javascript you can't control the client who executes your code. That's why you have to use some tooling to make sure it works even for the crappiest browser you support.
I guess if you're investing for the long term, avoid anything with machine learning, as it's overpriced...
> No it isn't.

It isn't because most other dependency managers don't allow dependencies to bring their own versions of other dependencies, instead you're stuck with version conflicts preventing you from upgrading to a recent version of library X because you're also using library Y and both libraries have a dependency on library Z (but the new release of library X uses an incompatible version of it).

I was a Python developer in a previous life and in any Django project this would eventually happen and require you to either replace these unmaintained dependencies or ignore their version requirements and pray for the best (which was usually a gamble because semver wasn't a thing).
The documentation provided by individual banks seems better than the documentation on the Open Banking site. For example, here is the documentation provided by

- Barclays:
https://developer.barclays.com/documentation/dc72e132-2951-4...

- Santander: https://developer.santander.co.uk/sanuk/external/open-bankin...

- List of others: https://openbanking.atlassian.net/wiki/spaces/DZ/pages/19104...
Or just doesn't care about the wellbeing of the majority and prefers pollution with cars, sprawled cities, pushing people with low incomes always further from the center (where they might still work), yada yada. It's a terrible stance for both ecological and social matters.
> In Swift, you cannot currently create classes in code, enhance and swizzle, implement proper proxies

In pure Swift, that is. These are all possible if you reach into the Objective-C runtime, as I'm sure you know well. Overall, though, I do agree with you: I'm unsatisfied with the current reflection API in Swift and I really think that this is something that the core team should focus on some time.
Wow, at least one other person shared my opinion. Clickbait article/post title. Also, the use-case for simple sales coupons mentioned in his article are not even close to the kind of things a company like Amazon does AI on in the recommendation process of WHAT product to advertise to each customer (as mentioned by another poster, I would not expect to see a discount for breast pumps when I just bought something for myself-male-such as men's deodorant).
This begs the question--where are you from? I think your experience is likely fairly atypical.
CVE-2018-0229 flaw in #SAML implementation threatens CISCO products
Yep, I guess that's similar to what's described in the article, although on a vastly different scale - it's a concern to me, but not that often.

Interestingly, this was exacerbated when I was walking with crutches. Even though I was even less of a danger, the ticking sound of the crutches did feel like I was sounding more threatening.
Its not just every VC, its a good percentage,maybe the majority of developers out there.
Yes, I think I agree. But OTOH I thought different colours for different lines of text would be annoying and distracting, but that’s what this app does, and apparently it works.

So I guess test it and variations?
In the area where I live there aren't very many coloured people. When I (rarely) walk at night I find myself estimating the social category of people. If I see a young man in sweat pants and a baseball cap, I think 'possible Chav' (low class white person here in the UK) and I'm wary. Low income young men are more likely to be a possible source of trouble.

I think in the UK and US since black people are far more likely to be in a low income category than not, race becomes a proxy for that.

Funnily enough, when I see a black person in the street at night here I'm more likely to have a positive attitude. Most of the coloured people living round here do so because they are quite successful. Also because there aren't many black people, such a person is perhaps more likely to feel out of place or potentially feel threatened or need help.
I was referring more to the tools themselves than the landing page.
Why it took a long time to build the tiny link preview on Wikipedia
Tesla used to (still does?) put larger batteries in their cars than ordered, and limits them with software:

http://www.thedrive.com/sheetmetal/12820/tesla-is-silently-p...

So if these numbers are from the owners reading them from the dash... they wouldn't know their 40kwh battery was actually a 60kwh battery that was software limited. That's a lot of spare capacity to hide aging.

Remember when they flipped a switch to allow Florida owners to use the range of their larger batteries to escape the hurricane:

https://techcrunch.com/2017/09/09/tesla-flips-a-switch-to-in...

Larger batteries than purchased are clearly quite widespread with Tesla.
> so over leveraged that it wouldn't take that much for the bank to require a mortgage holder to put in more money to cover the decreased value of the property

That's an interesting contract. For a primary mortgage that would be very unusual in the US. (For a secondary line of credit against the home, the bank would likely freeze the line of credit if the value dropped too far.)
Perhaps also pertinent on these lines is the principle of "Unconditional Positive Regard" [0] it is easy to infer the worst possible motives when somebody does something bad, but it can be helpful to assume that they did in fact mean well, when attempting to step into their shoes.

[0] https://en.wikipedia.org/wiki/Unconditional_positive_regard
I used to love YNAB, but once I started needing real support for multiple currencies, I had to stop using it. I haven't found a daily driver replacement since, and I've tried around 10 apps. I even started writing my own, although it's not done. Does HoneyMoney support multiple currencies?
How are buying agents compensated in the UK?
> It's also the case for Firefox.

Which version(s)?

I'm on 59.0.2 and that www.erlang.org doesn't throw any certificate errors for me.
SBCL is quite good at the static checking even in the imo. Not sure what level of maturity it was at 10 years ago though.
Neither will the next big failure.

What has AI really given us so far? Amazon / Youtube recommendations and some self driving cars that have crashed in unexpected ways. (Have they managed to debug that yet?)
First of all, a market cap of $300 billion could not be spoofed. The trading volume is enormous, and far beyond what any conspiracy of price pumpers could fabricate, and there is plenty of anecdotal evidence of significant fiat investments being made into cryptocurrency all the time.

Second, me saying people are not underestimating it is not an endorsement of it as an investment. If I were endorsing it, I would say the opposite; that people are underestimating it.
No competition. Tesla's wont be sold that much as soon a all the subsidies disappear.
You can only do that if you have a Google account. For everyone else yve Recommendations feed is an uneditable, unhidable list of click bait and dross.
You have to keep in mind that this economic migration to and from cities is something that's been happening for centuries and likely since the beginning of human civilization. The US is a particularly great illustration of this since it's such a new nation. Every single city in the US was a remote backwoods not all that long ago. And then the same process happens over and over. Some city begins to attract more and more people. This results in development and growth. Those that succeed during this period rise to the top and thrive. Some of those that do not move on and try to take part in what becomes the next great opportunity.

Take San Francisco for instance. The thing that brought it from being a backwoods to a major city was the gold rush of 1849, which is also where the name of the football team 'The San Francisco 49ers' comes from). You get a huge influx of people, lots of money and resources flowing, and the next thing you know it's one its way to becoming one of the largest cities in the US with massive growth.

However, the opposite can also happen. For instance Detroit used to be one of the most desirable cities in the US. It was the 4th largest city in the nation with a surplus of great high paying jobs. This wealth and opportunity ended up attracting vast numbers of people - more than there was opportunity for. And then on top of that what jobs and opportunities there began declining. And next thing you know people are leaving the cities and droves and today it's home to countless empty buildings, the second highest crime rate, and one of the highest unemployment rates in the country.

In the end the point is that cities, and the demographics within them, are in constant shift. Thing's change over time and it's always up the individual to find where the greatest 'value' is, with the meaning of 'value' certainly varying quite heavily from person to person.
I'd like you to know I got the sarcasm, even if nobody else did :)
> But that seems dishonest.

It's not. The bank has calculated and accepted the risk and factored it into your interest rate and other charges. Considering it to be dishonest is financially equivalent to considering a (not fraudulent) insurance payout to be dishonest.

Think about it this way. The bank has effectively bought an insurance policy to protect itself against this event and is paying the premium out of your interest charges. Now does it seem dishonest?
Capital gains didn’t apply because of the unique rules around primary residences — our new home cost more than the old. In fact it’s better than other investments in this particular way!

Regarding property tax, yes, but dividends are taxed as well and you generally are going to pay capital gains in the specific situation I’m describing if you replace “primary residence” with “stock”. Primary residences with mortgages have better tax treatment, really. If it didn’t I wouldn’t have a mortgage.
Show HN: Role Gate – Play-by-chat app that grew to 1000+ users in its first week
Mobile App Success Story: SoloLearn
Have you tried Kitty? https://github.com/kovidgoyal/kitty,
Pretty good too in terms of performance
Leverage cuts both ways, though. As someone who just sold their house, the costs are a percent of the sale. You don't get taxed on the sales (generally) but you will probably have to pay real estate sales costs. So yes, if your home increases by 3%, you've gained 15% growth on your investment, but if the sale cost is 5% of the the sale price, then things aren't so clear anymore.

The real calculation is the total monthly cost of ownership plus any sale prices on the buying and selling ends over the period, relative to the total costs of renting.

Over the 10 years we've owned our home, yes, it's been worth it to buy, because we can recoup money that would have gone to someone else.

However, over a short period, the sales costs would dwarf any returns we would get.

The sales costs are largely fixed on both ends, as a percent of home value, and diminish as a total percent of gains over the period of ownership. So the period of ownership is relevant.

This in turn is relevant because your mobility becomes relevant.

The previous market we were in too, was so overpriced relative to the rental cost that we actually saved money over the period by renting rather than buying. Then the Great Recession happened.

Either strategy makes sense depending on your mobility risk and the market. I don't think it's clear that one or the other is generally better.
Distributed platform for rapid Deep learning application development
“Not touching the EU” is a meaningless phrase.

If they raise money from any limited partners in the EU it’s pretty easy to make a case they have an EU business. The physical location is irrelevant - that’s the whole point of the GDPR.
Please. XML specification is much shorter than that of YAML, for example, even though XML 1.0 includes a simple grammar-based validation spec (DTD). "A markup language"? What does it mean? Are there any special "data-transfer languages" we neglect? :) Data gets serialized; we need to mark different parts of it; XML can totally do it. For some cases it's not the best fit, but nothing is.
The Nutrition Source: science-based guidance for healthy living
Ask HN: How do you maintain privacy policy/terms and conditions documents?
We're about to update a handful of website terms and conditions/privacy policies ahead of the GDPR enforcement deadline.

We have a number of different platforms and I've been trying to determine the best way of maintaining a master document that can easily be updated in multiple places (accepting some manual intervention).

From experience, I find that Finance/Legal are happiest editing on MS Word (slowly moving this to collaboration in Google Docs), but both of these solutions output kludgy/semi-formatted HTML. Every minor change can lead to extensive HTML reformatting.

More recently I'm using a Markdown file but this isn't friendly for the Legal side of the business, and it struggles with nested numbered-bullet points.

Does anyone have any success stories of making this work for all parties?!
>because no one locks down a dependency like is-string to a patch version.

Is this actually true? Is this not what "package-lock.json" is intended to solve?

(Not primarily a javascript developer)
Yes but go can do tree shaking and will not include packages in the binary that aren't used (that also means parent packages if a subpackage does not reference those).

I'm mostly pointing out that despite vanilla go's complete lack of version pinning, the community has (had to necessarily) step up and solve this by engineering.

Tree Shaking in JS should be trivial if the parent package implements no code and imports should be easy if they aren't of a dynamic nature (ie, "require('package/' + variable)") I'm not sure what is stopping NPM or Yarn from doing this atleast in the packaging step
> If you trace physical goods you need to trust people not to lie about their nature, if you have trust then you don't need a blockchain and can use something a lot cheaper and more efficient

I think the point is, though, that you can trust someone to be honest whilst someone is watching them, but you can't necessarily trust them not to change their minds at some point in the future. If it's all on the blockchain you have an immutable historical record.
how slow performance gains are relative to other sports

Stephen Jay Gould's analysis of why nobody hits .400 anymore is relevant here, I think. His argument, basically, is that there have been massive performance gains over the past 75-ish years, just in a way that deceptively depresses many of the statistics we compare to from earlier days.

The overall ceiling of physical performance for a human hasn't changed much in that time frame, and generally doesn't seem to change much in modern developed countries. The occasional people who win the genetic and upbringing lotteries today have much the same physical capacity as their predecessors of decades ago. But thanks to improved training, conditioning, coaching and selection, the average major-league player today is vastly better than in, say, the 1940s or 50s. Which means the gap between the very best professional players and the average professional player is much smaller than it used to be.

So take Mike Trout as an example. He's consistently the pick for the best player active in the major leagues today. He put up a .326 batting average in his rookie year, and that's the highest he's gotten as a major-league player. Which seems bad compared to Ted Williams' .406 in 1941 (the last time any major-league player had a batting average of .400 or better in a full season).

But, of course, Ted Williams faced the average player of 1941, and Trout faces the average player of today. The gap between Trout and the average major-league player today is far smaller than the gap between Williams and the average player in 1941. And if Trout got to face 1941-level competition, he'd be absolutely slaughtering them, as we can see from his earlier statistics: in his final minor-league stint before being promoted to a full-time major league player, he put up a .403 average.

To really appreciate Mike Trout, we have to turn to more modern statistics rooted in a better understanding of what leads to winning baseball games. Only three players have had a season at 10 or more fWAR in this millennium, and two of them -- Barry Bonds and Alex Rodriguez -- had careers tainted by steroid use. The other player to do it is Mike Trout. In 2012 he was good for 10.0, with the next best player being the Giants' Buster Posey at 7.6 and only 26 players in the major leagues breaking even 5.0 fWAR. In 2013 he reached 10.1, and only 25 players were at 5.0 or higher.

But, of course, modern baseball statistics are unfamiliar and often impenetrable to people used to batting average, home runs, RBIs, and (for pitchers) ERA and win-loss records. Which means it's hard to explain how we know that Trout -- who, by virtue of being on the Angels, has only been in the playoffs once, where his team got swept in the first round -- is so amazingly good at baseball, and how good the average player now is, which in turn means the perception, based on the older stats, is that players must not have gotten much better since the old days, or may even have gotten worse.
It feels much saver to be in his/her own home.

No money? Than don't repair stuff. it sucks but is still better and cheaper than being evicted.
Interpreted as a Rivest S-expression, the example given above conforms to the "advanced transport representation" [1], and so can automatically and straightforwardly be converted to the "canonical representation" [2].

In an important sense, then, I'd claim that it is a "canonical S-expression".

The reason this works is because SPKI S-expressions aren't just a grammar for a syntax, they also come with [3] a total /equivalence relation/, which is exactly what JSON lacks and which is what makes JSON such a pain to work with.

In other words, SPKI S-expressions have a semantics. JSON doesn't.

Lots of other "modern" data languages also lack equivalence relations, making them similarly difficult to use at scale.

[ETA: Of course, your point about lacking common data types is a good one! My fantasy-land ideal data language would be something drawing from both SPKI S-expressions and BitTorrent's "bencoding", which includes integers and hashes as well as binary blobs and lists.]

---

[1] Section 6.3 of http://people.csail.mit.edu/rivest/Sexp.txt

[2] Section 6.1 of http://people.csail.mit.edu/rivest/Sexp.txt

[3] The SPKI S-expression definition is still a draft and suffers a few obvious problems - ASCII-centrism and the notion of a "default MIME type" being two major deficits. Still, I'd love to see the document revived, updated, and completed. Simply having an equivalence relation already lifts it head and shoulders above many competing data languages.
What disgusts me the most about the JS ecosystem these days is that even the most basic developer tools have tons of depencies.

Let's say you want to use webpack and TypeScript on a project: Boom. 425 packages installed. And that's before you have written a single line of code. (It's probably worth noting that the offender is webpack. TypeScript seems to be dependency-free.) [1]

Every single one of those 425 packages could have a malicious postinstall script that deletes your entire home directory or sends your private ssh keys to a remote server.

npm makes it too easy to add dependencies to a library.

[1]: If you also want webpack-cli that's another cool 441 packages for you.
Python and js apps have the same order of magnitude of speed.

But pyqt apps are way faster than electron
> The downside is it now similarly becomes challenging to formalize the machine's intuition

Tooting my own horn here: I'm taking up this challenge professionally. There are new methods being discovered to extract interpretations for humans out of a model[1][2][3]; and I'll gladly work and provide advice and implementations on this, as an independent contractor.

Having an interpretable/explicable ML pipeline helps in:

* Knowing what drives your customers' buying patterns

* Knowing where to look for signals closer to the root, helping go from correlation to causation

* Orienting your marketing messages. You don't engage users the same way if they're a repeat buyer or a "hot now" lead

* Concentrating on "would buy our product but won't because of X", which is where the market share battle lies; as opposed to "definitely would buy" and "definitely wouldn't buy", which are cases where you have little influence on.

* Listing buying patterns exhaustively, enabling you to discover low noise niches

* Providing auditable explanations of the decisions the model has taken, if it has been required legally

Here is my email: pro@benoit.paris

[1] https://distill.pub/2018/building-blocks/

[2] https://www.darpa.mil/program/explainable-artificial-intelli...

[3] http://interpretable.ml/
There are lots of interesting problems with interacting with external underdocumented systems.

I find edit and continue really useful for making something worm with these.

Are these out of the reach of "professional programmers"? Can they only work on systems with fully specified models, with exacting requirements?
It's mostly frustrating for me that the state of art for dynamic programming had not really advanced and instead in many cases is an objective regression.
If you're renting, you most certainly _are_ losing money on the deal vs. what you'd pay if you owned _exactly_ the same property.

Assuming you could have made the same down-payment and gotten the same deal on a mortgage as the landlord.
Goodbye Facebook - cutting Facebook tools, buttons, plugins from our platform
A look at JWT vulnerabilities and mitigations
Hmm...like making every arithmetic operation a potential crash site?
The Ultimate Guide to Mobile App Analytics
>I indicated that it was actually quite effective without ML, and that it was easier to explain to users this way. They kept prodding around on the ML stuff, and how we might be able to use ML to accomplish roughly the same thing.

Just call what you do ML.

IBM, for one, never shied from calling any old BS "AI" and branding it Watson.