skysight-inc/HackerNews huggingface dataset:
> I am not surprised that digital marketing is not that effective. What does surprise me is why its taken this long to figure that out.

It is effective, and cheap at that. I run a small-ish left wing FB site and Twitter account, and regularly do small scale advertising - either to rally people up for a demonstration, or to annoy right-wingers. Proper targetting makes this incredibly easy, and judging by the numbers it works out just fine.
How Shor's Algorithm works (2007)
This isn't how accounting works. You can't just buy a warehouse and expense it in year one. You have to depreciate it overtime. You can look at their FCF its about 10 billion. Their market is 50x of that. For comparison walmart is ~20 billion and they are worth half of Amazon.
"Based on replicating nature"? Fiber optic cable? Microchips? Mass spectrometers? Atomic weapons? Even humans first technology, sharp stone tools, isn't really a replication of nature. Maybe some examples would help me understand what you mean.
Exactly. The entire industry seems to have no idea how to accurately weight "related" items by price or durability.
inline assembly e.g. intermixed with C/C++ code is not supported. You can still have separate files that are ASM only.

It's more hassle, but still possible to use.
> As to the motivation for the correct step: can you point me to a resource that explains this? Not sure I follow...

You write an equation involving division by the gradient. This is an illegal operation (one cannot divide by a vector), and your final recipe doesn't do it. As far as I can tell, you are writing down the incorrect, illegally-vector-inverting formula as motivation for the correct formula involving the (inverse of the) Hessian. All I am suggesting is that you say explicitly something like "Of course, this formula as written is not literally correct; one cannot actually divide by a vector. The correct procedure is explained below."

(Incidentally, speaking of inverses, another poster (https://news.ycombinator.com/item?id=14881265) has mentioned that it may be a bit confusing to speak of the inverse of a matrix rather than the reciprocal, since (as I interpret that other poster's point) the reciprocal of a matrix is just its inverse. I might prefer to say something like "We write $H_{\ell(\theta)}^{-1}\nabla\ell(\theta)$ rather than $\frac{\nabla\ell(\theta)}{H_\ell(\theta)}$ to emphasise that we are inverting a matrix, not a scalar, so that the order of multiplication matters.")
Not cheap, nor plastic, but it sounds like the Bollinger B1 is calling your name:

https://www.theverge.com/transportation/2017/7/27/16052118/b...
> You are a web design business…

Another way to look at this is that you're not a web design business (your perspective), you're a "solve my online problem" business (customer perspective). Yes, you're designing a web site, but in the service of increasing sales, improving discoverability for their potential customers, etc.

It seems like you intuitively know this, in which case the "extras" are potentially a distraction, but also an opportunity to bundle services into an ongoing "subscription".
My point was you can side-load apps. As for that specific API even the official docs seem a bit confused:

"Except when you use the NEHotspotHelper class, you do not need to obtain entitlements from Apple to use Network Extension classes. However, you still need to enable the Network Extension entitlement via the Developer portal."
All I can say is thanks for taking the time to write this. For the life of me I could not figure out what did the op do wrong.
I search everywhere about the difference between Newton's Method and Gradient Descent? but I couldn't find something that useful. Can u suggest any website/ article where I can learn the difference?
>We don't get to decide anything without government approval

ELI5:

* How does the "decide" process and "approval" process work?

* Who is the decider? (What are their qualifications/bkgrd? (Are they ex workers of the tech sector in .CN that have xp in building all the things?

* What are the implications to cost/design/[whatever-i-dont-know-to-ask]?

* ELI5 'submission for approval' and 'time to approval'

* do .cn gov employees work/are-embedded-at any given
company?

* is it as totalitarian as it sounds or is it efficient?

* what cut does the government take?

* Who is reputable in the US to ensure a product can get built in china?
"It still saves them"

I've done the same, its a classic corporate warfare tactic, there exists an agreed on paper form to standardize the demarcation point between two warring internal groups (and all internal groups are at war because of stack ranked management reviews, formal or otherwise...), someone on the home team was manual data entering that paper form into "something" now you can push that labor cost onto the other side who now do the manual data entry for the home team. For bonus points toss each data entry into a database and provide some manner of weekly reporting via web or email cron job, maybe some dynamic error checking at data entry time. Classic weapon of internal politics. I've made a lot of money off this, historically.

There's a whole universe of MBA companies out there that see computers abstractly as mere fancy electric typewriters, merely a modern Linotype machine, its just a modern mimeograph nothing more, a way to format manual data entry for other humans to read and manually data enter in something else. Computers that don't compute or have flowcharts, merely manual human I/O devices. Adding the slightest bit of automation can blow the minds of business people.

Beware of the inner product effect. A simple web form that emails the ticketing system in restricted cases can eliminate enormous amounts of manual data re-entry if it happens enough, but trying to turn it into a generalized tool will merely result in a "simple web form" that becomes more complicated than the ticketing system everyone was avoiding because it was too complicated for end users. Don't write a feature complete 100% wrapper for JIRA with the expectation it'll be simpler, thats not going to turn out well. Of course in corporate warfare sometimes punishing the other internal department is more important than making things simpler and faster for all, and if it gets your team out of the data entry task and puts them on the data entry task, well, the guy on your team who signs your paychecks is going to be happy with you.
Always entertaining to read about these stories.

The elevator shaft issue with the bigger computer was fun. We once rented some co-located space in a data center that had a small door and 19" servers couldn't pass without inclining them (which got tiring and stressful after a few servers, specially if we heard some loose component moving in the chassis). Nothing compared to having to modify the building for a different computer!

Wikipedia has a great page about this era: https://en.wikipedia.org/wiki/Timeline_of_computing_1950–79
FSSnip usually has those things. If you're talking that minimal I would just look up existing C# docs and translate them over to F#... ASP.NET Core is very lightweight w/ Kestrel though.

[1] http://fssnip.net/7OR/title/Simple-HTTP-Server
[2] http://fssnip.net/search/http%20server
Everyone at my company uses it with Nuclide, which still seems to be the best IDE combo for codebases that use Flow.
Freedom of speech isn't a blanket "call it speech and you can now do it unrestricted" rule. It is the freedom to communicate with interested parties about the matters of your choosing. It is not supposed to be a freedom to perform mass campaigns of dishonesty in economic or political domains.
Yes, I have. Not currently doing this work.

As a client, I won't actually do business with anyone who doesn't understand the need to fund those activities.

They will get swamped and fail, or the work will degrade and potentially fail as almost nothing works in a vacuum.

At a minimum, funding staying current, dealing with changes, dependent system changes, and the communication needed to make it all work will generally exceed the margins made on new projects.

If the goal is to make money and keep some of it, a retainer, or maintenance plan is needed.

Or, the expectation of paying time and materials on all post project work needs to be there, and it needs to be lean, no hassle.

My current arrangement is like this. Not quite enough to warrant a retainer, but enough that it needs to be paid for, unless I want my partner to starve or be unavailable to me when I need them.

Bundling hosting into this is a no brainer. They know that host well, you can always move, and it's efficient.
It's great to read someone challenging the hype, but this analysis is a bit too negative. I'd point out that AlphaGo did something a little bit like the author's toddler learning to squeeze through the back of her chair. AG learned its new moves by playing against itself. I still wonder how they avoided over-fitting here. Imagine two twins who loved to play and learn about go, and played constantly against one another, and in the process discover new plays they both believe to be unbeatable. You'd expect some of their new moves to be weak when actually tested against other players. But AG's new moves really were strong.

Admittedly, games with simple scores are the only scenarios where this really kicks in. But then again, the stock market could fit this model.
The comment right below has 2 links:

Gradient Descent explored similarly to this post: http://thelaziestprogrammer.com/sharrington/math-of-machine-...

The difference between the two: https://www.quora.com/In-optimization-why-is-Newtons-method-...
"Especially when your opinion goes against what pretty much everyone else in the field thinks" -> Most Deep learning people are consumers or hardware manufacturers, very few researchers in the field, and most people that have deep knowledge of it would agree with this guy.
It may be hard to believe, but read the forums (especially of other cars with Tesla drivetrains, like the Mercedes-Benz EV or the Toyota RAV 4 EV) and you'll see many tales of woe regarding the motors. Tesla makes their motors in-house, so it's not surprising they've been working out the kinks for years, given their sales volume (until recently).
Add/Manipulate Table and Get Height of Ttable in Existing PDF File in Java Apps
Isn't it wonderful that the iPhone is a walled garden, and with the flick of a switch, an entire category of software cannot be ran on it?

The fact that this switch exists is the real tragedy here - not the fact that Apple chose to use it.
This is impressive,

> associate road test editor Erick Ayapana had penciled me into a 2.0-liter Alfa Romeo Giulia to get here, and it feels like a wet sponge by comparison.
Can we please make this a recurring question

Wouldn't it be worth doing a trial period of manual posts first, to gauge the amount of interest?
Tesla does advertise the EPA range.
What should be my next step to become better programmer?
i'm sure this is a very useful product, and i understand that "addict" contains the letters "AD", but naming something "addict" while half the US drowns in opioids seems a little weird
It's a good question: https://electronics.stackexchange.com/questions/58236/why-do...
> Conclusion: Noone cares about GitHub. Noone is gonna read it. Everyone is gonna ask for it nonetheless, cause it’s hype.

True. We don't ask for GitHub links because they are pointless from a screening perspective. I'm happy to talk about them during an interview, but if I see a github link in a CV, there's little chance I will visit it. (Source: I'm a hiring manager at a tech company, have been in a similar position at previous jobs for several years).

The fact is, as a hiring manager, I would have no time at all to do anything if I start looking at all my candidates' github links. I would be more than happy to look at a portfolio showing products built by them, if they look interesting, I can ask technical details during the interview.
The Vulcan might not fly in 2019 but it will fly by 2021. ULA still has money coming in and it will get more government contracts for quite a while, at least until Blue Origin New Glenn flies, so that will require BE-4 anyways.
In short, Newton's method uses second order derivative information in the search direction, while gradient descent only uses first order derivative. In between, there are "quasi-newton" methods which include generalizations of the "secant method". I should also mention that there are all sorts of ad-hoc approaches for attempting to increase the convergence rate of gradient descent, e.g., pre-conditioning, "momentum" terms, etc.
Mao was as close to a dictator as you can get.
I often see an inverse correlation between being able to "talk about code" and actually producing good code.

That is not only between persons: I can get myself into a talker's mindset in a couple of days just by reading, drawing diagrams and generally doing anything that increases self confidence.

My actual coding abilities drop in the process because the ability to see abstract graphical patterns decreases.

On my best coding days I'm barely able to utter a coherent sentence at the end of the day.
Show HN: Write thank you letter to your favor project
That's still considered broad targeting.
It is idiotic that you can't compile and install something on a device you own.
I can only speculate about the justification for these clauses. The contract was written by a group of 3rd-party lawyers who had little to none knowledge about the open-source community. My only guess is that they wanted the contributions to be tied to the company's name to increase their visibility, as in — "We [the company] contribute to X project(s); love us!" — putting myself in the shows of these lawyers, if an employee leaves, their contributions will stop being tied to the company and they will lose some visibility, that is the only reason I can think of right now. Your guess about "we pay you; credit is for us" also makes sense, they are the company's lawyers after all, they are not working to protect my rights but the company, so I didn't think too much about it when I signed, later I regretted my decision and quit.
I think his article Intelligence Without Representation is particularly worth reading at least once.

http://people.csail.mit.edu/brooks/papers/representation.pdf
Making AI training accessible to anyone anywhere without silicon valley bias
Sounds plausible for me. Some notions in their "obfuscated" texts suggested that they come from inside the US (not necessarily from US nationals, but at least they live or used to live in the US for quite some time).
Hmm... 1px? behance?
North Korea Learned from Libya War to Never Give Up Nukes
Sorry that is wrong, the BE-4 did not blow up. A test of a component failed, problems like that are normal and they are still within the planed time line.

The idea that if the had success they would 'trumpet it' is simply wrong, this has not been true in the past for Blue Origin and nothing has changed.

Raptor might be further along now but we don't have the information.
Indeed, DL-based AI is likely to continuue - rightfully so - for non-safety critical domains (translation, games, ...) It's crazy that few people notice that real world applications with safety issues (where 99.9% accuracy is a joke) - ex: self driving vehicles - require much more that what DL has to offer. The AI community's sloppiness when it comes to safety and robustness engineering will lead to the some serious problems (when it comes to real-world industrial applications). But yeah there will still be jobs for people playing around.
> I am genuinely curious why none of the recent Deep NLP approaches work here.

To my understanding, it's because deep learning is fundamentally aimed at pattern-recognition, but makes no attempt to have a model of "reality" underlying that recognition.

I've seen some people argue that these models of reality are, themselves, nothing more than higher orders of pattern-recognition, and therefore ought to be amenable to deep-learning type methodologies. I have my doubts. Deep learning requires reinforcement training from big datasets, and the process of modelling reality does not often accommodate.

The process of learning to see things, navigate through space, etc., is something that you do through reinforcement training -- more or less like a neural network. Newborn babies don't see "things": they see colours and lights and motion, and it takes a couple of years of training before they reliably classify those sensory inputs. That's very much a big data exercise: every instant your eyes are open, you're collecting more data and training on it.

In contrast, consider the schema "The council denied the protesters a permit because they [feared/advocated] violence." The reason you can parse that sentence is not because you have trained yourself on a dataset of thousands of councils and tens of thousands of protests, and can therefore now recognise which might be fearing violence and which might be advocating it. Instead, you've built a model of the world out of vastly sparser data, via structured logical inferences. Which just isn't remotely the way that deep learning works.

So current AI techniques seem to have gotten very good at matching (and surpassing) a subset of cognition, but I'm not convinced that they will scale to cover these kind of schemas. I do think that fundamentally new ML approaches will be needed for these kind of domains. Neural Networks seem to be good at cerebellum and occipital lobe sort of tasks, but we'll need something else for the frontal lobe.
The value of free Supercharging is fairly small for most people. If you drive 12,000 miles/year and 25% of your driving is Supercharged, you'll end up paying something like $100/year in Supercharger fees for a Model 3.
Fielding serious offers for my startup http://commando.io. Looking for 3-4x ARR.

Commando.io is a web based SSH platform for running commands on servers. The commands (recipes) are centrally stored and versioned and there is a complete execution history. There is an API, CLI, and iOS app for running commands on servers on the go as well.

If interested see HN profile for my contact info.
Interesting how this has 57 points and as of now no comments.
"For models, Musk could look to Google co-founders Larry Page and Eric Schmidt, who have moved in and out of daily management at Google." Submarine promotion announcement for Eric Schmidt to co-founder of Google.

Good article. Musk seems less structured than Bezos and his "regret minimization framework". He will leave when it makes sense to and is boring. Success is boring.
Yeah but still, men and women "targeting" is basic stuff

What online ads allow you to target is "men between 28-32 from eastern Europe who like Formula 1 and Classical Music"
freedom of speech helps. where are grievances to this shitty policy aired to the govt by the public?
Facial recognition API for Python and the command line
Sounds like you should have gotten to the 2nd round at least
The calculus answer is that gradient descent approximates the shape of your optimization space with the first order taylor expansion at your currently optimized point (first derivative information), and Newton's method approximates it with the second order taylor expansion (first and second derivative information).

The geometric answer is that gradient descent approximates the shape of your currently optimized point by a plane that is tangent to the current point, whereas Newton's approximates it with a quadratic "bowl" using the local curvature. In gradient descent you have to take short steps down the path, because you know your approximation cannot be true (you will not always be able to lower your error by moving in the same direction, if you assume there is some minimum error). In Newton's method you can assume that your bowl approximation is correct and go straight to the bottom of that bowl. If your space really does look like a bowl, you will get to the bottom super quickly.

The ELI5 answer: you are a ship captain looking for the lowest point in a part of the ocean. You cannot afford to send a submersible down, so your approach is to send an anchor down to check the depth and take a panoramic photo a few feet around the anchor. After analyzing the results of each anchor drop you can move your ship anywhere and try again. In gradient descent you look for the lowest point on the edge of the camera's vision and move in that direction. You can move as far as you'd like, but you will not go too far because you know the drop will not go on forever. In Newton's method you will look at the curvature in the pano and extrapolate it out assuming it is a "bowl" shape. For example maybe your anchor is on an underwater J-shaped cliff getting less steep as it goes north, so you map it out as a U shape that starts going up again if you go a mile north. So, you move your ship a mile north and try again there. In gradient descent you would have moved maybe 100 feet north then checked again. Newton's method can intuitively get you to the low point in very few anchor readings, but crunching the numbers of the bowl can be very time-consuming (this becomes relevant in higher dimensions). In this analogy, the long and lat coordinates in the ocean are your two parameters to be optimized, and the height of the ocean floor is the error in your optimization. You want to find the parameters that give you the lowest error.
Ive got http://www.pedalr.com - a marketplace for people who love bikes.

It's been completely refactored, has a decent mailing list and list of followers. I'd be interested in selling it or linking up w the right cofounder w some energy to help me relaunch, test, learn, and grow the product.

Contact me for a look behind the landing page.
How stable are swift releases now? What are some good resources to start learning swift?
src: https://news.ycombinator.com/item?id=14880573
>>> Even Google Translate, which pulls off the neat trick of approximating translations by statistically associating sentences across languages, doesn’t understand a word of what it is translating.
>> This is just another incarnation of "AI is the thing we haven't done."
> I don't think so - it appears to be an objectively correct assessment of the current state of the art.

How deep an understanding is required to meet the threshold? The skepticism feels like "no true Scotsman" applied to the definition of understanding.

I observe the following in young children when exposed to a new word:

0. First exposure to totally new word used in a sentence with more familiar words.

1. Brief pause

2. Mimic pronunciation 1-2 times

3. Process for minutes, hours, or days.

4. Use the word in a less than 100% correct way

5a. Maybe hear the phrase repeated back with the error "corrected" (hello internet)

5b. Maybe hear more usage of the word in passing from others (with varying degrees of "correctness")

6. Recurse for life.

At what point did the person understand the word? How is AI translation substantially different?

I'm not sure I understand any word in a way that would satisfy AI skeptics.
I understand that we all at HN know about the media censorship, but when you are trying to convince someone who disagrees, some evidence would be very useful.
We have made several iOS apps which have been very well reviewed and sell consistently, but we're not terribly good at marketing. We're open to an offer if someone wants to purchase them and market them to the right audiences.

Apps: https://itunes.apple.com/us/developer/nikki-degutis/id119686...

Contact: email in profile
"Nobody cares about your code" piece of advice doesn't make sense to me. There are people who cares about your code, I had projects in GitHub I'm really proud of
If Bitcoin resolves its scaling issues, we may see hyperbitcoinization. This is potentially a much larger event than the widespread adoption of smart contracts in the financial world.

http://nakamotoinstitute.org/mempool/hyperbitcoinization/
I have a simpler model; no bluetooth or color screen, but it has a microSD card slot. 4GB base memory, with a 16GB card in it. I think it was about $20 when I bought it years ago, and it's still going.