Why squared error? (2014) - <AUTHOR> http://www.benkuhn.net/squared <comment><USER2> Why squared error? Because you can solve the equation to minimize squared error using linear algebra in closed form.

Why L2 regularization? Same reason. A closed form solution exists from linear algebra.

But at the end of the day, you are most interested in the expectation value of the coefficient and minimizing the squared error gives you E[coeffs] which is the mean of the coefficients.

<comment><USER3> I don't think this is any more convincing than the article's reasons. There are closed forms to lots of things that aren't interesting.

<comment><USER4> I cannot speak for eanzenberg but I think his comment was less about his personal justification and more about the rationalizations that have been used in the history of stats.

Gauss quite openly admitted that the choice was borne out of convenience. The justification using Normal or Gaussian distribution came later and the Gauss Markov result on conditional distribution came even later.

Even at that time when Gauss proposed the loss, it was noted by many of Gauss' peers and (perhaps by Gauss himself) that other loss functions seem more appropriate if one goes by empirical performance, in particular the L1 distance.

Now that we have the compute power to deal with L1 it has come back with a vengeance and people have been researching its properties with renewed almost earnest. In fact there is a veritable revolution that's going on right now in the ML and stats world around it.

Just as optimizing the squared loss gives you conditional expectation, minimizing the L1 error gives you conditional median. The latter is to be preferred when the distribution has a fat tail, or is corrupted by outliers. This knowledge is no where close to being new. Gauss's peers knew this.

<comment><USER5> > Gauss quite openly admitted that the choice was borne out of convenience.

That's quite interesting. Do you have a reference for that?

From my understanding, the popularity of the least squares method came (at least in part) from Gauss' successful prediction of the position of Ceres. Was this just because people not using least squares were not able to calculate it?

<comment><USER6> It's in the original paper in which he derives the normal distribution. Well worth a read. I last had a copy of it in the fourth basement down in the university library about fifteen years ago - it might be still there.

</comment></comment></comment></comment></comment><comment><USER7> There is a Kaggle competition right now that uses mean absolute error, and this makes the problem substantially harder. For a practical discussion of techniques used to solve machine learning problems that use mae see the forums in: [https://www.kaggle.com/c/allstate-claims- severity/forums](https://www.kaggle.com/c/allstate-claims-severity/forums)

As touched upon in the article, the objective not being differentiable is a big deal for modern machine learning methods.

<comment><USER8> No it isn't.

Differentiability is important if you want to have an closed-form formula _and_ derive it in front of undergraduates.

<comment><USER7> This is the difference between practice and theory. In theory differential objectives don't matter, in practice for medium to large datasets they make machine learning a lot faster. Speed is critical, as you need to be able to iterate quickly. The solution most commonly used on Kaggle is to transform the target feature and then minimize mean squared error, but there is some systematic uncertainty introduced by this.

</comment></comment></comment><comment><USER9> For minimizing the square of the errors I think the good reason is because, assuming your data has gaussian probability distribution, minimizing the square error corresponds to maximizing the likelihood of the measurement, as you and others have said.

Why do we assume gaussian errors? There is seldom a gaussian distribution in the real world usually because the probability for large error values doesn't not decay that fast. We use it because the math is easy and we can actually solve the problem assuming that.

<comment><USER10> That's a summary of the article.

<comment><USER9> Yes, sort of. But I think he says a lot of unnecessary things not getting at the root of the issue.

I left out some detail I should have said, like what is so special about a gaussian that makes the math easy. So I will say it.

A measurement can infer a probability distribution for what the measured quantity is. A second measurement, on its own, also infers some probability distribution for what the measured quantity is. It we consider both measurements together, we get yet another probability distribution for what the measured quantity is. The magic is that if we had a gaussian distribution for the measurements, then the distribution for the combined measurements is also a gaussian. This is not true in general. As long as we have gaussian distributions we can do all the operations we want and the probability distributions are gaussian and can be fully described by a center point and a width. (Forgive me for the liberties I am taking here.) The basic alternative to exactly solving the problem is to actually try to carry around the probability distribution functions, which is not practical even with very powerful computers.

</comment></comment></comment><comment><USER11> The best explanation is probably that squared error gives you the best fit when you assume your errors should normally distributed.

Things like the fact that squared error is differentiable are actually irrelevant - if the best model is not differentiable, you should still use it.

<comment><USER12> "if the best model is not differentiable, you should still use it."

I'm not sure I would say that - neural nets are "near everywhere differentiable", for example. Without differentiability we're stuck with, for example, discrete GAs for optimization, and you can throw all your intuition out the window (not to mention training/learning efficiency).

<comment><USER13> A few misconceptions I should correct in this comment.

\- There is plenty of existing technology for handling non-differentiable function. Functions like the absolute value, 2-norm, and so on have a generalization of the gradient (the subgradient) which can be used in lieu of the gradient.

\- That functions are "almost everywhere differentiable" (i.e. the non- differentability lies in a manifold of zero measure) makes these functions behave pretty much like smooth ones. This is often not the case as optima often conspire to lie exactly on these nonsmooth manifolds.

<comment><USER14> And error measures involving sum of absolute values (i.e., L1 norm) are central to methods like lasso ([https://en.wikipedia.org/wiki/Lasso_(statistics)](https://en.wikipedia.org/wiki/Lasso_\(statistics\))) and their cousins.

</comment></comment></comment></comment><comment><USER15> I asked that early in my career.

We want a metric essentially because if we converge or have a good approximation in the metric then we are close in some important respects.

Squared error, then, gives one such metric.

But for some given data, usually there are several metrics we might use, e.g., absolute error (L^1), worst case error (L^infinity), L^p for positive integer p, etc.

From 50,000 feet up, the reason for using squared error is that get to have the Pythagorean theorem, and, more generally, get to work in a Hilbert space, a relatively nice place to be, e.g., we also get to work with angles from inner products, correlations, and covariances -- we get cosines and a version of the law of cosines. E.g., we get to do orthogonal projections which give us minimum squared error.

With Hilbert space, commonly we can write the total error as a sum of contributions from orthogonal components, that is, decompose the error into contributions from those components -- nice.

The Hilbert space we get from squared error gives us the nicest version of Fourier theory, that is, orthogonal representation and decomposition, best squared error approximation.

We also like Fourier theory with squared error because of how it gives us the Heisenberg uncertainty principle.

Under meager assumptions, for real valued random variables X and Y, E[Y|X], a function of X, is the best squared error approximation of Y by a function of X.

Squared error gives us variance, and in statistics sample mean and variance are _sufficient statistics_ for the Gaussian; that is, for statistics, for Gaussian data, can take the sample mean and sample variance, throw away the rest of the data, and do just as well.

For more, convergence in squared error can imply convergence almost surely at least for a subsequence.

Then there is the Hilbert space result, every nonempty, closed, convex subset has a unique element of minimum norm (from squared error) -- nice.

<comment><USER4> Ah but square error is not a metric, its square root is a metric.

Many nice properties of the square loss (in fact un-fucking-believably nice properties) stem not from the fact that its square root is a metric but from the fact that it is a Bregman divergence. Another oft used 'divergence' in this class is KL divergence or cross-entropy.

Bregman introduced this class purely as a machinery to solve convex optimization problems. His motivation was to generalize the method of alternating projection to spaces other than a Hilbert space. But it so turned out that Bregman divergences are intimately connected with the exponential family class of distributions, also called the Pitman, Darmois, Koppman class of distribution. It takes some wracking of the brain to come up with a parametric family that does not belong in this class if one is caught unprepared, almost all parametric families used in stats (barring a few) belong to this class.

One may again ask why is this class so popular in probability and statistics, the answer is again convenience, they are almost as easy as Gaussians to work with, they have well behaved sufficient statistics, and their stochastic completion gives you the entire space 'regular' enough distributions with finite dimensional parameterizations.

You mentioned conditional expectation. So one may ask what are the loss functions that are minimized by conditional expectation. Bregman divergences are that _entire_ class. Of course square loss satisfies it too (more importantly L2 metric on its own does not, it is the act of squaring it which does this).

Very interesting stuff (at least to me)

<comment><USER16> This is very interesting. Thank you.

What book would you recommend for this discussion?

<comment><USER15> product space. Two examples are the real line and the 3 space we live in (at least locally ignoring general relativity).

But the biggie points about the Hilbert space definition are (A) the other, somewhat astounding (vector space), examples and (B) how much can do in Hilbert space that is close to good old high school plane (2D) and solid (3D) geometry and close to good, old freshman calculus with limits, convergence, etc. So, in particular, in Hilbert space we get the Pythagorean theorem of plane geometry and perpendicular projections as shortest distance from a point to a plane in solid geometry -- two biggies for pure/applied math. We get the triangle inequality. With the inner product, we get angles and orthogonality. And the core data we need for projections are just some inner products.

The most important examples of a Hilbert space are, for positive integer n, the set of real numbers R and the set of complex numbers C, the vector spaces of linear algebra R^n and C^n -- e.g., C^n is the set of all n-tuples of real numbers. Then as in whatever first course you had that talked about vectors and dot (inner, scalar) products, with those inner products R^n and C^n are Hilbert spaces.

The part about _complete_ is a generalization of completeness in the real numbers, that is, the biggie way the real numbers are better than just the rational numbers. In short, for example, intuitively, in the real numbers, if a sequence appears to converge, then there really is a real number there for it to converge to. Of course, that's not true in the rational numbers since, e.g., can have a sequence of rational numbers that converges to the square root of 2 but doesn't really converge in the rational numbers because square of 2 is not a rational number. This stuff about "appears to converge" is called _Cauchy convergence_ and is a weak definition of convergence. The point about completeness is that Cauchy convergent sequences really are convergent, that is, have something to converge to and do converge to it (essentially in the sense of limits you saw in calculus or high school algebra). If are taking limits to define or approximate what really want, then also really want completeness so that what converge to exists. So, that's completeness -- for a Hilbert space, we insist on that.

Of course, need a background in linear algebra. So, for a first book, get any of the popular ones. If you wish, concentrate on the more _geometrical_ parts and do less on the _algebraic_ parts \-- e.g., if there is a chapter on group representation theory, Galois theory, linear algebra over finite fields, where things go wrong when the field is the rationals, or algebraic coding theory, then feel free to leave that material for later. Likely should pay attention to dual spaces, but if wish can go light on adjoint transformations since they are less interesting when have an inner product. Curiously, can go light on change of basis for linear transformations, that is, the difference between vectors and coordinate vectors. Concentrate on dimension, linear independence, linear transformations, maybe touch on quotient spaces, use Gauss elimination as a good example of such things, eigenvalues and eigenvectors, orthogonality, and the polar decomposition (the core of factor analysis, singular value decomposition, matrix condition number, and more). If there is a little on the associated numerical analysis, then go ahead -- e.g., learn to accumulate inner products in double precision -- sure, take 10 minutes to see how to add iterative improvement to Gauss elimination and matrix inversion. For pseudo- inverses, cute material, but, especially for your question, likely won't see it again and can skip it.

Then if have some time, take a fast pass through the classic, Halmos, _Finite Dimensional Vector Spaces_. It was written in 1942 when Halmos had just gotten his Ph.D. under J. Doob (e.g., as in _Stochastic Processes_ and, more recently, _Classical Potential Theory and Its Probabilistic Counterpart_ ) and showed up at the Princeton Institute of Advanced Study and asked to be an assistant to John von Neumann, likely the inventor of Hilbert space. Well, Halmos wrote his book to be a finite dimensional version of linear algebra as if it were Hilbert space, which is commonly infinite dimensional. So you get a gentle introduction to Hilbert space. You get good at eigenvalues and eigenvectors, the polar decomposition, orthogonality, transformations that preserve distances and angles, etc. You get a lot of geometric intuition.

BTW, at one time, Harvard's Math 55 used Halmos, Baby Rudin (below), and a book by Spivak as the three main references.

Get a start on probability and statistics. A college junior level course should be sufficient. Don't take the course too seriously since will redo all the good parts from a much better foundation soon! Note: Elementary stat courses common get all wound up about probability distributions. Well, they do exist, are at the core of probability theory, and are very important, both in theory and conceptually, but, actually, in practice usually they require more data than you will likely have, especially in dimensions above 1. So, in practice, mostly can't actually see the actual data of the _distributions_ you are working with! You should hear about the uniform, exponential, chi-squared, and Gaussian and go light on the rest. The Gaussian is profound and won't go away even in practice although is less important in practice than long assumed in, say, educational statistics.

Then take a good pass through at least the first parts of Rudin, _Principles of Mathematical Analysis,_ AKA _Baby Rudin_. For the exterior algebra in the back of the more recent editions, well, likely get that elsewhere, say, now in English, directly from Cartan. The first parts of Baby Rudin cover metric spaces well enough. So, in Baby Rudin, get good at working with the limits, completeness property, compactness, etc. of mathematical _analysis_ , that is, not algebra, geometry, topology, or foundations, although the metric space material is the same as part of the part of topology called _point set_ topology.

Then learn more in mathematical analysis, in particular, measure theory. Measure theory essentially replaces the Riemann integral you learned in calculus and Baby Rudin. 'Bout time! Net, measure theory is a slightly different way to use limits to define the integral (areas, volumes, etc.) -- the first, biggie difference is that do the partitioning on the Y axis instead of on the X axis. The biggie reason: The resulting integral easily handles the pathological cases, especially involving limits, that the Riemann integral struggles with. Don't worry: The integral of, say, x^2 over [0,1] is still the same, IIRC, 1/3rd, right? But consider the function f: [0,1] --> R where f(x) = 1 if x is rational and 0 otherwise. Then the Riemann integral of f over [0,1] does not exist, but the measure theory integral does and gives 0 for the result.

There are at least two now classic books, Royden, _Real Analysis_ and Rudin, _Real and Complex Analysis_. But there are more, and likely more can be written. For Rudin, can f'get about the last half on functions of a complex variable.

Royden is easier to read. Rudin has the math more succinctly presented. Some people believe that Rudin is a bit too severe for a first version; but if get used to how Rudin writes, he's really good.

There in Rudin get good introductions to Banach space (a complete, normed linear space, that is, assume a little less than for a Hilbert space) with a few really surprising theorems, Hilbert space with an isomorphic argument that they are really all the same, a really nice chapter on Fourier theory (Baby Rudin does Fourier series; R&CA does the Fourier integral), and some nice applications.

With that background in measure theory, then take your good pass through probability. So, probability becomes a measure as in measure theory except the values are always real and in [0.1]. A _random variable_ finally gets a solid, mathematical definition -- it's just a measurable function ( _measurable_ is very general; in practice and even in nearly all of theory, essentially every function is measurable; in the usual contexts, it takes some cleverness to think of a function that is not measurable). And expectation is just the measure theory integral (with meager assumptions).

And in probability get some assumptions don't see in measure theory -- independence and conditional independence, and these two yield wonders nothing like in just measure theory.

Go somewhere; look at something; get a number; then that's the value of a _random variable_. In practice, suppose there are 20 random variables, you have numerical values for 19 of them, and you can argue that those 19 are not independent of the 20th and that you have some data on how they are dependent, then you have a shot at estimating the 20th. Presto, bingo, get a rich as James Simons, do _machine learning_ , get big houses, Cadillacs, Ferraris, a yacht, a private jet -- maybe!

Also get to use the Radon-Nikodym theorem (proved in both Royden and Rudin, and in Rudin with von Neumann's cute proof) for the grown up version of conditioning (i.e., _Bayesian_ ) and, from there, in stochastic processes, Markov processes and martingales (astounding results).

Books include L. Breiman, _Probability_ , K. Chung, _A Course in Probability Theory_ , J. Neveu, _Mathematical Foundations of the Calculus of Probability_. And there are more. IIRC Breiman and Neveu were both students of M. Loeve at Berkeley -- sure, can also get Loeve's _Probability_ in two volumes. Of these, Neveu is my favorite; it's elegant; but for most readers it is too succinct.

Hilbert space again? It turns out, the set of all real valued random variables X such that E[X^2] is finite is a Hilbert space. Yes, completeness holds; with some thought, that result seems astounding, like there is no way it could be true, but it is.

Now just derive grown up versions of most of the main results of elementary statistics for yourself from what you have learned. E.g., for the Neyman- Pearson result on most powerful hypothesis testing, just use the Hahn decomposition from the Radon-Nikodym theorem.

And, with the Radon-Nikodym theorem, get a grown up version of sufficient statistics, right, based on a classic paper by Halmos and Savage.

Along the way will notice that, the last time I looked, Baby Rudin defined the Riemann integral on closed intervals of finite length, but right away probability and statistics want to integrate on the whole real line, the whole plane, etc., do change of variable manipulations with such integrals, etc. Well, for the prerequisites, those are in measure theory where the first version of its integral applies also on the whole real line, the whole plane, and much more. Measure theory also give you the clean, powerful versions of differentiation under the integral sign and interchange or order of integration.

Ah, why bother to teach the Riemann integral at all? :-)

<comment><USER16> Thanks very much for your suggestions.

I have a utilitarian understanding of mathematics (vector spaces, SVD, orthogonality, invariances, etc.) and over time appreciating the underlying characteristics/relationships, which I recently got a taste of from T. Wickens, _The Geometry of Multivariate Statistics_.

I look forward to understanding measure theory and related math.

</comment></comment></comment></comment></comment><comment><USER17> I am no math expert, but I have always thought about it like this. The squared error is like weighting the error by the error. This causes one big error to be more significant than many small errors, which is usually what you want. Am I on the right track?

<comment><USER18> No, that's exactly why absolute error is better. "Big errors" are called outliers, they're (relatively) rare, often caused by bad data (measurement errors, typos, etc.) and substiantially influence the outcome of your calculation. In other words, squared error is _less robust_.

But squared error is easier to compute. So, in practice, what you do is you remove outliers (e.g. cap the data at +-3sigma) then use squared error.

<comment><USER19> > So, in practice, what you do is you remove outliers (e.g. cap the data at > +-3sigma) then use squared error.

But if you are say fitting a function to the data, you can't tell beforehand which data-points are the outliers. So in that case perhaps you need an iterative approach of removing them (?)

</comment></comment></comment><comment><USER20> Squared error represents the underlying belief that errors in various dimensions, or errors in independent samples, are linearly independent. So they add together like orthogonal vectors, forming a vector whose length is the square root of the sum of the squares. Minimizing the square error is a way of minimizing that square root without the superfluous operation of calculating it.

</comment><comment><USER21> It's fine to list some reasons for using squared error, but you really can't decide on the error function without referring to a problem you're trying to solve.

Just look at the success of compressed sensing, based on taking the absolute value error seriously.

<comment><USER22> Which is basically the entire message of the last section.

</comment></comment><comment><USER23> "inner products/gaussians" \- the absolute value (and also cuberoot of absolute cubes, fourth root of fourth powers) also define inner products. Likewise, there are "gaussian-like formulas" which take these powers instead of squared.

However: if you look at the shape of the squareroot of sum squares, it's a circle, so you can rotate it. If you take the absolute, it's a square, so that cannot be rotated; the cuberoot of cubes and fourthroot of fourths, etc. look like rounded edge squares, and that cannot be rotated either, so if you have a change of vector basis, you're out of luck.

With the gaussian forms of other powers, none of them have the central limit property.

<comment><USER24> What kind of inner products are defined by the absolute value, cuberoot of absolute cubes, fourth root of fourth powers? I never heard of that and would be glad to learn about it.

<comment><USER25> You may find it interesting to read about Lp norms, and their relationship to inner products on vector spaces. I think the OP is mixing up norm and inner product terminology. This happens often because you derive an norm from any inner product, but the other way may not exist.

If you plot on the plane the distance = 1 line, then L_1 gives you a diamond, L_2 a circle, L_inf a square. [More precisely, the unit circle under the related metric (distance function) looks like those euclidean shapes]

</comment></comment></comment><comment><USER26> The Bayesian formulation for the likelihood function would make this squared error explicitly clear.

<comment><USER27> For Gaussian uncertainty. Which still makes it a much more natural assumption than any other I know.

</comment></comment><comment><USER28> My explanation for squared error in linear approximation always was: because it minimizes the thickness of the line that passes through all the data points.

(Per the old math joke - you can make a line passing through any three points on a plane if you make it thick enough.)

</comment><comment><USER29> Or why use variances when there are standard deviations (the square root of the variance) which have more easily interpreted units? One commonly cited reason is that one can sum variances from different factors, which one cannot do with standard deviations. There are other properties of variances which make them more suitable for continued calculations[1]. This is why, for instance, variances are often utilized in automated optimization packages.

[1] [https://en.wikipedia.org/wiki/Variance#Properties](https://en.wikipedia.org/wiki/Variance#Properties)

</comment><comment><USER30> Interesting discussion. Not sure about the breakdown between ridge regression and LASSO though. The difference is not in the error term but in the regularization term.

</comment><comment><USER31> Squared error because the uncertainties in independent, normally distributed random variables add in quadrature. I expect that this could be proved geometrically using Pythagoras's theorem, so in that sense the comments about orthogonal axes are vaguely on the right track.

Normally distributed variables because the central limit theorem.

It isn't all that complicated.

</comment><comment><USER32> Why not KL-Divergence, which measures the error between a target distribution and the current distribution? From the perspective of Information Theory, it is the best error measurement.

Oh, and let's not forget that for a lot of problems minimizing the KL- divergence is the exact same operation as maximizing the likelihood function.

<comment><USER33> kl divergence has no nice theoretical properties other than 'it is the answer to these questions'

it is also extremely poorly behaved numerically and in convergence

<comment><USER4> I am sorry but I have to call bullshit on this.

To give just a taste for the nice properties of KL, if you are using a layer 1 NN with the sigmoid function as the transform, using square loss gives you an explosion of local minima. OTOH using KL in its place would have given you none. Numerically accuracy is pretty much a non-issue, people have known how to handle KL numerically since the last 40 or so years.

BTW using KL on equivariant Gaussian gives you square loss, apparently the loss you prefer.

<comment><USER34> if your problem is ok with the asymmetry of KLD

</comment></comment></comment></comment><comment><USER12> Another pro tip - absolute error magnitude is the convex hull of non-zero entry count for vectors (l_0 norm in some circles). So in the convex minimization context (and for most other smooth loss terms in general) you end up with solutions with more zero entries and few possibly large non-zero entries.

</comment><comment><USER35> Also see [http://www.leeds.ac.uk/educol/documents/00003759.htm](http://www.leeds.ac.uk/educol/documents/00003759.htm).

</comment><comment><USER36> Somewhat related; here's my attempt at explaining Cross Entropy:

[http://heliosphan.org/cross-entropy.html](http://heliosphan.org/cross- entropy.html)

</comment><comment><USER37> Square often corresponds to power in systems.

<comment><USER37> I noticed this got voted up and down more than usual. Maybe a little elaboration:

Square often corresponds to power/energy in systems AND energy (integral of power) is preserved. That relationship between physics and math allows a lot of useful transformations.

</comment></comment><comment><USER38> Why geometric mean?, I would ask.

<comment><USER21> "Why addition?", I would ask.

Different problems, different tools. You can't ask "why geometric mean?" without referring to a specific problem you're trying to solve.

<comment><USER38> What is a problem geometric mean solve? That was my question the entire time.

When people ask "why machine learning?" the answers are "machine learning can do these things blablabla", not "you must specify the problem you're trying to solve".

<comment><USER39> > What is a problem geometric mean solve?

It gives you a way to average together two things that have units that have nothing to do with each other and then compare two such averages and have the comparison make sense, as long as your units were consistent.

As a silly example, say you want to average 1kg and 1m and compare that average to the average 2kg and 0.5m. With arithmetic mean, ignoring the fact that it's nonsense to add different units, you could get numbers like (1+1)/2 = 1 and (2+0.5)/2 = 1.25 if you use kg and m, but numbers like (1 + 100)/2 = 50.5 and (2 + 50)/2 = 26 if you use kg and cm. Notice that which one is bigger depends on your choice of units. On the other hand, the geometric mean of the two examples is always the same as long as you use consistent units: 1 for both if you use kg and m, and 10 for both if you use kg and cm.

In practice, this sort of operation is only useful if you have multiple measures of some sort along different axes (think performance on 3 different performance tests) and you're being forced to produce a single average number. Again, a fairly silly thing to do, but _very_ common: just about every single performance benchmark does this.

<comment><USER38> Thank you very much. Great examples.

</comment></comment></comment></comment></comment><comment><USER40> cause linear algebra is a beautiful framework to think in.

</comment><comment><USER41> To make it positive and to amplify it (as a side-effect).

BTW, "error" is a misleading term - it communicates some fault, at least in the common sense. Distance would be much better term.

So, "squared distance" makes much more sense, because negative distance is nonsense.

<comment><USER42> Well it will only amplify values > 1.

<comment><USER43> That's not correct. Even though the magnitudes of the value in isolation shrinks, the relative magnitudes are still amplified which is what matters.

Consider values 1/2 and 1/4: in the original space it's double but in the squared space it becomes 1/4 and 1/16 so the difference is 4x. Also relevantly if you compare eg 0.9 and 1, the gap between them is amplified after squaring.

<comment><USER42> Those values aren't compared individually, they are summed to calculate the deviation, the result of that sum will be reduced if the values are < 1.

</comment></comment></comment></comment><comment><USER44> An honest question - do we even need statistics when we have machine learning? Statistics to me appears as a hack/aggregation of data we couldn't process at once in the past; these days ML + Big Data can achieve that and instead of statistics we can do computational inference instead. To me this looks like looking back to "old ways" for a reference point instead of looking forward to the unknown but more exciting.

<comment><USER10> Machine learning is often considered a statistical technique. The main difference seems to be that in traditional statistics, people derive practice from theory, whereas in ML people will try out techniques and figure out the theory later. That's really just a cultural difference. The techniques for analyzing ML models are all statistical to begin with.

Statistics, as a field, already used general-purpose optimization algorithms before modern ML techniques came about, so in that sense, ML just fits into an existing position in the statistical toolbox (like replacing a chisel with a 3D printer). In the other direction, statistical techniques like cross- validation are necessary for you to get your ML correct.

<comment><USER44> There is much more in ML than just statistics. I was basically asking why the "statistics filter" is so often on in ML. Neural networks don't seem a statistical technique, even if somebody uses them for regression. Yes, there is an overlap, but no, ML != statistics. As you mentioned, non-linear optimization is used in statistics on meta-level however nobody claims statistics is operations research or vice versa.

<comment><USER4> _In what way is neural network not a statistical technique ?_

very curious to hear about your point of view. Statistics is not linear regression and ANOVA, or whatever catalogue of techniques in a freshman book, not even the library of techniques available in R.

Statistics is the application of probability (or more broadly, math) to data.

That said statisticians did miss the neural net wave because of their flippant reaction to it. They said, "oh well yet another non-parametric function approximator we have worked out the asymptotics 30 years ago".

To paraphrase someone wise: asymptotically we are all dead. Not enough heed was paid to that. Among their other lacks were expertise in algorithms and optimization. Mind you, optimization has been at the core of their craft from their very genesis, its just that they did not feel it important enough to ride the cutting edge of research on optimization. Note: you cannot do maximum likelihood with solving an optimization problem. Gauss was doing it several hundreds of years ago for statistics. If I go on a bit further with my rant, they got a bit carried away with their fetish over bias and asymptotic normality. They missed the wave, sure.

But all said and done, by any accepted definition of statistics, NN is very much also statistics.

<comment><USER45> Statisticians are either Frequentists, or Bayesianists. Fundamental to both of these approaches is the involvement of probability. A technique that does not have a probabilistic interpretation is not a statistical technique. This is also largely related to the difference in goals between statistics and machine learning: statistics is primarily about inference, and machine learning is primarily about prediction. You can predict without necessarily making any meaningful probabilistic statements about data. There's not really much to infer without saying something probabilistic.

<comment><USER4> Neural nets have absolutely been associated with probalistic interpretations. Good resources would be David McCay and Radford Neal. Both their approaches are Bayesian. A far more trivial way to associate a probabistic interpretation is to claim that the neural net is the conditional expectation.

And who says frequentist and Bayesian are the only two views. Where would you shelve prequential statistics then ? Or nonparametric regression

Prediction has definitely been a part of statistics but often, as you rightly claim, as a byproduct. And yes i would characterize the focus of stats and ml exactly as you did

<comment><USER45> The point is not whether somebody has ever tried to associate neural nets with probability, sorry if my previous comment made it seem that way. The point is that neural nets are not fundamentally tied to it. You can try to tie them to probability, but you certainly don't have to, it mostly isn't, it isn't mostly taught that way, and the big open problems in the field don't involve it.

Statistics works the other way. You basically always start with some kind of probabilistic model. And then, if you even bother with prediction, you work towards prediction from the probabilistic model. With stats you don't need to interpret or add probability after the fact, it's already there.

Obviously stats and ML are enormous fields, with quite some overlap. And people tend to go after low hanging fruit; if many people who studied neural nets have formal probability backgrounds it simply makes sense that _someone_ will write a paper on it. And I'm generalizing here (same goes with frequentist & Bayesian comment). But there absolutely is justification for saying "neural nets are not really a statistical technique".

<comment><USER4> I agree with a lot what you are saying, but rest assured its colored by personal rationalizations one has made while learning these things and not objective facts.

nns are in no way less fundamentally associated with probability than say linear regression. In both cases you can start with a probability model and derive the final form as a logical consequence, or you can start with the final form and slap a consistent probabilistic model on top.

The main thing is that any test you come up with that carves nns away from stats is going to carve a whole lot of other things that people have no trouble calling them stats. This controversy essentially stems from the need to claim a technique for ones own tribe and not concede to another. I am making no moral claim here just an observation and neither is it a novel one.

BTW I am firmly from the ml camp and not stats. I enjoy poking a little bit of fun at statisticians and try being gracious about their criticism of ml. That said i feel no need to make a groundless claim to a technique when they have no less rights to it in terms of objective claims. Rights steeped in culture, fiat and history are a different matter.</comment></comment></comment></comment></comment></comment></comment></comment>


Lethal Injection Problems Lead States To Re-adopt "Gruesome" Execution Methods - <AUTHOR> https://firstlook.org/theintercept/2015/04/24/gruesomedeathpenalty/

<comment><USER2> I think this is a good thing. Giving 'lethal injection' feels like we are treating a patient where the person is not a patient, he is a criminal and a traitor to his people.</comment>


San Francisco Wants Homeless to Leave Tent Camp, but Some Vow to Fight - <AUTHOR> http://www.nytimes.com/2016/02/27/us/san-francisco-wants-homeless-to-leave-tent-camp-but-some-vow-to-fight.html <comment><USER2> I just learned of an interesting program in San Francisco called Lava Mae. They have re-purposed transportation buses into showers and toilets on wheels to serve homeless of SF. It is an interesting project that is using some innovation to solve a social problem.

[http://lavamae.org/](http://lavamae.org/)

Lava Mae Launch [Video] [https://www.youtube.com/watch?v=up9TfkcJNxk](https://www.youtube.com/watch?v=up9TfkcJNxk)</comment>


LambdaMOO takes a new direction (1992) - <AUTHOR> https://www.cc.gatech.edu/classes/cs8113h_98_spring/LTAND.html <comment><USER2> Still don't think I have ever connected to LambdaMOO, although I spent some time on ifMUD which is in the same neighborhood.

Reading these docs now, if I understand right, in between LTAND and LTAND2 was the Mr Bungle/Dr. Jest incident ( [https://www.villagevoice.com/2005/10/18/a-rape-in- cyberspace...](https://www.villagevoice.com/2005/10/18/a-rape-in-cyberspace/0) ), which maybe fills in some of the unspoken context around "We Are Reintroducing Wizardly Fiat" and "The wizards will no longer refrain from taking actions that may have social implications. In three and a half years, no adequate mechanism has been found that prevents disruptive players from creating an intolerably hostile working environment for the wizards. The LTAND ideal that we might somehow limit ourselves solely to technical decisions has proven to be untenable.".

I wonder if that's the whole story or if trolls were a huge problem in that community for the early 90s?

<comment><USER3> I was there pretty much right at the beginning of LambdaMOO. I left before I ever saw a troll. In fact, this is the first time I actually realised there was ever a problem. It's very sad. It was a _great_ community. Incredibly giving and helpful. Also Pavel Curtis was a really good maintainer. He was always willing to look into reported problems and would always accept patches. If he modified them, he always did it with grace.

</comment></comment><comment><USER4> I spent an enormous amount of time on "Snow Crash", a MOO that I think was similar and was popular around the same time. This was in the middle or late 90s. The world was incredibly rich---everything in it was an object that was programmable if you had the right ownership permissions. I don't remember the language it used, or if it even was a standard one, but looking back it was unbelievably robust. I was able to make a "video recorder" that could be turned on, record all the events in a given place, and replay them later to make it all seem like it was happening again; a couple then used this to record their in-world wedding, which I only learned about months later.

I have DESPERATELY searched for years for any commentary on this community slash game, with no luck. I can even remember the telnet address: sapporo.sensemedia.net. Port 8888, maybe? Ah, alas.

<comment> o-__-o Sounds very much like LambdaMOO. Back in the day there were mirror sites you could use if your ping was absurdly high its possible that sapporo host was just a mirror. Snow Crash sounds familiar, but it reminds me of an area inside LambdaMOO. That world was huge, I remember several times ending up in brand new areas that I would spend hours exploring because I never knew how to get back -- I jumped out of a helicopter once, landed on a remote island that had a HUGE underground complex. Another was a party room in the clouds... the imagination could run wild there. I also remember things that could "playback" events (like you could look into a mirror and see who used to be in that room)

<comment><USER4> This sounds entirely right. Because I also have a recollection of that party room in the clouds. Actually, it's further refreshing my recollection---I think the main area was called "the Sprawl" or something like that? Maybe it WAS just a mirror, and the reason I haven't ever been able to find anyone else talk about it is that I was under a teenage misimpression about the name.

EDIT: AHA. Sensemedia apparently had a virtual community consisting of MANY themed MOOs, termed "The Sprawl," of which Snow was one; ChibaMOO was I guess the famous one, and was based on Neuromancer. This is more progress than I have made on this in years, so thanks!

<comment> o-__-o :D

What blows my mind is that all of this existed almost 30 years ago and nothing quite like it exists today (maybe second life... maybe).

I remember snow crash but I don't remember much about it. I spent virtually my entire dialup childhood on lambdamoo. Lambda was a giant mansion and you would start in a closet. One of the bottom floors of the mansion had a mirror which replayed things that recently happened in the room. The mansion was huge but I would constantly leave and explore the street. That's how you ended up in crazy places like a helicopter or random ass island. Still don't remember how I ended up in someone's self built house. All I remember is they were having a party and it was freaking awesome reading through everything at 4am hoping my parents wouldn't barge in and yell at me lol. Good memories :)

If anyone with the handle "dags" sees this, hello from your helicopter buddy

<comment><USER5> Check out Sindome: [https://www.sindome.org/](https://www.sindome.org/) it's a cyberpunk MOO been online since '97 and is running a modified LambdaMOO.

</comment></comment></comment></comment></comment><comment><USER6> LambdaMoo, BayMoo (and the now defunct ParkMoo) were all favourite haunts of mine in the mid-90s when I was at university. Perhaps I'm feeling nostalgic for those innocent pre-social-media times: I'm in the process of writing a Python "MOO" as a weekend fun project -- a sort of interactive literary world inhabited by other writers / creators / explorers.

If you're interested in this sort of thing, Richard Bartle's "Designing Virtual Worlds" is the classic to read.

</comment><comment> core-questions I loved LambdaMoo. It was installed on our high school network in maybe '97 and I created whole worlds in there for friends to explore, including interactive functions. Had no idea I was writing "lambdas" at the time, it was just an interesting way to build a text adventure from within itself.

<comment><USER7> Wow, I had no idea LambdaMOO was capable of that. I knew that MUSH/MOO like systems were rich but not to that extent. It was essentially proto-Minecraft in a sense?

<comment><USER8> LambdaMOO is a fully programmable text-based virtual reality.

Here's the LambdaMOO programmer's manual: [https://hayseed.net/MOO/manuals/ProgrammersManual.html](https://hayseed.net/MOO/manuals/ProgrammersManual.html)

Webpage: [https://www.moo.mud.org/](https://www.moo.mud.org/)

Other resources: [https://github.com/johndoe771/lambda-moo- programming](https://github.com/johndoe771/lambda-moo-programming)

And for LambdaMOO itself telnet to lambda.moo.mud.org 8888.

<comment><USER5> More resources for those interested, probably the most comprehensive and up to date site: [https://lisdude.com/moo/](https://lisdude.com/moo/)

<comment><USER9> Very cool! Last I looked Lambda Moo and related topics seemed to lead to a maze of defunct ftp sites and occasionally a web site. Glad these aren't completely lost to time.

</comment></comment></comment></comment></comment><comment><USER10> Related from last year (just one comment): [https://news.ycombinator.com/item?id=20405167](https://news.ycombinator.com/item?id=20405167)

Related from 2017: [https://news.ycombinator.com/item?id=14075439](https://news.ycombinator.com/item?id=14075439)

</comment><comment><USER11> I spent an inordinate amount of time on Lambda in college, thanks to some friends that introduced me to it freshman year. It became the formative experience for a lot of my online time. I still log on there and a couple others, although these days it's really just to talk with one or two people that I don't have another connection to—the population on Lambda is a pale fraction of what it was at the peak in the nineties.

Some friends and I do still run our own MOO that we've been running since college on one person's computer or another. Through the years, it's outlasted ICQ, AOL, and every other form of instant-messaging doodad out there. It's simple, straightforward, and requires only an SSH client to connect to it from any device (since I run it on a Unix box, I just SSH to that and resume my session using tmux). Remarkably persistent, and a really nice way to continue talking with my friends every day.

</comment><comment><USER12> LambdaMOO was awesome, and an incredibly practical introduction to OOP and security! Quc's cage for example. Who has permissions to call your characters move() method? What about who called them? Lets iterate the call stack or we'll either be stuck here forever, or we can never type "go north" again!

You have a Star Trek ship parent object, and these ships can fire at each other? Ok. Script to: Make this object into a star trek ship; Move me into it; Lock onto the other ship at this location; Fire all six photon torpedoes; Move me out of it; chparent back to $thing. Emit appropriate text to users in the room "Klingon warbird decloaks..." Bonus, do it again, get all six photon torpedoes back, because init() method called when chparenting.

</comment><comment><USER13> i ran it on my laptop (colour screen!) 20y ago. mapped my apartment into it, with triggering objects etc.

<comment><USER14> Do you mean to say that you could interact with the virtual world and change things in the physical world? Like, drop an item in a virtual room to turn off a light in a real one?

</comment></comment><comment><USER15> I lived through all of this, was part of the community at the time this was happening. Very interesting times. I was a teenager at the time, so I look back at some of it with a bit of cringe. But I made some good friends there that I still have to this day.

These announcements led to some interesting political experiments.

</comment><comment><USER16> A good(?) history of LambdaMOO's creation:

[https://undark.org/2018/07/20/wilo-evans-broad- band/](https://undark.org/2018/07/20/wilo-evans-broad-band/)

</comment><comment><USER17> I spent a _ton_ of time on Lambda. Such a cool slice of computer history.</comment>


Upcoming Australian Android phone delayed indefinitely over screen resolution - <AUTHOR> http://www.theage.com.au/news/digital-life/mobiles--handhelds/articles/aussie-google-phone-launch-aborted/2009/01/16/1231608964531.html

<comment><USER2> I remember reading - probably in comments here on News.YC - that different screen resolutions could be a downside of the Android platform. How is it possible to get to "within days of shipping the product to customers" before deciding whether inconsistent resolutions are acceptable or not?

My only guess is that Kogan got the run-around from Google and decided to take the risk and go ahead with development rather than wait around for a clear answer and launch too late. If that's the case, this seems like a very bad play from Google.

That's only speculation though. Does anyone who's been watching Android more closely have some insights?

</comment><comment><USER3> Ouch. This "Android" paradise is sounding farther-fetched each day.</comment>


I need a short 'cartoon' video demonstrating how to use my website. Suggestions? - <AUTHOR>

I would like a short video showing the main features of my website. I've tried to film myself, but it mostly gives an impression of 'cheap' or 'corny'.
Futhermore, if I ever change the CSS or anything else just a little bit, then the video isn't matching the site anymore.
I would like a video that shows a 'cartoon' version of the main features. (Edit: by cartoon I mean simplified.)
Also, being French Canadian, I have a big accent that can be off-putting; a more traditional American or British voice would be great.
Is there a firm offering to make such videos? Any good/bad experiences with them?
Thank you very much in advance! <comment><USER2> You can receive quick quotes from talented producers and even supervise/manage your video production project with Hatchvid - [http://hatchvid.com](http://hatchvid.com)

PS. shameless plug

</comment><comment><USER3> This might sound crazy, but it'd be worth looking at Fiverr. You won't get world-class talent, but you can find competent motion graphics and video people on there, and decent voiceovers too.

<comment><USER4> Second Fiverr. Some of these guys/girls have impressive show reels!

</comment></comment><comment><USER5> I would go for these guys: [http://en.wikipedia.org/wiki/Common_Craft](http://en.wikipedia.org/wiki/Common_Craft)

</comment><comment><USER6> Many of the ones that are out there are made by a company named Epipheo. [http://epipheo.com/](http://epipheo.com/)

</comment><comment><USER7> I don't know the answer to your question, but, have you considered a screencast instead, showing actual use?

<comment><AUTHOR> I have; I plan to make one for a more detailed explanation. For the landing page however, I would like something short and sweet :-)

Thanks for the suggestion!</comment></comment>


JQuery Events: MouseOver / MouseOut vs. MouseEnter / MouseLeave - <AUTHOR> http://www.bennadel.com/blog/1805-jQuery-Events-MouseOver-MouseOut-vs-MouseEnter-MouseLeave.htm

<comment><USER2> The demo (just above the comments) in the jQuery docs is helpful.

<http://api.jquery.com/mouseenter/>

</comment><comment><USER3> Ben Nadel's blog is worth a daily read. Probably half of his content these days is about JavaScript. Ben and his partner just raised $1.5 million for their Invision app that lets you build interactive prototypes using PhotoShop.

[http://techcrunch.com/2012/02/22/invision-raises-1-5m-for- be...](http://techcrunch.com/2012/02/22/invision-raises-1-5m-for-beautiful- interactive-prototypes/)

<comment><AUTHOR> thanks

</comment></comment><comment><USER4> jquery has been around for a while and so has this post. but nonetheless a good reminder what a pain js would be without it.

<comment><AUTHOR> yeah you are right

</comment></comment><comment><AUTHOR> a real confusing point for quite of us developers.</comment>


Show HN: OpenERP launches a new eCommerce in Python/Bootstrap [video] - <AUTHOR> http://www.youtube.com/watch?v=pp7OCqWoRjQ

<comment><USER2> I guess you work for OpenERP, is there a reason why you use "tweet to pay" links on your website for these videos? It's absolutely annoying.

In this context, what is up with the hyperbole and the dissing of Drupal?

Now, about this tool, great video for consultants and marketing guys, but from a technical stand point, where can I learn more about it? Some questions:

\- Is there structured content? Content types? \- How does templating work? \- How does your WYSIWYG approach impact large and interconnected websites? I personally think WYSIWG is usually a fool's errand.</comment>


I Went from Grad School to Prison - <AUTHOR> http://www.cosmopolitan.com/politics/a29775/cecily-mcmillan-grad-school-to-jail/ <comment><USER2> What does society _gain_ from a prison like that?

Imagine a person spends 10 years in that situation, and gets out, what the hell are they supposed to do now? They have no job skills, no life skills, they're psychologically damaged. The rate of re-offense isn't high because 'bad people are bad', it's high because we take people in bad situations, and break them further. We take someone almost-functional, and make them completely disfunctional.

And anyone who disagrees with this model isn't "tough on crime".

<comment><USER3> > What does society gain from a prison like that?

No, you have to ask, what does a prison like that gain from society?

Once you understand that people are merely a resource for government, law enforcement and business, things make more sense.

Think about that as you drive by a speed trap, that the police and city undoubtedly portray as a safety operation, but where the cops hide themselves because they're not trying to discourage speeding, they're trying to collect taxes.

Think about that when you see various police organizations stating their opposition to legalizing marijuana. Is their objection that it may promote other, still illegal drugs, or that it would reduce LEO job opportunities? Their only proper stance on the issue, as LEO organizations, is to enforce the laws that society tells them to.

Think about that as an apparently overzealous prosecutor bullies a plea bargain out of someone with the threat of life in prison. She's not overzealous, she doesn't care about society; the victim/defendant is merely a line on her resume. When a prosecutor's job performance depends on _convictions_ (rather than truth), then she's going to maximize convictions.

Think about that as you, just a normal citizen who is no threat to anyone, are monitored, threatened and controlled by unconstitutional surveillance and police tools that are supposedly intended for, you know, actual terrorists threats.

<comment><USER4> > Once you understand that people are merely a resource for government, law > enforcement and business, things make more sense.

Then why is it that other places in the world, such as Norway, prisons are not like American prisons? You're claiming that the state in America is simply an outcome of universal properties of Government, but this outcome is not universal.

You can't just flippantly say "Things are bad 'cause Gubmint'", when it's clear that other places have overcome these challenges.

<comment><USER3> I didn't say it's universal, and Norway is apparently a place where reason wins more than America. They are also differently structured governments and societies, and the people have different formative experiences.

We are not uniform, and no outcome is inevitable. The way things are in America are probably more likely in America than elsewhere because of our unique shared myths and the wieldability of government by evil people.

</comment></comment></comment></comment><comment><USER5> Ms. McMillan was convicted, unanimously, by a jury of her peers. The video of her assault on the police officer is widely available and can be viewed here: [https://www.youtube.com/watch?v=devvY1cCVFE&oref=https%3A%2F...](https://www.youtube.com/watch?v=devvY1cCVFE&oref=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DdevvY1cCVFE&has_verified=1)

You can draw your own conclusions, but what I see is somebody preparing, consciously, to use the force of slamming their elbow into a police officer's face in order to escape from that officer.

Perhaps you disagree with a lot of things -- police militarization, incarceration rates in this country, unequal outcomes and opportunities based on socioeconomic factors.

But I don't see how a fair-minded person can agree that we ought to go around solving our problems by elbowing officers of the law in the eyeball: [http://www.nytimes.com/2014/04/15/nyregion/officer- testifies...](http://www.nytimes.com/2014/04/15/nyregion/officer-testifies- about-encounter-with-occupy-wall-street-protester.html?_r=0)

The woman in this case has repeated problems with the law (and the truth) and was arrested again last year for impersonating a lawyer in the subway and interfering with an arrest: [http://www.nytimes.com/2014/07/18/nyregion/occupy-wall- stree...](http://www.nytimes.com/2014/07/18/nyregion/occupy-wall-street- protester-is-out-of-jail-and-back-in-court.html)

As the prosecutors say in that case: "This new arrest mirrors what was on display throughout the trial: the defendant's utter contempt for the police and the important job they do on a daily basis."

Fact-free magazines like Cosmopolitan may display sympathy for people like Ms. McMillan, but here at HackerNews we should not.

<comment><USER6> _You can draw your own conclusions, but what I see is somebody preparing, consciously, to use the force of slamming their elbow into a police officer 's face in order to escape from that officer._

What is the point of the wording "preparing consciously"? Can you prepare for something unconsciously? If someone gropes you from behind, some conscious effort is required to defend yourself.

 _Fact-free magazines like Cosmopolitan may display sympathy for people like Ms. McMillan, but here at HackerNews we should not._

Please don't use the term "we". I'm not a sociopath and I have sympathy for people who were treated unjustfully.

<comment><USER7> >What is the point of the wording "preparing consciously"? Can you prepare for something unconsciously?

The point was probably to emphasize the premeditation of it, and I agree it was needlessly verbose. But yes, you can prepare unconsciously, like when your body has the fight-or-flight response.

</comment></comment></comment><comment><USER8> Name one important person who ended up in jail for more-or-less-violent protesting, and ended up having this as a "self transforming" experience. I can name at least one... _Adolf Hitler_ ([http://en.wikipedia.org/wiki/Adolf_Hitler](http://en.wikipedia.org/wiki/Adolf_Hitler)) ...and I think Mr. _Joseph Stalin_ followed a similar career path, though I know less about the details (but here's a cute mugshot of him: [http://en.wikipedia.org/wiki/Early_life_of_Joseph_Stalin#med...](http://en.wikipedia.org/wiki/Early_life_of_Joseph_Stalin#mediaviewer/File:Stalin%27s_Mug_Shot.jpg) )

So, here you go:

Q: How do you severely radicalize a random semi-moderate non-violent protester?

A: Send him/her to jail.

Thankfully this girl kept a cool head and drew a sane interpretation from it, but seriously, what are you Americans trying to do, 'cook up' the next generation of world-wrecking monsters in some insane social-engineering experiment?!

<comment><USER9> She was convicted of a violent offense.

Are you disputing the verdict or the punishment?

<comment><USER10> The punishment, from the sound of it.

<comment><USER9> OK, how does the punishment for hitting a police officer in the US compare to other countries?

<comment><USER11> It's worth noting that the punishment here was pretty over the top even by US standards. In suburban middle America a college girl with no priors who bruises a cop will not ordinarily get charged with a _felony._ Some cities would even take the charge that she was manhandled by cops seriously and investigate it.

<comment><USER9> That seems like you're disputing the charge/verdict, not the punishment.

What is the normal punishment for felony assault of a police officer for someone with no priors?

The reason this distinction is important is because disputing the charge/verdict requires getting involved in the case and seeing the prosecution's evidence.

<comment><USER11> I was actually with you until here:

> The reason this distinction is important is because disputing the > charge/verdict requires getting involved in the case and seeing the > prosecution's evidence.

I don't agree at all but I also think we've seen enough of the evidence that there's not really much factually to dispute. (We know a lot more than the jury did, which is a very common and very depressing situation but also a separate topic)

Anyhow, felony charges are usually reserved for people who do a lot of damage, on purpose, and who have prior convictions. I agree that there's a distinction to be made between the seriousness of charges and the severity of those charges associated punishment - they're determined by different parts of government, if nothing else - but I don't think as much hangs on it as you seem to, vis a vis the way different places treat crimes more or less severely. Heavy handedness in one area correlates with heavy-handedness in the other, after all.

</comment></comment></comment></comment></comment></comment></comment><comment><USER12> _I 'll agree not to charge you with a felony and potentially lock in you in cage if you agree not to have a third party examine the facts of this case, and instead plead to a misdemeanor and pay a fine._

Is there any possible reform for the disaster that is the plea system? I realize it's necessarily in some situations but the result is you are never not guilty of some crime. The prosecution virtually never loses. Truth and justice are nowhere to be found.

<comment><USER13> >Is there any possible reform for the disaster that is the plea system?

Prohibit prosecutors from going to trial with charges any higher than they offered in the plea deal. If they offer to (say) let you plead to only misdemeanor drug possession, then they shouldn't be allowed to then prosecute for felony drug possession, resisting arrest, plus anything else they can think of simply because you dared to ask for a jury trial. Sentencing can still be somewhat variable, which would offer an actual incentive to those that know they're caught-holding-the-bag guilty, but would not be such a ruinously life destroying difference that it would frighten the innocent into taking the pllea when they did nothing wrong. The argument against plea bargain reform from prosecutors is always along the lines of "the courts could never handle all those cases, so we have to discourage jury trials as best we can", and frankly, that line of reasoning is bullshit. If there aren't enough resources to offer a jury trial to every offender, then I say we have a crisis of constitutional proportions, and dealing with it by gaming the system to force people to forego their rights simply to improve efficiency is a far more evil thing than most of the crimes people plead to.

</comment></comment><comment><USER14> > The judge didn't allow evidence that my attorney wanted to show the jury, > including a range of videos of the incident.

That's the part I find the most shocking in this article. Aren't trials supposed to be fair? What are the kind of evidences that are not allowed to be shown?

<comment><USER5> NYTimes: "Jurors were also shown several YouTube videos of Officer Bovell, 35, in March 2012 getting elbowed in his left eye by Ms. McMillan, 25, who is charged with assaulting a police officer. She has maintained that Officer Bovell groped her breast, and that she reacted without knowing he was a police officer. He has denied that he touched her breast.

The shaky videos showed Ms. McMillan, a labor organizer, in a bright green dress, jumping back and planting her elbow in Officer Bovell's face.

"She crouched down and lunged backward, elbowing me in the eye," Officer Bovell said. "It's like a white light in the face.""

[http://www.nytimes.com/2014/04/17/nyregion/officer-stands- by...](http://www.nytimes.com/2014/04/17/nyregion/officer-stands-by-his- account-of-scuffle-at-occupy-wall-street-protest.html)

<comment><USER14> I don't disagree with what you are saying, but you are not answering my question.

</comment></comment></comment><comment><USER15> I live in NYC and I didn't read the entire article, but what stood out for me (beneath the picture) was the $167K per year that each prisoner on Rikers costs NYC!

The story of her re-arrest for confronting a police officer in the NYTimes story mentioned in the first comment suggests some underlying contributing psychological issues.

</comment><comment><USER16> One day a civilized nation will look back on America prisons as we now look back on the Bastille.

<comment><USER17> You don't need to wait for the future: [http://m.theatlantic.com/international/archive/2013/09/why-s...](http://m.theatlantic.com/international/archive/2013/09/why- scandinavian-prisons-are-superior/279949/)

</comment></comment><comment><USER18> the grad school -> prison movement isn't new.. i recall a friend who, as far as i know, went from a ritzy grad school to jail based on some fairly radical eco-"terrorism".... incarceration is a really scary thing, and there's a gamut of types there. i have a feeling that this was an experience for the article's author for sure, but, although it may be a platitute, i feel as though i ought to say, "it could be worse."

</comment><comment><USER19> I'm glad she wrote this. In prison you don't just "serve your time". Instead you are abused and neglected, criminally so.

Source: My brother went to prison with a 70 year sentence. He was 17.

</comment><comment><USER20> I don't love cops, but you don't hit cops. No matter what the cop does, you don't hit the cop. 58 days... she got off with a slap on the wrist.

<comment><USER21> Sure you do. A person has the right to defend themselves when assaulted, even if the assaulter happens to be wearing a shitty tin badge and carrying a gun and night-stick. We should not mistake the so-called "authority" of the State as having any legitimacy that can trump our innate rights, of which self- defense is about as fundamental as they get.

</comment></comment><comment><USER22> prisons in the US are unconscionable. the abuse by guards, the prevalence of prison rape (by an HIV-positive inmate, no less), the capricious behavior by the staff. It is a shameful system.

</comment><comment><USER23> "... And the right to a fair trial...' Yeah right...

</comment><comment><USER24> world done fucked up

</comment><comment> guard-of-terra Someone should become dead for this.</comment>


Test&Code 30: Legacy Code with M. Scott Ford - <AUTHOR> http://testandcode.com/30 <comment><AUTHOR> M. Scott Ford is the founder and chief code whisperer at Corgibytes, a company focused on helping other companies with legacy code.

Topics include:

* How M. Scott Ford got into forming a company that works on legacy code. * Technical debt * Process debt * Software testing * The testing pyramid * iterative development * kanban * readable code and readable test code</comment>


The Dark Side of Dark Mode - <AUTHOR> https://tidbits.com/2019/05/31/the-dark-side-of-dark-mode/ <comment><USER2> As long as a display works by shining a light through something I'll prefer dark mode, staring into a lamp for 8-10 hours a day is just unpleasant.

I already have the gamma turned way down on my screens, redshirt installed and make sure the scene behind the monitor is well lit all of which help but a good dark code theme (gruvbox usually) makes all the difference.</comment>


Why Garbagemen Should Earn More Than Bankers - <AUTHOR> http://evonomics.com/why-garbage-men-should-earn-more-than-bankers/ <comment><USER2> The larger the ratio of the size of the financial industry to the size of the productive economy at the same standard of living, the less efficient the economy is. Growth in the financial industry is like growth in the amount of oil you have to add to keep your engine from seizing up.

<comment><AUTHOR> Sounds about right. There's a strong argument that the bigger the size of the financial industry the less innovative and the more extractive an economy becomes.

</comment></comment><comment><USER3> Just because you don't understand the value someone provides to society doesn't mean they don't provide any. That's why planned economies don't work.

<comment><AUTHOR> All economies are planned in some way. That's just human history. Once you get to a certain level of large scale organization it's impossible to avoid some level of centralization.

<comment><USER3> But not to the level where bureaucrats pick wages. That way lies poverty and strife.

<comment><USER4> Then they shouldn't pick rules on who gets the right to strike either.

<comment><USER3> Who does that? In the US only government employees and "essential" services don't have the right to strike.

That's the deal when you work for the government, and you know it going in.

</comment></comment></comment></comment></comment><comment><USER5> It seems to me that the bankers were just as necessary to the Irish as garbage workers were to New Yorkers. Why couldn't the people of New York set up their own program for trash disposal just like the Irish set up their own replacement banking system?

<comment><AUTHOR> Because it's a job most people don't want to do and that has little reward?</comment></comment>


Ask HN: If a Bitcoin crash was to occur, what would it entail? - <AUTHOR> <comment><USER2> "Only when the tide goes out do you discover who's been swimming naked" \- Warren Buffett

Also don't forget our IRS friends, every time you sell a crypto coin to cash out or buy another, you owe Uncle Sam a big junk of your profits - for a holdings less than a year, more likely equal to your income tax bracket and if you think you are anonymous, think again and consider how likely your exchange is to turn all their records to the US government...

<comment><USER3> this is applied to the 1 year period

from my understanding, if bitcoin went down 50% based on the value you had invested in the beginning of the year, you pay nothing of taxes, because you had no profit. and you only pay taxes if you sell it.

i frequently trade in the stock market and that's how it works there though.

<comment><USER2> Exactly, if you held it for one year or less, it is a considered a short-term gain and is taxed as ordinary income. Depending on your tax bracket for 2017, that could range from a tax rate of 10 percent to 39.6 percent.

[https://www.cnbc.com/2017/11/30/bitcoin-investors-beware- the...](https://www.cnbc.com/2017/11/30/bitcoin-investors-beware-the-irs- wants-its-cut-and-you-may-not-know-it.html)

</comment></comment></comment><comment><USER4> The difference in Bitcoin is that it is relatively insulated from other securities. A bitcoin crash would have little to no impact on anything but other coins, and even then the impact would likely be minimal for more stable coins.

<comment><USER5> And, if Bitcoin did crash for good, another coin would probably take its place.

</comment></comment><comment><USER6> it crashes all the time.

[https://www.bloomberg.com/news/articles/2017-11-16/bitcoin-h...](https://www.bloomberg.com/news/articles/2017-11-16/bitcoin- hits-record-as-monday-s-29-plunge-fades-to-memory)

<comment><USER7> it crashes all the time

[https://99bitcoins.com/bitcoinobituaries/](https://99bitcoins.com/bitcoinobituaries/)

</comment></comment><comment><USER8> same as any other crash: a lot of people would lose unrealized gains and some would have unrealized losses. the use of leverage could exacerbate the fall</comment>


Ask HN: Is HN Web Accessible? - <AUTHOR> It seems that HN works well when checking accessible to color blind people with vischeck, but has anyone has issues with using screen readers? Also, anyone have issues that HN should improve for accessibility? <comment><USER2> The text of your post (like all posts) is shown in medium-gray text, presented against a light-gray background. This is pretty low contrast and would not be good for low-vision HNers.</comment>


SearchYC is shutting down - <AUTHOR> http://blog.searchyc.com/#so-long-and-thanks-for-all-the-fish

<comment><USER3> SearchYC has actually been tremendously valuable to me and my startups in the past. Hacker News is such a treasure trove of information, anecdotes and friends and your service was the gateway to that.

I used SearchYC as my "google for startups" I honestly cannot reiterate how useful your service was. I wish you'd keep it going as I still use it over the Hacker News Search (habit, more features, search within search results, being able to search for specific comments from users, etc etc.)

A friend was having relationship problems in part due to his startup, and I explicitly remember him saying "I looked on SearchYC and found tons of other posts from founders in the same boat" (this was when you had the curated post categories)

Seriously, thanks. (my startup is kind of in crunch at the moment but I had been meaning to reach out to you guys when I saw your service went offline a few weeks ago, i couldn't let you guys go without me - and probably the majority of the community - giving you guys some thanks and credit)

<comment><USER4> Speaking of treasure troves, I hope someone outside YC is archiving HN, so that should something happen HN can be restored without depending on YC to be able/willing to restore it.

Of course, I trust YC to have their own backups as well.

</comment></comment><comment><USER5> Thankyou so much for creating and running SearchYC, sad to see it go. It was a really great resource and well executed!

<comment><USER6> Agreed. Thank you much for providing us with this great resource!

</comment></comment><comment><USER7> Instead of shutting down, can't you hand it off to someone? Please!

<comment><USER8> _Instead of shutting down, can't you hand it off to someone? Please!_

Just to be sure, you know search isn't going away right? Its now integrated in. I'm not sure an external search is currently needed.

<comment><USER9> Search is integrated? Where is it?

<comment><USER10> Scroll all the way down.

<comment><USER11> Is that a UI design pattern to decrease server load?

There's nothing intrinsically wrong with search in the footer, but it defies 15 years of convention and thus takes a big hit to discoverability.

<comment><USER12> How long has it existed? How did I miss this?

<comment><USER13> Yeah I nearly missed it too.

<http://news.ycombinator.com/item?id=2619736>

</comment></comment></comment></comment></comment></comment></comment><comment><USER14> Thank you for creating and maintaining SearchYC these last few years. I used it more than any site except, well, HN. (My apologies for the server load.)

</comment><comment><USER15> Would you be willing to release the extensive HN dataset you have collected?

</comment><comment><USER16> Thank you for running searchyc all of these years. It was a tremendously useful service.

</comment><comment><USER17> With SearchYC gone, is there still a way to get an RSS feed for a user's comments?

<comment><USER18> Now we don't have comment scores I'm afraid I have to add:

^ this

I've been using SearchYC to archive my comments via the RSS feed. Can I do this some other way?

<comment><USER19> Not RSS but you can get the data in JSON format using HNSearch.

This will give your 10 most recent comments: [http://api.thriftdb.com/api.hnsearch.com/items/_search?filte...](http://api.thriftdb.com/api.hnsearch.com/items/_search?filter\[fields\]\[username\]\[\]=pbhjpbhj&filter\[fields\]\[type\]\[\]=comment&sortby=create_ts%20desc&pretty_print=true)

Documentation here to tweak for your needs: <http://www.hnsearch.com/api>

EDIT: Added filter to return only comments; previously included submissions, too.

</comment></comment></comment><comment><USER20> I echo the sentiment of many other HNers. Thank you for all the great work, and the invaluable resource. I can't count how many times it has served me in the past.

I wish you the very best - I am almost expecting something even more kickass out of you guys soon.

</comment><comment><USER21> "If I have seen further it is by standing on the shoulders of Giants." - <USER2> Newton

I believe there are quite a few of us here at Hacker News that could claim you, Mike and Jerry, as our giants.

Respect.

</comment><comment> brown9-2 SearchYC was an invaluable resource and a great tool - thanks for the work!

</comment><comment><USER22> I'm sad to see it go. I use it every week when creating my Hacker Newsletter and since it has been down I've had a hard time using HNSearch as effectively. Search really was just one component to it, it also had a great way to browse Ask HN threads.

</comment><comment><USER23> I'm deeply unhappy to see you guys close down. My experience is that your results are easier to use and more accurate. Just one instance of an annoyance is here:

<http://news.ycombinator.com/item?id=2704753>

But although I wish you would continue to include SearchYC in your future work, I wish you all the best in whatever you put your time and efforts towards.

</comment><comment><USER24> Turn it into free software! It would be invaluable to the community and you would get a hell of a reputation for it I think, especially from hackernews users.

</comment><comment><USER25> Can't thank you guys enough. If you have a paypal link where we can send you some beer money, I'm sure you'd collect a few rounds worth. Cheers!

</comment><comment><USER26> SearchYC has been an invaluable tool for me. It's easily tripled the value I've gotten from Hacker News.

Thank you for all your work on it.

</comment><comment><USER27> SearchYC was no less than kickass. Thank you so much.

</comment><comment><USER28> Amazing site and resource, thank you so much for your time running it. Really appreciated it.

</comment><comment><USER29> no time adding new feature is OK. but if it is hosting costs, HN users can donate or get a sponsor.

if you want a maintainer, I am willing to takeover from where you are leaving

</comment><comment><USER30> Is this not ironic <http://d.pr/x9Ri> :)

Seriously though, all the best, and thanks for all the years of good service!

</comment><comment><USER31> Don't know how to rephrase what's already been said so I'll just say it again. Thanks so much for providing an awesome service!

</comment><comment><USER32> Great tool all these years, thank you.

</comment><comment><USER33> SearchYC -- particularly the ability to sort results by date -- has been very useful to me.

</comment><comment><USER34> If you still need rss feeds you could use <http://jetsli.de> (launching in ~2 weeks)

You will be able to search for 'geeky news' also on other services than hackernews.

</comment><comment><USER35> It was a really useful and great tool. I used it a lot. Thank you!

</comment><comment><USER36> Thanks to everyone involved. It was invaluable.

</comment><comment><USER37> Thank you so much for creating SearchYC. I loved it. When I research something, SearchYC is a great resource. Respect.

</comment><comment><USER38> What did SearchYC give you over a google search like "site:news.ycombinator.com <search term>"?

</comment><comment><USER39> Thanks a lot, i lost count of the hours i spent in searchyc searching for old threads, simply great.

</comment><comment><USER40> I especially loved the Ask HN archives -- they are undoubtedly the best advice for entrepreneurs.

</comment><comment><USER41> Thanks for creating this, guys. Any chance you might consider sticking your code on Github?

</comment><comment><USER42> There goes the arc forum search, until, maybe, we get our version of HNsearch.

</comment><comment><USER43> Maybe Greplin could swoop in and fill this void. It would be nice of them.

<comment><USER44> There is a search at the bottom of HN now

</comment></comment><comment><USER45> Not going to say nothing new, but it's been really useful for me. Thanks.

</comment><comment><USER46> It's still the better search interface. Hope you make the code available.

Thanks.

</comment><comment><USER47> <http://www.hnsearch.com/>

</comment><comment><USER48> Thank you!

</comment><comment><USER49> Thank you!

</comment><comment><USER50> Thank You

</comment><comment><USER51> Thanks guys!

</comment><comment><USER52> Thanks guys. It helped me on more than one occasion.</comment>


Apple Now Allows Free Trials for iOS Apps - <AUTHOR> https://www.pcmag.com/news/361581/apple-now-allows-free-trials-for-ios-apps?amp=1 <comment><USER2> Remember "lite" versions of apps? With that gloss texture placed on top of the app icon? Crazy it took this long for free trials to become a thing on the AppStore.

<comment><USER3> Those weren't free trials: they were limited versions; they were already long ago replaced by using in-app purchase to upgrade a single app.

</comment></comment><comment><USER4> This reminds me of shareware and game demos from the 1990s!

</comment><comment><USER5> This is just a policy change so now you are allowed to have a free app that stops working after some time and can be unlocked with an in-app upgrade. There's no new category or technical solution.

</comment><comment><USER6> Are charges immediately posted for trials on our card? Or they simply authorize and then void them if we cancel?</comment>


Ask HN: Usable remote desktop over WAN - <AUTHOR> I've been looking at remote desktop options on various platforms to see which ones are actually usable over a typical residential connection (for me, 20M downstream). The results have been disappointing overall.
This was a subjective, rather than scientific, test using a few random apps (browsers, text editor, terminal).
On Linux (Xubuntu 14.10 in my test environment):
* NX 4 - it's OK, but still not usable enough in my opinion (over LAN with or without simulated latency/rate limit). Still quite a noticeable lag, scrolling in a Web page for example is not very smooth.
* x2go - required some tweaking and it's about as good as NX 4, maybe slightly worse.
* various VNCs - abandoned because RFB is useless with latency
* X11 forwarding - some apps are OK over LAN, some apps like Chrome are worse than VNC. Add a bit of latency and it's completely useless.
Over WAN I've used MS RDP on 2008R2 to dedicated servers about 30ms away and it has never been anything near usable in my experience. NX on Linux is far more usable.
I also tested Amazon AppStream, which was by far the smoothest and best experience. I used Chrome as the test app and the Windows client. Once the client reached 60 FPS (at 1280x720), it was almost indistinguishable from running locally, even with the 60ms latency to us-east-1. Scrolling was smooth, even on YouTube. YouTube videos were smooth. Unfortunately, it's proprietary, only available hosted from AWS, and Windows-only for the streaming host.
AppStream is the bar to meet or exceed and none of the other solutions get anywhere close. It demonstrated what's possible with a good streaming H.264 implementation. I don't want to use AWS and I want to use Linux, so unless someone knows of another competitive option I've missed, I'm going to try to put together my own H.264 remote desktop implementation and see what I can do. (I may also do some testing with other encoders, H.265, VP8, VP9 at least.) <comment><USER2> You can try teamviewer though it's pricey

<comment><AUTHOR> Thanks. I just tried it but unfortunately it's not any better than the others - even over LAN at 1280x720 with "optimize for speed" enabled, it's still not anywhere near usable. (I also verified, with a packet capture, that communication was on my LAN and not proxied through a TeamViewer intermediary server.)</comment></comment>


Mark Cuban on How To Cash Out At The Top  - <AUTHOR> http://www.alleyinsider.com/2008/3/mark_cuban_on_the_joy_of_cashing_out_at_the_top <comment><USER2> I'd have to imagine Kernen was joking.

<comment><USER3> In 2000? Maybe not. It was very different back then.

In any case, even if you don't think Broadcast.com or some of his other business ideas are the smartest ever, you can't fault the guy for being smart enough to take the money and run.</comment></comment>


Your startup probably isn't a platform - <AUTHOR> http://finance.fortune.cnn.com/2011/08/22/your-startup-probably-isnt-a-platform/

<comment><USER3> How is Amazon not a platform? Completely leaving aside AWS (which the author does mention):

a. it has zillions of stores selling through it b. a whole generation of small businesses have been built by people selling used books, cds and dvds. c. anyone can publish their own books, cds and dvds d. anyone can use their fulfillment services and payment services e. digital video and music f. kindle

<comment><USER4> It's an (albeit extremely large and diverse) e-commerce website, not a platform. What software do you build that runs on top of Amazon?

<comment><USER5> _It's an (albeit extremely large and diverse) e-commerce website, not a platform. What software do you build that runs on top of Amazon?_

Ignoring AWS, there are lots of complex systems you can build on top of Amazon. One example is Fulfillment by Amazon. You can integrate this service with your ecommerce website, and Amazon can warehouse your inventory in its facilities nationwide, charging you based on the volume of your inventory. They will automatically select a warehouse closest to your clients that has your item in stock and pack and ship it, and even order more inventory from your supplier when a certain warehouse inventory level falls below a certain threshold. It is basically S3 backed CloudFront for physical goods.

<comment><USER6> Fulfillment seems like a service. You don't build a business on someone else's warehouse, you use their warehouse to support your business. Unless someone's writing a "Fulfillment by Amazon Client" and selling it, I guess.

<comment><USER5> _You don't build a business on someone else's warehouse_

From the article:

 _A software platform is truly a foundation on which entire businesses can be built. It encompasses not just a technical infrastructure but also a user experience framework, usually some form of a selling channel, and a defined large-scale developer ecosystem_

If that isn't the very definition of Amazon, I don't know what is. In theory I could build an entire business on top of Amazon's infrastructure (and not just a software business). I could have outsourced labor write or edit content using Mechanical Turk. I could publish it using Amazon's self-publishing tools. I could sell it through Amazon's marketplace or on an ecommerce site hosted on AWS. I could collect payments using Amazon FPS. I can ship products using Fulfillment by Amazon. You most certainly can build a business using someone else's warehouse, just as Heroku and RightScale can build businesses on someone else's datacenter. Fulfillment is a service, but is is a PaaS - <USER2> robust logistics platform as a service.

<comment><USER6> I read the article. This thread started with "S3/AWS aside." I think "Amazon" isn't a platform because it's not a single thing ("Facebook"), but Amazon offers a number of services which can be used in concert. Maybe those are actually the same in practice, but they exist in different spaces in my brain.

</comment></comment></comment></comment></comment></comment><comment><USER7> This guy appears to be frustrated because he is using the term platform in a very narrow sense. By his definition it requires a "developer ecosystem." Whereas the people he hears use the term are referring more correctly to a two sided market.

<http://en.wikipedia.org/wiki/Two-sided_market>

</comment><comment><USER8> "At least for me, pitching a business as a platform when it really isn't tends to degrade what might be a really interesting vision and story in and of itself."

I suppose, but that's a pretty small downside. For others pitching yourself as a platform can lead to more buzz, more possibilities and of course...maybe a higher valuation. And, fact is you can't know where things are going so you might as well build & position yourself as a platform if it makes sense.

<comment><USER9> So, vigorously insist it's a duck even if it can't quack. Sleaze is the fastest route to funding?

I'm all too reminded of Saturday's NYT article[1]

[1] [http://www.nytimes.com/2011/08/21/technology/silicon- valley-...](http://www.nytimes.com/2011/08/21/technology/silicon-valley-booms- but-worries-about-a-new-bust.html)

<comment><USER8> "vigorously insist" & "sleaze"? Someone may have a case of the Monday's...:) Maybe you read something into it that wasn't written (you'll notice I ended w/ "if it makes sense.") Anyways, point was you don't know where you'll end up so think big and go for it.

<comment><USER9> I will concede my Mondays half-caffeinated status at the point of writing. My tone was a bit strong but I stand by my commentary. :)

</comment></comment></comment></comment><comment><USER10> The use of platform, in the creative agency space is somewhat different. A platform doesn't need to have an eco-system if it creates value for the consumer and develops a community around that value. In this sense Nike+ is a platform. Twitter is too.

The idea that a startup is only a platform if one can build on top of it, may be taking the idea too far.

</comment><comment><USER11> "or perhaps your a dev tools business"

Your / you're error in published article.. ugh

</comment><comment><USER12> What you are building is either an app or a platform. If you try to make it both you will fail. And there are very few (if any) examples of successful platforms that started as a platform.

<comment><USER12> Surprised this got down voted. Maybe I should explain further.

Point 1: If a startup tries to be both an "app" _and_ a "platform" it will fail.

In my experience, when building a new product, teams need to make a choice very early on whether they are building something that is all about end-user value/experience ("an app") or all about 3rd party value ("a platform").

Groups that attempt to do both lack focus. They don't do a great job building a great user experience and they don't do a great job building the platform because these two things are in tension. Example: enabling a 3rd party plug-in model requires the creation of UI that is often complex from a user's perspective. The investment in that UI to make it great (especially at an early stage when you don't really _know_ what or how many 3rd party extensions will get built) could be _better_ spent on building end-user-valuable functionality in the core of the product.

This point is all about focus. To be successful startups need extreme focus.

Point 2: There are very few (if any) examples of successful platforms that started as a pure platform.

Name a few successful "platforms". Before you do, for the sake of argument, agree with me that "successful platform" implies the platform has a vibrant set of independent partners who both contribute to and "profit" from the platform. I could also use the term "virtuous platfrom" which has come to mean a platform composed of a multi-sided market where each side of the market derives value in a symbiotic cycle with the others.

[EDIT added link] [http://en.wikipedia.org/wiki/Virtuous_circle_and_vicious_cir...](http://en.wikipedia.org/wiki/Virtuous_circle_and_vicious_circle)

The "virtuous platform cycle" that most people use as the prototypical example is the Windows ecosystem. The market sides were: Windows, Intel, OEMs (e.g. Compaq, DELL), IHVs (e.g. ATI, HP printers), ISVs (e.g. Lotus, Adobe, Office), retailers & channel (e.g. Egghead), and of course, end users.

All of these "market sides" drew great value from one another as part of the Windows ecosystem. They all made money, hand-over-foot, because the "virtious platform cycle" snowballed.

There are many examples of other platforms with a virtuous cycle today. Google's search engine, Apple's iOS, and on a smaller scale something like Minecraft (although that's a nascent example).

For each example YOU come up with, ask this question: What came first, "end user value" or "developer/3rd party platform"? In my case, I struggle to find examples where the platform STARTED as something that was focused on developer/3rd party value. You might argue Java, but then I'd say "in what way did the creator of that platform make any money off the platform?".

Discuss. :-)

<comment><USER13> Isn't every social network out there a counter to #1? e.g. Facebook, Twitter

Both are Apps, and both allow you to write code against them (or maybe Twitter doesn't count because it doesn't run on their site?)

<comment><USER12> Both examples actually re-enforce my argument because _when they started_ they were all about delivering value to their end-users. Yes, twitter exposed an API really early on, but their _focus_ was delivering on the end user promise of short messages.

Facebook did not add an API until much later and "being a platform" did not become a priority until recently.

<comment><USER14> Steam for games?

<comment><USER12> Steam is primarily a channel. [http://en.wikipedia.org/wiki/Distribution_channel#The_distri...](http://en.wikipedia.org/wiki/Distribution_channel#The_distribution_channel)

</comment></comment></comment></comment></comment></comment><comment><USER15> The argument about Amazon is wrong - it may account for 3% _now_ , but it's still a work in progress, a growing business. Much as the e-commerce business was unprofitable for most of the early life of Amazon, because it was still growing.</comment>


GetGoing (YC12) Introduces Platform to Make Travel Easier & More Affordable - <AUTHOR> http://www.marketwire.com/press-release/GetGoingcom-Introduces-Its-Pick-Two-Get-One-Platform-Make-Travel-Easier-More-1764849.htm

<comment><AUTHOR> I'm certainly guilty of pimping a YC company by submitting this, but their approach to validated segmentation is really interesting.

After so many mentions of airline reservation systems by pg (below), I'm wondering if GetGoing is using LISP?

<http://paulgraham.com/gh.html> <http://www.paulgraham.com/icad.html> <http://www.paulgraham.com/carl.html></comment>


Ask HN: Hard Drive Shortage? - <AUTHOR>

Has anyone heard about the coming hard drive shortage? I heard from our corporate parts supplier that "there will be a price increase on computers and computer related products that come with a hard drive because of supply allocation problems due to flood in Thailand", and today the local computer store is saying "Due to a global hard drive shortage, there will be a purchase limit of 2 hard drives per customer until further notice." <comment><USER2> BBC news has some articles:

(<http://www.bbc.co.uk/news/technology-15534614>)

Hard drives also need rare earth minerals for the magnets, and there are going to be greater shortages of those over coming years.

</comment><comment><USER3> Yes, it's all over the tech news.</comment>


Show HN: Subscription billing dashboard. Thoughts? - <AUTHOR>

I am currently working to release a subscription/recurring billing dashboard and customer management platform built with AngularJS, JQuery, and PHP/mySQL.
Unlike existing solutions (i.e. Chargify/Sassy) this solution is self-hosted. I hope to fill the void where customers do not want a third party handling their data or to have to pay additional charges per transaction or monthly.
This is more than an admin theme that you could buy on Themeforest or somewhere. It comes pre-built with Stripe integration (working to get other processors integrated as well) so all you have to do is install and go. No complicated setup or complex programming to deal with webhooks from processors.
Anyway, I plan to release the first version next week and wanted to see what everyone's thoughts were. I have a landing page setup for more details.
http://paypeek.com <comment><USER2> Looks pretty good. I'll submit my email to test if you need.

Send me an email the address I submit clickbrain.com</comment>