Forecasting s-curves is hard - <AUTHOR> https://constancecrozier.com/2020/04/16/forecasting-s-curves-is-hard/ <comment><USER3> Leaving modeling up to the professionals is completely the wrong lesson. Time series forecasting from business, epidemiological or economic data forecasting is hard so you should always take the results with a large grain of salt. The professionals get it wrong too and just like the weather nearby points from the model are more likely to be accurate than distant ones.

Smoothing helps. Estimating error helps. Domain knowledge helps. Experience in applying the model helps. There are techniques other than the one demonstrated in the article. Sometimes those help. There are also professionals who create models which are used to support the bias of the modeler or the modelers employer (i.e. cherry picking).

Politicians and pundits more often than not take the results of these models and draw conclusions which are unwarranted or at least highly uncertain without mentioning the uncertainty or only giving it lip service.

<comment><USER4> True. Also, if you read Superforecasting[1] you'll learn about several experiments where "non-experts" beat experts by a lot by just being well informed. Even when the experts had access to confidential data.

[1] [https://www.amazon.com/Superforecasting-Science- Prediction-P...](https://www.amazon.com/Superforecasting-Science-Prediction- Philip-Tetlock/dp/0804136718)

<comment><USER5> I'm very skeptical of evidentiary patterns laid out in NYT bestseller style books.

<comment><USER6> You are right to be. Tetlock spun the conclusions of the project far boyond what was warranted, especially because the project was heavily flawed.

I participated in the initial part of the project, figured out the winning strategy very quickly, and then dropped my participation immediately. How to be "super at forecasting" in Tetlock's eyes? Have lots of spare time in the middle of the day when professional are at work, because that is when questions get posted, and easy points can get made. (To win a prize that is less than the hourly rate of a professional.) Hmm, no wonder that the conclusion was that people with no expertise but plenty of spare time can beat the pros...

<comment><USER4> You mean beat the people that actually do that for a living?

</comment></comment></comment></comment></comment><comment><USER7> This is accounted for in more professional methods which estimate error. During a period of exponential growth, the error 6 weeks out is very sensitive to tiny errors occurring in immediate measurement.

It's not so much that fitting exponential or S-curves is _hard_ as much as even a very good fit is likely to have very significant error bars.

The hard parts about modeling the current situation involve the poor and limited data. The dynamics of the system are highly variable and unobserved and thus require clever tricks to reduce uncertainty by expert knowledge, clever alternative signals, and leveraging other models.

So ultimately, it's just not the forecasting but the response. People rarely handle uncertainty very well and a situation with uncertain exponential growth ends up with exponential uncertainty. Making policy decisions where the predicted outcome spans orders of magnitude is tremendously challenging.

<comment><USER8> Exactly. And I have a very recent example, yesterday somebody responded to one of my replies with:

"Cuomo claimed to need 40k ventilators after the lockdown was put in place. He ended up needing only a fraction of that. Obviously, without the lockdown, there would likely be more needed. Of course, modeling has errors, but this is an extremely large error, and one that has direct policy implications." (1)

My response was that it wasn't "a large error" but what only 6 more days of exponential growth would have made still insufficient:

[https://news.ycombinator.com/item?id=22911465](https://news.ycombinator.com/item?id=22911465)

Also as an illustration of the "uncertainty" one can see the shaded areas clearly here:

[https://covid19.healthdata.org/united-states-of- america](https://covid19.healthdata.org/united-states-of-america)

As I write this, the model on that page was last time updated based on the data up to the 15th of April, only 4 days ago, and its best estimate for number of deaths in USA on 19th of April was 37,056. Also as I check the [https://www.worldometers.info/coronavirus/country/us/](https://www.worldometers.info/coronavirus/country/us/) it's already 40,423. But the 95% error area for 19th is 39,721-56,108. It's that hard. Also at this moment the projection for 1st of June is 60,262 (34,063-140,106) and we can compare all these values after some later updates, e.g. in 6 and 12 days.

1) careful observers know that there is one more talked about person who is used the same pseudo-argument before.

<comment><USER8> Rechecked the page: currently data up to 20th April, 22th of April page updated:

June 01, 2020 projection: 67,444 (48,058-122,203)

Today's number of deaths in the U.S., according to worldometers: 47,808. Today is April 23.

<comment><USER8> Next recheck: currently death data up to 25th April.

June 01, 2020 projection: 73,621 (56,562-128,167)

Total number of deaths in the U.S., worldometers: 56,803. Today is April 28.

<comment><USER8> Next: currently last death non-projected data: May 01: 65,249.

June 01, 2020 projection: 112,073 (91,586-155,417)

Total number of deaths in the U.S., worldometers: 74,114. Today is May 06.

</comment></comment></comment></comment></comment><comment><USER9> I was thinking the same thing. Exponentials are so sensitive to almost nothing at the beginning and then it is too late to react. The only solution to not be late in reacting is to over react.

I was correct in my predictions of the disease coming to North America and the stages it would entail but I was wrong about the timeline because so as it happened it happened so fast.

<comment><USER10> Similar situation, and I knew it could happen very fast (3 day doubling periods do that), but without data I still wouldn't have predicted (and can't now) where it would get bad and where it would middle around.

What we do now know is that it changes VERY fast so you can't wait for your ICUs to fill up or a lot of people will die... you have to act early. And if it came once this fast... it can again, if our guard goes down (and it will).

</comment></comment><comment><USER11> > In other words, data enthusiasts (such as myself) should leave the modelling > up to the professionals.

This is really important to understand, and unfortunately often overlooked, especially by economists, who often work with good data and solid mathematical background but no prior domain knowledge and no reference to literature of the given field.

<comment><USER12> I have not seen a single useful model from professional epidemiologists so far -- at least nothing that would guide me better than "look at China and Italy and consider that it might also happen here."

<comment><USER13> I would argue that they're all useful but its very tough to be accurate when confounding actions are occurring to the problem. For example, in the US, the increase in social distancing measures and mask usage couldn't be easily baked into the original models. Forecasting accurately is very tough. Look at what's been said about Renaissance and its Medallion fund, they're right a little over 50% (they just leverage very heavily). Think about that, the most consistent and dominate hedgefund might only be right about 51% of the time. The issue with just using historical data is the data might be different. But I'm sure all of their models were using data or at least some domain knowledge from the spread in Italy and China.

<comment><USER14> >> For example, in the US, the increase in social distancing measures and mask usage couldn't be easily baked into the original models.

The IHME model - one of the most cited models - explicitly factored this into their model.

<comment><USER15> "Starting April 17, we began using mobile phone data to better assess the impact of social distancing across states and countries. These data revealed that social distancing was happening to a larger degree than previously understood, and even before social distancing mandates went into effect."

This suggests that social distancing happened 1-2 weeks sooner than their model anticipated. This will give you a much smaller number.

What is the policy you believe should have been followed instead? Are you saying that social distancing should have been abandoned? Why do you believe that this would result in fewer deaths?

<comment><USER14> No, I am saying they factored it into their models and also undershot their priors that were readily available. If they looked at OpenTable data, they would have realized people were voluntarily socially distancing before state authorities told them they should. In fact, OpenTable data showed that even on the day that the Mayor of NYC told people to go to sit-down restaurants and see movies in person (in early March), y/o/y data showed a net 30% reduction in reservations for restaurants in NYC.

People were voluntarily taking precaution well before our government bureaucracies enforced it, and it was available in public datasets. IHME just didn't look hard enough, apparently. Despite what the media keeps pushing by finding pockets of idiots, people aren't stupid. The most at-risk populations realize this and tend to shelter in place on their own and take precautions.

de Blasio on March 11th telling people to go out: [https://ny.eater.com/2020/3/11/21175497/coronavirus-nyc- rest...](https://ny.eater.com/2020/3/11/21175497/coronavirus-nyc-restaurants- safe-dine-out)

OpenTable data showing a voluntary reduction of restaurant activity _despite_ de Blasio's encouragements: [https://ibb.co/r2R9xnT](https://ibb.co/r2R9xnT)

<comment><USER15> What policies are you saying were misguided and should not have been taken based on the predicted elevated death toll?

How does one evaluate the OpenTable data and feed it into the model they were using to estimate the amount of social distancing?

<comment><USER14> Extended lockdowns are _positively_ correlated with deaths per million residents.

[https://twitter.com/boriquagato/status/1251943418860728320](https://twitter.com/boriquagato/status/1251943418860728320)

I'm saying that lengthy lockdowns, shelter-in-place, and shutting down non- storefront businesses did not provide much value, if any, and possibly had a negative effect.

The IHME trended into 95%+ CI territory over half the time. So when posters here say "well the variance was high," that's accounted for in the confidence intervals, which they also missed on wildly.

The models justified harsh authoritarian action, and the actual data way, way undershot it. Popular sentiment is "oh well, it saved lives at least" but few, if any, are looking at the acute - and more importantly, chronic - <USER2> costs of these policies that may or may not have even helped beyond just telling people what the risks were.

>> How does one evaluate the OpenTable data and feed it into the model they were using to estimate the amount of social distancing?

Pretty simple; it was clear that news of COVID-19 alone was enough to cause people to stop going to restaurants and start basic social distancing protocols on their own without government mandating it.

EDIT: Shockingly, a bunch of downvotes without explanation are forthcoming.

<comment><USER16> One thing that you and others arguing in the same way seem to neglect is that there is a huge economical cost to a significant fraction of your society dying. This is not an either or situation. I want to see your economic models that the economy would have been fine with 0.5-1% of the population dying within a short period (actually everywhere the health system got overloaded the rate was significantly higher). Let's not even talk about the loss in confidence in the government because they failed to react.

<comment><USER13> This is a great view point that I'd never thought of or seen put forward. Is there any good information into the economic impact of the Spanish Flu or even the Bubonic Plauge?

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER17> "It may not be surprising that in the exponential growth phase the estimate is very bad, but even in the linear phase (when 40+ points are available) the correct curve has not been found. In fact, it is only once the data starts to level-off that the correct s-curve is found. This is especially unhelpful when you consider that it can be quite hard to tell which part of the curve you on; hindsight is 20-20."

Does this perhaps explain why we are so bad at calling the top of economic bubbles and similar phenomena? Maybe there literally is not enough information until the very end. It's not that we're dumb. It's that we can't do the mathematically impossible.

I have a sense that this might be a very important and profound principle that might explain a lot of seemingly irrational behaviors.

<comment><USER18> I believe our supposed inability to predict economic bubbles (and other financial crises) is mostly just tautological: those crises we _can_ predict never happen because they are "self-falsifying prophesies": If enough people believe the stock or housing market is overheating, they will stop buying or might even speculate against further rising prices.

Thus, any bubble that does manage to grow to significant size before bursting is necessarily "unforeseen". (of course because of that thing with the monkeys, the keyboards, and online message boards, the will always be plenty of people that _did_ see it coming, but I would wait to buy their book until they do it a second time).

I know it's always trendy to hate on economists (some people have taken that idea all the way to creating cryptocurrencies and reinventing economics along the way). But comparing, say, the 2008 crisis with the 1920 or even the 1970s, I can't shake the feeling that maybe economists have become slightly better over time. The gold standard fandom that was all the rage for a while essentially rests on the idea that interventions by central banks are _worse than doing nothing_ , and the evidence now seems overwhelming that we can do better than this (admittedly low) benchmark.

<comment><USER19> I think Taleb makes a similar point in his books - if you can forsee an economic crisis, you take steps to avoid it and therefore it doesn't happen. But then everyone asks why you were wrong in the first place, as the crisis didn't happen

</comment></comment></comment><comment><USER20> > However, in my experience "intuition" and "mathematics" can often be hard to > reconcile.

They can be hard to do, but you must absolutely reconcile them for your effort to be fruitful. Until they are separated, your intuition or your mathematics is wrong, and you can't know which is.

</comment><comment><USER21> What's even harder is that you're usually trying to forecast for a reason other than pure academic curiosity. Like, should I be worried about the coronavirus? Should we stay inside? When can we reopen business? But the decisions people take determine the curve and the predicted curve determines people's decisions. If you decide not to social distance, you're changing the future. For that reason, forecasting is better done under different scenarios. For example, don't tell people approximately X (+/\- a lot) will die. Tell them approximately X (+/\- a little less) will die if we don't social distance and approximately Y (+/\- a little less) will die if 95% of us self- quarantine.

It's harder than just fitting a curve, but that's kind of the point. If you want actionable predictions, it _is_ harder.

</comment><comment><USER22> Is hard because for example to predict case load, at any point on the curve you could have policy changes, surprise increase of case load for various reasons(sudden increase of tests), these are just a few of possible hundreds of high impact events that can impact length and slope at any point

<comment><USER23> Yes, exactly. It's tempting to try to compute coefficients X weeks in, so we can forecast X+26 weeks in the future.

But the coefficients aren't fixed. Coefficients change drastically due to public policy, individual actions in response to news and social media, culture of local communities, degree of compliance with public policy, travel between regions with different rates of infection, etc.

So you're not fitting a curve to the data, you're modeling dynamic human actions which are not nearly as easy to forecast.

</comment></comment><comment><USER24> Well, considering all s-curves are exponentials at the beginning, it naturally flows that you can only make an accurate model after the inflexion point.

Since we're on HN, I'll plug a question I've had for a long time:

Do IPOs/liquidity events/exits/etc all happen when the right price has been determined; ie when we can see what size the company will be, and it is no longer useful to capitalize it further? When all growth paths have been explored. Is that time the inflexion point?

What does the conversation look like with the VCs when they see the inflexion point?

<comment><USER25> > considering all s-curves are exponentials at the beginning

Sorry, but I hear a lot of people saying this and it's driving me crazy. S-curves are S-curves from the beginning, not exponentials. It can be useful to use an exponential growth model at the beginning of the curve for short- term forecasting, but these two models will diverge dramatically at the S-curve inflection point.

Not that we shouldn't plan as if exponential growth will occur in a crisis like we're in now, but many people I know don't understand these dynamics and it has lead to a lot of undue panic.

<comment><USER26> Thank you for this response. All smooth curves are also approximately linear at all points, but that doesn't mean that we can't usefully predict and model an appropriate non-linear fit.

<comment><USER27> Not disagreeing, what you say seems right to me, but it ("All smooth curves ...") also seems like the sort of result that there might be a counterexample to, like a smooth curve that at no scale has a linear approximation. Maybe a fractal with a smoothly varying generator??

For curves of infection rates I don't doubt your verity.

<comment><USER28> "Smooth" is a term of art in math that means continuously differentiable infinitely many times. These functions are a subset of those functions that are differentiable once.

Differentiable once means, by definition, approximable everywhere by a line at small enough scale.

<comment><USER27> Yes, I know that much.

Imagine a sine wave, except when you look at it at 1000x magnification it's a sin + cos. It looks smooth, and at x = π you think that the derivative will be -1 but in fact it's 0 because you don't have a sine crossover there you have a cosine trough.

Except at 1000000 times magnification (ie another 1000) the cosine curve that forms that apparent sine curve is itself a sine curve. So everything is switched again.

f(x) is something like sin(x)+cos(ax)/a+sin(a^2x)/a^2+cos(a^3x)/a^3+ ... (sin(x.a^j)/a^j+cos(x.a^j+1)/a^j+1+ ...

for a=some arbitrary large number. Something like that, I'm a bit rusty, sorry.

At whatever scale you look at the curve the derivative is always wrong: you zoom in on the sine, at the peak it's got a cosine, so the d/dx is -1; but zoom in and the cosine has a sine at the crossing point, so the d/dx is 0; but zoom in and ...

The curve is provably smooth, it's sinuses all the way down, but nowhere can you tell the derivative as it's fractal???

That's what I had in mind.

Anyway, I thought their might be clever curves of that type.

<comment><USER28> A curve with the properties you describe is not smooth, by definition, since it is not differentiable.

> At whatever scale you look at the curve the derivative is always wrong: you > zoom in on the sine, at the peak it's got a cosine, so the d/dx is -1; but > zoom in and the cosine has a sine at the crossing point, so the d/dx is 0; > but zoom in and ...

This is pretty much the definition of something not being differentiable. "Differentiable" means that the approximations to the derivative (i.e., difference quotients) converge to some fixed value as the scale they're measured at approaches the infinitely small.

You might be interested in the Weierstrass function, which seems to be the sort of thing you're getting at with your idea: [https://en.wikipedia.org/wiki/Weierstrass_function](https://en.wikipedia.org/wiki/Weierstrass_function) . Continuous everywhere, but differentiable nowhere.

Edit: the specific function you wrote down is not differentiable (at least not everywhere). For example, at x=0, its derivative, if it had one, should be cos(0) + cos (a * 0) + cos (a^2 * 0) + ... , but that series clearly diverges.

<comment><USER27> cos(0) being 1, sin (0) begin 0; that series is 0 + 10e-3 + 0 + 10e-9 + 0 + 10e-15 + ... when x=0 (excuse my sloppy notation, I'm on a phone). Looks convergent to me, somewhere between 0.001000001000001 and 0.001000001000002 ?

Have you studied fractal dimensions formally, might I know the background you're speaking from?

Yes, I'm imagining, as a first example, something akin to a Weierstrass function but without the discontinuities.

[https://www.desmos.com/calculator/c6tbl4zr9j](https://www.desmos.com/calculator/c6tbl4zr9j)

<comment><USER28> Yes, the original function converges (not its derivative). The terms in the derivative are no longer divided by a^k. The a^k from the argument to cos/sin cancels then out (it becomes a multiplier, because of the chain rule).

So the series for the derivative is 0 + 1 + 0 + 1 + ..., which doesn't converge.

Not sure what this has to do with fractal dimensions. This is a simple question of definitions. The word "smooth", in math, literally implies, by definition, that the function is everywhere approximable by lines.

If you don't think it does, can you state the formal definition of "smooth" that you're using?

The Weierstrass function doesn't have discontinuities, btw.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER29> I am bothered the animation does not include confidence intervals or error bars for the fit. The way these confidence intervals would shrink as more data points are available would tell a just as important part of the story.

<comment><USER30> yes, a better problem definition would in this case reveal that the estimate becomes quite good (in my definition) around the 50% time mark, when the transition from ^x to ^-x takes place. More specifically, I mean that although the stable point is still off by e.g. 25%, the important thing is that the stable _time_ of the curve is well-estimated. you know it's no longer increasing exponentially...

</comment></comment><comment><USER31> I suppose that the s-curve with 3 parameters that the author is talking about is the logistic function. In general, if you consider a differentiable function of three parameters and try to determine and interval for the values of the parameters of that model then the length of that interval is bound by the ratio of the error in the data over the derivative with respect the parameter. For example estimating the parameter k (wikipedia logistic growth rate) with points such that x near x0 = (wikipedia midpoint of the sigmoid) is hard, since the derivative of the function with respect to k at x=x0 is zero. So mathematically this seems to be a well known fact when one try to estimate parameters from datapoints.

<comment><USER32> > the length of that interval is bound by the ratio of the error in the data > over the derivative with respect the parameter

This is interesting! Could you expand on this a bit? Why is the length of the interval bound by the ratio of the data error over the derivative?

<comment><USER31> The general case require some work and conditions. But to give a hint, the case of only one parameter is an application of the mean value theorem (1). Suppose a model (y = f(p,x) ) with only one parameter p0 and an exact point (x0,y0) (that is y0=f(p0,x)) and a data point (x0,y1) such that y1-y0=error in the data. And that there is a value p1 of the parameter such that f(p1,x0) = y1, then y1 - y0 = f(p1,x0) - f(p0,x0) = f'(sigma) . (p1-p0), so that p1-p0 = (y1-y0)/f'(sigma) that is (error in the parameter) = (error in the data)/(derivative with respect to the parameter) where sigma is between p0 and p1. The general case is a generalization of this idea using the mean value inequality.

(1) [https://en.wikipedia.org/wiki/Mean_value_theorem](https://en.wikipedia.org/wiki/Mean_value_theorem)

</comment></comment></comment><comment><USER33> I made a R web-app in Shiny which does curve fitting of a Four Parameter Log Logistic function (which is the S-curve discussed in the article) against the John Hopkins data:

[https://joelonsql.shinyapps.io/coronalyzer/](https://joelonsql.shinyapps.io/coronalyzer/) [https://github.com/joelonsql/coronalyzer](https://github.com/joelonsql/coronalyzer)

"Sweden FHM" is the default country, which is a different data source, it's using data from the Folkhälsomyndigheten FHM (Swedish Public Health Agency), which is adjusted by death date and not reporting date as the John Hopkins data is.

</comment><comment><USER34> One thing to note (from looking at the graph) is that the noise seems to be additive with constant standard deviation (and presumably floored floored such that the sum doesn't go negative).

That means that there is huge relative error initially (we have 10 infections +/\- 100), and very little relative error eventually (we have 1000000 infections +/\- 100).

I assume the forecasts would be better if the error were multiplicative (in other words, with standard deviation proportional to the current value).

However, I think the main point stands: the forecasts get much better once one approaches the inflection point.

</comment><comment><USER35> I did the covid19 week1 and week2 kaggle competitions (I think they had 4 of them) below. If you are interested, this is a fun way to play around with the data, and it shows how hard it is.

This I tried: \- Weibull Gamma distributions, but it was impossible to find good parameters for the distributions without exploding. It would only work if I put in an additional parameter saying 99% of the population wouldnt get it. It would come up with good shapes of growth but the predictions were far too below actual values in the future.

\- Logistic curves. usually great for countries that already ran up the curve but terrible for ones still in the exponential phase (as the article states). Also kind of useless for countries that didnt even begin their journey up the curve.

\- light gbm: good for predicting the next day but terrible many days out. It seems other counties curves do not help that much

\- SARIMAX: really good but later predictions would explode, like showing 4 million deaths in france, etc.

I tried to get around these by ensembling them together, but overall I did very poorly at predicting coronvavirus. I still want to get better at this, so if anyone has any good suggestions, please share. Also you can check out what other kagglers have done as well

[https://www.kaggle.com/c/covid19-global-forecasting- week-1](https://www.kaggle.com/c/covid19-global-forecasting-week-1) [https://www.kaggle.com/c/covid19-global-forecasting- week-2](https://www.kaggle.com/c/covid19-global-forecasting-week-2)

<comment><USER36> I briefly studied mathematical biology, and remember there being some debate about whether tumor growth was more accurately modeled by logistic growth or Gompertz growth [1]. I'd be curious to know whether your fits get better or worse if you replace your logistic-based model with a Gompertz-based model.

[1] [https://en.wikipedia.org/wiki/Gompertz_function](https://en.wikipedia.org/wiki/Gompertz_function)

<comment><USER35> Thanks, Ill take a look into it. It seems hn has no place for discussion these days and will down-vote anything.

</comment></comment></comment><comment><USER37> This article is about estimating a an s-curve in the real world. The point it: You cannot get a sense of the end by observing the beginning... even in the super idealized case of the data coming from a noisy s-curve. This learning obviously transfers over to the real world, where the data is going to be strictly worse than the idealized case (i.e. it won't be a perfect s-curve). Its a great article, with applications to pandemic forecasting.

</comment><comment><USER38> >S-curves have only three parameters, and so it is perhaps impressive that they fit a variety of systems so well

no it does not. it just so happens that the solution of certain differential equations produces an s-shape. it has nothing to do with having only 3 parameters. Very sophisticated models with many of parameters and conditions can produce this shape.

<comment><USER39> The author was probably talking about the logistic function being parameterized by 3 parameters, x0, L, and k [1]. The point the author is probably making is that if you are fitting a perfect logistic model to data, 3 data points should be sufficient to determine 3 parameters that unambiguously parameterize the curve.

A separate set of 3 parameters also parameterizes the SIR compartmental model ([https://en.wikipedia.org/wiki/Compartmental_models_in_epidem...](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model)), the solution of which also looks like a logistic curve. But this is a model- based (dynamics-based) solution whereas one may be interested in just fitting the logistic model based on the assumption that it's going to be logistic.

[1] [https://en.wikipedia.org/wiki/Logistic_function](https://en.wikipedia.org/wiki/Logistic_function)

</comment></comment><comment><USER40> A turning point in my understanding of S Curves came when I encountered an article that showed an S Curve as the cumulative area under a normal distribution.

Which is great _if_ your velocity on the project retains a normal distribution over time. But missing requirements, bad risk analysis, and team dynamic changes can make for a long velocity tail.

Which means the last 10% of the project accounts for the other 90% of the project time.

If however you have managed to do the important work first, you drop less important features and ship 95% of the planned features on time.

<comment><USER34> It's a common misconception, but the S curve generated by disease, for example, with exponential growth at the beginning, corresponds not to the normal distribution, but the logistic distribution, which has fatter tails than the normal.

The bell curve drops to zero with exp(-x^2), that is extremely fast.

The logistic distribution (derivative of the S-curve described here) drops to zero with exp(-|x|), that is exponentially, but not as fast as the normal distribution.

[https://en.wikipedia.org/wiki/Logistic_distribution](https://en.wikipedia.org/wiki/Logistic_distribution)

</comment></comment><comment><USER41> Would be intrigued to see what the story is like with priors on the parameters and a credible interval on the output.

</comment><comment><USER42> I've loved s-curves for decades. Way back in the lab, to analyze ligand- binding assays. And not that long ago, as a litigation consultant.

> For technological changes, can the final level-off be reasonably estimated?

It helps a lot if it's 0% or 100% :) Given that, you get a decent long-term fit, after about half the time to plateau.

</comment><comment><USER43> Forecasting solutions to a differential equation is hard, especially when there are infinitely many solutions. It is a matter of keeping constants up to date in light of new information. If those constants are wrong, the whole model is essentially useless.

<comment><USER44> > Forecasting solutions to a differential equation is hard, especially when > there are infinitely many solutions.

Not sure what you mean by infinitely many solutions, but this is not true in general. A silly example is

y'(t) = Ct,

where, for most distributions, you would only need a few points to get both C and the initial condition to reasonably high accuracy (~O(sqrt(n))). More complicated examples exist that have much more interesting dynamics, but their general trajectories are just not as sensitive/chaotic (w.r.t. the initial parameters).

> If those constants are wrong, the whole model is essentially useless.

I think what makes this hard is not _if_ they're wrong, but rather, being even just a tiny bit wrong makes the whole future prediction change drastically. In other words, two possible inferred parameters which are statistically indistinguishable given our current observations will yield incredibly different outcomes under many of these models.

> It is a matter of keeping constants up to date in light of new information.

Indeed! :)

</comment></comment><comment><USER45> To predict the s-curve for epidemic, you need to know R0.

The upper limit of the curve is 1 - 1/R0. If you use mitigation and effective reproductive number the number is 1 - 1/Rt, where Rt varies with mitigation effort.

</comment><comment><USER46> One of the only good things to come out of this pandemic is the increased emphasis on S-Curves. Models tend to use the predictive power of exponential curves to estimate the steep part of the S-Curve but these predictions are short term and are best applied to planning scenarios.

What is missing from this article is the relationship between S-Curves and Bell Curves. We can use the Rules of Thumb associated with the Normal Distribution to think about peak growth rate and standard deviations.

The health data.org curve fitting is a decent Fermi Estimate based on observed data. Models are always wrong but sometimes they are useful and I hope we start to discuss the underlying key assumptions used in each case rather than focusing on their imperfect predictive power.

</comment><comment><USER47> the article correctly points out that 3 parameters need to be estimated but then jumps directly to modeling those 3 parameters with 3 points and that that will always be wrong.

that's not the right intuition. you could model a logistic curve with just 3 points if the error in those measurements tended toward infinity. the further apart the points are, the less tight the error bars need to be.

the problem with real-world modeling/curve-fitting is that measurements are super noisy and the errors in them are significant.

</comment><comment><USER48> I tried to do some exploration with the coronavirus data to get some idea on the final number. One of the best plot I found that could tell the final number is plotting the percentage of cases in the next week with the cases till now. It is like negative half parabola and the time it meets the x axis will give the final number per country.

This is the final plot: [https://i.imgur.com/o54t0Ts.png](https://i.imgur.com/o54t0Ts.png). You could make a good guess from the data how many people it will affect in the lifetime per country by continuing the same pattern till it reaches x axis.

</comment><comment><USER49> >Many of us will have learnt in school that if there are three parameters to be found, you need three data points to define the function.

4 points are needed in my universe.

</comment><comment><USER50> Yes, as in the OP, S curves can be challenging to work with, in particular, to use, say, early in the history of smart phones, to make long term projections from the number of smart phones sold each day for each of the last 30 days.

But there is some good news: The data used can vary, and in some cases good projections can be easier to make. Can see, e.g., for COVID-19 the recent

[https://news.ycombinator.com/item?id=22898015](https://news.ycombinator.com/item?id=22898015)

[https://news.ycombinator.com/item?id=22897967](https://news.ycombinator.com/item?id=22897967)

[https://news.ycombinator.com/item?id=22900104](https://news.ycombinator.com/item?id=22900104)

[https://news.ycombinator.com/item?id=22902667](https://news.ycombinator.com/item?id=22902667)

In the third one of those we have that the projection from a FedEx case is the solution to the first order ordinary differential equation initial value problem

y'(t) = k y(t) (b - y(t))

There for data we used y(0) and b. Then we guessed at k. Had we used values of y for the past month, we could have picked a better, likely fairly good, value for k.

Lesson: Fitting an S curve does not have to be terribly bad.

The key here is the b: The S curve of the solution is the logistic curve, and it rises to be asymptotic to b from below. Knowing b helps a LOT! When have b, are no longer doing a projection or extrapolation but nearly just an interpolation -- much better.

For FedEx, the b was the capacity of the fleet. For COVID-19 the b would be the population needed for herd immunity (from recovering from the virus, from therapeutics that confer immunity, and a vaccine that confers immunity).

Knowing b makes the fitting much easier/better. To know b, likely need to look at the real situation, e.g., population of candidate smart phone users, candidate TV set owners, market potential of FedEx (as it was planned at the time), or population needed for herd immunity for the people in some relatively isolated geographic area.

Then in TeX source code, the solution is

y(t) = { y(0) b e^{bkt} \over y(0) \big ( e^{bkt} - 1 \big ) + b}

Can also use a continuous time discrete state space Markov process subordinated to a Poisson process. Here's how that works:

Have some states, right, they are discrete. For FedEx, that would be (i) the number of customers talking about the service and (ii) the number of target customers listening. Then the time to the next customer is much like the time to the next click of a Geiger counter, that is, has exponential distribution, that is, is the time of the next arrival in a Poisson arrival process (e.g., the time of the next arrival at the Google Web site). So at this arrival, the process moves to a new state where we have 1 more current customer and 1 less target customer. Then start again to get the next new customer.

The Markov assumption is that the past and future of the process are conditionally independent given the present state; so that justifies our getting to the next state using only the current state -- given the current state, for predicting the future, everything before that is irrelevant.

What is a Markov process, what satisfies that the Markov assumption, can depend on what we select for the state -- roughly the more we have in the state, the closer we are to Markov. In particular, if we take the whole past history of the process as the state, IIRC every process is Markov. But Markov helps in something like the FedEx application since that state is so simple.

We get to use continuous time since the time to the next change of state is from a Poisson process whose arrival times are the continuum -- that is, we don't have to make time discrete although it is true that the history of the process (one sample path) has state changes only at discrete times.

So, for state change, and for some positive integer n we have some n possible states, then for i, j = 1, 2, ..., n, we can have some p(i,j) which is the probability of jumping from state i to state j, that is, we have an n x n matrix of transition probabilities.

[p(i,j) is the conditional probability of entering state j given that the last state was i.]

For two jumps, square that matrix. Now there is a lot of pretty math -- get some limits and eigenvectors of states, etc. Actually fairly generally there is a closed form solution to the process. Alas, often in practice that closed form is useless because the n and the n x n are so large, maybe n^2 in the trillions. E.g., in a problem I solved for war at sea, there were Red weapons, Blue weapons, on each side some number types and some number of weapons of that type. The states were the combinatorial explosion. Then there were the one on one Red-Blue encounters where one died, the other died, both died, or neither died. The time to an encounter was the next arrival of Poisson processes, also Poisson. Well, that was an example where there was a closed form solution but n and n x n were wildly too large for the closed form solution but running off, say, 500 sample paths via Monte-Carlo was easy to program and fast for the computer. So, sure the software reported the average of the 500 sample paths. On a PC today, my software would be done before could get finger off the mouse button or the Enter key.

This approach is fairly general. And since what I did included attack submarines, SSBN submarines, anti-submarine destroyer ships, long range airplanes, etc., there should be no difficulty building such a model for COVID-19 that included babies, grade school kids, ..., nursing home residents, people at home, people working nearly alone on farms, ....

Back to S curves, IIRC dropping out of the math for the n x n matrix and its powers is an S curve. So, in a broad range of cases, always get an S curve although a different curve depending on, yes, the p(i,j) and the initial state. Uh, when no one is left sick, the Markov process handles that as an _absorbing state_ \-- once get there, don't leave.

For the n in the billions, the n x n is really a biggie. So, for the submarine problem I did,

J. Keilson, _Green 's Function Methods in Probability Theory._

asked "How can you possibly fathom that enormous state space?". That is a good question, and my answer was: "After, say, 5 days, the number of SSBNs left is a random variable. It is bounded. So it has finite variance. So, both the strong and weak laws of large numbers apply. So, run off 500 sample paths, average them, and get the expectation within a gnat's ass nearly all the time. Intuitively, Monte Carlo puts the effort where the action is.". Keilson was offended by "gnat's ass" but liked the math and approved my work for the US Navy. That question and answer are good to keep in mind.

There is more in, say,

Erhan Çinlar, Introduction to Stochastic Processes, ISBN 0-13-498089-1, Prentice-Hall, Englewood Cliffs, NJ, 1975.

For why the arrival times have exponential distribution and why we get a Poisson process, Çinlar has a nice simple, intuitive, useful axiomatic derivation. There is more via the renewal theorem in

William Feller, _An Introduction to Probability Theory and Its Applications, Second Edition, Volume II_ , ISBN 0-471-25709-5, John Wiley & Sons, New York, 1971.

<comment><USER51> There's quite a bit more to COVID-19 prediction than standard derivation of a logistic curve found in introductory differential equation books. SIR models would perhaps be the simplest place to start.

[https://en.wikipedia.org/wiki/Compartmental_models_in_epidem...](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#Bio- mathematical_deterministic_treatment_of_the_SIR_model)

<comment><USER50> Nice reference. The discussion is mostly about _SIR_ (for susceptible, infected and recovered) models but also with several generalizations to handle more details, e.g., losing immunity and getting infected again. A lot of this material goes back, say, to 1927.

First cut it appears that the differential equation I gave is an SIR model except with R = 0, i.e., once are a customer of FedEx then remain one.

At one point article finds the logistic curve as I did -- maybe for the same situation.

The article does touch on stochastic models, but I didn't see discussion of a Markov assumption or Poisson process for time to the next infection.

There is an unclear reference to operator spectral radius, and maybe that is related to the eigenvalues, vectors I mentioned.

Whatever, especially with the Wikipedia reference, it appears that we are a step or two beyond the OP.

The work I did and described here I did decades ago and is not at all related to the math of my startup now. So, here I was able to describe some of my old work, but I'm doing other things now.

For the differential equation I gave, the solution as I derived it needed only ordinary calculus and not the additional techniques of differential equations. I just did the derivation in a hurry at FedEx just as a little calculus exercise. I didn't consult a differential equations book. I discovered that the solution was the logistic curve only by accident years later.

The Wikipedia article is nice.

<comment><USER51> Yes that's I nice way to run into logistic. Some introductory books use that as a motivating example, usually the second one. SIR is too simple to be accurate, but is ok enough to paint with broad brush strokes. Sometimes simple models are still useful as long as one does not read too much into the output.

Statistical epidemiology is quite heavily invested in stochastic differential equation. Wikipedia would hardly suffice as an academic, up to date and an exhaustive bibliography.

<comment><USER50> Sounds like the next step up would be stochastic optimal control, the field of my Ph.D. dissertation.

Ah, been there, done that, got the T-shirt, and doing other things now!

<comment><USER51> Heh ! that's why I said this [https://news.ycombinator.com/item?id=22937262](https://news.ycombinator.com/item?id=22937262)

BTW you mention Prof. Bertsekas a few times, did you ever run into Pof. Tsitsiklis ? He has done some work in the area of epidemics around 2015. Mentioning just in case you knw him

</comment></comment></comment></comment></comment></comment><comment><USER52> There is no such thing as an 'exponential curve' in our finite world. And the derivative of the sigmoid is a bell curveish thing.

<comment><USER53> When you're talking about a highly contagious disease it's more or less a distinction without a difference. It's exponential until very close to the point where a significant percentage of the vulnerable population is infected. It's not super interesting to point out that it's no longer growing exponentially, when half the population has it already.

<comment><USER52> It is interesting to think about how long the exponential trend will continue before becoming a sigmoid.

</comment></comment></comment><comment><USER54> The reason it is hard is that it is using a stateless model to approximate an inherently stateful and often chaotic process.

Take tech adoption for example. Often incorrectly represented as s-curves, they are the result of the inherent cost/benefit of the technology combined with the stateful diffusion process of communication and inherently human resistance to change. And being inherently stateful, you can have chaotic influences in that diffusion process. For example, the idea of microservice architecture had an absolutely massive diffusion jump the moment Amazon sent out that now-famous email mandating adoption. It wasn't linear, it wasn't exponential...it was a discrete step, and a very large one at that. These are everywhere too, because communication doesn't propogate like bacteria grows, it propagates due to extremely non-linear levels of influence. Bill Smith, 45 year old mid-level programmer for a tiny Midwest bank, will never have the tech-adoption influence levels of a Steve Jobs or Alan May or Linus Torvalds.

A better option for modeling would be to use Monte Carlo methods or systems methods. Something that acknowledges the inherent statefulness of the process.

<comment><USER18> > Often incorrectly represented as s-curves, they are the result of the > inherent cost/benefit of the technology [...]

You're taking from two completely different levels of abstraction here. Tech adoption rather obviously happens in an S-shaped curve, at least sometimes. See the article for examples. And of course that shape, and its exact parameters, are the result of some underlying processes.

These two things aren't contradictory. Outside air temperature follows a roughly sinusoid curve. It's the result of the earth turning and therefore alternating between night and day. But it's still sinusoid.

And the S-curve does acknowledge state, or it would just be exponential.

Yes, there are different approaches to disease modelling, such as agent- and rule-base simulations. Unfortunately, they tend to be really bad because we just don't enough data to satisfactorily simulate societies at the level necessary for this application.

<comment><USER54> But having an s-shaped curve is not the same thing as being a sigmoid function, in the same way that having a bell-shaped density is not the same thing as having a gaussian distribution. There a tons of processes out there that can approximate well with either of those two, while being extremely different in extrapolation.

One thing that can immediately disprove mathematical sigmoid modeling: curve symmetry. If the early adoption exponential growth is not exactly the same shape as the late adoption slowdown, then you don't have a sigmoid function.

And that's the problem: if you're using a single function to model the result of two (or more) separate processes with distinct mechanics and parameters, you're going to have the same exact pitfalls as you would trying to model a bimodal process with a single probability distribution. Namely that they might fit well with interpolative methods, but completely fail with extrapolative methods (like forecasting!).</comment></comment></comment>


When to Avoid Using A.T.M.'s - <AUTHOR> http://bucks.blogs.nytimes.com/2011/02/06/when-to-avoid-using-a-t-m-s/

<comment><USER2> I thought this article was going to be about how to properly punctuate and pluralize abbreviations.

<comment><USER3> I thought this too at first, but now I think A.T.M.'s is okay in some editorial circles as a plural non-possessive. It's used in many other NYT articles - as is non-possessive G.I.'s.

"...some writers still pluralize initialisms in this way. Some style guides continue to require such apostrophes - perhaps partly to make it clear that the lower case s is only for pluralization and would not appear in the singular form of the word, for some acronyms and abbreviations do include lowercase letters." [http://en.wikipedia.org/wiki/Acronym_and_initialism#Represen...](http://en.wikipedia.org/wiki/Acronym_and_initialism#Representing_plurals_and_possessives)

</comment></comment><comment><USER4> A friend of mine that banks with BofA showed me that any credit card will do to open the door. I don't know if this is still the case, however.

</comment><comment><USER5> CC-actionable locks are possibly one of the more irresponsible things banks are doing to their customers. They should be banned.

<comment><USER6> One of the interesting things IME about those swipe locks is that you don't have to use a credit/debit card, you can use _anything_ with a mag-strip. If you're suspicious about a lock, I suppose you could try using your AAA card, reward card or something else that doesn't have any sensitive info.

<comment><USER7> For a while, the door reader at one of my banks only looked for the first discernable digit. Insert the card a fraction, and bzz went the door lock.

</comment></comment></comment><comment><USER8> Cash should be made obsolete. I don't use it most of the time, I don't need it most of the time and I _never_ prefer using it to a digital solution.

Only thing I buy regularly with cash is lunch in smaller restaurants.

<comment><USER9> Cash will never be obsolete until non-cash purchases can be made anonymous/paper-trailless. The black market is most likely 10%-50% of the market (depending on where you live, see [http://en.wikipedia.org/wiki/Black_market#Comparison_with_re...](http://en.wikipedia.org/wiki/Black_market#Comparison_with_regular_economy)).

<comment><USER10> Agreed, the only thing cash has going for it the privacy/freedom it provides from being tracked.</comment></comment></comment>


Best HTPC motherboard? - <AUTHOR> http://www.minimotherboard.com/article/asus-at3n7a-i-as-the-best-choice-for-your-full-hd-mini-htpc/

<comment><USER2> The best HTPC MB has a PCI slot so he can use a MPEG2 TV-capture card. I'd still take the Zotac for the wifi. The wifi card is removable so I could replace it with another miniPCIe device (Broadcom Crystal HD for example).

Also, stations are dropping/have dropped analog broadcast so an MPEG2 encoder is completely irrelevant. You'd be better served with 2 USB tuners that are the size of a stick of gum.

</comment><comment><USER3> I think that any motherboard with NVIDIA ION chipset is good for building a HTPC...</comment>


Heart Patient Risk From iPad2 Found by 14-Year-Old - <AUTHOR> http://www.bloomberg.com/news/2013-05-09/heart-patient-risk-from-ipad2-found-by-14-year-old.html <comment><USER2> Bloomberg, in typical sensationalist fashion, calls this a "dangerous iPad2 flaw" when defibrillator patients are already educated about the risk of magnets and Apple's manual informs users of the magnets and warns against this exact problem.

They continue (in the video) and lump pacemakers into the warning even though their own article specifies that pacemakers were unaffected: "The iPad2 didn't interfere with four pacemakers or a loop-recorder, which were also tested."

tl;dr - 14 year old's dad does her science fair project for her and doesn't win. Nobody learns anything and the news gets it all wrong.

<comment><USER3> Not fair.

It was her idea, she concocted the theory, investigation and reported the results. Insofar as she didn't do part of the full experiment, no teenager should be allowed to carry out casual experiments involving shutting off life- support equipment on heart patients - duh, let an adult handle that bit. That she didn't win doesn't mean anything more than the winners were better projects, drama aside.

People DID learn something. Awareness is spreading about the issues of proliferation of magnets strong enough to cause false inputs to implanted medical devices.

The only thing the news got wrong was the subtle jab at Apple, rather than a more neutral reference to the many magnet-containing products which have similar odds of the same problem.

<comment><USER4> > It was her idea, she concocted the theory, investigation and reported the > results.

As far as Bloomberg reports. Her father is hinted as being a heart surgeon ("given her father's specialty") but it's never explicitly pointed out in the article.

She's obviously extremely intelligent—and I have no qualms with a 14 yo getting some hints from their parents for a project idea—but the article seems designed to sensationalise an issue that has long existed.

"Apple story == page views".

<comment><USER5> Dad is a cardiac electrophysiologist.

</comment></comment></comment></comment><comment><USER6> See also [1] for a case caused by the hard drive of a laptop.

Fundamentally, this is a consequence of the decision of manufacturers such as Medtronic to use a magnetic field to temporarily disable implantable cardioverters/defibrillators. Note that magnetic fields do different things for ICDs produced by different manufacturers.

[1] = <https://www.ncbi.nlm.nih.gov/pubmed/22353166>

[2] = <http://www.medscape.com/viewarticle/585151_2>

</comment><comment><USER3> Having a pacemaker, I've been quietly paranoid about _any_ magnets coming anywhere close to it. That includes keeping the iPad away from the area, both because of magnets and the radio transmitter.

One of the first warnings I got about having one was to not dangle earbuds around my neck where they would hang in front of my left shoulder. Other warnings about keeping distance from generators, magnets, shoulder-pressed cell phones, store theft-deterrent sensors, and even chainsaws (cardiologist & I got a kick out of wondering which would kill me first: the alternator's fields knocking out the pacer, or my passed-out fall into the still-spinning blade). MRIs...just don't even go there.

The problem with magnets is more complex than perhaps obvious. There's bad input, and then there's damage. What the girl is referring to is quite legitimate: defibrillators and pacemakers typically allow proximity of a magnet as a "master override" function. When I suffered an unrelated instance of atrial flutter (heart decided to run at 350bpm, pacemaker hit the brakes at 150...for 12 hours), the ER doc not having a diagnostic system handy forced the pacemaker to reduce to a much more humane rate by placing a good-sized magnet over the pacemaker; this allowed a more relaxed 2-hour wait for a technician to arrive and better diagnose & manage the problem (the subsequent drive-thru heart surgery is another story). She's raising awareness that sometimes this "master override" function can be induced improperly and even unknowingly, leaving the patient in a high-risk condition without knowing until periodic diagnostics or medical failure. A related issue she may tangentially raise awareness of is outright damage, with induced currents overstimulating the patient or frying the electronics (see aforementioned chainsaw issue).

She's raising a good issue. With the proliferation of tiny super-magnets, an enduring technical problem must be revisited: how to provide a low/no-impact way to essentially "hit the reset button" on an implanted medical device when there is no visible/external contacts? Not long ago, using a strong magnet to activate a magnetic switch was a sensible solution. Today, stronger magnetic fields are coming closer more often, risking activating a "safe/master mode", and there's not much way to alert anyone to the condition.

To address a few other points: Yes, any small but strong magnets like the magsafe plug are a possible problem. It's not an EMI problem so much as false input to a magnetic sensor. Defibrillators and pacemakers face the same basic problem, but the former moreso because the "master override" function needed is a function shutdown (need a way to turn it off and keep it off), while the latter responds in a more benign manner.

Glad to hear someone raising awareness of the issue. Amused that it's a child figuring it out and publicizing it. Thanks, kid!

<comment><USER7> Very informative post!

One comment, the magnet for magsafe power supplies is actually in the laptop, not the plug.

Have you heard about people implanting a tiny magnet in their pinky finger? I think they've been mentioned on HN before... could give you insight about magnetic fields around you (assuming it was weak enough not to be a cause for concern itself).

EDIT: past story on the implants.

[http://www.iamdann.com/2012/03/21/my-magnet-implant-body- mod...](http://www.iamdann.com/2012/03/21/my-magnet-implant-body-modification)

<https://news.ycombinator.com/item?id=3734130>

<comment><USER3> Glad to know that magnet isn't where I thought it is.

Yeah, I've heard of the magnetic finger modification. I promptly was concerned about it, not quite sure why but favor caution; sure wouldn't want one given the possible consequences. Since earbud magnets are enough to warrant concern, I'll avoid what is likely more powerful.

Woolrich has a line of shirts I want which feature magnetic "button". Can't convince myself they wouldn't be a problem.

</comment></comment></comment><comment><USER8> The construction of her acronym shows how brilliant (and snarky about acronyms) she truly is.

<comment><USER9> I can't find the acronym. Is it in the video? Because I'm not watching a video.

<comment><USER10> I think this is the title (I had to web-search) <https://www.usc.edu/CSSF/Current/Projects/33084.pdf>

"EMIT: Does iPad Use in Patients with Implantable Cardiac Rhythm Devices Cause Electromagnetic Interference?"

(EMIT looks intended to be an acronym, but what do the letters stand for?)

<comment><USER5> You can see it in this picture: <http://www.bloomberg.com/image/iZbO1N0jA7eY.jpg>

As I commented above, this is usual practice for cardiology publications. And her dad is a cardiology subspecialist.

</comment></comment></comment></comment><comment><USER6> > " _Walter Chien, a cardiac electrophysiologist, helped his daughter > coordinate the patient testing._ "

Maybe this is why she didn't win first place? At any rate, if this finding holds up, this is a serious problem.

 _Edit_ : "the project was beat out for the top spot by work on electromagnetics and on the effect of punctuation mark placement in keyboards on carpal tunnel syndrome."

So I guess she's just up against really stiff competition.

<comment><USER11> Assuming that she was competing for the Intel Science and Engineering Fair (ISEF), as a former judge, I can say that her project has a major red flag. One of the disqualification criteria applied to projects is to examine if an Institutional Review Board (IRB) properly vetted her project and approved experimention on human subjects. From my perspective, if her paperwork was anything less than pristine, signed, and notarized, I could not imagine a judging committee electing it to first place.

<comment><USER3> I did the ISEF as a kid (speech recognition on an original IBM PC). Seriously tough competition there.

</comment></comment></comment><comment><USER12> Would this happen with any sufficiently powerful magnet? Also, does anybody know why the magnets would turn off a pacemaker? Pacemakers try very hard to filter out EMI.

Edit: Implanted defibrillators, not pacemakers

<comment><USER13> If the pacemaker develops a fault, or you're seeing your doctor and they want to see your natural rhythm, or several other situations where you want to go 'natural' for a while. There's a leaf switch inside - it's a design feature, not a fault.

You do need a decent magnetic field next to the pacemaker - having it near a fridge magnet while you open the fridge looking to sneak a snack isn't going to do it.

I used to work as a neuro tech, and we would occasionally reprogram vagus nerve stimulators (sort of a defibrillator for the vagus nerve). Every few minutes it would send a charge up the vagus nerve, which could scramble rhythms that might be developing into a seizure. You could dell when it was firing if the person was talking - every few minutes, their voice would go funny for a couple of seconds as the vocal chords are innervated by the vagus. It wasn't the most pleasant experience, but you did get used to it, and it was better than the general level of seizures you'd have to have to get one implanted in the first place.

</comment></comment><comment><USER14> I wonder if the magsafe (magdeadly?) socket has the same issue; it is probably weaker overall since you can hold an ipad up by its cover but can't come close to holding a macbook air by its plug.</comment>


Show HN: Positiviser, a browser extension that hides negative content - <AUTHOR> https://github.com/artdgn/web-positiviser <comment><AUTHOR> Chrome or Firefox extension that finds negative elements on the page and makes them less visible, or hides them completely.

This works for YouTube, Twitter, Facebook, various news sites, etc, even HN :)

Negativity is calculated using sentiment analysis, either only in browser, or optionally using a deep learning model running locally in the background. A variety of scoring and restyling controls are exposed via the menu, and the options can be saved per site or globally.

It's a fun project, and there's plenty of ideas to try (both for scoring, UX, restyling), so will be happy to hear any feedback, thoughts, or suggestions!

</comment><comment><USER2> > _translations = {

> r'covid|cases|pandemic|virus|coronavirus': 'sickness',

> r'trump': 'dictator'

>}

<comment><AUTHOR> That's a simple way to work with those few special out-of-vocabulary negative words that are part of a lot of negative content.

It doesn't mean that everything with those words is automatically negative though, they're just replaced by known words.

A nice feature would be to be able to add / edit them of course (but I haven't got around to that).</comment></comment>


Ask HN: Which camera drone would you recommend based on own experience? - crypto-jeronimo <comment><AUTHOR> Our family bought a DJI Spark over Christmas. Been very happy with it. For social media sharing even for doing footage for your film making friend, shooting in 1080p is more than enough. We got the plus pack that had a case and extra battery. Well worth it.

We had a few months of intense use and then it's on the shelf mostly now. Take amazing footage, so fun to fly around. Battery good too. Seriously it's got 2km range you'll probably not want to fly more than 700m cause it if ever came down you'd loose it further than that. And it already flys to the legal limit in Australia (100m up I think it is).

If you had a few more bucks the DJI mavik air looks amazing but really for your first drone the DJI Spark will do everything you want and more and if you really become a big fan you can always trade up.

Oh and the build quality and design are solid too, these things feel like the MacBook of drones IMHO.

My 2 cents.

<comment> crypto-jeronimo Thank you so much for the expressed opinion!</comment></comment>


Neural ramifications of nootropic drugs in the healthy developing brain - <AUTHOR> http://journal.frontiersin.org/Journal/10.3389/fnsys.2014.00038/full

<comment><USER2> Its good to see article like this. Many just don't realise that drug boost usage may result in serious damage. So far the best brain booster is healthy lifestyle and practice.

</comment><comment><USER3> I'm not sure I'd refer to psychostimulants as nootropics, although I'm aware many people do. The neurological tradeoffs involved in catecholamine reuptake inhibition are relatively well-known and uncontroversial; to me, much more interesting are the implications of long-term racetam use. History (and homostasis) seems to indicate that there are probably tradeoffs involved, and the fact that we have no idea what they are makes me more than a little nervous.

</comment><comment><USER4> The tradeoff between 'performance' and 'plasticity' is an interesting framework. One with larger ramifications later in life. It's also one that vexes the "education" industry, and has broadeer applicability outside the realm of the discussed paper. Definitely an interesting topic.

</comment><comment><USER5> We changed the url from [http://io9.com/smart-drugs-could-be-impairing-the- brains-of-...](http://io9.com/smart-drugs-could-be-impairing-the-brains-of- young-peop-1576908559).</comment>


The Inventor of Underwear with Pockets - <AUTHOR> http://priceonomics.com/the-inventor-of-underwear-with-pockets/ <comment><USER2> [http://www.scottevest.com/v3_store/Travel- Boxers.shtml](http://www.scottevest.com/v3_store/Travel-Boxers.shtml)</comment>


Ask HN: Data scientists, what does your workflow look like? - <AUTHOR> Hi HN-ers,<p>I'm doing some research on data scientists and learning more about:<p>- what company size should you start having a data scientist on board? - as a data scientist, what does your workflow look like? - do you have any side projects? if no, why not?
- how does your output look like for your data science work? (Excel, slides, API, database updates, etc.) <comment><USER2> One of the following:

RStudio > write.table(x, "clipboard", ...) > paste to excel > email data to BA who makes slide

Python/Anaconda + jupyterlab nbs + sklearn

Excel + Solver + PowerPoint

Obviously track everything on Git etc.

Many more things you can do here:

Use R notebooks, Jupyter notebooks, even have a build server and make each one of your projects an R package

<comment><AUTHOR> Interesting. Do you code in both Python and R? What would be your rational for picking one over the other?

Also, would you have a need for a build server?

<comment><USER2> Yes code in both. Depends on who I am working with really, prefer R but python much easier to integrate in a production environment.

I don't have a need personally but some companies that are doing industrial scale modeling (on the order of building and maintaining thousands of models) do use a build server to Basically check that code is formatted properly and can have a model run in a somewhat automated fashion.

<comment><AUTHOR> What makes productionizing R difficult?

I'm curious about whether the rise of Python in data science is really just because of the lack of flexibility to integrate with other systems. I've read that R seems to do better in data science/analytics work but when it comes to integration, it's more challenging.

And any insights on which companies are already doing industrial scale modeling?</comment></comment></comment></comment>


What iPads Did To My Family - <AUTHOR> http://chucksblog.emc.com/chucks_blog/2010/05/what-ipads-did-to-my-family.html <comment><USER5> Please don't take this as being too critical, but I used my bread-maker every other day for 2 weeks after buying it. I can't remember the last time I got it out the cupboard.

<comment><USER6> The article makes it sound like much more than a novelty.

I don't have an iPad yet, but I do use my Cuisinart bread-maker twice a week to make delicious and substantial wholewheat bread for way less than you would pay at a store.

<comment><USER7> The money and the taste is all well and good, but the real reason I kept using mine (until I started travelling): the smell. Waking up in a house that smells like fresh baked bread is just heaven.

<comment><USER8> * goes to dig unloved breadmaker from the back of the cupboard _

</comment></comment></comment></comment><comment><USER9> OK, after reading this article, I finally get it. (Or at least have a reasonable working hypothesis.)

The iPad does to the internet what the iPod did to music.

I don't really need that done to the internet for me, but I can see how millions of people would really like it.

<comment><USER10> I like your hypothesis.

I bought a 16gb iPad the week after they came out. I didn't really have a need for it, I just wanted to see if it was interesting. I had a very simliar experience as the author.

My wife has little but distain for my gadget obsession, but within minutes she wanted one for herself. Our three-year-old adores it. I put some educational games and cartoons on it, and she can get to it all on her own. Literally every person I've shown it to has wanted one.

Love it or hate it, but don't ignore it.

<comment><USER11> _Want_ one is the keyword. I've played with my coworkers iPad quite a bit and can see how it could fit into my life. But at the same time its not a need - not in the way I need my smartphone. Its a big shiny toy that I want but only when its convient.

<comment><USER12> Most of what we own are wants, let's be clear.

<comment><USER13> accidentally downvoted

</comment></comment></comment></comment></comment><comment><USER14> \- No Flash, no multitasking, no Camera!

\- Just a big iTouch.

\- 256MB RAM!? Pfft.

\- WTF, not widescreen?!

\- LCD bad for reading.

\- $500?? Overpriced.

\- Can't see why anyone would need one.

\- Fills a need that isn't there.

\- Looks uncomfortable.

\- You have to pay $99 to publish on it.

\- Yeah, if you like to peck and type.

\- It's gonna destroy the culture of content creation.

\- Fine if you don't need to get real work done.

\- Only fanboys will buy one.

\- Give me a netbook so I can program.

\- People who are buying iPads are sheep.

\- It would not sell if it weren't for the marketing.

\- Sure it's selling well now, but what about in 6 months/1 year/2 years …

\- Sure it's selling well now, but no true intelligent scotsman …

\- Just a toy.

\- I can't believe people are buying this without being able to articulate why.

\- Look, someone says they stopped using it! I was right.

\- No wireless. Less space than a nomad. Lame.

28 days and $1,000,000,000 in revenues later, still reasons Apple doesn't give a flying frack about, and maybe a sign that some of us should reevaluate how much we know about the buying habits of the non-comic book guys, and that there might be something there that warrants more than a knee jerk cmdrtaco response.

<comment><USER15> All of the above are good points that can be exploited by competing products, i am not sure why you are suggesting that apple should close its eyes on this!

I will never buy a second iPhone, i will look for a good Android based phone, and using the same logic i will wait for a good iPad like product from HP or HTC

<comment><USER16> If people are in the market (or simply want) a tablet-sized computing device, they're going to have the iPad at the front of their mind. If they play with one at an Apple Store or at a friend's house and aren't convinced (or are pissed it doesn't run Flash) then they will fall into Category B which is where a dozen or more companies will compete.

You basically have the iPad, and everything else, just like for music players there's the iPod and then everything else. Features on a comparison matrix don't get consumers excited. The iPad has a LOT of people excited.

</comment></comment></comment><comment><USER17> Wow, a house full of technology and it can be replaced by a single ipad. Makes you wonder why they never did more with all the stuff they've got, it seems like they were seriously under-using it before.

Good thing nobody ever had a complicated spread sheet, a document on letterhead for the office or a small programming job to do!

It's genius how Steve Jobs managed to identify that most people now use their computers as a media consumption device, I see mine as power tools.

<comment><USER18> >Good thing nobody ever had a complicated spread sheet, a document on letterhead for the office or a small programming job to do!

At home? Most people don't.

Every computer I've watched my parents buy over the past 15 years, they buy Microsoft Office as a reflex response. When I visited for the holidays and helped migrate them to their iMac, I noticed the number of Word and Excel documents they had accumulated over this period.

About 40.

Outside of school assignments, what need does a child have for a productivity suite? Close to zero, and even then, 98% of what they do for writing assignments can be accomplished by a simple typewriter or standalone electronic word processor. Programming? Don't get me started...

My sister bought a 15" MacBook Pro over the holidays, and a copy of Office. She doesn't work on documents, just logs in to Outlook Web Access and maybe reviews something sent to her. She does the real work, well, at work. Aside from occasionally loading new photos into iPhoto after one of her 2 yearly vacations, it's a $1700 web browser and media consumption device.

My non-IT friends? The same. Webmail. Facebook. Funny pictures of cats.

The computer in the traditional sense is becoming a lot more niche. Why? It's what people want.

It's like the new 2011 Mustangs are hitting dealer lots. I want the V8, a 6-speed, the performance axle and the good brakes. Out of the dozens of Ford dealers in a 100 mile radius, only ONE dealer has manual transmissions. The only reason that dealer has them is because it's Bob "Win on Sunday, Sell on Monday" Tasca -- a car enthusiast, catering to other enthusiasts.

We're computer enthusiasts. The virtues of SSDs are just as foreign as the virtues of powersliding to someone who's, frankly, just not that interested and just wants to get something done inexpensively and with a minimum of fuss that doesn't involve a debugger or heel-toeing.

<comment><USER17> Just looking at the stuff I'm running right now and wondering how I'd do without:

      - xchat (maybe that does work on the iPad, it could be)

      - <USER2>

      - <USER3>

      - 20 or so terminal tabs logged in to just about as many servers

      - about 20 browser windows, each with multiple tabs

      - an ide (currently intellij, I hate it but that's what I'm using)

And I'm not even programming right now (haven't touched the ide in a few days, but it's still running), just staying informed, in touch and doing some systems maintenance.

Just thinking about having to 'downsize' to a single non-multi tasking machine without a clearly defined local file store that I can back up to a server gives me headaches.

edit: I missed open office and okular, different desktop, same box.

It would be like looking at the world through a very small key-hole.

<comment><USER19> _It would be like looking at the world through a very small key-hole._

Oh, yes. _Heck_ , yes. I've found that to be a big reason why I love my iPad.

Remember all that psychological research on multitasking? The research that concludes that even the people who claim to enjoy multitasking and have "lots of practice" at it are less productive and more stressed when trying to do more than one thing at a time?

Have you ever seen Cory Doctorow's theory of ebooks? The one that holds that long-form prose on computer screens hasn't worked, _not_ because we're holding out for better screen quality -- many of us white-collar workers spend eight or more hours a day reading from our existing screens, after all -- but because our computers, and our habits when working with them, are so centered around distraction that we can't make ourselves concentrate when sitting in front of a computer? You start to read the novel from the browser window, but then your email beeps. Or, god help you, your mind starts to wander, your hands hit a key-combo, and _you_ check your email as a sort of spinal reflex. And then you notice the RSS feed icon blinking, and then Twitter updates, and then a Skype call comes in, and before you know it you're reading Wikipedia about some band from the eighties while simultaneously watching a YouTube video of some kittens. Or (ahem) posting to _Hacker News_.

The iPad is _relentlessly focused_ on one app at any given time. This is obviously a problem for some tasks -- yes, you can't really program on the iPad at the moment, or even blog very effectively; that's why iPad owners also have computers -- but for many other uses it is downright refreshing, even Zen. You do one thing. Then you do something else.

<comment><USER17> > Or (ahem) posting to Hacker News.

Guilty as charged. You have me almost convinced that I should cut down my screen real estate to something a bit more modest and a maximum of one open window at a time.

Maybe I should try that for a week and see how it works and do a write-up.

<comment> X-Istence Since you are cutting down, could I get that extra screen real estate?

When I am programming I have one terminal window open for subversion, the IDE I am programming in, and the web browser for documentation.

When all three are on different screens life is awesome, if I have to move my mouse to switch between windows it becomes a burden and annoying. Having even one extra screen is an absolute must as a programmer. I don't think I could live with just a single screen.

Now on a day to day browsing machine, sure, one screen, like the iPad is perfect. Hell, I tend to sit on the couch with my iPhone browsing the web while watching TV.

</comment></comment></comment></comment></comment></comment><comment><USER20> My iPad is still getting about 3-4 hours use a day - and it still, 30 days later, is pretty popular at parties, restaurants, etc... I'll check back in a couple months. New Apps (PopSci, Time, Penultimate, Sketch, Alice, etc...) keep my interest, mostly. Though Videos, Field Runners, PvZ, Safari, Kindle, WSJ, Mirror's Edge, Marvel, GoodReader, and Email are in pretty much constant rotation.

It's unclear to me if I'll still be as addicted in six months, but the form factor certainly is appealing.

One thing nobody talk about the battery life - It's _not_ 10 hours. It's 10 Hours of _use_. Used on and off, I'll go close to three days before needing to plugin. That's very nice.

</comment><comment><USER21> About 10 days after getting my iPad, it's hardly been turned on in the past few days. I think the ergonomics of it suck - it's heavy, and it gives me a crick in the neck when I use it.

YMMV.

<comment><USER22> This is one thing I really don't get about the iPad. I learned a bit of ergonomics in college, and the iPad doesn't make a whole lot of sense to me.

If you are sitting on your couch with your feet up dicking around on the internet (apparently the iPad primary use case), and you want to look at the screen at the proper viewing angle, either it needs to be tilted up by you holding it at an angle with your wrist (ergonomic no-no) or you need to tilt your head down (ergonomic no-no).

Considering that you probably want to use your hands to tap on it, that means it is likely resting in your lap and your head is tilted down putting strain on your neck.

Color me sensitive (broke my neck rock climbing about a year ago and still get some pains) but that doesn't sound fun over a long period of time.

I really think the amazing thing about the iPad is the screen no the form factor. Give me a macbook pro with a multi-touch screen and dual boot Mac OS X and iPad OS. Tell me you wouldn't prefer that. When you want multitouch, you have it, when you want a keyboard, you have it. When you want to sit back and watch a movie you have a built in stand .

<comment><USER23> What about the ergonomics of a book or magazine? Aren't those effectively the same as an iPad?

<comment><USER24> A magazine is a lot lighter than the iPad, so that makes a huge difference. Also you don't have to continuously touch the pages of a magazine while using it, which is another significant difference. Finally a magazine is bendy meaning you can change it's size and shape if and when the need arises.

<comment><USER25> Magazines may be lighter, but I'm fairly sure that the hardcover I'm reading right now (David Weber's _A Mighty Fortress_ ) is about as heavy as an iPad.

</comment></comment></comment></comment></comment><comment><USER26> You know, as much crap as Apple gets, they really have revolutionized the way people use technology. My mom, who a month could do nothing more than open FF on her computer, has been asking me for weeks when the 3G iPad is coming out. I have never seen her so excited to buy any sort of product. And that's why I'm not quick to judge Apple when it comes to their strict policies. They've shown time and time again that they know what they're doing, and they reach out to new, previously non-techy people with every product, inciting within those people a desire to use technology. This all might sound cultish, sure, I love my Apple products, but they hands down have the best computing products on the market for every day people. And I think it's their desire to control and perfect every level of the user experience that has gotten them to that point.

</comment><comment><USER27> After playing with Safari for a couple of weeks, I've recently started flexing the iPad Photos and Music software and it has been a revelation. My post-80s history with Apple starts with the iPod in 2004, and the iPad Music app is _really_ fucking good. I want to mount this thing in my car, in my kitchen, in my living room. The music software puts the iPhone/iPod touch to shame. Too bad it doesn't fit in my pocket.

And Photos, it's so much fun to play with the "Places" map, I immediately went in to iPhoto and started tagging the rest of my photos. I don't think I will ever use my laptop to browse or show off my photo library again. I can pull up the Places map, hand it off to whoever what to see my photos, and let them go nuts. It's a supremely compelling experience.

I bought an iPad because I wanted to see how a pure touch interface could change traditional human-computer interaction; I haven't been disappointed.

And the iPad App Store isn't even available in my country yet. The iPhone/iPad-only WWDC sold out in 8 days, and I can't wait to see what developers come up with over the next year. It should be a hell of a toboggan ride.

</comment><comment><USER23> It's a good thing the iPad does have excellent battery life because in our family, it's being used almost all day long. The other night I completely lost my wife and daughter for hours as they played Plants vs Zombies together way past bedtime. My five year old has already requested one for her birthday.

<comment><USER28> My son, 8, loves to have me watch him play plants vs zombies. (I'm way better, btw. My technique, which seems like the only logical one, is to buy tons of sun producing items in the beginning and fend off the zombies with the free or cheap shooters. At the end of levels I have an excess of 1000 sun points)

<comment><USER29> 2 Onions at the end of the outer (and middle lane). With spikes to the right of them if there are vehicles. Sunflowers behind those. Then you're down to 2 lanes to defend - and heaps of sun. That covers about 90% of the levels.

<comment><USER12> You just blew my mind. I'm trying to make it 40 rounds on the unlimited waves level and I think you may have just given me the strategy I need..

<comment><USER29> I've only got it on the iPhone - so I can't play unlimited. I'm not sure how it would go.

It still has a couple of weaknesses - 2 onions will deal with any walking zombie + pole vaulters. It wont stop: * vehicles -- use a row of spikes * gargantuar -- cob cannons, etc. * Miner Zombies -- These are a pain. * pogo zombies -- I haven't got a great strategy for them.. - cob cannons, cat-tails, etc. (Or just put a pea-shooter (maybe the reverse one) to deal with these and the miners. * Flying zombies - the blower plant or the cat-tails. The risk is that it can fall behind your onions - and then eat through all of your sunflowers.

Basically - it works awesome for almost every level.. But probably not for unlimited.

</comment></comment></comment></comment></comment><comment><USER30> I lent mine to a 13-year old kid from a small provincial town in the Philippines and, within an hour, it was like he'd been using one his whole life. And I couldn't steal it away from my 62-year old father while waiting for a flight. And I do remember both of them smiling. This thing is absolutely amazing.

</comment><comment><USER31> Annecdotally, I have similar findings. The iPad has replaced 90% of my laptop use at home and the office. The laptop only comes out when I need to do Real Work™, things that require multiple ssh sessions, browser windows, chat, email, etc, all at the same time.

For email, web browsing and those blasted causal games, it's glued to my fingers.

<comment><USER32> Surprisingly, this seems to be an indication that MS could easily make a good Win7 tablet. They only need to get a good finger-friendly email client, web browser, and IM client.

The argument thta people make is that once you go into some apps then the mouse/keyboard nature is exposed, but it appears that most people never dive into those apps. And in those cases, if the Windows tablet had an easy way to use a keyboard (convertible & detachable) then it would have the best of both worlds.

The only thing Windows doesn't have today is the app ecosystem, and I suspect we'll see that with Windows Phone 7 (and since all the apps are managed, the port will be easy, if not binary compatible).

</comment></comment><comment><USER33> In the very near future the "TV time" of many families like this will be all of them sitting around watching separate movies or shows on individual iPads or similar devices. The shared family dinner has already fallen victim to modern ways. Now the traditional TV-watching paradigm of the past sixty years is going by the wayside.

<comment><USER34> Not necessarily. They can always IM each other during dinner.

</comment></comment><comment><USER35> "She was smiling."

It sounds almost if she became happy again. Scary stuff.

</comment><comment><USER36> This reinforces what I've been telling people for the last month or so - <USER4> iPad is disruptive to the PC. Maybe it isn't as good as a PC in a lot of situations, but it provides sufficient functionality and speed that people will choose it for its ease of use and portability. And, in a few generations, you will be able to do anything you can on all but the highest-end PCs.

Sure, most people will still use PCs at work, but at home, you will just whip out your tablet.

</comment><comment><USER37> I'm wondering on only one thing at the moment. How long does it take for people to realize that most of their productivity/business software can be used on iPad as well.

I can imagine people traveling (sales people?) with iPads 3G checking their inventories in real-time, doing their forecast reporting, sending emails, reacting to on-line customer enquires...

Do you think this is something that will happen or my imagination just plays tricks on me? :)

</comment><comment><USER28> I played with iphone os 4 today at work, and once multitasking hits the ipad, it's really going to be lights out of how much time is spent on a "traditional" computer.

</comment><comment><USER38> Has anyone set a grandparent up with an iPad?

I'd love to buy one for my grandma. She has a Dell right now, and she uses it only for e-mail. She seems to constantly be running into problems with Outlook Express (marking items as junk, blocking senders, etc.).

I'd like to find her a device that's friendly and easy to use.

The iPad seems like that device. The only thing it can't do is print photos in e-mail messages (easily). I'm not sure if that's a deal breaker, and I think the usability trade off might be worth it. She could always go back to the Dell for printing.

If anyone's set up a novice with an iPad, I'd love to hear about your (and their) experience. I'm especially curious about whether you set it up as a stand-alone device or if you showed the user how to sync with PC.

</comment><comment><USER39> "There's an old saying: To a small boy with a hammer, everything looks like a nail. As programmers, we call into the same trap all too often. We learn about some new technology or solution, and we immediately begin seeing places to apply it."

I got that quote from the post on patterns in Python, but believe it applies equally well here.

</comment><comment><USER40> It might be a good idea to have a follow up for this article in 6 months to see if the results can be sustained...What iPads Did To My Family After 6 Months.

</comment><comment><USER41> What a happy iFamily

</comment><comment><USER42> How come iPad is popular among facebook users when [farm]ville apps don't work?

</comment><comment><USER43> Spot on. Ipads around my home and office seem to mutiply like rabbits. Most people can't articulate why they need one but the little things like competently clearing your email from bed or sitting through 7 hrs of airport travel hell and still have 50% battery are exceptional.

<comment><USER44> I understand why it's fun to play with a new (limited) device. I have an ipod touch. But I worry when people rush to buy $500 devices without being able to articulate why they need it. It sounds like good marketing and impulse shopping. I'm very curious to see how this plays out in the long run.

<comment><USER45> It's hard for me to worry too much when the vast majority of such people love and rave about the product after they've had it for a while.

</comment></comment></comment><comment><USER46> IT shows perfectly why technology does not brings intelligence. You can give them the best hardware and still they wont become next Google or Facebook founders or use it in any other productive endevour! Seriously most of the human race does not deserves the power of computing!

They wont lolcats on "magical devices!" long live America! What a waste!

<comment><USER47> Wow, judge much?

Throughout time, people have used technology to incrementally make their lives better. Not everybody has the skills, goal, or even desire to found the next Facebook or Google. The people who make the most money in the technology space recognize this about their customers and don't look down on them about it.

And, just because you are ragging on intelligence, it is "want"...

<comment><USER46> Until now! They are now competing with cheap labor/intelligence from India and China. And Later worse with machine intelligence!

Frankly the inly "skill" that you could rely on 20-40 later is programming and creativity.

<comment><USER48> you are the first true troll I have seen on HN

<comment><USER49> You see more if you turn on "showdead" in your profile.

</comment></comment></comment></comment></comment><comment><USER50> Replace all the taps in the article to faps and it becomes funnier.</comment>


Ask HN:  How to escape the momentum trap? - <AUTHOR>

Just this last Friday I launched a Kickstarter project (http://bitly.com/kxz0E2) that I thought would be really interesting for the community I live and work in - namely - the startup community.  You can see from my prior posts that I was working on getting the project right for awhile in the runup to the launch, and I have to confess that when I flipped the switch I expected something to happen...except that nothing really did!  Of course, I don't mean this literally - I spent a great deal of time publicizing the project on Facebook and Twitter and Linkedin and every mailing list to which I belong, but the project didn't seem to immediately catch.
This is probably fine, since building buzz about something like a web series takes some time, and there are so many competing messages out there.  But I have already figured out a couple things, which I thought I would share.
Momentum - the trendy word for it is traction - is a funny thing.  No one is interested in joining your cause unless you have it, and you can't have it unless you have people joining your cause.  To be honest I haven't discovered a way to get out of this after just a few days of running the Kickstarter project.  Bloggers respond with "come back when you have some pledge traction."  So here's what I plan to do - I'd be curious to get some suggestions on these items:
1)  Make personal pledge requests to interesting people: even if they only pledge $1, if they are OK with me sharing their participation then hopefully this helps with momentum.
2)  Cameos: this one is tough - I only have 6 slots.  Robert Scoble agreed to take one of them, which is a great start, but I need to build on that this week.
3)  Personal presentations at meetups:  I have contacted a bunch of meetups and asked if I can make announcements at their next session.  I'll be doing several of these a day for the next few weeks.  I'm hoping that a personal request gets retweets, if not pledges
4)  Mailing lists:  I've reached out to every list for which I am eligible that has a readership that might be interested:  startups, school, Yahoo alumni, Disney alumni.  Where else to go?
There's another thing too, which is that I haven't gotten any direct feedback on the project itself - I don't know if I come across in the video as committed and passionate, which I am, or something else.  I don't know if the description is long enough or detailed enough...any thoughts? <comment><USER2> _Momentum - the trendy word for it is traction - is a funny thing. No one is interested in joining your cause unless you have it, and you can't have it unless you have people joining your cause. To be honest I haven't discovered a way to get out of this after just a few days of running the Kickstarter project._

Well shoot, if I knew a way to solve that problem I'd be a very, very rich man right now.

<comment><AUTHOR> I don't know if I'm trying to become very rich - but I'm definitely trying to get my project funded! Let's be honest - trying to drum up a discussion about the process is an attempt to get through the process. How meta!

</comment></comment><comment><USER3> I'm wondering what these folks are getting in return -- the people you are asking for help from (permission to make announcements and so on). Maybe there is something in it for them and maybe you aren't getting that across very well. You have stated you will make no profit from it. Lots of not-for-profit projects are promoted with a combination of desperation/begging and high-horse lecturing ("this is the RIGHT thing to do!" kind of spiel). I've spent plenty of time doing volunteer work and did a fair amount of reading on the topic and what not. Even if you don't expect to turn a profit personally, what value are you bringing to the table to hook potential supporters? Because begging and lecturing typically don't accomplish much.

<comment><AUTHOR> That's a very good question. I think the starting point is seeing a series made about their lives and realities, though perhaps this isn't enough. The way the Kickstarter project itself is structured, of course, is that contributing gets you tshirts, movie credits, dinner with the cast, and so on - there are tons of rewards. But as for why someone would like or retweet it/spread the word...I'll have to give it some thought. Perhaps have some rewards based on retweeting? Something like a random draw of people who retweet and the person gets a role in the production?

<comment><USER3> I have no idea what you need to be offering. Just hoped to get you thinking strategically on "what's my motivation?" (from their perspective). FWIW: A scene from "Take the Lead" comes to mind where he has his Aha! moment about how to sell underprivileged kids on learning ballroom dancing. If you haven't seen the movie and have no idea what I am talking about, here is a 10.5 minute clip that should cover the gist of it: [http://www.youtube.com/watch?v=ZGrnv- FnWxA&feature=relat...](http://www.youtube.com/watch?v=ZGrnv- FnWxA&feature=related)

<comment><AUTHOR> bookmarked for tomorrow - will revert.</comment></comment></comment></comment>


Istio 0.5 - <AUTHOR> https://istio.io/about/notes/0.5.html <comment><USER2> So it looks like the Helm Chart lives here:

[https://github.com/istio/istio/tree/master/install/kubernete...](https://github.com/istio/istio/tree/master/install/kubernetes/helm)

The release notes (and several Github issues) indicate this chart isn't working for this latest release.

Would be great to see a working Helm Chart as a requirement for further releases.</comment>


My personal manifesto as a developer and entrepreneur - <AUTHOR> https://medium.com/@andyware/a-manifesto-31ca87f82320 <comment><USER2> > There are so many people who are trying to tell everyone how to 'make it' in > this world but how can you do that when it's not the same for everyone?

I think some general principles apply to almost everyone. Surround yourself with smart people and show them your work in progress. Iterate fast, whatever iteration means in your context. Be intellectually humble and ready to realize you were wrong about something. And many more.

Most of them bear repeating because they're hard to actually do every day. If it was easy to show off your half-baked prototypes, you wouldn't need 1000 blog posts encouraging you to go do it right now.

</comment><comment><USER3> Are you the author?</comment>