skysight-inc/HackerNews huggingface dataset:
>You are the one who is saying I gave a either-or.

That's not the case (plus I'm not the parent who you were responding to).

The parent wrote: "Having to work for someone else will lead to less freedom of choice than working for yourself."

And you responded: "That's just silly. If you really wanted to get away from all human dependence, the path to that is clear, go live out in the woods off the grid."

That's the dictionary definition of the slippery slope fallacy.

The parent didn't say anything about "really [wanting] to get away from all human dependence", so why bring this up? He just said that working for yourself is more free than working for someone else.

And you continued that "Whatever it is you want, holding down a stable, reliable job is a great way to move closer to it" freelancing or creating one's own business is not an option, and everybody is better of to achieve their goals by "holding down a stable, reliable job".

Tens of millions of people started straight into either freelancing or getting their own business while young or even straight out of college. An office or salaried job has never been a requirement, and has become even less so nowadays.

>I said nothing of the sort! I know lots of successful entrepreneurs. That's a great path to take if you want to do it. But you get there by learning about entrepreneurship and marketing, not by wishing and wishing you can quit your job.

Who said about "wishing you can quit your job"? TFA and other people in these comments spoke about actually quitting your job. Or not starting with one in the first place, but doing your own thing from the start. Heck, the article is about the guy completing 10 years doing his thing, not about them "wishing they can quit their job".
Leveraging Machine Learning with Ruby
Bacteria Found in Alzheimer's Brains
>I dunno. I jumped from school to the military, then out of the military into the workforce. I've had spiritual adventures and travel adventures.

And the message from those adventures was "keep your day job and don't try anything independent"?
As a regular Guardian reader I don't recognise any of that stuff.
Running hacker-proof software on hacker-not-proven hardware will not solve the issue. Only the attack vector will become harder and more expensive in development. It may become a death for hack-kiddies but surely there will be somebody who is willing to pay.
https://webtask.io is another alternative. A lot easier to deploy and manage, but only runs Node.js/JS.

/disclaimer: Work at Auth0, Webtask is one of our products.
I like your comment. The real question is whether they are conscious.

The analogy between deep neural networks and the brain has proven to be very fruitful. Other analogies may as well. See our upcoming paper for more info.

https://grey.colorado.edu/mediawiki/sites/mingus/images/3/3a...
That's Bill Stone's entire reason for existing these days - check these out:

http://stoneaerospace.com/

https://en.wikipedia.org/wiki/DEPTHX

https://en.wikipedia.org/wiki/ENDURANCE
You're too focused on marketing, and you eat meat and obviously don't care how your choices impact the planet.
I figured Glassdoor was not crazy!

You have to wonder, if most people who say things like "You only make 200k, you should try $x" are such great developers that they don't realize they are unique, or if they are full of it.
> In other tools, hitting re-run would simply replay the job, with the same state it was executed in the first run.

If the job failed, you get this. Otherwise I figure that the idea is that you're satisfied with those versions of the resources acted on by the job.

Disclosure: I work for Pivotal.
I don't believe this for a minute.

Sure, lots of Enterprise apps use Python 2 because that was what they could build with.

I'm a Python developer and I don't know anyone who doesn't use Python 3 by default.

I understand this is purely anecdotal, but it seems outlandish to say that Python devs mostly use Python 2.
That's a hell of a last sentence.
Just want to echo this. Same upsides and observations.

> Way less beer drinking (and when I do, it's Miller Lite or something comparably light on carbs)

Michelob Ultra's great and readily available as well with 2.6g carbs per bottle (vs 3.2g carbs for miller lite)

I've been doing strict keto (i.e. less than 25g of net carbs per day) since April. Down ~15 lbs (180 -> 165) as well.
Yeah, except it's kinda hard to find jobs/stores/daily-places-that-you-need-to-go that don't require transportation on the road, but it's kind of easy to find jobs/stores that don't require going into underwater caves or jumping off cliffs/airplanes or the like. If only one out of thousands of people ever needed to drive on the road, we'd probably criticize them too. The entire argument is so ridiculous that I can't even believe I have to lay it out like this.
>This is a fantastic answer - thank you, Eli.

Thanks! I've been doing a lot of amateur reading in cog-sci and theoretical neurosci. The subject enthuses me enough that I'm applying to PhD programs in it this upcoming season.

>So what do you think of the original article?

Thorough and accurate. I'll give a little expansion via my own thought. One thing taught in every theoretically-focused ML class is the No Free Lunch Theorem. In colloquial terms it says, "If you don't make some simplifying assumptions about the function you're trying to learn (and the distribution noising your data), you can't reliably learn."

I think experts learn this, appreciate it as a point of theory, and then often forget to really bring it back up and rethink it where it's applicable. All statistical learning takes place subject to assumptions of "niceness". Which assumptions, though?

Seems to me like:

* If you make certain "niceness" assumptions about the functions in your hypothesis space, but few to none about the distribution, you're a Machine Learner.

* If you make niceness assumptions about your distribution, but don't quite care about the generating function itself, you're an Applied Statistician.

* If you make niceness assumptions about your data, that it was generated from some family of distributions on which you can make inferences, you're a fully frequentist or Bayesian statistician.

* If you want to make almost no assumptions about the generating process yielding the data, but still want just enough assumptions to make reasoning possible, you may be working in the vicinity of any of cognitive science, neuroscience, or artificial intelligence.

The key thing you always have to remind yourself is: you are making assumptions. The question is: which ones? The original article reminds us of a whole lot of the assumptions behind current deep learning:

* The "layers" we care about are compositions of a continuous nonlinear function with a linear transform.

* The functions we care about are compositions of "layers".

* The transforms we care about are probably convolutions or just linear-and-rectified, or just linear-and-sigmoid.

* Composing layers enables gradient information to "fan out" from the loss function to wider and wider places in the early layers.

* The data spaces we care about are usually Euclidean.

These are things every expert knows, but which most people only question when it's time to look at the limitations of current methods. The author of the original article appears well-versed in everything, and I'm really excited to see what they've got for the next part.
Are there currently any ERC tokens with applicable utility other than wild speculation and claims about unproven future features which the ERC20 tokens are created in completely arbitrary (artificial scarcity, sidestepping SEC IPO investments) limited amounts for?
135 at 5'8" is 20.5 BMI. Why would she want to go much below that?
>A key responsibility of being a parent is to make sure arrangements are made for you children should you die.

Could you even get life insurance if you engage in high-risk activities?
This illustrates my problem with "the keto phenomena" (also including Atkins and its ilk).

My problem is because my parents have been on the Atkins diet for about 5 years now. They objectively have lost weight, they claim to feel much better, more energetic, etc. I don't have a way to argue that this could just be because they are healthier, fitter, and slimmer now. That the reason could just as easily be because they consume less calories and a no-longer-absurd amount of carbs. To them, it's all because of Atkins.

The reason I have a problem with it is that I'm not convinced that long-term, severe carb restriction (and the consequential increase in fat in the diet) is healthy. Their cholesterol is rising, they are consuming alarming amounts of high-fat foods such as bacon and butter, and I don't think they ever actually learned how to "diet" because they still eat a lot, but because it's low carb, they don't gain weight. They still drink and eat a lot of "sweet food," because they never tackled those cravings, just instead of sugar now they get aspertain or whatever it's called. Lots of coke zero, atkins bars, etc.

I can't find any evidence that long term atkins dieting is unhealthy, so I can't convincingly link the increase in cholesterol (or convince them that high cholesterol levels are unhealthy) or increase in kidney stones to their diet. To them if I ever am able to point out a negative change before and after their diet, it's simply due to old age (kidney stones for example).
Your private keys should be fine, no matter how the fork happens. In every scenario discussed everyone keeps their old coins, as far as I'm aware.
My problem with Keto was that it made me dumb. I found that when I was in ketosis, my brain just wasn't as sharp. Tasks that should have taken 5 minutes took half an hour. My memory recall was not as good (not that it was great to begin with). I know it was Keto causing it, because my brain would go right back to normal in under half an hour if I drank a can of soda.

Agree with everything else though. It definitely changes the way your body works (the whole "sweating acetone" thing took some getting used to), and it's a killer way to lose weight.
What do you think about a bayesian interpretation of the above as MAP/MLE?

https://arxiv.org/abs/1706.00473
>I agree, particularly since there's been 100s of contributors to the project. Which makes Blockstreams impact much less valuable than the original comment implied.

This always get's bandied about. There are less than 20 people responsible for the vast majority of the commits in the last year, the other 90+ are small scattered changes. Easy to verify, go look at the commit logs. Oh, and 3 of the top Contributors aren't even working on core anymore.

>SegWit does nothing of the sort. Lightning Network and Sidechains, which can be built on top of SegWit, certainly reduces off-chain fees. SegWit itself only reduces the fees on witness data, but that's moot for the time being. SegWit will have little impact on fees right now.

Segwit is all about sidechains, that's the main application, and the real purpose. Side chains mean transactions off chain. Transactions off chain mean less fees for Bitcoin miners. It's not rocket science. The only way they make it back is if the settlement costs become huge.

> That's actually false. Block sizes aren't really an issue for the miners, they are an issue for the nodes. Each miner only needs single full node. What increasing the block size does is make running a full node (which has to store the entire block chain) extremely expensive. The larger the block sizes, the more expensive a full node becomes.

The reason nodes are expensive is because they are doing full historical validation which is almost completely useless and unnecessary. Minor changes to the Bitcoin block header would make that completely irrelevant. The nodes need all data ever argument is total junk.

>LN is an opensource implementation of something not dissimilar to smart contracts. Calling that control is laughable.

How much does it cost to open and close a channel on the main bitcoin blockchain at a 1 megabyte limit with millions of users? It's not a free thing, it's very expensive. To say that won't result in centralization is ridiculous. LN is not a solution without larger block sizes.
Because maybe she wants to look strapped as fuck? Why does anybody push their fitness levels beyond "healthy average?"
There are some really good not chicken strips in the frozen section now.

There's a world of difference between fraduent babyfood and obvious meat substitutes.

The conflation of the two is academic dishonesty (and FUD) of the worst kind.
If you think Richard Branson ever worked even 40 hours a week in his life the way an employee does, you'd be surprised.

He had a hippy indy prog record company that got lucky with an unexpected hit record. The rest is investing, self-marketing, and networking, hard work (a la Jobs or Carmcak or Gates etc.) was seldom required.
> While it has amazing CI pipeline capabilities, we ultimately didn't select Concourse because it felt much more like a CI toolbox, requiring some development to put those tools to use.

This is actually a brilliant insight. I'd never thought of it that way. And I'm a one-eyed raving Concourse fan.
See: Kant on a priori notions of space-time.
My understanding is that innovation comes from reinforcement learning during self-play (rather than supervised learning of pro games), and thus goes against the best moves suggested by AlphaGo's policy network, in turn pushing it towards new options.

In a sense, it seems innovation arises when the value network forces the policy network to expand the search space because an apparently unlikely move leads to downstream positions deemed favorable.
I'm sure a lot of those dark market coins are going through BitPay.
Define success? Define happiness? I get a pretty nice dopamine hit when I solve a hard problem. Money doesn't make me happy but maybe that's because however much I get, it's never enough.
Just imagine what will happen when state actors start to use this as part of psychological warfare...

Future looks grim!
Food waste happens at every level of the food distribution system. Produce discarded in a field becuase it didn't meet some ripeness or quality standard(likely unchanged), produce that rots or is damaged during transport to local distribution channels(could be reduced by meal prep), food that rots or is unsold at the retail level (obviated by meal prep), food purchased by consumers that goes unused(obviated), and food waste that gets landfilled(instead of getting composted at meal prep place).
Briefly, Pipes worked with feeds and data streams. IFTTT triggers on events.

https://google.com/search?q=ifttt+yahoo+pipes

https://www.import.io/post/5-yahoo-pipes-alternatives-that-a...
Please don't: https://news.ycombinator.com/item?id=14785597
My brother did a fair amount of mountain climbing in his 20s, but stopped around the time he had kids. He specifically said "I don't want to go again until they're old enough to remember me." Done properly, the risks are limited, but they do exist no matter how careful you are.

Now he's hit about that right time and I don't know if he even still has the desire. Age changes a lot of that. Still, it does seem like he made the responsible choice to me.
(1) FRANCE AND SWEEDEN BURN ,MUSLIM AGENDA
> Nothing is risk-free

You can determine a threshold though?

> can even agree what "reasonable parenting" means.

So much as we can agree what reasonable <anything> means - which we can, as there are all sorts of laws regulating various aspects of life. We can, to some degree, legislate "reasonable driving", or "reasonable flying".
Heads up for the Gitlab people here-- The user preferences link under try it yourself 404s for me, both when logged in and out. Excited to try out these changes though!

Edit: Appears to be the same path but with `about` subdomain
IMO it helps to treat calorie-counting as something that is fundamentally inaccurate, but still "good enough" as long as the errors are consistent.

Meanwhile, the moving-average of your weight is closer to some kind of ground truth, and can be used to recalibrate the calorie budget.
"The SJW is the odious little Eichmann with his jackboots up on a fair trade coffee table..."

Really??? That's rich coming from /pol
While I know nothing about this project or the creators, I find your comment very ignorant. If you don't like it, go do something else of your own.

At least the creators spent time and effort doing something they find valuable. That's 100x better than your comment.
> Despite not being required to cut calories, participants were eating an average of 500 calories less, and what they were eating was high quality.

That's what happened to me. I tried to lower carbs to 40% of calories, not to lose weight but rather to see what affect it would have on blood sugar levels. My A1c had crept up to 8.1, and that was while on two different diabetes medicines.

I made no attempt to limit calories...just to make sure that 40% or less came from carbs. In fact, to reach that I started increasing the size of some meals. For example, if I got a sandwich which would normally be 50% carbs, I would order that sandwich with double meat and/or with full mayonnaise instead of light mayo or no mayo, because meat and mayo are calories without carbs, and so lower the carb ratio.

I found that this made the food more satisfying, and I ended up eating less kind of automatically. Over the next 18 months I lost 140 pounds that way. A1c is down to 5.0 now, and that is after three months with no diabetes medicine.

Long term, I'm eating about 30-35% carbs, and about 45% fat.
>lean-meats

Well, it's not easy to do it "cheap," in most people's mind of what "cheap" food is - the little muffins at star bucks, or mcdonalds, the free cereal at work (when there are no high-protein, low-carb options).

So it's not "easy" because you're no longer even capable of eating what 80% of the people you know are. If you had even half of one of the muffins at work, you'd be thrown out of ketosis and you'd need half the week getting your diet back on track.

That being said, if you already have a strong habit of cooking and eating the majority of your meals instead of eating out or eating the free food at work, a low carb diet is a remarkably "easy" way to lose weight, because it doesn't come with that shitty feeling of grumpy hunger that (I at least) one gets from low-calorie dieting.
> maybe that was your original point that I misunderstood.

Sort of. If you're a journalist working for a media company, from the viewpoint of the media company, the value you're producing isn't your content, except indirectly. The direct value you produce is in attracting eyeballs, and hence ad revenue. So if your goal is to produce value with your content and get paid for that, you're not going to achieve that goal working for a media company, except as a lucky side effect.

If you're self-employed and being supported by your readers (say, by Patreon contributions), then you are producing value with your content, directly. That was my understanding of the goal of the author of the article.

> I'd say in many of those jobs you have the self-determination to choose any topic. You just can't guarantee that there won't be any blowback of you pick some topic of controversy.

You can't guarantee no blowback if you pick a controversial topic no matter how you are getting paid. The difference is in how that plays out. If you work for a media company, your future depends on the people running the media company and whether they will support you. If you work for yourself, your future depends on your readers and whether they will support you.
Numeraire for example (NMR) is a ERC-20 token which has a real-world application today.
OP is providing feedback on the project just like you are providing feedback on OP's comment.
>Yes, and you, unlike the author of this piece, appear to grasp the basic fact that nobody will pay you for this.

You'd be surprised. Actually, there are tons of freelancing jobs that pay you just for this.

Not to mention that the "author of this piece" has been making money for 10 years now, and getting paid, thank you, for doing his thing.

So maybe it's not him, but you that don't understand what's available?
Over 30 years of momentum.
I don't have the first clue about finance but I'm really interested in learning. I'm willing to share whatever I have to offer in return (it's not much -- but sometimes a new perspective can mean the world).

rug3y@teknik.io
> That's not what I said.

Says the guy who translated "actively go around risking your life" into "taking risks"?
Those exact same words (with rider replacing diver) were the first ones uttered to me by my Motorcycle Safety Instructor when I first took the course back in the the 90's. He was an ex-CHP officer with ~1 million miles under his belt, and had a lot of stories, gruesome and glorious.

It was a great class, and he was a wonderful teacher. Scared the foolhardy, sobered the romantics, and conveyed a career's worth of lessons on riding safe. I've been accident-free since starting in '94, and I genuinely do think a large part of it was his initial, excellent instruction.
Careful, that's the sort of thinking that led to the last 'AI Winter': assuming that if enough rule-based expert systems were built, general-purpose systems could be assembled from them and/or enough could be learned to build general-purpose systems.

Now, it is worth noting that DL models are already being assembled together (often with a coordinating DL model to switch between them). This can have the advantage of the smaller models being reusable to some extent (certainly more than expert systems ever were) but is not a panacea. The results are still essentially bespoke models rather than general purpose ones.

Deep Learning obviously has a lot more mileage left in it, given that much human mental labor is 'just' training and using our general-purpose intellects for what amount to a series of rather narrowly defined tasks, but it won't surprise me if there is a wall of some sort lurking just over the horizon that will require a different approach (albeit one that may still be called 'deep learning') to cross.

OTOH, it does seem as though the folks at DeepMind are fairly aggressively pursuing whatever is on the other side of that particular horizon:

https://deepmind.com/blog/neural-approach-relational-reasoni...

https://deepmind.com/blog/cognitive-psychology/

https://deepmind.com/blog/imagine-creating-new-visual-concep...
some pig
Love the idea. Clearly, a ton of effort put into this, congrats on shipping. But if you're going to abstract away Google Sheets, you might as well make a thicker abstraction. I want to use Google Sheets like a key-value store or some Mongo-like API, without thinking about worksheet names, column names, or underlying worksheet-specific implementation details. Your custom designed API is too much of a leaky abstraction, seems that it's not too far from me learning and using the Google Sheets API directly. Consider how other libraries like diskDB abstracted away the filesystem behind a Mongo-like API.
> 95% of people who diet gain that weight back and more within 1-5 years.

This keeps being touted again and again as if it's some grand criticism of diets. Obviously if you stop following a diet, its results will stop applying to you. If you follow a diet that restricts your calories and causes you to lose weight, and you then stop following the diet and eat whatever you ate earlier, of course you will gain back your weight.

Healthy eating is a lifestyle decision and a lifestyle change. You have to understand what your diet was trying to achieve, and then try to apply similar concepts to your general everyday life.