skysight-inc/HackerNews huggingface dataset:
I have a slightly smaller pad arm that I've connected to a deck (sitting) chair and now that's become my reading chair.

A remote would be really neat though.
Show HN: Rules-based labelling tool for NLP
I am wearing one. Switched from smartwatch. A silver colored version is nice looking too
France too. It's a shame to eat McDonald's in a country with so much great food, of course, but getting a burger covered in blue cheese served with a beer is a bit better than the standard experience.
> absurd that you'd buy a laptop with a bunch
> of "hardcoded" ports that you can't ever change.

This is great for multi-monitor users. Depending where in _my setup_ the other monitors are, I don't want the power and video cables, or docking port cable, to cover part of the external monitor. Being able to move the ports' locations is genius.
Hey HN!

After working in ML for more than a decade, I became frustrated over time with the lack of tools to create baselines using simple rules and heuristics. It is well known that most business problems can achieve decent baselines using only heuristics. So this is why I have just open-sourced DataQA, a rules-based labelling tool for NLP:

- Quick labelling: You can create complex rules using regular expressions to help you label your text faster.
- Search engine: DataQA also ships with a search engine (local elasticsearch database) so you can search your documents.
- Easy installation: Only need to install a single python package!
- Easy use: upload your data as csv files.
- Privacy: No data ever leaves your computer.

I'm hoping to get some feedback, and I'm open to hear about feature requests or ideas for extensions. I will be around to answer questions.
Anecdotally, I never had any success with this. I bought several cheap but powerful (100W-300W) 12VDC Peltier elements off Amazon. With the hot side bonded onto either a massive chunk of aluminium or a CPU cooler, the cold side never reached less than minus a few degrees C. Open air mounted or with some cling foil insulating it from air (ambient was probably around 25-30C). I also tried stacking a second (up to 50W) Peltier on top of the first, but that was even less successful. Just to say that it's not quite that trivial to get to -20C using Peltiers. If anyone likes to share their more successful experience, I'm all ears!
This is fantastic news and a model for others to follow. Going to scope 3 ("indirect emissions from making products and services, e.g., from purchased materials, business travel, etc.") really is going above & beyond what other businesses are doing - and not only that, they're even offsetting 125% AND donating the same amount as a "promise bank" to buy carbon capture tech when it's available.

Couldn't be prouder to be a DDG user.
I went into ML when I realized that this piece of advice is now wrong, at least in computer vision.

It was a few years ago. I had to classify pictures of closed and opened hands. I thought surely I don't need ML for simple stuff like that: a hue filter, a blob detector, a perimeter/area ratio should give me a first prototype faster and given the little amount of data I had (about a hundred images of each), not worth the headache. I quickly had a simple detector with 80% success rate.

Then as I was learning a new ML framework, I tried it too, thinking that would surely be overengineering for a poor result. I took the VGG16 cat-or-dog sample, replaced the training set with my poorly scaled, non-normalized one, ran training for a few hours and, yes, outperformed the simple detector that took me much longer to write.

Now in computer vision, I think it makes sense to try ML first, and if you are doing common tasks like classification or localization of objects, setting up a prototype with pre-trained models has become ridiculously easy. Try that first, and then try to outperform that simple baseline. In most case, it will be hard and instead worth improving the ML way.
i enjoyed all of this writeup except this sentence:

> He then opened Evernote to look up his AWS database credentials.

which somewhat scared me.
I mean; he did. https://www.youtube.com/watch?v=rYdvBZxPhLY

Additionally; laws follow humans, not the other way. If you're a person in a profession that is not allowed to argue for better conditions: then that's the best way to get the worst possible conditions.

Seems like a false economy to force people to accept the conditions that they get with no recompense. A bitter FTC employee is much worse than a strike. A bitter FTC employee can kill hundreds.
This quite strongly depends on the composition of the metorite and the angle at which it enters the atmosphere.

The Chelyablinsk meteor entered at a very shallow angle & was a chondrite with little internal strngth, so it disintegrated over a longer distance forming a “line burst” rather than a single point airburst.

Had it entered more steeply, then it would have concentrated it’s KE over a much smaller area, causing much more damage.

A nuclear bomb may take a few ms, but after a few 100ms, you’ve got a dense, hot plasma in a small volume. Sounds a lot like the result of a metorite airburst! And indeed, people use the same codes that are used to model nuclear explosions to model meteor airbursts.
Should start putting that on smartphone screens.
Using the tech company label as if it was a random startup is not really relevant given the place Facebook has in our societies. Also it's not 00's "don't be evil" anymore, governments should absolutely shake down ad tech IMHO.
AI, The next revolution leading us into the 4th Industrial Revolution
Unfortunately in big companies, pointing in the right direction isn't good enough: then you have to fight inertia, inter-team, inter-site disputes to finally do the right things :-(
My Tex Shinobi keyboard has perfect IBM-like trackpoint.
The website is dead, Hacker News just killed it.

archive.org mirror: https://web.archive.org/web/20210721043921/https://terem.tec...
You're making money off of the death of your fellow man and you're trying to tell us that it's okay. It's not okay, you're helping them.
My mother likes to tell the story of the first time I encountered a dead mouse:

"Why isn't he moving?"

--"Well, he's in the sky." (One way we say "in heaven" in my native language.)

"But he's right there."

--"Well, his soul is in the sky."

"..."

--"..."

"And why isn't his tail in the sky?"
Well the latest compute module does expose its pcie. People have already bolted nvme drives on to it.

It’s not blindly fast (iirc it’s only a single pci lane and to get the full benefit out of m.2 nvme drives you need 4 lanes)

It’s just that the pci lane is used by the USB3 host controller on the RPI4
It's great that they're measuring but claiming to be carbon negative by purchasing offsets is quite a stretch. It really dilutes the true meaning of it. Also, the offsetting markets are very murky indeed. In many cases, offsets are IOUs that haven't been realised yet. Also, offsetting credits often come from projects where you can't even know or guarantee the lifetime effect of the projects, like reforestation - who guarantees that those projects will be around in ten years?
Airbus reveals the next generation of CityAirbus
If they could be replaced then the notion of it being illegal because they were "absolutely required" should be called into question.

You can't make it illegal to strike on the grounds of it being dangerous; then fire everyone for it with little or no issue: it just tells us that the reasons for it being illegal were bullshit.
Tips for an efficient and productive Work from home
Yes. Someone paid a fortune for that document.
Remotion: Make videos using React
Since DDG uses Bing does this factor in the carbon emissions of Bing?
That doesn't make it impossible to get such configuration options. If I can choose how much RAM I want, they could just as well offer the "no RAM" option so I can keep the two modules of my old laptop. Instead they lie to our faces claiming RAM has to be soldered on for some reason, making that impossible. Same with SSDs etc.

I would understand it if a manufacturer offered some ultra high-end model with custom storage like in the PS5. But if the Framework laptop can have swappable RAM and SSDs with almost the same thickness as a (insufficiently cooled) Macbook it's obvious why that stuff is soldered in.
> When it was his turn, they immediately started telling him not to be afraid and that it wasn't going to hurt. He sat on his dad's lap, his dad holding him tight, while his mom desperately tried to deflect his attention by waving a teddy bear in front of him and non-stop talking to him.

"Here's a glass of water. It's definitely not poison and it won't kill you. It's not going to burn your throat when you drink it. Now let me fixate you in case you start violently thrashing around. Just drink the water and think of something else. We'll get through this together, okay?"

I think anyone would freak out at this point, even if it really is just water.
You realize that by litigating this stuff, it all goes out into the open, right
Yeah, it kinda seems like buying offsets does nothing to actually reduce real Co2 emissions and its more "fund a project that aims to maybe, possibly, sometime in the future reduce Co2 emissions"

I think these projects are worthwhile, and do need investment, but carbon offsetting seems like a disingenuous term to use here. Especially if the stuff that is funded doesn't even work out.
> I assumed there was some sort of tip sharing deal between them

Oh sweet summer child...

Some people are nice... and some people dont get their fair share especially when they are replaceable.
"The most peaceful and helpful nuclear explosion " lol
From the article, they say that they are Going Carbon Negative. But they are not there yet (because it is technically not feasible yet). A more accurate title would be: "DuckDuckGo Is Going Carbon Negative"
> How much are you supposed to care about someone you married who wants to divorce you?

Well, if you dont care much about them, they are likely to divorce you. By the sound of it, this dude did not cared about relationship one bit. God knows what faults she had, beyond jealousy, because OP choosen not to trash the ex. Which is noble.

But she was done with relationship for reasons that had nothing to do with money. He was emotionally checkout out for months too. Expecting money windfall to fix that all is asinine. Even if she decided to cancel divorce for half those money, it would be marriage for money and nothing more.
No shame in it. If you like their burgers eat them.
IIRC the CEO of Twitter did lie to congress recently and nothing happened to him. It seem like its not a lie if you cant proof that what someone said was known to be wrong at that time by the person who said it.
James Clapper's lies while under oath suggest it needn't be.
They do in Spain too. You can even buy gluten free beer in McDonald's in some parts of Spain.

Here in Scotland the soft drink Irn Bru is served, as this is more popular than Coca Cola.
As others pointed out the term Hero is not used in a conventional way in this article. Usually when Hero Developer is portrayed as a problem, it means there is one person who solely takes upon himself to solve all problems without consulting or delegating responsibilities with anyone else in the project. Then that person is praised for it by management, growing the developer's ego and belittling everyone else in the process.

On the other hand, the term Hero in this article and research refers to a developer who is most involved with other people in the project, and has the most communication with everyone else, and thus shortening the communication gaps and misunderstandings. Reminds me a lot of a hack against Conway's Law. This person also happens to do most of the work, but that might as well be accidental, since this person (in these github projects) is the nominated responsible person for that project.

And the bias for "publicly available open source projects on github" is just horrible. It is like inspecting the color of bananas and then proving that avocados are in fact yellow.
Well the main sound you hear when a train arrives in a station is the sound of brakes. Its frequency and volume changes as the train slows down. You'll need to analyze the physics of that before extracting doppler shift from it.

Also, depending on the track used, there may be trains passing by without braking, so you will need at least a classifier to sort these two cases.

I'd argue that using ML to build such a classifier is almost always a time saver.

And if you have the ML pipeline there, why not try to train it to recognize the speed while we are at it? It will likely find out about doppler shift but also do things that would take ages to code manually:

- Use volume levels and volume level differences
- Use the clicks at rails junctions to evaluate the speed
- Recognize the intensity of the braking/engine running
- Use cues like rails vibration at certain speed
- Adjust for air pressure difference when it hears the rain

All of that for free. Nowadays, going ML first is becoming a pretty good idea actually.
Link11 DDoS Report at Mid-Year Reveals 33% More Attacks
Link11, Europe’s leading IT security provider in cyber resilience, has published its annual Link11 DDoS Report for the first half of the year. The report shows that DDoS criminals were once again very active between January and June 2021, launching a record-breaking number of attacks.
> Do you see how the corrected statement, actually gives people something to engage with, instead of someone merely asserting the amoral, unopinionated fact of "They have a board seat"?

Yes and no:

Yes, I understand that to some people, obviously including you, "They have [bought] a board seat" is an "amoral, unopinionated fact".

But no, that's not "instead of merely". Looks to me like the problem is that you don't see that to many people, "They have [bought] a board seat" is not just an "amoral, unopinionated fact" but in itself already a moral punchline to engage with. There is no need for any tweets to "intend to imply" anything. Whether any non-beneficial consequences have followed or are immediately to expect is, in their view, irrelevant: That's just not how shit is supposed to work, i.e. wrong in itself.

And frankly, it's a bit baffling to me how you and those who argue like you seem to have such a hard time understanding this view. I mean, it's one thing not to agree with it, but you're all coming off as if it hasn't even occurred to you that it's possible to see things that way. Makes me wonder if you're seriously so blind, or if it's some underhanded debating tactic. Whichever it is, in my eyes it seriously weakens your argument.
> Validation is then as easy as running the model on some examples outside the training set.

Only if you can trust the data gathered from that validation to be representative. You can do that easily when you understand the statistics your model is doing - which is the case with an "old-school" ML solution, but not so with DNNs.

This gets worse the more complex your problem is. I can expect a DNN to pick up the correct frequency patterns in audio time series quickly, as it stands out in the solution space - but with more variables, more criteria, we know it takes ludicrous amounts of data for the model to start returning good results, and it still often fixates on dubious variables.

And then you have to ask yourself - what are your error bars? With a classical approach to estimating train velocity from sound, your results will be reasonably bounded, and won't surprise you. With a DNN, all bets are off.

> How would you determine the characteristics? From a dataset of examples, I guess.

And physics. In this case, a human can apply their understanding of physics to determine what characteristics to expect, verify they exist in the dataset, and encode that knowledge in the solution. A DNN will have to figure this out on its own, and we have no good way to verify it did it correctly (and isn't just overfit on something that's strongly but incidentally correlated).

I agree there are plenty of problems where we don't have a good "first principles" solution - where we're just looking for correlations. DNNs automate this nicely. But such models belong to the category of untrusted ones - they might seem to work now, but because of their opaqueness, we can't treat past performance as a strong indicator of reliability.

> Well ML is often basically a statistical model with lots and lots of parameters.

Yes. But I think it matters if people know what those parameters do.
Agreed. I actually made a quick edit to include that, and then saw you made the same point.
This might be more fit for StackOverflow, but I have a related question.

I have a Go application that runs in Kubernetes, where memory usage steadily increases until it's at around 90% of the cgroup limit, where it seems to stabilize. As far as I can tell, Go GC uses the container memory limits to navigate it's total memory usage (this might be the fault of the OS not reclaiming what Go has already freed(?)).

However, my issue is that in this app, I also call out to cGo, and do manual memory allocations in C++ every 10-30minutes. This works well, except when the container is stabilized at a high memory usage, and my manual allocation brings it over the limit, thus forcing kubernetes to terminate it. (These allocations should as far as I know not be leaking. For a short while, I have two large objects allocated, and 99.9% of the time it's only one)

So, what I'd ideally want is to be able to specify a target heap size for GoGC, and then have a known overhead for the manual allocation. But as far as I'm aware, this isn't possible (?)

Does anyone have any experience with something like this, or see any obvious avenues to pursue to solve the termination issue?
Two decades later and the movement has succeeded. Agile’s underlying principles and values are now table stakes for any organisation. The degree to which an organisation is applying agile is now a matter of small increments of productivity rather than a revolutionary game-changer.

So it's not dead, it has become standard operating procedure.
Join AsyncAPI Hackathon and win $5000
Significance of Performance Testing in Ensuring Holiday Readiness of Apps
Isn’t that what «Ask HN:» is for? You can also just post a title without any link.

So you are basically asking for a subset of HN? To avoid echo chamber I think the links is a good thing.
WhatsApp uses the Signal encryption subsystem and they only allow one other machine.

What is FB doing that allows this on Whatsapp but not Signal.

What a WhatsApp users opening themselves up for here?
This is highly irrelevant to the post you replied to.
> Classifying communism as a monotheistic religion is... an interesting take

Somewhat related. According to Primo Levi (a famous holocaust survivor), there were two classes of people who stood up well in the hell of Auschwitz. One was religious people such as Yahweh's Witnesses, and another was ... Marxists.

Levi totally got lost for words when, shortly after being liberated by Russian army, he chatted with a fellow French prisoner about how lucky they were to survive the Nazi camp, and they did so by only a slight twist of fate, and got a surprising reply: "... but Joseph was there!"

Of course, the French man was talking about Joseph Stalin. He never lost his faith in the camp.
You are right, every business should be working to minimise the impact they have rather than offsetting it. Carbon free is better than carbon neutral[1], of course, but ultimately you can't be entirely carbon free. Running servers, moving bits around the internet, and just running a business uses CO2. To try to offset what you can (and more in the case of DDG!) is still a positive climate action.

[1] Carbon free means not causing carbon to be released; carbon neutral is offsetting the carbon that's released by capturing it somewhere else.
I think the author was focusing more on general applications (given his research & industrial background). In computer vision & NLP, the field is a bit advanced and it's harder to come up with rules. The promise of Auto-ML is bigger in these two fields.
The low level of RAM will make it hard to support epub files out of the box. They are pretty nasty to deal with as they are actually zip files containing html and css. So to render an epub file accurately you need to parse and render html.
In the US, when you have a lot of money you are above the law.
I love financial engineering stuff like this that leaks out into more 'real world'/physical things (being not a finance professional). DDG has purchased so much carbon footprint offsetting whatevers that it's 'carbon negative'... it is... funding (in carbon allowance) 'carbon positive' companies? I'm (hopefully) obviously joking - you can plant lots of trees without saying 'its ok to burn more coal now' - but it's fun to think about isn't it.
Didn't think of that.

Good idea!
The other issue with offsetting is that it's an easy trick to wash over any increases in carbon output. Outputs increased by 50% in a year? Just offset more. The aim should be to minimize output as much as possible, rather than brushing it under the rug of questionable offsetting.
Huge amounts of data was necessary in the early models and is still when you want to win DL competition.

I fine tuned YOLOv5 with a few dozens hand-labelled images to make an object detector in a semi-controlled environment.

The idea that you need a million images to train a detector or a classifier is now totally wrong. Fine-tuning can be done on a very small dataset.
You've said everything I wanted to say.

I therefore pledge to reduce my carbon footprint to zero for 300 years after my 120th birthday.
Show HN: Nussknacker
Hello from the Europe (PL). What are the odds of getting one of those if you live outside US/CA?
Oddly missing the embedded-in-windows Chakra? (And chakracore)
Indeed, it dilutes at best.

We're to expect that, as demand increases, whoever is selling the negative carbon will eventually double-sell the same negative carbon.

I suspect that whoever is buying has no way to check.
In Croatian the word is vrtić, which means little garden.
Guy claims to have evidence of who Satoshi Nakamoto is.
This guy claims [0] to have evidence of who Satoshi Nakamoto is. Basically he claims it's Adam Back because he used the same linker for Hashcash as for (a very early version) Bitcoin.

Sounds thick milky to me, what you guys think?

[0] https://www.youtube.com/watch?v=_iMwixTBsb0
> i.e. the only "salary" is that you can add working in a 3 star restaurant to your CV.

Spain has salaries? I thought that was largely a US concept.
I am astonished at the omission of ChakraCore, open sourced by Microsoft as what Edge used to use, but sadly abandoned by them after they switched Edge to Chromium: https://github.com/chakra-core/ChakraCore. But it looks like it’s still chugging along as a community effort rather than being completely abandoned.

ChakraCore is definitely one of the most notable and significant implementations after the big three.
But the therapist did not said that "properly functioning brain takes suicide into account as a possible choice all the time". That is much stronger statement then "you are not at risk of suicide".

If is completely normal and usual for properly functioning adults to not see suicide as a possible choice all the time. Suicide ideation is not normal state of being.
It's what companies always mean when they write about carbon offsetting. I don't see the problem tbh. Buying offsets drives up the price of these offsets and that in turn spurs investment in less carbon intensive alternatives.
That's an exagerrated take. I think schools assigns group projects to students, so that they can learn all kinds of group dynamics, mooching off of harder working people being merely one of them.
If you have a point to make you should make it. I have no idea who this is (or why what is on his wikipedia is relevant) and I don't know what you mean with (what I assume is euphemistically stated) "contextualised".