skysight-inc/HackerNews huggingface dataset:
> WSL is passable for running Docker containers, and that is if you add a ton of complicated socket forwarding machinery and Window-side service management automation to it, which is what Docker Desktop does.

That's exactly the reason why I mentioned simple docker containers.

Once I needed to make some of them work with macvlan/ipvlan, and docker desktop on mac and windows were both completely unusable.
I would love to work on an open source project I am passionate about.

So in the interests of finding this passion, I journal everyday. I wrote down 450 journal entries, each one is an idea for computing software or a startup.

You can find links to them on my profile.

I recommend you take up journalling and dig into your ideas and thoughts. And write things down for rereading later. Someone might like a thought you wrote and contact you. I've had people contact me and be interested in my GitHub projects.

If you want serious people, join the product hunt community.
It’s cool and all, and I don’t mean to knock it in any way, but how is this not entirely “about novel aesthetics”?
The "Concrete use case: intersecting ray" section implies that it might be reasonable to do logical ops by first intersecting each Bézier-represented curve against the segment-representation of the other, then stitching together the Bézier-represented boundaries of what remains. (with the last step made fiddly by the fact that calculated intersection points of A with Segments(B) are likely to only be "reasonably close" to intersection points of Segments(A) with B)
This is likely at the top because you've manage to hide your irrational dislike of Firefox underneath a semi-plausible veneer of technical complaints, while others who more directly get upset at percieved political differences haven't been upvoted as much.

But it's still the same BS.

HDR on Youtube is not 'the basics'. Don't be silly. And you listed it twice because you had nothing better to add.

If you want to attack the lone remaining independant browser maker because of some political disagreement you have with them, then fine. That is also stupid, but at least it's honest.

Don't lie to us or yourself by scraping together this frankly absurd list of minor gripes and passing it off as a serious technical complaint about how Firefox is managed and developed.
The visible remains of ring forts in Ireland are commonly referred as “fairy forts”. In many areas of the country there is a taboo associated with interfering with or disturbing them.
> as happened in much of the world at the height of the pandemic

The median age of death in Australia is ~83yo. It's not far off from comparable European countries which are all around 80yo too. ( I don't consider the US comparable for a number of reasons, western Europe is generally the baseline we draw for most health metrics)

The UK has a median age of 83yo also, despite their horrific death toll and lax attitude compared to Australia. [1][2][3]

So the onus here is on you to prove evidence to back up such a statement that it's "healthy young people dying in the thousands"?

[1] https://www.ons.gov.uk/aboutus/transparencyandgovernance/fre...

[2] https://www.ons.gov.uk/aboutus/transparencyandgovernance/fre...

> Meanwhile, deaths among those aged 44 or younger made up under 2% of the total

[3] https://www.theguardian.com/world/2022/jan/16/what-do-we-kno...
https://ibb.co/Fz383Qm

Is that preview on the right not a preview?

Or are you talking about thumbnails?

AFAIK, Gnome is currently planning to get the file picker out of GTK and make it part of Nautilus, thus reusing Nautilus as file picker.
The dilemma is that although there are recommendations like fast.ai to get your hands dirty quickly into ML/DL, none of the good AI researchers and practitioners got there via these quick tutorials. They got there via rigorous linear algebra, traditional ML, statistics and related computer science knowledge.

I would say try fast.ai for a quick taste of what ML/DL is like, and then go back to linear algebra, deep learning and stats courses from top schools while picking a personal project goal to achieve (e.g. reproducing a popular CVPR/ICML paper results or building your own XXX) Once you go through a full lifecycle of building something from scratch, you will have a much better understanding about where you are and wanna go from there.
If by offender you mean the bot, then I'm confused. The bot asked for robots.txt in a plaintext format. The bot was delivered some binary garbage that it couldn't parse. The bot continued to ask for an updated robots.txt, and continued to be told that the binary garbage was the intended content. What more was it supposed to do, exactly? The offending party here is the broken hosting platform.
Real-time visualization with React and D3.js
These types of anecdotes make the actual practice of both ML and AI seem rather, well, less than scientific. There is supposed to be Ph.D. level math behind all of this, yet an amateur with admittedly no ML background is part of the team.

In Star Wars, it takes Luke Skywalker years to learn to use a light saber skillfully. Then in The Force Awakens, some ex-Stormtrooper with no training picks up the light saber and within 5 minutes is a pro. Kinda ruined the mystique of Star Wars, just like people jumping into ML with no training ruins the mystique of ML.
> if you're a farmer, you'd commute

Most farmers work from home don’t they? All the ones I know do.
Designing Offices with Machine Learning
Show HN: Cookieless Conversion Attribution with Pathview
Morning HN.

I worked on a cloud CMS and then pivoted an analytics feature to a standalone SaaS. Pathview focuses on the conversion path rather than general analytics. I want to help users optimize conversions.

It doesn’t use cookies, contains 160-characters of JavaScript, and leverages HTTP Messaging for a modern take on an old-school analytics approach.

I’m close to launching a public beta test and could use a sanity check. Any advice, feedback, or questions for this first-time developer?

-sb
This. Back in the day you could find a custom ROM for pretty much every phone, and most of them were quite good actually. Now the only phones that get interesting ROMs are the Google pixels. Ahh, times have changed.
In the short term, kinda curious what would happen to all the people who are sustained by the economic activity of the things you deem superfluous. Someone works those jobs. Provides lunch, cleans the offices, sets up the furniture and AV, maintains the building, etc...

The pure fact that it provides increased economic activity and therefore is able to support different jobs and the second/third order consequences of those makes perfect sense to me.

In your stay at home model, what would all those people do instead?
Cascading failures in drives is a fairly common occurrence unless you're actively trying to avoid it. People will typically just buy several of the same drive, from the same source, because it's easy and seems to make sense. What you've likely done is bought several of the same drive, from the same manufacturing batch, and then brought them all online at the same time. If there's a manufacturing issue, or just an expected lifetime for those drives, you're going to hit at almost the same moment across all your drives.

The ideal here is to buy similarly specced drives from multiple manufacturers to reduce the risk. At the very least buy from multiple suppliers to reduce your risk of getting drives from the same batch if this is something you're going to care about.
I highly recommend looking at Kyle Boggeman.[1]
He focuses on exercising for long term health using calisthenics and really opened my eyes at looking at exercise volume differently e.g. from fixed volume per exercise to accumulated volume over a week.

[1] https://www.youtube.com/user/Kbogea/videos?view=0&sort=da&fl...
Can confirm.

While data engineer is an excellent role for a fresh graduate, the data engineering profession shares many similarities with the SRE/IT professonional.

The best data engineers are the folk who had job title of DBAs of yonder year.

You will always be the supporting cast, rarely the star.
I guess it's horses for courses. One person's pain as a measurement of success in the gym is another person's reason not to work out until the pain is totally gone - which might mean they never get off the starting blocks.
> The book is full of great lines too e.g. "New York is not a city that plans; it creates situations and then deals with them"

This sounds like a good line to describe America in miniature.
Ask HN: What are your favourite Cybersecurity RSS feeds (Podcasts, Blogs, News)?
What are your favourite RSS/Atom feeds you consume to stay up to date with all things Cybersecurity in terms of News sites, Podcasts, Blogs or even YouTube channels?

Ideally anything that isn't already covered by sites like allinfosecnews.com.

Stuff like:

general news in the infosec space
threat reports
in depth research
career related/educational stuff
new products/tech
vulnerabilities
Im not really following kernel dev much, but I am a C and C++ programmer. Has the Rust panic() issue been resolved?

Does the lack of a standard, or even specification (code isn't a specification), not put a stop to this? I would expect a language in the kernel I run to at least have a stable specification or standard. I might be the old man screaming at the cloud.
I like that order, it matches how I'd work with a Javascript list of objects:

users.filter((u) => u.lastName == "foo").map((u) => [u.id, u.firstName]);

Python looks like the original SQL if you do it as a list comprehension:

[ (user.id, user.firstName) for user in users if user.lastName == "foo" ]
Somehow I just knew it would be Hampton’s video. Yes, this guy and his channel, hybridcalisthenics, is amazing at breaking workouts down into progressive sets for beginners .
That’s great. Apple employees who don’t like this decision are free to go work for a company like that.
I second learning the statistics/probability basics.

Your first model should always be something that predicts a constant value, or maybe in really complicated cases something like a linear/logistic regression. Then you have a baseline to compare more advanced approaches to. But in order to understand how to use linear regression well, you need to understand how it works in the first place.

Also experiment structure, sampling design, hypothesis testing, etc. will tell you a lot about what conclusions you can and cannot draw from observational data, which is what a lot of ML is about.
Linux has big GUI problems, we all know about it. But the HN discussion on Linux desktop is constantly partisan and very low quality, I am extremely disappointed. This is a celebration, if you have nothing good to say, just move on. This is a free desktop environment for a free operating system.

Happy birthday GNOME. You might not have as many fans as years ago, but I appreciate the courage of trying something new. I miss GNOME 2, but yours is still my favourite desktop on the PC bar none. Looking forward to the next 25 years.
Normally these pages are filled with people who prefer old gnome. I thought I'd weigh in and say I prefer gnome 3 by a significant margin.

* I don't use border click targets to resize, I hold alt + click to resize / move windows.

* I use "expose" like functionality to switch windows or alt tab.

* Nautalis isn't great, but I don't spend much time in there anyway.

* I prefer the gnome 3 launcher (Windows key + start typing), than the menu system

I barely see gnome, it lets the applications take centre stage, and I spend all my time in them anyway.
I like seeing actual numbers about what Good is meant to be for this metrics - it's easy for a devlops advocate to highlight a metrics and push for further investment in it, despite it not being a major weakness of the organisation otherwise.
> The solution is to just squat more frequently.

I've found stretching also is important prior to exercise.
I guess were all waiting for fields in the semi-shade of solar panels with a slew of robots powered by said panels, automating sowing to reaping.
I play Stadia. Couples of weeks ago, ToS change. Couple of days ago, an ad on my LG TV that if i sign up for Stadia i get 3 months free, and the Stadia app is very prominently shown in the Applications menu. Same on my ISP's Android TV box. Each month ~10 games get added to the Stadia Pro tier.

Stadia isn't going anywhere soon, or the people working in the division haven't been told and have continued spending money left and right with partnership deals, which would be a weird waste of money.
I can only talk from my own experience but there's other way than just straight up go to ML/DL and crank out new models. That way is to come from the application domain side. [0]

For me that was robotics, with the motivation that traditional method felt like it wouldn't scale outside static environments so I started to look in to ML/DL (Deep Reinforcement Learning really) and from the looks of it I'm not alone. [0]

Now I do research in it, without a PhD nor taking any courses in it at my masters. (except one DL course where we had to code everything including gradient flow from scratch. No framework)

Frankly, going pure DL at research level today seems like a steep uphill; the top labs and research institute(including industries) are the ones that are producing (and notably training) most of the SOTA models. Getting in to those circles are your best bet but then a PhD at a top university under a top professor is the best bet, and competition to get in to those are insane

[0] https://www.natolambert.com/writing/path-into-ai
I was always more of KDE person, but I've always liked GNOME.
Reset a File Input in React
Hi dang.

But this is my 22 years of experience in software development working for various companies from all over the world, including a number of companies in Asia.

I have experience working directly for about 30 companies and indirectly for about 50 companies.

I worked for a core security team for one of the largest phone makers from Korea. I don't want to name the company but when I say the actual privacy was not a huge concern this is actually my own experience working with them.

These are facts just like saying most Americans are overweight. You might not like it, but that does not change the fact.

Here, some more explanation about groups of people, facts, prejudice and racism: https://news.ycombinator.com/item?id=32480914
Strong types are an obvious and IDE parseable way to express limited contracts with in the application. There are different methods with stronger and weaker level of that. There are codebases where they add a lot of value. In other big code bases strong types add no value or negative because they are to strong or to weak. For many applications they help.

Other ways to manage large code bases include TDD with sufficient unit tests (can give more insight then just type errors).
The only time I look at usernames is when looking at thread replies to see if the person replying is the same.
https://www.deeplearningbook.org/ and http://incompleteideas.net/book/the-book-2nd.html are excellent resources for supervised and reinforcement learning, respectively, and some knowledge of statistics and probability go a long way. But I think by far the most important thing is to just start training models, even very small ones, and developing an intuition for what works and what the failure modes are.

- Get really comfortable with matplotlib or your graphing library of choice. Plot your data in every way you can think of. Plot your models' outputs, find which samples they do best and worst on.

- Play around with different hyperparameters and data augmentation strategies and see how they affect training.

- Try implementing backprop by hand -- understanding the backward pass of the different layers is extremely helpful when debugging. I found Karpathy's CS231n lectures to be a great starting point for this.

- Eventually, you'll want to start reading papers. The seminal papers (alexnet, resnet, attention is all you need, etc) are a good place to start. I found https://www.youtube.com/c/YannicKilcher (especially the early videos) to be a very useful companion resource for this.

- Once you've read some papers and feel comfortable with the format, you'll want to try implementing something. Important tricks are often hidden away in the appendices, read them carefully!

- And above all, remember that machine learning is a dark art -- when your dataloader has a bug in its shuffling logic, or when your tensor shapes get broadcast incorrectly, your code often won't throw an error, your model will just be slightly worse and you'll never notice. Because of this, 90% of being a good ML researcher/engineer is writing tests and knowing how to track down bugs. http://karpathy.github.io/2019/04/25/recipe/ perfectly summarizes my feelings on this.
Strongly disagree.

There's a vast amount of work that doesn't involve unethical recommendation systems.

Expand your horizon outside the Bay Area.

The plurality of work I see is straightforward computer vision/NLP applications.
Linus limited his scope to something that doesn't matter for users.

I think this is a valid criticism.

It's admirable to do the dishes, but the house is also on fire, so nobody will be able to enjoy the clean plates, so what's then even the point of doing the dishes?

In fact, in this analogy he could have saved the kitten instead of done the dishes.

Err, back from analogy land: ABI stability makes it harder to make things better, improving and replacing APIs. This is expected. But here we are in the worst of both worlds. Thanks to the kernel we are slowed down in improvements, and thanks to kinda-userspace (i.e. vdso & ld-linux), and userspace infra (libc, libstdc++, libm) we don't have ABI compatability either.

So it's lose-lose.
There's so much resources it's hard to pick… If you're already studying CS and like to learn by building, I'd recommend to do that for ML too!

Pick a real problem, try to build a ML solution for it and while doing so keep a list of things you'd like to dig deeper into. Then go back to that list and pick one item to study, and iterate.

Happy to have a chat and give you specific pointers if you'd like (email in profile), I got my master in ML in 2016 and applied it in the industry since.
reasonable guess, but no: I was thinking of an access that either completes out of L1 or fails.
if the software needs context, that's on it to keep.
Where I'm from, I've not seen a native shellfish in quite some time. Corbicula fluminea, aka "Asian clam," has been the only clam we've been seeing the past few years. We've been to a few state parks and hatchery tours where they're attempting to grow more native shellfish and reintroduce them. I hope they succeed.
Ah, got ya, I thought you guys were trying to kill me.

Regarding Samsung, you know once you’re familiar with something you just know the way that things work.

I know that in America everyone relies on data, but in fact the most impactful businessmen in the world lean on intuition and give Hindi as an example for a place where people just know things. Where people rely on themselves rather than on numbers. Data lies, numbers as well. Intuition never lies.

As of myself I indeed understand the situation with Samsung Cafe keyboard and it’s been a trend for quite a time. See reference, Pixel’s voice function that Google did. It’s available only on Pixel to choose Google’s Pixel over other phones. Samsung did it as well.

My goal eventually is to be attractive enough to do the same thing on the business side level as well.
not only in tech, but also in business practices. I live in Germany and don't speak German. Spotify refuses to send me any communication in English. Amazon Prime refuses to serve me English-language content, and so on. When trying to get my language switched on both I got the stock response of basically "we know this is a problem, but we're not going to fix it because it affects too few people".
These are engineering limitations, not "light speed limitations"
You could set easier goals for the winter - a slightly higher calorie intake, a bit less exercise, give yourself an extra cheat day, whatever works.
> You can construct the orthonormal vector by taking the cross product between our first bond, and an arbitrary unit vector.

Arbitrary not gonna work. The cross product will sometimes be zero vector, and the algorithm will fail. The cross product will sometimes be a vector of very small length, not long enough to give accurate direction, and the algorithm will glitch.

One reliable way is finding index of the smallest absolute coordinate of the input vector, and using that unit vector.

Another minor thing, I think most libraries are storing these 4 numbers in xyzw order, while this article uses wxyz order for the formulae.
A book on the technology and engineering of the Hayabusa2 mission published
> code isn't a specification.

Code in the form of a test is.

It shows how you can interact with the system/component/unit under test.
Yes! Everything I learned when I was young told me a) you have to be *very serious* about working out if you are going to do it and b) it's mostly about looking good.

Both of those turned out to be huge fallacies. I'm 40 now and not very serious about working out, but I do it reliably (on an irregular schedule) and I don't target specific muscles etc to try to make myself look a certain way. The result is that I am just plain healthier and feel great. That has many second order effects.
Very sensible. If everyone had adopted that viewpoint then there'd be far fewer "antivaxxers" in the world. Vaccines as pure commercial products you buy for your own use, with governments getting out of the way as much as possible, would be far less controversial.

Unfortunately governments can't stay away from the idea of forcing everyone to vaccinate in reach herd immunity, a concept that seems to appear and disappear depending on what public health wants you to do today. How many people have to take it to reach this threshold? Nobody knows because the people who are supposed to know what it is make it up in order to generate compliance (Fauci admitted to doing this in the New York Times).

For people to start trusting vaccines again, they need to be treated like any other drug. The idea of policy-created herd immunity is attractive but too destructive to tolerate. Once governments think they have a magic policy solution to a problem they can't resist pushing it regardless of consequences.