skysight-inc/HackerNews huggingface dataset:
It beggars belief that gnome devs seem somehow totally incapable of understanding why one would want a proper thumbnail view in the file picker. Have they never had to find a specific image in a directory of many images? If you told me their response to the file picker issue is some kind of elaborate troll or gaslighting operation I would believe you.
I personally almost always use an important/unimportant and urgent/not urgent entry in my communications. Helps alleviate quite a bit of stress, IME.
> nasty little posts

When Wikipedia entries, written in part or completely by Croats, are quoted that is "nasty". <sigh>

> your ethnic cleaning Serbian dogma

I and most other Serbs condemn all atrocities and crimes committed by Serbs or in Serbian name. Explain one thing to me: How come that in Serb-dominated countries/entities (Serbia and Serb-entity of Bosnia and Herzegovina) there are still Muslims/Boschniaks, Croats, Albanians, Hungarians... while in today's Croatia, Muslim/Croatian part of Bosnia and Herzegovina and occupied part of Serbia ("Kosovo") there are almost no Serbs? In Sarajevo alone, prior to the war (1991 census), over 150.000 Serbs have lived. According to the last (2013) census there were less than 9.000. But, yeah, "ethnic cleaning". <sigh>
A friend of mine joined the top french Maths school when we were in our twenties. I remember him in high school making small gestures with his fingers while solving problems. (He would also breathe heavily through his nose).
Learning how to do machine learning and learning how to clear data is a bit like asking what the best way to become an award winning author with very neat penmanship is.

One has nothing to do with the other.

That said data engineering at scale pays a lot more than deep learning but is also a lot less fun. Figure out which you'd rather do.
i am currently doing the 2019 part 2 of the course......a study group would be amazing
he's allowed to use his iPhone and has 1GB of data monthly
i'd like to get into engineering management, but not sure how to transition, since these roles often require many years of experience as an engineering manager.

i tried to propose this where i am, but as a contractor, there's no need for it. i'm basically just here to follow orders.

i do like mentoring junior devs.
> Read research publications and try to implement them.

In my opinion, jump straight into this! Learn prerequisites as you need them.

I found Goodfellow's book [1] to be helpful to learn some basics.

But don't think you need to read the whole book before you start reading and implementing research papers.

If you try and build up all the fundamentals thorougly, you run the risk of going down a very deep rabbit hole e.g. learning real analysis so you can learn measure theory so you can learn measure theoretic probability theory so you can learn stats properly etc.

You can be a productive researcher and patch up the fundamentals over time.

[1] https://www.deeplearningbook.org/
Think they were being hyperbolical. Your example makes sense though
Recreating Doom on a Homebrew 8-Bit CPU
yes, no, no. that's an easy one :)
Last time I dabbled with tup, I remember it was not trivial to support any process that generated more than one output. My use case back then was lex / yacc: you can generate a source and a header file with them (and even some other output files), but it was not trivial to capture these dependencies with tup.
i got the same thing, but without the ring. When I got antibiotics it had already started affecting my nervous system.

I had to eat antibiotics for 3 months.

Despite having Lyme disease I am not protected. If a vaccine comes In will take it without hesitation.
SpaceX has offered $25,000 reward to anyone who can hack their satellite Starlink. The spacecraft engineering company has invited researchers to ‘test’ Starlink for security in a “non-disruptive” way.
Yup, a plus from me about the clean 90's look, hope it will stay that way.

Searched for a term, gives +400k results, up to page 80 and still going. Another big plus from me since I just love to search low ranked results, sometimes you find true gems in obscure pages.
One of the best sensible videos I've watched on the topic. Firas might not look it, but he's trained a lot of authoritative athletes
Happy Birthday, MATE . (<https://mate-desktop.org>)

What is called "GNOME" today is something else.
VC won't touch anything with an export ban (the potential market size has just been reduced from 8 billion people down to 300 million)

Scientists stop research in the area because they typically can't publish papers on it in international journals.

Companies already in the field have to fire all their remote non-US workers and non-US customers. And that usually, together with the lack of new VC funding, kills the company.

The only exception is if the US military pays for R&D instead, but in that case typically everything is kept secret and nobody gets to use it except some missiles.
Without further ado, here is a huge list of ebooks resources: https://custom-writing.org/blog/free-books-online
fast.ai openly declares it doesn't have complete pipelines for audio, especially ASR.

They're also very open that areas like ASR and RL take a ton of compute to replicate SOTA. This shouldn't be used to substantiate any sort of criticism of fast.ai the library since it is just wrappers on pytorch. Maybe on the course.fast.ai page they could make some sort of disclaimer that some areas like ASR and yet to achieve the quantum leap of performance that we've seen in other domains.

I disagree completely that fast.ai is for toy problems. It's a development tool that in my experience provides for more rapid iteration than starting in vanilla pytorch. Use it for toys, for challenges, for research, or in industry. You can always rewrite your solution in pytorch once stable if there is benefit in doing so.

I agree with others that fast.ai is a great place to start.
maybe engineering management...something with less hands on coding. i'd like to be able to breath and take a longer term view in my work, instead of constantly being hounded by deadlines..
Read “AI a modern approach“ and do the MIT and Stanford courses that are available in Youtube.
Then you can go deeper into the branches presented in the book and courses.
The problem I’m seeing now, ist that everybody seems to think AI/ML is NN. Nothing further from truth!
Drawing Filled Quadrilaterals and Triangles
https://www.taipeitimes.com/News/feat/archives/2022/08/09/20...
I can see how weekend = daily workout could be a perfunctory interpretation of the same results as in the title post.
Where I live Lyme disease and TBE are the only ones you really need to worry about, and such is the case in large parts of Europe. TBE vaccines are common with a high rate of protection. A lyme disease vaccine will be very welcome.
You look at a billion and one swans. For the first billion you see nothing but white swans, yet for the final one you look at you see a black swan. You now cannot say swans are universally white. All it takes is a single exception to break a rule.

This is one of the many issues I have with social science - lack of falsifiability. Social science effectively lacks any degree of falsifiability simply because everything is based on vague probabilistic distributions which vary dramatically based on subtle nuances like asking an identical question two different ways, or testing on different populations in cases where the hypothesis is suggested to generalize - as here.

If there was any notion of falsification, it would all be false. In many ways it's quite analogous to astrology which, if you are unaware, was indeed considered a science, studied in universities for hundreds of years. It's end, somewhat ironically, came not from scientific objection to the fact it suffered the same issues as the social sciences today, but from the Church who deemed it heretical in claims of defacto divination.
Crypto's Largest Collapse with Terra's Do Kwon
Looks like something a website would work for. Auto scale by assigning groups to different instances, keep chat history local to the instance where it happened, maintain a list of time windows on what instance a certain group chat happened centrally. After some time make the instances archival, move of active chats to other instances, attach readonly to servers serving history. Client side on fail to contact last known active instance for a group, ask central where it moved. Central handles auth, gives out irrevocable cookies for entire domain. Group switches are send as silent message to connected clients. Client->Server connection is HTTP(s) POSTs Sever -> client is an ongoing download of a file on the server that is throttled to only send when new stuff comes and the TCP connection kept open server side. In case of loss of connection download is continued where it left off.

80 million evenly spaced messages a day is something a single server could do too buti went for something more fancy.
What would exactly be that "something"?

Statically linking glibc was always strongly discouraged. Even if you did it, nss and iconv are always dynamically loaded, and they need symbols from glibc itself, so your statically-glibc-linked binary would need to load dynamic libc anyway!

No, something doesn't need to check it, today it exists only to serve broken applications (and systems broken by their admins). Its semantic doesn't allow to express the needs of resolvers today (like per-interface or per-zone resolving or other mechanisms, like mDNS).

How the resolver is configured is its internal issue; apps should just use getnameinfo().
A 6 wheel dump truck is a vehicle with a dump body and 6 wheels. It can be called as 4×2 dump truck or small dump truck.
I've never spent more than 400 or 500$ on a laptop. Plenty of good enough stuff on the second hand market if you go after business laptops. But it's never my main machine (I can't understand doing anything productive with 14").
You might want to fix the xss issue...
From what I've heard (and my own very little experience) the ideal is somewhere in the middle. You need to allow 1-2 days after excercise for muscle growth. So doing the same excercise every day might actually inhibit growth a bit, and once a week is too infrequent.

You also don't need to do crazy long workouts. It is possible to set the muscle stimulus pretty quickly. The idea is that you train right up till muscle failure (which sounds scarier than it is). What's I've seen recommended is about three sets of each 8-12 repetitions per exercise. You choose the weight so that you cannot do more than 12. Splitting it up in sets helps you edge closer to the point of exhaustion. It is important that you don't overdo it of course.

In the end though, any exercise is better than no exercise, and as long as you have something you can stick to and works for you, it's fine.
it really is bizzare and cruel, when you break it down in the cold light of day.
I believe Mozilla has been losing market share, but not users, while the market has grown substantially, often on platforms that have banned alternative browsers.

Maybe that's now changed, but if it has, and Mozilla is actually losing users, I'd say it probably has more to do with those platforms choices than any particular woman you don't like.
In smaller teams/companies one gets to wear multiple hats. However, the term "Data engineer" was specifically created by/for ML folks to get rid of unpleasant repetitive work that has to be done but nobody looks forward to it.
Is he going to run `git checkout v3.6` and make them start over?
Definitely, this is just an extreme version of it.
Autonomous driving BMW causes fatal accident
https://archive.ph/sMpXv

Quote:
If the universe is expanding, a strange optical illusion must exist. Galaxies (or any other objects) in expanding space do not continue to look smaller and smaller with increasing distance. Beyond a certain point, they start looking larger and larger. (This is because their light is supposed to have left them when they were closer to us.) This is in sharp contrast to ordinary, non-expanding space, where objects look smaller in proportion to their distance.

I just woke up and I don't get this...
Setting up containers with systemd-nspawn
When you think of social media, sites like Facebook and Twitter probably come to mind immediately. But messenger apps have actually caught up to social networks in terms of users. And increasingly messenger apps are being used for marketing.

The leader in most of the world is WhatsApp (a pun on the phrase “what’s up”), with 1.5 billion monthly active users around the world.
Obligatory: But can it run Crysis?
Anyone have any recommendations for a product that implements a referral program with reasonable defaults on top of Stripe subscriptions?
SQL's SELECT does both restriction and projection; indeed, it always does projection, but the restriction is optional:

select id, first_name from user;

So it's not crazy to equate SELECT with projection.

If we're making up new syntaxes for relational calculus, i think something pipey would feel most natural:

from user | restrict last_name = $last_name | project id, first_name;

Letting you repeat stages as you see fit (this is somewhat silly example):

from user
| project *, is_boss = role in ('manager', 'executive')
| restrict last_name = $last_name and is_boss
| project id, first_name;

You need some sort of converging fork to do joins, maybe:

from user
| restrict role = 'executive'
> boss
from department
| restrict location = 'HQ'
> hq_dept
join < boss < hq_dept on boss.id = hq_dept.head_id
| project first_name, last_name, dept_name

Or maybe something better!
SRE = Site Reliability Engineer?
This, 100%. That said, most data scientists don't do what you would consider real work (meaning, I assume, interesting work with significant mathematical/analytical meat). There just isn't a lot that's both interesting and useful to private-sector rent-seekers whose opinions of your work determine whether or not you advance.

Most of the people doing real ML in industry are prestige hires--they're hired because their names draw people in, but basically get to work on whatever they want--and you need a top-10 PhD at an absolute minimum to be eligible for those.

The ugly truth about industry is that 99.9997% of it is flow capture based on power relationships, found artifacts (i.e., corruption opportunities) within the state, and the implementation of very simple processes but in a way such that the threat to executive reputations as a first priority, and profit as an important second one, are minimized. This doesn't exactly make a market for ML innovation, unless your boss for some weird reason still cares about being a co-author on your papers (which his bosses will pressure him not to let you publish, because after all, this publishing is a distraction from your paid work).

On the other hand, if you want to be able to afford a house in the Bay Area, and to be tapped for (indeed, most likely forced into, both due to losing interest in and being unhireable for IC work) management in your mid-30s... then go for industry. The poison carrot will make you sick but it will kill you more slowly than poverty, so that ain't so bad, now is it?
they keep them right next to the bootstraps.
Thanks GNOME for facilitating me meeting XFCE
Any self-hosted ticket system sound good. eMail can be a source for ticket creation however tickets are processed via a web interface.

https://osticket.com is one such software
WrongMove
> I meant that the glibcs are potentially ABI-incompatible both ways, not just that they’ll fight if you try to load both of them at once. Specifically, if the bundled (thus loaded) glibc is old, 2.U, and you try to load a host library wants a new frobnicate@GLIBC_2_V, V > U, you lose, right? I just don’t see any way around it.

Yup. So the answer is to minimize the amount of loaded host libraries, ideally 0. If that cannot be done, the builder of that host library will have to make sure it is backward compatible.

> But suppose the year is 2025, and a shiny new GPU architecture has come out, so groundbreaking no driver today can even lay down command buffers for it. The vendor is kind enough to provide an open-source driver that gets into every distro, and the userspace portion compiled against a distro-current Glibc ends up referencing an AVX-512 memcpy@GLIBC_3000 (or something).

> I load a flatpak using Gtk3 GtkGLArea from 2015.

Ideally, that driver would be build as an extension of the runtime your flatpak uses. I.e. everything based on org.freedesktop.Platform (or derivatives like org.gnome.Platform and org.kde.Platform) has extensions maintained with appropriate Mesa and Nvidia user space drivers.

So if new open source driver, if it is not part of Mesa, or you are not using above mentioned runtimes, would need to be build against the 2015 runtime. The nice thing is, that the platforms have corresponding sdks, so it is not a problem targeting specific/old version.
If you program for 30 minutes you spend first 15 minutes filling your short-term memory with all the relevant details to do 15 minutes of work and throw it away.
I think he meant "real work" from the perspective of the overeducated people who tend to end up in (and resent being in) corporate data science roles.

From the boss's perspective, the grungy IT support stuff is closer to real work (although the only thing that's actually respected in management, because it's what they do) than the shiny ML stuff that one star hire is allowed to do because it makes the company look cooler than it actually is.
> waking up 2 hours earlier

> having to commute 1-2 hours after work

> possibly losing money on the commute, trains whatever, traffic, road rage

> loss of personal space, dealing with noise,

> loss of control of environment, people talking over your head, coming and going, so many visual and audio distractions, temperature

> cannot use your own bathroom, sometimes have to queue for a piss

> less time with your family

> have to wear uncomfortable work clothes

> have to sit at a desk for 6-8 hours like a pleb

> people coming to your desk to tell you they sent you an email about a slack message they sent

this is off the top of my head, i'm sure there are more.

but the biggest problem is that we;ve seen the last 2 years its not necessary to work from office, just because - it's cruel and unfair.
No matter what, sooner or later you'll find yourself watching Minecraft letsplay at 3 a.m.
You actually can do that using inline assembly, it's just a very obscure trick. Many years ago I wrote a tool that generated a header file locking any code compiled with it to older symbol versions. It was called apbuild but it stopped being maintained some years after I stopped doing Linux stuff. I see from the comments below that someone else has made something similar, although apbuild was a comprehensive solution that wrapped gcc. It wasn't just a symbol versioning header, it did all sorts of tricks. Whatever was necessary to make things Just Work when compiling on a newer distro for an older distro.
What I've heard is the exact opposite, you should wait until the muscle soreness is on the way out (2-3 days if you are a beginner) until training that muscle again, to give it time to build new muscle. I've also found that I only get bad DOMS when I haven't trained a muscle in a while. If it happens every time, I am training slightly different spots every time because my execution is not clean.

(Side note, I find the highly technical term DOMS funny, as if that is some syndrom that has just recently been discovered. It's just plain old muscle soreness, the German term translates even roughly to "muscle hangover").
What are the latency characteristics like for io2? You’re mentioning near-local-nvme performance, but describing throughput (IOPS can be deceptive as they are pipelined, and as such could still give you 2ms latency at times).
:)
Thank you, fixed.
I wonder how many times fsync (or equiv) is called for this. Because IO load typically heavily depends on write queuing and syncing smartly (see other databases). The article does not describe this in detail, but it feels like the assumption and requirement is to fsync for every write operation separately (instead e.g. of combining).
> Can you expand on that? I have done this using mapy.cz and AlpineQuest dozens of times in the last ten years.

I've just pulled up the summit of Snowdon on mapy.cz[0] to find something we can compared with a traditional 1:25000 walking map of the same area, an extract from the OS Explorer map can be found on page 5 of this PDF[1]

To my mind, the difference is very striking.

If you are stuck in the outdoors in poor visibility, every feature on the map can be useful. mapy.cz gives a good overview of the summit and the routes to and from it, but compared with the OS map there is a lot of detail missing. Not least contour lines!

[0] https://en.mapy.cz/zakladni?x=-4.0756102&y=53.0701102&z=17
[1] https://www.ordnancesurvey.co.uk/documents/resources/map-rea...
My 2c (not exhaustive for what you want to do, probably):

1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model).

2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of good suggestions on this thread, I won't chime in), and you miss the fact that, sometimes, a simple decision tree, or dimensionality reduction approaches (e.g. PCA or ICA) can yield an incredible value in a very short time on huge datasets.

I had written a fairly short post about it when I finished my georgia tech path https://www.franzoni.eu/machine-learning-a-sound-primer/

3) It can take a lot of time to become effective in ML, effective as in, what you _manually create_ is as effective as picking an existing trained model, fine tune it, and use it. This can be frustrating: low hanging fruits are pretty powerful and you don't need to understand a lot about ML algorithms to pick them up.

4) Consider MOOCs or online classes. I took Georgia Tech OMSCS, I can vouch for it and some classes force you to be a data scientist and read papers as well, and you can have "real world" recognition and discuss with your peers, which is useful!
It's ridiculous this is down voted. Client is a fork of Signal on all platforms.
The fix is made, use it.

Arguments are usually just "ad hominem" against blockchain.
1. Clarify your goal.

Do you want to:

a) Become an academic in mathematics/statistics.

b) Become an academic in computer science with a focus on artificial intelligence.

c) Become a MLE in "regular" statistical applications. Aka bayesian classification, "core" statistical principles.

d) Become a specialized computer vision/natural language processing focused MLE.

e) Become a generalist software engineer who can whip out the above if needed.

In no way is e) the inferior option.

Generalists who can write code fast with 100% test coverage and pristine logging are by far the segment the industry has the shortest supply of.

There are TONS of math guys. Vanishingly few Principal Engineers who can write a design document and lead a project.

(Machine learning customers are OBSESSED with test coverage and verifiability. Believe it or not, multinational corporations generally don't want to unleash a {your_adjective_here}ist algorithm on the world.)

2. Study the above, properly.

To study the math, Elements of Statistical Learning/Algorithms by Goodfellow.

Start on page 1, do every second exercise. Publish a summary of every chapter you finish with your answers to GitHub.

3. Pursue your goal in a publicly verifiable manner.

See:

https://news.ycombinator.com/item?id=32071137
I'm currently using Heisig's RTK; it's been great. I wonder if you simply don't like the system or if you think the pedagogy is ineffective/inefficient.