skysight-inc/HackerNews huggingface dataset:
This application requirement really bothered me as someone who's autistic and dyslexic. I think visually, and while I have valid ideas and unique perspectives, I sometimes struggle to convert my visual thoughts into traditional spoken/written language. AI tools are invaluable to me - they help bridge the gap between my visual thinking and the written expression that's expected in professional settings.

LLMs are essentially translation tools. I use them to translate my picture-thinking into words, just like others might use spell-checkers or dictation software. They don't change my ideas or insights - they just help me express them in a neurotypical-friendly format.

The irony here is that Anthropic is developing AI systems supposedly to benefit humanity, yet their application process explicitly excludes people who use AI as an accessibility tool. It's like telling someone they can't use their usual assistive tools during an application process.

When they say they want to evaluate "non-AI-assisted communication skills," they're essentially saying they want to evaluate my ability to communicate without my accessibility tools. For me, AI-assisted communication is actually a more authentic representation of my thoughts. It's not about gaining an unfair advantage - it's about leveling the playing field so my ideas can be understood by others.

This seems particularly short-sighted for a company developing AI systems. Shouldn't they want diverse perspectives, including from neurodivergent individuals who might have unique insights into how AI can genuinely help people think and communicate differently?
AI companies don't think so, is clearly the implication.
There is a balance to be struck. You obviously don't expect a SWE to begin by identifying rare earth metal mining spots on his first day.

Where the line is drawn is context dependent, drawing the same single line for all possible situations is not possible and it's stupid to do so.
Yeah

I'm thinking this definitely pushes the start of life in the bigger universe some billion years before the start of life on Earth. Possibly a lot of billion years.

(though you still need a couple of generations of stars to get carbon, nitrogen and phosphorus)
F-strings is one of my favorite features of Python to be honest.

That doesn't automatically mean it's a good idea in C++, knowing C++ there are gonna be a whole lot of gotchas which aren't in Python, but it means that, at least in my opinion, how F-strings worked in Python is an argument in favor of them rather than against them.
It's almost like what people have been saying for years: there's the promise of AI and the reality of AI - and they're 2 very different things. They only look similar to a layman without experience in the field.
I think it's a lot more complicated than that. I think it can be used as a tool for people who already have knowledge and skills, but I do worry how it will affect people growing up with it.

Personally I see it more like going to someone who (claims) to know what they're doing and asking them to do it for me. I might be able to watch them at work and maybe get a very general idea of what they're doing but will I actually learn something? I don't think so.

Now, we may point to the fact that previous generations railed at the degeneration of youth through things like pocket calculators or mobile phones but I think there is a massive difference between these things and so-called AI. Where those things were tools obligatorily (if you give a calculator to someone who doesn't know any formulae it will be useless to them), I think so-called AI can just jump straight to giving you the answer.

I personally believe that there are necessary steps that must be passed through to really obtain knowledge and I don't think so-called AI takes you through those steps. I think it will result in a generation of people with markedly fewer and shallower skills than the generations that came before.
> Typewriter monkeys as a service almost. // Still useful for the odd task here and there

1) Paramount task: searching in naturally structured language, as opposed to keywords. Odd tasks: oh yes, several tasks of fuzzy sophisticated text processing previously unsolved.

2) They translate NN encodings in natural language! The issue remains about the quality of /what/ they translate in natural language, but one important chunk of the problem* is in a way solved...

Now, I've been probably one of the most vocal here, shouting "That's the opposite of intelligence!" - even in the past 24 hours -, but be objective: there are also progresses ...

(* Around five years ago we were still stuck with Hinton's problem of interpreting pronouns as pointers in "the item won't fit in the case: it's too big" vs "the item won't fit in the case: it's too small" - look at it now...)
I'm a Garmin user as I find the hardware to be very reliable but the market is in deep need of an Open Source alternative.

Let's not forget Garmin was victim of a ransomware attack in 2020 and they finally paid $10 million after days of blackout and they never really addressed the issue.
Is this a statement on behalf of Sentry.io? I see the company mentioned in this very weird and seemingly pro-fascist blog post, the author works there, and no disclaimer.

I am not inclined to do business with this firm based on this very off-putting statement.
> he’s just a dumbass.

Which is precisely the point. In a plutocracy, a monarch serves at the behest of the plutocrats. He is the CEO and they are the Board of Directors over the country.

Trump doesn't need to be smart, he just needs to have sway over the single-party Management, which he does. The "destroy shit and burn bridges" is the job of Management, who ensures the security of investments made by the Board.

What the comment above you is pointing out is that major banks are stockpiling gold likely because a major crash is around the corner.

Now, what they haven't said is that these current events are actually a part of the NRx (Neo-reactionary) manifesto proposed by Curtis Yarvin at BIL in 2012. Yarvin's playbook served as a major piece of the guiding philosophy behind Project 2025. People like Peter Thiel, J.D. Vance, and yes Elon Musk, have all been taking notes from Yarvin's proposals for a hard-reset on the U.S. government. Even DOGE is an implementation of Yarvin's RAGE (Retire All Government Employees).

Likely, the plan is to sunset the FDIC (another New-Deal safeguard that slows corporate gains) and replace the ACH with with a new and private system backed by plutocratic investment. In order to do this, the plutocrats must take control of the Treasury's payment systems, and the FBI must be neutered to prevent investigation into this. These two things are happening now.

Next, the economy must be destabilized in order to set the precedent for either legislature or emergency powers that dissolve the FDIC and reroute ACH transfers through the new system (likely Musk's X). This destabilization is what the tariffs are for.

When all forms of cash-flow are under plutocratic control, it becomes very unlikely that domestic funds could be raised to oppose them, no matter how politically or socially motivated the masses may be. Elections can even continue if the people want, but the funds won't make it to the opposition, the news of that won't make it to the majority of the general populace, and there won't be an organization that is able to investigate why.

Trump doesn't need to know or understand any of this, however. All he needs to do is sign off on what the Board of Plutocrats tell him to. Whether they can actually pull off this switch-over is the real question.

The top 10% of the U.S. controls 60% of it's wealth, and 813 of it's 345,000,000 people are billionaires. You and I aren't rich enough to do anything but wait and see what happens. But hey, in a couple centuries, Rome will spit in two again, so there's that at least.
I'd like to see those communities surviving to the same standards of living without having motor vehicles around them either. You want to build walls out of concrete ? Or wood even ? Well, have fun transporting all that in a cart.

Those communities have phones. Manufactured goods. They're watching Game of Thrones. If you remove motor vehicles all of that simply isn't possible due to the loss of productivity.

While I agree that preventing the intrusion of motor vehicle in residential areas would be good (ie have main roads where they're allowed, and small residential streets where they aren't. Lots of cities in Europe shifting to this currently), saying that the negatives outweigh the positives "in a lot of cases" I think is completely incorrect.
> we have superette, a small surface store, branded like the super/hyper but within the city center

I think we visited a few of those when we had a family holiday to France last year. They seemed really nice.
I strongly agree with this comment. Anecdotal evidence time!

I'm an experienced dev (20 years of C++ and plenty of other stuff), and I frequently work with younger students in a mentor role, e.g. I've done Google Summer of Code three times as a mentor, and am also in KDE's own mentorship program.

In 2023/24, when ChatGPT was looming large, I took on a student who was of course attempting to use AI to learn and who was enjoying many of the obvious benefits - availability, tailoring information to his inquiry, etc. So we cut a deal: We'd use the same ChatGPT account and I could keep an eye on his interactions with the system, so I could help him when the AI went off the rails and was steering him into the wrong direction.

He initially made fast progress on the project I was helping him with, and was able to put more working code in place than others in the same phase. But then he hit a plateau really hard soon after, because he was running into bugs and issues he couldn't get solutions from the AI for and he just wasn't able to connect the dots himself.

He'd almost get there, but would sometimes forget to remove random single lines doing the wrong thing, etc. His mental map of the code was poor, because he hadn't written it himself in that oldschool "every line a hard-fought battle" style that really makes you understand why and how something works and how it connects to problems you're solving.

As a result he'd get frustrated and had bouts of absenteeism next, because there wasn't any string of rewards and little victories there but just listless poking in the mud.

To his credit, he eventually realized leaning on ChatGPT was holding him back mentally and he tried to take things slower and go back to API docs and slowly building up his codebase by himself.
My experience? My experience is you have a lot of people who claim to care about Ukraine, but then go on to say things such as “they should just give up the land”.

The above conversation is a good example of this. Ukrainian people are very patriotic especially now, and flags are a highly symbolic and meaningful subject. And not wanting to listen to “just don’t use a flag for languages”, which is a ridiculously simple ask, to then pretend it’s not a big deal for those who lost their homes and families?

GP has no idea what it’s like to be be in the exact case they claim to care about. So yes, I’ll take $10 for some drone parts, instead of trying to educate those who don’t listen to the simplest asks.
This has a poetic tone to it.

However, not sure what to think of it. So AI should help people on their job and their interview process, but also not? When it matters? What if you're super good ML/AI, but very bad at doing applications? Would you still have a chance?

Or do you get filtered out?
> numerical linear algebra stuffs like iterative solvers

Been there; done some of that:

Once worked on numerical linear algebra, e.g., Gauss-Seidel. Then ran into the M. Newmann numerically exact technique based on (i) multiply by a suitable power of 10 to have only whole numbers, (ii) for a list of prime numbers, solve the system in the integers modulo each prime, (ii) construct the multi-precision rational results using the Chinese remainder theorem.

From a course, a rule: "For numerical calculation, multiply and divide freely, add OK, but avoid subtraction, especially avoid subtracting two numbers whose difference is small, i.e., nearly equal."

One day, talking with Richard Bartels, mentioned that once I wanted a random unitary matrix so generated some random vectors and applied the Gram-Schmidt process, and right away Bartels responded that Gram-Schmidt is "numerically unstable" to which I replied "Wondered about that so applied Gram-Schmidt twice".
In general, Bartels has done a lot in numerical methods.

In summary, in my experience, for many decades, a LOT has been done on numerical linear algebra, including iterative methods, for the simplex algorithm, etc. E.g., at one time, used Linpack -- it seemed terrific; on a computer with a 1.8 GHz clock, called Linpack 11,000 times a second.

As I recall, in numerical linear algebra there are some fundamental issues having to do with the eigenvectors of the polar decomposition. E.g., can argue that for these issues, sometimes Gauss-Seidel cannot work well.

Might look at some of the Golub LU decomposition work, e.g., in linear programming.

If you can find some problems where can get new, correct, significant results, okay. Maybe can get some problems from some of the current AI work.

Examples: (1) Took an industrial problem, did some math and computing, and got an engineering style solution. Some other students in the department did much the same but for different practical problems. (2) Had a course in optimization, in the summer went over the notes word by word and rewrote the notes, got deep into the subject, found an unanswered question, got a solution, along the way found a surprising, general result, wrote a paper, got it accepted right away at Mathematical Programming, published later elsewhere. (3) Working in some AI to monitor systems, wanted some results with meager assumptions so used the general result tightness. So, each of these is an example of how to find a problem and get some results.

But broadly for some decades, "numerical linear algebra", including iterative approaches, is a 'well plowed field'.
It's possible you might adopt a different attitude if one day you're diagnosed with rapid onset Altzheimer. At that stage you'd be forgiven for muttering 'basic science be blowed'. Keto (or whatever) offered some relief for my friend Bill, I'll give it a try given it's my survival at stake.

Plate tectonics was suggested in 1913 and not supported (to put it politely) at that point by 'basic science'. It took until 1960 to be accepted. A paradigm shift was needed as Kuhn explained.

Meanwhile, this paper (2024) https://www.sciencedirect.com/science/article/pii/S127977072...
'Effects of ketogenic diet on cognitive function of patients with Alzheimer's disease: a systematic review and meta-analysis'

concludes "Research conducted has indicated that the KD can enhance the mental state and cognitive function of those with AD, albeit potentially leading to an elevation in blood lipid levels. In summary, the good intervention effect and safety of KD are worthy of promotion and application in clinical treatment of AD."
I use it for this; found out about it when I installed Bazzite on my ROG Ally and it came with Waydroid.

The other useful feature for me is using the Android apps for media subscriptions that only enable offline downloads in the app and not in the browser so I can use them on the go.
Use LLM. But do not let it be the sole source of your information for any particular field. I think it's one of the most important disciplines the younger generation - to be honest, all generations - will have to learn.

I have a rule for myself as a non-native English speaker: Any day I ask LLMs to fix my English, I must read 10 pages from traditionally published books (preferably pre-2023). Just to prevent LLM from dominating my language comprehension.
Sure.

We do not need to rely on foreign labor which is a tiny fraction of American labor to maintain our quality of life. Lots of things need to be reorganized to make this work and some people with a lot of wealth will have a lot less certainly.

But foreign manufacturing of nearly everything is relatively new, you have to remember. America was plenty prosperous not so long ago before we started exporting so many jobs to Mexico, then China and beyond. We had the highest wages in the world then too.
How does that process handle people who have been out of work for a few years and can pass a take-home technical challenge (without a LLM) but cannot remember a convincing level of detail on the specifics of their past work? I’ve been experiencing your style of interview a lot and running up against this obstacle, even though I genuinely did the work I’m claiming to have done.

Especially people with ADHD don’t remember details as long as others, even though ADHD does not make someone a bad hire in this industry (and many successful tech workers have it).

I do prefer take-home challenges to live coding interviews right now, or at least a not-rushed live coding interview with some approximate advance warning of what to expect. That gives me time to refresh my rust (no programming language pun intended) or ramp up on whichever technologies are necessary for the challenge, and then to complete the challenge well even if taking more time than someone who is currently fresh with the perfectly matched skills might need. I want the ability to show what I can do on the job after onboarding, not what I can do while struggling with long-term unemployment, immigration, financial, and family health concerns. (My fault for marrying a foreigner and trying to legally bring her into the US, apparently.)

And, no, my life circumstances do not make it easy for me to follow the common suggestion of ramping up in my “spare time” without the pressures of a job or a specific interview task. That’s completely different from when I can do on the job or for a specific interview’s technical challenge.
I just checked and he narrated the audiobook! Listening to the preview now and it sounds as good as you'd think.
(Update: it’s visible for me now.)
Sounds like someone who switches jobs and technologies every year, so someone else has to clean up the mess
> listless poking in the mud

https://xkcd.com/1838/
Not just Europe. Many companies in the US will benefit, too. As will companies in Asia, Africa and the Middle East. These are the first truly frontier-grade models released under a friendly license. The most potent non-reasoning model before this point was Mistral Large, and it has serious restrictions on allowed uses (research only).
You say "thread safe", I say "dead lock".
Feeling "Lazy" is just an emotion, which to me has nothing to do with how productive you are as a human. In fact the people not feeling lazy but hyped are probably more effective and productive. You're just doing this to yourself because you have assumptions on how a productive/effective human should function. You could call it "stuck in the past".

I have the idea that there are 2 kinds of people, those avidly against AI because it makes mistakes (it sure does) and makes one lazy and all other kinds of negative things, and those that experiment and find a place for it but aren't that vocal about it.

Sure you can go too far, I've heard someone in Quality Control Proclaim "ChatGPT just knows everything, its saves me so much time!" To which I asked if they heart about hallucinations and they hadn't, they'd just been copying whatever it said into their reports. Which is certainly problematic.
Can you Paint with all the Colors of the Wind?

https://music.youtube.com/watch?v=MpLnqOvGuMs&si=jfMiKab7GOC...
Full-stack sum types with TypeScript, Rust, and PostgreSQL
Ah, well now I feel old :D
The military industrial complex making overtures the incoming administration.
You can always interview in person. This has often been the norm, after some initial screening. I think it's the best option.

Even remotely, normally a coding interview isn't a candidate typing things for 45 min on a screen. There are interactions, follow-up questions, discussions about trade-offs and so on... I suppose it's possible for a good candidate to cheat and get some extra points, but the interview isn't broken yet.

You could also let the candidate use AI, and still gather all the relevant signals.
> LLMs just make you lazy.

Yeah, and I'd like to emphasize that this is qualitatively different from older gripes such as "calculators make kids lazy in math."

This is because LLMs' have an amazing ability to dream up responses stuffed with traditional signals of truthfulness, care, engagement, honesty etc... but that ability is not matched by their chances of dreaming up answers and ideas that are logically true.

This gap is inevitable from their current design, and it means users are given signals that it's safe for their brains to think-less-hard (skepticism, critical analysis) about what's being returned at the same moments when they need to use their minds the most.

That's new. A calculator doesn't flatter you or pretend to be a wise professor with a big vocabulary listening very closely to your problems.
Conway's Law (2022)
> Moloco ads is a DSP network

A digital signal processing network? A bit annoying to introduce an acronym without defining it. Great article otherwise.
Cutting-edge Renault Filante concept hopes to break EV efficiency records
I just got a new Garmin Vivoactive5 this week, because my old one broke (also vivoactive3). For now I am quite happy with it, but I bought it knowingly, that Garmins APIs and possibilites are not _optimal_ at all.

I looked into getting a Apple Watch series 10, but ultimately decided against it (battery lasting not longer than 1.5days / no real use for messaging or smart apps a side from health / 2x price compared to the vivo).

I would love to find an _open_ watch, that allows for hacking, nice APIs, self-hosting of dashboards even, nice apps et cetera. I feel like there is market for people like me. Maybe the rebirth of the pebble will be able fill this void?
Orb stack is great but it requires a paid license
Google offers cash to quit for workers who aren't 'deeply committed'
When you go for a job as a forklift operator, nobody asks you to demonstrate how you can manually carry a load of timber.
Foreign function interfaces aren't that scary. Any competent $lang developer ought to be able to read the docs and roll their own wrapper.
Being promoted is about being given progressively more ambiguous problems.

Sounds like no one really prepped you for this.

Your job is to untwist the culture and get the group productive.

I suggest you do a lot of listening and learning before you start pushing hard.

Earn trust, and be a model collaborator, and as you earn respect, use it to understand and resolve the interpersonal dynamics.

Your job isn't about programming anymore, it's about people. Sorry.
The young are always the easiest to indoctrinate.
Where Poka-Yoke and Cybernetics Meet: Users determine the purpose of any device
Learning comes from focus and repetition. Talent comes from knowing which skill to use. Using AI effectively is a talent. Some of us embrace learning new skills while others hold onto the past. AI is here to stay, sorry. You can either learn to adapt to it or you can slowly die.

The argument that AI is bad and anyone who uses it ends up in a tangled mess is only your perspective and your experience. I’m way more productive using AI to help me than I ever was before. Yes, I proofread the result. Yes, I can discern a good response from a bad one.

AI isn’t a replacement for knowing how to code, but it can be an extremely valuable teacher to those orgs that lack proper training.

Any company that has the position that AI is bad, and lacks proper training and incentives for those that want to learn new skills, isn’t a company I ever want to work for.
Given the current state of US government I wouldn't be surprised if soon it would introduce sanctions against anyone trying to use Deepseek.