skysight-inc/HackerNews huggingface dataset:
Only someone who has never built anything of significant complexity and utility can think that putting natural language encoding between you and the bytes is a net positive.

AppleScript might convince me of your argument.. ;-) But, seriously, we've been putting abstractions between "us and the bytes" ever since Fortran and COBOL appeared (and indeed, earlier). We can argue about the quality and expressiveness of those abstractions, and there are a lot of arguments against natural languages in this task, but the broad idea of putting things in between developers and machines is sound so it's worth continuing to explore IMHO.
One suggestion is to put pictures of what it looks like at the top.

It's the first thing people are going to be interested in.
The tool of choice for cutting undersea cables & pipelines seems to currently be tankers dragging anchors across the seafloor. There's no economically viable protection able to withstand forces like that.
A while ago, dynomight went in and tried to explain what's going on, and why LLMs are bad at chess.

See: https://dynomight.net/more-chess/

HN Discussion of that article: https://news.ycombinator.com/item?id=42206817

(Edits: It turns out "it's complicated").
That sounds incredibly efficient.
Depends on your money raised and your leverage. YC companies are typically top tier founders with tons of leverage and capital options.
I love it. Respect to “brick” your actual steam deck OLED no less. I think I would have gone and ordered a broken one from eBay (don’t know prices availability etc since it seems to be repairable).
Interesting article and congrats on the original solutions, but wow!

Is there any other field where applicants are subjected to this?

Nepalese Ghhurkas perhaps ?

I once backed out of an interview when they sent me an 85 page manual on how to prepare for the interview with them.
It was not even a FAANG company, just a small IT solution with mediocre benefits.
Deepseek is indeed better than Mistral and ChatGPT. It has tad more common sense. There is no way they did this on the “cheap”. I’m sure they use loads of Nvidia GPUs, unless they are using custom made hardware acceleration (that would be cool and easy to do).

As OP said, they are lying because of export laws, they aren’t allowed to play with Nvidia GPUs.

However, I support DeepSeek projects, I’m here in the US able to benefit from it. So hopefully they should headquarter in the States if they want US chip sanctions lift off since the company is Chinese based.

But as of now, deepseek takes the lead in LLMs, my goto LLM.

Sam Altman should be worried, seriously, Deepseek is legit better than ChatGPT latest models.
The HTML Tags Memory Test [0] game refers to 114 tags. Your link got 113. I wonder what's missing?

[0]: https://codepen.io/plfstr/full/zYqQeRw
> If you get that with two different planes, you probably have a pretty accurate position (a few hundred meters).

What?! I've seen the viral guy on social media who can pinpoint places in the world by looking at a photo but... a few hundred meters based on the angle of the sun/moon? That's wild!
how do you know the output from AI is correct and covers all details?

Same way as any other code. You look at it, ask the 'author' to explain any parts you don't understand, reason through what it's doing and then test it.
Everyone is a Bayesian these days; it's become so fashionable to throw "priors" around like it means anything.
Okay this all sounds great, what specs do I need on the machine connected to the TV? Will a Raspberry Pi 3 work? Pine64? Atomic Pi? (That's x86_64, intel Celeron)

Anything more than that and the value proposition goes way down. For every 50 grams lighter I am willing to lose 1fps. For every fan removed I will drop an entire resolution (4k -> 2k -> FHD...) Change "lighter" to heavier if it makes sense. My comfort and aesthetic matters more than competition quality, pixel perfect yadda

I keep starring these remote display projects but none have convinced me to provision a client machine for the purpose yet.
You're thinking of SQL.
The lack of handling identity is a showstopper for ActivityPub. I cannot for the life of me figure out why they didn’t go the extra and make identity portable.
> The only use case I have for <div> is in a details/summary where there is no CSS to select the contents of a <details> element, excluding the <summary>.

div > *:not(summary)
As a community member I wholeheartedly disagree. Hacker News is one of the few places I can go and not be inundated with the political pocket sand nonsense. In fact, I think this entire post is probably off topic. With respect, please get your politics out of here.

From the community guidelines:

On-Topic: Anything that good hackers would find interesting. That includes more than hacking and startups. If you had to reduce it to a sentence, the answer might be: anything that gratifies one's intellectual curiosity.

Off-Topic: Most stories about politics, or crime, or sports, or celebrities, unless they're evidence of some interesting new phenomenon. Videos of pratfalls or disasters, or cute animal pictures. If they'd cover it on TV news, it's probably off-topic
I find it is good at writing about 90% of tests, either from requirements or existing code.

It is terrible once you get 3 or 4 tweaks into main parts of the code base. Just like autocorrect degrades over time.
Your example is extreme, of course kenyan labelers contribute a minuscule fraction of value and they would be easily replaced by other workers if needed. Replacing the engineers is harder.

Labelling data is easier (and therefore cheaper) than doing machine learning.

OpenAI often argues for socialism-light, aka today's social democracies that leech on the middle class and favor politicians and the rich elite class.

It doesn't argue for full blown socialism with equal pay for any job. I think the historical proof is just too large to argue otherwise, which is why socialist politicians are going for the UBI alternative.

Same crap, but hey it will totally work this time.
Because it validates US propaganda that virus originated in PRC, instead of PRC position that they were merely first to detect and warn. There is no reason to give WHO unfettered access based on US/western propaganda - that's stupid amount of soverign leverage to cede because Trump/Pompeo wants to pressure PRC via WHO. PRC has also asked WHO to investigate Fort Detrick, until US reciprocates, which they haven't? Does US have something to hide? Why are they trying to deflect? See how easy it question motives / intentions / credibility based on inaction. Ulimately no one has to believe the CCP propaganda, but in this scenario the alternative is literally from "we lied, we cheated, we stole" Pompeo, uttered when he was director of CIA no less. Hence CIA pointing fingers mean nothing on PRC credibility, if anything it reinforces it.
> integrate some browser and autonomous test loop into Cursor

Doesn't this exist yet? It's such an obvious idea I'd be astonished if no-one has done it.
Local open-source Android 3D scanner
No it's American, you probably confuse them with Mistral AI.
Yeah working in the data space I see a ton of customers using Iceberg and some using Delta Lake if they're already a Databricks shop. Virtually no Hudi.
This probably only works if you glue a bunch of high level, popular APIs together. It might work, but will be fragile and expensive.
Bro, did you use Deepseek? That shyt is better than ChatGPT. No cards being thrown here.
> It's offshoring 2.0

Doesn’t this analogy break down inasmuch as offshoring eventually worked?
I’d expect $50-$100m. 1/40 the value of Rockstar energy drinks.
1. The Pragmatic Programmer.

2. Writing Tools: 50 Essential Strategies for Every Writer.

3. Diffusion of Innovations.

4. The Little Book of Common Sense Investing.

5. Implementing Lean Software Development: From Concept to Cash.

6. Writing to Learn.

7. On Writing Well.

8. Pragmatic Thinking and Learning: Refactor Your Wetware.

9. Thinking In Systems: A Primer.

10. The Startup Owner's Manual: The Step-By-Step Guide for Building a Great Company.
Show HN: AI-powered tool that breaks down complex topics into bite-sized outline
Hey everyone! I’m excited to share my side project, StudyGuide, which uses AI to simplify just about any topic you can think of. If you’ve ever struggled with breaking down huge chapters or complicated subjects, this tool might help.
What it does:
You type in a topic (say, “Quantum Mechanics” or “French Revolution”).
The AI then generates a detailed outline, broken down into smaller, easier-to-digest subtopics.
You can click into each subtopic to go deeper, eventually drilling down to specific sub-subtopics or key concepts.
Why I built it:
I’ve always found that half the battle in learning is figuring out where to start and how to structure my study notes.
I wanted a simple interface: type a topic, get a structured breakdown, and explore further as needed.
If you want to try it out, here’s the link: https://app.tivateonline.com/studyguide/
Feel free to leave your thoughts in the comments – I really appreciate any feedback or suggestions for improvement!
Don't use a hosted service. Download the model and run it locally.
Precisely. This lets any of us have something that until the other day would have cost hundreds of millions of dollars. It's as if Linus had published linux 2.0, gcc, binutils, libc, etc. all on the same day.
Getting to the truth is a valid motivator for a bit over half of the country.
Chain of thought prompting ("think step by step") only encourages the model to break the problem into steps, which allows it to incrementally build upon each step (since the output is fed back in as part of the input).

Reasoning requires more than chain of thought, since it's often not apparent what the next step should be - you (human, or model) may go down one path of reasoning only to realize it's going nowhere, and have to back up and try something else instead. This ability to "back up" - to realize that an earlier reasoning "step" was wrong and needs to be rethought is what was mostly missing from models that (unlike o1, etc) hadn't been trained for reasoning.

The reason non-reasoning models can't reason appears to be because this type of chain-of-consciousness thought (thinking out loud, mistakes and all) when trying to figure out a problem is hugely underrepresented in a normal training set. Most writing you find on the internet, or other sources, is the end result of reasoning - someone figured something out and wrote about it - not the actual reasoning process (mistakes and all) that got them there.

It's still not clear what OpenAI had to do, if anything, to help bootstrap o1 (special hand-created training data?), but basically by using RL to encourage certain types of reasoning pattern, they were able to get the model to back-up and self-correct when needed. DeepSeek-R may well have used o1 reasoning outputs as a bootstrap, but have been able to replicate RL training to encourage self-correcting reasoning in the same way.

One interesting aspect of DeepSeek-R is that they have shown that once you have a reasoning model, you can run it and use it to generate a bunch of reasoning outputs that can then be used as normal training data to fine-tune a non-reasoning model, even a very small one. This proves that, at least to some degree, the reason non-reasoning models couldn't reason is just because they had not been trained on sufficient self-correcting reasoning examples.
Commoditize your complement has been invoked as an explanation for Meta's strategy to open source LLM models (with some definition of "open" and "model").

Guess what, others can play this game too :-)

The open source LLM landscape will likely be more defining of developments going forward.
Anthropic is not French. You probably meant Mistral, that is a French company, but a niche player in this AI game.
In Chicago, the "L" network also has stretches that run down expressway medians, along with the elevated lines and the subway sections downtown.
When I took the job, I trusted my employers' word that the role's tasks required being in-person, against my own suspicions. That, combined with no other prospects, meant either I took the potentially raw deal or face destitution.

Now, I've had worse jobs, but that doesn't mean it's right that this one is arranged the way that it is. Not for me or for the business.

You're saying that no one has standing to criticize a deleterious deal after someone has taken it, and I have to reject that. It's the kind of attitude that excuses all kinds of exploitation because "you knew what you were getting into." In reality, you have people forced into definitively bad situations to avoid potentially worse ones, or ambiguity regarding the conditions that doesn't resolve itself until they're in the thick of it.

Go back to basics: if an employer is made aware that their work conditions are unreasonable, they should strive to make them reasonable, even if they have leeway not to because of a prior agreement. That's just an ethical reality. Such conscientiousness also engenders loyalty and trust; tapping the employment contract instead is why workers are happy to serve out their "term" and then quiet quit or actually quit.
its actually surprising how many difficult puzzles can be solved by a very small look ahead and playing the only move that doesn't lose. i've even seen strong GM solve puzzles like this. this is especially useful when the first move in the puzzle is very clear but there might be 5 or 6 reasonable candidate moves in reply and its just a waste of time to compute each variation.
this is not actually an accurate list
> Usage of the phrase "first principles" has sprawled into many different things since (I think) Musk first mentioned it as a way to learn

In pop culture in 2010+ sure, but he was essentially parroting Feynman IIRC.

"How to learn AI from first principles?"

Start with https://en.wikipedia.org/wiki/Zermelo%E2%80%93Fraenkel_set_t... and eventually you'll get to AI, exercise left to the reader ;)
iceberg is plauged with the problems it tries to solve, like being too tied to spark just to write data
ChatGPT in 2023 was a fun toy, but just that.

Claude in 2025, especially with Project feature is far better. It can complete CRUD project on its' own, and all I have to do is to fix glaring issues and design API before.

Which might be not impressive to someone, but it is good at that. And few years ago, it would not be possible.
But seriously, why do modern engineers try to reinvent the wheel for everything??

this x1000.

80%￶ of everything done since '01 has been the result of crap makework bullshit jobs.
> (In fact, the reason they ask leetcode questions isn't to test your IQ, it's to know if you've read the obvious and available literature.)

Rather: it tests whether you are sufficiently docile and devoted to be willing to cram lots of leetcode exercise books that have no relevance for the programming concepts that the job will involve, just for a lottery ticket for a somewhat well-paid position.

I know that there is so much more to programming and related topics that is sooo much deeper (in particular if non-trivial mathematics becomes involved) than these leetcode-style brainteasers. So I strongly prefer to read about such deeply intellectually inspiring topics related to programming instead of jumping through the idiotic hoops that other people want me to.

Indeed, I thus fail the test for docility and devotedness, but I honestly can't take organisations seriously that demand such jumping through hoops.
Interesting. I try to use prepared statements to avoid redoing the planning. But since the database schema is small compared to the data, the cost of query planning is quickly negligible compared to running an generic query that is inefficient.
huh what? We use iceberg extensively, never used spark.
I see your point of view, but I would argue that many hackers would find interest in the fact that an idolised tech magnate made such an extreme gesture.

In my (non American) point of view this transcends left vs right arguments, but this is exactly why I am curious what this community would have to say. It is an interesting topic, if only from an anthropological perspective.
Man, they say China is the most populated country in the world, I’m sure they got loopholes to grab a few thousands H100s.

They probably also trained the “copied” models by outsourcing it.

But who cares, it’s free and it works great.
Same. I only stopped because managing storage became a problem - three huge games came out that I wanted to play.

Were I to do this again I wouldn't do ryzen I'd do at minimum a threadripper, so that the guest can get a USB pcie card and a GPU, so literally every device windows sees and talks to is virtualized. Usb keyboard, mouse, soundcard, etc.

I think LTT did an epyc build where 1 epyc ran 3 full "Desktops" with GPUs, nvme, for each virtual machine dedicated. I just need the one!
As an experienced developer, that’s also how I use it. What I’m finding is that it generally rabbit holes as I give it new errors that it’s previous fix has produced.

However, usually after three or four of those kind of fixes, I can walk it back to the starting point before the initial error, and I now know how to prompt it to produce correct code, because I now have a better mental model of how the thing is supposed to work.

This has been super helpful in my process of learning new things, as well as relearning things I haven’t worked with in a while.
Facts, them Chinese VCs will throw money to win.
Why should the bubble pop when we just got the proof that these models can be much more efficient than we thought?

I mean, sure, no one is going to have a monopoly, and we're going to see a race to the bottom in prices, but on the other hand, the AI revolution is going to come much sooner than expected, and it's going to be on everyone's pocket this year. Isn't that a bullish signal for the economy?
In a lot of places, due to tax revolts there are now property tax caps which effectively prevent this from happening.
The current state of so-called AI does not provide much meaningful assistance in software development beyond basic tasks such as explaining workflows, breaking down thought processes, and performing simple conversions. I believe that generative AI, in its current form, is not true artificial intelligence. Rather, it is a sophisticated prediction engine that lacks genuine reasoning or understanding.

True AI should be capable of comprehending problems and devising its own solutions, rather than merely generating statistically likely outputs. Until AI reaches that level of cognitive ability, its applications in the real world remain limited, and much of what we see today is largely hype.

Tokenization and embeddings merely help models predict the most probable next token, a process that is executed at scale using vast computational resources. This is not intelligence but large-scale probabilistic prediction. The terminology used in computer science, especially in recent years, can often be misleading.
The thing with Prolog is that it’s beautiful for solving very specific and well-defined combinatoric problems. Steer away and you hit a wall.
I am stockholder in snowflake and iceberg's ascendance seems to coincide with snow's downfall.

Is the query engine value add justify snowflake's valuation. Their data marketplace thing didn't seem to have actually worked.
Historically, I think it's akin to when Russia conquered Russia and renamed St. Petersburg to Leningrad
If you have time of the image accurate to the minute, and you know that the plane, sun and observer were on a line, the observer has to be on the ground track of the shadow of the plane during this minute. If you have that from two planes on different tracks, the observer has to be at the intersection of the ground tracks.

This method is not just from measuring the angle of moon/sun, but using the plane (which has a known position) as a marker. Just by using the angle of sun and moon, you need to be extremely precise. 1 degree error is 1/360 of the circumference of earth distance, that’s about 100km.
Coping will continue until moral improves.
The last 20 years of programming tells me that this isn’t the case.

This is only the case for new projects which don’t yet have users. Add users to even the simplest project and it evolves into a special snowflake with never before seen edge cases.

That’s why low code solutions are great for prototyping but eventually always explode into a nightmare of complexity.
>> capital city after its conqueror would be a gigantic “fuck you” to the people

As how most every port city in North America is named by whatever western explorer first put it on a map? From Botany Bay to Vancouver, Los Angeles and even Virginia USA, placenames are pulled from the culture of the conquerors. Only when one gets into the hinterlands do local names appear.
This is the kind of question that makes my spidey senses get all tingly. This happens more on SO than here though. It’s either you’re doing it wrong and just have no idea why this request is bad, or they know it’s bad and tried to phrase it in an innocent enough way to hopefully get an answer that would otherwise get ignored.
I would argue that stuff like Iceberg is really aimed at Data Platform Engineers, not BI analysts. Companies I've worked with in the past have 10-15 people on a Platform team that work directly with stuff like this, to offer analysts and data scientists a view into the company's data.
Claude 3.5 Sonnet did rather well.

I get the impression it often does as well or better than o1 on many tasks, despite not being a reasoning model.
You misunderstand, the repo lists an MIT license which requires attribution. You want people to give you credit if they use this LLM-generated code.

LLMs which were trained on the works of thousands of other developers with similar licenses, who are offered no similar credit here.

It also claims copyright of the code as though you have authored it, but you're claiming here to have used LLMs to generate it. Seems like trying to have it both ways.
> The state went a record 98 of 116 days providing up to 10 hours of electricity with renewables alone

Nice...but "up to 10" < 24, and 98 < 116, and "how easy was the first 1/4?" is generally a crap indicator of how easily a job can be finished.
Am I the only one to be worried about using the DeepSeek web app due to how my data will be used? Since this is China.

I was looking for some comment providing discussion about that... but nobody cares? How is this not worrying? Does nobody understand the political regime China is under? Is everyone really that politically uneducated?

People just go out and play with it as if nothing?

LLMs by their nature get to extract a ton of sensitive and personal data. I wouldn't touch it with a ten-foot pole.
Stalingrad, Léopoldville, Karl-Marx-Stadt, Constantinople. Many such cases!
This is also why all of the auto union are against electric cars. Some person working in their garage will no longer be able to work on cars and get a job as a mechanic/technician and instead will need to have the experience of an engineer.

It's very similar to the horse/buggy and car arguments of years past.
> All IT workers are exempted from mobilization.

This isn't entirely true. A friend of mine (who works in IT) got drafted. In 3 days he ended up getting a pregnancy certificate faked, so he and his girlfriend could get legally married quickly (for immigration options elsewhere) - and fled the country.

The draft is unequal, they are concerned about their IT worker shortage. The majority of drafting is happening in impoverished areas of the country, and ethnic minorities. Often people think they'll just be doing a driving job or something, and end up in combat. But nobody is truly safe. Your friend is taking a large gamble by visiting.
Have you tried Sentry with replay?
You'd lose a lot in efficiency. A power bank designed for this would have 2 cables and 1 or 2 wires, power transfer cables and sense/control wires. My engineering brain says "don't do this it's stupid and I think OP was making a joke I don't get"