MurphyA/hacker-news huggingface dataset:
by photonthug | 2025-04-08 22:33:06+00:00
> There’s no way to use “rules and facts” to express concepts like “chair” or “grass”, or “face” or “justice” or really anything. Any project trying to use deterministic symbolic logic to represent the world fundamentally misunderstands cognition.

Are you sure? In terms of theoretical foundations for AGI, AIXI is probabilistic but godel-machines are proof based and I think they'd meet criteria for deterministic / symbolic. Non-monotonic and temporal logics also exist, where chairness exists as a concept that might be revoked if 2 or more legs are missing. If you really want to get technical then by allowing logics with continuous time and changing discrete truth values, then you can probably manufacture a fuzzy logic where time isn't considered but truth/certainty values are continuous. Your ideas about logic might be too simple, it's more than just Aristotle
by hug | 2025-04-08 22:33:06+00:00
It’s wildly and hugely unrealistic.

The rule of thumb that employees actually cost a business roughly twice their salary is based on two things:

1. Retention. Hiring costs are “huge”, and so if you have a higher or lower average retention, may make up a disproportionate cost compared to salary. Ramp up time and institutional knowledge loss is no joke either.

2. A spread of average wages. 500k is not average, and a huge number of the costs are relatively fixed. $1,000 a month worth of software licensing isn’t an uncommon number and is fully 1/3 of the salary of a $3k a month or $36k/year junior clerk. It’s peanuts when you look at it next to a $500k/year salary. It may be that the clerk is, all in, costing the company 3x their salary after indemnity insurance and so on. The dev will never reach 10%.
by antics | 2025-04-08 22:34:42+00:00
I actually have read a chunk of the relevant TAOCP, but didn't mention it because the comment was already kind of long. I remarked to a friend the other day that it's kind of funny that we've a little bit come full circle on asymptotic analysis in that we are now counting FLOPs, and it is not a little bit like what Knuth does here. :)
by ggm | 2025-04-08 22:35:00+00:00
I am reminded of Lenin's dictum: ‘The Capitalists will sell us the rope with which we will hang them.’ -And wonder if it applies to populist leaders as well as the soviets.

I think about Musk/Bezos/Zuckerberg quite a lot in this context. They hitched their wagon to the Trump train, and now they are hoist on their own petard.
by XorNot | 2025-04-08 22:35:37+00:00
It's also just practically not possible: the distribution of resources is that it is, and for the expense of conquering and securing an expansive empire to get them, strategic alliances and diplomacy will be cheaper (not to mention that just because something is within your empire, it hardly means you necessarily exert any real sovereignty or control over it - the conquered peoples will hardly feel like citizens).
by ggm | 2025-04-08 22:37:25+00:00
Well run services don't step outside the boundaries of their authority willingly. They demand a paper trail of justification, lest somebody come looking for the reason behind the action. You would think the US Marshals are a legally constituted body, with regulations over entry, arrest/detention, evidence collection, rights to arms and legal constraints on their use. A budget even, demands a line item, and some accounting.

So if its well run, we should be able to know how this was authorised, on what basis. And if it's not well run, isn't that a major issue in itself?
by tibbar | 2025-04-08 22:37:49+00:00
The payoff for this kind of creation needs to be evaluated over time. The creator not only makes money for the puzzle, but they also gained e.g. a worldwide audience and a great resume booster. In the future, if they want to launch another puzzle or write a book or get a job doing this, the door is wide open. There is no such upward path as an uber driver, unfortunately.
by toomuchtodo | 2025-04-08 22:38:13+00:00
China has nothing to lose, they remain committed to exporting to the global market. The US quality of life was built on deflation from importing inexpensive goods at scale. China’s pain will be temporary as they reconfigure their export system, US pain will be permanent. There are not enough US workers or time to build what is being parroted to replace imports with domestic production. Xi is sophisticated, Trump is not.

https://news.ycombinator.com/item?id=43617956 (citations)
by antics | 2025-04-08 22:39:15+00:00
Yep, I mentioned this elsewhere in the threads—Knuth does do this quite a bit in TAOCP. I think in a lot of ways we've ended up completely full circle, in that we are now counting FLOPs. This time though I think we have very little hope for something as nice and pretty as asymptotics. At least ones that are useful.
by groby_b | 2025-04-08 22:39:23+00:00
That depends entirely on how you raise the funds. Yes, you can say "Here's the growth rate we'd get without your money - based on that, this investment gets you an ROI of x%."

With x% high enough, sure, you can get VC money without too many strings. (Also, reading the Series B post, they were planning to invest - just in organic growth instead of the usual growth hacking)

And if you read the Series C post, you'd know what they're spending on - GPU (and general) cloud interconnectivity.

There's really not much need to guess, Tailscale's financing announcements are about as open as you can get.
by genocidicbunny | 2025-04-08 22:39:55+00:00
From my experience, in the early programming classes that have switched from Java to Python, one of the reasons is the verbosity of Java. When you're trying to teach the basics of programming -- variables, some simple text IO, loops, functions, the structure imposed by Java can be a little overwhelming. I recall a lot of other students struggling with getting past all the little things you had to do just to get a "Hello World" written. Python is simpler to gradually introduce those concepts with, especially with very those who are just starting out. And especially when you're dealing with a quarter system, which many schools use, as a teacher you had a very limited amount of time to get through that sort of material.
by lazyasciiart | 2025-04-08 22:40:04+00:00
> Japan always does the hard thing

Talk about reductive, but also wrong. Not being racist, for example, is the harder option for most people, and not the one encouraged in Japan.

Another one: Japan still has children growing up in orphanages because it is considered weird to take in someone else’s child.

Office life is 50% time spent correctly pandering to your bosses feelings, and they have made so little effort to include women in the workforce and make parenting compatible with a good job that nobody wants to have kids.
by dccoolgai | 2025-04-08 22:40:28+00:00
Unfortunately, when cheap goods are sourced outside the US "innovation" usually just means "slave labor" or "flouting environmental regulations".

Not the case for Europe, etc. but most Americans are better off without competing against slave labor so billionaires can become trillionaires while they lose social security, healthcare, etc. I'll pay an extra 20 percent for well-made things that aren't produced by indentured servants.
by jacobr1 | 2025-04-08 22:40:31+00:00
Are you using a paid version? Do you use web-search? And have you tried alternatives like Claude?

I've mostly switched to using Claude these days, with MCPs for websearch and fetching specific remote or local files. It answers questions generally very accurately (from the source documents it identifies) and includes citations.

I've found that people that haven't really tried the latest models, and just rely on whatever knowledge is in the model training are really missing out on the potential power. GPT4o+ and equivalent models really changed the game. And using tools to do a search, or pull in your code, or run a db query or whatever enables them to either synthesize information or generate context relevant material. Not perfect for everything, but much better than a year ago, or what people are doing with the free systems.
by smt88 | 2025-04-08 22:41:42+00:00
Investors hate uncertainty and these tariffs will raise costs for a lot of startups to the point they can't be profitable.

Investors will likely increase investment in other countries or keep their money liquid for a while to see what happens.

US tech is effectively dead unless the tariffs go away, and it may still be dead if the go away in a way that feels like it could be temporary.

Best case scenario is for SCOTUS to rule they're unconstitutional in the very near future.
by luqtas | 2025-04-08 22:41:46+00:00
if you are visiting a city and you are exclusively using the bus, in 2 or 3 travels the card pays itself for not dealing with coins and physical money... you can literally buy them in 5 minutes, no need to have a CPF exposed or whatever [they are called TOURIST CARDS for a reason]

around Paraty -> Angra dos Reis region you can literally visit/stop in more than 25 beach spots with the buses who circulate that area

and it's really nice that it helps the bus drivers. it even saves some time on embarking, which can add up in longer routes. godspeed on a single card (state agnostic) for the entire public transportation system (metro, bus, trains) in Brazil
Ask HN: Tell me a problem you face as a Dev, that you'd pay to see solved
by evenoroddman | 2025-04-08 22:43:33+00:00
Hey everyone, I am looking to solve some genuine problems as part of my next project.

I have been working as a software engineer for a decade now and recently took a break from the full time job. I am trying to navigate the space of solo development without drowning in debt. For context I’ve recently worked on an online code editor - scriptpad. Have previously worked on developer productivity and low code tools, as part of my job.

I would really appreciate it if you could share your pain points to help me paint a picture.

All ideas are welcome. Hopefully this thread acts as inspiration not just for me but for all those who are looking for some actual problems that are worth solving.
by slicktux | 2025-04-08 22:43:47+00:00
That’s a self fulfilling prophecy…say that is the case. The one thing that won’t change is the jobless and skill less Americans; I understand that the road to hell is paved with good intentions but at this point we can’t help but not ignore the homeless, jobless and skill less problem. Though we can solve it by just creating a UBI, right (sarcasm)?
by bryant | 2025-04-08 22:44:12+00:00
The main reminder here (not even really a novel takeaway - this ends up happening like clockwork in some shape or form every few generations) is that laws can only be enforced after the damage is done.

Short of overwhelming activism e.g in the form of recall elections where applicable, constitutional conventions, etc., the degradation of both the rule of law and the standing of the United States on the world stage will continue for the next 21 months. And there's every reason to believe that voters suffering today will again be swayed through misinformation to vote against their own interests in the November 2026 congressional elections.
by superlopuh | 2025-04-08 22:44:30+00:00
I once used Sentient[0] to solve a similar puzzle in a different game that I think made a puzzle that required a constraint solver as a joke on the player. I'm a bit saddened to see that the repo hasn't been updated in 6 years, and a node update broke the binary installed on my computer, I find it a much more ergonomic environment than Prolog, hopefully someone else will pick up the mantle.

[0]: https://sentient-lang.org/
by Laremere | 2025-04-08 22:44:48+00:00
I've been playing and enjoying this game since it was first posted on hn. Doing this is absolutely key to getting a good score. Eg, for the final answer in today's puzzle, I had the outer answer, and had to work backward 3-4 layers. The other good thing to do is, if you have an answer to work forward a couple layers. Once you know that an answer makes the next couple questions make sense, you're much less likely to give an incorrect guess.
by ForceBru | 2025-04-08 22:44:56+00:00
I don't think this is _literally_ a search through vector embeddings.

LLMs store embeddings of individual tokens (usually parts of words), so a result of an actual search will be top-k embeddings and the corresponding tokens, similar to the output of a Google search. You could extract the initial matrix of embeddings from some open-weights model and find tokens closest to your query. However, it's not clear why do this. OP got coherent text, so that's not search.

It's _similar_, though, because attention in LLMs basically looks for most similar tokens. So to answer the question about the term, the LLM had to create a stream of tokens that's semantically closest to the given description. Well, this is somewhat like a search, but it's not exactly the same.
by voidfunc | 2025-04-08 22:45:01+00:00
At some point you realize almost all of these studies are pointless and you're talking about shaving or adding negligible amounts of time off your life while ignoring more serious risks like driving or inhaling smog and brake dust daily. It's not an excuse to go crazy and eat and drink total shit, but I've largely given up on modern medicine and nutrition having any fucking clue beyond calories in / calories out.
by photonthug | 2025-04-08 22:45:15+00:00
I think I'd remain interested in more conclusive proof one way or the other, since by your logic everything that's currently unknown is unknowable.

Regardless of whether the project of explainable / understandable succeeds though, everyone should agree it's a worthy goal. Unless you like the idea of stock-markets, resource planning for cities and whole societies under the control of technology that's literally indistinguishable from oracles speaking to a whispering wind. I'd prefer someone else is able to hear/understand/check their math or their arguments. Speaking of 6-8000 years since something happened, oracles and mystical crap like that should be forgotten relics of a bygone era rather than an explicit goal for the future
by nlarew | 2025-04-08 22:45:20+00:00
You're making a huge jump from "gathering anonymous counters to understand how many people use the thing" to "harvest all the data you want and monetize it".

If they were tracking my identity across sites and actually selling it to the highest bidder that's one thing that we'll definitely agree on. This is so so far from that.

You're welcome to build and use your own MCP browser automation if you're so hostile to the developer that built something cool and free for you to use.
by barnav2424 | 2025-04-08 22:45:24+00:00
Location: Cambridge, MA

Remote: no

Willing to relocate: Only considering San Francisco / Bay Area Technologies: MCP, vLLM, TogetherAI, HuggingFace, tensorflow, pytorch, react, nextjs, flask, fastapi, celery, rabbitmq

Led work across real-time control systems, LLM-driven semantic execution engines, RL-based human-AI collaboration, and autonomous motion planning with multimodal perception — I learn fast and can juxtapose being scrappy with being detail-oriented. Won HackMIT 2024 and am a researcher at MIT CSAIL's Data Systems Group.

Resume/CV: https://www.arnavadhikari.com, https://www.linkedin.com/in/arnavwad PDF provided on request

Email: arnavwad@mit.edu
by n_ary | 2025-04-08 22:45:42+00:00
I still use search engine. LLMs are great but often get outdated by 6-12 months. Usually, I search for random coding topics and asking LLMs will often reproduce the outdated top(most voted) answer verbatim. However, there are some nice folks at SO/SE who come back later and update the answers or submit a new one, but LLMs often don’t return these but continue producing various spin off of the top answer by modifying variable names or adding/removing comments.

Hence, search still remains my hope until SO and the likes decay.

Additionally, many search engines now already generate quick summaries or result snippets without a lot of prompt-fu, hence LLMs have actually become 40:60(llm:search) ratio day to day.
by c7b | 2025-04-08 22:45:45+00:00
> By having central banks attached to these projects, it helps build a testbed so private sector players can then extend on.

Again, you can get all of that without needing a CBDC, just have the central bank build and run a regular payment system. It gets substantially more complex once you make it a CBDC, making the chance of success even smaller. For what gain? You actually introduce some tangible risks for the financial system, the fact that it's regulated doesn't eliminate that. See eg [0]: "Threats to financial intermediation in steady state arise mainly in situations where the central bank balance sheet expands, and triggers adjustment mechanisms that lead to more costly or less stable funding of the banking system, while in crisis times run risk may increase." The typical way to address those risks are holding limits, which add operational complexity (you need an overflow logic, you need a draft logic if you want to enable payments greater than the holding limit), and put some limits on programmability [1].

[0] https://www.imf.org/en/Publications/WP/Issues/2024/10/11/Cen...

[1] https://ieeexplore.ieee.org/document/10628652
by crvdgc | 2025-04-08 22:46:05+00:00
A perspective from a friend, who recently gave up trying to get into concept art:

Before AI, there was out-sourcing. With mass-produced cheap works, foreign studios eliminated most junior positions.

Now AI is just taking this trend to its logical extreme: out-sourcing to machines, the ultimate form of out-sourcing. The cost approaches to 0 and the quantity approaches to infinity.
by alright2565 | 2025-04-08 22:46:09+00:00
Through incredible human ingenuity, we have convinced the two most abundant elements in the earth's crust to give us free energy. Once they are no longer effective, after several decades of service, we put them back in the earth where we got them.

Is this a problem worth solving?

Is it a problem worth giving any amount of thought to at all, when the alternative is killing people both directly through pollution and indirectly through climate change?
by dijksterhuis | 2025-04-08 22:46:25+00:00
I actually have two settings in the AI features settings area: one for "chat" and another for "assist" -- which I think is the word salad summaries? I've got chat set to Off and assist set to Never.

Searching 'octatrack' has the wikipedia page on the top right as a summary box thing. no ai word salad for me :shrug:
by antics | 2025-04-08 22:46:30+00:00
I just meant that there are lots of problems where I think TCS could have contributed but didn't, because the relevant analysis was not acceptable at FOCS or SODA (or whatever). A good example is the original transformers paper, which is vastly over-complicated relative to (say) a later treatment[1] by Ruslan Salakhutdinov's lab, which shows that attention is "just" plain-old Nadaraya-Watson kernel smoothing—which if you don't know is a weighted average of points covered in elementary grad stats.

I'm not saying it was TCS people would be "better" at discovering this or anything like that, I'm just saying that the opportunity for contribution from TCS people is very high, and because the fields are not well-integrated you often have to wait years to uncover what ML concepts "really" are. I think working together would benefit both ML and TCS!

[1]: https://arxiv.org/pdf/1908.11775
by lesuorac | 2025-04-08 22:47:04+00:00
It's an economic theory - https://en.wikipedia.org/wiki/Mercantilism

The general idea is that when you buy stuff you're losing wealth so if you never buy anything then you never get poorer. Since we constantly dig gold up from the ground we still get richer over time even if nobody buys our stuff.

This is also why its importantly to gain colonies (Greenland/ Canada) or mineral rights from other countries so that the amount of stuff we have increases.

IMO, it's a theory that's missing the forest for the trees but it's historically very popular.
by jongjong | 2025-04-08 22:48:42+00:00
> In terms of software quality, I would say the code created by the AI was worse than code written by a human–though not drastically so–and was difficult to work with since most of it hadn’t been written by the people whose job it was to oversee it.

This is a key insight. The other insight is that devs spend most of their time reading and debugging code, not writing it. AI speeds up the writing of code but slows down debugging... AI was trained with buggy code because most code out there is buggy.

Also, when the codebase is complex and the AI cannot see all the dependencies, it performs a LOT worse because it just hallucinates the API calls... It has no idea what version of the API it is using.

TBH, I don't think there exists enough non-buggy code out there to train an AI to write good code which doesn't need to be debugged so much.

When AI is trained on normal language, averaging out all the patterns produces good results. This is because most humans are good at writing with that level of precision. Code is much more precise and the average human is not good at it. So AI was trained on low-quality data there.

The good news for skilled developers is that there probably isn't enough high quality code in the public domain to solve that problem... And there is no incentive for skilled developers to open source their code.
by 0xbadcafebee | 2025-04-08 22:48:59+00:00
-x does not show output/errors when pipefail triggers. It tells you a pipe has started, and that's it. No idea what specific part of the pipe failed, or why, or what its return status was.

It's not that pipefail doesn't handle a case, it's that it doesn't tell you what happened. It does not report what failed or why. Your shell just exits with a mystery return code.
by jacobr1 | 2025-04-08 22:49:45+00:00
It is more subtle than that. It is trained on "everything" so the specific adjectives and words you use in your prompt will cause it to generate very different responses. The fine tuning causes it to prefer by default more reliable/professional responses ... but that is not because the training data is weighted toward them as such. If you mention specific publications, or forums, it will give you responses more likely to come from those.

Looking at the reasoning traces for for the new reasoning models you can actually see how fine tuning is moving toward having models list the assumptions around data sources, which should be trusted, list multiple perspectives and then summarize, resulting in better answers. You can do that today with non-reasoning models, but you need to prompt engineer it to ask for that explicitly. This process of identifying not just extant content, but teaching systems how to approach problem analysis (instruction tuning, reasoning traces, etc ...) will be key to influencing how the models work and increasingly how they are differentiated.
by purple-leafy | 2025-04-08 22:49:47+00:00
Hey congratulations!

Did you approach Atlantic, or did they approach you?

I’ve had a word game in-mind for years, and several attempts to implement it. It’s a tricky one but I’ll crack it one day :)

My plan is to be acquired by a news company.

So, how did you go about this? I have some ideas myself