MurphyA/hacker-news huggingface dataset:
by Aurornis | 2025-09-26 15:49:23+00:00
COVID wfh was a weird time. The company I worked for was remote before COVID. Oddly, COVID was a boost to basically every metric, including the ones not tracked for productivity like even number of Slack messages sent or raw number of PRs. I think everyone was just closed off from their out of house obligations, especially families with kids who went to a lot of activities that got cancelled. So they worked instead or were just more rested and less exhausted from everything else in their lives.

The problems started mostly with cohorts hired remote during COVD. Something about COVID wfh attracted a lot of remote candidates with not so great intent: The overemployed people getting multiple jobs, the side hustle bros who needed a paycheck and healthcare while they worked on their startup idea, the 4 Hour Workweek people who tried to travel the world and answer Slack once a day or other people who generally just weren’t interested in actually doing work while remote. It started to add up over time. There were also the people who cancelled daycare and tried to watch kids while they worked, people who were never at their keyboard for some reason, a guy who was always catching COVID or going to a funeral whenever you needed to schedule a meeting. It really wore everyone down. I wished we could have stuck with the pre-COVID remote crew because for some reason everything changed when everyone started WFH.
by guywithahat | 2025-09-26 15:49:31+00:00
Honestly I wonder if it's more than that. China has preemptively banned basically all major US social media sites (and many tech companies) to the benefit of their own local brands. Those brands are now entering the US market, and so I could see why the US might want to try and block them, although I suppose I wish social media sites were just allowed to operate in each respective country.
by barbazoo | 2025-09-26 15:49:49+00:00
Article addresses that somewhat

> On the one hand, this output is not very surprising. AI researchers who create AI language models like the kind that power ChatGPT have known for years that these models can synthesize realistic permutations of information learned from those texts. It's how every AI assistant today works.

> But what makes this episode especially interesting is that a small hobbyist model trained by one man appears to have surprised him by reconstructing a coherent historical moment from scattered references across thousands of documents, connecting a specific year to actual events and figures without being explicitly taught these relationships. Grigorian hadn't intentionally trained the model on 1834 protest documentation; the AI assembled these connections from the ambient patterns in 6.25GB of Victorian-era writing.
by api | 2025-09-26 15:50:12+00:00
Socialism (of the authoritarian centralized sort) is state ownership of the means of production.

Fascism is private ownership but state control of the means of production through political and economic pressure, or if that fails by direct edict. The state gets all the benefits of owning the means of production but none of the responsibility, and also gets to pick winners. The winners it picks are generally people aligned with the regime, so over time the business world gets stacked with partisans.

(I'm talking about the economic component of fascism, which usually comes with other ideological components like ultra-reactionary social doctrines and extreme nationalism.)

Capitalism in the free market sense is private ownership and private control, with businesses having a significant amount of independence from the state. This independence is both economic and ideological, e.g. free enterprises openly criticizing or even opposing the political class. Fascism seeks to stamp this out and force business to align with state authority.
by ratelimitsteve | 2025-09-26 15:50:28+00:00
TrumpTok is gonna be grooooooooooooosssssssssssssssssss. I don't for a second think that they're going to take what is, by their admission, the most sophisticated and far-reaching propaganda engine in human history and carefully balance the conflicting duty of delivering maximum value while not taking advantage of people.
by 999900000999 | 2025-09-26 15:50:31+00:00
I believe if you create something like a task manager for the coding agents, think something hosted on the web like Jira, you can work around this.

I started writing a solution, but to be honest I probably need the help of someone who's more experienced.

Although to be honest, I'm sure someone with VC money is already working on this.
by tstrimple | 2025-09-26 15:50:43+00:00
> With that said, I think the lot of conspiracy that just doesn't really hurt anyone but the believer. Aliens, moon landings, illuminati, etc. Kind of the modern day opiate of the masses.

I'm more and more convinced that these are gateway conspiracy theories. That's where my dad started innocently enough. I remember listening to AM Coast to Coast with him. Art Bell and those "innocent" conspiracies. A couple decades later he's a completely different person with completely different beliefs all still aligned to conspiracy theories he picked up on AM radio. But now it's about Muslims taking over the country and wanting to kill or convert us all and the Clinton's and Obama's plans to enable Sharia takeover.
by loudmax | 2025-09-26 15:50:57+00:00
I wouldn't take it for granted that Claude isn't re-reading your entire context each time it runs.

When you run llama.cpp on your home computer, it holds onto the key-value cache from previous runs in memory. Presumably Claude does something analogous, though on a much larger scale. Maybe Claude holds onto that key-value cache indefinitely, but my naive expectation would be that it only holds onto it for however long it expects you to keep the context going. If you walk away from your computer and resume the context the next day, I'd expect Claude to re-read your entire context all over again.

At best, you're getting some performance benefit keeping this context going, but you are subjecting yourself to context rot.

Someone familiar with running Claude or industrial-strength SOTA models might have more insight.
by tracker1 | 2025-09-26 15:51:03+00:00
I've been running the alpha for about 6 months at this point... it's been relatively solid, but there's been a few rough edges (mostly around keyboard navigation in the apps). Most of what I expect to work is now working without issue. I've also been running a mainline 6.16 kernel, for better gpu support (RX 9070XT).

I've got no idea on where Accessibility lands right now though... At least in terms of screen readers. As I've been having vision issues even beyond using very large monitors I've had to rely on zoom features in apps/browsers. It's generally been a better experience than some of the rougher edges of Windows at work. I need to figure out if/how to scale RDP sessions.
by nickpsecurity | 2025-09-26 15:51:39+00:00
I still don't have a GPT3-class model that was trained without copyright infringement. I'd have so many uses for it from research to production. What's stopping me is the $30 million training cost for 180B models. Even a 30B like Mosaic cost over a million dollars.

So, I strongly disagree unless we're talking about the five or six companies that already spend tens of millions on training and keep repeating that. Outside of them, the medium to large models are done infrequently or one off by a small number of other companies. Then, most of us are stuck with their pretraining efforts because we can't afford it ourselves.

On my end, I'd rather see a model that drops pretraining costs to almost nothing but costs 10-32x more to do inference. My uses would produce mere MB of output vs hundreds of GB to TB that pretraining requires. A competitive use that costs 32x current prices would probably be profitable for me. Optimizations, which are plentiful for inference, might bring it down further.
by RedNifre | 2025-09-26 15:51:57+00:00
I vibe coded an invoice generator by first vibe coding a "template" command line tool as a bash script that substitutes {{words}} in a libre office writer document (those are just zipped xml files, so you can unpack them to a temp directory and substitute raw text without xml awareness), and in the end it calls libre office's cli to convert it to pdf. I also asked the AI to generate a documentation text file, so that the next AI conversation could use the command as a black box.

The vibe coded main invoice generator script then does the calendar calculations to figure out the pay cycle and examines existing invoices in the invoice directory to determine the next invoice number (the invoice number is in the file name, so it doesn't need to open the files). When it is done with the calculations, it uses the template command to generate the final invoice.

This is a very small example, but I do think that clearly defined modules/microservices/libraries are a good way to only put the relevant work context into the limited context window.

It also happens to be more human-friendly, I think?
by revel | 2025-09-26 15:51:59+00:00
This is one cause but another is that agents are mostly trained using the same sets of problems. There are only so many open source projects that can be used for training (ie. benchmarks). There's huge oversampling for a subset of projects like pandas and nothing at all for proprietary datasets. This is a huge problem!

If you want your agent to be really good at working with dates in a functional way or know how to deal with the metric system (as examples), then you need to train on those problems, probably using RFT. The other challenge is that even if you have this problem set in testable fashion running at scale is hard. Some benchmarks have 20k+ test cases and can take well over an hour to run. If you ran each test case sequentially it would take over 2 years to complete.

Right now the only company I'm aware of that lets you do that at scale is runloop (disclaimer, I work there).
by atonse | 2025-09-26 15:52:11+00:00
Do we know if LLMs understand the concept of time? (like i told you this in the past, but what i told you later should supersede it?)

I know there classes of problems that LLMs can't natively handle (like doing math, even simple addition... or spatial reasoning, I would assume time's in there too). There are ways they can hack around this, like writing code that performs the math.

But how would you do that for chronological reasoning? Because that would help with compacting context to know what to remember and what not.
by Applejinx | 2025-09-26 15:52:22+00:00
OK, so it sounds like this is a 'I know for certain I can't code without AI and that I get nothing coherent done, and now I'm doing the greatest things all the time, so you're demonstrably wrong!' argument.

I would ask, then, if you're qualified to evaluate that what 'you' are doing now is what you think it is? Writing off 'does it lead to other problems' with 'no doubt, but' feels like something to watch closely.

I imagine a would-be novelist who can't write a line. They've got some general notions they want to be brilliant at, but they're nowhere. Apply AI, and now there's ten million words, a series, after their continual prompts. Are they a novelist, or have they wasted a lot of time and energy cosplaying a novelist? Is their work a communication, or is it more like an inbox full of spam into which they're reading great significance because they want to believe?
by PaulHoule | 2025-09-26 15:53:15+00:00
In Ithaca we have crazy hills so it is a good plan to take the bus up and then ride down although E-bikes change that equation.

In Ithaca we have great bus service between the Ithaca Commons, Cornell and the Pyramid Mall. Before the pandemic we had a bus every 15 minutes at the mall which was great -- it's still pretty good. There are 5 buses a day during weekdays to the rural area where I live. These are well timed for the 9-5 worker at Cornell and I'm going to be taking the late one back today because I'm going to go photograph a Field Hockey game over in Barton Hall and the timing is right -- it's OK but we did have more buses during the pandemic.

Bus service is not so good to Ithaca College. When I've tried to make the connection with my bus I've concluded that I might as well walk up the hill the IC rather than wait for the bus.
by ljm | 2025-09-26 15:53:57+00:00
For finicky issues like that I often find that, in the time it takes to create a prompt with the necessary context, I was able to just make the one line tweak myself.

In a way that is still helpful, especially if the act of putting the prompt together brought you to the solution organically.

Beyond that, 'clean', 'well written' and 'maintainable' are all relative terms here. In a low quality, mega legacy codebase, the results are gonna be dogshit without an intense amount of steering.
by magicalhippo | 2025-09-26 15:54:09+00:00
I'm not an expert but it seemed fairly reasonable to me that a hierarchical model would be needed to approach what humans can do, as that's basically how we process data as well.

That is, humans usually don't store exactly what was written in as sentence five paragraphs ago, but rather the concept or idea conveyed. If we need details we go back and reread or similar.

And when we write or talk, we form first an overall thought about what to say, then we break it into pieces and order the pieces somewhat logically, before finally forming words that make up sentences for each piece.

From what I can see there's work on this, like this[1] and this[2] more recent paper. Again not an expert so can't comment on the quality of the references, just some I found.

[1]: https://aclanthology.org/2022.findings-naacl.117/

[2]: https://aclanthology.org/2025.naacl-long.410/
by ARandumGuy | 2025-09-26 15:54:20+00:00
If you want a deep dive into chess cheating, including a lot of wild stories, Sarah Z put out an entertaining Youtube video [1] a couple of months ago that explores the concept. It's a long video, but well worth the watch.

[1]: https://www.youtube.com/watch?v=ZtN-i-IkRWI
by amdsn | 2025-09-26 15:54:33+00:00
Getting any kind of information from a chess engine would be sufficient to gain an edge for a good player. Even something as simple as a nudge that there is a high value move in a position with no information about what the actual move is could be enough. Big chess tournaments tightly control phones and other devices for this reason. That's on a single-match level. On a tournament level there have been allegations of collusion where players will intentionally arrange their own matches to either be quick draws (to get a break to focus on other matches) or to give points to a designated player to help them win the tourney, Fischer famously accused Soviet chess players of doing this.
by techblueberry | 2025-09-26 15:54:58+00:00
I often wonder what the substantive risk is here. On the one hand, like I expect the media I interact with to vibe with me. I think if I logged into youtube and it looked like Rumble, I wouldn't use it. Not out of principal, not because I hate free speech, but it's like browsing the channels on the tv. I pick websites I vibe with and not ones I don't. But if you could make the front page show me the things I do vibe with, cabin porn and all that, I probably wouldn't care if Alex Jones was there?

And maybe this is part of the point, do you want it to be in the air that Youtube is the place you go to listen to Alex Jones and Nick Fuentes? If that was in the air, again, I would probably just organically move away from it. But this is just a numbers game, there are far more things in this world that I don't participate then that I do participate in, and youtube would obviously prefer that I choose to participate there.
by jonahbenton | 2025-09-26 15:55:24+00:00
I used to like these in old magazines that would have multiple clue sets for the same puzzle layout. Try the hard one, then do the easy one. The mechanic in an interactive context strikes me as challenging. Is it just time? Is there a hint submechanic? Is there a points mechanic? Is there a difficulty labeling (like give me the Saturday version first, then switch to Wednesday). How much cognitive load to add to the game? I'm also a little skeptical that the (implied) llm generation of clues or hints could be implemented without frustration. It seems like an interesting idea to explore but many paths in that game design tree seem like they could be frustrating rather than fun.
by ascagnel_ | 2025-09-26 15:56:12+00:00
For a first run-through, I'd recommend the "Zero Rando" edition of the [Deus Ex Randomizer](https://github.com/Die4Ever/deus-ex-randomizer/) mod, since that's strictly limited to quality-of-life and bug fixes. If you're playing on a Steam Deck or with a controller, it integrates a radial menu for augmentations, which will help a ton when mapping the mouse-and-keyboard controls onto a gamepad.
by cuttothechase | 2025-09-26 15:56:16+00:00
It is pretty clear that the long horizon tasks are difficult for coding agents and that is a fundamental limitation of how probabilistic word generation works either with transformer or any other architecture. The errors propagate and multiply and becomes open ended.

However, the limitation can be masqueraded using layering techniques where output of one agent is fed as an input to another using consensus for verification or other techniques to the nth degree to minimize errors. But this is a bit like the story of a boy with a finger in the dike. Yes, you can spawn as many boys but there is a cost associated that would keep growing and wont narrow down.

It has nothing to do with contexts or window of focus or any other human centric metric. This is what the architecture is supposed to do and it does so perfectly.
by NohatCoder | 2025-09-26 15:56:27+00:00
While it is good to see some Bayesian statistics in use, I wouldn't in this case put so much emphasis on an exact calculated probability that he did or din not cheat, the prior in this case is simply too wishy-washy for that.

The sound conclusion is that this is not evidence of cheating, but it is not evidence of the contrary either.
by BinaryIgor | 2025-09-26 15:56:35+00:00
As expected, Postgres - especially with Unlogged Tables - is fast enough; additionally, having less things to run on your infrastructure and fewer integrations to manage in your app is a hugely underrated benefit.

I am just a little bit surprised on the relatively low write performance for both Postgres and Redis here; but as I can see, the tests were run on just 2 CPUs and 8 GB of RAM machine. In my experience, with 8 CPUs, Postgres can easily handle more than 15 000 writes per second using regular tables; I would imagine that it can easily be 20 000+ for the Unlogged variety - who needs more than that to cache?
by PaulHoule | 2025-09-26 15:56:51+00:00
Many of the emotionally disturbed and criminal people aren't actually homeless, and many homeless people are basically law abiding and not so crazy.

About a year ago I went to NYC and it was a bit surreal. It didn't really seem unsafe but boy I saw a lot of people (mostly white) propping open the emergency exits so other people could sneak in just around the corner from New York Guard troops supporting the NYPD. Video ads on the subway were oddly calibrated: "Don't sleep on the subway because it makes you vulnerable to crime", "Don't jump the turnstile because we have roughly 30 programs that could get you free or reduced fares" together with ads for deodorant.
by davedx | 2025-09-26 15:56:53+00:00
> It needs to understand product and business requirements

Yeah this is the really big one - kind of buried the lede a little there :)

Understanding product and business requirements traditionally means communicating (either via docs and specs or directly with humans) with a bunch of people. One of the differences between a junior and senior is being able to read between the lines of a github or jira issue and know that more information needs to be teased out from… somewhere (most likely someone).

I’ve noticed that when working with AI lately I often explicitly tell them “if you need more information or context ask me before writing code”, or variations thereof. Because LLMs, like less experienced engineers, tend to think the only task is to start writing code immediately.

It will get solved though, there’s no magic in it, and LLMs are well equipped by design to communicate!
by zahlman | 2025-09-26 15:57:17+00:00
> Their prices are completely made-up since we don’t have the technology to remove carbon

All prices are made-up. We pay in currency which lacks intrinsic value, and simultaneously multiplying or dividing all the numbers by ten wouldn't effectively change anything.

Moreover, we aren't really "pricing carbon" in the first place; we're placing a Pigouvian tax on polluting. It's not as if someone will pay (or be paid) to take the carbon off the producer's hands. In fact, as you highlight, there is no consumer who could do so.

Moreover, "technology to remove carbon" is completely irrelevant to setting the level. Determining the right level is the same kind of optimization problem as price discovery, but it's neither based on supply-and-demand for the carbon itself, nor on compensating anyone for dealing with the carbon. It's based on figuring out what keeps the economy going while moving the world towards net zero.

(Although we do have such "technology" — for example, the trees that people are planting to get these credits. The credit is paid for an action that demonstrably removes CO2 from the air, and is scaled according to the expected removal and how much would be charged for polluting that much.)
by jonex | 2025-09-26 15:57:30+00:00
It's the difference between proportional voting vs winner takes it all. In the latter case you can't really hold politicians accountable, as you will have to choose between effectively throwing your vote away or voting for the one opposition candidate, that often will be just as bad.

While the UK have some level of representativeness, each circuit has a winner takes it all structure, making change quite hard to achieve on a larger scale.
by cosmic_cheese | 2025-09-26 15:57:32+00:00
Unfortunately Gnome extensions suffer from breaking frequently due to how they work.

The best design for extensions specifically is with a capable, well-defined, stable public API that can be hooked into by a scripting language. The extension APIs should be exposed with both official bindings for popular languages as well as plain C headers, so other language bindings can be easily written and extension authors can use Python or Lua or Ruby or whatever it is they like to write.
by supertrope | 2025-09-26 15:57:33+00:00
2/3 of public transit budgets in wealthy countries is hiring employees. Vehicle costs are not the headline cost. However this cost does needs to be managed. Transit agencies are running on shoe string budgets.

Until recently the US Federal Government funded capital expenses but never operating expenses. This lead to outcomes such as the feds distributing grant money with the requirement that buses must last at least 12 years and transit agencies refreshing their buses on the 12 year mark. Buying a natural gas bus or battery electric bus lowers OPEX and the increased CAPEX is picked up by the feds.
by t43562 | 2025-09-26 15:57:38+00:00
At one time Gnome ejected a portion of its user base in order to become something that would work on tablets and phones as well as desktops - at least that seemed like the rationale. At that time it looked like there might be a market for linux devices with a UI and I don't know for sure but I think some money was spent to achieve this which is why objections by users were of so little importance.

Nowadays phones seem like a duopoly that cannot be challenged and tablets aren't very important anymore. Linux doesn't matter on the desktop because browsers are the UI and the apps run in the cloud. The whole GNOME/KDE/whatever effort is a bit moot.