MurphyA/hacker-news huggingface dataset:
by zoklet-enjoyer | 2025-07-22 01:02:48+00:00
I recently started getting notifications and pictures of mail from a place I moved out of in 2013. I think I must have had informed delivery set up for that address back then and the new resident signed up and now I'm getting notifications again?

The first time I noticed this was after I had renewed my passport. I got a notification saying that I was expecting a package in Tacoma. I was so confused, thinking maybe someone has stolen my CC number. The ln I remembered my passport should be on the way. I freaked out, contacted my ex and asked her to go back there and talk to the current resident to see if they had my passport. Then I got another notification, logged into the USPS website, and saw that I had notifications for this guy's mail at my old house.
by gavmor | 2025-07-22 01:03:02+00:00
I was hoping he'd touch on how writing is "embodied" or "extended" cognition, ie how it allows for manipulation and reorganization of ideas in ways that are functionally similar to what occurs within the mind a la Clark and Chalmers' “Otto’s notebook” thought experiment (in which an Alzheimer's patient has written all of his directions down in a notebook to serve the function of his memory).

Or how the thoughts we have as we are writing shape our understanding, and so we come out with not only a written composition, but a new frame of mind; AI generated writing allows us to preserve our frame of mind—for better or worse!

There is something to be said for the ways in which coding, too, is an exercise in self-authoring, ie authoring-of-the-self.
by doritosfan84 | 2025-07-22 01:03:22+00:00
I’ve been trying unsuccessfully to convert my team to jujutsu. I feel like what would be great is a page that really shows some common but complicated operations in git and how much easier they are in jujutsu. Something like the elevator pitch here but expanded on without the depth of Steve’s tutorial.

Maybe what I need to do is do a demo so people can see and ask questions.
by jameskilton | 2025-07-22 01:04:15+00:00
> That is like asking how we can make our cities better for cars, or our workplaces better for the furniture (emphasis mine)

I love this analogy and am going to use it.

This is a fantastic article. In the end, everything is still, and will always be, about people. We ignore and forget that at our peril.

Thanks!
by Skunkleton | 2025-07-22 01:04:55+00:00
Its needed on my ubuntu 24.04 system

$ cat test.sh #!/usr/bin/env bash -c "echo hello" $ ./test.sh /usr/bin/env: ‘bash -c "echo hello"’: No such file or directory /usr/bin/env: use -[v]S to pass options in shebang lines $ ./test.sh # with -S hello
by lukezeller | 2025-07-22 01:05:08+00:00
(Another member of the team behind the benchmark here) The first month performed well because (1) the models effectively leveraged historical precedent - they could identify similar transactions from past data and apply established patterns, and (2) the starting balances were clean, so they were more easily able to understand / track down discrepancies.

> Was all of the context there without tool calls in the first month?

We provided schemas for the GL and source data in the system prompt, but none of the actual data. The model had to use its tools (SQL and python script) to understand / analyze historical data.

> In the later months that seem like tool calls weren’t happening. That should have been happening to inform the context?

We actually didn’t find that they stopped calling tools entirely. Instead, they weren’t able to make sense of the information fetched with tools (for example, a bank account starting balance that was >$100000 different from the starting balance on the supporting bank statement). They’d tend to either do nothing or just do a first pass without deduplicating / cleaning up. This created a feedback loop where incorrect balances led to more errors and made subsequent months increasingly difficult to process accurately.

This didn’t make it into the report, but another interesting behavior we observed w.r.t tool usage (with Claude in particular): if a tool failed 2-3 times (for example, runtime error in python code) Claude would tend to abandon it entirely for the rest of the session. Interestingly, this happened even when it knew how to fix the errors: on a couple of early runs, I observed Claude fixing a python bug (with the edit_tool tool) but then abandoning without even attempting to rerun, and reverting to SQL-only for the rest of the session.
by Lyngbakr | 2025-07-22 01:05:31+00:00
I feel pretty dense, because I still struggle to get my head around automatically adding changes to a revision. Sometimes, I'll make a change locally to a file that I'll use during the development process that I have no intention of committing. With regular git, I never stage that file so there's no danger of accidentally pushing my change to the remote repo, but it seems with jj I'll need to somehow unstage that change or something to prevent this. Perhaps it's just habit, but I feel more comfortable explicitly saying what I want to commit rather that defaulting to everything. Or have I totally misunderstood jj?
by alkh | 2025-07-22 01:06:25+00:00
Thanks, but the main question is how come the behaviour is still the same whether you pass the flag or not? I would get it if it just failed without "-S" but it works as intended. I am wondering if this is cause I might not be using the GNU version of env, so this is less relevant? Edit: looks to be the version thing indeed, this doesn't work for someone on Ubuntu
by doritosfan84 | 2025-07-22 01:07:20+00:00
I don’t think simplified model is even the best selling point, but it’s definitely up there. IMO one of the killer features that you absolutely cannot get in git is universal undo. For example, rebases can always be a little tedious and tricky no matter how experienced you are. Not only does jj make that whole process easier and safer to work through, but if you do still manage to get to a state where you just want to go back it’s literally just an undo away.

I’d give [1] a read if you’re interested.

1. https://zerowidth.com/2025/what-ive-learned-from-jj/#commits...
by oneplane | 2025-07-22 01:08:17+00:00
Meanwhile, Citrix has been on fire causing much worse things (you can just grab any session you want and become anyone who's already logged in). Who needs to break into SharePoint when you're becoming someone who's already got access... including to everything else (not just SharePoint)

It's patchable, but it's been two times in a row now, and patching is always slow and incomplete.
by mcswell | 2025-07-22 01:08:21+00:00
BTW, I decided I'd see if there was an XPath library for Prolog (once my favorite programming language, and with some conceptual/ algorithmic similarity to XSLT, I think, although XSLT has nothing like Prolog's backtracking). Turns out there is (at least) one: https://www.swi-prolog.org/pldoc/man?predicate=xpath%2f3.

There's a comparison of Prolog and XSLT v1 here: https://arxiv.org/pdf/1912.10817v1 (warning: poorly translated from the Russian original, and dated).
by ghssds | 2025-07-22 01:09:08+00:00
This article remind me of this quote in Conan the Barbarian:

Mongol General: Hao! Dai ye! We won again! This is good, but what is best in life? Mongol: The open steppe, fleet horse, falcons at your wrist, and the wind in your hair. Mongol General: Wrong! Conan! What is best in life? Conan: To crush your enemies, see them driven before you, and to hear the lamentations of their women. Mongol General: That is good! That is good.

What makes life worth living is not to have won in the past and you can now be satisfied with your situation but to win right now and to be making progress right now. You call it the Boogeyman, I call it the essence of what it is to be alive.
by kingstnap | 2025-07-22 01:09:32+00:00
It's still better than YouTube's search.

I once was looking for a video I watched a few months ago with a history search. Nothing brought it up.

I found it with text match in my browser history.

Out of curiosity, I tried every combination of words in its title, including the full title verbatim, and it did not show up. It is truly astonishing how bad it is.
by ninkendo | 2025-07-22 01:09:46+00:00
Let’s compare my quality of life to a king 500 years ago.

I have air conditioning.

I have running water and a toilet.

I have antibiotics. And all sorts of other medicine that actually works.

When I have to get an operation done I can go under anesthesia rather than it being literal torture. And I won’t be killed by an infection resulting from the tiniest cut.

I have literally endless entertainment. More than I can ever consume in a lifetime.

I have access to just about any piece of information that has ever existed and been made public.

I don’t have to worry about being killed by an uprising.

I can go on.
by heisenzombie | 2025-07-22 01:10:15+00:00
Quick plug here for a simple Jupyter kernel I created:

https://github.com/tobinjones/uvkernel

It’s a pretty minimal wrapper around “uv” and “iPython” to provide the functionality from the article, but for Jupyter notebooks. It’s similar to other projects, but I think my implementation is the least intrusive and a good “citizen” of the Jupyter ecosystem.

There’s also this work-in-progress:

https://github.com/tobinjones/pep723widget

Which provides a companion Jupyter plugin to manage the embedded script dependencies of noteboooks with a UI. Warning — this one is partially vibe-coded and very early days.
by garciasn | 2025-07-22 01:10:16+00:00
Rural fiber at my lake home went from $35/mo for 100/100 to $89.95 this year. On a 12mo contract.

Starlink got my business after VZW forced their 5G boxes to use 5G and not allow forced LTE usage. 5G is unusable there with 60-100/0.03. I force my phone to use LTE and all is well but 5G just does not work.

I hate giving Elon money but it’s the only affordable month-to-month option now.
by dimatura | 2025-07-22 01:10:35+00:00
I've found myself adopting this philosophy for a specific use case: monitoring ML training jobs. It's pretty common to see people output training metrics (loss, validation accuracy, etc) every N batches, iterations or epochs. And that does make sense for a lot of reasons, and it's pretty simple to do. But also when you're exploring models that might have wildly varying inference latencies, or using different hardware, or varying batch sizes, or using a differently sized dataset, all of those might end up reporting too infrequently to get an idea of what's happening or too frequently and just spamming too much output.

Checkpointing the model every N iterations/epochs/batches has a similar problem - you may end up saving very few checkpoints and risk losing work or waste a lot of time/space with lots of checkpoints.

So I've often found myself implementing some kind of monitoring and checkpointing callbacks based on time, e.g., reporting every half an hour, checkpointing every two hours, etc.
by wmlive | 2025-07-22 01:11:59+00:00
We are not really in a position to dictate what the GNUstep project ought to do. I for one am rather thankful for what they have achieved so far. Especially since they seem to mainly lack the man power which its critics probably won't provide either.

Not being a software developer myself, I've resorted to loosely following the progress of GNUstep over more than 20 years, in the hope that it will eventually become a viable option. But apparently capable people found more interest in KDE, GNOME, and similar projects based on either Qt or gtk+, and then hardly anyone cared anymore.

By all means, if anyone here is capable enough is wanting to still contribute to the GNUstep project, please do! At the current stage of the project it would be a pity if all the efforts made over the years would be wasted by not supporting it anymore.
by sfink | 2025-07-22 01:12:14+00:00
It was meant as more of an illustration than a persuasive argument. LLMs don't have much of a distinction between thinking and writing/saying. For a human, an admonition to not say something would be obeyed as a filter on top of thoughts. (Well, not just a filter, but close enough.) Adjusting outputs via training or reinforcement learning applies more to the LLM's "thought process". LLMs != humans, but "a human thinking" is the closest regular world analogy I can come up with to an LLM processing. "A human speaking" is further away. The thing in between thoughts and speech involves human reasoning, human rules, human morality, etc.

As a result, I'm going to take your "...so it is by definition impossible to _not_ think about a word we must avoid" as agreeing with me. ;-)

Different things are different, of course, so none of this lines up or fails to line up where we might think or expect. Anthropic's exploration into the inner workings of an LLM revealed that if you give them an instruction to avoid something, they'll start out doing it anyway and only later start obeying the instruction. It takes some time to make its way through, I guess?
by RiverCrochet | 2025-07-22 01:13:04+00:00
Long expiration times = compromised certs that hang around longer than they should. It's bad.

Note that you can make your own self-signed CA certificate, create any server and client certificates you want signed with that CA cert, and deploy them whenever and wherever you want. Of course you want the root CA private key securely put somewhere and all that stuff.

The only reason it won't work at large without a bit of friction is because your CA cert isn't in the default trusted root store of major browsers (phone and PC). It's easy enough to add it - it does pop up warnings and such on Windows, Android, iOS and hopefully Mac OS X, but they're necessary here.

No, it's not going to let the whole world do TLS with you warning-free without doing some sort of work, but for small scales (the type that Let's Encrypt is often used for anyway) it's fine.
by 0cf8612b2e1e | 2025-07-22 01:13:56+00:00
No that is correct, and also a habit I am trying to break. The reasonable argument is that we should stop running code with untracked state. Either the changes are important and should be committed or not. Otherwise you are recording code versions that never truly existed during development.

Where this gets extra sticky for me is tooling which refuses to distinguish repo wide config vs a local only version. VSCode being a huge offender where there is only a ‘launch.json’ and no ‘launch.local.json’ suitable for per host customization (eg maybe I am already running something on port 8888, so I need to map it to 9000, that does not mean a quirk of my environment should be committed).
by joegibbs | 2025-07-22 01:14:03+00:00
I think it would be good to combine traditional OCR with an LLM to fix up mistakes and add diagram representations - LLMs have the problem of just inventing plausible-sounding text if it can't read it, which is worse than just garbling the result. For instance, GPT4.1 worked perfectly with a screenshot your comment at 1296 × 179 but if I zoom out to 50% and give it a 650 × 84 screenshot instead, the result is:

"There's multiple fundamental problems people need to be aware of. - LLM's are typically pre-trained on text tokens and then extrapolated out to longer context windows (it's easy to go from 4000 text tokens to 4001). This is not possible with images due to how they're tokenized. As a result, you're out of distribution - hallucinations become a huge problem once you're dealing with more than a couple of images. - A PNG at 512x 2048 is 3.5k more tokens than the raw text (so higher inference costs and slower responses). Going lower results in blurry images. - Images are inherently a much heavier representation in raw size too, you're adding latency to every request to just download all the needed images.

Their very small benchmark is obviously going to outperform basic text chunking on finance docs heavy with charts and tables. I would be far more interested in seeing an OCR step added with Gemini (which can annotate images) and then comparing results.

An end to end image approach makes sense in certain cases (like patents, architecture diagrams, etc) but it's a last resort."

It mostly gets it right but notice it changes "Pdf's at 1536 × 2048 use 3 to 5X more tokens" to "A PNG at 512x 2048 is 3.5k more tokens".
by andrewmcwatters | 2025-07-22 01:14:41+00:00
Sorry to read that you’re dealing with this. My primary email is apart of some vast international spam program where the mail servers responsible for sending me spam forge headers, falsify source record information, lie about which downstream service provider is hosting them, etc. It’s really quite interesting, but more so than that, it’s annoying.

I pondered my problem for a few days, thinking about what sort of external service I could use to surgically remove these numerous, daily, very specifically identifiable spam emails before I stumbled on a thread on StackOverflow where some people discussed using Google App Scripts to do this very thing.

I’d recommend searching the web for that sort of topic. You’ll find that there’s a way to set up an hourly script job that will wipe this spam completely off your mailbox and find some peace.

Hope this points you in the right direction. The idea of having to use yet another Google service to fix an existing separate one is such a stupid labyrinthine experience, but at least it beats having to set up a job on a VPS for this.

If you had this issue with another mailbox service provider, a VPS approach would probably be necessary, though.
by stouset | 2025-07-22 01:15:05+00:00
In jj you tend to use `jj split` to break changes apart. The selected bits become the first revision, the remaining bits become the second revision.

I tend to do a bunch of work then split into small, bite-sized revisions. Often I split something out to a parallel revision (e.g., a separate branch) if it’s an independent thread of work like a bugfix elsewhere or a documentation fix. This is an obvious one-liner in jj but a bunch of annoying branch-switching and stashing in git.

You can also use a `git add`-style workflow. Create a new revision with `jj new`. Do it again. Make your changes, then `jj squash -i/--interactive` to select the bits you want to include. Keep making changes and squashing into the previous commit you’re building up until you’re happy. Conceptually just think of @ (the current revision) and @- (the previous revision) as the working copy and the staged copy, respectively.
by conception | 2025-07-22 01:17:20+00:00
I very much have LLMs go through rule sets all the time? In fact, any prompt to an LLM is in fact, a rule set of some sort. Can you say plausible but I think what you mean is probable. When you give an LLM rules most of the time the most probable answer is in fact follow them. But when you give it lots and lots of rules and or fill up its context sometimes the most probable thing is not necessarily to follow the rule it’s been given, but some other combination of information that it is outputting.
by PaulHoule | 2025-07-22 01:19:25+00:00
"shows that rival stock prices often rise when a merger is blocked"

There's definitely a story that many mergers are irrational. People in many parts of the US are worried about losing our local pharmacy because of a series of failed mergers. CVS buying the Caremark PBM was probably the last successful merger in the sector. Notably the UK chain Boots bought the US chain Walgreens which was a mistake because Boots (in a country where pharmaceutical prices are artificially low) has no idea of the dynamics of the business in the US (a country where pharmaceutical prices are artificially low.)

Boots made a mistake which has been common in that industry which is to think that it could greatly improve "front of store" sales of packaged food, beer, shampoo and stuff in Walgreens. On one hand there's the probably that after you've paid for health insurance and paid for your drugs you don't have money to buy anything in the front of the store. There's also the problem that retail is fiercely competitive and anything you could get front of store at Walgreens you could get at a supermarket or Target or Walmart or Amazon for less -- and you can fill your prescriptions there too.
by mcswell | 2025-07-22 01:19:44+00:00
Religions (I happen to be a Christian) have answers to this. For most of them, physical or economic progress is not the answer, at least not the main answer.

That said, all but the most religious people have a mix in their head (and I include myself in this) between what their religion says and what the culture around them says; and that mix usually destroys, in whole or in part, whatever sense of peace or fulfillment the religion could supply.
Advising is the next step for LLMs, a change in how they answer questions
by Taikhoom10 | 2025-07-22 01:20:06+00:00
I am not some sort of AI expert but this is what I personally think is going to be the next big step for llms. For simplicity I will be using ChatGPT throughout rather than llm. (First Mover Advantage perk) The next big leap isn't agentic capabilities or search through AI. Those are more of mediums of through which AI will progress into AGI one day. I think. What I want to talk about is the way llm's answer questions. I wont talk about if there right or wrong, more of how they just answer. Rather lets say I ask ChatGPT to help me write a title for a post on a forum. It will help me do that, now one can argue it might not do a good job. But rather say it responds by saying, don't post on this said forum right now because there isn't a lot of viership at this time. Peak post times are in the early morning. And of course there can still be a option to go through with the title brainstorm thing, but that advice is crucial. Llm's are a bunch of yes men by default, they need to be advisors. And good ones that too. A king needs a advisor that actually critiques the mistakes and provides better advice, not agree with whatever the king has to say. Otherwise the king would not need a advisor. The same way the llms today need to provide advice sometimes, rather than saying yes. I know this might be counterintuitive but humans make mistakes. Like ordering overly expensive coffee, llms should help us reduce those mistakes as well.
by acdha | 2025-07-22 01:20:11+00:00
Tesla buys most of their batteries from Japanese and Chinese companies like Panasonic and CATL. Some of those are assembled in factories like the Nevada gigafactory where Panasonic runs part of the factory. They’ve struggled with quality and capacity issues, which I think really just hits the idea that China spent decades on infrastructure and it’ll take similar amounts of time to catch up.
by PaulHoule | 2025-07-22 01:20:29+00:00
What's funny about it is that even though it follows the instructions your writing instructor gave you to always write a conclusion for your articles (and that probably are the instructions in the prompt for an LLM) it does not follow the instructions that I got from everybody I ever did content marketing with: to always end an article with a "call to action", which is why this article was up for hours before anybody noticed the site it was on.
by sdiupIGPWEfh | 2025-07-22 01:21:36+00:00
For the record, having suffered the effects of extreme dehydration before, if someone insists on aiding me in the process of dying (against my wishes) please get it over with quickly. End it with the morphine right off. Dehydration is a miserable process; the immediate misery of thirst aside, the delirium, paranoia, and irritability are not the least bit merciful to inflict on someone, in my personal experience.