MurphyA/hacker-news huggingface dataset:
by bsenftner | 2026-03-10 11:16:43+00:00
I gave this advice to a non-developer friend yesterday: There is a huge interest in creating all types of automation. Don’t waste your time doing this also. Someone else is going to create a way to automate things that is much more secure than the extremely insecure manners the crowd is experimenting with right now. Let them experiment, make messes, and probably figure out this method, that method, and 10 others people dream up and try are not good for long term use. Let them drive wreaked infrastructure for a while, and then pick up the working, clean and safe methods 2–3 years from now. Long after this early experimenters crowd have cognitively damaged themselves and intellectually exhausted themselves multiple times over. Let their pain be your future easy solution.

But who am I kidding, this is fashion and has nothing to do with tech.
by coldtea | 2026-03-10 11:16:53+00:00
> I don't buy the 10x efficiency thing: they are just lagging behind the performance of current SOTA models. They perform much worse than the current models while also costing much less - exactly what I would expect.

Define "much worse".

+--------------------------------------+-------------+-----------+------------------+
| Benchmark | Claude Opus | DeepSeek | DeepSeek vs Opus |
+--------------------------------------+-------------+-----------+------------------+
| SWE-Bench Verified (coding) | 80.9% | 73.1% | ~90% |
| MMLU (knowledge) | ~91 | ~88.5 | ~97% |
| GPQA (hard science reasoning) | ~79–80 | ~75–76 | ~95% |
| MATH-500 (math reasoning) | ~78 | ~90 | ~115% |
+--------------------------------------+-------------+-----------+------------------+
by andy12_ | 2026-03-10 11:17:02+00:00
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world itself is the bottleneck, you can just put a vision-action LLM on a reinforcement learning loop in a robotic/simulated body.
by ZeroCool2u | 2026-03-10 11:18:44+00:00
Regardless of your opinion of Yann or his views on auto regressive models being "sufficient" for what most would describe as AGI or ASI, this is probably a good thing for Europe. We need more well capitalized labs that aren't US or China centric and while I do like Mistral, they just haven't been keeping up on the frontier of model performance and seem like they've sort of pivoted into being integration specialists and consultants for EU corporations. That's fine and they've got to make money, but fully ceding the research front is not a good way to keep the EU competitive.
by lapcat | 2026-03-10 11:18:44+00:00
Here's a Finder window screenshot from Mac OS X 10.0 Cheetah: https://512pixels.net/wp-content/uploads/2018/08/10-0-Cheeta...

Notice two things:

1) The window chrome with traffic lights and title is entirely separate from the toolbar, not unified with the toolbar.

2) The top of the window is rounded, but the bottom of the window is not!

I think the old design was superior for several reasons, one of which is that it made the windows much easier to drag around the screen. In any case, though, even if there's an argument about concentricity and window controls, it makes no sense that the bottom of the window has the same corner radius as the top when the toolbar is only at the top.
by adrian_b | 2026-03-10 11:21:02+00:00
Only ordinary dye-based ink will run if the paper gets wet.

It is easy to find either roller pens or ink for fountain pens that are pigment-based, lightfast and waterproof. It is very easy to test yourself that with such ink water has no effect whatsoever over the written text (after a minute or so of drying after writing). For example, there are many kinds of Uni-Ball roller pens with waterproof ink, but there also many other brands with similar products. (Only fountain pens, roller pens and gel pens may have pigment-based ink, the paste ink used by ball-point pens is easily washed by alcohol or other organic solvents.)

In my experience with graphite on paper, it is a much worse choice, because the writing will fade over the years, due to the rubbing of the paper sheets from each other.

The pigment-based black inks also use carbon, like graphite pencils, but any ink contains not only a pigment, but also a glue that binds the pigment to the paper, so it will not be rubbed out by touching it.
by mehulimukherjee | 2026-03-10 11:21:09+00:00
Hi HN,

Over the past year I’ve been working on ExtractPDF4J, an open-source Java library for extracting tables from real-world PDFs.

Many document processing pipelines rely on PDFs like bank statements, financial reports, or invoices. In practice these files are inconsistent: some are text-based, others are scanned images, and many contain irregular layouts or multi-page tables.

Most existing tools in this space are Python-based (like Camelot or Tabula). In JVM-heavy environments this often means running a separate Python service or building a hybrid stack.

ExtractPDF4J was designed to solve this problem directly in Java.

Key ideas behind the project:

• Hybrid parsing strategies (stream + lattice detection)
• OCR fallback for scanned documents
• CLI and service modules for production workflows
• Maven Central distribution for easy integration

The latest release also introduced a BOM module to simplify dependency management and a full documentation site.

Project:
https://github.com/ExtractPDF4J/ExtractPDF4J

Docs:
https://extractpdf4j.github.io/ExtractPDF4J/

I’d really appreciate feedback from people who have dealt with messy PDF extraction problems. Suggestions and contributions are welcome. Star the repo for more reach to the Java community.
Thank you!
by throw0101c | 2026-03-10 11:21:25+00:00
> Rama may turn out unrecognizable after the Hollywood script jockeys have been through with it, as happened to Foundation.

Denis Villeneuve (Dune, Blade Runner 2049, Arrival, Prisoners, etc) is set to direct and is writing the script as well:

* https://en.wikipedia.org/wiki/Rendezvous_with_Rama#Film

Though it may be a little while since he first has to release adaptation of Dune Messiah, and a Bond film is probably next:

* https://en.wikipedia.org/wiki/Denis_Villeneuve#Upcoming_proj...
by presentation | 2026-03-10 11:22:14+00:00
One time when I was living in Shanghai, I accidentally took the train to the wrong airport and had to take a cab to the other one. The cabbie was driving on the highway right at the speed limit, and I was worried I wouldn’t make my flight. I asked him if he could rush a bit, but he replied that he would not speed because 100% he would get a ticket.

It only doesn’t work if the system is half assed. But I agree that in low speed pedestrian areas, the built form is a better solution, but knowing you will get caught is also effective (if you accept the privacy tradeoffs).
by brodo | 2026-03-10 11:22:21+00:00
The takeaways at the very bottom of the page are valuable:

> Overall, having spent a significant amount of time building this project, scaling it up to the size it’s at now, as well as analysing the data, the main conclusion is that it is not worth building your own solution, and investing this much time. When I first started building this project 3 years ago, I expected to learn way more surprising and interesting facts. There were some, and it’s super interesting to look through those graphs, however retrospectively, it did not justify the hundreds of hours I invested in this project.

The whole "qualified self" movement might be more about OCD and perfectionism than anything else.
by 1718627440 | 2026-03-10 11:22:31+00:00
Because comments are a bad fit to encode the evolution of code. We implemented systems to do that for a reason.

> The VCS history has to be actively pulled up and reading through it is a slog

Yes, but it also allows to query history e.g. by function, which to me gets me to understand much faster than wading through the current state and trying to piece information together from the status quo and comments.

> history becomes exceptionally difficult to retrace in certain kinds of refactoring.

True, but these refactorings also make it more difficult to understand other properties of code that still refers to the architecture pre-refactoring.

> I have never understood the idea of relying on code history instead of code comments. It seems like it's all downsides, zero upsides.

Comments are inherently linear to the code, that is sometimes what you need, for complex behaviour, you rather want to comment things along another dimension, and that is what a VCS provides.

What I write is this:

/* This used to do X, but this causes Y and Z
and also conflicts with the FOO introduced
in 5d066d46a5541673d7059705ccaec8f086415102.
Therefore it does now do BAR,
see c7124e6c1b247b5ec713c7fb8c53d1251f31a6af */
by sergF | 2026-03-10 11:22:35+00:00
That makes sense, and this is actually close to what I keep ending up with in different projects.

I usually start with something simple, then add a task table, then locking, retries, then some kind of worker process, and eventually it turns into a small job system anyway.

At some point it starts feeling like I'm rebuilding the same queue/worker setup over and over, which is why I'm wondering if this should live outside the app entirely.

Thanks, this discussion is really helpful.
by DrProtic | 2026-03-10 11:22:36+00:00
I’m ignorant?

I didn’t see any more brutality than I saw from US regime and especially ICE.

I never saw a person shot in the face in Iran but I saw in the US. Should we bomb US?

Also, 30k dead means there would be at least some proof. Once Iran reinstated internet after protests NO VIDEOS of killings showed up. Protesters decided not to record any of the killings?!
by JAlexoid | 2026-03-10 11:23:01+00:00
Funny thing is that building an LLM isn't as complex as you might think.

But the problem of attribution is easily understandable to any human with a modicum of intelligence.

Imagine that you have a trillion input images, with every single one having a source associated.
When training they go through lots of processes and every single image contributes a varying degree to a subset of 8billion parameters. That alone would produce a dataset that is 1T * 8B to just say how much a particular image contributed to the output...

To mimic intelligence the output is also randomized - the association is not static and every single pixel in the output has it's own lineage.

So as you can probably imagine that to calculate the actual source weights on the output you'd require to do at least 8e+21 calculations per output pixel... and require double precision floating point while you do it.

We know how to do it. It's just ridiculously expensive.

(The above example is for demonstrative purposes only)
by zelphirkalt | 2026-03-10 11:23:21+00:00
> I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation.

Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way.

Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not.

The problems are structural/architectural. I think it will take another 2-3 major leaps in architectures, before these AI models reach human level general intelligence, if they ever reach it. So far they can "merely" often "fake it" when things are statistically common in their training data.
by A_D_E_P_T | 2026-03-10 11:23:25+00:00
You could have continual learning on text and still be stuck in the same "remixing baseline human communications" trap. It's a nasty one, very hard to avoid, possibly even structurally unavoidable.

As for the "just put a vision LLM in a robot body" suggestion: People are trying this (e.g. Physical Intelligence) and it looks like it's extraordinarily hard! The results so far suggest that bolting perception and embodiment onto a language-model core doesn't produce any kind of causal understanding. The architecture behind the integration of sensory streams, persistent object representations, and modeling time and causality is critically important... and that's where world models come in.
by chilmers | 2026-03-10 11:25:02+00:00
I think a primary concern when Apple evolves their new design language nowadays is competitive differentiation. Because so many people try to clone their UI, they seek to add visual elements like frosting, glass, squircles, etc. that are difficult or impossible to achieve in competing platforms. Gradually others catch up and they need to evolve it again. Liquid Glass seems like an aesthetic choice made purely for the technical difficulty of the simulated physics necessary to accurately recreate it.
by 1718627440 | 2026-03-10 11:25:30+00:00
> In fairness to GP, they said VCS, not Git

I did say VCS, but I also don't know what Git is missing in this relation.

> Other VCSes did support graph histories.

How does Git do not?

> Still, "3rd dimension" code reasoning (backwards in time) has never been merged well with code editing.

Maybe it's not perfect, but Git seems to do that just fine for my taste. What is missing there?
by peacefulPanda | 2026-03-10 11:25:45+00:00
I built a lightweight sandboxing tool that works on macOS and linux in Nix. Wrap any agent binary so that it only has write access to CWD, and the tools, state directories and environment variables it needs. I built this as a development tool for people wanting to run agents in YOLO mode, not as a comprehensively secure sandboxing tool. Network access is left open.
by apignotti | 2026-03-10 11:26:35+00:00
Only node is supported as of version 1.1, but the next version is fully focused on command line tooling (git, bash, ssh, grep, ...).

See the launch blog post for our full timeline: https://labs.leaningtech.com/blog/browserpod-10

Also, could I ask you to quickly edit your previous comment to clarify you were benchmarking against the older project?
by throw0101c | 2026-03-10 11:26:51+00:00
> Lindsey Graham has been pretty mask-off in wanting to take control of Iran's oil resources and, in his words, "make a ton of money."

Which kind of set all of this off back in the day: UK/US oil companies controlled Iranian oil, the legislature nationalized it, the Shah suppressed the legislature and portions of the population:

* https://en.wikipedia.org/wiki/1953_Iranian_coup_d%27état

the population got tired of being pushed down, and so eventually fought back:

* https://en.wikipedia.org/wiki/Iranian_Revolution
by Anthony-G | 2026-03-10 11:27:13+00:00
One of the reasons I like mutt is that I can easily edit the “From:” header (I have Postfix configured with multiple email aliases for my primary email account).

In my experience, most mail user agents allow editing the Subject header. Even Outlook Web Application that I have to use in work allows me to change the Subject line when a conversation thread has veered off-topic, e.g., “Server replacement [was: Windows 11 upgrades of client PCs in Accounts]“
by dTal | 2026-03-10 11:27:27+00:00
What exactly is the difference between "a machine-readable contract for what the output has to be" and "source code"?

What is the difference between an "agent" and a "compiler"?

For that matter, what is the difference between "I got an agent to provide a high level description" and a decompiler?

What is the difference between ["decompiling" a binary, editing the resulting source, recompiling, and redistributing] and [analyzing the behavior of a binary, feeding that description into an LLM, generating source code that replicates that behavior, editing that, recompiling and redistributing]?

Takeaway: we are now in a world where software tools can climb up and down the abstraction stack willy nilly and independently of human effort. Legal tools that attempt to track the "provenance" of "source code" were already shaky but are now crumbling entirely.
by overrun11 | 2026-03-10 11:28:21+00:00
The article is about compute cost though. By "lose money on inference" I mean the assertion that inference has negative gross margins which a lot of people truly believe. This is important because it's common to reason from this that LLM's are uneconomical and a ticking time bomb where prices will have to be jacked up several orders of magnitude just to cover the compute used for the tokens.
by TMWNN | 2026-03-10 11:28:28+00:00
> We spent our entire days in SuperCalc 3 and dBase III, and some of the fancier staff actually got to use 1-2-3. I think we used both because 1-2-3 had copy protection and SuperCalc didn't? But 1-2-3 was clearly better.

InfoWorld said in 1986 that SuperCalc 4 competed well with 1-2-3. <https://books.google.com/books?id=Zi8EAAAAMBAJ&pg=PA35> Did you have experience with that version?
by wiz21c | 2026-03-10 11:29:02+00:00
within 20 years, everyone will be developing software which will be copyrighted partly by AI and be behind walled gardens. Sure you'll be able to do things locally but everything (security clearance, walled garden, government's control etc) but it will forever remain at the level of "tinkering".

If you are 50 years old or more, the computing you were born with (you own the computer, you own the programs) will be gone. Copyleft only makes sense if you own the computer.

That makes me sad.
by keybored | 2026-03-10 11:29:25+00:00
> That being said, to outright ban a technology in 2026 on pure "vibes" is not something I'd say is reasonable.

The response to a large enough amount of data is always vibes. You cannot analyze it all so you offload it to your intuition.

> It leaves stuff on the table in a time where they really shouldn't. Things like documentation tracking, regression tracking, security, feature parity, etc. can all be enhanced with carefully orchestrated assistance.

What’s stopping the maintainers themselves from doing just that? Nothing.

Producing it through their own pipeline means they don’t have to guess at the intentions of someone else.

Maintainers just doing it themselves is just the logical conclusion. Why go through the process of vetting the contribution of some random person who says that they’ve used AI “a little” to check if it was maybe really 90%, whether they have ulterior motives... just do it yourself.
by ghostlyInc | 2026-03-10 11:29:36+00:00
LLMs tend to pick up recurring metaphors from training data and reinforcement tuning.

Words like “goblin”, “gremlin”, “yak shaving”, etc. are common in engineering culture to describe hidden bugs or messy systems. If those appear often in the training corpus or get positively reinforced during alignment tuning, the model may overuse them as narrative shortcuts.

It's basically a mild style artifact of the training distribution, not something intentionally programmed.
by giancarlostoro | 2026-03-10 11:29:47+00:00
And Microsoft's Azure. It's on all 3 major cloud providers. Which tells me, they can make profit from these cloud providers without having to pay for any hardware. They just take a small enough cut.

https://code.claude.com/docs/en/microsoft-foundry

https://www.anthropic.com/news/claude-in-microsoft-foundry
by ForHackernews | 2026-03-10 11:30:10+00:00
https://medium.com/state-of-the-art-technology/world-models-...

> One major critique LeCun raises is that LLMs operate only in the realm of language, which is a simple, discrete space compared to the continuous, complex physical world we live in. LLMs can solve math problems or answer trivia because such tasks reduce to pattern completion on text, but they lack any meaningful grounding in physical reality. LeCun points out a striking paradox: we now have language models that can pass the bar exam, solve equations, and compute integrals, yet “where is our domestic robot? Where is a robot that’s as good as a cat in the physical world?” Even a house cat effortlessly navigates the 3D world and manipulates objects — abilities that current AI notably lacks. As LeCun observes, “We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.”
by bhasinanant | 2026-03-10 11:30:19+00:00
I'm definitely biased here, but the OpenClaw hype is making people disregard the economics of it all.
Building Auto-CRM.com, my primary concern was building a system that runs well while not costing 200$ per month to keep up, and of course, while also maintaining security. I assume the good guys at Folk, Pipedrive, etc also had similarly priorities.
A lot of good work is being done within the OpenClaw ecosystem regarding RAG and memory, but specialised orchestration process to be a more reliable system.
by zelphirkalt | 2026-03-10 11:30:36+00:00
I guess the sheer amount and also variety of information you would need to pre-encode to get an Einstein at 40 is huge. Every day stream of high resolution video feed and actions and consequences and thoughts and ideas he has had until the age of 40 of every single moment. That includes social interactions, like a conversation and mimic of the other person in combination with what was said and background knowledge about the other person. Even a single conversation's data is a huge amount of data.

But one might say that the brain is not lossless ... True, good point. But in what way is it lossy? Can that be simulated well enough to learn an Einstein? What gives events significance is very subjective.
by saagarjha | 2026-03-10 11:30:54+00:00
> If you're building code specifically to run in emulation, use RISC-V: builds faster, smaller code, runs faster.

I don't really think this bears out in practice. RISC-V is easy to emulate but this does not make it fast to emulate. Emulation performance is largely dominated by other factors where RISC-V does not uniquely dominate.
by Habgdnv | 2026-03-10 11:30:57+00:00
Linux users are still behind on this innovation. Big corporations with money can move much faster. The closest I got on Fedora was a "donate to the author" button in a game launcher I installed last week. I saw it once. That pretty much exhausts my "modern platform" experience for the past few months.

Still waiting for the copilot button in cat | grep.
by CoastalCoder | 2026-03-10 11:31:00+00:00
I'm pretty late to this discussion, but...

I was somewhat horrified to discover that the STL ended up with a special role in the language spec. (IIRC, one of the tie-ins is initializer lists.)

IMHO it's far wiser to leave the standard library as something that isn't needed by the core language, and where users can (at least in principle) provide their own alternative implementation without needing compiler hacks.

I.e., those details are inherent in the definition of "library" in the C/C++ world.
by ckolkey | 2026-03-10 11:31:01+00:00
I've gotta disagree with you here - it's not uncommon for me to be diving into a library I'm using at work, find a small issue or something that could be improved (measurably, not stylistically), and open a PR to fix it. No big rewrites or anything crazy, but it would definitely fit the definition of "drive by change" that _thus far_ has been welcomed.