MurphyA/hacker-news huggingface dataset:
by patcon | 2025-06-02 23:53:30+00:00
I so strongly agree with this.

I've never been a completionist. I'm distracted. I like novelty. I make half-form experiments, but mostly provide deeply immersive conversations for the benefit of a small group of people around me, some of whom go on to make/do things that I proudly feel my influence on. The final 10% of work often defeated me.

Now, I just make it happen. The pain is gone. Something else can carry the last 10%, and my ideas get realized by me (and AI).

I can't even conceive how this will change the world en masse. Knowledge domains (not just tech, but science, art, etc) have been built of the things that the type-A completionists knew how to see. The others didn't finish, and what they saw of the world has largely been marginalized.

The mind of ADHD and creative types is about to get much more represented in the output products of civilisation. This is going to be a big recalibration, and I think a re-balancing toward something more healthy tbh
by OhMeadhbh | 2025-06-02 23:53:40+00:00
people do not believe AI is fad because of NFT mania. we believe LLMs are a fad because they're over-hyped and they under-deliver. just my $0.02. note that i didn't say "AI is Bullshit." but I will say some of the AI hype is bullshit. and one thing i've noticed is there are so many caveats for making accurate statements about LLMs, it's hard to make short statements that aren't wrong (unless you have all those exceptions and caveats.)

come at me.
by AtlasBarfed | 2025-06-02 23:53:52+00:00
Everyone keeps thinking AI improvement is linear. I don't know if this is correct, but it's just my basic impression that the current AI boost came from instead of being limiting yourself to the CPU and its throughput adding the massive amount of computing power in graphics cards.

But for each nine of reliability you want out of llms everyone's assuming it's just a linear growth. I don't think it is. I think it's polynomial at least.

As for your tasks and maybe it's just cuz I'm using chat GPT, but I asked it to Port sed, something with full open source code availability, tons of examples/test cases, a fully documented user interface and I wanted it moved to Java as a library.

And it failed pretty spectacularly. Yeah it got the very very very basic functionality of sed.
by citizenpaul | 2025-06-02 23:54:15+00:00
I watched someone do a similar demonstration( https://news.ycombinator.com/item?id=44159166)live at an event. They ended up doing something like 3 pull requests to get the original change. Then had to do 4 more to get it to fix and put back things it removed. Not exactly efficient, and it was painful to sit there and be like I could have had it done manually 20x by now while we painfully waited for the AI to do the changes.

I've never been able to get it to work reliably myself either.

The internet just tells me to prompt harder. Lots of "grind-set" mentality energy around AI if you ask me. Very little substance.
by bigmadshoe | 2025-06-02 23:54:28+00:00
I agree with the main take in this article: the combination of agents + LLMs with large context windows + a large budget of tokens to iterate on problems can probably already yield some impressive results.

I take serious issue with the "but you have no idea what the code is" rebuttal, since it - to me - skims over the single largest issue with applying LLMs anywhere where important decisions will be made based on their outputs.

To quote from the article:

People complain about LLM-generated code being “probabilistic”. No it isn’t. It’s code. It’s not Yacc output. It’s knowable. The LLM might be stochastic. But the LLM doesn’t matter. What matters is whether you can make sense of the result, and whether your guardrails hold. Reading other people’s code is part of the job. If you can’t metabolize the boring, repetitive code an LLM generates: skills issue! How are you handling the chaos human developers turn out on a deadline?

The problem here is that LLMs are optimized to make their outputs convincing. The issue is exactly "whether you can make sense of the result", as the author said, or, in other words: whether you're immune to being conned by a model output that sounds correct but is not. Sure, "reading other people’s code is part of the job", but the failure modes of junior engineers are easily detectable. The failure modes of LLMs are not.

EDIT: formatting
by sureglymop | 2025-06-02 23:54:29+00:00
I think Supabase just have bad defaults... Especially for non technical people that they market to.

I think by default they have some sort of public schema which is the default schema and for which PostgREST is enabled. There may be a checkbox to change that during setup but it's not checked by default.

Instead, one should probably use a different schema for the actual tables and create views that define the public API in the public schema.

I just googled this and it seems to be even recommended in the PostgREST documentation itself: https://postgrest.org/en/v10/schema_structure.html#
by magicalhippo | 2025-06-02 23:54:49+00:00
About 15 years ago or so, webscale was everything and MongoDB was webscale[1][2] hence being used everywhere.

Has it become a legacy product, or are there still good reasons for using it in a new project in 2025?

[1]: https://www.youtube.com/watch?v=b2F-DItXtZs

[2]: https://news.ycombinator.com/item?id=1636198
by blibble | 2025-06-02 23:55:02+00:00
I have no interest in adapting, I'd rather be redundant

but I don't think that's going to happen

and no amount of prophesying (what you're doing here) will change my mind

I think the more likely outcome is a repeat of the 2000s: ungodly amounts of outsourced code leading to paralysis and near collapse

except this time it'll be far more widespread, thanks to the US tech industry ramming their slop down everyone's throat
by palata | 2025-06-02 23:55:19+00:00
> If they can get the AI to make working code and explain why it works, do they need to know more than that, unless they want to be top in their field?

Making working code is the easy part. Making maintainable code is a completely different story.

And again, being able to explain why something works requires superficial knowledge. This is precisely why bugs pass through code reviews: it's hard to spot a bug by reading code that looks like it should work.
by srhtftw | 2025-06-02 23:55:26+00:00
Many tools make lesser developers more productive (to a point) but they fail to improve the productivity of talented professionals. Lots of "no/low" code things come to mind. But here's a tool that made kentonv 2x productive at a task that's clearly in his wheelhouse. It seems under the right conditions it can improve the productivity of developers at the opposite end of the spectrum.

What other tools could do that?
by mmooss | 2025-06-02 23:55:30+00:00
Right. I'm repeating what actual experts say - including settled, consensus conclusions from decades of expertise. I'm not doing personal hot takes and if I did, they should be ignored. I'm not even posting expert hot takes, which also aren't so valuable (but much more valuable than my own).

I responded to this comment: "Destroying their entire strategic aviation forces would not meaningfully impact MAD."
by timewizard | 2025-06-02 23:55:39+00:00
The law is divorced from the new reality. The law is intended to protect lives and the commercial airline industry. It did not envision such simple automated nuisances arising from licensed operations. The FAA has also shown itself quite willing to disrupt or alter aircraft operations in order to reduce the amount of noise around an airport.

I would not expect the federally charged drone free for all to last very long once this activity picks up in earnest.
by divtiwari | 2025-06-02 23:55:44+00:00
Location: New York

Remote: Open to any form of work (Remote, Hybrid, In-office)

Willing to relocate: Yes

Technologies: Java, Python, Scala, C, bash, Golang, SQL (Oracle, SQL Server, SQLite), JavaScript, HTML, CSS, Linux, Kubernetes, Docker, Kata Containers, Apache Ignite, Caffeine, gRPC, Spring, Prometheus, Grafana.

Résumé/CV: https://drive.google.com/file/d/1AtqD3KNSGepJzbthrpjGO33knVc...

Email: divyaankt21 [at] gmail [dot] com

Hi, I'm Divyaank! I am looking for Software Engineering roles focused towards backend, data, infrastructure and distributed systems. I possess around five years of experience in the Systems domain spanning across industry and academia. I am proficient in Java, Python, Kubernetes, SQL, bash and have dabbled with Scala, C, Go, JavaScript, Spin/Promela. I am affiliated with the File systems and Storage Lab (FSL) (https://www.fsl.cs.stonybrook.edu/). I have worked on implementing Containerization support (using Kubernetes, Docker and Kata Containers) for Swarm Verification (https://spinroot.com/swarm/) used by Metis (https://github.com/sbu-fsl/Metis). This work is currently under submission with me as a co-first author. Previously, I used Metis to discover kernel hang bugs in the Journaled File System and submitted patches to the Linux kernel to fix these bugs[https://lore.kernel.org/lkml/8c7b1d37-758a-46b2-b8aa-afd3331..., https://lkml.org/lkml/2024/7/5/899]. Additionally, I have also volunteered as an Artifact Evaluator at ASPLOS '25 and FAST '25 conferences.

Before FSL, I worked for three years in the FinTech industry at MSCI Inc., first as a Site Reliability Engineer (earning a fast-tracked promotion in the process) and then as a Backend Engineer.
by benmmurphy | 2025-06-02 23:55:49+00:00
Is the problem with LLMs and Rust really a problem with the training data or a problem with it being harder to get something to compile in Rust? I presume its easier to get something to compile in the dynamically typed languages, and then slightly more difficult in statically typed languages and then a step up with Rust which also forces you to deal with the lifetime of objects. But maybe with LLMs its different and they can do static typing easier than dynamic but they just really struggle with structuring a program to properly deal with ownership.
by Exuma | 2025-06-02 23:56:09+00:00
my record is 160 and it makes me mad that ~145-153 is my "wall". no matter how much i relax my hands and do technique I cannot move beyond it. if i google advice/videos, its all people trying to get above 110 or so. I'm with tumbleweeds ... man it would be sweet to type 200 though.
by yeahwhatever10 | 2025-06-02 23:56:37+00:00
I use it as a SO stand in as well.

What I like about Chatbots vs SO is the ability to keep a running conversation instead of 3+ tabs and tuning the specificity toward my problem.

I've also noticed that if I look up my same question on SO I often find the source code the LLM copied. My fear is that if chatbots kill SO where will the LLM's copied code come from in the future?
by capnrefsmmat | 2025-06-02 23:57:36+00:00
For introductory problems, the kind we use to get students to understand a concept for the first time, the AI would likely (nearly) nail it on the first try. They wouldn't have to fix any non-working code. And annotating the code likely doesn't serve the same pedagogical purpose as writing it yourself.

Students emerge from lectures with a bunch of vague, partly contradictory, partly incorrect ideas in their head. They generally aren't aware of this and think the lecture "made sense." Then they start the homework and find they must translate those vague ideas into extremely precise code so the computer can do it -- forcing them to realize they do not understand, and forcing them to make the vague understanding concrete.

If they ask an AI to write the code for them, they don't do that. Annotating has some value, but it does not give them the experience of seeing their vague understanding run headlong into reality.

I'd expect the result to be more like what happens when you show demonstrations to students in physics classes. The demonstration is supposed to illustrate some physics concept, but studies measuring whether that improves student understanding have found no effect: https://doi.org/10.1119/1.1707018

What works is asking students to make a prediction of the demonstration's results first, then show them. Then they realize whether their understanding is right or wrong, and can ask questions to correct it.

Post-hoc rationalizing an LLM's code is like post-hoc rationalizing a physics demo. It does not test the students' internal understanding in the same way as writing the code, or predicting the results of a demo.
by lolinder | 2025-06-02 23:57:50+00:00
> Meanwhile, software developers spot code fragments seemingly lifted from public repositories on Github and lose their shit. What about the licensing? If you’re a lawyer, I defer. But if you’re a software developer playing this card? Cut me a little slack as I ask you to shove this concern up your ass. No profession has demonstrated more contempt for intellectual property.

This kind of guilt-by-association play might be the most common fallacy in internet discourse. None of us are allowed to express outrage at the bulk export of GitHub repos with zero regard for their copyleft status because some members of the software engineering community are large-scale pirates? How is that a reasonable argument to make?

The most obvious problem with this is it's a faulty generalization. Many of us aren't building large-scale piracy sites of any sort. Many of us aren't bulk downloading media of any kind. The author has no clue whether the individual humans making the IP argument against AI are engaged in piracy, so this is an extremely weak way to reject that line of argument.

The second huge problem with this argument is that it assumes that support for IP rights is a blanket yes/no question, which it's obviously not. I can believe fervently that SciHub is a public good and Elsevier is evil and at the same time believe that copyleft licenses placed by a collective of developers on their work should be respected and GitHub was evil to steal their code. Indeed, these two ideas will probably occur together more often than not because they're both founded in the idea that IP law should be used to protect individuals from corporations rather than the other way around.

The author has some valid points, but dismissing this entire class of arguments so flippantly is intellectually lazy.
by jim1994 | 2025-06-02 23:57:53+00:00
Location: Kamloops, BC, Canada Remote: Yes Willing to relocate: No Technologies: TypeScript, Node.js, React, Next.js, Express PostgreSQL, MongoDB, AWS, RabbitMQ, Jest and more Résumé/CV: https://drive.google.com/file/d/1goKIGfII__h6bV9BHCpl1mZrsMX8cbDa/view?usp=sharing Email: zijin@ualberta.ca I am a full-stack software developer with multiple years of experience in tools like TypeScript, React, and Express, and am comfortable working with AWS. I recently developed Rabbitory (https://rabbitory.github.io/), a self-hosted RabbitMQ-as-a-Service platform that simplifies deployment and management of RabbitMQ brokers by offering users an intuitive control panel built with Next.js.
by bradly | 2025-06-02 23:57:56+00:00
> Woodworking is also an art form. But most people just need furniture, fixtures, and structures. Nobody would take seriously the idea that new construction all be done with sashimono joinery in order to preserve the art form, but somehow we're meant to take seriously the idea of hand-dovetailed CRUD apps.

How many furniture makers did you talk to forming this opinion? The metaphor does not line up with either my software of furniture experience. I work with production furniture shops that choose not to use CNCs to avoid the soul being sucked out of the work. This is not a rare stance to take and this is not "japanese joinery" woodworking. This is real work, balancing the means of production with optimal quality. There is all sorts of arguments on whether cncs or using a domino or whatever is "real" woodworking, but the idea that this choice of quality does not exist in woodworking and so we shouldn't have it in software is not my experience.
by a_bonobo | 2025-06-02 23:58:23+00:00
I find the Konwinski Prize to be very interesting in this context. 1 million dollars to whoever's open source LLM solves >90% of a set of novel Github issues.

https://www.kaggle.com/competitions/konwinski-prize/

Currently, the #1 spot sits at a score of 0.09, not 0.9. A far cry from being useful. I know that open source models are not as good as closed source, but still, we're a long way from LLMs being good for code on their own.

And that supports OP's point - these tools aren't AGI, they produce trash that needs evaluation, but they're still useful.
by lsllc | 2025-06-02 23:58:55+00:00
> >My brother for example built a thing with Microsoft copilot that helped automate more in his manufacturing facility (used to be paper).

> I have harped on this endlessly as a non-programmer working a non-tech job, with 7 "vibe-coded" programs now being used daily by people at my company.

Aren't AI coding agent(s) just the next iteration of democratizing app development? This has happened before with Microsoft Access (even Visual Basic), or going back further FoxPro, dBase & Clipper etc? With all of these tools, non-programmers had been able to create apps to help them with their businesses.
by gbacon | 2025-06-02 23:59:05+00:00
Pulling on the thread of your intuition about needing infinite levels, a regular language is one that can be recognized by a finite automaton, so requiring infinite memory or arbitrarily high counting suggests that the language is not regular. It’s not perfect; we prove it as below.

Assume for purpose of contraction that our language L of balanced parentheses is regular. Then L has pumping length p ≥ 1 such that every w ∈ L where |w| ≥ p can be decomposed w = xyz where |y| ≥ 1 and |xy| ≤ p. It is called the pumping lemma because we can “pump” the non-empty substring y either up or down, i.e., xy⁰z = xz and xyⁿz are also all in L. In formal languages, exponentiation notates character or string repetition.

We don’t know p and may not cherry pick it. Pretend you are playing a game against an adversary who gets to select arbitrary p ≥ 1, and then you use that p to choose w ∈ L to spoil the regular language party. With the language of balanced parentheses, no matter what p the adversary selects, you can force the leading xy prefix to contain all left-parentheses by choosing w = (ᵖ)ᵖ. The substring y cannot be empty, so pumping down gives xy⁰z = (ᵖ⁻ᵏ)ᵖ belongs to L, a contradiction.

One of my favorite test questions for automata theory classes is to have ChatGPT attempt a proof that a language is not regular and then have students correct the proof. You linked to a lecture by Michael Sipser, and the undergraduate version of the course I teach uses his excellent textbook.

https://www.amazon.com/Introduction-Theory-Computation-Micha...

His lecture on the pumping lemma for regular languages is

https://youtu.be/KAySmSEGc9U?t=1807
by smeeger | 2025-06-02 23:59:12+00:00
you are. just change a few words around and you would be reading the confidently incorrect predictions of essentially all scientists and engineers in 2010. you say LLMs wont get us there… and you personally would probably have said word2vec couldnt get us past the turing test… and here we are. citing the existence of a current technology as evidence that another technology, related or not, cannot exist, is lazy and stupid. the simple fact is that there has been an explosion in the progress recently… a corresponding explosion of funding and the specific purpose of every single dollar of research is to create AGI, whether through LLMs or some other framework. to dismiss this situation as totally unconcerning is literally FOOLISH
by Xss3 | 2025-06-02 23:59:22+00:00
This. And most of the time the code isn't that complex either. The complexity of a software product often isn't in the code, it's in the solution as a whole, the why's of each decision, not the how.

However whenever I've faced actual hard tasks, things that require going off the beaten path the AI trains on, I've found it severely lacking, no matter how much or little context I give it, no matter how many new chats I make, it just won't veer into truly new territory.
by bigger_cheese | 2025-06-03 00:00:04+00:00
I can share my experience I am not a software engineer. I am the "other" type of Engineer (chemical engineer). I tend to use AI in the same way I use google or Stack Overflow, mostly when I get stuck on syntax and my code editor isn't helping me, I know what I am attempting to do "should" be possible but I can't get the syntax right. For example a recent question I asked was

"Python, create an xarray with two dimensions from a pandas df"

It gave me a few lines of example code which was enough for me to figure out where I had messed up the syntax in my own code.

I have seen one of my junior coworkers copy+paste entire chunks of code from chatbot conversations and to be honest what he has produced is underwhelming the code is poorly structured difficult to reason about I have low confidence he understands what the bot has produced (and why it did things the way it did) and I don't have high confidence we'd be able to trust the accuracy of the figures this code was outputting.
by palata | 2025-06-03 00:00:04+00:00
> After all, lots of people sucked at math before them too.

A calculator doesn't do maths, it does arithmetic. People sucked at maths, but I'm pretty sure they were better with arithmetic.

> At the end of the day, it's the average productivity across a population that matters.

You're pushing my example. My point is that AI may actually make the average developer worse. Sure, also more productive. So it will reinforce this trend that has been in the software industry for more than a decade: produce more but worse software.

Productivity explains why we do it. It doesn't mean it is desirable.
by jacquesm | 2025-06-03 00:00:14+00:00
You now have 18 comments in this thread. All of them shallowly criticizing the comments you reply to, including appeals to (vague) authority and a whole bag of tricks to make it seem as if the original commenter is clueless and you hold all the cards. I also don't see you take up any position of your own. What is your point with all this? That Ukraine should just roll over and accept that they're going to get bombed without ever striking back? That they should take into account all of the geopolitical effects of their moves before they think about their own survival? I can't make heads or tails of all of the words you've spent on this subject. Please enlighten.
by sethkim | 2025-06-03 00:00:17+00:00
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by creakingstairs | 2025-06-03 00:00:20+00:00
Sure there have always been people bad at math. But basic arithmetic is not really math. We used to drill it into kids but we no longer do so and I can usually see the difference between generations. For example, women in my mother’s generation were not prioritised for education but they often are pretty quick at arithmetic. But kids and young adults I come across pull out their phones for basic additions and divisions. And I find myself pulling out my phone more and more often.

I mean it’s not the end of the world and as you’ve said the raw number of people of numerate people are rising thanks to technology. But technology also seem to rob people of motivation to learn somewhat useful skills and even more so with LLMs.