MurphyA/hacker-news huggingface dataset:
by mgraczyk | 2025-06-02 22:07:58+00:00
I have done everything from architecture design for a DSP (Qualcomm), to training models that render photos on Pixel phones, to redoing Instagrams comments ranking system. I can't imaging doing anything without LLMs today, they would have made me much more productive at all of those things, whether it be Verilog, C++, python, ML, etc. I use them constantly now.
by the_snooze | 2025-06-02 22:08:30+00:00
I feel like the dismissal of mental models is a direct consequence on the tech industry's manaical focus on scale and efficiency as the end-all be-all values to optimize.

Nevermind other important values like resilience, adaptability, reliability, and scrutability. An AI writes a function foo() that does a thing correctly; who has the know-how that can figure out if foo() kills batteries, or under what conditions it could contribute to an ARP storm or disk thrashing, or what implicit hardware requirements it has?
by rglover | 2025-06-02 22:08:47+00:00
> So where will the experts come from?

They won't, save for a relative minority of those who enjoy doing things the hard way or those who see an emerging market they can capitalize on (slop scrubbers).

I wrote this post [1] last month to share my concerns about this exact problem. It's not that using AI is bad necessarily (I do every day), but it disincentivizes real learning and competency. And once using AI is normalized to the point where true learning (not just outcome seeking) becomes optional, all hell will break loose.

> Perhaps there is another way to develop the skills

Like sticking a fork in a light socket, the only way to truly learn is to try it and see what happens.

[1] https://ryanglover.net/blog/chauffeur-knowledge-and-the-impe...
by hiAndrewQuinn | 2025-06-02 22:09:05+00:00
Definitely not. I took this same basic idea of feeding videos into Whisper to get SRT subtitles and took it a step further to make automatic Anki flashcards for listening practice in foreign languages [1]. I literally feel like I'm living in the future every time I run across one of those cards from whatever silly Finnish video I found on YouTube pops up in my queue.

These models have made it possible to robustly practice all 4 quadrants of language learning for most common languages using nothing but a computer, not just passive reading. Whisper is directly responsible for 2 of those quadrants, listening and speaking. LLMs are responsible for writing [2]. We absolutely live in the future.

[1]: https://github.com/hiandrewquinn/audio2anki

[2]: https://hiandrewquinn.github.io/til-site/posts/llm-tutored-w...
by DebtDeflation | 2025-06-02 22:09:12+00:00
> Some of the smartest people I know share a bone-deep belief that AI is a fad — the next iteration of NFT mania

It's not that it's a fad. It's that the hype has gotten way ahead of the capability. CEOs laying off double digit percentages of their workforce because they believe that in 6 months AI will actually be able to do all those jobs and they want to get the message out to Wall St to juice the stock price today.
by mrguyorama | 2025-06-02 22:09:55+00:00
Russia has also "paused" it's compliance with New Start as of February 2023, which is odd.

Though they are unlikely to exceed the treaty warhead numbers, as nukes are expensive. It's too bad really, the START treaties were some of the best "reduce military spending" treaties ever made. Trump called it a bad deal back in 2017 and told Putin that he didn't want to extend it because it was too favorable, which is dumb since it was only ever a boon to both parties.

Despite all the bluster and bullshit and "Super duper turbo America killer 9000" weapons like the Satan, Russia does like the START treaties and wanted to keep them going, at least until the war.

As of today(!), the US has suspended the VISAs of Russia nuclear weapon inspectors as retaliation.
by simonw | 2025-06-02 22:10:06+00:00
Why does that seem weak to you?

It seems obviously true to me: code hallucinations are where the LLM outputs code with incorrect details - syntax errors, incorrect class methods, invalid imports etc.

If you have a strong linter in a loop those mistakes can be automatically detected and passed back into the LLM to get fixed.

Surely that's a solution to hallucinations?

It won't catch other types of logic error, but I would classify those as bugs, not hallucinations.
by keeganpoppen | 2025-06-02 22:10:38+00:00
this is one of those fascinating cases where i agree with none of the arguments, but vehemently agree with the conclusion… it ordinarily would give me pause, but in this case i am reminded that nonsense arguments are equally applicable to both sides of the debate. if the arguments actually had logical connection to the conclusion, and i disliked the arguments but liked the conclusion, that would be real cause for introspection.
by grey-area | 2025-06-02 22:10:44+00:00
I’d love to see the authors of effusive praise of generative AI like this provide the proof of the unlimited powers of their tools in code. If GAI (or agents, or whatever comes next …) is so effective it should be quite simple to prove that by creating an AI only company and in short order producing huge amounts of serviceable code to do useful things. So far I’ve seen no sign of this, and the best use case seems to be generating text or artwork which fools humans into thinking it has coherent meaning as our minds love to fill gaps and spot patterns even where there are none. It’s also pretty good at reproducing things it has seen with variations - that can be useful.

So far in my experience watching small to medium sized companies try to use it for real work, it has been occasionally useful for exploring apis, odd bits of knowledge etc, but overall wasted more time than it has saved. I see very few signs of progress.

The time has come for llm users to put up or shut up - if it’s so great, stop telling us and show and use the code it generated on its own.
by NotAnOtter | 2025-06-02 22:11:06+00:00
Pay walled, so won't read the actual article.

That said, I'm not sure why this is always so popularly discussed in Eng forums. The reality is, our work is inherently async, and 90% of IC's could work 1 days/week if we magically squished all our work in that time frame. Many in practice get away with a 4 day work week. My company has a no-meeting Friday and I'm full remote, if I've finished my work I just quietly take it off.

So the reality is this: a 4 day work week for IC's means either asking them to work less or forcing them into a schedule they already had access to but opted to not take.

Why would a business be incentivized to have their staff work less or force them into an uncomfortable schedule that doens't adhere as nicely to the realities of parenthood or other adult commitments.
by lukko | 2025-06-02 22:11:13+00:00
London, UK Remote: Yes Willing to relocate: No Technologies: Swift, Metal, Figma, UI/UX, Houdini, Real-time graphics & audio CV: lukehale.com; https://uk.linkedin.com/in/ukehale

Multi-disciplinary doctor and design engineer - created Lungy (lungy.app). Would love to help on interesting projects part-time (10-20 hours per week)

email: hello [at] lungy [dot] app
by spankalee | 2025-06-02 22:11:59+00:00
I would double check that this doesn't have performance implications before going wild. Those are extra tokens that need to be parsed, and browsers do a lot of work to speed up selector matching, including building caches of class names to nodes.

It could be that browsers populate these caches even for unused class names because it's difficult to know at that point in time whether a class name is used or will be used in the future. A ton of unused class names could explode the cache, evict important classes... who knows?

It could also be that the cache is built on demand as selectors are matched and having a ton of unused classes doesn't matter at all.

The point is that it's quite implementation dependent and requires some testing to know. To be safe, I'd just stick to comments because they are very, very cheap, in all browsers and parsers.
by fellowniusmonk | 2025-06-02 22:12:57+00:00
Over the last years? As in two years or more? Could you explain that a bit more?

I consider "LLM stuff" to be all inclusive of the eco-system of "coding with LLMs" in the current threads context, not specific models.

Would you still say, now that the definition has been clarified, that there has been slow progress in the last 2+ years?

I am also curious if you could clarify where we would need to be today for you to consider it "fast progress"? Maybe there is a generational gap between us in defining fast vs slow progress?
by ghaff | 2025-06-02 22:13:09+00:00
I took the MIT MOOC CS Intro a few years back for a combination of refresher (though I wasn't actually a CS major) and just to kick the tires. Good Lord, that would have kicked my butt back when I was a freshman absent prior programming experience, admittedly when computer programming was much less a norm than it is today.

I had a high school BASIC class but that was about it.
by pona-a | 2025-06-02 22:13:22+00:00
> It’s projection. People say “LLMs can’t code” when what they really mean is “LLMs can’t write Rust”. Fair enough! But people select languages in part based on how well LLMs work with them, so Rust people should get on that.

How is it the responsibility of the Rust community that there weren't enough metric tons of free code for the machine to slurp up? And the phrasing makes it sound like it's the community's fault for not feeding OpenAI enough code to be stripped of its license and authorship and get blended into a fine latent soup. It's a lot like people coming to a one-man FOSS project with a laundry list of demands, expecting to be treated with the religious reverence of a major enterprise contract.

The whole tone, the pervasive "use it or you'll be left behind"—where users saying they don't want or need it only proves further evidence of its imminent apotheosis—superficially reminds me of previous FUDs.

And how is it not concerning that the thing described as intelligent needs billions of lines to generalize a language a human can learn from a single manual? Will it need hundreds of kLOC to internalize a new library, or even its new version, beyond in-context learning? The answer is yes; you are choosing to freeze the entire tech stack, when fixing its abstractions could actually save you from boilerplate, just so the machine can write it for you at $200 a month with a significant error rate.
by bityard | 2025-06-02 22:13:39+00:00
I was a big fan of Star Trek: The Next Generation as a kid and one of my favorite things in the whole world was thinking about the Enterprise's computer and Data, each one's strengths and limitations, and whether there was really any fundamental difference between the two besides the fact that Data had a body he could walk around in.

The Enterprise computer was (usually) portrayed as fairly close to what we have now with today's "AI": it could synthesize, analyze, and summarize the entirety of Federation knowledge and perform actions on behalf of the user. This is what we are using LLMs for now. In general, the shipboard computer didn't hallucinate except during most of the numerous holodeck episodes. It could rewrite portions of its own code when the plot demanded it.

Data had, in theory, a personality. But that personality was basically, "acting like a pedantic robot." We are told he is able to grow intellectually and acquire skills, but with perfect memory and fine motor control, he can already basically "do" any human endeavor with a few milliseconds of research. Although things involving human emotion (art, comedy, love) he is pretty bad at and has to settle for sampling, distilling, and imitating thousands to millions of examples of human creation. (Not unlike "AI" art of today.)

Side notes about some of the dodgy writing:

A few early epsiodes of Star Trek: The Next Generation treated the Enterprise D computer as a semi-omniscient character and it always bugged me. Because it seemed to "know" things that it shouldn't and draw conclusions that it really shouldn't have been able to. "Hey computer, we're all about to die, solve the plot for us so we make it to next week's episode!" Thankfully someone got the memo and that only happened a few times. Although I always enjoyed episodes that centered around the ship or crew itself somehow instead of just another run-in with aliens.

The writers were always adamant that Data had no emotions (when not fitted with the emotion chip) but we heard him say things _all the time_ that were rooted in emotion, they were just not particularly strong emotions. And he claimed to not grasp humor, but quite often made faces reflecting the mood of the room or indicating he understood jokes made by other crew members.
by timr | 2025-06-02 22:13:50+00:00
For what it's worth, I basically accept the premise of the GP comment, in the same way that I would accept a statement that "loggers who don't use a chainsaw will be put out of business". Sure, fine, whatever.

I still think the tone is silly and polarizing, particularly when it's replying to a comment where I am very clearly not arguing against use of the tools.
by hiAndrewQuinn | 2025-06-02 22:14:24+00:00
I'll take the opposite view of most people. Expertise is a bad thing. We should embrace technological changes that render expertise economically irrelevant with open arms.

Take a domain like US taxation. You can certainly become an expert in that, and many people do. Is it a good thing that US taxes are so complicated that we have a market demand for thousands of such experts? Most people would say no.

Don't get my wronf, I've been coding for more years of being alive than I haven't by this point, I love the craft. I still think younger me would have far preferred a world where he could have just had GPT do it all for him so he didn't need to spend his lunch hours poring over the finer points of e.g. Python iterators.
by Philpax | 2025-06-02 22:14:28+00:00
An important note is that the models you can host at home (e.g. without buying ten(s of) thousand dollar rigs) won't be as effective as the proprietary models. A realistic size limit is around 32 billion parameters with quantisation, which will fit on a 24GB GPU or a sufficiently large MBP. These models are roughly on par with the original GPT-4 - that is, they will generate snippets, but they won't pull off the magic that Claude in an agentic IDE can do. (There's the recent Devstral model, but that requires a specific harness, so I haven't tested it.)

DeepSeek-R1 is on par with frontier proprietary models, but requires a 8xH100 node to run efficiently. You can use extreme quantisation and CPU offloading to run it on an enthusiast build, but it will be closer to seconds-per-token territory.
by simonw | 2025-06-02 22:14:50+00:00
tptacek wasn't making this argument six months ago.

LLMs get better over time. In doing so they occasionally hit points where things that didn't work start working. "Agentic" coding tools that run commands in a loop hit that point within the past six months.

If your mental model is "people say they got better every six months, therefore I'll never take them seriously because they'll say it again in six months time" you're hurting your own ability to evaluate this (and every other) technology.
by mgraczyk | 2025-06-02 22:15:13+00:00
Deliberate practice, which may take a form different from productive work.

I believe it's important for students to learn how to write data structures at some point. Red black trees, various heaps, etc. Students should write and understand these, even though almost nobody will ever implement one on the job.

Analogously electrical engineers learn how to use conservation laws and Ohm's law to compute various circuit properties. Professionals use simulation software for this most of the time, but learning the inner workings is important for students.

The same pattern is true of LLMs. Students should learn how to write code, but soon the code will write itself and professionals will be prompting models instead. In 5-10 years none of this will matter though because the models will do nearly everything.
by mwcampbell | 2025-06-02 22:15:17+00:00
I think where tptacek is right, though, is that if we're going to hold this position without hypocrisy, then we need to respect copyright as long as it exists. He's right that many of us have not done that; it's been very common to violate copyright for mere entertainment. If we want the licenses of our own work to be respected, then we need to extend that respect to others as well, regardless of the size of the copyright holder.
by ChrisMarshallNY | 2025-06-02 22:15:17+00:00
I use AI every day, basically as a "pair coder."

I used it about 15 minutes ago, to help me diagnose a UI issue I was having. It gave me an answer that I would have figured out, in about 30 minutes, in about 30 seconds. My coding style (large files, with multiple classes, well-documented) works well for AI. I can literally dump the entire file into the prompt, and it can scan it in milliseconds.

I also use it to help me learn about new stuff, and the "proper" way to do things.

Basically, what I used to use StackOverflow for, but without the sneering, and much faster turnaround. I'm not afraid to ask "stupid" questions -That is critical.

Like SO, I have to take what it gives me, with a grain of salt. It's usually too verbose, and doesn't always match my style, so I end up doing a lot of refactoring. It can also give rather "naive" answers, that I can refine. The important thing, is that I usually get something that works, so I can walk it back, and figure out a better way.

I also won't add code to my project, that I don't understand, and the refactoring helps me, there.

I have found the best help comes from ChatGPT. I heard that Claude was supposed to be better, but I haven't seen that.

I don't use agents. I've not really ever found automated pipelines to be useful, in my case, and that's sort of what agents would do for me. I may change my mind on that, as I learn more.
by disambiguation | 2025-06-02 22:15:26+00:00
Man the redbull is oozing off this post, talk about sipping rocket fuel.

I mean a tool is a tool, nothing wrong with that - but most of the resistence stems from AI being shoved down our throats at warp speed. Its already everywhere and I can't opt out, that stinks.

As for the skepticism in terms of adoption and usefulness, its mainly a question of whether or not it will continue improving - there's no way to no what lies ahead, but if it came to a grinding halt today well then the high water mark just isn't all that impressive.

> Yeah, we get it. You don’t believe in IPR. Then shut the fuck up about IPR. Reap the whirlwind.

This is the point that matters, and I don't think everyone is on the same page that LLMs are essentially over glorified data laundering.

The industry would get just as much "value" if we declared a jubilee and wiped out all licenses and allowed unlimited plagiarism (Looking at Zuckerburg and his 10 TB of pirated data). In fact, if AI owners published their training data sets with a capable search engine, I would bet money of it out performing LLMs in most cases. Why waste all that man power reinventing Netflix again? Just copy paste the code and give everyone their time back, sheesh.

> Kids today don’t just use agents; they use asynchronous agents. They wake up, free-associate 13 different things for their LLMs to work on, make coffee, fill out a TPS report, drive to the Mars Cheese Castle, and then check their notifications. They’ve got 13 PRs to review. Three get tossed and re-prompted. Five of them get the same feedback a junior dev gets. And five get merged.

I'm in a role that is behind the times, using a bespoke in-house framework that is immune to the benefits of LLMs, so I don't get to see what you see - so as a skeptic, I'm not convinced this isn't just the illusion of speed. I have not seen convincing results, show me the amazing things being made by AI (AI tooling itself does not count) - but yes, maybe that's because its all siloed into walled gardens.

> But something real is happening. My smartest friends are blowing it off. Maybe I persuade you. Probably I don’t. But we need to be done making space for bad arguments.

Yeah all the arguments have been made, good and bad, we're all waiting to see how it plays out. But I'd rather take the side of being a skeptic - if I'm right then I'm in the right place. If I'm wrong, that's cool too, I don't mind playing catch-up. But fully embracing the hype is, IMO, tantamount to putting all your eggs in one basket, seems like a needless risk but if that's worth it to you to get ahead then by all means, slurp up the hype.
by stock_toaster | 2025-06-02 22:15:31+00:00
I think the more "general" (and competent) AI gets, the less being an early adopter _should_ matter. In fact, early adopters would in theory have to suffer through more hallucinations and poor output than late adopters.

Here, the early bird gets the worm with 9 fingered hands, the late bird just gets the worm.
by tsimionescu | 2025-06-02 22:15:40+00:00
This all works something like this: an "agent" is a small program that takes a prompt as input, say "//fix ISSUE-0451".

The agent code runs a regex that recognizes this prompt as a reference to a JIRA issue, and runs a small curl with predefined credentials to download the bug description.

It then assembles a larger text prompt such as "you will act as a master coder to understand and fix the following issue as faithfully as you can: {JIRA bug description inserted here}. You will do so in the context of the following code: {contents of 20 files retrieved from Github based on Metadata in the JIRA ticket}. Your answer must be in the format of a Git patch diff that can be applied to one of these files".

This prompt, with the JIRA bug description and code from your Github filled in, will get sent to some LLM chosen by some heuristic built into the agent - say it sends it to ChatGPT.

Then, the agent will parse the response from ChatGPT and try to parse it as a Git patch. If it respects git patch syntax, it will apply it to the Git repo, and run something like `make build test`. If that runs without errors, it will generate a PR in your Github and finally output the link to that PR for you to review.

If any of the steps fails, the agent will generate a new prompt for the LLM and try again, for some fixed number of iterations. It may also try a different LLM or try to generate various follow-ups to the LLM (say, it will send a new prompt in the same "conversation" like "compilation failed with the following issue: {output from make build}. Please fix this and generate a new patch."). If there is no success after some number of tries, it will give up and output error information.

You can imagine many complications to this workflow - the agent may interrogate the LLM for more intermediate steps, it may ask the LLM to generate test code or even to generate calls to other services that the agent will then execute with whatever credentials it has.

It's a byzantine concept with lots of jerry-rigging that apparently actually works for some use cases. To me it has always seemed far too much work to get started before finding out if there is any actual benefit for the codebases I work on, so I can't say I have any experience with how well these things work and how much they end up costing.