MurphyA/hacker-news huggingface dataset:
by spankibalt | 2025-06-02 22:43:51+00:00
> That doesn’t mean PCs were somehow more capable in the 80s though ;)

It clarifies specifics relating to my personal experiences with the discussion matter, addressing (perceived) realities of a local market. How people use computers is of the utmost relevance; a fact which you, given your lamentations here, certainly must have internalized.

> I think that’s probably rose tinted glasses on your part then. The pain was real. Games seldom “just worked” and you often had to be really knowledgeable in your system to get stuff working.

No rose-tinted glasses here. And I believe you that your and others' pain was real. Many people could not work their heads around a PC; many of 'em fell for cheap SX clunkers with other substandard components, ffs. That's obviously an inherent problem of such an open platform: PCs are highly individual in many subtle ways; a trade-off one had, and still has, to negotiate in one fashion or another.

> You’re comparing theoretical top of the line PC hardware (which nobody actually owned and no one had written software for yet) with commodity Amigas and STs.

I'm comparing hardware available on the market (with key system components coming together in 1987/88, and games supporting such top-of-the-line hardware showing up in numbers from '88 onwards). I also spoke to economical realities in nearly every post in this disc; I am well aware that 16-bit home birds had a technical lead for a short while, and were an even better value proposition for many people a while longer. For some, just as valid, this still holds true.

> And even those top of the line PCs still missed a few tricks that made some genres of games better on Amigas and Atari STs, like fast blitters.

Yes, already addressed by referring to Prince of Persia 2 and Aladdin (1993/94!).

> It wasn’t until the era of ray casting 2.5D 1st person shooter that PCs started looking better than their counterparts.

So, your stylistic (genre) preference maps it into the time between 1991 (with Hovertank 3D in April as well as Catacomb 3-D in November) and Wolfenstein 3D (May 1992). Okay.

With mine it begins earlier, largely because of proper 3D-titles: Deathtrack (1989, PC-exclusive), LHX: Attack Chopper (1990, no Amiga/Atari port), and Red Baron (1990, got the Amiga slideshow in 1992), as well as the odd non-3D action title here and there, e. g. Silpheed (1989, no Amiga/Atari port).

One can probably go even back to 1988, for at least parity in certain markets and their segments, if one compares the technological edge in an intellectually honest fashion, i. e. what the platform, hardware and software, was really technically capable of.

And productivity software, part of the deal, is of course its very own world.
by tart-lemonade | 2025-06-02 22:43:59+00:00
The amount of time I spend going back and forth between the implementation and the test cases to verify that the tests actually fully cover the possible failure cases alone can easily exceed the time spent writing it, and that's assuming I don't pull the branch locally and start stepping through it in the debugger.

The idea that AI will make development faster because it eliminates the boring stuff seems quite bold because until we have AGI, someone still needs to verify the output, and code review tends to be even more tedious than writing boilerplate unless you're speed-reading through reviews.
by simonw | 2025-06-02 22:44:07+00:00
> The counter-argument as I see it is that going from “not using LLM tooling” to “just as competent with LLM tooling” is…maybe a day? And lessening and the tools evolve.

Very much disagree with that. Getting productive and competent with LLM tooling takes months. I've been deeply invested in this world for a couple of years now and I still feel like I'm only scraping the surface of what's possible with these tools.
by ipieter | 2025-06-02 22:44:12+00:00
Distributing inference per layer, instead of splitting each layer across gpus, is indeed another approach, called pipeline parallelism. However, per batch there is less compute (only 1 gpu at a time), so inference is slower. In addition, the orchestration of starting the next batch on gpu #0 while gpu #1 starts is quite tricky. For this reason, tensor parallelism as I described is way more common in LLM inference.
by kevinsync | 2025-06-02 22:44:19+00:00
Not to derail, but NFT mania (part of the opening salvo in the article) was the giant shitshow that it was -not- because the concept of unique digital bits in the possession of a single owner was a bad idea (or, the concept of unique verification of membership in a club was a bad idea) -- it was a diarrhea-slicked nightmare because it was implemented via blockchains and their related tokens, which inherently peg fluctuating fiat value to the underlying mechanisms of assigning and verifying said ownership or membership, and encourages a reseller's market .. not to mention the perverse, built-in economic incentives required to get nodes to participate in that network to make the whole thing go.

Had NFTs simply been deployed as some kind of protocol that could be leveraged for utility rather than speculation, I think the story would be a complete 180. No clue personally how to achieve that, but it feels like it could be done.. except that, too, would have been completely perverted and abused by centralized behemoths, leading to a different but terrible outcome. Can you imagine if all data became non-fungible? Convince all the big identity vendors (Google, Apple, etc) to issue key pairs to users that then get used by media companies to deliver audio and video keyed only to you that's embedded with maybe some kind of temporal steganographic signature that's hard to strip and can be traced back to your key? It's not just cracking AACS once and copying the bytes. It becomes this giant mess of you literally can't access anything without going through centralized authorities anymore. Then build more anti-patterns on top of that lol. Prolly better that it was mostly just monkey JPEGs and rug pulls.

Anyways, I'm so far off topic from what's actually being discussed -- just couldn't help myself from veering into left field.
by TheRoque | 2025-06-02 22:44:23+00:00
The C abstracting the assembly or the GC a abstracting away memory management work because they were possible to implement in a deterministic and reliable way (well, in the case of garbage collection, not all the time)

But I don't think that's a similar situation for LLMs, where the hallucinations or failure to debug their own issues are way too frequent to just "vibe code"
by orthoxerox | 2025-06-02 22:44:23+00:00
When my old mouse died, I bought a vertical mouse and sold it after a week. I was just too cumbersome to use. When I use a mouse, I keep the forearm and the wrist static and move it mainly with the tips of my thumb and pinkie. This was impossible with a vertical mouse, which expects you to grab it with the whole hand.

I found a regular mouse instead, but a decidedly asymmetrical one. This way my hand can stay in a less pronated position while still controlling the mouse the way I am used to.
by w10-1 | 2025-06-02 22:44:31+00:00
> I typically just use the index/middle finger on my left hand that covers most of the left hand side of the keyboard, depending on word (index might reach for the 'y' key sometimes) and the pinky for shift key. Right hand uses mostly the index, ring (for hitting backspace and enter) and thumb (for spacebar).

fwiw, you might be avoiding the ulnar nerve usage. Perhaps that makes things easier for your brain/cerebellum and spine, which then only have to coordinate movement in one nerve pathway.
by whockey | 2025-06-02 22:44:47+00:00
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Previous to this I started Plaid (plaid.com), and at Column I'm focused on keeping an extremely lean and experienced team. We're currently 10 experienced engineers that all report to me. We are looking for an ambitious infrastructure, product, and backend engineer (one of each) that wants to build incredible software from first principles.

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by taurath | 2025-06-02 22:45:13+00:00
> I snapped a photo of a bistro's daily specials hand-written on a blackboard in Chinese, copied the text right out of the photo, translated it into English, learned how to pronounce the menu item I wanted, and ordered some dinner.

> Two years ago

This functionality was available in 2014, on either an iPhone or android. I ordered specials in Taipei way before Covid. Here's the blog post celebrating it:

https://blog.google/products/translate/one-billion-installs/

This is all a post about AI, hype, and skepticism. In my childhood sci-fi, the idea of people working multiple jobs to still not be able to afford rent was written as shocking or seen as dystopian. All this incredible technology is a double edges sword, but doesn't solve the problems of the day, only the problems of business efficiency, which exacerbates the problems of the day.
by mjburgess | 2025-06-02 22:45:29+00:00
OK, great. All I'm saying is until we really have videos (or equivalent empirical analysis) of these use cases, it's hard to assess these claims.

Jobs comprise different tasks, some more amenable to LLMs than others. My view is that where scepticism exists amongst professional senior engineers, its probably well-founded and grounded in the kinds of tasks that they are engaged with.

I'd imagine everyone in the debate is using LLMs to some degree; and that it's mostly about what productivity factor we imagine exists.
by cube2222 | 2025-06-02 22:45:32+00:00
I think this is actually a really good point. I was just recently thinking that LLMs are (amongst other things) great for streamlining these boring energy-draining items that "I just want done" and aren't particularly interesting, but at the same time they do very little to help us juggle more complex codebases right now.

Sure, they might help you onboard into a complex codebase, but that's about it.

They help in breadth, not depth, really. And to be clear, to me that's extremely helpful, cause working on "depth" is fun and invigorating, while working on "breadth" is more often than not a slog, which I'm happy to have Claude Code write up a draft for in 15 minutes, review, do a bunch of tweaks, and be done with.
by pedalpete | 2025-06-02 22:45:48+00:00
The prevalence of apnea is likely highly over-estimated. ResMed and Philips, who both make billions off of CPAP, push these high figures.

Measures of OSA (Obstructive sleep apnea) are somewhat poor, more focused on the amount of pauses in breathing, rather than oxygen desaturation, which is the secondary measure. Note, you don't breath 100% of the time when you're awake either.

I work in sleep and neurotech, and at a large sleep conference last year, a talk was given about better methods to measure apnea. It was clear these measures would have significant challenges in being adopted because it would result in less people being diagnosed as needing CPAP.

Lastly, the impact of CPAP in improving cognitive function is inconclusive [1]. Not that it doesn't have an affect. It does in some people, but not in others.

[1] https://www.sciencedirect.com/science/article/abs/pii/S13899...
by Terr_ | 2025-06-02 22:45:52+00:00
Explaining the joke: https://www.cnbc.com/2022/06/30/why-blood-makes-up-over-2poi...

That said, I'm sure I recently saw some blog-post about how the 2-2.5% amounts are significantly overblown because that's counting export-categories of products can sometimes contain human blood derivatives as an ingredient, rather than straight-up RBCs/plasma.

Hmmm, nothing yet on a quick Google search, but that doesn't mean as much these days...
by rockfishroll | 2025-06-02 22:46:04+00:00
Biopure was a company doing something similar in the US. They imploded in the early 2000s, but they had created an "oxygen therapeutic" (blood substitute) by isolating hemoglobin based oxygen carrying molecules FROM COW BLOOD!

The fact that they weren't using whole red blood cells meant the product was typeless, room temp stable, and better at perfusing around arterial blockages and into tissue since the molecules were so small.

Unfortunately, the company was kind of a mess. They managed to get licensed for sale in South Africa, and in the US for the veterinary product, but never managed FDA approval. It's a shame. Everyone could see the promise of the product, and it really actually worked, but they just couldn't seem to make the business viable.

https://en.wikipedia.org/wiki/Biopure

Edit: When I say they imploded, I really mean it. They got prosecuted for misleading statements to investors about the state of US clinical trials, and the legal proceedings became farcical.

"On March 11, 2009 [Senior VP] Howard Richman pleaded guilty in U.S. District Court and admitted he had instructed his lawyers to tell a judge he was gravely ill with colon cancer. He also admitted to posing as his doctor in a phone call with his lawyer so that she would tell the judge that his cancer had spread and that he was undergoing chemotherapy."

That guys was sentenced to 3 years in prison. Here's hoping this new blood substitute has a happier outcome!
by buremba | 2025-06-02 22:47:07+00:00
It's interesting that Snowflake went shopping for Crunchy Data over Neon. While Neon focused on bringing compute and storage separation to OLTP, Crunchy Data focused more on bringing OLTP/PostgreSQL closer to OLAP with DuckDB and Iceberg.

In a way, Crunch Data was a competitor to Snowflake as they literally name themselves as "Postgresql Data Warehouse" but correct me if I'm wrong. Neon sounds more complementary to Snowflake as they were struggling with an OLTP backend, namely their Unistore product, which was announced 3 years ago but never went into general availability due to its scalability issues.

Maybe Neon was 4x more expensive, but this acquisition sounds more like an answer to Databricks than a strategic acquisition if I'm being honest. Apparently, Crunchy had $30M ARR, so it's 8x ARR, which is a cheaper answer to Databricks.
by bob1029 | 2025-06-02 22:47:33+00:00
I am finding the most destructive aspect of LLM assistance to be the loss of flow state.

Most of the time I can go faster than these tools if I have confidence in myself and allow the momentum to build up over the course of 20-30 minutes. Every time I tab out to an LLM is like a 5 minute penalty over what I could have done unaided on a good day.

Getting the model prepared to help you in a realistic domain often takes a few minutes of arranging code & comments so that it is forced toward something remotely sane. I'll scaffold out entire BS type hierarchies just so I can throw a //TODO: .... line in the middle somewhere. Without this kind of structure, I would be handling unfiltered garbage most of the time.

It's not that these tools are bad, it's that we need to recognize the true cost of engaging with them. ChatGPT is like a jackhammer. It will absolutely get you through that concrete slab. However, it tends to be quite obnoxious & distracting in terms of its operational principles.
by anotherevan | 2025-06-02 22:47:35+00:00
Using AI to generate subtitles is inventive. Is it smart enough to insert the time codes such that the subtitle is well enough synchronised to the spoken line?

As someone who has started losing the higher frequencies and thus clarity, I have subtitles on all the time just so I don't miss dialogue. The only pain point is when the subtitles (of the same language) are not word-for-word with the spoken line. The discordance between what you are reading and hearing is really distracting.

This is my major peeve with my The West Wing DVDs, where the subtitles are often an abridgement of the spoken line.
by imiric | 2025-06-02 22:47:47+00:00
> People coding with LLMs today use agents. Agents get to poke around your codebase on their own. They author files directly. They run tools.

I'll be damned if I give up control of my machine to a tool that hallucinates actions to take using hastily put together and likely AI-generated "agents". I still want to be the primary user of my machine, and if that means not using cutting edge tools invented in the last 6 months, so be it. I don't trust the vast majority of tools in this space anyway.

> I’m sure there are still environments where hallucination matters.

Still? The output being correct matters in _most_ environments, except maybe art and entertainment. It especially matters in programming, where a 99% correct program probably won't compile.

> But “hallucination” is the first thing developers bring up when someone suggests using LLMs, despite it being (more or less) a solved problem.

No, it's not. It's _the_ problem that's yet to be solved. And yet every AI company prefers chasing benchmarks, agents, or whatever the trend du jour is.

> I work mostly in Go. [...] LLMs kick ass generating it.

I also work mostly in Go. LLMs do an awful job generating it, just as with any other language. I've had the same shitty experience generating Go, as I've had generating JavaScript or HTML. I've heard this excuse that the language matters, and IME it's just not the case.

Sure, if you're working with an obscure and niche language for which there is less training data, I suppose that could be the case. But you're telling me that there is no good training data for Rust, the trendiest systems language of the past ~decade? C'mon. Comparing Rust to Brainfuck is comical.

I won't bother responding to all points in this article. I will say this: just as AI doomsayers and detractors deserve criticism, so does this over-the-top praising. Yes, LLMs are a great technology. But it is also part of a wildly overhyped market that will inevitably crash as we approach the trough of disillusionment. Their real value is somewhere in the middle.
by lapcat | 2025-06-02 22:48:05+00:00
> If you were trying and failing to use an LLM for code 6 months ago, you’re not doing what most serious LLM-assisted coders are doing.

This sounds like the "No true Scotsman" fallacy.

> People coding with LLMs today use agents. Agents get to poke around your codebase on their own.

That's a nonstarter for closed source, unless everything is running on-device, which I don't think it is?

> Part of being a senior developer is making less-able coders productive

Speak for yourself. It's not my job.
by danudey | 2025-06-02 22:48:11+00:00
In this specific case, the 'static binaries' and 'cross-compilation' aspect aren't relevant, as vanta is a dynamically linked binary with multiple library dependencies; it has to link against libpcap, which also pulls in some infiniband libraries on my system, plus libdbus which pulls in libsystemd, libgcrypt, libgpg-error, libcap, and libs lz4, lzma, and zstd. In fact, the only library that tcpdump links against that vanta doesn't is libcrypto.

Note that none of this has to do with vanta itself; it's solely because it depends on libpcap, and libpcap depends on all of those other libraries. Still, it does mean that cross-compiling isn't notably easier than just building tcpdump itself.
by wordofx | 2025-06-02 22:48:24+00:00
It is absolutely hilarious to read the responses from people who can’t use ai make attempts to justify their ability to code better than ai. These are the people who will be replaced. They are fighting so hard against it instead of learning how to use it.

“I wrote 400 lines of code I don’t understand and need months to understand it because ai obviously cant understand it or break it down and help me document it”

“Speed is what caused problems! Because I don’t know how to structure code and get ai to structure it the same it’s obviously going rogue and doing random things I cannot control so it’s wrong and causing a mess!!!”

“I haven’t been able to use it properly so don’t know how to rein it in to do specific tasks so it produces alot of stuff that takes me ages to read! I could have written it faster!!!”

I would love to see what these people are doing 1-2 years from now. If they eventually click or if they are unemployed complaining ai took their jobs.
by paulsutter | 2025-06-02 22:48:32+00:00
The best I can offer skeptics is the more you work with the tools the more productive you become. Because yes the tools are imperfect.

If you've had a dog you know that "dog training" classes are actually owner training.

Same with AI tools. I see big gains for people who spend time to train themselves to work within the limitations. When the next generation of tools come out they can adapt quickly.

If this sounds tedious, thats becuase it is tedious. I spent many long weekends wrestling with tools silently wrecking my entire codebase, etc. And that's what I had to do to get the productivity improvements I have now.
by Aloisius | 2025-06-02 22:48:46+00:00
Thanks but no thanks. If history is any guide, citizens almost always end up in far worse shape in actual dystopian nightmares when states collapse.

I'll happily take stability living under an "oligarchy" with relatively weak and divided oligarchs, in a country with free elections that can still effect change, even if it happens at a glacial pace.
by jwpapi | 2025-06-02 22:49:02+00:00
I think one of the smartest decisions of my career was that I learned NEO when I was 16, which is like DVORAK, but with german letters. It also has a lot of hotkeys for important coding characters like ({*\/ on the home row.

I reached 130wpm in german once and I can’t fathom how much better it made my life. Coding is not awkward at all. This along with the thinkpad nipple makes a really smooth life at the computer. Maybe sometimes even a bit too much.

I totally can confirm that trying to go faster is not helping. I remember that even during Fortnite grind sessions, where you did edit parcours. Your best bet to go faster is to go perfect. You’ll almost not notice how you get faster. It is actually the same for the piano or for tennis a lot of coordination is best learnt by not making mistakes.
by bicx | 2025-06-02 22:49:04+00:00
This is exactly what I’ve experienced. For the top-end high-complexity work I’m responsible for, it often takes a lot more effort and research to write a granular, comprehensive product spec for the LLM than it does to just jump in and do it myself.

On the flip side, it has allowed me to accomplish many lower-complexity backlog projects that I just wouldn’t have even attempted before. It expands productivity on the low end.

I’ve also used it many times to take on quality-of-life tasks that just would have been skipped before (like wrapping utility scripts in a helpful, documented command-line tool).