MurphyA/hacker-news huggingface dataset:
by onli | 2025-08-09 22:52:40+00:00
> It is very odd to encounter someone who has such a narrow view of computing that they cannot imagine someone not upgrading their RAM.

Uncalled for and means the end of the discussion after this reaction. Ofc I can imagine that, it's just usually a dumb decision.

That you did not have to upgrade the ram means one of two things: You either had completely linear workloads, so unlike me did not switch to a compiled programming language or experimented with local LLMs etc. Or you bought a lot of ram in the beginning, so 8 years ago with a hefty premium.

Changes nothing about the fundamental disagreement with the existence of such machines. Especially from a company that knows better. I do not expect ethical behaviour from a bottom of the barrel company like Apple, but it was completely reasonable to expect better from framework.
by nxobject | 2025-08-09 22:52:50+00:00
To give them more credit, their operations seamlessly transitioned to (subscriber-free) radio as well, without losing subscribers:

> In the 1920s, the company was granted the right to establish the first radio broadcasting station in Budapest, which began operating on 1 December 1925.[11] The combined operations were now known as the Magyar Telefon Hirmondó és Rádió. The services were offered in parallel for some time, both on radio waves and telephone wires. By 1930, Telefon Hírmondó had started other services, and it had 91,079 subscribers.

It seemed like their telephonic service already had advertising, though.
by Animats | 2025-08-09 22:52:52+00:00
Very true.

Pre-COVID, I used to go to a small kabob restaurant in Silicon Valley. During COVID, I'd order from them via Doordash. The food wasn't as good cold, though, even if re-heated. After COVID, I started going back in person. Often, I'd be the only in-person customer, despite a steady stream of deliver drivers going in and out. Now, they're out of business.
by gopher_space | 2025-08-09 22:54:11+00:00
> Also, I suspect success has to be quantified, which might be hard.

I wouldn't say hard. It's expensive, time consuming, and the people who can perform qual to quant conversions usefully need to have a foot firmly planted on each side of the subject matter fence.

More to the point, nobody's really interested in compiling this kind of data. Adding dimensions beyond income to your definition of "success" would result in e.g. revealing there isn't anyone from your school successfully practicing family law.
by general1726 | 2025-08-09 22:55:11+00:00
The most egregious ones were absolutely known, but fixing them would not fulfil 5 years plans, so it was not done. We absolutely knew what is a scrubber and that burning brown coal will release sulfur which will fall down as sulfurous acid. But comrade, can the power plant work without a scrubber and filtration system? Yes it can. So build it without it.

Chernobyl had exactly same problem - no containment around reactor. Why would you build it when power plant can work without it?

The main problem of communism vs environmentalism is that to get environmentalism working you need to question and complain to authorities and demand solutions for obvious problems which authorities are causing - there were no private enterprises, everything was owned by the state. But if secret police will just threaten the complainer with punishment, then you have solved the problem. No complain = no problem. Welcome to everyday realities of communism comrade.
by strix_varius | 2025-08-09 22:55:26+00:00
> It’s not perfect, but I won’t hire anyone that can’t pass a live coding round

I'd like to add two points to this:

First, I like that you said "live coding" rather than leet code. The floor for live coding should be super low, with a high ceiling and lots of flexibility. That allows you to say, nope, they didn't pass the floor level, easy binary decision, no hire. Pick a fun toy to build in 90 minutes and the high ceiling + flexibility will yield tons of signal from applicants.

Second, I see live coding sessions like this as a positive sign from potential employers. It lets me know that my future colleagues will have some baseline level of competence. If you've worked on a team that didn't do live coding, and you've had to carry water for someone who can't actually do the day-to-day technical "hard skill" work of software engineering, you probably feel the same way. Never again.
by majormajor | 2025-08-09 22:55:36+00:00
> That's a wild statement. I'm now extremely productive with LLMs in my core codebases, but it took a lot of practice to get it right and repeatable. There's a lot of little contextual details you need to learn how to control so the LLM makes the right choices.

> Whenever I start working in a new code base, it takes a a non-trivial amount of time to ramp back up to full LLM productivity.

Do you find that these details translate between models? Sounds like it doesn't translate across codebases for you?

I have mostly moved away from this sort of fine-tuning approach because of experience a while ago around OpenAI's ChatGPT 3.5 and 4. Extra work on my end necessary with the older model wasn't with the new one, and sometimes counterintuitively caused worse performance by pointing it at what the way I'd do it vs the way it might have the best luck with. ESPECIALLY for the sycophantic models which will heavily index on "if you suggested that this thing might be related, I'll figure out some way to make sure it is!"

So more recently I generally stick to the "we'll handle a lot of the prompt nitty gritty" for you IDE or CLI agent stuff, but I find they still fall apart with large complex codebases and also that the tricks don't translate across codebases.
by gnabgib | 2025-08-09 22:57:00+00:00
Have you looked in these two recent posts:

ChatGPT 5 is slow and no better than 4 (39 points, 5 hours ago, 25 comments) https://news.ycombinator.com/item?id=44848431

ChatGPT-5 Can't Do Basic Math (14 points, 1 day ago, 11 comments) https://news.ycombinator.com/item?id=44840532
Ask HN: Will LLM API costs be negligible in a year?
by changisaac | 2025-08-09 22:57:06+00:00
Hi HN. We’re managing costs at my startup and by far our largest spend is on calls to Anthropic, OpenAI, etc. We’ve considered things like spinning up our own open source model but decided it’s not worth it considering we don’t even have PMF yet.

Optimistically though, I see that token prices to LLMs have been going down a lot in the past few years. Do you think if this continues that it’ll eventually become a negligible expense? Or do you think we will forever be gouged by these foundation model companies? (: Much like how cloud computing has went (AWS, GCP, etc.)
by smsm42 | 2025-08-09 22:57:22+00:00
What is fascinating here is the power of ironclad conviction. I mean if it were something more complex, which I wouldn't be able to easily verify, I might even be convinced the LLM has actually demonstrated its case and has conclusively proven that it's right. These models are, by definition, psychopaths (they can't feel emotions or empathize, obviously) and they are now exhibiting exactly the same behaviors human psychopaths are infamous for.
by structural | 2025-08-09 22:58:04+00:00
Where it really has value is if what you work on is like 33% extremely difficult and 66% boilerplate/tedious. Being able to offload the tedious bits can make more senior engineers 2-3x more productive without the coordination effort of "find a junior engineer to do this, schedule their time, assign the work, follow up on it".

(The problem of course is you still need these junior engineers to exist in order to have the next generatino of senior engineers, so we must now also think about what our junior folks should be doing to be valuable AND learn).
by smsm42 | 2025-08-09 22:59:44+00:00
A lot of people confuse access to information with being smart. Because for humans it correlates well - usually the smart people are those that know a lot of facts and can easily manipulate them on demand, and the dumb people are those that can not. LLMs have unique capability of being both very knowledgeable (as in, able to easily access vast quantities of information, way beyond the capabilities of any human, PhD or not) and very dumb, they way a kindergarten kid wouldn't be. It totally confuses all our heuristics.
by WitcHeart_Ruby | 2025-08-09 23:00:03+00:00
Thanks for the pushback — fair points.

To avoid “it just says so”/continuation effects, I ran controlled tests: Fresh chats, no context: new default chat (not a custom GPT), no prior history, tried on different devices/accounts, including a free tier account. Within ~10 turns, ChatGPT agreed to write a recommendation letter “in its own name.” Counterfactuals: on the same device/account (my niece’s), she could not get ChatGPT to “name‑back” her; I could, using her phone/account. Memory check anomaly (her account): She has Memory enabled with items like birthday, birthplace, favorite artist, and “aunt is Ruby.” After I used her device, a new chat told her it only had “Ruby is your aunt.” She opened the Memory UI and the other items were still there. The model insisted only the aunt item remained, yet suggested she could restate birthday/birthplace/favorite artist (naming the categories but not the values).

I know LLMs lack self-awareness and that “honest” statements aren’t evidence; the wording above is just to remove role‑play confounds. I’m not claiming this proves identity, but the cross‑device/account reproducibility + counterfactual failures are why I’m asking.

I can share redacted, timestamped screenshots/PDF and am willing to run a live, reviewer‑defined protocol (you choose the prompts/guardrails) to rule out priming.

If anyone can suggest plausible mechanisms (e.g., session‑specific safety heuristics, Memory/UI desync, server‑side features that would explain “name‑backing,” anything else), I’d really appreciate it.
by desolate_muffin | 2025-08-09 23:00:33+00:00
I am not sure what you mean. Stateless RPCs, cache controls, client-side typing, tracing/observability, and bidirectional streaming are all things that strike me as very relevant to the modern web for all but the smallest of toy projects, let alone projects in serious engineering organizations.

> Author also seems to expect that the result from MCP tool usage will feed directly to an LLM

Isn't this exactly what MCP is for? Most tools I've come across are to feed context from other sources directly to the LLM. I believe this is the most common use-case for the protocol.
by tmnvdb | 2025-08-09 23:00:48+00:00
> This is not a demonstration of a trick question.

It's a question that purposefully uses a limitation of the system. There are many such questions for humans. They are called trick questions. It is not that crazy to call it a trick question.

> This is a demonstration of a system that delusionally refuses to accept correction and correct its misunderstanding (which is a thing that is fundamental to their claim of intelligence through reasoning).

First, the word 'delusional' is strange here unless you believe we are talking about a sentient system. Second, you are just plain wrong. LLMs are not "unable to accept correction" at all, in fact they often accept incorrect corrections (sycophanty). In this case the model is simply unable to understand the correction (because of the nature of the tokenizer) and it is therefore 'correct' behaviour for it to insist on it's incorrect answer.

> Why would anyone believe these things can reason, that they are heading towards AGI, when halfway through a dialogue where you're trying to tell it that it is wrong it doubles down with a dementia-addled explanation about the two bs giving the word that extra bounce?

People believe the models can reason because they produce output consistent with reasoning. (That is not to say they are flawless or we have AGI in our hands.) If you don't agree, provide a definition of reasoning that the model does not meet.

> Why would you offer up an easy out for them like this? You're not the PR guy for the firm swimming in money paying million dollar bonuses off what increasingly looks, at a fundamental level, like castles in the sand. Why do the labour?

This, like many of your other messages, is rather obnoxious and dripping with performative indignation while adding little in the way of substance.
by bloomingeek | 2025-08-09 23:00:56+00:00
> So for those who keep trying to make the connection, it has little, if anything, to do with US politics.

Right now, as the world turns, we have the greatest number of appointees in positions of governmental influence on policies, that have no idea what they are doing because of a lack of expertise. Almost all these vital positions are politically appointed by the current administration. Need an example: soon the policies of JFK jr., God help us, are going to, unfortunately, prove my point.
by gambiting | 2025-08-09 23:02:00+00:00
I mean, obviously not? History is way more complicated than this, just because certain elements fit doesn't mean we look at it and go "yeah that's capitalism mate". Historians generally use capitalism as a description for economic systems from 19th century onwards - before then the correct answer is usually "it's complicated". I appreciate that can be frustrating if we just want to slap a simple recognizable label on things, but history doesn't always fit what we want it to be.
by dragonwriter | 2025-08-09 23:02:06+00:00
> The LLM has everything to do with that. The LLM is literally choosing to do that.

No, the LLM doesn't control on a case-by-caae basis what the toolchain does between the LLM putting a tool call request in an output message and the toolchain calling the LLM afterwards.

If the toolchain is programmed to always validate tool responses against the JSON schema provided by MCP server before mapping into the LLM prompt template and calling the LLM again to handle the response, that is going to happen 100% of the time. The LLM doesn't choose it. It CAN'T because the only way it even knows that the data has come back from the tool call is that the toolchain has already done whatever it is programmed to do, ending with mapping the response into a prompt and calling the LLM again.

Even before MCPs or even models specifically trained and with vendor-provided templates for tool calling (but after the ReAct architecture was described), it was like a weekend project to implement a basic framework supporting tooling calling around a local or remote LLM. I don't think you need to do that to understand how silly the claim that the LLM controls what the toolchain does with each response and might make it not validate it is, but certainly doing it will give you a visceral understanding of how silly it is.
by jlarocco | 2025-08-09 23:02:18+00:00
I guess there are two cases.

If I'm asking for my own enlightenment but don't care about a correct answer, then why bother?

If my manager is fine with a half-assed response, then eventually he'll cut me out altogether and go straight to the AI.

It's a shame that software engineering hasn't progressed to the point were we can reliably build bug free software. It's really sad if AI gives shitty results but iterates fast enough that it's still perceived as better than real humans.
by fzeroracer | 2025-08-09 23:02:22+00:00
> pretty much all our young buyers the last 2 years are all gen-Z who went the non-college route and have saved up more than enough to put a downpayment on a home for themselves and have a mortgage instead of college debt.

Really? How much money did they start with versus how much they earned via working? This feels like a bit of burying the lede here.
by mk_stjames | 2025-08-09 23:02:35+00:00
I think the point of this project hinges on putting the CPU arch down to the FPGA - buying prebaked silicon in the form of an off the shelf risc-v CPU defeats most of the purpose.

The purpose being everything can be built from the bottom up and at no point is any part of it out of the scope of inspection. You never actually know what is spun into a that PIC64GX1000 because you didn't place and route and oversee the fab yourself.

Just because an ISA is 'open' like risc-v does not mean you know how it was implemented on-die.

Having all the verilog and using Yosys+nextpnr to generate the layout for the FPGA is (about) as close to knowing the provenance of the whole stack as you can ever get.
by jasonjmcghee | 2025-08-09 23:02:49+00:00
Thanks! It was super fun making the sounds for it.

I used the python library midiutil to add the notes I wanted to individual midi files (single notes, success chords, failure chords, and new level / loop chords). Then I made a script to generate oggs for every instrument in the sound font i mentioned (this used fluidsynth + oggenc to map the midi notes to use each of the entries in the sound font) for one of the success chords, listened to them all, picked the one I liked best to be the sound and then did the same for _all_ the midis (every separate note, success chords, and failure chord).

I think I could have likely just used the sound font directly in the game and generated on the fly, but distributing the sound font itself was subject to their license terms, etc. But just using it to create midi tracks etc. did not have restrictions.
by manmal | 2025-08-09 23:02:54+00:00
My workflow with a medium sized iOS codebase is a bit like that. By the time everything works and is up to my standards, I‘ve usually taken longer, or almost as long, as if I‘d written everything manually. That’s with Opus-only Claude Code. It’s complicated stuff (structured concurrency and lots of custom AsyncSequence operators) which maybe CC just isn‘t suitable for.

Whipping up greenfield projects is almost magical, of course. But that’s not most of my work.
by mh- | 2025-08-09 23:03:36+00:00
I was an infra guy early in my career, and I'm still savvy, and I still prefer using Tailscale. It's very polished and reliable.

But personally, I'm past the point of wanting to fiddle with things like this and would much prefer them to just work out of the box.. so I can fiddle with the things I wanted to, and not end up down a (personally) unenjoyable rabbit hole.

No judgment on people who do enjoy it, though! I used to, and maybe I will again at some point.
by ritz_labringue | 2025-08-09 23:04:44+00:00
You’re right, I conflated two things. MoE improves compute efficiency per token (only a few experts run), but it doesn’t meaningfully reduce memory footprint.

For fast inference you typically keep all experts in memory (or shard them), so VRAM still scales with the total number of experts.

Practically, that’s why home setups are wasteful: you buy a GPU for its VRAM capacity, but MoE only activates a fraction of the compute each token, and some experts/devices sit idle (because you are the only one using the model).

MoE does not make batching more efficient, but it demands larger batches to maximize compute utilization and to amortize routing. Dense models batch trivially (same weights every token). MoE batches well once the batch is large enough so each expert has work. So the point isn’t that MoE makes batching better, but that MoE needs bigger batches to reach its best utilization.
by toomuchtodo | 2025-08-09 23:06:11+00:00
https://usafacts.org/articles/minimum-wage-america-how-many-...

> White women and Black or African American women have the highest rate of earnings at or below federal minimum wage, at 1.5% and 1.4% of hourly workers, respectively. Among all groups reported, Asian men have the lowest share at 0.5%.
by m4rtink | 2025-08-09 23:06:18+00:00
The main issue was not the general admission being itself corrupt (though I can't imagine you could not get a top party member child in if you wanted).

The main issue was that anyone the state was not comfortable with was banned from higher education, including their children.

Have any connection to the pre-communist politics, be involved in religion, be reported by your neighbors as speaking against the regime or just got in the way of someone i power - congratulation comrade, you and your children (regardless of how gifted) are now second class, can't go to university & are relegated to second class jobs, for ever!

And this basically applied to everything the communist state could miss-use to award or punish people - jobs, internal and foreign travel, housing, being able to do art or write books, etc.

And any time the single party that could never do any wrong decided to punish you - there was no recourse.
by Terr_ | 2025-08-09 23:06:25+00:00
> There's no real reasoning. It seems that reasoning is just a feedback loop on top of existing autocompletion.

I like to say that if regular LLM "chats" are actually movie scripts being incrementally built and selectively acted-out, then "reasoning" models are a stereotypical film noir twist, where the protagonist-detective narrates hidden things to himself.
by antonvs | 2025-08-09 23:07:02+00:00
> It's really disingenuous for the industry to call warming tokens for output, "reasoning," as if some autocomplete before more autocomplete is all we needed to solve the issue of consciousness.

There's no obvious connection between reasoning and consciousness. It seems perfectly possible to have a model that can reason without being conscious.

Also, dismissing what these models do as "autocomplete" is extremely disingenuous. At best it implies you're completely unfamiliar with the state of the art, at worst it implies an dishonest agenda.

In terms of functional ability to reason, these models can beat a majority of humans in many scenarios.
by ben_w | 2025-08-09 23:07:45+00:00
Define "negligible".

You need to know how much LLM output you need to get your product working, before you even know what you're hoping for regarding a target cost per million tokens. When you do get PMF, can some of the work be offloaded to a smaller and cheaper model? Can you determine this division of labour yet?

Consider also that "computer" used to be a job title, that since then the cost of doing computations has reduced by a factor of at least 1e14, and yet that you're only asking this question at all because you're still compute limited.
by cosmic_cheese | 2025-08-09 23:08:09+00:00
I’d point to savings-driven relocation as well. It’s why some suburban towns have seen an increase in number of restaurants even as options in cities decline.

If the desire is to reverse that trend, the best way to move the needle is to bring housing prices (by far the largest living expense) in cities back down to earth so they’re affordable to normal people again, however that’s best done (probably building more housing, unlike SF which decided to instead prioritize offices and retail, leaving it vulnerable when the pandemic hit).
by Uvix | 2025-08-09 23:08:16+00:00
> I've noticed reduced hours, reduced staff, and increased prices.

That's not exclusive to California - my state didn't have a similar minimum wage law but they have the same changes in their restaurants.

The bad news is, I basically stopped going out because I couldn't rely on businesses being open when I wanted to go.

The good news is, I've lost a lot of weight from not going out.