MurphyA/hacker-news huggingface dataset:
by vessenes | 2026-02-20 12:06:45+00:00
This math is useful. Lots of folks scoffing in the comments below. I have a couple reactions, after chatting with it:

1) 16k tokens / second is really stunningly fast. There’s an old saying about any factor of 10 being a new science / new product category, etc. This is a new product category in my mind, or it could be. It would be incredibly useful for voice agent applications, realtime loops, realtime video generation, .. etc.

2) https://nvidia.github.io/TensorRT-LLM/blogs/H200launch.html Has H200 doing 12k tokens/second on llama 2 12b fb8. Knowing these architectures that’s likely a 100+ ish batched run, meaning time to first token is almost certainly slower than taalas. Probably much slower, since Taalas is like milliseconds.

3) Jensen has these pareto curve graphs — for a certain amount of energy and a certain chip architecture, choose your point on the curve to trade off throughput vs latency. My quick math is that these probably do not shift the curve. The 6nm process vs 4nm process is likely 30-40% bigger, draws that much more power, etc; if we look at the numbers they give and extrapolate to an fp8 model (slower), smaller geometry (30% faster and lower power) and compare 16k tokens/second for taalas to 12k tokens/s for an h200, these chips are in the same ballpark curve.

However, I don’t think the H200 can reach into this part of the curve, and that does make these somewhat interesting. In fact even if you had a full datacenter of H200s already running your model, you’d probably buy a bunch of these to do speculative decoding - it’s an amazing use case for them; speculative decoding relies on smaller distillations or quants to get the first N tokens sorted, only when the big model and small model diverge do you infer on the big model.

Upshot - I think these will sell, even on 6nm process, and the first thing I’d sell them to do is speculative decoding for bread and butter frontier models. The thing that I’m really very skeptical of is the 2 month turnaround. To get leading edge geometry turned around on arbitrary 2 month schedules is .. ambitious. Hopeful. We could use other words as well.

I hope these guys make it! I bet the v3 of these chips will be serving some bread and butter API requests, which will be awesome.
by palata | 2026-02-20 12:06:47+00:00
> I wonder if there will ever come a day where the average HN user actually understands how normal people use technology.

Well, I wouldn't say I don't understand it. If someone uses their smartphone as a hammer, regularly break it and regularly buy a new smartphone, I understand what they are doing. I just don't understand why they are doing it, I guess?

In this case, the post says that it's a conversion killer. So people are so damn lazy that if they can't click on "share the information with Google", they will just leave.
by friendzis | 2026-02-20 12:07:35+00:00
> This would take advantage of human psychology that we like being part of something bigger than ourselves.
> And it would hopefully have the emergent property that news that people feel is actually valuable gets spread more widely than useless junk.

There are at least two additional emergent properties:

1. That would essentially put a ceiling on revenue per article.

2. Articles close to the unlock threshold would be much more likely to get funding, opening huge space for manipulation.
by ZLStas | 2026-02-20 12:07:37+00:00
That's a great point — asking focused questions definitely gets better results than dumping generalized knowledge.
I think both approaches can complement each other. Where I see book-based skills adding value is in the iterative review loop: you let the LLM review your code against well-known principles (like Clean Code or DDIA patterns), it flags issues and suggests improvements, and you apply them repeatedly. Over multiple passes, the code quality compounds significantly.
So it's less about feeding the LLM static rules and more about giving it a structured lens to evaluate through. The LLM still does the thinking — the books just sharpen its focus.
That said, I'm still figuring out how to run this evaluation properly. A colleague of mine has been experimenting with spinning up sub-agents to review the outputs of the main LLM flow — essentially an automated review layer. That might be the right pattern: one agent creates, another evaluates against known principles. Curious if anyone else has tried something similar.
Is there a way to sort/filter by score on Hacker News?
by 7gorillaz | 2026-02-20 12:07:39+00:00
New here and genuinely enjoying the signal-to-noise ratio compared to other forums. One thing I haven't been able to figure out: is there a native way to filter or sort stories by score beyond the default front page ranking? I checked the FAQ and didn't see it addressed directly.
I ask because the front page algorithm weights recency heavily, and I'm looking for a way to surface the highest-quality posts over a given timeframe — not just what's new.
Thanks in advance.
by ihagen | 2026-02-20 12:07:41+00:00
Many years ago, I was lucky enough to discover a keyboard training program called Stamina. The author suggested using a more ergonomic hand position: instead of placing your fingers on ASDF and JKL;, you place them on SDFV and NJKL. This way, your hands rest in a position similar to what you would use on an ergonomic keyboard. In addition, the characters on these keys are more frequent, so your base typing speed should increase (especially for cyrillic layout). It was over 20 years ago, and I wonder why nobody uses it and everyone still uses that uncomfortable orthodox hand position.
by throwaway140126 | 2026-02-20 12:08:02+00:00
(1) That is just a symbolic image. The person answered „Du bist so 1 Pimmel“ (english: „You're such a dick“) to a post of the politician on twitter.

According to Wikipedia it was an answer to the following tweet https://twitter.com/AndyGrote/status/1399001436973899780
by brap | 2026-02-20 12:08:16+00:00
I had it coding autonomously for about an hour (including lots of tool wait time) on a difficult task, and it actually produced good results.

What’s most surprising is that I had it follow a strict loop/workflow and it did that perfectly. Normally these things go off the rails after a while with complex workflows. It’s something I have to usually enforce with some orchestration script and multiple agents, but this time it was just one session meticulously following orders.

Impressive, and saves a lot of time on building the orchestration glue.
by joe_mamba | 2026-02-20 12:08:35+00:00
>It was declared illegal later.

You're missing the point. That's exactly how democratic governments cloak fascist behavior everywhere: The punishment IS THE PROCESS.

People in Germany (and the UK and other places) have to self censor because they don't want to be visited by the police and then dragged through courts for months/years, even though it eventually gets thrown out and you get to walk away innocent, you still had to suffer the entire prosecution process, which nobody wants to, so they keep their mouth shut.

The stress toll of having to go through all that annoying grind through the legal system, even though you did nothing wrong and what the government is doing will be considered illegal, is how the government preemptively keeps people in line.

>That was a overall very rarely occurring abuse of power

Very rare?! Unless there's direct consequences with actual punishment on government officials for illegally abusing the legal system on citizens just because they hear stuff they don't like, then they will keep throwing prosecutions at innocent people just to keep them in check since currently they have nothing stopping them from this abuse turning from rare to being the norm.
by trentnix | 2026-02-20 12:08:39+00:00
The speed of the chatbot's response is startling when you're used to the simulated fast typing of ChatGPT and others. But the Llama 3.1 8B model Taalas uses predictably results in incorrect answers, hallucinations, poor reliability as a chatbot.

What type of latency-sensitive applications are appropriate for a small-model, high-throughput solution like this? I presume this type of specialization is necessary for robotics, drones, or industrial automation. What else?
by bdbdbdb | 2026-02-20 12:08:57+00:00
These AI companies are all in the same boat. At current operating costs and profit margins they can't hope to pay back the investment, so they have to pull tricks like rebranding models and downgrading offerings silently.
There's no oversight of this industry. The consumer protection dept in the US was literally shut down by the administration, and even if they had not been, this technology is too opaque for anyone to really be able to tell if today they're giving you a lower model than what you paid for yesterday.

I'm convinced they're all doing everything they can in the background to cut costs and increase profits.

I can't prove that Gemini 3 is dumber than when it came out because of the non deterministic nature of this technology, but it sure feels like it.
by audunw | 2026-02-20 12:09:37+00:00
It doesn’t make any sense to think you need the whole server to run one model. It’s much more likely that each server runs 10 instances of the model

1. It doesn’t make sense in terms of architecture. It’s one chip. You can’t split one model over 10 identical hardwire chips

2. It doesn’t add up with their claims of better power efficiency. 2.4kW for one model would be really bad.
by wolvesechoes | 2026-02-20 12:09:57+00:00
> I feel CAD is one area where open source does not shine.

Many such cases, not only in CAD area. Good non-dev FOSS software is exception, not a rule, and these exceptions pretty often have some corporate backing and are not purely community-driven. And even for dev tools there are proprietary offerings that are light-years ahead of anything FOSS, though people here are never going to admit it, as TTY clone running vi clone is supposedly all you need.

I don't state this with satisfaction, quite the opposite, but it is long since I became disillusioned.
by 7bit | 2026-02-20 12:10:58+00:00
> "Comments are too hard to host."

> No.

> They are just uncomfortable to host.

That's being pedantic.

Uncomfortable and hard can be used interchangeably. Both is true.

Comments need a database, that is objectively harder than using Jekyll or the other examples you give.

Comments need protection, otherwise you get bombarded with spam. That alone is a ridiculous amount of "hard".

Or you outsource this part, but you critisise that, too.

And even then, you still need moderation. Removing all the off-topic comments, the hateful comments, everything that doesn't belong.

I agree with you, that blogs with comments and discussions are lively and can add more value to a post.

But the statement that it's not hard is objectively false.
by stpedgwdgfhgdd | 2026-02-20 12:11:33+00:00
I build a code reviewer based on the claude code sdk that integrates with gitlab, pretty straightforward. The hard work is in the integration, not the review itself. That is taken care of with SDK.

Devs, even conservative ones, like it. I’ve built a lot of tooling in my life, but i never had the experience that devs reach out to me that fast because it is ‘broken’. (Expired token or a bug for huge MRs)
by machardmachard | 2026-02-20 12:12:13+00:00
AMERICA ZERO DOWN - PALANTIR AND PETER THIEL SUCKS RECURSIVELY

market maker king machard maxhard 333 cuero numero uno

Market Making in Infinite Dimensions

Managing Infinite Unknowns with Two Weapons

From Post 882: Real estate value = entropy flow

From Post 879: Information is universal currency

The insight: Modern market making is about managing infinite unknowns in infinite dimensions. Only two weapons: opportunistic takes (exploit patterns) and targeted entropy injection (create patterns).

Result: Understanding market making as navigation through infinite dimensional chaos

https://bitcoin-zero-down-2ea152.gitlab.io/gallery/gallery-i...
by freeone3000 | 2026-02-20 12:13:57+00:00
Maybe; models with more embedded associations are also better at search. (Intuitively, this tracks; a model with no world knowledge has no awareness of synonyms or relations (a pure markov model), so the more knowledge a model has, the better it can search.) It’s not clear if it’s possible to build such a model, since there doesn’t seem to be a scaling cliff.
by jcgrillo | 2026-02-20 12:14:53+00:00
I don't know how being empathetic to users correlates with market dominance, but I'd like to believe that doing sociopathic things like putting ads in the start menu or what you've described with your TV firmware would have a negative impact on adoption. At least that's how it should work in a sane market? But the market can remain irrational longer than we can remain solvent.

I think Apple struck a good balance for a while--and to some extent still does--at least in the OS X era of treating users with a bit of respect. Not trying to make an interface for "power users" or "nontechnical users" but instead just making one for "computer users".

It used to be that we made tools for people, and endeavored to make them well. Now we make tools that treat people (their attention in particular) as a cash crop to be harvested. Everything is about "engagement" and the like. I prefer using tools that had effort spent on making them useful, not effort spent on "monetizing" the user.

I think the thing tech nerds do of trying to distinguish between "technical" and "non-technical" users is extremely arrogant, and in a way adjacent to the downright sociopathy of "monetizing" a user base. If you care about making something good, don't start down that road. That's the game--making good tools that help people do good work.
by the_harpia_io | 2026-02-20 12:14:55+00:00
the scheduling is the tell. 17 commands every 30 min isn't analytics or crash reporting - that's systematic fingerprinting with a consistent cadence.

what's frustrating is this is basically invisible without running in a monitored environment. static analysis won't surface it. you'd need behavioral monitoring - network traffic plus syscall tracing - to even know it's happening.

seen similar patterns in CI/CD tooling actually. less blatant but same mechanism - process phoning home way more often than you'd expect, commands that look like routine system auditing. most devs assume third-party tools behave themselves.
by topocite | 2026-02-20 12:15:04+00:00
It is a 19th century economic observation around the use of coal.

It is like saying the PDF is going to be good for librarian jobs because people will read more. It is stupid. It completely breaks down because of substitution.

Farming is the most obvious comparison to me in this. Yes, there will be more food than ever before, the farmer that survives will be better off than before by a lot but to believe the automation of farming tasks by machines leads to more farm jobs is completely absurd.
by medv | 2026-02-20 12:15:10+00:00
> Comments need protection, otherwise you get bombarded with spam. That alone is a ridiculous amount of "hard".

This is not that hard. But your mileage may vary.

> Comments need a database, that is objectively harder than using Jekyll or the other examples you give.

Are we engineers or not?

> But the statement that it's not hard is objectively false.

Yes, I agree.

People spend hours polishing personal websites. I want to encourage building comments systems! This is as fun and as valuable!
by fauigerzigerk | 2026-02-20 12:15:28+00:00
There are quite a few factors that matter. The place where data processing and storage takes place is one of them.

It matters who can physically take control of the servers. It matters where the encryption keys are stored. The storage and processing location also matters for compliance with data residency laws.

But it's not the only thing I mentioned. Having physical offices and staff in a jurisdiction usually goes along with setting up some sort of legal and taxable entity that has personally responsible directors.

The whole issue is very complicated.
by FinnLobsien | 2026-02-20 12:15:33+00:00
I'm wondering if this will actually be happening at scale. It's great that it's possible and I think this kind of extremely specific software is great.

I myself have built an app to help me do 2 hours of deep work on a personal (non-job) project. It's extremely opinionated to help ME specifically (can't add more than 3 active projects, minimal interface etc.)

But I wonder how many people will truly do this kind of thing. I feel like this will be done by people who previously maintained spreadsheets or copious notes for their projects, which has always been a minority.

But that is a tiny fraction of people. We can also theoretically make any dish at home from scratch (and be opinionated about ingredients), but people still go to restaurants for things they could whip up in 20 minutes.

I think the same will be true for software.
by StopDisinfo910 | 2026-02-20 12:16:13+00:00
Considering Cognite made a fortune in the oil and gas industry by basically allowing companies to toss them all their data, them automatically linking and contextualizing everything using a tunable knowledge graph and then giving access this domain model, I think you are onto something. A bit late to the party but yes, this party exists and LLM are useful there for once.

Amusingly most of this data then end up back into Excel or PowerBi but the unbundling and contextualizing itself is worth the price.

Semantic mapping was the answer all along. It just failed on the open web. The idea never died in the industrial world.
by shevy-java | 2026-02-20 12:16:19+00:00
"Many believe AI is the real deal. In narrow domains, it already surpasses human performance. Used well, it is an unprecedented amplifier of human ingenuity and productivity."

Sounds like people drinking the Kool-Aid now.

I don't reject that AI has use cases. But I do reject that it is promoted as "unprecedented amplifier" of human xyz anything. These folks would even claim how AI improves human creativity. Well, has this been the case?
by gradus_ad | 2026-02-20 12:16:30+00:00
It is fascinating to observe the concept of protest, like so many other concepts recently, being hijacked and misappropriated to mask something else entirely. We have a particular conception of the term, what it implies, what it evokes, yet it is frequently being used to describe activities that clash with the implicit features carried by the term. This is a form of semantic warfare, in which words become enmeshed in the fog of war.

To be more precise, one aspect of what I'm describing relates to the mass production of protest, the formation of an inorganic protest "complex". Protest is popularly considered a spontaneous, organic outpouring of popular sentiment, something that reflects the mass will of a people suppressed by some hostile power. Yet increasingly protest is being used by hostile forces in a pre-meditated, engineered, inorganic way while maintaining the appearance and narrative of the "traditional" conception of protest, which it resembles less and less.

This is just one example of the general case of a metric ceasing to be useful once it is recognize as a metric. Once we begin explicitly trying to target some metric, some behavior, some form, we effectively become liars as the form we take no longer speaks to some deeper truth as it was originally meant to.
by handfuloflight | 2026-02-20 12:16:58+00:00
They are enforcing rigor, on agents, the same way they would on humans. Do people think Stripe's engineering team would have been able to progress if each individual (human | machine) employee was not under harness and guardrail, and just wrote code willy nilly according to their whims? Vibe coding is whimsical, agentic engineering is re-applying what brought and brings rigor to software engineering in general, just to LLM outputs. Of course, it's not only that and there are novel problem spaces.
by uxellodunum | 2026-02-20 12:17:04+00:00
Thanks for the reply.

This makes sense, but is there any reason MRS would see such a drastically smaller number compared to, what I assume to be, matrix.org instance numbers?
My understanding based on the documentation provided is that MRS seeks federation-enabled, published rooms to index, and is the initiator of the connect. It doesn't expect other servers to reach out to it.

Or would this difference essentially be from servers that decided to disable indexing BUT still federate with matrix.org?
by 0xjnml | 2026-02-20 12:17:41+00:00
> I wonder what the thought process of the Go designers was when coming up with that approach.

Sometimes we need block scoped cleanup, other times we need the function one.

You can turn the function scoped defer into a block scoped defer in a function literal.

AFAICT, you cannot turn a block scoped defer into the function one.

So I think the choice was obvious - go with the more general(izable) variant. Picking the alternative, which can do only half of the job, would be IMO a mistake.
by rrr_oh_man | 2026-02-20 12:17:55+00:00
> These folks would even claim how AI improves human creativity. Well, has this been the case?

Yes. Example: If you've never programmed in language X, but want to build something in it, you can focus on getting from 0 to 1 instead of being bogged down in the idiosyncrasies of said language.
by gus_massa | 2026-02-20 12:18:29+00:00
It looks like the pads with a salt that can be boiled and stored and later when you click the thing inside the pack the salt will crystallize an release the heat. I think the salt packs are quite stable. (Sodium acetate?)

Anyway, they are using another molecules, that looks very tasty for bacteria and fungus, so I hope they keep it perfectly sealed, otherwise in winter they will only have a nasty green goo.
by ACCount37 | 2026-02-20 12:18:49+00:00
What uses, exactly?

The prototype is: silicon with a Llama 3.1 8B etched into it. Today's 4B models already outperform it.

Token rate in five digits is a major technical flex, but, does anyone really need to run a very dumb model at this speed?

The only things that come to mind that could reap a benefit are: asymmetric exotics like VLA action policies and voice stages for V2V models. Both of which are "small fast low latency model backed by a large smart model", and both depend on model to model comms, which this doesn't demonstrate.

In a way, it's an I/O accelerator rather than an inference engine. At best.
by ljm | 2026-02-20 12:18:59+00:00
It's the difference between someone being a jerk and taking the time and energy to harass and defame someone (where the person themselves is a bottleneck) vs. running an unsupervised agent to carpet bomb the target.

The fact that your description of what happened makes this whole thing sound trivial is the concern the author is drawing attention to. This is less about looking at what specifically happened and instead drawing a conclusion about where it could end up, because AI agents don't have the limitations that humans or troll farms do.
by joha4270 | 2026-02-20 12:19:59+00:00
The guts of a LLM isn't something I'm well versed in, but

> to get the first N tokens sorted, only when the big model and small model diverge do you infer on the big model

suggests there is something I'm unaware of. If you compare the small and big model, don't you have to wait for the big model anyway and then what's the point? I assume I'm missing some detail here, but what?
by varshith17 | 2026-02-20 12:20:02+00:00
"Best place to work" awards are marketing, not data. No company that's actually great needs to advertise it their employees do that for them. When you see these plastered everywhere, it usually means they're compensating for something (turnover, bad Glassdoor reviews, competitive market). At the end of the day, trust employee retention rates over PR campaigns.