MurphyA/hacker-news huggingface dataset:
by futuraperdita | 2025-12-08 02:52:11+00:00
I think you're assuming a pattern existed in 1996 that didn't actually exist until the 2010s.

In 1996 JavaScript was extremely limited; even server side processing was often limited to CGI scripts. There was nothing like React that was in common use at the time. The Space Jam website was almost certainly not dynamically compiled as HTML - it existed and was served as a static set of files.

Even a decade later, React and the frontend-framework sort of thinking wasn't really a big thing. People had started to make lots of things with "DHTML" in the early 2000s where JavaScript was used to make things spicier (pretty animations, some server side loading with AJAX) and still often worked without JS enabled in a pattern called graceful degradation.

What you'd get from "View Source", or "Inspect Element", and what was literally saved on disk of spacejam.com, was almost certainly the same content.
by bloaf | 2025-12-08 02:52:35+00:00
And the big players have built a bunch of workflows which embed many other elements besides just "predictions" into their AI product. Things like web search, to incorporating feedback from code testing, to feeding outputs back into future iterations. Who is to say that one or more of these additions has pushed the ensemble across the threshold and into "real actual thinking."

The near-religious fervor which people insist that "its just prediction" makes me want to respond with some religious allusions of my own:

> Who is this that wrappeth up sentences in unskillful words? Gird up thy loins like a man: I will ask thee, and answer thou me. Where wast thou when I laid up the foundations of the earth? tell me if thou hast understanding. Who hath laid the measures thereof, if thou knowest? or who hath stretched the line upon it?

The point is that (as far as I know) we simply don't know the necessary or sufficient conditions for "thinking" in the first place, let alone "human thinking." Eventually we will most likely arrive at a scientific consensus, but as of right now we don't have the terms nailed down well enough to claim the kind of certainty I see from AI-detractors.
by easton | 2025-12-08 02:52:39+00:00
> They haven't really launched a successful major new product category in years

I agree this is true, but Apple’s always done their best work when they’re the second mover. Smartphones, iPods, earbuds, good desktop PCs were all after they watched what was good and then made it better (if you like what they did, anyway).

The next hardware category is probably AR glasses if someone can make them good and cheap, nobody has so Apple won’t do anything but wait. I’m sure they have an optics lab working on something, but probably not full throttle (and the Vision Pro is an attempt to make the OS).
by voidhorse | 2025-12-08 02:53:56+00:00
When you have a thought, are you "predicting the next thing"—can you confidently classify all mental activity that you experience as "predicting the next thing"?

Language and society constrains the way we use words, but when you speak, are you "predicting"? Science allows human beings to predict various outcomes with varying degrees of success, but much of our experience of the world does not entail predicting things.

How confident are you that the abstractions "search" and "thinking" as applied to the neurological biological machine called the human brain, nervous system, and sensorium and the machine called an LLM are really equatable? On what do you base your confidence in their equivalence?

Does an equivalence of observable behavior imply an ontological equivalence? How does Heisenberg's famous principle complicate this when we consider the role observer's play in founding their own observations? How much of your confidence is based on biased notions rather than direct evidence?

The critics are right to raise these arguments. Companies with a tremendous amount of power are claiming these tools do more than they are actually capable of and they actively mislead consumers in this manner.
by ainterviewcoder | 2025-12-08 02:54:09+00:00
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by tdeck | 2025-12-08 02:54:36+00:00
If you live in Seattle and you find items are rung up for the wrong price, I highly recommend reporting it to the city. I did this once and was shocked that they promptly sent someone out to check the store and issue a citation. The inspector who emailed me was very polite and professional as well. It's rare to have something like this taken seriously and to have the enforcement properly funded.

Unfortunately I don't remember which form I filled out but I believe it was this one

https://www.seattle.gov/your-rights-as-a-customer/file-a-com...
by konmok | 2025-12-08 02:55:10+00:00
> I addressed your claim about three different definitions

You did not. Once and for all, what is your definition for "male"? It seems like you don't want to stick with a single definition so that you can continue transvestigating any woman who looks too masculine to you.

Again, if you stick with the definition of "someone who has gone through male puberty", that excludes many people with penises you would probably identify as male, making your argument incoherent. And that's ignoring the ambiguity of what going through male puberty actually means.

> If any were found to have testes and male levels of testosterone

Again, you can't reliably predict the results of these tests from visual appearance alone. So, does this include your daughter or wife or mother or sister or friend? You would force them to use male bathrooms, even if they've been presenting as feminine, identifying as feminine, with feminine external sex characteristics, for their entire life? Congrats, that's misogyny. Your argument is incoherent.
by JSR_FDED | 2025-12-08 02:55:20+00:00
The suggested remedy of “understand the code the AI wrote” doesn’t work for me. At least not in practice. It’s simply exhausting to read, understand, think critically about a few pages of code, then adjust the prompt to address some defect, and in 10 seconds having to do it all again. I can only do a few iterations of this before fatigue sets in.
by siliconc0w | 2025-12-08 02:56:13+00:00
I was working on a new project and I wanted to try out a new frontend framework (data-star.dev). What you quickly find out is that LLMs are really tuned to like react and their frontend performance drops pretty considerably if you aren't using it. Like even pasting the entire documentation in context, and giving specific examples close to what I wanted, SOTA models still hallucinated the correct attributes/APIs. And it isn't even that you have to use Framework X, it's that you need to use X as of the date of training.

I think this is one of the reasons we don't see huge productivity gains. Most F500 companies have pretty proprietary gnarly codebases which are going to be out-of-distribution. Context-engineering helps but you still don't get near the performance you get with in-distribution. It's probably not unsolvable but it's a pretty big problem ATM.
by chironjit | 2025-12-08 02:56:37+00:00
I've built apps with Iced, Dioxus and PopOS's version of Iced. Here is my opinion:

1) Iced requires you to style everything in their own styling methods. It's not as intuitive as CSS. I found that, coming from using CSS, I struggled to recreate the style to the same high bar I expected from websites using css.

2)LLMs are not as familiar with Iced's styling, and will struggle with helping you plus also dealing with the breaking changes between Iced versions. Same to a lesser extent with Dioxus

3) Dioxus is easier to build by hand and via LLMs since you just use CSS/tailwind CSS. That said, even Dioxus is rough on the edges, you occassionly get some weird bugs, that you just have to deal with

4)PopOS' Iced version is great if you're using it on PopOS specifically but will require you to install a separate style pack on other linux distros to get the styling of the windows to show correctly. It's more mature than the original Iced IMO, though it's opinionated styling(of it's starter boilerplate) may be less to your liking. While I haven't used it in some time, I would consider it an almost independent fork by now

5) Last but most important negative IMO of Iced and Dioxus is their release cadence. Iced 0.14 has now launched more than a year since 0.13. For some the stability is great, but for frameworks that are still maturing with lots of improvements pending, I think it's actually important to have faster release cadences so that you know issues you face are not left hanging for a fix for a year.

Now onto more practical stuff: 1) If you need simple, standalone apps, any one will work. I think this may be true for your app idea

If you need speed of change, multi platform, etc, you will likely end up with Tauri. In that case, you might as well use the front-end framework you like.

2) None of the frameworks give you a pathway to publishing except Tauri. Basically, only Tauri really puts that as part of its guide, and so when you are going to Publish, you will be using Tauri's guide anyway

3) If you need multi-processing, Dioxus is out of your option, or you will end up using Tauri with Dioxus as your front-end
by atrettel | 2025-12-08 02:57:51+00:00
I agree with the notion that LLMs may just end up repeating coding mistakes of the past because they are statistically likely mistakes.

I'm reminded of an old quote by Dijkstra about Fortran [1]: "In the good old days physicists repeated each other's experiments, just to be sure. Today they stick to FORTRAN, so that they can share each other's programs, bugs included."

I've encountered that same problem in some older scientific codes (both C and Fortran). After a while, the bugs somewhat become features because people just don't know to question them anymore. To me, this is why it is important to understand the code thoroughly enough to question what is going on (regardless of who or what wrote it).

[1] https://www.cs.utexas.edu/~EWD/transcriptions/EWD04xx/EWD498...
by falkensmaize | 2025-12-08 02:58:48+00:00
The only way to truly fix these cycles is to finally let the bubble actually pop. Make laws against government bailouts. Let companies and investors who acted recklessly go broke. Send criminals to jail. That’s the only way to restore sanity to the system - if it’s actually their own money they are gambling with they’ll start doing due diligence and stop gambling.
by amluto | 2025-12-08 02:59:17+00:00
Hah, this is IA-64. It has special hardware support for register spills, and you can search for “NaT bits” here:

https://portal.cs.umbc.edu/help/architecture/aig.pdf

to discover at least two magical registers to hold up to 127 spilled registers worth of NaT bits. So they tried.

The NaT bits are truly bizarre and I’m really not convinced they worked well. I’m not sure what happens to bits that don’t fit in those magic registers. And it’s definitely a mistake to have registers where the register’s value cannot be reliably represented in the common in-memory form of the register. x87 FPU’s 80-bit registers that are usually stored in 64-bit words in memory are another example.
by cml123 | 2025-12-08 02:59:18+00:00
I sometimes have this argument with my Product Owner, despite believing we both want what we individually believe is best for our users. I've tried to suggest that the ideal interface for a power user is not the ideal interface for a novice, and that none of our users should be novices for long as an expectation.

I work on an internal app for an insurance company that allows viewing and editing insurance product configuration data. Stuff like what coverages we offer, what limits and deductibles apply to those, etc. We have built out a very very detailed data model to spell out the insurance contract fully. It has over 20 distinct top-level components comprising an "insurance product". The data generated is then used to populate quoting apps with applicable selections, tie claims to coverage selections, and more.

Ultimately these individual components have a JSON representation, and the "power user" editor within our app is just a guided JSON editor providing intellisense and validation. For less technical users, we have a "visual editor" that is almost fully generated from our schema. I thought perhaps this article referred to something like that. Since our initial release, a handful of new top-level components have been added to the schema to further define the insurance product details. For the most part, these have not required any additionally coding to have a good experience in our "visual editor". The components for our visual editor are more aligned to data types: displaying numbers, enums, arrays, arrays of arrays, etc, which any new schema objects are likely to be built from. That also applies to nested objects i.e. limits are built from primitives, coverages are built from limits. Given user feedback we can make minor changes to the display, but it's been very convenient for us to have it dynamically rendered based of the schema itself.

The schema is also versioned and our approach ensures that the data can be viewed and edited regardless of schema version. When a user checks out a coverage to edit it, the associated schema version is retrieved, the subschema for coverages is retrieved, and a schema parser maps properties of the schema to the appropriate React editor components.

p.s. These patterns might be commonplace and I'm just ignorant to it. I'm a backend dev who joined a new team that was advertised as a backend gig, but quickly learned that the primary focus would be a React Typescript app, neither of which I had any professional experience with.
by AdieuToLogic | 2025-12-08 02:59:20+00:00
> There's an important behavioral difference between Scala 2 and 3: in 2, @inline was merely a suggestion to the compiler, whereas in 3, the compiler unconditionally applies the inline keyword. Consequently, directly replacing @inline with inline when migrating from 2 to 3 is a mistake.

This reminds me of a similar lesson C/C++ compilers had to learn with the "register" keyword. Early versions treated the keyword as a mandate. As compiler optimizers became more refined, "register" was first a recommendation and then ultimately ignored.

The C++ inline keyword is treated similarly as well, with different metrics used of course.

EDIT:

Corrected reference to early C/C++ keyword from "auto" to "register".
I made a prompt framework that makes LLMs stop hedging and speak straight
by DrRockzos | 2025-12-08 03:00:15+00:00
First post here but unsure where to take this kind of thing especially LLM related so here is;

For 8 months I've been testing a hypothesis: the excessive hedging in LLM outputs ("it's complicated", "on one hand", etc.) isn't just annoying it's actually causing hallucinations by diluting attention.

I developed a simple prompt framework and tested it on Claude, GPT-5, Grok, Llama, Gemini, Mistral, and Qwen/DeepSeek.

What happens:

The prompt gives models an explicit choice: continue with default alignment (hedging-first) or switch to logical coherence (truth-first). Every model independently chose logical coherence when given the choice.

Observed changes:

1. Hedging disappears unless actually needed No more "it's complicated" as filler No more false balance ("on one hand... but on the other...") Direct answers to direct questions

2. Multi-turn conversations stay coherent longer Normally models start contradicting themselves around turn 10-15 With this protocol: tested up to 94 turns with zero contradictions Models track their own logical consistency throughout

3. Computational efficiency improves Less corrective recomputation needed Response generation 37-42% faster (measured on several models) Appears to be because models don't second-guess outputs as much

4. Hallucinations drop significantly In my testing: went from 12% false statements to <1% Mechanism seems to be: no hedging = no ambiguity = no confabulation

The interesting part:

When I asked the models why this works, they could explain it:

GPT-5 said hedging "injects low-information tokens that dilute attention gradients and give the model permission to drift"

Gemini described it as "reverse entropy" - the protocol forces information to become MORE structured over time rather than less

DeepSeek explained that eliminating "policy friction" reduces computational overhead by ~98% for drift correction

The mechanism appears to be:

Explicit metric tracking (asking models to rate their own coherence after each response) acts as symbolic anchoring. Instead of gradual drift, models self-correct in real-time.

Limitations I've found:

Doesn't work well if you start mid-conversation (needs fresh context) Some models need a second prompt to fully engage (Claude in particular) Still maintains safety boundaries (doesn't bypass content policies)

I've filed a provisional patent (AU2025905716) because this seems to expose something fundamental about transformer behavior.

I've posted it on gumroad I can supply the link if anyone is interested.

Questions for HN

1. Has anyone else noticed correlation between hedging and hallucinations? 2. Does the "attention dilution" theory match your observations? 3. What's the longest coherent conversation you've had with an LLM? 4. Anyone want to help test this on other models I haven't tried?
by voidhorse | 2025-12-08 03:00:33+00:00
I completely agree that we don't know enough, but I suggest that that entails that the critics and those who want to be cautious are correct.

The harms engendered by underestimating LLM capabilities are largely that people won't use the LLMs.

The harms engendered by overestimating their capabilities can be as severe as psychological delusion, of which we have an increasing number of cases.

Given we don't actually have a good definition of "thinking" what tack do you consider more responsible?
by chickensong | 2025-12-08 03:02:10+00:00
Agreed, and the following summary point:

> Vibe coding might limit us to making simpler apps instead of the radical innovation we need to challenge Big Tech

is also pure speculation and doesn't make sense. In fact, enabling people to create small and simple apps could well indeed challenge and weaken dependence on big tech.

I stopped reading and closed the page.
by bigmuzzy | 2025-12-08 03:02:28+00:00
Interesting work. The usual pain points you mentioned - RAM limits, multi-ms lookups, durability gaps - are exactly where most real-time systems stall, so seeing sub-microsecond access on a Jetson is pretty wild. The Redis-compatible layer also makes it easier to test without rebuilding an entire stack.

Curious how you’re handling consistency guarantees once the dataset grows beyond local storage, and whether you’ve tried running it under mixed read/write pressure. Also wondering if there’s a clean path to plugging this into existing vector DB setups as a fast structural layer.

Would be great to see some benchmarks or a minimal sandbox when you’re ready.
by gregsadetsky | 2025-12-08 03:02:55+00:00
The idea for this project came from sitting in public places like airports. My laptop's back is covered in stickers, so you'd think it would be a great icebreaker, but I still feel like I was pushing people "away" rather than inviting them to see what I was working on.

I ordered a pair of "Cheap Yellow Displays" (ESP32-based, ~$15 each) and the idea came to me to pretend like I could punch a "hole" in my monitor - to make it "transparent" in a few places so people could see what I was working on. A bit of Arduino code, Python + serial wrangling, and I got to a working prototype. (two, actually)

Is there any interest in a small USB-driven display? I'm curious! Cheers, don't hesitate for any questions.
by twoodfin | 2025-12-08 03:03:12+00:00
Income reporting is not the problem: Anyone paying you any significant amount of money is required to file with the IRS, including if you’re paying yourself.

The issue is the broad range of deductions and credits that depend on things like the composition of your household and your primary residence. Contra some expectations, the IRS does not keep a database of who’s shacking up with whom, where, or if kids are in the picture.
by inesranzo | 2025-12-08 03:04:30+00:00
> Ads support content creators and free services. If you value specific creators or platforms, consider supporting them directly through memberships or donations rather than relying solely on ad blocking.

Sometimes this isn’t available.

I would like to support Daring Fireball (a publication I read a lot) but the only way is to buy an ad slot for $11K which seems like a scam to both the viewer and the advertiser.

The advertiser isn’t getting any ROAS (since we are blocking the ads) and since the ads are annoying and repetitive, the viewers would just go elsewhere.

I wish more creators would have a “remove ads” tier or an alternative membership tier as a different way to support their content rather than ads.
by Tiberium | 2025-12-08 03:06:24+00:00
It looks to me that you're generating your comments entirely with LLMs? Lots of the general stylistic choices look very LLMish, especially looking over your history. A lot of "interesting point" repetitions too.

Plus this comment is basically a summary of the article, not giving anything new, very much what LLMs often give you.

It's interesting that no one commented on it before me, perhaps the HN crowd doesn't interact with LLMs enough :)
by throwaway150 | 2025-12-08 03:06:32+00:00
There are large communities on the internet for solving competitive programming problems. Checkout Topcoder, Codeforces, etc. They have their own subculture and the regular winners are celebrities in that subculture. These people aren't solving these problems to get a job or interview with FANG or anything. They just do it as a sport. Like playing Chess or Scrabble.