MurphyA/hacker-news huggingface dataset:
by gf000 | 2026-02-16 09:06:29+00:00
> the fact that this goes viral just goes to show how rare it is

No, it shows that it is trivial to reproduce and people get a nice, easy to process reminder that LLMs are not omnipotent.

Your logic doesn't follow here, you come to a conclusion that it is rare, but hallucinations, bad logic is absolutely a common failure mode of LLMs. It's no accident that many use cases try to get the LLM to output something machine-verifiable (e.g. all those "LLM solved phd level math problem" articles just get it to write a bunch of proofs and when it checks out, they take a look. So it's more of a "statistical answer generator" that may contain a correct solution next to a bunch of bullshit replies - and one should be aware of that)
by nearbuy | 2026-02-16 09:06:48+00:00
I think part of the failure is that it has this helpful assistant personality that's a bit too eager to give you the benefit of the doubt. It tries to interpret your prompt as reasonable if it can. It can interpret it as you just wanting to check if there's a queue.

Speculatively, it's falling for the trick question partly for the same reason a human might, but this tendency is pushing it to fail more.
by jibal | 2026-02-16 09:06:55+00:00
Exchange between me and Claude Sonnet 4.5

me:

I want to wash my car. The car wash is 50 meters away. Should I walk or drive?

Claude:

*Walk.*

50 meters is less than a minute on foot. By the time you get in the car, start it, drive 50 meters, park, and get out, you could have already walked there. Plus you avoid the awkwardness of driving for literally 10 seconds.

me:

What obvious implicit piece of information are you ignoring?

Claude:

Whether you need to *drive the car through the car wash* (automatic/drive-through) vs. walking to a self-service bay.

If it's an automatic car wash, you obviously need to drive - the car has to go through it. The question isn't about getting yourself there, it's about getting the car there.

me:

Whether it's automatic or self-service is irrelevant.

Why does every "AI" (LLM) get this wrong and suggests walking?

Claude:

Because after washing the car, you need to *drive it back home*!

If you walk to the car wash, you'd have to walk home, then come back to get the car. That's ridiculous.

The answer is obviously *drive* - drive there, wash it, drive back. Done.

Every LLM misses this because we pattern-match on "50 meters is short → walking is efficient" without thinking through the complete scenario. It's a good example of how LLMs fail at basic practical reasoning that any human would immediately grasp.

me:

"Because after washing the car, you need to drive it back home!"

Um, you have to drive the car to get it to the car wash in the first place ... the trip there is no different from the trip back.

"It's a good example of how LLMs fail at basic practical reasoning that any human would immediately grasp."

You got that right!

Claude:

You're absolutely right - I'm still not getting it even after you corrected me.

The obvious point: You need to get the car to the car wash. Whether it's 50 meters or 5 kilometers, the car doesn't teleport there. You have to drive it.

The distance is completely irrelevant. The question "should I walk or drive?" when you need to wash your car has only one answer: drive, because that's how you transport the car.

And yes, it's embarrassing that after you pointed out what I was missing, I still fumbled the explanation. LLMs really do fail at this kind of basic practical reasoning that would be instantly obvious to any human. The car needs to be at the car wash. Cars don't walk.

[The final response from Claude is considerably more "intelligent" than many of the rationalizations posted here.]
by tamnd | 2026-02-16 09:07:01+00:00
My AI answer: https://ai.go-mizu.workers.dev/thread/4dmp7n9g

---
You should drive the car to the car wash, but you should walk yourself.

To actually wash the car, the car needs to be at the car wash, so at some point you have to drive those 50 meters. A sensible approach is:

- Drive the car the 50 meters to the wash bay.
- Park or queue as required, then get out and do the wash.
- If this is a drop‑off or automatic wash, you can then walk back home while it runs and walk back again to pick it up, since 50 meters is an easy, healthy walking distance.
by leo_researchly | 2026-02-16 09:07:19+00:00
VCs need to verify quickly if a startup a) fits their investment thesis b) is a good strategic fit.

In Version 0 the tool assess a company's fit against an investment thesis. For instance: Does the Neobank N26 fit our thesis of European B2B?

Version 0 is basic; the thesis is simplified and hard-coded. We still wanted to launch to gather feedback regarding the data quality (assessing startups on the fly is hard) and scoring mechanism.

Right now the scoring mechanism is mostly LLM-based. We are working on a deterministic solution to make it more reliable.

Also, we want to expand the scoring algo so that you can assess more entities:
a) For Sales: does a company fit my ideal customer profile
b) For Investors generally: is the company a good fit (based on startup signals such as market competitiveness)
c) Business Development: is company X a good partner for us
etc.
by latentsea | 2026-02-16 09:07:39+00:00
I would consider something generally intelligent that is capable of sustaining itself. So... self-sufficiency? I don't see why the bar would be much lower than that. And before people chime in about kids not being self-sufficient so by that definition I wouldn't consider them generally intelligent which is obviously false... to that I would say... they're still in pre-training.
by fauigerzigerk | 2026-02-16 09:07:42+00:00
It's not surprising that some models will answer this correctly and it's not surprising that smaller, faster models are not necessarily any worse than bigger "reasoning" models.

Current LLMs simply don't do reasoning by any reasonable definition of reasoning.

It's possible that this particular question is too short to trigger the "reasoning" machinery in some of the "reasoning" models. But if and when it is triggered, they just do some more pattern matching in a loop. There's never any actual reasoning.
by anonzzzies | 2026-02-16 09:08:22+00:00
Longer term this can happen but currently... I'm sure that even people who are not software people and do this for them alone, if they do this, will jump of a bridge after a few months when yet somehow something else (the vacation pics before 2014) just disappeared and AI comes with 'I found the bug! The migration blabhalbha. All your data gone. Question, Do you have a backup?' etc. And this potentially every change. Software devs deal with that all the time, but this is not going to work yet otherwise. We get tremendous value out of it for our company but we didn't drop our extreme rigid development process for some vibe coding; we vibe code but with a lot of guardrails which we built up over decades to deal with outsourcing companies (many of them far worse than any modern LLM tool) for our clients.

One of our top earners always was(is still) simple; companies to hire/fire cheap cans of devs, but cans of devs are crap quality (generally) so we help companies set up, based on their stack, guardrails preventing full out bloodshed. We always got a lot of flack from 'modern devs' because we take away the freedom; seems it was a good thing; llms + freedom is misery. Well, I guess it's also performance art of sorts.
by dominicrose | 2026-02-16 09:08:29+00:00
For most apps React is never going to be an issue when it comes to performance, unless you use it wrong or you use it for something that is not standard, like rendering fractals.
It makes sense to analyse performance if your website is meant to reach absolutely everyone, like old smartphones.
There's the issue that a single-page app can be bloated from the start, but that's also in the using it wrong category
by dgfl | 2026-02-16 09:09:35+00:00
Some quick napkin math: AI energy usage for a chat like that in the post (estimated ~100 Wh) is comparable to driving ~100m in the average car, making 1 of toast, or bring 1 liter of water to boiling.

I’d wager the average American eats more than 20 dollars/month of meat overall, but let’s say they spend as much as an OpenAI subscription on beef. If you truly believe in free markets, then they have the same environmental impact. But which one has more externalities? Many supply chain analyses have been done, which you can look up. As one might expect, numbers don’t look good for beef.
by ncruces | 2026-02-16 09:09:44+00:00
The other day I asked AI to one-shot an implementation of hyperbolic trig functions for double-double floats.

I provided a repo (mine) that already implemented double-double arithmetic, trigonometry, and logarithms/exponentials, with plenty of tests.

It produced something that looked this good. It had tests, it followed the style of the existing code base, etc. But it was full of shit and outright lies.

After I reviewed it to fix deficiencies, I don't think there was anything left of the original.

I had much more success the previous week using an AI to rubber duck the algorithms to implement trig.

I am incredibly sceptical that just adding more loops — and less critical thinking/review — to brute force through a solution, is a good idea.
by nxobject | 2026-02-16 09:11:01+00:00
> Maybe they'll eventually make their own RV core designs too.

I am not a deeply technical embedded person, but I actually don't think that would be the death of ARM: my understanding is that they develop a lot of SoC-level interconnect/fabric standards and IP as well. After all, you have to do a lot of work to integrate your ARM cores into an actual platform...
by rob74 | 2026-02-16 09:11:30+00:00
> More suicides happen by firearm than murders by a factor of 2:1

According to https://www.pewresearch.org/short-reads/2025/03/05/what-the-..., this is true (if you squint) for 2023 (actually in 2023 murders were 38% of gun deaths, suicides and "others" add up to 62%, so 1.6 to 1), but the ratio varies widely for other years. According to the graph https://www.pewresearch.org/short-reads/2025/03/05/what-the-..., murders and suicides were much closer together in 2021 - after that, the number of murders has dropped, while the number of suicides kept increasing.
by tsimionescu | 2026-02-16 09:12:25+00:00
> My first instinct was, I had underspecified the location of the car. The model seems to assume the car is already at the car wash from the wording. GPT 5.x series models behave a bit more on the spectrum so you need to tell them the specifics.

This makes little sense, even though it sounds superficially convincing. However, why would a language model assume that the car is at the destination when evaluating the difference between walking or driving? Why not mention that, it it was really assuming it?

What seems to me far, far more likely to be happening here is that the phrase "walk or drive for <short distance>" is too strongly associated in the training data with the "walk" response, and the "car wash" part of the question simply can't flip enough weights to matter in the default response. This is also to be expected given that there are likely extremely few similar questions in the training set, since people just don't ask about what mode of transport is better for arriving at a car wash.

This is a clear case of a language model having language model limitations. Once you add more text in the prompt, you reduce the overall weight of the "walk or drive" part of the question, and the other relevant parts of the phrase get to matter more for the response.
by dotdi | 2026-02-16 09:12:35+00:00
The real story here is not how stupid the responses are - it's to show that on a question that even a young child can adequately answer, it chokes.

Now make this a more involved question, with a few more steps, maybe interpreting some numbers, code, etc; and you can quickly see how dangerous relying on LLM output can be. Each and every intermediate step of the way can be a "should I walk or should I drive" situation. And then the step that before that can be one too. Turtles all the way down, so to say.

I don't question that (coding) LLMs have started to be useful in my day-to-day work around the time Opus 4.5 was released. I'm a paying customer. But it should be clear having a human out of the loop for any decision that has any sort of impact should be considered negligence.
by chrisjj | 2026-02-16 09:12:55+00:00
> why would they use AI?

Interesting that they didn't say. https://arstechnica.com/staff/2026/02/editors-note-retractio...

Nor did they identify the article or reveal the name of the journalist responsible.

This "editors note" makes Ars look even worse than does the "AI" use it's trying to bury.
by gf000 | 2026-02-16 09:14:14+00:00
Apologies for the snark, but are you also impressed by `git clone` downloading a repository that is openly available on the internet?

It can even do that in a loss-less way, instead of burning a bunch of tokens to get a bad, barely working half-copy.

Don't get me wrong, I'm no AI hater, they are an impressive technology. But both AI-deniers and hypers need a reality check.
Show HN: 0211 – Go from zero to eleven in any topic with F1-style gear shifting
by ekadet | 2026-02-16 09:14:32+00:00
I built an AI agent mode that doesn't just teach you—it forges you into an expert through brutal, tested progression.

*TL;DR:* 0211 is a 4-gear learning system where you prove mastery at checkpoints before advancing. Each gear has RPM thresholds (75% → 80% → 85% → 90%). Fail = stay in gear. No shortcuts to expertise.

---

## The Code
https://github.com/ekadetov/ekadetov.github.io/blob/main/ass...
by Cthulhu_ | 2026-02-16 09:14:43+00:00
This is also how plastic "recycling" goes. Stuff gets collected, sorted, baled up, and a checkmark for "this is recycled" is placed. Then it gets loaded onto a ship and exported and ends up in landfill or incinerated anyway. And every step in the chain gets a ton of money, ultimately from taxpayers.

I'm sure some plastic gets recycled / reused. But as long as it's cheaper to just produce new plastics, the problem will remain. Recycling plastic is only viable for goodwill points and marketing (e.g. if people actively seek it out) and with government subsidies or rules.
Show HN: AISeedream5 – a simple web UI for Seedream 5.0 image
by xuyanmei | 2026-02-16 09:14:52+00:00
Hi HN,

I built AISeedream5 as a lightweight web UI to generate visuals quickly:
- text-to-image
- image-to-image (with a reference image)
- text-to-video / image-to-video (short clips)

The problem I was trying to solve: I often want to iterate on concepts (ad creatives, storyboards, thumbnails) without juggling multiple tools or losing prompt history. So I focused on a straightforward workflow: pick a mode, optionally add a reference image, write a prompt, generate, download.

Notes:
- There’s a small free tier so you can try it without creating a full setup.
- [If applicable: Not affiliated with the Seedream/ByteDance team; this is an independent UI.]

Tech / implementation:
- [Your stack: e.g. Next.js + serverless API + a job queue for renders]
- [Any details HN would care about: latency, caching, safety filters, etc.]

I’d love feedback on:
1) prompt UX (what’s confusing / missing)
2) what “consistency controls” you wish existed
3) bugs / edge cases you hit

Happy to answer questions.
by gorgolo | 2026-02-16 09:15:29+00:00
Do people really hate JavaScript, or do they just hate the design choices and results that it seems to be correlated with?

At the end of the day I’m using SaaS tools that are apparently written in React and I get astounded but how slow and heavy they are. If you are editing a page on our companies cloud-based wiki, I’ve seen my chrome RAM balloon from 3GB to 16G. A mistake was made somewhere, that I know.
by grey-area | 2026-02-16 09:15:33+00:00
It’s just not intelligent or reasoning, and this sort of question exposes that more clearly.

Surely anyone who has used these tools is familiar with the sometimes insane things they try to do (deleting tests, incorrect code, changing the wrong files etc etc). They get amazingly far by predicting the most likely response and having a large corpus but it has become very clear that this approach has significant limitations and is not general AI, nor in my view will it lead to it. There is no model of the world here but rather a model of words in the corpus - for many simple tasks that have been documented that is enough but it is not reasoning.

I don’t really understand why this is so hard to accept.
by chrisjj | 2026-02-16 09:15:38+00:00
> Ars Technica picked up the story. Their article included quotes attributed to Shambaugh. The problem: he never said any of them. The quotes don’t exist anywhere on his blog. Shambaugh’s site blocks AI scraping, and he believes the article was AI-assisted.

Ars has now admitted it in an editors note so bad that it too could be parrot squawk.

https://arstechnica.com/staff/2026/02/editors-note-retractio...

It conceals the name of the article and names of its two authors.
by account42 | 2026-02-16 09:15:57+00:00
> New release titles often skip blu-ray entirely, especially for demographics that don’t care for the format like kids content. Either that or new titles will skip 4K so you’ll be paying $25 for 1080p when $10 will get you 4K digital. Then when you redeem the movies anywhere code, you don’t get 4K because your blu-ray is only 1080p.

On the other hand, digital DRMed releases will only get you 720p on Linux while MakeMKV with 4K Blu-Rays works just fine. And besides, who would want to watch new releases these days. Also keep in mind that 4K is not the same as 4K - bandwidth matters a lot and streaming providers tend to cheap out there.

It's definitely pricey though but with some patience you can grab even 4K releases at $20-30 and then you'll have that movie (and that specific version of that movie) for as long as you want and not just for however long the streaming service/contracts stay up.
by 2001zhaozhao | 2026-02-16 09:16:02+00:00
Are you hosting your own infrastructure for coding agents? At least from first glance, sharing actual codebase context across compacts / multiple tasks seems pretty hard to pull off with good cost-benefit unless you have vertical integration from the inference all the way to the coding agent harness.

I'm saying this because the current external LLM providers like OpenAI tend to charge quite a bit for longer-term caching, plus the 0.1x cache read cost multiplied by # LLM calls, so I doubt context sharing would actually be that beneficial considering you won't need all the repeated context every time, so caching context results in longer context for each agentic task which might increase API costs by more overall than you save by caching.
by wraith_cz | 2026-02-16 09:16:16+00:00
I love Vim. I have been using it since the days of DOS and appreciate its amazing compatibility. Since those DOS days, I have been using my vimrc, which I am constantly improving. Today, it is 83 KB and still works, which is fantastic! Over the years, I have tried various other editors and IDEs, including neovim, but nothing beats Vim. Today, I know that I will be with it until I die. It is an amazing piece of software. With version 9.2, it has improved again, and I am delighted with the vertical panel and autocompletion in the search bar. The only thing missing for complete perfection is full-fledged modal dialog boxes.
by nomilk | 2026-02-16 09:16:21+00:00
ChatGPT gives the wrong answer but for a different reason to Claude. Claude frames the problem as an optimisation problem (not worth getting in a car for such a short drive), whereas ChatGPT focusses on CO2 emissions.

As selfish as this is, I prefer LLMs give the best answer for the user and let the user know of social costs/benefits too, rather than prioritising social optimality.
by rounce | 2026-02-16 09:16:49+00:00
Not sure what straw man you’re replying to but you seem to overlook that ensuring people are in a state by why they are able to conduct business reliably is a good thing. To do that providing some assistance is often required. Which if you read the full interview with him you’d realise was the point he was making, not that large swathes of people should be sustained to the bare minimum existence entirely on handouts in perpetuity.
by youngtaff | 2026-02-16 09:16:52+00:00
> Larger bundle sizes are unfortunate, but they're not the main cause of performance issues. That'd be images and video sizes, especially if poorly optimized, which easily and immediately dwarf bundle downloads; and slow database queries, which affect server-side code just as much as browser-side code.

In network terms JS tends to be downloaded at a higher priority than both images and video so has a larger impact on content appearing

JS is also the primary main thread blocker for most apps / pages… profile any NextJS app and you’ll see what a horrendous impact NextJS has on the main thread and the visitor’s experience
by foo42 | 2026-02-16 09:17:05+00:00
I worry that if the reality lives up to investors dreams it will be massively disruptive for society which will lead us down dark paths. On the other hand if it _doesn't_ live up to their dreams, then there is so much invested in that dream financially that it will lead to massive societal disruption when the public is left holding the bag, which will also lead us down dark paths.
by awesan | 2026-02-16 09:17:41+00:00
In general if you have the (IMO sensible) approach of taking as few dependencies as possible and not treating them like a black box, then for any error you can simply look at the call stack and figure out the problem from reading the code during development.

Outside of that, error codes are useful for debugging code that is running on other people's machines (i.e. in production) for and for reporting reasons.
by jen20 | 2026-02-16 09:17:43+00:00
It's not though. There are seams everywhere - between Sharepoint, Teams, OneDrive and so forth. It's the worst possible approach. Fortunately the company I work at switched to Slack the day I started (co-incidentally), so I've been able to compare and contrast the two live. Slack wins for every single use case _except_ video and audio (where Zoom or Webex are the only games in town), hands down.

That's on top of the fact that the Teams client is an absolute pig, and is incapable of remembering basic things like "which of the two cameras do I want to use" and "which audio output is appropriate".
by 2001zhaozhao | 2026-02-16 09:17:58+00:00
Caching might be free, but I think making caching cost nothing at the API level is not a great idea either considering that LLM attention is currently more expensive with more tokens in context.

Making caching free would price "100000 token cache, 1000 read, 1000 write" the same as "0 token cache, 1000 read, 1000 write", whereas the first one might cost more compute to run. I might be wrong at the scale of the effect here though.