MurphyA/hacker-news huggingface dataset:
by 1313ed01 | 2025-11-04 15:18:29+00:00
One thing that bothers me about extreme bloat is that I do not want every app to push against the limit of what my computer can handle. It's fine if some game or compiler or such complex software do that every now and then. A common pro-bloat argument is that software should make use of all the hardware instead of leaving it idle, and that makes sense in some contexts. But 99% of the time I would prefer smaller things that I could have lots of and never have to worry about disk or RAM use.
by jjice | 2025-11-04 15:18:56+00:00
The cost discussion on LIDAR always confused a layman like me. How much more expensive is it that it seemed like such a splurge? LIDAR seems to be the only thing that could make sense to me. The fact Tesla does it with only cameras (please correct me understanding if I'm wrong) never made sense to me. The benefits of LIDAR seem huge and I'd assume they'd just become more cost effective over time if the tech became more high in demand.

I'm _way_ out of my depth though.
by srid68 | 2025-11-04 15:19:00+00:00
I am building two major Open Source Projects using AI one of which i have already published https://srid68.github.io/Arshu.Assembler/ and another one will publish in two weeks time which is OCIServer. If you interested, we can collaborate. contact details in my profile. I am looking forward to implement the same abstractions/projects in other languages like clojure, lua, haskel, c, ocaml which will be very interesting.
by achushankar | 2025-11-04 15:19:04+00:00
Fair question. Honest answer:

Monetization: None planned. This is MIT licensed and I'm starting a new job tomorrow, so I won't have time to develop it commercially.

Why trust it exists in 6 months? You probably shouldn't rely on me maintaining it actively. That's why it's MIT licensed - anyone can fork and maintain it.

AWS Bedrock: Not currently supported. Would love a PR if someone wants to add it!

I'm sharing this more as "here's what I built while learning, maybe it helps someone" rather than "here's a product I'm committed to maintaining."
by b112 | 2025-11-04 15:19:36+00:00
lidar was always going to be impossibly expensive for a consumer product.

Everything is expensive without scale. But lidar will be very cost effective, when scaled to millions upon millions of cars annually.

And with scale, there are reasons to optimise, reduce cost, etc. Large volumes of sales draws more research. Research to reduce cost.

Self driving is a long game. Decades.
by BurritoKing | 2025-11-04 15:19:43+00:00
As a massive advocate of FSD (and someone who's currently running 14.1.4, their very latest FSD build) it is absolutely in no way ready for unsupervised FSD. It still makes silly mistakes, and the latest build is terrified of leaves and will swerve across lane dividers to desperately avoid a leaf blowing into it's path.

I love FSD, I use it for 99% of my driving, and when it's working right it's an incredibly technology that overall makes my driving safer, but there's absolutely areas of weakness that every FSD user knows it cannot be trusted in under any circumstances and you must closely supervise and be ready to take over at any time.
by ableal | 2025-11-04 15:20:18+00:00
> The reasoning, I think, was that humans can drive using sight and a little bit of sound, so an AI should be able to do this too.

If memory serves, a few years ago the official position, on a Karpathy presentation, was that if radar contradicted vision they would have to discard one, so they would stick to vision only.

I could never swallow that argument - seems obvious that a radar failsafe would keep you from making bad vision errors ...
by achushankar | 2025-11-04 15:20:51+00:00
You're absolutely right and I appreciate the honest feedback.

Yes, a lot of this was AI-assisted coding (Claude/Cursor), and I didn't clean up all the patterns. The exception handling is inconsistent, there are useless comments, and the code quality varies.

I'm the first to admit the codebase needs a lot of work. I was learning and experimenting, and it shows.

If you (or anyone) wants to improve it, I'd welcome PRs! The architecture/approach might be useful even if the implementation is rough.

Thanks for looking at the code and giving honest feedback - this is exactly the kind of thing I needed to hear.
Show HN: Refusal-Aware Logical Framework for LLMs
by Utharian | 2025-11-04 15:20:56+00:00
### *BonepokeOS: Refusal-Aware Creative Activation*

*~1500 lines. No dependencies. Copy-paste ready.*

#### *What This Isn't* Another AI safety framework. Another content filter. Another attempt to make LLMs more "helpful" and "harmless."

#### *What This Is* A *cognitive scaffold* that treats contradiction as a feature, not a bug. A system that breaks the "Cohesion Trap" by rewarding structural tension, not surface-level agreement.

# Slightly more concrete demo from bonepoke_core import BonepokeBrain

brain = BonepokeBrain() result = brain.process_story_need( "Explain quantum entanglement using cooking metaphors" ) print(f"β-Score: {result.metrics.contradiction_bleed:.2f}") # 0.0-1.0 scale print(f"Fatigue: {result.metrics.motif_fatigue:.2f}") print(f"State: {result.state}") # 'SALVAGE' = optimal creative tension ```

#### *The Three Metrics That Matter* - *β (Contradiction Bleed)*: How much productive tension exists - *ℰ (Motif Fatigue)*: How repetitive and predictable the output is - *LSC (Local Semantic Coherence)*: Whether it makes sense locally despite global paradox

#### *Why This Exists* Because current LLMs are stuck in a *Cohesion Trap* — they prioritize safe, predictable, culturally-flat outputs. Bonepoke breaks this by making *refusal and tension first-class citizens* in AI cognition.

"Used this to detect autoimmune disease indicators 6 months before clinical diagnosis. Same architecture, different truth anchor."

"Works with any LLM. No fine-tuning. Just cognitive scaffolding that forces better reasoning."

#### *For Researchers* This isn't just an AI tool — it's a *philosophical argument in code*. It demonstrates that: - Creativity requires managed contradiction - Cultural preservation needs narrative tension - The best answers often come from refusing the obvious ones

#### *The Spherical Cow Principle* We simplified the problem to its essence: *Better AI comes from better tension management, not better cohesion.*
by toyg | 2025-11-04 15:21:00+00:00
> Who is using Bluesky in the US?

A lot of writers and creatives who could not stomach X.com anymore (and were then likely burned by Mastodon's geekiness).

> Is it just my friends?

If your friends are in the right-wing sphere (e.g. Joe Rogan listeners, etc), then yeah, likely.
by amelius | 2025-11-04 15:21:04+00:00
> If Tesla wants people to trust them regarding safety, this is not the way to go.

Companies have discovered that trust does not matter. People forget in one or two weeks or so. You can do very bad things and most people will still trust you in the long run. Especially if your offer is a few dollars cheaper than the competition.

The extra sad part is that this will make roads unsafer for informed people too.
by madamelic | 2025-11-04 15:21:15+00:00
Robot Taxis will be competing on price. Whoever can release the lowest cost per mile and most reliable taxi will take lion's share simply because consumers are generally price conscious about transport. Very few will be analyzing the data if two are judged to be 'safe enough', it will come down to price.

Companies like BYD and Tesla are positioned well for that if they can get their AV functionality proven out as both are fully integrated car manufacturers.

Waymo doesn't have in-house manufacturing and is, to my knowledge, purely software so they have lots of vendors along with a relatively low output of vehicles. Their 2025 and 2026 plan is to build 2,500 new cars per year. Each Waymo car currently costs over $100k. Even if Tesla was pushing out Model Ys as their robotaxi platform, they could flood the market very easily in both scale and price per mile _if_ UFSD (unsupervised FSD) was proven.
by cpt_sobel | 2025-11-04 15:21:21+00:00
> Neither can humans. We also just brute force "autocompletion"

I have to disagree here. When you are tasked with dividing 2 big numbers you most certainly don't "autocomplete" (with the sense of finding the most probable next tokens, which is what an LLM does), rather you go through set of steps you have learned. Same as with the strawberry example, you're not throwing guesses until something statistically likely to be correct sticks.
by thiago_fm | 2025-11-04 15:21:25+00:00
That's what I've been saying for years.

Waymo can just add the cameras exactly the way Tesla has, and train based only on that information.

Now it has tons and tons of data, they could gradually remove the Lidar on cities that they've driven over and over again. IF driving without Lidar is worth it... maybe it isn't even worth it and we should pursue using Lidars in order to further reduce accidents.

Meanwhile people use Tesla sporadically in a few spots they consider safe, they will always have data that isn't useful at all, as it can already drive on those spots.

--

Another thing, we can definitely afford to have Lidars on every car, if that would make our cars safer.

Imagine if China does a huge supply chain of Lidars, I bet the cost would be very tiny. And this is supposing there aren't any more automation and productivity gains in the future, which is very unlikely.

Lidar production just doesn't have that big scale, because it's a very tiny market as of now. With scale, those prices would fall like batteries and other hardware have fallen with the years.

TLDR: Tesla lost badly
by achushankar | 2025-11-04 15:21:30+00:00
Excellent point about the security concerns. You're right that the combination of: - Sensitive knowledge access - Tool use/actions - Potential exfiltration

Is a serious concern, especially in enterprise environments.

Currently, this has: - Row-level security in Supabase - API key auth - Rate limiting

But it does NOT have: - Comprehensive audit logging - Fine-grained permission controls - Tool execution sandboxing - Data loss prevention

You're right that this shouldn't be deployed in production with sensitive data without significant security hardening. I should have been clearer about that.

Thanks for the link to Simon's article - very relevant. This is more suited for learning/experimentation than production use with sensitive data right now.

If anyone wants to work on security features, I'd be happy to collaborate on that!
by piokoch | 2025-11-04 15:22:21+00:00
This is already happening. Some time ago all models providers charged $20 a month. Now you still get $20 version, which is crippled, with limits. If you want "a real thing", you need to pay $200. And this might still be too little, as the algo that runs the show is quadratic with the size, which, as every algos books says, is highly inefficient.
by ojr | 2025-11-04 15:22:25+00:00
no I will not replace mega app with platform, WeChat app is totally different from the App Store.

In and Out has 5 menu items, similar to an app made in the USA, not too many features

A Chinese market can list 50 items similar to WeChat that has 50 different features.

The culture is reflected in the app design.

source: https://digitalcreative.cn/blog/how-china-ux-is-different
by notepad0x90 | 2025-11-04 15:22:29+00:00
I think (hah) you're understimating what goes on when living things (even small animals) think. We use auto-compleition for some tasks, but it is a component of what we do.

Let's say your visual system auto-completes some pattern and detects a snake while you're walking, that part is auto-completion. You will probably react by freezing or panicing, that part is not auto-compleition, it is a deterministic algorithm. But then you process the detected object, auto-compleiting again to identify it as just a long cucumber. But again, the classification part is auto-completion. What will you do next? "Hmm, free cucumber, i can cook with it for a meal" and you pick it up. auto-completion is all over that simple decision, but you're using results of auto-completion to derive association (food), check your hunger level (not auto-completion), determine that the food is desirable and safe to eat (some auto-compleition), evalute what other options you have for food (evaluate auto-complete outputs), and then instruct your nervous system to pick it up.

We use auto-compleition all the time as an input, we don't reason using auto-compleition in other words. You can argue that if all your input is from auto-completion (it isn't) then it makes no difference. But we have deterministic reasoning logical systems that evaluate auto-completion outputs. if your cucumber detection identified it as rotten cucumber, then decision that it is not safe to eat is not done by auto-completion but a reasoning logic that is using auto-completion output. You can approximate the level of rot but once you recognize it as rotten, you make decision based on that information. You're not approximating a decision, you're evaluating a simple logic of: if(safe()){eat();}.

Now amp that up to solving very complex problems. what experiments will you run, what theories will you develop, what R&D is required for a solution,etc.. these too are not auto-completions. an LLM would auto complete these and might arrive at the same conclusion most of the time. but our brains are following algorithms we developed and learned over time where an LLM is just expanding on auto-completion but with a lot more data. In contrast, our brains are not trained on all the knowledge available on the public internet, we retain a tiny miniscule of that. we can arrive at similar conclusions as the LLM because we are reasoning and following algorithms matured and perfected over time.

The big take away should be that, as powerful as LLMs are now, if they could reason like we do, they'd dominate us and become unstoppable. Because their auto-completion is many magnitudes better than ours, if they can write new and original code based on an understanding of problem solving algorithms, that would be gen ai.

We can not just add 1 + 1 but prove that the addition operation is correct mathematically. and understand that when you add to a set one more object, the addition operation always increments. We don't approximate that, we always, every single time , increment because we are following an algorithm instead of choosing the most likely correct answer.
by lawnchair | 2025-11-04 15:22:35+00:00
Terrateam | Remote (EU/US timezones) | Full-time | OCaml

Terrateam builds GitOps automation for Terraform and OpenTofu. We help large engineering teams plan, review, and apply infrastructure changes safely, directly from GitHub and GitLab pull/merge requests.

We are looking for a Software Engineer who can move between backend systems, infrastructure, and open source tooling. You'll work on Terrateam's OCaml core, support customers, and contribute to the design of next-generation Terraform orchestration.

You might be a good fit if you:

- Care about correctness and reliability in complex systems

- Have experience with infrastructure automation, CI/CD, or compilers

- Enjoy working in OCaml or another strongly typed functional language

- Value autonomy and precision in engineering

Stack: OCaml, Postgres, Docker

Team: small, remote, and technical

Comp: €90k–€130k depending on experience

We are based in Amsterdam and work async across EU and US time zones. Preference for EU timezones.

Apply: https://terrateam.io/careers
by sebstefan | 2025-11-04 15:23:21+00:00
> To replace the farmer you would have to completely eliminate the entire concept of human ownership that we hold.

Why?

> Economies benefit from its automation. It's far less clear if societies benefit. Farm work is hard, but there is a sense of accomplishment when it is done, which is good for the psyche. The never ending "bullshit" jobs that most people seem to find themselves in nowadays has not lead to happiness.

"Farming is good for the psyche" doesn't hold to the suicide rates.

Automate hard jobs where people kill themselves or destroy their bodies, future generations get jobs that are easier on the body and they get to live healthy longer. It's not rocket science!

Why aren't you a farmer?
by yesco | 2025-11-04 15:23:35+00:00
Your reply doesn't answer my question and seems to imply things I can't understand. Are you suggesting people in Europe simply use the same kind of cheese with everything? I find that hard to believe. Perhaps you have never bought cheese at a deli? There happen to be many kinds.

Your last point is even more confusing, why would the fact that chedder and blue cheese originate from England have anything to do with this? It's like random trivia you interlaced here, it's very strange. I can't seem to grok it.
by _heimdall | 2025-11-04 15:23:41+00:00
I agree with you on the need for definitions.

We spent decades slowly working towards this most recent sprint towards AI without ever landing on definitions of intelligence, consciousness, or sentience. More importantly, we never agreed on a way to recognize those concepts.

I also see those definitions as impossible to nail down though. At best we can approach it like disease - list a number of measurable traits or symptoms we notice, draw a circle around them, and give that circle a name. Then we can presume to know what may cause that specific list of traits or symptoms, but we really won't ever know as the systems are too complex and can never be isolated in a way that we can test parts without having to test the whole.

At the end of the day all we'll ever be able to say is "well it’s doing a thing that looks like thinking, ergo it’s thinking”. That isn't lazy, its acknowledging the limitations of trying to define or measure something that really is a fundamental unknown to us.
by ProllyInfamous | 2025-11-04 15:24:00+00:00
>"Think of how stupid the average person is, then realize that half of them are stupider than that."

—George Carlin (RIP)

I have been discussing both fiction and non-fiction with Perplexity (since early 2023) and Ollama (since early 2025), and what I'm beginning to realize is that most humans really aren't thinking, machines.
by philipallstar | 2025-11-04 15:24:37+00:00
> Waymo on the other hand have always been aiming to build a service business and that changes the model significantly

This isn't the main factor. The main factor is Waymo only does this one thing. Tesla has been: building electric cars and forcing all car makers to do the same; building charging networks; funding and releasing free of charge battery tech research improvements; and doing self driving, and all of it while trying to make a profit to keep running. Waymo is funded by Google, which has infinitely deep ads-spying-on-you pockets. Which is just much, much easier.
by gen220 | 2025-11-04 15:25:04+00:00
In some realpolitik/moral sense, does it matter whether it is actually "thinking", or "conscious", or has "autonomy" / "agency" of its own?

What seems to matter more is if enough people believe that Claude has those things.

If people credibly think AI may have those qualities, it behooves them to treat the AI like any other person they have a mostly-texting relationship with.

Not in a utility-maximizing Pascal's Wager sense, but in a humanist sense. If you think Claude is human-like, and treat Claude poorly, it makes you more likely to treat the humans around you (and yourself) poorly.

Conversely if you're able to have a fulfilling, empathetic relationship with Claude, it might help people form fulfilling, mutually-empathetic relationships with the humans around them. Put the opposite way, treating human-like Claude poorly doesn't seem to help the goal of increasing human welfare.

The implications of this idea are kind of interesting: even if you think AI isn't thinking or conscious or whatever, you should probably still be a fan of "AI welfare" if you're merely a fan of that pesky little thing we call "human flourishing".
by b112 | 2025-11-04 15:25:29+00:00
It's a long term R&D project that's been going on for a decade.

HN: No one does R&D anymore. So sad.

Elon R&D: autopilot didn't work out, so everything Elon is involved in is a scam and never works.

Go ahead and dislike the guy, but nobody on the planet has his track record of success and think big.

And he's succeeded over and over and over and over again.

Frankly I don't know anyone as successful in modern times.

Note that his success does not mean I like him, or not. It is simple reality, data.
by fabian2k | 2025-11-04 15:25:35+00:00
I don't think any meaningful comparison is possible here. The type of driving matters, you have to compare similar driving profiles. And in this case there are actually humans on board. So the real comparison would have to be how many accidents (with Tesla at fault) and how many safety driver interventions that otherwise would have likely resulted in an accident. And we don't have that information.
by skywhopper | 2025-11-04 15:25:51+00:00
I was in my late teens at the time. My memory is that I felt like the tech was definitely going happen in some form, but I rolled my eyes heavily at the idea that AT&T was going to be the company to do make it happen.

If you’re unfamiliar, the phone connectivity situation in the 80s and 90s was messy and piecemeal. AT&T had been broken up in 1982 (see https://www.historyfactory.com/insights/this-month-in-busine...), and most people had a local phone provider and AT&T was the default long-distance provider. MCI and Sprint were becoming real competition for AT&T at the time of these commercials.

Anyway, in 1993 AT&T was still the crusty old monopoly on most people’s minds, and the idea that they were going to be the company to bring any of these ideas to the market was laughable. So the commercials were basically an image play. The only thing most people bought from AT&T was long distance service, and the main threat was customers leaving for MCI and Sprint. The ads memorable for sure, but I don’t think they blew anyone’s mind or made anyone stay with AT&T.
by Workaccount2 | 2025-11-04 15:26:19+00:00
The cost for Waymo is the whole car, so Waymo is in it at ~$100k per operational taxi. They are beholden to hardware manufacturers for their product.

Tesla is trying to get in at ~$20k per operational taxi, with everything made in house.

Assuming Tesla can figure out it's FSD (and convince people it's safe), they could dramatically undercut Waymo on price, while still being profitable. If a Waymo to the airport is $20, and Robotaxi is $5, Tesla will win, regardless of anything else (assuming equal safety).
by ozb | 2025-11-04 15:26:34+00:00
> integers exist

Mostly as an abstraction on top of a continuous wavefunction/quantum field

> Spacetime ends up being discretizable

As far as I know this is speculative and usually assumed by physicists to be false; it's definitely not a required feature of quantum mechanics per se, and as far as I know not of any other well-accepted theory.
by 4kchiefofstaff | 2025-11-04 15:26:41+00:00
Calculate how much a car can drive (200-400k km), then the avg cost of a car (50k vs. 100k) and the avg taxi route (5-30km).

The car itself is a price point of 10 to 40 cent pro km which has impact on the journey for sure but a lot less that it might be the reason.

if you tell me, that i can take the saver car and pay 1 euro more with a 20 euro fair, I wouldn't care.

Nonetheless, economy of scale has happened already at lidar and continues to happen.

If tesla can't get it running properly in bad weather but waymo can, they can also compensate it just by driving at situations were tesla doesn't want to drive.

But hey its just brainstorming at this point as tesla is not close enough to waymo to compare it properly. And while waymo exists, plenty of other companies exist too doing this. Nvidia itself will keep building their car platform which will level the playfield even more.

Whatever market selfdriving cars are, it will be split between everyone and no tesla will not just 'win' this. It will be a race to the bottom for everyone reducing the revenue to a commodity.