MurphyA/hacker-news huggingface dataset:
by jahsome | 2026-02-24 08:05:45+00:00
There was absolutely nothing backhanded about anything I said. I regret if it came across that way.

I wish you'd have elaborated on specifics and actually tried to understand, rather than telling me what I believe. I can see now you just want to be angry at someone, and I'm no longer interested in engaging with you. In any case, I'm genuinely sorry for whatever I've done to activate you, and I wish you well.
by godelski | 2026-02-24 08:05:46+00:00
> This is like a restaurant that releases a new feature that they will no longer defecate in your food.

Thank god, at least there's one restaurant not serving literal shit.

You're analogy works but you can't forget that there other restaurants. That the other restaurant not only aren't making promises to not defecate in your for but they're actively advertising how much shit they can shove in a sandwich. Even the bread is made of shit!

So thank fucking god. At least there's one place where I don't have to eat shit. The bar is so fucking low it doesn't matter if they spit in it or you find the chef's ball hairs, at least it isn't shit.
by 5o1ecist | 2026-02-24 08:06:06+00:00
This is all a bit unfair. I can't speak about the others, but testing Perplexity like this and comparing her with the others doesn't do her justice.

For Perplexity specifically it matters a lot how you trained her thought processes. A smarter user with smarter thoughts changes the outcome of her output.

As a personal comment ... they've changed Perplexity to GPT5 recently (at least for me) and it has been a massive intellectual downgrade in comparison to the Sonar-Perplexity I had been running with my own, deliberately trainend, thought patterns and thinking processes. It's been only a few days and I hope GPT5 catches up, otherwise it's just a massive disappointment.
by watwut | 2026-02-24 08:06:37+00:00
Not everything is both sides. In fact, both sides is usually wrong and in this case too.

> Manufacturers have a strong argument against right-to-repair from the perspective of system integrity and safety - one can imagine unintended consequences and liability cascades from imperfect repair.

No they dont. This is not even honest argument. There is no liability cascade from bad repair, in normal setup you loose liability when doing own repair. That is it. There is nothing new or obscure about it.

> The authorized dealer model isn't just about a monopoly - it’s about a guaranteed standard of care.

As long as it is not mandatory. The moment you make it mandatory, it is about monopoly.
by verisimi | 2026-02-24 08:07:09+00:00
I would assume it would be not be regulated by government, so without constraints on age, restrictions on what you can do - you know, like reality.

And I know that government attempts to regulate reality too, but if you drive at 35 where the limit is 30, or speak to someone dodgy to get some marijuana or whatever, and get away with these and other heinous crimes, you're good!

The distinction really is whether you bake regulation into the technology or not. And it seems that technology is actually the new legal system. Or perhaps that should be the 'pre-legal system' as it won't allow you to do those things it determines as 'wrong'. Which is absolutely fine if you think government really does know best, or hell on earth for everyone else.
by tosti | 2026-02-24 08:07:16+00:00
I'm not a math expert, but if I want to pre-order the book I can save money and dead trees on the eBook. It's half the price but comes with DRM. I'm not selling my soul and decrypting a book is quite a math problem.

So I'll be downloading this one from Anna and save even more money. I'm poor :(
Ask HN: Agentic search vs. RAG – what's your production experience?
by yamarldfst | 2026-02-24 08:07:29+00:00
There's been a clear shift from 2023's you need RAG to 2026's agentic search outperforms RAG.

We made the switch at Cosmico when building AI-native applications with Claude Code. Agentic search gives us better accuracy but at higher cost per query.

For those running production systems:

1. What prompted your switch (or decision to stick with RAG)?
2. What broke during the transition?
3. What's your hybrid approach (if any)?

Specifically curious about code search vs. document retrieval use cases, and how you handle the latency/cost trade-offs.

Our context: building custom software with AI agents in weeks, not months. Accuracy matters more than speed for our workflow, but that's not universal.

What's working (or not working) for you?
by wonderwhyer | 2026-02-24 08:07:40+00:00
The debate around local vs API vs subscriptions feels mostly anecdotal.
I tried building a tool that compares them using “quality-adjusted tokens per dollar.”

The idea:

Tokens per dollar

Weighted input/output pricing (75/25 assumption)

Benchmark-normalized quality (Arena, Aider, SWE-bench)

Early results surprised me (local often loses economically unless privacy is heavily valued).

I’m mostly looking for critique of the methodology:

Is quality-adjusted tokens per dollar even the right metric?

Is normalizing ELO to % defensible?

What benchmarks am I missing?
by niyikiza | 2026-02-24 08:08:29+00:00
Good distinction, but I wonder if it's worth going further: context integrity may be fundamentally unsolvable. Agents consume untrusted input by design. Trying to guarantee the model won't be tricked seems like the wrong layer to bet on.
What seems more promising is accepting that the model will be tricked and constraining what it can do when that happens. Authorization at the tool boundary, scoped to the task and delegation chain rather than the agent's identity. If a child agent gets compromised, it still can't exceed the authority that was delegated to it. Contain the blast radius instead of trying to prevent the confusion.

(Disclaimer: working on this problem at tenuo.ai)
by mrklol | 2026-02-24 08:08:37+00:00
Firefox mentions the following ones:

"- Translations, which help you browse the web in your preferred language.

- Alt text in PDFs, which add accessibility descriptions to images in PDF pages.

- AI-enhanced tab grouping, which suggests related tabs and group names.

- Link previews, which show key points before you open a link.

- AI chatbot in the sidebar, which lets you use your chosen chatbot as you browse, including options like Anthropic Claude, ChatGPT, Microsoft Copilot, Google Gemini and Le Chat Mistral."
by Borealid | 2026-02-24 08:09:57+00:00
Would you say a game is running at 90fps if, 45 times per socond, two frames are produced, the second of which is a linear interpolation of the frame before and after it?

How about if the two frames are 100% identical?

Does either of these situations differ substantially from what is being discussed, wherein the render pipeline can only produce a new render 45 times per second?
by BrunoVT1992 | 2026-02-24 08:11:24+00:00
GUID / UUID v8 is a flexible, application-defined identifier format that provides a standardized container for custom schemes. Unlike other GUID / UUID versions, v8 does not prescribe how most bits are generated, allowing applications to define their own internal structure while producing valid GUID / UUID strings.

GUID / UUID v8 is designed for advanced use cases where predefined versions (v4 and v7) don't fit. It requires careful design and documentation, as uniqueness and ordering guarantees depend entirely on the application's implementation.
by renegat0x0 | 2026-02-24 08:11:26+00:00
I think the cost and work remains the same. What has change is efficiency. Previously people had to manually program byte after byte. Then came C and streamlined it, allowing faster development.

With python I can write a simple debugging UI server with a few lines.

There are frameworks that allow me to complete certain tasks in hours.

You do not need to program everything from scratch.

The more code, the faster everything gets, since the job is mostly done.

We are accelerating, but we still work 9 to 5 jobs.
Show HN: ImgPakt – Client-side image compressor, nothing leaves the browser
by isuatfurkan | 2026-02-24 08:11:52+00:00
Hey HN,

I built ImgPakt because every image compressor I found uploads files
to a server. TinyPNG, Compressor.io, iLoveIMG — they all require
sending your images over the internet.

ImgPakt compresses images entirely in the browser using Canvas API
and Web Workers. There's no fetch(), no XMLHttpRequest, no upload
endpoint in the code. Files literally cannot leave your device.

Technical details:
- FileReader → offscreen Canvas → toBlob() at target quality
- Web Workers keep the UI thread free during batch processing
- Supports JPEG, PNG, WebP, AVIF
- 5 preset quality profiles instead of a manual slider

Free tier: 5 images per batch, all profiles.
Pro ($3.99/mo): unlimited batch, format conversion, AVIF.

I'd appreciate any feedback on the compression approach or UX.
Happy to answer questions about the architecture.
by kingstnap | 2026-02-24 08:13:05+00:00
I think frame or even multi frame generation combined with Asynchronous Reprojection / Frame Warp might be a very good idea.

https://youtu.be/f8piCZz0p-Y?si=OLq9iZUjuRMYKPDo

If you have never heard of it, the basic idea is that you make low FPS feel responsive in first person games by having the mouse motion warp the existing frame independently of when a new frame is actually rendered.

This could be combined with some AI techniques to help sort out the edge artifacts you get from this.
Show HN: AegisMind Discover – cross-domain hypothesis generation from papers
by aegismind_app | 2026-02-24 08:14:03+00:00
I built a system that reads research papers across unrelated domains and tries to surface hypotheses that neither field would have generated on its own.
The Discover page is where it publishes findings: https://aegismind.app/discoveries
It's very early — only three discoveries so far — but the core idea is what I want feedback on.
The problem it's trying to solve:
Science is siloed. A breakthrough in mycology might have direct implications for network routing. A discovery in chronobiology might reframe how we think about database consistency. Nobody reads across all of it, and even when they do, the connection is usually accidental.
How it works:

The autonomous "Right Brain" service ingests papers across domains continuously
Multiple models (GPT, Claude, Gemini, Mistral, Grok) analyze papers in parallel
A synthesis layer looks for structural or mechanistic similarities across domain boundaries
Hypotheses are only published when they pass a novelty and coherence threshold — it won't surface things either field already knows

What I'm genuinely uncertain about and want feedback on:

How do you evaluate whether a cross-domain hypothesis is actually interesting vs. superficially pattern-matched? This is the hardest part of the novelty filter.
Which domain combinations would you most want to see? (We're currently not curating this — it's whatever the system finds structurally similar.)
Should findings link directly to the source papers?

Only three discoveries published so far, so the page is sparse — but I'd rather share the idea early than wait until it looks polished.
by codeflo | 2026-02-24 08:14:33+00:00
All of the examples on the linked page seem to be "good" outputs. Attribution sounds most useful to me in cases where an LLM produces the typical kind of garbage response: wrong information in the training data, hallucinations, sycophancy, over-eagerly pattern matching to unasked but similar, well-known questions. Can you give an example of a bad output, and show what the attribution tells us?
by godelski | 2026-02-24 08:15:01+00:00
Sure, but then the people complaining would need to recognize that that's not happening. All the AI features are local models. The only thing not local is that side window you can open that can connect to a chatbot. It's also easy to disable.

Honestly, I think people just like complaining. I think they like complaining about Firefox even more. There's plenty to complain about, but aren't there bigger fish to fry right now? Seems like complaining about some minnows while we're being circled by a bunch of great whites
Show HN: VVMList – Vulnerable VMs organized by attack techniques
by jayhowye | 2026-02-24 08:15:18+00:00
Hi HN,

I built VVMList for cybersecurity learners to easily find specific techniques used on specific machines.

A few years ago, I originally created VVMList to track the machines I completed and the techniques I used to solve them. After some time, I semi-abandoned the project.

At the end of 2024, I revisited the idea and decided to expand it into something bigger. More like a structured "ctf bible" for learners who want to study techniques in a more organized way.

For this, I read through many machine writeups from Vulnhub, HackMyVM, VulNyx and HackTheBox. I extracted and categorized the techniques used in each machine.

This process led to VVMList v3. when I first started, the project included around 250 machines. With v3, it grew to over 1200 machines, which allowed me to reach the original scope I had in mind.

In the 2026 annual update (v3.2), I added newly released machines from the platforms, integrated ctfmirror.com (my another project) for all machine images and integrated archive.org for all writeups. The goal was to provide a more centralized solution and reduce the risk of resources disappearing over time.

I'd really appreciate feedback from the community. Does the structure make sense? Are there techniques or machines that you think are missing? Any suggestions to improve usability or make it more useful for learners are very welcome.

Thanks for taking a look.
by gritzko | 2026-02-24 08:15:30+00:00
I was the happy owner of ThinkPad X1 Extreme g1. It had that bug out of the box, new original battery. Once it thermal throttles, it never goes back to full GHz. It throttled pretty soon, cause big CPU small chassis. Yes, I had a script like that.

It is still somewhere on a shelf, so maybe its day will come again.
by henrikschroder | 2026-02-24 08:15:55+00:00
"Fine, I won't connect then!1!!"

What's wrong with you, you're responding to literally the opposite of what I said? You are free to connect, to seek your roots, figure out weird quirky things from the culture of your ancestors, and nurture as much fondness for Ireland and Irishness as you please. No-one in Ireland (Note, I'm not Irish!!) is gonna object to any of that.

The one thing, the ONE FUCKING THING we're asking you not to do is to call yourself Irish, because that will guaranteed piss off everyone you meet in Ireland.

How is this difficult to do or understand? We're asking one thing.

Everything else is up for grabs. You can appropriate as much culture as you please, real, fake, stereotypical, exaggerated, whatever. Grab it, use it, do it, perform it, that's fine. You don't need to excuse yourself or justify yourself or claim ancestry or heritage or anything. Absolutely no-one will gatekeep the culture. Enjoy it, all of it! Do this one thing, and real Irish people will be super happy to share their culture with you.
by adebayoj | 2026-02-24 08:16:01+00:00
Completely agree with all your points!

Here is what this model does: it `rewrites` the model's activations (during pre-training) into supervised + unsupervised concepts that are then decoded into tokens. So at pre-training, we constrained the model with 33k supervised concepts (e.g., sports, toxicity, alignment, demographic variables), and then have more (101k) unsupervised concepts for the model to learn as well.

Overall, the architecture and loss functions of this model allow you to answer the following questions:
1) Which token in the context caused a chunk (group of tokens) to be generated?
2) which high level concept (supervised or unsupervised) caused the
3) perhaps more interestingly, in a single forward pass, we can tell you which training chunk led to the output of the model as well.

We do all of this for the single steerling model which is 8B parameters trained on 1.5T tokens. First time any model of this scale has achieved this level of interpretability by design.

would be happy to answer more questions.
by grey-area | 2026-02-24 08:16:04+00:00
"The lobster gets the attention. The engine gets quietly forked into production."

Is there any way to stop LLMs generating text like this?

Is it really better than just writing it yourself? I guess generating blog posts is lower-effort and thus wins in this attention economy people think they are competing in.

The actual engine is here:
https://github.com/badlogic/pi-mono

An interesting idea to have a bit more control over what your 'agent' is doing and keep it simple. Some of the prompts do give me pause though, why do we talk to text generators as if they are people, have we found this works best, or is it a sort of cargo-cult?

https://github.com/badlogic/pi-mono/blob/main/.pi/prompts/is...

I love that he's telling his tools not to trust people in his comments here!
by adrian_b | 2026-02-24 08:16:16+00:00
The article is about vertebrates, not about animals in general.

The vertebrates originally had at least 3 eyes, 2 lateral eyes and one in the middle of the head.

Most vertebrates of today have lost the median eye, though there are a few species, like tuatara, which still have it.

The research reported in the article has shown evidence that the retina of the lateral eyes of the vertebrates has evolved not from the original retina of those eyes, but from the retina of the middle eye, which had a different structure, i.e. different photoreceptors.
by Fizzadar | 2026-02-24 08:16:39+00:00
All of these agent wrappers/claws/whatever seem to encourage wildly irresponsible usage of AI. Sure it can get stuff done in what is effectively a simple loop but without review we’re just playing with fire.

I was experimenting with Claude code doing similar (don’t need wrappers really) and the result was a huge amount of mediocre code and my weekly limit burnt. The code “works” but oh my the duplication and potential bug surface is off the chart.

Stopped my experiment and back to human in the loop plan->execute cycle which is way more effective.

Thankfully I don’t think we’re at jarvis levels yet.
by krisoft | 2026-02-24 08:16:44+00:00
Theory of mind won’t help you answering this question. It is obviously an underspecified question (at least in any contexts where you are not actively designing/thinking about some specific industrial process). As such theory of mind indicates that the person asking you is either not aware that they are asking an underspecified question, or are out to get you with a trick. In the first case it is better to ask clarifying question. In the second case your choosen answer depend on your temperament. You can play along with them, or answer an intentionally ridiculous answer, or just kick them in the shin to stop them messing with you.

There is nothing “mathematical” about any of this though.
by piokoch | 2026-02-24 08:16:59+00:00
"unintended consequences and liability cascades from imperfect repair" - I would say this is not the greatest argument. If someone messes with the equipment outside official channel, loses equipment guarantee, so there is no liability on the producer side.

If some firmware is buggy and pose cybersecurity issues, hiding that will not help, as sooner or later someone will discover those bugs anyway (and will turn good old John Deere into B-class horror movie serial killer machine).

So I am not buying manufacturers arguments. If they were honest, they would said openly they want to earn money on overpriced service because CEO wants to earn more and stakeholders are after him.
by theshrike79 | 2026-02-24 08:17:00+00:00
Scoped memory is a good thing, I _want_ ChatGPT to cross-reference stuff when I'm in my Homeassistant project so I don't need to keep re-explaining the basic setup every time.

But when I do a one-off question, I don't want it assuming stuff because of something I discussed with it 6 months ago.
by riffraff | 2026-02-24 08:17:33+00:00
> Companies rely on the fact they can hire people who already know photoshop/xero/webpack/etc rather than having to train them from scratch on in house tools.

Yeah, I've seen perfectly good flexible in house products abandoned because it was just easier to hire people who knew Salesforce or whatever.

But the true AI Believer would object you don't need to hire anymore, you can just get more agents to cold call or whatever :)
by mseepgood | 2026-02-24 08:18:10+00:00
> We would immediately build better telescopes to track it precisely, refine its trajectory models, and begin developing propulsion systems capable of interception

That's not what would happen. We wouldn't mobilize. We'd fragment. Within days, the prediction would be declared partisan. One bloc would call it settled science; another would call it statistical hysteria. Billionaires would quietly commission private shelters while publicly funding studies questioning whether the asteroid even qualified as "large." News panels would debate whether the projected impact zone was being unfairly politicized. Conspiracy channels would insist the asteroid was fabricated to justify global governance. Others would insist the real asteroid was being hidden. Amateur analysts would flood the internet with homemade trajectory charts proving the professionals wrong. Death threats would arrive in astronomers' inboxes faster than research grants.
by mort96 | 2026-02-24 08:20:04+00:00
Who has expressed a desire for Firefox to become "an AI browser"?

Because that's the source of the complaints. I don't want to use an "AI browser", kill switch or not. If this "AI browser" dies because of their mission to destroy community goodwill, good. I'm sick of giving the benefit of the doubt every time they royally fuck up. This situation where they're the steward of the only non-Google browser is not tenable, something needs to change.
by Incipient | 2026-02-24 08:21:22+00:00
My understanding is that frame generation uses motion vectors to (slightly?) adjust the scene to produce a "highly plausible" next frame to drop in before the following "real" frame.

I've only seen videos, so from a somewhat unrealistic perspective, it seems like an acceptable compromise for low end hardware in particular.

Boosting 120hz to 240hz admittedly seems silly.
by pjmlp | 2026-02-24 08:21:33+00:00
IBM i (nee AS/400) also uses a database for its lower layers, and even though it isn't that common, database systems like MS SQL Server and Oracle can also be fully used to write applications with Web UIs, e.g. APEX, or mapping Web API endpoints to stored procedures.

They can also make use of raw partitions, thus having a server that is all about the RDMS and nothing else.
by renegat0x0 | 2026-02-24 08:21:35+00:00
Age verification has always been about normalization of it, and about mitigating snow flake blame.

- I always give my children unlocked devices, because I know what they are doing

- Internet is not a safe space, and there will always be means of circumventing protections. Age verifications do not protect anybody

- Parents do not want to 'rise children' they give phones to kids and expects youtube to show kids only good stuff. They expect this from platform
by arkh | 2026-02-24 08:22:04+00:00
But if push come to shove any other engineer can come in and debug your senior engineer code. That's why we insist on people creating easy to change code.

With auto generated code which almost no one will check or debug by hand, you want at least compiler level exactitude. Then changing "the code" is as easy as asking your code generator for new things. If people have to debug its output, then it does not help in making maintainable software unless it also generates "good" code.
by 7777777phil | 2026-02-24 08:22:08+00:00
The token comparison is what jumped at me.. Half the context window for the same work means mainstream tools are burning your tokens on system prompts and MCP plumbing before you even start.

I wrote earlier why the agent stack is splitting into specialized layers, and this is a good example of what drives it. Monolithic tools waste the most on their own overhead. https://philippdubach.com/posts/dont-go-monolithic-the-agent...