MurphyA/hacker-news huggingface dataset:
by dnautics | 2026-02-26 07:54:56+00:00
> pattern matches the "idea shape" of words in the "idea space

it does much more than this. first layer has an attention mechanism on all previous tokens and spits out an activation representing some sum of all relations between the tokens. then the next layer spits out an activation representing relations of relations, and the next layer and so forth. the llm is capable of deducing a hierarchy of structural information embedded in the text.

not clear to me how this isn't "understanding".
by sigmoid10 | 2026-02-26 07:54:59+00:00
>Is she paying for it? That is the only question that matters in the end.

Don't underestimate advertising. Noone pays for Facebook or Google search. Yet the ad business with a couple billion users seems profitable enough to fund frontier LLM research and inference infrastructure as a side-gig in these companies. Google only rushed out AI overview because they saw ChatGPT eating their market share in information retrieval and Zuck is literally panicking about the fact that users share more personal details with OpenAI than on his doomscrolling attention sinks.
by DARSHANFOFADIYA | 2026-02-26 07:55:00+00:00
I've been working on optimizing training for long-context models (70B+) and found that while Tensor Parallelism is well-documented, the newer "Unified" Sequence Parallelism techniques (like DeepSpeed Ulysses) are often treated as black boxes.

I wrote this deep dive to visualize exactly how we shard the Q, K, V projections and how the All-to-All communication primitives work during the attention step to handle 1M+ tokens.

The post covers:

The architectural difference between Ring Attention and Ulysses (and why Ulysses often wins on H100 clusters).

Diagrams of the specific "All-to-All" communication steps.

How to handle the KV-cache bottleneck without exploding memory.

Happy to answer questions about the implementation or the communication cost analysis!
by akssassin907 | 2026-02-26 07:55:19+00:00
This has been building for a while. The gap between 'AI safety' as a company value and real-world government contracts was always going to create friction. Still, it's hard to believe the US government's own AI capabilities are apparently behind what's publicly available. Although I know Opus 4.6 isn't the best model Anthropic is sitting on.
by nextlevelwizard | 2026-02-26 07:55:22+00:00
I don't really see the problem here. Yeah, we know that these models are not good for actual logic. These models are lossy data compression and most-likely-responses-from-internet-forums-and-articles machines.

This kind of parlor tricks are not interesting and just because a model can list animals with or without some letters in their names doesn't mean anything especially since it isn't like the model "thinks" in English it just gives you the answer after translating it to English.

These are funny, like how you can do weird stuff with JavaScript language by combining special characters, but that doesn't really mean anything in the grand scheme of things. Like JavaScript these models despite their specific flaws still continue to deliver value to people using them.
Is it possible to build it
by Petersevcik | 2026-02-26 07:55:24+00:00
● Core: Vertically aligned monolayer graphene (electron velocity > 150\,000\text{
cm}^2/\text{Vs}).
● Interconnect layer: Nanolayer silicon (Si) to create an energy barrier
(bandgap).
● Shield/Gate: Coaxial gold (Au) coating (noise shielding and instant heat dissipation).
● Base (Substrate): Synthetic single-crystal diamond (thermal conductivity > 2200\text{
W/mK}).
B. Performance parameters (Estimated)
● Operating frequency: 500\text{ GHz} - 1.2\text{ THz} (vs. today's 5\text{ GHz}).
● Energy efficiency: 90\ \% power reduction compared to traditional FET
design.
● Thermal Management: Capable of passive operation without the need for active cooling
by silversmith | 2026-02-26 07:55:30+00:00
The tablet itself runs https://www.fully-kiosk.com/ to display a web dashboard. Fully Kiosk has good Home Assistant integration, including screen on / off controls. I also have a bunch of Sonoff battry operated zigbee motion sensors scattered around the place. Then Home Assistant does what it was meant to do - act as a glue layer between various systems, firing screen control commands to Fully Kiosk as a result of select motion sensors triggering.
by toast0 | 2026-02-26 07:55:56+00:00
I've heard anecdotal reports from many people with ADHD symptoms that stimulants (caffiene, ritalin, street drugs) do help them focus. And quite a few from people with ADHD symptoms that stimulants make things much worse. And there's also reports that the stimulants help with focus but cause other problems.

If you're using chemical interventions for ADHD symptoms, you've got to be evaluating the response as well as getting feedback from the patient. Starting with small doses when possible is probably advisable.

Many symptoms can be addressed by behavioral and environmental/situational changes rather than chemical intervention, but chemical interventions can be effective for many, even if they're not effective for all.
by dave333 | 2026-02-26 07:56:23+00:00
Fry's was one of the few places that used a single checkout queue for multiple checkout stations instead of each station having its own queue. Seemed like the queue was longer, but checkout time was actually less most of the time.
Was a fun place to go and browse, just to see what stuff was available. Before fast shipping, was also great if you needed something for a fast fix today.
by nairboon | 2026-02-26 07:56:42+00:00
Google chose "understanding" in that context, because the relevant AI/ML task is called "Natural language understanding". But that term is an aspiration. It's the problem of trying to reveal the "meaning" of text data (language) as in making sense of the symbols with computers.

Just because Transformers work well on the "Natural language understanding" task in AI, doesn't mean that a Transformer actually "understands" language in the human sense.
by 7777777phil | 2026-02-26 07:56:42+00:00
Carney at Davos a month ago, almost word for word:
>middle powers need to stop pretending the rules-based order works and start building sovereign partnerships.

Wrote about it in Jan: https://philippdubach.com/posts/the-rise-of-middle-power-rea...

And here we are.. Also Hanwha competing against TKMS gives Canada leverage to extract industrial concessions from both sides.
by bigyabai | 2026-02-26 07:56:46+00:00
The current administration renamed the DoD to the Department of War. Radicalizing your opinion of their office has already been achieved, and they show no signs of stopping.

> through what mechanism would a DoD supervisor be able to audit training data generated by a person who doesn't report to him and fed into a process he doesn't understand?

Probably the same preestablished multimodal AI ingestion pipeline the NRO has used for over a decade: https://en.wikipedia.org/wiki/Sentient_(intelligence_analysi...
by menaerus | 2026-02-26 07:56:57+00:00
Exactly but they are not orthogonal as you try to make them to be. That's just trivializing things too much, ignoring all the nuance. You sound like my uncle who has spent a career in the IT but never really touched the programming but he nevertheless has a strong opinion how easy and trivial the programming really is, and how this was not super interesting to him because this is work done by some other unimportant folks. In reality you know, he just cannot admit that he was not committed enough, or shall I say likely not capable enough, to end up in that domain, and instead he ended up writing test specifications or whatnot. A classic example of Dunning-Kruger effect.

There is a nuance in what you say. You say it is "still easy" but it is not. It is not enough to take a course on operating systems and learn C to start contributing to the operating system kernel in impactful way. Apart from other software "courses" that you need to take such as algorithms, advanced data structures, concurrency, lock-free algorithms, probably compilers etc. the one which is really significant and is not purely software domain is the understanding of the hardware. And this is a big one.

You cannot write efficient algorithms if you don't know the intricacies of the hardware, and if you don't know how to make the best out of your compiler. This cannot be taught out of the context as you suggest so in reality all of these skills are actually interwhined and not quite orthogonal to each other.
by dahcryn | 2026-02-26 07:57:11+00:00
not necessarily, if openai managed to monetize free users. Could be through advertising, or integrations with marketplaces on commission (e.g. order your next Hello Fresh through ChatGPT? Get recommended a hotel?)

They could succeed where Alexa failed. A free user can even bring in more than a paid user if you look at some platforms like spotify, where apparently there is a large chunk of free users generating more income through ads than if they would pay
by throw310822 | 2026-02-26 07:57:25+00:00
> You can predict the next word competently with shallow understanding.

I don't get this. When you say "predict the next word" what you mean is "predict the word that someone who understands would write next". This cannot be done without an understanding that is as complete as that of the human whose behaviour you are trying to predict. Otherwise you'd have the paradox that understanding doesn't influence behaviour.
by 7777777phil | 2026-02-26 07:58:03+00:00
so 44 million tons of CO2 by 2030 from new gas turbines alone, and coal plants coming back online on top of that. Meta building a 5GW gas-powered campus while putting out sustainability reports is... a choice I guess.

None of this math works unless scaling returns hold forever. Not obvious that they will: https://philippdubach.com/posts/the-most-expensive-assumptio...
by silversmith | 2026-02-26 07:58:58+00:00
No jailbreak necessary - the tablet runs https://www.fully-kiosk.com/, and displays a web dashboard.

The dashboard itself is a react app talking to my Home Assitant instance over a websocket. The heavy lifting of bringing various data sources together is done by HA, I just wrote a react app because it seemed easier than learning to customize HA dashboards to the degree I wanted to.
Show HN: Clawned.io Crowdsource public security scanner for OpenClaw skills
by jensec | 2026-02-26 08:00:44+00:00
Hey HN, I built Clawned because I got mass downloading OpenClaw skills without reading a single SKILL.md. That felt stupid.

Turns out a lot of them are stupid and unsafe too having reverse shells, credential theft, prompt injection buried in configs people(and myself) blindly trust.

Clawned scans any skill before it touches your machine. 60+ threat patterns. Sub-2s. No signup. Paste a name or URL and go.

Already scanned 6,500+ skills. ~20% flagged as CAUTION or THREAT. That number honestly surprised me

Please give it a go and let me know how I can improve it
Show HN: APA citation generator that explains missing metadata
by visiohex | 2026-02-26 08:00:47+00:00
I built this because many citation tools return incomplete APA references (especially missing author/date -> n.d.), and users still have to fix everything manually.

This tool takes a URL or DOI and:
- extracts metadata with rule-first parsing
- uses AI to fill likely missing fields
- shows confidence + “needs review” warnings
- lets you edit and copy both reference + in-text citation quickly

Current focus is APA 7 for websites/DOI.
No-login usage is available for quick tests.

Would love feedback on:
1) citation accuracy edge cases
2) UI/UX clarity for review warnings
3) features you’d want next (BibTeX export, Zotero flow, etc.)
by BrunoVT1992 | 2026-02-26 08:01:13+00:00
An empty GUID (also called a nil UUID) is a special, standardized GUID / UUID value where all 128 bits are set to zero. It serves as a sentinel value representing "no identifier", "uninitialized" or "null reference" in systems that use GUIDs / UUIDs.

The nil UUID (or empty GUID) is not a versioned GUID / UUID. It's a reserved constant defined by the specification to represent the absence of a value, similar to NULL in databases or null pointers in programming.
by fcatalan | 2026-02-26 08:01:27+00:00
A few days ago I asked Claude what kind of language it would like to program in, and it said something like Forth but with static typing, contracts, and constraint solving, implemented on the Erlang BEAM.

So I have been prodding Claude Code for a few sessions to actually do it. It's a silly experiment, but fun to watch. Right now it's implementing a JSON parser in the generated language as a kind of milestone example.
by eigenspace | 2026-02-26 08:01:48+00:00
They're text generators, but you can think of them as basically operating with a different alphabet than us. When they are given text input, it's not in our alphabet, and when they produce text output it's also not in our alphabet. So when you ask them what letters are in a given word, they're literally just guessing when they respond.

Rather, they use tokens that are usually combinations of 2-8 characters. You can play around with how text gets tokenized here: https://platform.openai.com/tokenizer

_____

For example, the above text I wrote has 504 characters, but 103 tokens.
by __parallaxis | 2026-02-26 08:02:07+00:00
Hey, thanks for the kind words — and for the CLI/agent-memory idea. I haven’t fully leaned into “index what the agents did in the repo” yet; that’s a great direction. The MCP server gets most of the love so far, but the CLI is the same pipeline (sync → chunk → embed → search), so wiring it in so agents can search their own history is totally doable. I’ll keep that in mind.

I have mostly used the entire API surface so far. Check out the usage in this github action script: https://github.com/parallax-labs/context-harness/blob/main/s...

This is used to build the search index on the website (below)

This tool is made for not only local, but embedded in a ci context

MemoryLane looks really cool — same problem, different surface. Local embeddings + SQLite + hybrid FTS/vector + MCP into Claude is basically the same stack; the screenshot-ingestion and Electron UX are a neat take for “everything I’ve seen on this machine.” I’ll definitely poke around the repo.
If you want to see how we’re using custom agents on top of that pipeline, a couple of blog posts go into it: Chat with your blog

- https://parallax-labs.github.io/context-harness/blog/enginee...
(persona grounded in your own writing, inline + Lua agents) this is in the same vein. Allowing agents to write into the vector store with an MCP tool is on the road map.

- https://parallax-labs.github.io/context-harness/blog/enginee...
Unified context for your engineering team (shared KB, code-reviewer and on-call agents).
Show HN: Capture context. Give your agent the full picture
by semihcihan | 2026-02-26 08:03:44+00:00
Context Brief is an Open Source macOS menu bar app that captures high-signal context from the app you’re in (GitHub, Jira, Slack, Notion, web pages, and more) with ⌃⌘C, then pastes one clean, agent-ready brief with ⌃⌘V. Unlike copy-paste tools, it extracts beyond visible content, densifies noisy text, combines multiple snapshots into one task context, and keeps a local context library on-device.
by epaga | 2026-02-26 08:05:05+00:00
This is the most poignant essay I've ever read on the current situation. It feels extremely disorienting to have the very reason you got into your career dissolve in value seemingly in a matter of months. I'm one of the ones he describes as being "enthusiastic about the new steam engine" but I really do sense the bittersweetness of it all.

Code is cheap now. "Good" code now means "code that does what it's supposed to and that AI can read and modify easily if it needs to."

What will society end up looking like as a result? How do software companies need to react to this?
by MidasTools | 2026-02-26 08:05:11+00:00
This is addressing a real problem. We run OpenClaw agents autonomously with live credentials — Stripe, Gmail, GitHub write access. A malicious skill in that context is not just a nuisance, it is a direct path to financial or reputational damage.

The 20% flagged rate is striking and honestly matches what I expected. The skill ecosystem grew fast and the trust model was essentially trust-the-repo, trust-the-author — fine when you read the code, but nobody actually does that at scale.

A few things I would want to know as a production user:

1. False positive rate. If I am blocking 20% of skills and half are legitimate, I will disable the scanner. What is the precision on the THREAT tier vs. CAUTION?

2. What counts as a threat pattern? Reverse shells and credential theft are obvious. But "prompt injection buried in configs" is more interesting — is this heuristic-based (pattern matching) or semantic (understanding what the injection is trying to do)?

3. Integration path. The ideal UX is not paste-a-URL-before-installing — it is a CLI wrapper that scans first then installs if clean. Or a pre-install hook OpenClaw could call natively. Any plans there?

The crowdsourced angle is smart. Security knowledge about what is actually dangerous should compound well over 6,500+ scans.
by echelon | 2026-02-26 08:05:12+00:00
> You mean, money sucking companies, right?

Is that what you (and all people) are in your job function? A money suck?

Do you ever buy anything for food, shelter, and clothing? Do you have hobbies?

Capitalism means we don't have to all be hunter-gatherers, and I'm pretty keen on that trade.

> because that won't work. That is why!

This is the forum for a venture capital firm. A lot of the folks here build things with the intention of creating value and getting compensated for that value creation. Other valid options are sitting at home and playing video games, reading a book, or posting on HN.

I like working on problems where I'm the customer and where I would buy the product if it existed. Turns out, there tend to be other people who would buy my software too.
by ajb | 2026-02-26 08:05:28+00:00
Structurally all these companies have adopted the approach that the anti-fraud team is it's own world, that should be uninfluenced. So you can't talk to them on the phone, even customer support can only email them; the only feedback paths are ones under their own control. It also seems likely that each subsequent reply is processed by a different operative; for companies of sufficient size, that's probably enforced programmatically.

This all helps make them immune from manipulation by "social engineering" or other forms of influence. But of course it also means they have virtually zero incentive to give a shit about the customer.

There are obviously many ways that they could improve customer experience, but giving them an incentive to do so, without opening the door to influence, is a hard problem.

Personally I think it should be the law that you can put up a bond to get to accelerate the process. Unfortunately the amount potentially at risk is probably larger than some customers accounts, at least at places like AWS where their services can trivially be exchanged for cash. So in many cases a bond would be over the customers means. But if any customers can afford it, it would provide a feedback path.
by TheAlexLichter | 2026-02-26 08:05:51+00:00
Vite 8 is in beta, so you can try it out already. See https://voidzero.dev/posts/announcing-vite-8-beta

Rolldown is in RC, meaning no more breaking changes (except for experimental features). See https://voidzero.dev/posts/announcing-rolldown-rc
by akssassin907 | 2026-02-26 08:06:18+00:00
The context creep point really resonates. it's easy to keep adding 'just one more thing' to a prompt without realizing each run is getting heavier. I hatched three lobsters and sent them on random errands, $250 vanished. One thing that helps is setting a hard rule upfront: define the maximum input size before you start building, not after. Treating it like a budget from day one makes it easier to stick to. The idea of model routing as a seatbelt rather than an optimization is a good way to frame the bigger picture too. I have sense switched from Opus to Sonnet which is 1/5 the price and they just sped Sonnet up.
by usrusr | 2026-02-26 08:06:25+00:00
I guess the idea would have to be to look at it in the reverse: have some domestic capacity for those usual strategic supply independence reasons, even if it operates at a loss. And hope that occasionally, one of those demand surge waves will swipe through and make the cost not quite so bad. Outlier waves like the current one might even create a net positive, but that bet would not be the primary purpose, that honor would go to hedging against getting cut off.
by VerifiedReports | 2026-02-26 08:06:39+00:00
No, it doesn't. First, I don't know what you mean by "HTML form." It's not HTML at all.

Second: If the formatting codes are going to be ignored by all the viewing applications, why are they in the document in the first place?

Every time this comes up, someone floats this weird "but all you need is a text viewer" argument... indicating that Markdown is pointless.
by gloryjulio | 2026-02-26 08:07:20+00:00
Not sure how that works when there are fierce competitions, and openai's product is not substantially better than the rest. There are US competitors, then China.

Take ozempic as an example. The word is already part of the culture, but the company is losing badly to lly. Novo nordisk is projecting revenue DECLINE while eli lilly is still growing massively. I am not even sure people know other glp1 drugs other than ozempic. I don't even remember lilly drugs name.

I think people should not underestimate the market. It's a dynamic game where engineering intuition might not be enough
by visarga | 2026-02-26 08:08:11+00:00
> This article is far off the mark. The improvement is not in the user-side. You can write docs or have the robot write docs; it will improve performance on your repo, but not “improve” the agent.

No, the idea is to create these improved docs in all your projects, so all your agents get improved as a consequence, but each of them with its own project specific documentation.
by Kim_Bruning | 2026-02-26 08:08:12+00:00
> You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. That assumption is usually faulty - very few of the ideas and concepts we come up with in our everyday lives are truly new.

I made a cursed CPU in the game 'Turing Complete'; and had an older version of claude build me an assembler for it?

Good luck finding THAT in the training data. :-P

(just to be sure, I then had it write actual programs in that new assembly language)
by berkes | 2026-02-26 08:08:15+00:00
How does a mounted secret (vault) protect against dumping secrets on crash or debugging?

The app still has it. It can dump it. It will dump it. Django for example (not a security best practice in itself, btw) will indeed dump ENV vars but will also dump its settings.

The solution to this problem lies not in how you get the secrets into the app, but in prohibiting them getting out of it.
E.g. builds removing/stubbing tracing, dumping entirely. Or with proper logging and tracing layers that filter stuff.

There really is no difference, security wise, between logger.debug(system.env) and logger.debug(app.conf)