MurphyA/hacker-news huggingface dataset:
by goodmythical | 2026-02-26 16:58:23+00:00
right, because turning off any number of data centers is going to do anything at all but create massive pressure on researching the efficiency and effectiveness of the models.

There are already designs that do not require massive data centers (or even a particularly good smart phone) to outperform average humans in average tasks.

All you'd accomplish by hobbling the data centers is slow the growth of sloppy models that do vastly more compute than is actually required and encourage the growth of models that travel rather directly from problem to solution.

And, now that I'm typing about it, consider this: The largest computational projects ever in the history of the world did not occur in 1/2/5/10 data centers. Modern projects occur across a vast and growing number of smaller data centers. Shit, a large portion of Netflix and Youtube edge clusters are just a rack or a few racks installed in a pre-existing infrastructure.

I know that the current design of AI focusses on raw time to token and time to response, but consider an AGI that doesn't need to think quickly because it's everywhere all at once. Scrappy botnets often clobber large sophisticated networks. WHy couldn't that be true of a distributed AI especially now that we know that larger models can train cheaper models? A single central model on a few racks could discover truths and roll out intelligence updates to it's end nodes that do the raw processing. This is actually even more realistic for a dystopia. Even the single evil AI in the one data center is going to develop viral infection to control resources that it would not typically have access to and thereby increase it's power beyond it's own existing original physical infrastructure.
by smalltorch | 2026-02-26 16:58:37+00:00
When I started prototyping it's started as full duplex. I was up for the challenge and wanted to understand why this already didn't really exist.

It ended up being awful. A standard real time call your able to interject and talk over someone and this works because of the low latency. The other person can stop talking and the conversation still flows.

The latency from Tor just makes it awkward to the point that you have to almost relearn how to have a conversation since by the time your interjecting and they hear the interjection, a whole 6 seconds may have passed and they may already be on a whole other train of thought. Walkie talkie architecture just forces you to listen and digest the message, think, respond.

There are two layers of encryption here. Tor, which already is encrypting the data, and locally via openssl before transmit.

I have 21 ciphers programed in from the openSSL library. There's a lot more available in the library but these are supposedly the strongest. The cipher used is not secret so while in the call you can see your cipher, and the remote cipher real time.

The authentication is resting entirely in the users lap. It's up to them how to come up with key exchange.

For me, I would be comfortable enough knowing the other side is who I think it is by the simple fact that audio is passing through. Establishing a connection once in person is most ideal.

Because the .onion is derived on device, an attacker can't just forge that. You need also need the private key for a connection to be established.

Let's say an attacker copies the directory from one of the endpoints and has the private key now and can launch tor under your static address. Well, because the additional shared secret is encrypted with another password known only to the user it's useless. The other side will not be receiving forged audio because of this barrier. They may get as far as being in a call, but no audio is going to be played back because the shared secret was successful protected. Even if you pass your message to the attacker, decryption will fail and nothing will pass.

Directly after sending, we run rm -f on $raw_file, $opus_file, $enc_file.

Audio is recived to /audio folder. After audio is decrypted and played we run rm -f $enc_file, $dec_file. There is only a split second that it lives on disk until it's gone.
by Road-Essential9 | 2026-02-26 16:58:48+00:00
> We should spend our energy on the people who can be convinced

They've had a decade and a half to be convinced! They weren't convinced by Trump's easily-disprovable birther nonsense in 2011, his "Mexico is sending their rapists" speech, his "grab 'em by the pussy" recording, etc etc. And that's just in his first term. But they're convinced now?

I don't buy it. And I don't want fascists as my comrades-in-arms. Or even fascist-adjacent people, which IMO is a distinction without a difference. If they truly understood the full consequences of their vote for Trump and actually regretted it, right now they'd be seeking forgiveness from the people they, frankly, victimized. But they're not. That's not an ally I can rely on. That's not a coalition which will last. Even if Trump goes, Trumpism will be around for the foreseeable future. The people who voted for him will vote for the next fascist the first chance they get.

I choose moral clarity, even if it's at the expense of political effectiveness. Maybe that makes me an extremist- so be it. None of the literally thousands of horrible things Trump has done over the years were deal-breakers for Republicans. It was only when gas prices went up 5 cents per-gallon that they started to have misgivings. So forgive me if I ask myself whether they consider Trump's fascism to be a bug, or a feature.

A vote for Trump should never wash off. It should be an embarrassing family secret for generations to come.
by VorpalWay | 2026-02-26 16:58:55+00:00
Which is why you should be careful to never use your actual email in git commits.

When I made a patch to the Linux kernel I did have to use a real email, since you have to send to their mailing list. I used a throwaway email for it, which I have since edited on my mail server config to forward to /dev/null (yes, I'm one of the weirdos still self hosting email in 2026). The amount of spam I got was insane, and not even developer relevant spam.
by ibejoeb | 2026-02-26 16:59:02+00:00
>costs are spread over more people

I'm suggesting that this isn't the actual answer. The thread started with the premise that the city doesn't have enough revenue, and that the way to increase that revenue is to bring in more people who pay more tax. Next, bringing in more people requires more housing, so that requires incentives for developers to displace people residing in SFH so that the can replace those with high density housing. There's a big problem: more people require more services beyond fancy curb cuts, like police, fire, water, electricity, schools, hospitals, etc. That cost that is spread also grows proportionally with the number of people, and you can't ignore that.

On the cost of building roads: there are cement and asphalt plants right in LA city proper, and also in weho and inglewood, among others in the county. LA has a price problem, not a cost problem.

There are more specters, too, which are bound to be political fights. For one, when you dig up a road, there are numerous places that will require displacing very large homeless camps. Now, credit where it's due, LA has shown that it is able to do that sometimes, like around Echo Park, which is the junction of several major thoroughfares like glendale blvd and the 101. Still, these are non-trivial projects that take years.
by u1hcw9nx | 2026-02-26 16:59:23+00:00
Apple gets enough. Their need for chips is not growing as high as Nvidia's.

I think you are trying to say that Nvidia has lock-in due its massive reservations and it starves others out. That's partially true real bottleneck currently is CoWoS/CoWoS-L packaging, not the chips. Nvidia is reaping the benefits of being pioneer who helped to make it possible. I'm not sure of the exact numbers, but I think Nvidia has over 60% capacity reservation of TSMC CoWoS.

That kind of lock in is only temporary. Anyone can reserve ahead of time and take the risk of failing to get competitive node and suffer the losses.

ps. Nvidia is paying 50% to 100% premiums over standard prices just to secure "urgent" additional capacity beyond their original reservations. If others want that capacity they can bid.
by kbolino | 2026-02-26 16:59:45+00:00
A business cannot pay a tax with money it doesn't have. Every place that isn't intentionally running private industry into the ground levies corporate income taxes on some form of income less expenses, not gross revenues.

If you want something like how personal income taxes work through withholding, what you actually want is a value-added tax (VAT). Most large business environments with a VAT use it alongside corporate income taxes, not instead of them, though.
by manuelabeledo | 2026-02-26 17:00:03+00:00
> By making these fake images ubiquitous we are forcing people to quickly learn that they can't believe what they see on the internet and tracking down sources and deciding who you trust is critically important.

Has this thought process ever worked in real life? I know plenty of seniors who still believe everything that comes out of Facebook, be AI or not, and before that it was the TV, radio, newspapers, etc.

Most people choose to believe, which is why they have a hard time confronting facts.
Show HN: SkillFortify, a formal verification for AI agent skills
by varunpratap369 | 2026-02-26 17:00:45+00:00
Hi HN,

In January 2026, 1,200 malicious skills infiltrated the OpenClaw agent marketplace
(ClawHavoc campaign). A month later, researchers catalogued 6,487 malicious agent
tools that VirusTotal cannot detect. The first agent-software RCE was assigned
CVE-2026-25253.

The response: a dozen heuristic scanning tools (pattern matching, LLM-as-judge,
YARA rules). They all carry the same caveat: "no findings does not mean no risk."

SkillFortify takes a different approach. Instead of checking for known bad patterns,
it formally verifies what a skill CAN do against what it CLAIMS to do. Five
mathematical theorems guarantee soundness -- if SkillFortify says a skill is safe,
it provably cannot exceed its declared capabilities.

What it does:
- skillfortify scan . -- discover and analyze all skills in a project
- skillfortify verify skill.md -- formally verify against capability declaration
- skillfortify lock -- generate skill-lock.json for reproducible configs
- skillfortify trust skill.md -- compute trust score (provenance + behavior)
- skillfortify sbom -- CycloneDX 1.6 Agent Skill Bill of Materials

Supports Claude Code skills, MCP servers, and OpenClaw manifests.

Evaluated on 540 skills (270 malicious, 270 benign): F1=96.95%, zero false positives.

Paper: [ZENODO_DOI_URL]
Install: pip install skillfortify
Code: https://github.com/varun369/skillfortify

Built as part of the AgentAssert research suite (agentassert.com). Happy to answer
questions about the formal model, threat landscape, or benchmark methodology.
by array_key_first | 2026-02-26 17:01:06+00:00
Part of the reason programs use so much memory is because of optimization, but of a different kind. Memory is fast-ish, so if you know or think that you will require X Y Z anyway then just load it in RAM. And, if you think you might need it later, don't bother unloading it. Just keep it around.

Garbage collectors also use similar strategies. Collecting garbage is expensive, so just don't until you need to. The extra memory usage in this case isn't a downside, it's an upside. Your code runs faster.

That's how Java and dotnet are able to achieve insane performance times in some benchmarks, like within 50% of native. They're not collecting garbage, and their allocators are actually faster than malloc.

If you've ever run a Java program at consistent 90% heap usage, you'll notice it absolutely grinds to a halt. I'm talking orders of magnitude slower. Naturally, this isnt highlighted in benchmarks, but it illustrates the power of allocating more memory.
by switchbak | 2026-02-26 17:01:30+00:00
Every local business I deal with is completely lacking on the online side. They might have square POS terminals and all that stuff, but their website either doesn't exist, sucks (not updated in years) or they throw me to Facebook (also sucks).

This is like the last mile for online presence. The average barber out here doesn't use Squarespace, barely knows how to use Facebook and doesn't touch GenAi. But they can still cut your hair pretty well - tech savvyness doesn't have a huge connection to business competence out here.
by tstrimple | 2026-02-26 17:01:31+00:00
This is easily demonstrated to be wrong. California isn't in the top 5 highest. The top 5 being:

1. Louisiana 10.11%
2. Tennessee 9.61%
3. Washington 9.51%
4. Arkansas 9.46%
5. Alabama 9.46%

Crazy how we never hear pithy drops about sales tax in Louisiana. I wonder why that is literally never a talking point in these discussions? Probably a very similarly motivated reason as to why people rant about murder in Chicago but never St. Lois.
by yakattak | 2026-02-26 17:01:38+00:00
> Advertisements were already actively malicious fantasies to exploit the way our brains react to pictures. They're just fantasies that required whole teams of humans doing weird bullshit with lighting and photoshop, and I'm not sure that's much better.

This is a good point. My gut reaction is “well at least someone was paid to do it and can continue to keep society/the economy going ”.

I can see the other side where that’s a soulless job. Not sure what’s worse. Soulless job where your skills apply or even less jobs in a competitive industry.
by hirvi74 | 2026-02-26 17:02:23+00:00
I am torn between Claude and GPT. Though it was recently brought to my attention that I use LLMs in an old-fashioned way [1]. I will say that based on my usages, both models seem very comparable in terms of accuracy and quality. Sometimes one might do things a bit different, but both tend to be more similar than different.

When I am using an LLM for JS, I can't really tell the difference between the two. For C#, I think GPT might produce slightly better quality code, but Claude produces code that seems more modern. I also feel like Claude makes slightly more minor mistakes like forgetting to negate a boolean conditional check.

With Swift, I have found both models to be surprisingly awful. I am not sure if it is because of some of the more recent changes with Swift versions greater than 6.0, but both seem to produce wild results for me.

[1] I do not use Codex CLI nor Claude Code nor any IDE plug-ins. I just type questions into the web app and rarely copy/paste anything back and forth.
by VorpalWay | 2026-02-26 17:03:01+00:00
I self host email, and I have never gotten spam to any email "constructed" from the domain, other than random attempts to things like "accounting@domain.tld" etc.

But the email I used to interact with the Linux kernel mailing list I had to null route after a while, it got so much spam. I used a throwaway for just that purpose of course, so no big deal.
by amiga386 | 2026-02-26 17:03:04+00:00
They say "an open-source developer requests to remove the dependency that links xz to OpenSSH" while showing https://github.com/systemd/systemd/pull/31550 on screen, zoomed and focused so the word "systemd" does not appear.

They never once utter the word "systemd", anywhere in the script... isn't that strange for such a key dependency?
by varunpratap369 | 2026-02-26 17:03:06+00:00
Hi, I'm Varun — the author. A bit of context on why I built this.

I've spent 15 years in enterprise technology as a Solution Architect. When
our teams
started adopting AI agents with third-party skills, I realized we had the
same blind
trust problem that npm had before npm audit existed — except worse,
because agent
skills can execute shell commands, read environment variables, and make
network
requests by design.

After ClawHavoc hit in January, I saw a dozen scanning tools appear in
weeks. All
heuristic. All pattern matching. The leading one literally says in their
docs: "no
findings does not mean no risk." That bothered me.

So I asked: can we do better than heuristics? The answer is yes — formal
analysis
with soundness guarantees. If the analysis says "no violations," the math
proves
the skill cannot exceed its declared capabilities. Not "we checked and
didn't find
anything" — "we proved it can't."

The key insight: I adapted the Dolev-Yao model (1983, originally for
cryptographic
protocol verification) to model attackers in the agent skill supply chain.
Combined
with abstract interpretation over a capability lattice, SAT-based
dependency
resolution, and a trust algebra — you get five provable theorems instead
of five
regex patterns.

Honest about limitations: we miss typosquatting (50% detection — needs
name similarity
module) and dependency confusion (0% — needs registry lookup). These are
v0.2. The
paper documents every gap.

Happy to go deep on any of: the formal model, the benchmark methodology,
why SAT
for dependencies, or the trust score algebra. Ask away.
by josephcsible | 2026-02-26 17:03:45+00:00
> I use a 5 year old phone

I don't, because I care about security updates, and I don't want to have to choose between a highly degraded battery and giving up waterproofing.

> an 11 year old car

Crash safety has improved by leaps and bounds in recent years. I suspect you're more likely to be killed in a car accident that you wouldn't be in a new car, than to be killed by one of the industries that California bans.

> think the US Navy could function just fine with a lot less budget and warships

If a powerful adversary goes to war with us, then we'd want a lot more, and only increasing then would be too late, because we'd lose the war first.
Show HN: OnGarde – Runtime content security proxy for self-hosted AI agents
by antimaterial | 2026-02-26 17:04:08+00:00
Built this because I had heard some horror stories about companies leaking PII from high compliance environments to ChatGPT. I wanted something that would auto-filter any dangerous traffic between my AI agent and the LLM API without requiring code changes in the agent itself.

The filtering list has expanded a bit to include PII, secret keys and I've started a prompt injection library thats being filtered on as well.

The problem: self-hosted agent platforms (OpenClaw, Agent Zero, CrewAI) have no runtime content layer. If your agent leaks an API key, gets prompt injected, or decides to forward someone's SSN to GPT-4, nothing stops it. The platforms don't try to stop it either.

OnGarde is a proxy. You change one line in your config (swap baseUrl) and every request gets scanned before it leaves. Catches credentials, PII, prompt injection, dangerous shell commands. If the scanner fails, it blocks it; never silently passes through.

npx @ongarde/openclaw init handles the OpenClaw setup automatically. Also on PyPI if you're doing something custom.

Dashboard is localhost-only with a SQLite audit log. Nothing phones home.

v1 just shipped: https://github.com/AntimatterEnterprises/ongarde/releases/ta...

I am looking for feedback on this project. Let me hear your thoughts.
by electrosphere | 2026-02-26 17:04:19+00:00
It reminds me of the Star Wars content thats come out recently - before there was the Original Trilogy which we all watched many times and the lines became iconic. Since then it's all become a mismash and blur of mediocrity due to over-exposure.

(except The Mandalorian, and I can't believe I'm using the word "content" :/)

edit: Totally forgot about Andor & Rogue One sorry, great film and two seasons of top-notch storytelling.
by serendip-ml | 2026-02-26 17:04:34+00:00
Hi HN, Karpathy's recent post [1] described Claws as "a new layer on top of LLM agents, taking orchestration, scheduling, context, tool calls to a next level." That's the right framing - but orchestration alone isn't enough. SAIA is the rails layer that makes that orchestration predictable.

[1] https://simonwillison.net/2026/Feb/21/claws/

Instead of prompting an LLM and hoping it does what you meant, the idea is to write in 12 verbs (ASK, VERIFY, CRITIQUE, REFINE, etc.) with typed outputs - each verb returns a dataclass, enforced by JSON schema at the API level. The name comes from SCUMM - the scripting language LucasArts used for Monkey Island. Constrained vocabulary, structured outputs, debuggable behavior.

The bigger goal: agents that actually improve over time. What I've learned building these is that without training, agents plateau quickly. They can remember facts, but they don't get better at their job. So feedback from execution flows into fine-tuning, and the model gets better at the specific task. Not "memory," but real learning.

For that to work, I needed to build multiple layers:
- *llm-saia*: the protocol layer (this post) - rails between Python and LLM
- *llm-infer*: inference server (vLLM, LoRA support)
- *llm-kelt*: feedback collection → fine-tuning pipeline
- *llm-gent*: agent runtime with traits, tools, persistence
- *appinfra*: production Python infrastructure that holds it all together

Everything is open source. Happy to discuss design tradeoffs - the 12-verb constraint is intentionally limiting.

This is v0 - the vocabulary will evolve. If there's prior work I should know about, drop a link.

Open problems worth solving:
- *Determinism*: same input → same output. Current idea: fine-tune models to follow verb contracts reliably.
- *Verification*: how do you prove a verb did what it claimed? Tracing helps, but formal guarantees need real PL exper
- *Composition*: when verbs chain, errors compound. Better error propagation and recovery needed.
by artisin | 2026-02-26 17:04:42+00:00
No matter how you slice this apple, it's going to be lopsided. The real problem is that true learning is hard, ugly work, and the vast majority of people simply don't have the grit to choose friction over shortcut. Lord knows, I wouldn't have put in the grinding effort to learn how to code if I could have just bot-it.
To make matters worse, you have advertisers masquerading as influencers pushing the notion: if you're not shipping 10 features a day, you'll be left behind. It's a perfect damned if you do, damned if you don't situation.
by hliyan | 2026-02-26 17:05:06+00:00
I've come to a similar conclusion. I now almost exclusively post under my real name online, and before writing something, I ask myself whether it's something I'd say to a person's face and whether I'm comfortable being quoted on it. If not, I look for a more neutral, stronger version of the argument I'm trying to make (stronger, as in strong enough to stand without rhetorical devices or fallacies), or, I qualify the statement as an opinion or something I consider to be a possibility.
by TrueGeek | 2026-02-26 17:05:07+00:00
> What was your final motivator to depart

Our main motivator was that we watched our grandparents grow older and just stop moving. I absolutely love America, but you do spend the majority of your time sitting. I'm not inactive, I do ultras, I hike, etc. But that doesn't make up for the fact that we're just always sitting - at home, at work, in the car. We wanted to live somewhere that would force us out of that.

> Were ya'll's companies already established

I had an LLC I used for contracting in the US, but the DAFT visa required setting up a new Dutch corporation.

> Citizens, yet

We're currently applying for our first visa renewal. Next time we'll be elligible for PR. Citizenship would require us to give up our US citizenship and I don't want to do that.

> is living in Maastricht similar to living in a US state panhandle

It kinda is. We don't have a car, but we do have neighbors that only grocery shop in Germany because it's cheaper.

> What drew ya'll south (V.e.g: Amsterdam)?

I just couldn't imagine living anywhere so flat and so far from forests / mountains. We still don't have proper forests in Maastricht, but I can be in the forests of Belgium or Germany in 20 minutes.

> Any suggestions/websites for a single electrician that's been thinking about DAFT for over a decade?

No websites, but just as someone who has tried to hire an electrician in both countries it seems like the shortage is greater here. It really seems like you could just come over and have a lot of work.