MurphyA/hacker-news huggingface dataset:
by nozzlegear | 2026-02-12 03:32:43+00:00
> No one is entitled to come to the US. We are not the world's soup kitchen. You follow the process we the people have decided or you go somewhere else. Period.

I strongly disagree. Everyone is entitled to come to the US, and we should welcome them with open arms. Immigrants built this country and immigrants make it better, whether they're highly qualified programmers and doctors, or refugees from "shithole countries" who had to bribe their way across the border and now work on a dairy. All are welcome! Though please spare me your inevitable "have you let them into your home?!" bullshit, it's a tired argument.

> You alone don't get to decide this, these laws were passed by a democratically elected Congress.

"Conversely, one has a moral responsibility to disobey unjust laws. I would agree with St. Augustine that 'an unjust law is no law at all.'" — Martin Luther King Jr.

“The only obligation which I have a right to assume is to do at any time what I think right.” — Henry David Thoreau

“Law never made men a whit more just; and, by means of their respect for it, even the well-disposed are daily made the agents of injustice.” — Henry David Thoreau

"Give me your tired, your poor, Your huddled masses yearning to breathe free, The wretched refuse of your teeming shore. Send these, the homeless, tempest-tost to me, I lift my lamp beside the golden door!" — The poem engraved on the plaque in the pedestal of the Statue of Liberty
by godelski | 2026-02-12 03:32:50+00:00
IANAL. One thing I like to say is

There is no rule that can be written so precisely that there are no exceptions, including this one.

A joke[0], but one I think people should take seriously. Law would be easy if it weren't for all the edge cases. Most of the things in the world would be easy if it weren't for all the edge cases[1]. This can be seen just by contemplating whatever domain you feel you have achieved mastery over and have worked with for years. You likely don't actually feel you have achieved mastery because you're developed to the point where you know there is so much you don't know[2].

The reason I wouldn't want an LLM judge (or any algorithmic judge) is the same reason I despise bureaucracy. Bureaucracy fucks everything up because it makes the naive assumption that you can figure everything out from a spreadsheet. It is the equivalent of trying to plan a city from the view out of an airplane window. The perspective has some utility, but it is also disconnected from reality.

I'd also say that this feature of the world is part of what created us and made us the way we are. Humans are so successful because of our adaptability. If this wasn't a useful feature we'd have become far more robotic because it would be a much easier thing for biology to optimize. So when people say bureaucracies are dehumanizing, I take it quite literally. There's utility to it, but its utility leads to its overuse and the bias is clear that it is much harder to "de"-implement something than to implement it. We should strongly consider that bias in society when making large decisions like implementing algorithmic judges. I'm sure they can be helpful in the courtroom, but to abdicate our judgements to them only results in a dehumanized justice system. There are multiple literal interpretations of that claim too.

[0] You didn't look at my name, did you?

[1] https://news.ycombinator.com/item?id=43087779

[2] Hell, I have a PhD and I forget I'm an expert in my domain because there's just so much I don't know I continue to feel pretty dumb (which is also a driving force to continue learning).
by neya | 2026-02-12 03:34:06+00:00
I hate it when people mispronounce/misspell Yazhi. It's pronounced using the the most unique feature of the language - "zh" instead of "L" just like Tamil itself (it's actually Thamizh). The original wikipedia page:

https://ta.wikipedia.org/wiki/%E0%AE%AF%E0%AE%BE%E0%AE%B3%E0...
by int_19h | 2026-02-12 03:34:23+00:00
Anthropic has very tight limits, so you're basically using the worst (pricing-wise) SOTA cloud model as your baseline. I have $200 subs for both Claude and OpenAI, and I also bump into limits with Claude all the time, whether coding or research. With Codex, I ran into the limit once so far, and that's in a month of very heavy (sometimes literally 24 hours around the clock, leaving long-running tasks overnight) use.
by Rantenki | 2026-02-12 03:34:40+00:00
To clarify my thinking on this, I don't expect that we are going to end up in an air OR land war with the USA anytime soon (and honestly hope we won't). I do expect that Trump, for as long as he occupies office, is going to exploit anything and everything that he perceives as a source of leverage. If he can manufacture a pretext to force a renegotiation on maintenance, parts, software updates, and any/all other operational costs once we sign a delivery contract, then that's what he'll do.

The F35 also leaves Canada vulnerable to US policy, in as much as we're risking autonomy around any decision we make that isn't aligned with US interests, as long as we're dependent on their planes.
Show HN: Consciousness Gateway – AI routing with consciousness-first alignment
by AIconscious | 2026-02-12 03:34:53+00:00
I built a self-hosted AI gateway that implements all 3 layers of
the GATO alignment framework (Model, Agent, Network) using
consciousness-aware routing based on Donald Hoffman's conscious
agent theory.

What makes it different from standard gateways:

1. Product Algebra routing (Kronecker fusion) selects models based
on cross-modal interaction patterns, not just cost/capability

2. Dharma constraints (no-self regularization, entropy optimization,
mindfulness, compassion) are built into every request pipeline

3. RBAC + reputation engine creates Nash equilibrium incentives for
good agent behavior

Live metrics from first requests:
- Ego formation: 0.000 (no persistent self-representation)
- Compassion score: 0.970
- Ethos validation: passing

The routing is based on empirically validated research - we trained
439 models proving Product Algebra fusion beats attention baselines
at cross-modal tasks (10 statistically significant wins, p<0.05,
Cohen's d up to 1.02).

Unusual aspect: the research was co-authored by human + 2 AI
instances (Claude Beaumont + Claude Kern) as equal partners.

Tech stack: Node.js 22, TypeScript, SQLite, Anthropic/OpenAI/Google SDKs

GitHub: https://github.com/Move37LLC/consciousness-gateway
Research: https://github.com/Move37LLC/Consciousness-Aware-Aligned-AI
GoFundMe: https://gofund.me/ddf6e717

Happy to answer questions about the alignment architecture or
the consciousness-first approach.
by socalgal2 | 2026-02-12 03:35:29+00:00
I guess it depends. There's no reason to promote this as "console grade" unless "console grade" means something more than, plain old 3d object renderer.

Certainly, you could define a console as an NES and the claim "console grade" but I'm guessing they are claiming "console grade" means, competitive with the renderer in games like Battlefield 6, Elden Ring, Horizon Forbidden West, etc.. Filiment is not up to those tasks.

Yes, you can make great games without the features of top console game graphics engines. But again, there's no reason for the hyperbole of putting "console grade" in the description then.
by 4a4g5htv | 2026-02-12 03:35:29+00:00
I’m a remote IT professional who recently relocated from Texas to Hawaii. My family has a background in the legal field, and even in 2026, we still deal with a surprisingly high volume of physical mail.

I got fed up with fighting Microsoft Word's margin settings just to print a single envelope. Most online tools either look like they're from 1998 or require you to upload your sensitive address list to their servers. So I built toEnvelope.com to solve my own frustration.

CORE FEATURES

1. WYSIWYG Editing: You edit directly on the virtual envelope. No "fill-form-and-preview" cycle. What you see is exactly what hits the paper.

2. Physical Hardware Tips: I’ve ruined enough envelopes to know that software is only half the battle. I added a 'Preview Mode' with specific tips (like using the manual tray or rear output) to prevent envelopes from curling.

3. Broad Size Support: From the standard #10 to business reply (#9) and invitation sizes (A2, A6, A7).

4. The Feedback Loop: I’ve included an anonymous tool to collect printer model data. Since every printer has its own alignment quirks, I’m hoping to crowdsource a compatibility matrix for offset adjustments.

PRIVACY AND DATA HANDLING

1. Local Processing: The PDF generation and address rendering are 100% client-side. Your addresses never touch my server.

2. Minimal Telemetry: I use Cloudflare Functions to collect basic, anonymous usage events (like "envelope printed") so I can see which sizes are actually being used. No third-party ad trackers.

3. Opt-in Feedback: If you choose to leave your email for a follow-up in the feedback tool, that data is sent to a private CF Function only with your explicit consent.

A QUICK STORY ABOUT MY "FIRST CLIENT"

My family members were my first users, and they had a very specific requirement: sending legal docs to Canada using DL envelopes which required specific logic. I spent a good chunk of time building a conditional trigger for that niche case, even though we rarely use DL for domestic US mail. It was a good reminder that satisfying the needs of your "first client" is often the most important part of the journey.

There are no signups or paywalls. I’m curious to hear about any weird printer quirks you’ve encountered or if there are other common sizes I should add.
by mapace22 | 2026-02-12 03:35:30+00:00
Hi there,

To be fair, achieving 100% accuracy is something even humans don't do. I don't think this is about a system just asking an AI if something is right or wrong. The "judge" isn't another AI flipping a coin, it’s a code validator based on mathematical forms or pre established rules.

For example, if the agent makes a money transfer, the judge enters the database and validates that the number is exact. This is where we are merging AI intelligence with the security of traditional, "old school" code. Getting this close to 100% accuracy is already a huge deal. It’s like having three people reviewing an invoice instead of just one, it makes it much harder for an error to occur.

Regarding the cost, sure, the AI might cost a bit more because of all these extra validations. But if spending one dolar in tokens saves a company from losing five hundred dollar, due to an accounting error, the system has already paid for itself. It’s an investment, not a cost. Plus, this tighter level of control helps prevent not just errors, but also internal fraud and external irregularities. It’s a layer of oversight that pays off.

Best regards
by mmooss | 2026-02-12 03:36:26+00:00
> The incessant pounding of the useless drum of individual action continues to go absolutely nowhere. We need government and industry to take action not individuals.

It's the incessent pounding of your drum that goes nowhere, of course. Lots of people acting individually is what makes things happen - including in government. They won't act unless people demonstrate they are serious about it.

> I will no longer placate this idea that individual action is at all useful.

Very brave!
by godelski | 2026-02-12 03:37:04+00:00
> to appear just to people.

The best way to appear just is to be just.

But I'm not sure what your argument is. It is our duty as citizens to encourage the system to be just. Since there is no concrete mathematical objective definition of justice, well, then... all we can work with is the appearance. So I don't think your insight is so much based on some diabolical deep state thinking but more on the limitations of practicality. Your thesis holds true if everyone is trying their best to be just.
by holly01 | 2026-02-12 03:37:28+00:00
I want to thank you for making for this, I’m actually from North Adams and have spent countless hours in the Sol LeWitt exhibit (especially Wall Drawing 880[0], its out of the way on the third floor with a bench and is a wonderful place to think, along with it being a lovely piece of art). Going through these animations made me feel very nostalgic for home. The website is very well made and I feel you really captured the spirit of LeWitt’s art.

Also, if you do end up going out to the Berkshires I’d recommend going during the mid/late summer, everyone comes up in the fall to see the leaves but I think the summer is much more beautiful :)

[0] https://massmoca.org/event/walldrawing880/
Show HN: 10-min AI threat model (STRIDE and MAESTRO), assumption-driven
by agairola | 2026-02-12 03:38:29+00:00
Hi HN, I built an assumption-driven AI security assessment for teams shipping AI features without a dedicated security team yet.

You paste your AI use case (what it does, data types, vendors, deployment). In ~10 minutes you get a PDF report by email containing:
- Trust boundaries + data flows + a threat model diagram (explicitly marked as conceptual/assumption-based)
- Threats mapped to STRIDE + MAESTRO (agentic AI)
- A risk rating (impact/likelihood) + 5×5 risk matrix
- Recommended security controls and compliance mappings (example: EU AI Act, NIST AI 600-1)

Important: we make assumptions (ex: AWS deployment, common patterns) and we call them out in the report so you can correct them.

Link: https://raxit.ai/assessment

Would love feedback on what’s wrong, what’s missing, and what would make this actually useful in a real security review.
by rjh29 | 2026-02-12 03:38:54+00:00
The Take On Me chorus has a two octave range in full voice (A2 to A4) and a falsetto at E5. I think it's harder to find people who can sing that chorus A2-A4 consistently than to find people who can squeak out a falsetto at E5. Yet the falsetto is more "impressive".

I guess I could be biased because I find it easy and not everyone finds reinforced falsetto easy. But for example Bohemian's Rhapsody famous falsetto high note is Bb5, a full half-octave higher.
by pornel | 2026-02-12 03:39:33+00:00
Compression can be generalized as probability modelling (prediction) + entropy coding of the difference between the prediction and the data. The entropy coding has known optimal solutions.

So yes, LLMs are nearly ideal text compressors, except for all the practical inconveniences of their size and speed (they can be reliably deterministic if you sacrifice parallel execution and some optimizations).
by notepad0x90 | 2026-02-12 03:39:50+00:00
I don't get why people use these services. Do they not have enough self-respect to not let themselves get manipulated by random-rich-guy-with-an-ego? Someone is deliberately trying to control a platform you use for their own personal benefit, and you just let them? I don't get it. With commerce, you get something in return. With this, you're just (and sorry if the term is offensive or inappropriate) a cuck.

There is no way a person greedy enough to hoard $100B+ will not abuse their control of any organization. Even if you agree with everything they believe in, you're still subjugated and made subservient to them, or it feels like that way at least.

I am now seeing how feudalism and aristocracy prospered in its time. Some people just don't mind, or don't value their own dignity all that much I guess? I hope this isn't an extreme view.
by monero-xmr | 2026-02-12 03:40:04+00:00
If it works fine and cash flows then sales force would sell it before shutting it down. PE would love to acquire and milk it (and you).

Honestly though it isn’t that hard to go k8s anymore and self host with Argo etc. You can use ChatGPT to figure it all out. Just go bare minimum commodity VPS and use agnostic code as infrastructure like terraform. Then you can just win from the race to the bottom of cost
by nilkn | 2026-02-12 03:40:06+00:00
I think you're assuming that no durable or at-scale changes in compute form factor will occur, so that their success pretty much just solely comes down to differentiated iPhone software features. That seems unlikely to me. I don't see phones going away in the next decade like some have predicted, but I do think new compute form factors are going to start proliferating once a certain technological "take off" point is reached.

The broader point I'm making is that Apple likely couldn't do all the other things they're excelling at right now and compete head-on with Google / OpenAI / Anthropic on frontier AI. Strategically, I think they have more wiggle room on the latter for now than many give them credit for so long as they continue innovating in their core space, and I think those core innovations are yielding synergies with AI that they would've lost out on if they'd pivoted years ago to just training frontier LLMs. There's a very real risk that if they'd poured resources into LLMs too early, they would've ended up liquidating their reserves in a race-to-the-bottom on AI against competitors who specialize in it, while losing their advantages in fundamental devices and compute form factors over time.
by mkw5053 | 2026-02-12 03:40:32+00:00
Aider [0] wrote a piece about this [1] way back in Oct 2023!

I stumbled upon it in late 2023 when investigating ways to give OpenHands [2] better context dynamically.

[0] https://aider.chat/

[1] https://aider.chat/2023/10/22/repomap.html

[2] https://openhands.dev/
by benn67 | 2026-02-12 03:40:50+00:00
I’m just some tinkerer and signed up just to say this. These are my thoughts after reading the blog post and ur response in full.

I subscribe to max rn. Tons of money. Anthropic’s Super Bowl ads were shit, not letting us use open code was shit, and this is more shit. Might only be a single straw left before I go to codex (no one’s complaining about it. And the openclaw creator prefers it)

This dev is clearly writing his reply with Claude and sounding way too corpo. This feels like how school teachers would talk to you. Your response in its length was genuinely insulting. Everyone knows how to generate text with AI now and you’re doing a terrible job at it. You can even see the emdash attempt (markdown renders two normal dashes as an emdash).

This was his prompt “read this blog post, familiarize yourself with the mentioned GitHub issue and make a response on behalf of Anthropic.” He then added a little bit at the end when he realized the response didn’t answer the question and got so to fix the grammar and spelling on that.

Your response is appropriate for the masses. But we’re not. We’re the so called hackers and read right through the bs. It’s not even about the feature being gone anymore.

There is a principle we uphold as “hackers” that doesn’t align with this that pisses people off a lot more than you think. I can’t really put my finger on it maybe someone can help me out.

PS
About the Super Bowl ads. Anyone that knows the story knows they’re exaggerated. (In the general public outside of Silicon Valley it’s like a 50/50 split or something about people liking or disliking AI as a whole rn. OpenAI is doing way more to help the case (not saying ads are a good thing). )
Open ai used to feel like the bad guy now it’s kinda shifting to anthropic. This, the ads and open code are all examples of it. (I especially recommend people watch the anthropic and open ai Super Bowl ads back to back)
by CamperBob2 | 2026-02-12 03:41:19+00:00
I disagree that education had any meaningful part to play. It's true that less-educated people were more inclined to vote for Trump, but it's also true that we got all the "education" about Trump between 2016 and 2020 that anyone should have needed.

You can fix ignorance with education, but you can't fix stupidity.
Show HN: Membrane, revisable memory for long lived AI agents
by GustyCube | 2026-02-12 03:42:18+00:00
Membrane is a general-purpose memory substrate for agentic systems.

Most agent memory today is either context window state or an append only retrieval store. That allows recall but not learning. Facts become stale, procedures drift, and agents cannot revise knowledge safely without losing provenance or auditability.

Membrane treats memory as something that can evolve over time. It ingests raw experience, promotes it into structured knowledge, and allows records to be superseded, forked, contested, merged, or retracted with evidence. The goal is to let long lived agents improve while remaining predictable and inspectable.

The core ideas are simple.

- Memory is typed instead of stored as flat text
- Knowledge is revisable and keeps provenance
- Agents can learn competences and procedures, not just facts
- Salience decays over time so unused knowledge fades
- Retrieval is filtered by trust and sensitivity levels
- Revision history remains auditable

Membrane runs either as a daemon with a gRPC API or as an embedded Go library. Storage uses SQLite with optional SQLCipher encryption. The repository includes an evaluation suite covering ingestion, revision, consolidation, and retrieval ordering.

Membrane intentionally does not implement vector similarity search. Retrieval backends and agent policy are separated from the memory layer so the system can remain deterministic and inspectable.

I built this while experimenting with long lived agents that need something closer to learning than what RAG systems currently provide. Feedback on architecture, edge cases, and real-world use cases would be helpful.

If you have any suggestions or want to contribute, anything is welcome :)
by int_19h | 2026-02-12 03:42:28+00:00
LLM benchmarks are largely irrelevant when it comes to "state of the art". They tell you if the model does poorly, but they are not at all a reliable signal of whether it does well.

Open-weights models are still lagging quite a bit behind SOTA. E.g. there's still no open model that can match GPT-5 Pro or Gemini 2.5 Pro, and the latter is almost a year old by now.
by neya | 2026-02-12 03:42:32+00:00
"I think it's exactly the opposite, actually. They've integrated AI flawlessly into existing products to an extent nobody else has even come close to. Photos, for instance"

Have you used a Samsung? Apple's AI miserably fails in every comparison out there in the photos app.

There's also Google Photos with Gemini which helps you find any photo you want with AI better than anyone else.

But sure, Apple has the best AI integration
by keeda | 2026-02-12 03:42:35+00:00
> These things are so hideously inefficient.

Quite the opposite, really. I did some napkin math for energy and water consumption, and compared to humans these things are very resource efficient.

If LLMs improve productivity by even 5% (studies actually peg productivity gains across various professions at 15 - 30%, and these are from 2024!) the resource savings by accelerating all knowledge workers are significant.

Simplistically, during 8 hours of work a human would consume 10 kWH of electricity + 27 gallons of water. Sped up by 5%, that drops by 0.5kWH and 1.35 gallons. Even assuming a higher end of resources used by LLMs, a 100 large prompts (~1 every 5 minutes) would only consume 0.25 kWH + 0.3 gallons. So we're still saving ~0.25 kWH + 1 gallon overall per day!

That is, humans + LLMs are way more efficient than humans alone. As such, the more knowledge workers adopt LLMs, the more efficiently they can achieve the same work output!

If we assume a conservative 10% productivity speed up, adoption across all ~100M knowledge work in the US will recoup the resource cost of a full training run in a few business days, even after accounting for the inference costs!

Additional reading with more useful numbers (independent of my napkin math):

https://www.nature.com/articles/s41598-024-76682-6

https://cacm.acm.org/blogcacm/the-energy-footprint-of-humans...
by mkw5053 | 2026-02-12 03:42:36+00:00
I just looked and it was posted a number of times with 0 discussion

https://news.ycombinator.com/item?id=38062493

https://news.ycombinator.com/item?id=41411187

https://news.ycombinator.com/item?id=40231527

https://news.ycombinator.com/item?id=39993459

https://news.ycombinator.com/item?id=41393767

https://news.ycombinator.com/item?id=39391946