MurphyA/hacker-news huggingface dataset:
by crazy5sheep | 2026-03-01 04:53:34+00:00
The 1905 thought experiment actually cuts both ways. Did humans "invent" the airplane? We watched birds fly for thousands of years — that's training data. The Wright brothers didn't conjure flight from pure reasoning, they synthesized patterns from nature, prior failed attempts, and physics they'd absorbed. Show me any human invention and I'll show you the training data behind it.

Take the wheel. Even that wasn't invented from nothing — rolling logs, round stones, the shape of the sun. The "invention" was recognizing a pattern already present in the physical world and abstracting it. Still training data, just physical and sensory rather than textual.

And that's actually the most honest critique of current LLMs — not that they're architecturally incapable, but that they're missing a data modality. Humans have embodied training data. You don't just read about gravity, you've felt it your whole life. You don't just know fire is hot, you've been near one. That physical grounding gives human cognition a richness that pure text can't fully capture — yet.

Einstein is the same story. He stood on Faraday, Maxwell, Lorentz, and Riemann. General Relativity was an extraordinary synthesis — not a creation from void. If that's the bar for "real" intelligence, most humans don't clear it either.
The uncomfortable truth is that human cognition and LLMs aren't categorically different. Everything you've ever "thought" comes from what you've seen, heard, and experienced. That's training data. The brain is a pattern-recognition and synthesis machine, and the attention mechanism in transformers is arguably our best computational model of how associative reasoning actually works.

So the question isn't whether LLMs can invent from nothing — nothing does that, not even us.

Are there still gaps? Sure. Data quality, training methods, physical grounding — these are real problems. But they're engineering problems, not fundamental walls. And we're already moving in that direction — robots learning from physical interaction, multimodal models connecting vision and language, reinforcement learning from real-world feedback.
The brain didn't get smart because it has some magic ingredient. It got smart because it had millions of years of rich, embodied, high-stakes training data. We're just earlier in that journey with AI. The foundation is already there — AGI isn't a question of if anymore, it's a question of execution.
by jerf | 2026-03-01 04:53:54+00:00
Yet the humans of the time, a small number of the smartest ones, did it, and on much less training data than we throw at LLMs today.

If LLMs have shown us anything it is that AGI or super-human AI isn't on some line, where you either reach it or don't. It's a much higher dimensional concept. LLMs are still, at their core, language models, the term is no lie. Humans have language models in their brains, too. We even know what happens if they end up disconnected from the rest of the brain because there are some unfortunate people who have experienced that for various reasons. There's a few things that can happen, the most interesting of which is when they emit grammatically-correct sentences with no meaning in them. Like, "My green carpet is eating on the corner."

If we consider LLMs as a hypertrophied langauge model, they are blatently, grotesquely superhuman on that dimension. LLMs are way better at not just emitting grammatically-correct content but content with facts in them, related to other facts.

On the other hand, a human language model doesn't require the entire freaking Internet to be poured through it, multiple times (!), in order to start functioning. It works on multiple orders of magnitude less input.

The "is this AGI" argument is going to continue swirling in circles for the forseeable future because "is this AGI" is not on a line. In some dimensions, current LLMs are astonishingly superhuman. Find me a polyglot who is truly fluent in 20 languages and I'll show you someone who isn't also conversant with PhD-level topics in a dozen fields. And yet at the same time, they are clearly sub-human in that we do hugely more with our input data then they do, and they have certain characteristic holes in their cognition that are stubbornly refusing to go away, and I don't expect they will.

I expect there to be some sort of AI breakthrough at some point that will allow them to both fix some of those cognitive holes, and also, train with vastly less data. No idea what it is, no idea when it will be, but really, is the proposition "LLMs will not be the final manifestation of AI capability for all time" really all that bizarre a claim? I will go out on a limb and say I suspect it's either only one more step the size of "Attention is All You Need", or at most two. It's just hard to know when they'll occur.
Show HN: Engram – Memory for AI coding agents (2.5K installs, 80% on LOCOMO)
by tstockham | 2026-03-01 04:55:07+00:00
Engram gives AI coding agents persistent memory across sessions. 2,500 npm installs in the first five days.
The problem: every Claude Code or Cursor session starts from zero. You re-explain your architecture, conventions, past decisions. CLAUDE.md helps but it's manual and doesn't scale.
Engram runs as a native MCP server. One command to install, SQLite backend, no infrastructure.
Three tiers of memory:

Explicit: things you tell it to remember
Implicit: behavioral patterns extracted from how you work (low confidence, reinforced over time)
Synthesized: meta-observations generated during consolidation

The key architectural bet: intelligence at read time, not write time. Most memory systems extract and classify on ingest. Engram stores broadly and invests compute when you query, because that's when you actually know what matters. This is why it scores 80% on LOCOMO (arXiv:2402.17753) while using 30x fewer tokens than full-context retrieval.
Stack: TypeScript, SQLite + sqlite-vec, Gemini embeddings by default (any OpenAI-compatible provider works via ENGRAM_LLM_BASE_URL). Zero external dependencies.

Install: npm install -g engram-sdk && engram init
Comparison with Mem0, Zep/Graphiti, Letta/MemGPT: https://www.engram.fyi/compare
GitHub: https://github.com/tstockham96/engram
by noduerme | 2026-03-01 04:55:28+00:00
Speaking of Spanish food and sweetened french fries, I think berenjenas with miel de caña are a truly underrated breakfast competitor. And Spanish tortilla is also sort of a dark breakfast food... I make them to keep in the fridge. Although... heh. My fav thing to eat in Spain for breakfast are the small clams called almejas, sauteed in Manzanilla, with some crusty bread to soak up the boozy clam juice. Tragically, I became allergic to clams when I lived there.
by ph4rsikal | 2026-03-01 04:55:38+00:00
I build AI for banks. A couple of weeks ago, I had a meeting with a Google team on Legacy Modernization using Agentic AI. Let me phrase it this way. None of them were smart. My team and I were shaking our heads at the low skills of the team. While this might have been true 20 years ago, I am sure there are pockets of smart people somewhere. But Google now employs 200K people worldwide. I am certain that it's hard to keep up the quality.
by rodonn | 2026-03-01 04:56:26+00:00
There's been a huge amount of improvement in coding agent effectiveness since they ran that experiment. In a more recent follow up experiment, METR found 20% speed up from AI assistance and says they believe that is likely an underestimate of the impact. https://metr.org/blog/2026-02-24-uplift-update/

They are working on making a new measurement approach that will be more accurate.
by apparent | 2026-03-01 04:58:02+00:00
Are you aware that the Citizens United case was actually about a movie? It wasn't about handing someone a stack of cash. When I see perspectives like yours, I wonder what you would say is the right way to handle the question of whether someone can make a movie that portrays a candidate in a positive or negative light. It seems to be pretty clearly a matter of free speech (first amendment), so unless there's some other provision of the constitution that would override that, I don't see how it could be forbidden.

To the extent that a pretty big chunk of donations are used to fund very short movies (we call them ads) for or against candidates, I'm not sure how that can be distinguished. I could see how one would distinguish get-out-the-vote or other similar non-speech type activities, but those on the Left seem to not oppose such expenditures.
by card_zero | 2026-03-01 04:58:23+00:00
> Chasing happiness, moral righteousness, or political engagement just loops you back into your own head, helping people doesn’t. Feeling good is a side-effect of being useful, not the goal.

Presumably you imply that moral righteousness, too, is best attained intuitively, by being useful to others and helping them (to do whatever, like a useful idiot?) without conscious thought for what's right.

Or else you're saying "help people for no reason even though it isn't right, and you'll end up feeling good that way so it's fine".
by jessicest | 2026-03-01 04:58:25+00:00
This is true: in general the best fix for US democratic systems is to learn from more functional systems from overseas.

US is running on beta version democracy; it was wonderful for a trial run and we learned a lot from it, but unfortunately the country has been stuck without upgrades for a while. It'd be like trying to connect a Xerox PARC desktop to the modern internet.

Obviously it's absurdly nontrivial to shift it at this point but I do agree that age and term limits both seem to be stopgap solutions due to the challenge of implementing more effective strategies.

Consider Australia: of 226 parliamentarians, there's one aged 75+: Bob Katter.

I'd say there's three features of the au system that keep us relatively free of the absurd incumbency advantages in USA:

1. Compulsory voting makes it harder to solicit votes from a subset of the populace.

2. The Australian Electoral Commission is highly trusted as a neutral body, so Gerrymandering is rare.

3. None of our voting systems use First Past The Post; it's all ranked choice, babes!
by yanhangyhy | 2026-03-01 04:58:35+00:00
Many people might wonder why China doesn't step in to help Iran. From our perspective, we realized a long time ago that the Iranian government is too weak and fundamentally untrustworthy. They would sell out China's interests to curry favor with the United States, so China's cooperation with Iran is kept as free from political entanglements as possible. War is wrong, but at most China will sell some weapons—it won't get too involved."
by khnfrhn | 2026-03-01 05:02:27+00:00
Hey HN,

I built a tool that analyzes employee survey responses from a CSV or Excel file and returns themes, sentiment, and burnout signals in about 60 seconds.

No signup. No credit card. Drop your file, get insights.

Started building this after watching HR teams at small companies do this work manually in Excel, spending hours tagging responses just to find patterns. The AI does it for you.

Would love honest feedback on whether this is useful in practice.

Try it here: getdistill.tech

(The full product is still in progress -- survey builder, stakeholder reports, etc. check /roadmap but the analysis tool is live and free for you to try :) )
by CamperBob2 | 2026-03-01 05:03:12+00:00
The guidelines are a little hard to interpret. At https://huggingface.co/Qwen/Qwen3.5-27B Qwen says to use temp 0.6, pres 0.0, rep 1.0 for "thinking mode for precise coding tasks" and temp 1.0, pres 1.5, rep 1.0 for "thinking mode for general tasks." Those parameters are just swinging wildly all over the place, and I don't know if printing potato 100 times is considered to be more like a "precise coding task" or a "general task."

When setting up the batch file for some previous tests, I decided to split the difference between 0.6 and 1.0 for temperature and use the larger recommended values for presence and repetition. For this prompt, it probably isn't a good idea to discourage repetition, I guess. But keeping the existing parameters worked well enough, so I didn't mess with them.
by Lerc | 2026-03-01 05:04:31+00:00
Perhaps, but there is a difference in a reasoning system deciding on the best way to achieve the goal.

To get the predicted disastrous effects you need to be doing function optimisation without regard to the meaning of the function parameters. Yes, models can still game the system at inference time, but in much the same way as a human might game the system, it requires awareness that you are going against the intent of some rule.
by abraxas | 2026-03-01 05:04:32+00:00
The oligarchic system is not only poisonous to economic growth but also incredibly hard to dislodge. Most of you are American and likely thinking that this is an aberration that will be rectified come next election cycle but I believe you are going to get disappointed. People who scaled the government buildings in a coup attempt are not going to give up power willingly. And now they have coerced your oligarchs to come to their side and those who fail the loyalty test like Amodei are getting punished. You are not approaching Russia style kleptocracy. You're already there.
by carlio | 2026-03-01 05:05:31+00:00
An AMD AI max+ 395 - I use the one from frame.work (https://frame.work/de/en/desktop) with 128GB unified RAM and it can run a 120b model (gpt-oss:120b) just fine.

See Wendel's review here - https://www.youtube.com/watch?v=L-xgMQ-7lW0

There are other mini-pc manufacturers, the mainboard is the important part.
by Mond_ | 2026-03-01 05:05:32+00:00
Manjaro sells itself as "Arch, but more approachable". In reality, you'll often end up with "Arch, but with additional weird package management upgrade issues that are a byproduct of Manjaro's own repositories interacting with the arch on your system."

Instead of just having to track the arch repos, you suddenly have those and Manjaro's own stuff (and own package manager tool etc.), which is another point of failure. Every new bit of technology is another part that can fail.
by frez131 | 2026-03-01 05:07:07+00:00
I personally care deeply when content intended as communication is AI generated (much more so than if code is generated).

On the surface level, I find it a bit disrespectful when I'm communicating with someone who's just using an LLM to generate their responses. Imagine if you are talking to someone in-person, and they pull out a phone, generate a response then read it back out to you?

On a deeper level, if someone's generated a bunch of text and clearly hasn't devoted the time into generating/editing it that they're expecting me to invest while reading it, I'm just not going to read it.
by EPWN3D | 2026-03-01 05:10:27+00:00
There are some people who just have high cholesterol but none of the other risk factors. I'm one of them. I did a calcium score on my heart, and it came back clean. The cardiologist basically said my cholesterol is just part of who I am, and it's not causing problems.

If you're similar to me, you might want to get a second opinion. There are different kinds of LdL cholesterol, and the small, dense particles are the ones that cause blockages. Big puffy ones don't. I have mostly big puffy ones, but classifying them is a different test that has to be special-ordered.

I also have a very low resting heart rate, exercise regularly, have a high VO2Max, and have a healthy diet. So the claim that I was at major risk of a cardiac episode just didn't pass the smell test. If it wasn't for those things, I probably wouldn't have asked questions when my doctor said I should go on a statin.
by StevenThompson | 2026-03-01 05:15:06+00:00
I wrote a version of this post awhile back that gets into a bit more detail as to HOW to bolt on the determinism.

I'm glad to see others talking about it. One day we'll look back on this era the same way folks look back at the time before we validated inputs.

https://www.stevenathompson.com/effective-vibe-coding-best-p...
by cmeacham98 | 2026-03-01 05:15:31+00:00
An LLM can neither understand things nor value (or not value) human life. *It's a piece of software that predicts the most likely token, it is not and can never be conscious.* Believing otherwise is an explicit category error.

Yes, you can change the training data so the LLM's weights encode the most likely token after "Should we kill X" is "No". But that is not an LLM valuing human life, that is an LLM copy pasting it's training data. Given the right input or a hallucination it will say the total opposite because it's just a complex Markov chain, not a conscious alive being.
by zinodaur | 2026-03-01 05:19:07+00:00
> You're assuming things I didn't state. I don't particularly want mass AI surveillance at all

That's fair, sorry for that.

> considering how much more dangerous a government's mass spying is to its own citizens living in it 24/7, it's not unreasonable for that to be the focus

The US government is actively trying to influence politics in my country and spending huge amounts of money to do it. The US government is a much larger threat to us than our own government.

All of our tech is owned and operated by US companies, which means the US government has read/write access to all of our data. If we attempt to incentivize domestic software production (e.g. by taxing imported software, or by stipulating where our data can be stored and who can access it), the US government will destroy our economy. This has played out a few times recently.

I can't believe we were so foolish as to let this situation grow. Its going to be a painful few decades.
Show HN: Iosef, an iOS simulator CLI designed for agents
by riwsky | 2026-03-01 05:20:15+00:00
Iosef is an iOS simulator CLI designed for agents, inspired by simonw/rodney, facebook/idb, and joshuayoes/ios-simulator-mcp.

For those familiar with idb, some of iosef’s agent-oriented features include:

Directory-level sessions, so agents in different worktrees don’t interfere with each other’s simulators.

Automatic scaling of screenshots into the same coordinate space as the accessibility trees and interaction tools.

Selector-based interactions, to save LLMs from needing to re-consult accessibility trees or screenshots each turn.

Skills and MCP support.

=====

iosef’s absence from the pre-training corpus means agents need a little nudge (either via the skill or user prompt) to use it over idb or simctl—but once they’re using it, they have a easier time than they do with other tools. While written in swift, it’s packaged via pypi for easy running and installation via uv (eg “uvx iosef --help” or “uv tool install iosef”).

Is it nearly entirely vibe-coded? Yes—but I’ve found it helpful for my own use, so I share it in case others will, as well. And this post is entirely human-written, so at least there’s that.
by charcircuit | 2026-03-01 05:20:29+00:00
It can learn. When my agents makes mistake they update their memories and will avoid making the same mistakes in the future.

>Reinforcement learning, on the other hand, can do that, on a human timescale. But you can't make money quickly from it.

Tools like Claude Code and Codex have used RL to train the model how to use the harness and make a ton of money.
by SethMurphy | 2026-03-01 05:22:54+00:00
Wars are almost always about commerce, history has shown that. Ideology is used to back the motive publicly, but the reason for involvement is almost always trade or commerce. This case could be different, but it is not obvious to me that this case is any different. A simple example is WW1 where the US was forced to back the UK because of their large debt to US banks, despite them still being a colonist power at the time.
by giancarlostoro | 2026-03-01 05:23:12+00:00
S(c)am Altman has had this messed up story arc the entire time, the problem is most people are unaware. Remember when he declared himself the chairman of Y Combinator, they deleted it from YC's blog / website, he filed as YC's chairman with the SEC for years despite not actually being chairman. This was after he stepped down as CEO. Then there's all the sketchiness surrounding some companies he sold.

Guy's the worlds most successful grifter who has world economies by the... anyway...
by reliefcrew | 2026-03-01 05:23:13+00:00
> Say hypothetically the productivity boost from AI is 2x per employee

I believe this is a bad assumption. Most likely the boost is not normally distributed. So, although all employees become more productive, some employees become much more productive thereby making the others redundant.

As someone pointed out already... there are diminishing returns. In addition, there are marginal returns. Put the two together and you get ever diminishing marginal returns from the laggard employees.
by tabs_or_spaces | 2026-03-01 05:24:12+00:00
> OpenClaw has nearly half a million lines of code, 53 config files, and over 70 dependencies. This breaks the basic premise of open source security. Chromium has 35+ million lines, but you trust Google’s review processes. Most open source projects work the other way: they stay small enough that many eyes can actually review them. Nobody has reviewed OpenClaw’s 400,000 lines. It was written in weeks with no proper review process.

Yeah, but the world rewarded this by making it the fastest growing github project. The author gets on the podcasts, gets the high profile jobs from big tech. I'm more encouraged to do things this way than being security minded about all this.

And there's no accountability to this at all. If an agent leaks private data, the user is to blame and not the author. If Google bans your services for using api keys incorrectly, we cast the bad eye towards Google and not the maintainer than enabled and approved it.

There's just so much incentive for for "not reading code" and not developing secure code that is just going to get worse over time. This is the hype and the type of engineering that we all allow either by agreeing or by staying silent.

I agree with the author, but the world works off a different set of principles than what we're used to. I just see the world blindly trusting agents more.
by card_zero | 2026-03-01 05:25:25+00:00
"Degrowth" is misanthropic, I guess, unless you think the world is overpopulated structurally somehow, as in you'd like more humans but only in due course and under more favorable (global?) circumstances.

But I think looking at this in global terms is wrong-headed anyway, whether you're for or against. The question is whether a specific person should be a parent here and now in specific personal circumstances. So of course it tends to be selfish. It's not entirely selfish since others are involved locally, including the future child. But "the world needs more children" or "the world needs fewer children" is barely relevant at all.
by aix1 | 2026-03-01 05:25:29+00:00
As the contest entry page explains:

> ChatIOCCC is the world’s smallest LLM (large language model) inference engine - a “generative AI chatbot” in plain-speak. ChatIOCCC runs a modern open-source model (Meta’s LLaMA 2 with 7 billion parameters) and has a good knowledge of the world, can understand and speak multiple languages, write code, and many other things. Aside from the model weights, it has no external dependencies and will run on any 64-bit platform with enough RAM.

(Model weights need to be downloaded using an enclosed shell script.)

https://www.ioccc.org/2024/cable1/index.html