MurphyA/hacker-news huggingface dataset:
by aswegs8 | 2026-02-24 09:42:34+00:00
Funny how we now see AI go through developmental phases similar to what we see in young child development. In a weird convoluted way. Strawberry spelling and car wash aren't particularly intuitive as cognitive developmental stages.

E.g. well known mirror-test [1], passed by kids from age 1.5-2

Or object permanence [2], children knowing by age 2 that things that are not in sight do not disappear from existence.

[1] https://en.wikipedia.org/wiki/Mirror_test
[2] https://en.wikipedia.org/wiki/Object_permanence
Show HN: AI Headcanon Generator: Instant Ideas, Endless Stories
by avaj03 | 2026-02-24 09:43:05+00:00
I’ve been a fandom writer for a long time and also a very deep Tumblr user (like… headcanons, incorrect quotes, character analysis at 2am kinda deep). Writing has always been how I have fun w/ characters and stories I love.

I’m also a dev, and at some point I noticed smth pretty obvious: I love writing, but inspo doesn’t always show up when I want it to. Sometimes I really wanna write headcanons or relationship stuff, but my brain just goes empty lol.

So I ended up building a small tool for myself.

It’s an AI headcanon generator — you give it a few prompts, and it can generate character headcanons, relationship dynamics, random moments, or incorrect quotes. It’s not meant to replace writing or anything, more like a brainstorming buddy when you’re stuck or just wanna mess around.

I’m sharing it here bc it actually helped me get unstuck and made writing feel fun again, and I figured other fandom writers / Tumblr ppl might enjoy it too.

If anyone wants to check it out, here’s the link:
https://aiheadcanongenerator.org/

Would love feedback, ideas, or feature requests — esp from ppl who live in fandom spaces.
by potato-peeler | 2026-02-24 09:43:15+00:00
Looks very interesting. Is there a published paper/article on your algorithm? Would like to take a dab at implementing this on my own.

I could find this [0], but not sure if that represents the entire system? (Apologies, I am not that well versed in ML)

[0] - https://www.guidelabs.ai/post/scaling-interpretable-models-8...
by abhikul0 | 2026-02-24 09:43:39+00:00
Pi works great for local models in my short testing. Wanted to try out Skills and how small models work with these agentic tools for small tasks, mainly [browser-use](https://github.com/browser-use/browser-use).

Tried Mistral Vibe, Codex, Opencode, Claude with gpt-oss:20b, ministral 3b,8b, Nemotron3 nano 30b and GLM 4.6V; finally settling on gpt for its impressive pass rates. All the other tools inject upto around 7-10k tokens on the initial prompt while pi takes up ~1.5k. This works out to be quite usable for my m3 pro machine that can take a while when processing the huge initial prompts from other CLIs.

While I'm not doing any serious work, and the other tools could be tweaked to use a simpler System Prompt; pi felt quick and the llms did use the tool calls correctly without being confused with all the huge prompts being dumped on them.
by duttish | 2026-02-24 09:43:59+00:00
Hm. I don't think I follow "isn't translating to user numbers", could you elaborate?

Here's my thinking:
There's 100 users getting updates.
There's 40 users sending telemetry with AI enabled
There's 10 users sending telemetry with AI disabled

So we have 50 people not sending telemetry and using or not using AI. If we assume more likely but not overwhelmingly more it's 30 people.

So we end up with 40+20 with AI, and 10+30 without?
by lopis | 2026-02-24 09:44:14+00:00
While that is true, it doesn't change the sentiment behind “We have no cure. I don’t want to know.” if knowing the diagnosis doesn't help you personally. Sure you might have a sense of responsibility for mankind but you still know you can't do anything to save yourself.

With that said, lifestyle changes can slow down the onset of Alzheimer's, so knowing the diagnosis isn't totally useless.
by keiferski | 2026-02-24 09:44:49+00:00
Sure. But it doesn’t really seem clear to me that selecting for intelligence actually results in a better world. It’s a fallacy to think that intelligence = more rational or immune to human flaws, as a cursory glance at any “intelligent” social group should make obvious.

I think we’d be better off optimizing for conscientiousness or empathy, frankly. Even a world run by gardeners would probably be more beautiful and meaningful than one run by math geniuses.
by oinj | 2026-02-24 09:46:52+00:00
He never was a "spiritual leader". He was a med school professor. He had been ordained as a _upāsaka_, a lay Buddhist disciple, meaning he took the vow of following the five precepts and he was given this name by the people who ordained him, that's not a weird thing. He later was removed from the board of the meditation center he co-founded because he broke the precepts (adultery and lying about it to his wife, hardly a criminal)[1], so he was prohibited from teaching things. He died recently from cancer[2].

His approach in the book, which seems still taught at that center, probably because it's excellent, was to lay out a path for meditation practice that was inspired from classic Buddhist writings, but presented and explained with modern neuroscience.

[1] https://engagedharma.net/2019/08/19/culadasa-charged-with-se...
[2] https://culadasa.com/
by light_hue_1 | 2026-02-24 09:47:38+00:00
Writing code was always cheap! You could outsource for inconsequential amounts of money and get massive amounts of code in return. Yet, the vast majority of companies do not do so. Because coding is not the hard part of being a software engineer / programmer.

That's like saying that photography killed painting because it saved you from having to draw things. Drawing is basically free now, I just take the photo. But the number of painters (and by that I mean, artists who paint) is dramatically higher today than in 1800. Artists didn't die because of mechanical reproduction, they flourished, because that wasn't the problem they were solving.
by rapidata | 2026-02-24 09:48:12+00:00
All good ^^, its a fair point, we have come up with some fun ways to track peoples reliability over time. But the validation sets contain plenty of forced-choice questions, those that have an empirical true can be used directly to calculate a reliability, those that are subjective need to be re-asked after sometime to ensure consistency. People that don't pass thresholds would not be part of the 10'000 here.

But of course. If every human was told to take 3 minutes to deeply think about it and told that its a trick question, then they most likely will all get it right. But its the same with the LLMs, if you ask them like that they will get it right most of the time. The low effort is kinda the point here.
by pjmlp | 2026-02-24 09:49:08+00:00
Microsoft cross platform effort always pushed forward Web development and distributed systems, hardly "that's about it".

And if you mean before the whole .NET Core rewrite, many big corp server applications were already written in .NET, products like SharePoint, Sitecore, Optimizely, Dynamics 365, Four51,.... and plenty of server infrastructure for XBox and Windows games.
by llingelo | 2026-02-24 09:49:09+00:00
MCP server that wraps AzerothCore's SOAP interface into 28 GM tools. Talk to
your WoW private server through any LLM client instead of typing console
commands — bans, accounts, mail, guilds, teleports, etc.

https://github.com/Lingelo/azerothcore-wotlk-mcp

If you find it useful, a star on the repo goes a long way!
by shakna | 2026-02-24 09:49:11+00:00
> The issue is other sites really want to know if it's a link to the same resource or a different resource.

Thats what the canonical link is for, isn't it? [0]

RFC 6596 introduced it in 2012. Other websites, like search engines or social media, have been using it for a while.

[0] https://developers.google.com/search/docs/crawling-indexing/...
by close04 | 2026-02-24 09:49:16+00:00
> important regulation is truck speed limits, because drag grows

No I just meant that drag was a second thought when the regulation was conceived on either side of the ocean. The regulation was not created "because of drag". The main driver was road safety. Efficiency and pollution were secondary. There are other ways to achieve them like improving aerodynamics or power trains, and much of this cost is paid by the freighter alone. This is why speed limits didn't increase as trucks got more efficient and drag went down. Because there's no way to reduce the potential energy of a 40-50t vehicle (or up to almost 90t Finland) travelling at 130km/h.
by rahoulb | 2026-02-24 09:49:27+00:00
I'd add in "code is easier to write than it is to read" - hence abstraction layers designed to present us with higher level code, hiding the complex implementations.

But LLMs are both really good at writing code _and_ reading code. However, they're not great at knowing when to stop - either finishing early and leaving stuff broken, over-engineering and adding in stuff that's not needed or deciding it's too hard and just removing stuff it deems unimportant.

I've found a TDD approach (with not just unit tests but high-level end-to-end behaviour-driven tests) works really well with them. I give them a high-level feature specification (remember Gherkin specifications?) and tell it to make that pass (with unit tests for any intermediate code it writes), make sure it hasn't broken anything (by running the other high-level tests) then, finally, refactor. I've also just started telling it to generate screenshots for each step in the feature, so I can quickly evaluate the UI flow (inspired by Simon Willison's Rodney tool).

Now I don't actually need to care if the code is easy to read or easy to change - because the LLM handles the details. I just need to make sure that when it says "I have implemented Feature X" that the steps it has written for that feature actually do what is expected and the UI fits the user's needs.
by CJefferson | 2026-02-24 09:49:28+00:00
What? Are there billion dollar companies with huge staffs who are constantly trying to figure out how to sneak my child a gun all the time, at school, wherever they go?

I'd say this comparison is good -- we as a society have decided that people who provide alcohol, guns, and cigarettes are responsible if children are provided them. You don't get to say 'hey, you didn't watch your child, they wandered into my shop, I sold to them 2 liters of vodka and a shotgun'.
by zozbot234 | 2026-02-24 09:51:33+00:00
You'll find that all of these languages ultimately build FFI on top of C ABI conventions, though Swift's own internally stable ABI uses a lot of alloca() to place dynamically sized objects on the stack, in a way that's somewhat unidiomatic (the Rust folks are trying to back out of their alloca() equivalent). You can even interface to COM from pure C.
by A_D_E_P_T | 2026-02-24 09:51:42+00:00
It would be nice if there were an easier way to detect and filter those "reply guys." If LLMs were forced to watermark their output (possibly by using randomly-selected nonstandard ASCII characters in inconspicuous places, like "ｓ" instead of "s") it would have been trivial, but that ship has sailed. The most anybody can do is train another LLM to find offenders and make a list. Bot vs bot.
by keiferski | 2026-02-24 09:51:54+00:00
I just read this yesterday in Conversations with Walter Murch, a well-known film editor. Not exactly the same, but I do get the sense that Tao still feels the same way about math:

As I've gone through life, I've found that your chances for happiness are increased if you wind up doing something that is a reflection of what you loved most when you were somewhere between nine and eleven years old.

Interviewer: Yes—something that had and still has the feeling of a hobby, a curiosity.

M: At that age, you know enough of the world to have opinions about things, but you're not old enough yet to be overly in by the crowd or by what other people are doing or what you thinkyou “should” be doing. If what you do later on ties into that reservoir in some way, then you are nurturing some essential part ofyourself. It's certainly been true in my case. I'm doing now, at eight, almost exactly what most excited me when I was eleven.
by das-bikash-dev | 2026-02-24 09:52:24+00:00
Nice project, especially given the VRAM constraints. A few things I've learned
building production RAG that might help:

1. Separate your query analysis from retrieval. A single LLM call can classify
the query type, decide whether to use hybrid search, and pick search parameters
all at once. This saves a round-trip vs doing them sequentially.

2. If you add BM25 alongside vector search, the blend ratio matters a lot by
query type. Exact-match queries need heavy keyword weighting, while conceptual
questions need more embedding weight. A static 50/50 split leaves performance
on the table.

3. For your evaluator/generator being the same model — one practical workaround
is to skip LLM-as-judge evaluation entirely and use a small cross-encoder
reranker between retrieval and generation instead. It catches the cases where
vector similarity returns semantically related but not actually useful chunks,
and it gives you a relevance score you can threshold on without needing a
separate evaluation model.

4. Consider a two-level cache: exact match (hash the query, short TTL) plus a
semantic cache (cosine similarity threshold on the query embedding, longer TTL).
The semantic layer catches "how do I X" vs "what's the way to X" without hitting
the retriever again.

What model are you using for generation on the 8GB? That constraint probably
shapes a lot of the architecture choices downstream.
by seonglae | 2026-02-24 09:53:27+00:00
I made a rating leaderboard by making papers fight each other. Helping overlooked papers get the recognition they deserve.
How it works: papers are matched by rating, AI scores them on soundness, originality, experiments, clarity, and impact, then picks a winner. After thousands of battles, the leaderboard shows which papers hold up.
61K+ papers across NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL. Full reasoning shown for every match. You can rebut the AI's judgment if you disagree.
Random matches are free, no signup.
by kozinc | 2026-02-24 09:54:28+00:00
It may not change much as far as researching a treatment or cure goes, but it may help with other stuff, like being better prepared for the future, like organizing a health care worker or getting family members to help out or to look out for you, and other stuff, which you might not be able to do at a later stage of Alzheimers.

While there aren't any cures yet, certain treatments and lifestyle strategies may slow its progression, keep quality of life as long as possible and stuff like that for as long as possible. (And the sooner you start with that, the better)
by close04 | 2026-02-24 09:55:57+00:00
> the second of which is a linear interpolation of the frame before and after it

If I understand what you describe, this is generating a frame "in the past", an average between 2 frames you already generated, so not very useful? If you already have frames #1 and #2, you want to guess frame #3, not generate frame #1.5.
by emregucerr | 2026-02-24 09:56:03+00:00
People dismiss this as a meme too quick but I think this is a good thought experiment not only for drawing a comparison for energy consumption but learning efficiency. AI is often criticized for its low learning efficiency but if you compare it to a human it's not looking too bad. Let's say a human becomes an AGI-level learner by the time they are 14yo. Human vision is approx. 500 megapixels and that is approx. 1.7 Gb per second of vision data. That means it takes approx. 800 PETABYTES of data to 'pre-train' a human to become a well-enough generalist learner. Take Llama 4 from Meta whose training data set consisted of 30 trillion tokens - his is equivalent of 120 Tb which is a mere 0.12 peta-bytes.

I am well aware this is a flimsy napkin math at best but I find comparing LLM models to humans with a more serious tone is fun and useful thought.
by das-bikash-dev | 2026-02-24 09:56:57+00:00
Re: when to add a WebSocket gateway vs keeping it in the monolith —

I've built multi-channel chat infrastructure and the honest answer is: keep the
monolith until you have a specific scaling bottleneck, not a theoretical one.

One pattern that helped was normalizing all channel-specific message formats into
a single internal message type early. Each channel adapter handles its own quirks
(some platforms give you 3 seconds to respond, others 20, some need deferred
responses) but they all produce the same normalized message that the core
processing pipeline consumes. This decoupling is what made it possible to split
later without rewriting business logic.

On Redis pub/sub specifically: for a solo dev, skip it until you actually have
multiple server instances that need to share state. A single process with
WebSocket sessions in memory is fine for early users. The complexity cost of
pub/sub isn't worth it until you need horizontal scaling or have a separate
worker process pushing messages.

What's your current message volume like? That usually determines timing better
than architecture diagrams.
by KoolKat23 | 2026-02-24 09:57:11+00:00
In what sense? DNS blockers work generally do they not?
Adguard also censors google search results.

I don't see why your kid should be browsing reddit.

I mean even only allow whitelisted sites. As I say this can be standardized further.

These measures I truly believe do not need to be 100% foolproof so long as the hurdle is high enough that children give up it's fine. And these measures could potentially notify a parent of a suspected breach or attempt to game it, without intruding too much into the child's privacy.
by ACS_Solver | 2026-02-24 09:57:26+00:00
This is one of the most interesting questions to me about human brains, and as far as I know no significant progress has been made in answering it.

Some people appear to have a capacity for learning, retention and understanding that is well outside the normal range. People like Ramanujan or von Neumann, or Tao. They learn at a speed that far exceeds the speed of what we would consider gifted students, they reach a deep and intuitive understanding of the material, and go on to make many discoveries / inventions of which even one would be enough for an ordinary scientist to be considered successful.

It seems there is something very different about their minds, but just what is it that allows those minds to operate at such a level?
by yanosh_kunsh | 2026-02-24 09:57:31+00:00
You are absolutely correct, but I don't need it to be 100% bulletproof.

I'm using opencode as a coding agent and I've added a custom plugin that implements an .aiexclude check (gist (https://gist.github.com/yanosh-k/09965770f37b3102c22bdf5c59a...)) before tool calls. No matter how good the checks are, on the 5th or 6th attempt a determined prompt can make the agent read a secret — but that only happens if reading secrets is the explicit goal. When I'm not specifically prompting it to extract secrets, the plugin reliably prevents the agent from reading them during normal coding work.

My threat model isn't a motivated attacker — it's accidental ingestion.

That's also why I think this should be a built-in feature of coding agents — though I understand the hesitation: if it can't guarantee 100% coverage, shipping it as a native safeguard risks giving users a false sense of security, which may be harder to manage than not having it at all.
by znpy | 2026-02-24 09:57:56+00:00
> If you don't want to use the base system (which docker is NOT the base system on Linux)

There are many ways to manage "containers" on linux. I might agree with the fact that docker is not the base system (although it really depends on what distro you're using).

But I might also use something like systemd-nspawn or systemd-machined (see https://wiki.archlinux.org/title/Systemd-nspawn or https://en.opensuse.org/Systemd-machined) to handle those.

> I can delegate a ZFS dataset to a jail to let the jail manage it.

I could probably do the same.

> Do Linux containers have an equivalent to VNET jails yet? With VNET jails I can give the jail its own whole networking stack, so they can run their own firewall and dhcp their own address and everything.

I'm not sure, but most likely yes. Maybe not through docker. Docker isn't the only way to run containers in GNU/Linux though.
by Aeglaecia | 2026-02-24 09:59:17+00:00
https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing

a great link to share around !

now ive been wondering - what is the polite way to exit a conversation when it becomes obvious that your fellow interlocutor is merely a chunk of electric meat redirecting the output of sam altman? im talking blatantly obvious eg. 'its not x, its y' multiple times in the same paragraph.
by myrion | 2026-02-24 09:59:28+00:00
That's not how that works - they can prove they check by showing logs, rather than VPs. There's even legal limits on what identifiers they can store and for how long. But even ignoring that, they'd be storing only very limited disclosures.

The base registry stores identifiers of issuers and verifiers, not credential holders.

Even the status register does not contain the tokens themselves:

> Within these status lists, each index (i.e., status entry) documents the validity of one VC. The corresponding index is captured in the VC’s metadata to allow for a decentralized status information retrieval that does not require verifiers or the VC holder to contact the issuer.

Of course, each issuer needs to maintain a list of the credentials they have issued in order to be able to ever revoke them. That's unavoidable.
by NKosmatos | 2026-02-24 09:59:53+00:00
I agree 100% with the author, clean, easily readable and well structured URLs make the web a better place. URL is a hierarchical structure as introduced in the RFC1738 by a guy you might have heard, Tim Berners-Lee, the inventor of the World Wide Web :-)
https://www.rfc-editor.org/rfc/rfc1738

Easily readable URLs is something I learned in the 90s and I still try to enforce in everything I create.
by randomtoast | 2026-02-24 10:01:14+00:00
The problem is you haven't used the API, but you have used your ChatGPT subscriptions with personality, memories and possible customization. I can see for instance that your ChatGPT answers with emojis, while my ChatGPT subscription never does.

If you ask GPT 5.2 with high reasoning efforts in the API, you get 10 out of 10: drive.
by Borealid | 2026-02-24 10:01:25+00:00
I chose the two scenarios I did to illustrate that "frames per second" is clearly not meant to be measured in terms of times the display refreshed, but rather in terms of times content was actually rendered by the game engine.

In my opinion it is quite difficult to provide a definition of "fps" that somehow makes 45-fps-native-with-frame-doubling be counted as 90 but doesn't also make either of the ludicrous examples I presented be counted as 90.
by Lockal | 2026-02-24 10:02:57+00:00
[x] 
Anthropic
 Cursor built a broken browser from scratch by distilling and wrapping Webkit
Lyrics: Whatever happened to... the boy that I once knew?

[x] Anthropic built a broken C compiler from scratch by distilling GCC
Lyrics: The boy who said he'd be true...

[x] Anthropic was distilled
Lyrics: Oh, no, oh, no. Oh, no, no, no, no, no
by adamgold7 | 2026-02-24 10:03:06+00:00
Prompt guardrails are theater - they work until they don't. We ended up building sandboxed execution for each agent action. Agent proposes what it wants to do, but execution happens in an isolated microVM with explicit capability boundaries. Database writes require a separate approval step architecturally separate from the LLM context.

Worth looking at islo.dev if you want the sandboxing piece without building it yourself.