MurphyA/hacker-news huggingface dataset:
by em-bee | 2026-02-24 11:41:21+00:00
According to your line of thinking, anything that results from nature can be attributed to nurture because "we can't prove 100% that it isn't nurture". This is a terrific example of an unfalsifiable hypothesis.

all correct. but the reverse is true too. you can't prove that it is all nature.

both are beliefs. the reason why i side for nurture is because nature is used as an excuse to defend bad behavior or to discriminate. i have written about this before.

"men should not be teachers or single parents because it is in their nature to abuse children, and it is not in their nature to care for them"

i am exaggerating to make a point, the teacher was shared by someone in new zealand, the single parent one is common in germany and i believe in the US and many other place too.
by nephihaha | 2026-02-24 11:41:24+00:00
Latin was a lot more common than Greek in schools in the UK. But even that tended to be private schools after the sixties and seventies. Greek was a subset of that.

I was lucky, I had two or three excellent English teachers. Very inspiring and helpful. I wish I could say the same about mathematics (most of my teachers were terrible and one didn't even teach us how to do the problems)... Or my French teacher. I think we spoke better French than he did at the end. Since I spent a lot of my childhood in rural Scotland I was effectively bilingual anyway.
by noduerme | 2026-02-24 11:42:07+00:00
>> When did bald face lies become the norm in business ?

When capital has nowhere to go, and the middle class become gamblers and speculators, and people who work become 'superfluous', lots of bad things start to happen. At least, that was my takeaway from "Origins of Totalitarianism".

[edit] To be more specific: Lying (and foreign wars, too) become viable business models when the moneyed class has so much to invest that they no longer know what to do with it, and lack any new markets to pry open, or the education or creativity to produce anything new of value that isn't extractive, and even the extractive methods of generating wealth have begun to dry up locally. Call it a bubble, call it fascism; fascism is basically just a way of keeping a bubble from collapsing indefinitely by pirating neighboring peoples' wealth and cannibalizing one's own society. So there's not a great difference between that and the stated vision of the major AI companies ATM.
by kittikitti | 2026-02-24 11:42:27+00:00
This works by obfuscating the keys in memory with a root-access risk model. It will work but as I've been told when I tried the same thing for another purpose, this is security by annoyance. It sounds harsh but the same gatekeepers mentioned that this was only a psychological trick.

I dislike the gatekeepers so I will follow this implementation and see where it goes. Maybe they like you better.
by ossa-ma | 2026-02-24 11:42:57+00:00
These patterns overlap with formal writing advice because AI was trained overwhelmingly on academic papers, journals and professional writing so it inherited this style.

I completely understand - and do not intend to disparage - the use of these tropes. With the vetter and aidr tools I try to focus more on frequency analysis. I've tried to minimise false positives by tuning detection thresholds to match density rather than individual occurrences e.g. "it's not X, it's Y" is fine but 3x in one paragraph and suspicions flare.

But other tropes like lack of specificity and ESPECIALLY AIs tendency to converge to the mean (less risk, less emotion, FALSE vulnerability) are blatantly anti-human imo.
by TheKnack | 2026-02-24 11:43:01+00:00
I used to keep everything in Obsidian, but I recently switched to keeping notes in Obsidian and links and articles in Karakeep (self-hosted).

https://karakeep.app/

One of many things that I like about Karakeep is that when you save a link it captures both a screenshot and text from the page, and uses AI to create tags and a summary for the link. Basically it automatically categorizes everything that you save.
by bondarchuk | 2026-02-24 11:43:05+00:00
>Anthropic is studying whether its chatbot, Claude, is conscious or can feel “distress,” and allows Claude to cut off “persistently harmful or abusive” conversations in which there are “risks to model welfare”—explicitly anthropomorphizing a program that does not eat, drink, or have any will of its own.

It's really idiotic to posit that only things that eat and drink can suffer.

Final sentences:

>These tools may serve us. But to put them on the same plane as organic life is sad.

There you have it. "If machines could feel I would be sad. Therefore machines cannot feel."
by qsort | 2026-02-24 11:43:37+00:00
> In particular, we’re curious to hear the community’s take on our inclusion of AI-related topics

I think this is fine and if anything you should give it more space. It doesn't replace foundational understanding, but the course is explicitly about "practical" aspects, we can assume said foundational understanding is developed in other courses.

Something like "build your own agent" would be a great intuition pump. The model is doing the heavy lifting and a basic harness is a couple hundred lines of simple code. It could fit in a single lecture and it would be very high signal in my opinion.
by formerly_proven | 2026-02-24 11:43:57+00:00
You will never ever get decent 1% lows in most titles, the software stack is architecturally fucked in the popular engines and can’t do it. You would need a CPU that’s literally 100x faster than today’s top models for it to be able to compile shaders on-demand within a single frame without hitching. (Or maybe it’s more accurate to say that there’s a massive gulf between what the hardware/drivers need - compiled pipeline objects built/known ahead of time - versus what game engines are doing, building pipelines on the fly on demand, surfacing new permutations frame-by-frame)
by embedding-shape | 2026-02-24 11:44:21+00:00
Let me introduce you to the decentralized alternative to ISPs, connecting and collaborating with the new-ish wireless mesh networks that are still active and maintained. The three biggest AFAIK are Freifunk (Germany), Guifi (Spain) and NYCMesh (NYC/US?).

Basically, you can as a private individual set up a wireless node, talk with your nearest node that you have a visual line of sight to, and get connected to a completely separate network from the internet, where there is a ton of interesting stuff going on, and it's mostly volunteer run.
by vincentvandeth | 2026-02-24 11:45:47+00:00
Hard-coded checks before every action, plus a governance layer that separates "what the agent wants to do" from "what it's allowed to do."
The deeper issue: if your agent decides whether to issue a refund, you're solving the wrong problem with prompt guards. A refund is a deterministic business rule — order exists, within return window, amount matches. That decision shouldn't be made by an LLM at all.

In my setup, agents propose actions and write structured reports. A deterministic quality advisory then runs — no LLM involved — producing a verdict (approve, hold, redispatch) based on pre-registered rules and open items. The agent can hallucinate all it wants inside its context window, but the only way its work reaches production is through a receipt that links output to a specific git commit, with a quality gate in between.

For anything with real consequences (database writes, API calls, refunds), the pattern is: LLM proposes → deterministic validator checks → human approves. The LLM never has direct write access to anything that matters.

"Just hoping for the best" works until it doesn't. We tracked every agent decision in an append-only ledger — after a few hundred entries, you start seeing exactly where and how agents fail. That pattern data is more useful than any prompt guard.
by embedding-shape | 2026-02-24 11:45:52+00:00
Or use WiFi and decentralized networks, Freifunk, Guifi and NYCMesh already demonstrated it's possible, and you can easily with consumer hardware setup 1km links with directed antennas, so as long as you have line of sight to another already connected node, you too can participate and help build another network that is separate from the internet :)
by absynth | 2026-02-24 11:46:14+00:00
I have a different perspective. You'll probably hate it.

AI data centers should be operated according to two limiting factors.

1) No energy from grid. Can't use coal or fossil fuel energy sources. Must have plan to provide excess TO grid.

2) No use of fresh water from municipal or fresh groundwater for cooling. Can use waste water. Must transition to providing excess fresh water to common supply.

No loopholes. Massive penalties for use of loopholes or breaking rules, not limited to but including complete shutdown of data center.

Those two limits will spur innovation AND prevent AI being criticized for energy use. These rules would hard burn improvements in energy storage and renewables as well as other methods of energy production.

Give them five years to comply to some useful progress percentage. Plenty of time to come up with a transition plan and show sufficient progress to justify further extensions. Realistically it will take 20 years at least to fully realize this plan.

Don't bring up cost. If you do, let me remind everyone that the climate change issue is real enough to hurt now. There's the very real cost of not pursuing these rules. AI has had plenty of time to bootstrap off grid. Now it can begin to migrate to something else instead.

Those with experience with energy generation will realize this plan has ridiculously high reward for those who follow it. Have your cake and eat it too definitely applies.
by jason_oster | 2026-02-24 11:46:38+00:00
I agree it's probably monomorphization (speculation without looking at it). Generic function parameters might be the root cause, but number of dependencies is a combinatorial multiplier.

I've hit compiler bugs that behave this way. Here's one from an LLVM upgrade [1]. The test case I discovered apparently took over 20 minutes to compile, up from 26 seconds on stable! Their value tracking algorithm was accidentally quadratic.

[1]: https://github.com/rust-lang/rust/issues/137909
by zith | 2026-02-24 11:47:40+00:00
I must have missed some trends changing in the last decade or so. People have production secrets in the open on their development machines?

Or what type of secrets are stored in the local .env files that the LLM should not see?

I try to run environments where developers don't get to see production secrets at all. Of course this doesn't work for small teams or solo developers, but even then the secrets are very separated from development work.
by AJRF | 2026-02-24 11:47:43+00:00
Every time I go to purchase a hobby license for Wolfram / Mathematica I give up because their product offering is the most convoluted i've seen in my life.

Why can't I just pay some price and get the entire bundle of Wolfram One Cloud + API calls + LLM Assistant + This new MCP access + Mathematica?

I need to buy 5 different things - and how does that look for me the user, I need 5 different binaries?

They really should sort that out, I know they are losing money because of this.
I emailed their support once and ended up getting more confused.
by anon7000 | 2026-02-24 11:47:46+00:00
Disagree, I thought the Veritasium video was fantastic. You understand how the machine works in depth, the history of its development and challenges it encountered, and hear from people actively working on it. It’s a science lesson and history lesson. Like usual, they keep the video engaging and focused on the story, while still keeping a lot of depth with the science. It’s a great format
by koolba | 2026-02-24 11:47:51+00:00
> I think you might be underrating the value of even that enabling work. Some parents would not have the financial resources to provide those learning materials. And some parents would take a normative stance on how an 8 year old ought to behave.

And most modern parents would swamp the child with a bunch of mind rotting auto playing TV and video games. There's an account of Terence's time at university where he nearly fails his oral qualifying exams as he spent most of his time playing Civ rather than studying anything. Imagine the travesty for the world if 5 year old Terence had been handed an Xbox.
by quietbritishjim | 2026-02-24 11:50:29+00:00
Great quote!

Your final sentence seems a bit odd in that context though: Churchill's point is that Latin and Greek actually isn't useful at all, so it would follow that it isn't better to do both, especially as time to learn them would have a huge opportunity cost.

(If you think they're worth learning just for their own sake then that's another matter, but the quote seems to imply that Churchill wouldn't agree.)
by einr | 2026-02-24 11:51:02+00:00
The whole thing seems like it was designed in a parallel universe, or at least it reeks of some kind of a sunk-cost-fallacy design-by-committee thing.

Supposedly what happened is that the system was originally designed to have either an 8-bit CPU, or a 16-bit CPU with an 8-bit bus (cf. 8086/8088) like TI's own TMS9985, but at some point it was decided that they should instead cram their full 16-bit TMS9900 minicomputer CPU (!) into the thing. This decision basically tanked the whole architecture.

It was too late/too expensive to redesign the 8-bit support chips to 16-bit counterparts so they had to make some really out there decisions like "talk to the graphics chip and give it an address to read/write every time you want to use memory" and "software is written not in machine code, but in GPL (Graphic Programming Language), which is then interpreted by the CPU and turned into actual TMS9900 machine code"

Software on ROM cartridge for the system is stored in GPL and is fetched from ROM by the CPU (but wait! The ROMs are not in memory space like they would be on a sane computer; they are SERIAL ROMs read 16 bits at a time with memory mapped I/O) and interpreted to machine code. This is slow. When you write your own software in BASIC, however, this gets worse: now you're writing BASIC, which is being interpreted and turned into GPL, stored in video RAM, and then fetched back from video RAM and turned into machine code by the CPU. THIS IS EVEN SLOWER.

Needless to say, the BASIC on the TI99 is dramatically slower than the already slow implementations on other contemporary micros.

It DOES have a full 16-bit CPU which is theoretically much more powerful than a 6502 or Z80 but this wild-ass implementation of... well, everything, makes the system probably the least capable machine of the era.
by Lerc | 2026-02-24 11:51:06+00:00
That makes sense because It's a relevance problem, not a reasoning problem. Adding the hint that it is a test implicitly says 'don't assume relevance'

It is reading

I want to X, the X'er is 50meters away, should I walk or drive?

It would be very unusual for someone to ask this in a context where X decides the outcome, because in that instance it the question would not normally arise.

By actually asking the question there is a weak signal that X is not relevant. Models are probably fine tuned more towards answering the question in the situation where one would normally ask. This question is really asking "do you realise that this is a condition where X influences the outcome?"

I suspect fine tuning models to detect subtext like this would easily catch this case but at the same time reduce favourability scores all over the place.
by UI_at_80x24 | 2026-02-24 11:51:15+00:00
Could you please describe your use-case? How do you use it? How do you make use of it?

I use Claude code, so I understand that paradigm; I don't grok this though. Is it any different then going to a web page i.e. gemini.google.com and typing your query there?

Could this side bar have been a "search bar" at the top?
Now that I say it out lou, adding them to the 'search providers' isn't a bad idea.

Generally speaking I am against this being shoved at us, but I find it as a useful tool in a limited number of areas.
by panick21_ | 2026-02-24 11:51:26+00:00
> Governments cannot make you an alternative

There are many cases where we know for a fact this isn't true.

Many things that governments now do used to be private. Trains for example. Airbus wouldn't have happened without government. French movie industry and so on.

But I agree that its not as easy as government randomyl getting into every random entertainment market and trying to create competitors for everything.
Show HN: Reversing Games With LLMs (Give Claude Access to Memory Dump tools)
by dougwithseismic | 2026-02-24 11:51:38+00:00
Might be of interest to you here.

I'm learning about reversing Source 2 games by building an offset dumper / RTTI crawler / [Insert buzzword feature here] with an API that LLMs can use to debug memory in real-time.

It manual maps a dumper DLL with a web-socket server connected to memory read/write fns, so imagine Cheat Engine but Claude can control it, find offsets, patterns etc.

It started off as a 'Can this be done?' type challenge that's ended up with a live view in web + some LLM tool calls so they can dump memory in real-time. Watching Claude debug memory dumps and follow assembly looks kinda like that infamous Matrix scene to my untrained, noob eye.

I'm a guest in this space, and somewhat of a scrub, so I'm genuinely asking if this could be something helpful for some, or a nothingburger feature that's another 'LLMs built this thing for me' fart in the wind.

https://github.com/dougwithseismic/dezlock-dump

https://github.com/dougwithseismic/dezlock-dump/issues/17#is...
by vincentvandeth | 2026-02-24 11:52:19+00:00
I run a multi-agent orchestration system where each terminal has access to skill templates. The orchestrator (T0) picks which skill to assign based on the task — so I've spent months tuning how skill descriptions affect agent behavior.
What I found: the description is the entire selection surface. The agent doesn't read your code, doesn't check your tests, doesn't browse your docs. It reads the description and decides in one pass.

Three things that actually moved the needle:

Negative boundaries work better than positive claims. "Generates reports from structured receipts. Does NOT execute code, modify files, or make API calls" gets called correctly way more often than "A powerful report generation tool."
Trigger words matter more than you'd think. I maintain explicit trigger lists per skill — specific phrases that should activate it. Without those, the agent pattern-matches on vibes and gets it wrong ~30% of the time. With explicit triggers, that drops to under 5%.

Schema is the real interface. Clean parameter names with sensible defaults beat elaborate descriptions. If your tool takes query: string vs search_query_input_text: string, the first one gets called more reliably across models.

But here's the thing the "agent economy" framing gets wrong: you don't want fully autonomous tool selection. An agent choosing freely between 50 tools is like giving a junior developer admin access to everything — it'll work sometimes and break spectacularly other times. What works better is constraining the agent's scope upfront. Give it 3-5 relevant skills for the task, not your entire toolkit. Or build workflow skills that chain multiple tools in a fixed sequence — the agent handles the content, the workflow handles the routing.

The uncomfortable truth: you're not optimizing for "discovery" in the human sense. There's no brand loyalty, no trust built over time. Every single invocation is a cold start where the model reads your description and decides. That's actually freeing — it means the best-described tool wins, regardless of who built it.
by aleph_minus_one | 2026-02-24 11:52:40+00:00
> I knew OpenAI was in trouble the instant they chose Altman over Ilya Sutskever.

I am not so sure:

This decision rather tells something important about the priorities of the string-pullers behind the curtain:

They clearly want(ed) to monetize what is there, with the risk that only smaller improvements for the AI models will happen from OpenAI, and thus OpenAI might get outcompeted by competitors who are capable of building and running a much better model.

If this is the priority (no matter whether you like or despise Sam Altman), you will likely prefer Sam Altman over Ilya Sutskever.

If, on the other hand, a fast monetization is less important than making further huge leaps towards much better AI models, you will, of course, strongly prefer Ilya Sutskever over Sam Altman.

Thus, I wouldn't say that choosing Sam Altman over Ilya Sutskever is a sign that OpenAI is in trouble, but a very strong sign where the string-pullers behind the curtain want OpenAI to be. Both Sam Altman and Ilya Sutskever are just marionettes for these string pullers. When they have served their role, they get put back into the box.
by tobiaslante | 2026-02-24 11:53:24+00:00
I built a factory simulation with 30 machines (CNC, injection molding, assembly lines) that runs 24/7 with realistic production schedules, breakdowns, and quality issues.

On top of it sits an AI layer with 111 MCP tools.

So you can connect any LLM and query ERP, OEE, WMS, QMS, and scheduling data in real time.

The idea: Manufacturing AI is hard to learn because you never get access to a real factory. This gives you one — for free.

What you can do:
- Chat with a factory AI agent that has access to live production data
- Build visual AI workflows with a Node-RED-based flow editor
- Take challenges ("Get OEE above 85%", "Eliminate late deliveries")
- Bring your own LLM key (OpenAI, Anthropic, or local models)

Stack: Next.js, Express, Node-RED per-user pods on K8s, PostgreSQL, factory simulator in TypeScript. The MCP tools follow the Model Context Protocol spec.

Source: https://github.com/TobiasLante/openshopfloor

Would love feedback — especially from anyone working in manufacturing or building with MCP tools.
by simonw | 2026-02-24 11:54:23+00:00
In context, this bit is about how to deal with the fact that our intuitions on what's worth it vs not worth it in terms of the time it takes to build are likely out-of-date with these new tools:

> For now I think the best we can do is to second guess ourselves: any time our instinct says "don't build that, it's not worth the time" fire off a prompt anyway, in an asynchronous agent session where the worst that can happen is you check ten minutes later and find that it wasn't worth the tokens.

This shouldn't lead to huge maintenance burden because most of the time you'll throw away the result. It's a learning exercise.
by vlovich123 | 2026-02-24 11:54:58+00:00
Laszlo Polgar would disagree [1]. He contends that raising a genius is something you can actually target intentionally (whether or not you should). His proof being 3 daughters who became GMs and one that was a generational talent. As far as audibles go, that’s quite a flex. Yes, the daughters are not all equally talented and chess isn’t quite the same as math, but we’re talking about gradiation within an achievement only reached by much less than 1% of all active players. To me that’s genius level. Also, it’s not necessarily an accident that the youngest is the one to have attained the best result. Evidence is quite clear that older siblings can help their younger ones achieve more faster because the younger ones see it as a path to follow/if they can I can.

[1] https://slatestarcodex.com/Stuff/genius.pdf
by KronisLV | 2026-02-24 11:54:59+00:00
> Note: All "Server Auction" servers have a 3% price increase across the board.

Mine went from 36.30 EUR to 37.39 EUR, according to the e-mail that I got.

I'm actually very thankful that the Server Auction exists and lets some hardware be used like that, instead of becoming e-waste or something.

For what I get, those prices are more than reasonable. I feel a bit bad about the other prices that saw a way bigger of a hike.