MurphyA/hacker-news huggingface dataset:
by iainmerrick | 2026-03-10 10:19:35+00:00
That led me to https://www.folklore.org/Desk_Ornaments.html which is a very fun read. Interesting to note that the UI style of the DAs is actually not consistent at all, some have round corners and some don't.

I particularly like this Bill Atkinson tidbit at the end:

Bill Atkinson complained to me that it was a mistake to allow users to specify their own desktop patterns, because it was harder to make a nice one than it looked, and led directly to ugly desktops. [...] So he made MacPaint allocate a window that was the size of the screen when it started up, and filled it with the standard 50% gray pattern, making his own desktop covering up the real one, thus protecting the poor users from their rash esthetic blunders, at least within the friendly confines of MacPaint.

(He was totally right, making your own desktop patterns was fun but the standard checkerbard was far and away the best choice.)
by quietbritishjim | 2026-03-10 10:19:51+00:00
In the UK, speeding fines are also backed by "points" added to your license - get enough of them and you lose your license altogether for a while. It's similar in at least some other European countries.

That is a definite punishment for anyone that cares enough about driving that they were doing it in the first place, while also clearly not being revenue generating (in fact it prevents future fine revenue). I'm not sure that would wash in the car-centric States though (but it would make it an even juicier punishment). But since you don't get banned immediately, it's potentially low-impact on a per-ticket basis.
by rob74 | 2026-03-10 10:20:32+00:00
> Blanchard's account is that he never looked at the existing source code directly. He fed only the API and the test suite to Claude and asked it to reimplement the library from scratch. The resulting code shares less than 1.3% similarity with any prior version, as measured by JPlag. His conclusion: this is an independent new work, and he is under no obligation to carry forward the LGPL. Mark Pilgrim, the library's original author, opened a GitHub issue to object. The LGPL requires that modifications be distributed under the same license, and a reimplementation produced with ample exposure to the original codebase cannot, in Pilgrim's view, pass as a clean-room effort.

Another question which as far as I can see isn't addressed in the article: even if you accept that the AI-driven reimplementation is an independent new work, can you (even as a maintainer) simply "hijack" the old LGPL-licensed project and overwrite it (if the new code only is 98,7% different from the existing code, it's essentially overwriting) with your MIT-licensed code? You're free to start a new MIT-licensed project with your reimplementation, but putting the new code into the old project like some kind of cuckoo's egg seems wrong to me...
by tomovo | 2026-03-10 10:21:18+00:00
The justification by Apple is that it keeps the concentricity between window corner and the red/green/yellow window controls. Which, as you may notice, it does.

It's wrong though, because the window is the higher element in the hierarchy (container) and should not be affected by what is inside. It creates a larger inconsistency than the "consistency" it supposedly brings.
by fennecfoxy | 2026-03-10 10:21:55+00:00
Tibetans, uyghurs, etc? Factories full of North Korean workers under the watchful eye of their overlords, modern slavery even of your own people.

As a Kiwi I look at the US, Russia, China, etc as the same. Even the UK (where I now live) is a scarier place than back home.
by tveita | 2026-03-10 10:22:09+00:00
There's some odd choices here.

- 90 days is a very long time to keep keys, I'd expect rotation maybe between 10 minutes and a day? I don't see any justification for this in the article.
- There's no need to keep any private keys except the current signing key and maybe an upcoming key. Old keys should be deleted on rotation, not just left to eventually expire.
- https://github.com/aaroncpina/Aaron.Pina.Blog.Article.08/blob/776e3b365d177ed3b779242181f0045cd6387b3f/Aaron.Pina.Blog.Article.08.Server/Program.cs#L70-L77 - You're not allowed to get a new token if you have a a token already? That's unworkable - what if you want to log in on a new device? Or what if the client fails to receive the token request after the server sends it, the classic snag with use-only-once tokens?
- A fun thing about setting an expiry on the keys is that it makes them eligible for eviction with Redis' standard volatile-lru policy. You can configure this, but it would make me nervous.
by nachocoll | 2026-03-10 10:22:34+00:00
C is one of the more interesting vibe coding domains because the failure modes are so unforgiving. With Python or TypeScript, AI-generated bugs often manifest as logical errors or runtime exceptions you can trace. With C, the bugs can be memory corruptions that don't surface until much later, and the hallucinated function signatures or incorrect memory management patterns can produce code that works most of the time and fails spectacularly in edge cases.

That said, there are genuine wins in using AI for C — boilerplate generation for data structures, writing test harnesses, scaffolding platform-specific code you'd otherwise have to look up. The key discipline is the same as always: you have to read and understand every line before it touches a production codebase.

The Agile Vibe Coding Manifesto (https://agilevibecoding.org) has a principle that's especially relevant for C: "Generated systems remain deterministic and verifiable — verified through tests." For C specifically, that means not just running the tests but running with sanitizers (ASAN, UBSan) and being very deliberate about what invariants you're relying on.

Is this for embedded, systems software, or something else? The risk profile varies a lot by domain.
by mike_hearn | 2026-03-10 10:23:00+00:00
I'd love to be a fly on the wall when this argument is tried in front of a bankruptcy court. It drives me nuts. Of course there's evidence that they're selling tokens at a loss.

The only thing these companies sell are tokens. That's their entire output. OpenAI is trying to build an ad business but it must be quite small still relative to selling tokens because I've not yet seen a single ad on ChatGPT. It's not like these firms have a huge side business selling Claude-themed baseball caps.

That means the cost of "inference" is all their costs combined. You can't just arbitrarily slice out anything inconvenient and say that's not a part of the cost of generating tokens. The research and training needed to create the models, the salaries of the people who do that, the salaries of the people who build all the serving infrastructure, the loss leader hardcore users - all of it is a part of the cost of generating each token served.

Some people look at the very different prices for serving open weights models and say, see, inference in general is cheap. But those costs are distorted by companies trying to buy mindshare by giving models away for free, and of those, both the top labs keep claiming the Chinese are distilling them like crazy including using many tactics to evade blocksl So apparently the cost of a model like DeepSeek is still partly being subsidized by OpenAI and Anthropic against their will. The cost of those tokens is higher than what's being charged, it's just being shifted onto someone else's books. Nice whilst it lasts, but this situation has been seen many times in the past and eventually people get tired of having costs externalized onto them.

For as long as firms are losing money whilst only selling tokens, that means those tokens are selling at a loss. To not sell tokens at a loss the companies would have to be profitable.
Show HN: A mission-based game to help students apply math in real life
by firepegasus11 | 2026-03-10 10:23:10+00:00
I was the weakest at maths and still get nightmares about it. After years of trying, shipping, and playing, I realised that rather than turning each concept into a module, it's much better to provide a place where students can apply their learning and see the outcome.

With this, I present Owster Labs. This is a game-based learning module where you go through missions and find solutions. Unlike normal games, this is a very watered-down version of CFD. You have a UAV to work on: understand the situation, prepare your UAV to fly below mountains and evade enemy aircraft. Do your calculations, find the best path, and learn the art of trade-offs and how maths is used in real life.

The system will play the outcome based on your solution and provide feedback. The whole mechanism is focused on making it look great and less fearful for kids.

Kindly try it on your PC and let me know what you think.
by hansmayer | 2026-03-10 10:23:11+00:00
Of course you can use them for whatever you want. Its also not disputable that some people will be more careful than the other. The issue however is that the idiots who pushed for widespread usage of AIs in the companies, i.e. clueless MBAs, have also pushed them onto exactly the types you are mentioning - the ones who will screw things over because they are incompetent or don't care, or most likely - are both of those things. So it's not a criticism of people who are careful in their usage of LLMs in critical scenarios - it's a criticism of the morons who bought into the AI hype and really believe an LLM will produce equally great terraform code previously written by 10 engineers at the 1% of the cost.
by Protostome | 2026-03-10 10:23:21+00:00
Going to the gym every day doesn't mean lifting weights every day , at least not at first. Once you have advanced enough you can certainly lift everyday, focusing on different muscle groups.

The principle is to be active - treadmill, rowing machine, elliptical, etc on days you're not lifting weights are perfectly reasonable expectation after a few months of adaptation period.
You don't have to go to the gym of course, you can do all those activities at home with some very cheap and easy to obtain equipment like rubber bands and/or TRX, but the point I think OP was trying to make is to create opportunities for social interactions.

On days I miss workouts I feel much more groggy and tired, so working out over the years became a necessity which I don't really need to find motivation to do. If you feel bored and tired, try to couple workouts with audiobooks or podcasts, that helps to make the experience more enjoyable and even productive at times.

(I'm nearing 42, working out most of my life 5 days a week at least)
Most B2B Lead Generation Tools Create Contacts, Not Customers
by lakshmirk | 2026-03-10 10:23:23+00:00
Something interesting happens in many B2B companies.

Marketing teams generate thousands of leads every month. Dashboards look great, reports look impressive, and campaigns appear successful.

But when sales teams start reaching out, the story often changes. Emails bounce, many contacts never respond, and some people were never serious prospects in the first place.

The result is a huge list of contacts but very few real conversations.

This made me start looking deeper into how companies approach lead generation today. Many tools focus heavily on collecting data, but they don’t necessarily help teams identify the right prospects or decision-makers.

Some tools are designed for prospect discovery, some focus on email verification, and others automate outreach or capture leads from websites. When these pieces aren’t connected properly, businesses end up with volume instead of quality.

I found this breakdown of different lead generation tools and how businesses actually use them across prospecting, verification, and outreach. It explains how these tools fit into the overall lead generation workflow.

https://jarvisreach.io/blog/lead-generation-tool-for-every-business/

Curious to hear how others here approach this problem.
Do you focus more on volume of leads, or quality of prospects when building a pipeline?
by DrewADesign | 2026-03-10 10:23:27+00:00
The lead time on a national magazine ad was usually longer than a month and they generally weren’t tied to a specific magazine’s publishing schedule— they were probably parts of longer thematic/strategic campaigns. They probably also appeared in trade rags for other tech-heavy (mechanical engineering) or tech-adjacent (finance) publications.

The real reason ads look shittier now is the marketing world shifted their investment from the ads themselves to ad targeting. You just don’t need to make great ads if you can shove them in the face of the most receptive people at the right time. It’s also not feasible to make a few great ads when your marketing team has 8 different approaches tailored to specific demographics in multiple languages.
by RovaAI | 2026-03-10 10:23:57+00:00
The idea generation bottleneck you flagged is the real one. Everyone has access to the same LLMs now - the scarcity is knowing what to write about that actually resonates with your specific audience.

What works: use AI to mine your own existing channels first. Take your email replies, support threads, sales call transcripts - run them through Claude with a "what pain points recur most" prompt. You get a priority-ranked content backlog in 20 minutes that's grounded in what your audience actually says out loud.

Most people reach for AI to generate ideas and then struggle with quality. The teams getting real ROI flip it: use traditional signal (replies, reviews, community posts) to source the ideas, then AI to execute faster.

On the stack question - the distribution layer is where money is worth spending, not the generation layer. $89/month on SurferSEO to surface the right ideas beats $200/month on better generators every time.
by nickk81 | 2026-03-10 10:24:08+00:00
built a demo where you can chat with an entire AI leadership team — CEO, CFO, CTO, COO, a lawyer, a dev, an analyst, and an intern. Each has a distinct personality, expertise area, and communication style. You can talk to them individually or throw a question into a group chat and watch them respond from their own perspectives.

It's built on top of my customer support platform (99helpers.com), but I made this mostly to explore a question: what happens when you give AI characters persistent roles and let them interact with the same prompt from different angles? The group chat mode is where it gets interesting — ask something like "should we raise prices?" and you'll get the CFO's spreadsheet logic clashing with the COO's operational concerns.

Would love to hear what people think about multi-persona AI interactions as a UX pattern.
by endymion-light | 2026-03-10 10:24:53+00:00
I mean, i think if you want to perform mass surveilance, you can do it far cheaper and more efficiently via facial recognition, mobile phone surveillance and a variety of different other methods.

If you want reconstruction and training of robotic movement, this is far more appropriate. I believe we're going to see robots being able to "dream" in terms of analysing historical video information on spaces and improving movement and navigation.

So not mass surveilance, but probably there's a future of mass subjugation using robot enforcement.
by RovaAI | 2026-03-10 10:24:56+00:00
devonkelley's dashcam framing is right. The useful question isn't "how do I see what happened" - it's "how do I catch irreversible actions before they happen."

The failure modes from those incidents aren't really observability gaps. They're about permission scope and action reversibility. An agent deleting a database doesn't need better logging after the fact - it needs a clear model of what's reversible and what isn't, built into the execution loop.

What works: classify every action as either local/reversible (reads, file edits, drafts) or external/irreversible (sends, deletes, pushes, payments). The former runs autonomously. The latter gets a confirmation checkpoint with no exceptions. That one split eliminates most incident surface area without needing a dedicated SDK.

Langfuse/LangSmith are useful for cost tracking and debugging post-hoc. But they're tools for the team, not the agent. The reversibility model needs to be at the framework level.
by vmykyt | 2026-03-10 10:25:03+00:00
I have very naive question:

People in comments have assumption that Atropic 10 times bigger than chinese models so calc cost is 10 times more.

But from perspective of Big O notation only a few algorithms gives you O(N). Majority high optimized things provide O(N*Log(N))

So what is big O for any open model for single request?
by cucumber3732842 | 2026-03-10 10:25:34+00:00
A reasonably median person doing reasonably median things (so like within a couple standard deviations) has a right to be free from low effort harassment by the state at the unilateral whim of its agents. In the US this is codified across several of the amendments in the bill of rights.

That means the building inspector can't just waltz into your home uninvited, ICE can't kick in your door because your maid is brown and the government can't just put up cameras and start dragnet fining people for rules that only really exist on paper and are used to make it procedurally easier to go after behavior that's bad for subjective reasons.

Second, the fact that legislative bodies may pass and enforcers may enforce stuff that violates people's rights (rental inspections and civil asset forfeiture) come to mind because the size of the harm and the manner in which it is targeted keeps it below the "get everyone pissed off" level doesn't make it not a violation of people's rights. Funny you mention building code. The manner in which building (zoning really, since building code is basically the public adoption of 3rd party standards via the zoning code) code is written and enforces is complete dogshit and would not hold up to rigorous scrutiny (and generally does not, in the incredibly rare occasion is sees it), but between the fact that it's cheaper to comply and that the worse abuses are generally targeted at exactly the kind of people who no judge will have sympathy for (much like civil asset forfeiture was initially) mean the general population is not too up in arms about it since it's

Now, before you put words in my mouth as the kind of people who say things like "rights are not unlimited" are in my observation very apt to do, I'm not saying don't have building code. I'm saying don't violate people's rights to have it, or anything else. It's nowhere near enforced to the letter anyway so walking the actual letters back to match what can be enforced without violating people's rights should be no big deal. And the same is goes for like red light cameras.
by nananana9 | 2026-03-10 10:25:36+00:00
if (foo == true) { // checking foo is true (rocketship emoji)
20 lines of code;
} else {
the same 20 lines of code with one boolean changed in the middle;
}

Description:

(markdown header) Summary (nerd emoji):

This PR fixes a non-existent issue by adding an *if statement** that checks if a variable is true. This has the following benefits:

- Improves performance (rocketship emoji)
- Increases code maintainability (rising bar chart emoji)
- Helps prevent future bugs (detective emoji)

(markdown header) Conclusion:

This PR does not just improve performance, it fundamentally reshapes how we approach performance considerations. This is not just design --- it's architecture. Simple, succinct, yet powerful.
by yccs27 | 2026-03-10 10:25:57+00:00
The last line of the introduction

> By doing so, we aim to provide a novel paradigm [...]

also made me think of item 19 on your list:

> 10 points for claiming that your work is on the cutting edge of a "paradigm shift".

I'm sad though that you didn't call it the "Baez crackpot index"...
by jrecursive | 2026-03-10 10:26:10+00:00
I feel this. Almost exactly the same experience. 300bps in early days. At some point got a 1200bps. Then 2400, then 14.4 and 28.8 came fast. WWIV variants. CDC. 2600. Phrack. 612-341-2459 for local Telenet dialup at the time, my fingers still know the number unconsciously. War dialing! 5ESS. SS7. Bluebeep. 0700 bridges. Even in the early days of the internet, EFnet had BBS flavor to some extent. Certainly the warez. haha. A lot of the same people. What a time. I remember the first time I was going through Computer Shopper and found the BBS list. First one I ever called was Unlawful Entry... welp. If fate exists, it was busy that day.
by frwrfwrfeefwf | 2026-03-10 10:26:27+00:00
It gets worse: material things aren't real either.

It continues to get worse: Sensations are compounded in our experience to express semantics but the sensations themselves have no inherent semantics, so not only is knowledge arbitrary and pointing to nothing, but there's no objective truth to find, let alone the world and us not existing in any real sense.
by heresie-dabord | 2026-03-10 10:26:35+00:00
> 7200 years ago the Neolithic revolution was still in full swing

Older folks at Göbekli Tepe grumbled that climate change wasn't real. As far as they were concerned, the Sumer and Indus Valley kids were playing with fire and didn't know squat. The older generation just couldn't understand the crazy architecture over in Egypt and the slangy "new wave" movement at Salisbury Plain. It always seemed that the shiftless youth there just loitered and smoked and invented new expressions to frustrate communication. And everyone could agree that no one liked their so-called music!

https://en.wikipedia.org/wiki/G%C3%B6bekli_Tepe

https://en.wikipedia.org/wiki/8.2-kiloyear_event

https://en.wikipedia.org/wiki/Sumer

https://en.wikipedia.org/wiki/Indus_Valley_Civilisation

https://en.wikipedia.org/wiki/Old_Kingdom_of_Egypt

https://en.wikipedia.org/wiki/Stonehenge
by fennecfoxy | 2026-03-10 10:26:36+00:00
I mean sure, but in terms of cost per dollar/per watt of inference Nvidia's GPUs are pretty up there - unless China is pumping out domestic chips cheaply enough.

Also with Nvidia you get the efficiency of everything (including inference) built on/for Cuda, even efforts to catch AMD up are still ongoing afaik.

I wouldn't be surprised if things like DS were trained and now hosted on Nvidia hardware.
by manojpathak | 2026-03-10 10:26:39+00:00
HA after reading your question I noticed that new accounts are shown in green :-) and I am green too. I understand your pain and the issue is that it is inevitable. G-AI are being used everywhere so instead of writing in bad English like this why not ask G-AI to formalize my thoughts.

BTW I am curious, how you figure it out that its AI generated other then green?
Cold DMs don't work anymore. Here's what got me my first users
by deep1283 | 2026-03-10 10:26:40+00:00
Business owners get hundreds of cold messages daily. Yours gets ignored.
The approach that actually works: find people already talking about the pain your product solves.
Not cold outreach. Not ads. Just being in the right place when someone is already frustrated.
Search Reddit, HN, Twitter for people complaining about the exact problem you solve. Slide into that conversation naturally. That's a warm lead who already wants a solution.
The best distribution hack isn't marketing. It's being in the right place when someone is already in pain.
What's the most effective way you've found your first users?
by bluefirebrand | 2026-03-10 10:27:02+00:00
I'm still just so surprised any time I encounter people who think AI will be overall good for humanity

I pretty strongly think it will only benefit the rich and powerful while further oppressing and devaluing everyone else. I tend to think this is an obvious outcome and it would be obviously very bad (for most of us)

So I wonder if you just think you will be one of the few who benefit at the expense of others, or do you truly believe AI will benefit all of humanity?