MurphyA/hacker-news huggingface dataset:
by pear01 | 2026-03-10 16:02:34+00:00
China is also an ancient civilization. Americans view of themselves is highly inflated by the sheer luck of being two oceans away from everyone during both world wars. Save for Pearl Harbor there were no notable attacks on the American homeland. It's easy to be a superpower when the world destroys itself and you step into the breach. Millions of Soviet civilians died on their own homeland. In their own cities. Millions. Most Americans have no idea and can't really comprehend it. Even today a shocking number of Americans don't have a passport and really know nothing about the world beyond their shores. These people are overrepresented in an American Congress that is anti-democratic. People on the coasts, like in New York City are underrepresented in American government. The entire state of New York gets the same amount of senators as flyover states, many of which are welfare states (take more funds than they contribute). This is because in a modern economy what NYC produces is more valuable than what a state with barely any people in the middle of nowhere produces. Yet the middle of nowhere is represented more. It makes no sense.

The current administration is only convincing the world that America is a threat. We live in an age where two oceans offer far less protection than they did when America rose to superpower status. The fact Russian intelligence operatives can so easily infiltrate American political discourse is just one example. Watch any congressional hearing about cyber and you might be forgiven for thinking we have already been invaded. Beating up on third world pariah states impresses no one but the current administration. The United States bombs Iran but blinks at Russia. The administration started a trade war with China then backed off, not one meaningful concession was achieved.

Unless America reverses course fast the decline will only continue. The world will move on. No country is inevitable.
by orbital-decay | 2026-03-10 16:02:49+00:00
Yes, my point is "censorship and bots do work" does not equal "people don't care". People online often imagine themselves to be pretty informed about some other country, especially if they communicate with people from that country. This is delusional, I can't claim I know much about "average reality of living in Brazil" even though I communicate with people from there, follow some media, have traveled across it on a motorcycle, and was a guest to some friends there. This is even less true for current Russia.
by SlinkyOnStairs | 2026-03-10 16:02:49+00:00
In the long distant past of 4-5 years ago, it simply wasn't a problem. Few projects were overwhelmed with PRs to begin with.

And for the major projects where there was a flood of PRs, it was fairly easy to identify if someone knew what they were talking about by looking at their language; Correct use of jargon, especially domain-specific jargon.

The broader reason why "unknown contributor" PRs were held in high regard is that, outside of some specific incidents (thank you, DigitalOcean and your stupid tshirts), the odds were pretty good of a drive by PR coming from someone who identified a problem in your software by using it. Those are incredibly valuable PRs, especially as the work of diagnosing the problem generally also identifies the solution.

It's very hard to design a UX that impedes clueless fools spamming PRs but not the occasional random person finding sincere issues and having the time to identify (and fix them) but not permanent project contribution.

> and the hard part isn't writing code anymore but generating the spec

My POV: This is a bunch of crap and always has been.

Any sufficiently detailed specification is code. And the cost of writing such a specification is the cost of writing code. Every time "low code" has been tried, it doesn't work for this very reason.

e.g. The work of a ticket "Create a product category for 'Lime'" consists not of adding a database entry and typing in the word 'Lime', it consists of the human work of calling your client and asking whether it should go under Fruit or Cement.
by hkonte | 2026-03-10 16:02:51+00:00
Agreed on the judgment point. One place where structure helps: decomposing the prompt before you run it. Writing out role, objective, constraints, output format as separate fields forces you to answer "what do I actually want?" before the model does anything. You catch vague goals at prompt-writing time, not after you get a useless output.

That's the core idea behind flompt (https://github.com/Nyrok/flompt), a canvas where you build prompts from typed blocks and compile to XML. The act of filling in each block is itself a judgment exercise. Open-source if anyone wants to try it.
by yangro | 2026-03-10 16:03:06+00:00
The difference I've seen in PM teams isn't AI vs no-AI — it's structured vs ad hoc usage.

Ad hoc prompting (where each session starts blank) accumulates the cognitive debt people are worried about here. You get faster output but no compound value.

The teams that aren't degrading are the ones with persistent context: product knowledge, decision history, persona details loaded once and built on every session. Every new PRD or competitive analysis picks up where the last one left off instead of reinventing the product brief.

The "AI will ruin your team" concern is valid for the blank-slate approach. It's less applicable when the system carries institutional knowledge the way a senior PM would.
by robeym | 2026-03-10 16:03:07+00:00
I’ve been building a custom ERP/CRM system for the medical device manufacturing company I work at. We went through two commercial ERPs over the years and both were slow and painful to customize, so I started building a simpler replacement.

It handles inventory, work orders (MRP), purchasing, sales orders, accounting, everything we need. After a couple failed attempts earlier on, I rebuilt it with a much simpler stack and it’s now running the company day to day.

Still learning a lot about manufacturing workflows and how much unnecessary complexity most ERP systems accumulate. Now I'm trying to focus on ways that the software can help other companies in a similar ERP boat that we were some years ago.
by mschuster91 | 2026-03-10 16:03:08+00:00
> Which is to NOT use those platforms that require age verification.

That is getting harder and harder. Platforms that are not susceptible to age verification (yet?) are on their way out - when have you written an email the last time for personal (i.e. non-work, order or customer support related) reasons? A physical letter [1]? The (root) cause is, centralized platforms like Whatsapp are much much more convenient and on top of that network effects apply - when 90% of your social connections use Whatsapp exclusively, it's hard to not use Whatsapp as well.

And then you got digitalization of government services and banking. More and more governments push for the removal of paper forms and require a web service. Banking regulations enforce 2FA, which almost always comes in the form of a phone app. The web services require a browser and an OS, which may require age verification sooner than later (see the recent spat about California's law), and the phone apps are only available for the walled gardens of unrooted, Play Store certified Apple and Android phones - that can and will be forced to verify ages as well.

Hard cash is out as well, many governments have set hard caps on cash transactions due to "anti money laundering" laws, in other countries you need to have a bank account to pay for mandatory things like taxes or public broadcast fees [2], and an increasing number of vendors refuses to accept cash as well due to the associated handling cost and risk of fraud (i.e. employee theft) and robbery.

That last point alone will make it impossible to survive in society without engaging with one or more of the walled gardens.

And mercy be upon you if the US Government decides to put you on one of their black lists. No more banking, even as an European, because everything touches VISA/MC/SWIFT, your cloud accounts (and with it your phone and app stores), all gone, you are now an unperson [3].

[1] Some countries are already shutting down postal services over that, e.g. Denmark: https://www.theguardian.com/world/2025/dec/21/denmark-postno...

[2] https://www.verbraucherzentrale-niedersachsen.de/themen/rund...

[3] https://en.wikipedia.org/wiki/Executive_Order_14203
Show HN: CocoIndex-Code open source embedded AST-based code MCP
by georgehe9 | 2026-03-10 16:03:38+00:00
Hi HN friends, George here. I've been working on an embedded AST based code mcp that is super light weight and just works. I published my test on the repo, it has great effect on achieving and speeding up coding tasks and saves tokens.

Works with Claude, Codex, OpenCode and any coding agents, I'd love to learn from your feedback!
by majewsky | 2026-03-10 16:03:48+00:00
> It is very unclear whether the output of an AI tool is subject to copyright.

At least for those here under the jurisdiction of the US Copyright Office, the answer is rather clear. Copyright only applies to the part of a work that was contributed by a human.

See https://www.copyright.gov/ai/Copyright-and-Artificial-Intell...

For example, on page 3 there (PDF page 11): "In February 2022, the Copyright Office’s Review Board issued a final decision affirming
the refusal to register a work claimed to be generated with no human involvement. [...] Since [a guidance on the matter] was issued, the Office has registered hundreds of works that incorporate AI-generated material, with the registration covering the human author’s contribution to the work."

(I'm not saying that to mean "therefore this is how it works everywhere". Indeed, I'm less familiar with my own country's jurisprudence here in Germany, but the US Copyright Office has been on my radar from reading tech news.)
by yangro | 2026-03-10 16:04:01+00:00
Worth adding the PM use case, since most examples here are engineering-focused.

For product work, agent teams shine in three scenarios:

1. Parallel competitive analysis — 4 agents each covering one competitor simultaneously, results synthesized in <3 min vs 2 hrs
2. User interview synthesis — each agent handles a batch of transcripts, then a coordinator merges themes
3. Multi-stakeholder review — simultaneous CTO/CXO/CMO perspectives on a PRD before it goes to leadership

The async model works best here because you want each agent to reason independently before comparison. Consensus models collapse too fast to the first strong framing.

This is something I've been running in Claude Code for PM teams — the hard part isn't the agents, it's building enough shared context that they're reasoning from the same product model.
by marginalia_nu | 2026-03-10 16:04:05+00:00
Expert reviews are just about the only thing that makes AI generated code viable, though doing them after the fact is a bit sketchy, to be efficient you kinda need to keep an eye on what the model is doing as its working.

Unchecked, AI models output code that is as buggy as it is inefficient. In smaller green field contexts, it's not so bad, but in a large code base, it's performs much worse as it will not have access to the bigger picture.

In my experience, you should be spending something like 5-15X the time the model takes to implement a feature on reviewing and making it fix its errors and inefficiencies. If you do that (with an expert's eye), the changes will usually have a high quality and will be correct and good.

If you do not do that due dilligence, the model will produce a staggering amount of low quality code, at a rate that is probably something like 100x what a human could output in a similar timespan. Unchecked, it's like having a small army of the most eager junior devs you can find going completely fucking ape in the codebase.
Ask HN: How are you testing AI agents before shipping to production?
by harperlabs | 2026-03-10 16:04:07+00:00
We've been running reliability audits on AI agents before production deployment, and the failure patterns are consistent enough that I built a framework around them.

Some context on why this matters right now: Gartner predicted over 40% of AI agent projects will fail by 2027. In January 2026, a prompt injection in a customer support agent processed a $47,000 fraudulent refund. These aren't fringe cases anymore.

The 7 failure modes we see most often:

1. Hallucination under unexpected inputs — works perfectly in demos, invents data when the input is slightly off

2. Edge case collapse — null values, Unicode names (O'Brien, José, 北京), empty fields, concurrent requests

3. Prompt injection — if your agent processes external content, users can hijack its behavior through that content

4. Context limit surprises — agent works for 95% of conversations, then silently misbehaves when the context window fills. No error. Just wrong behavior.

5. Cascade failures — tool call #1 fails, agent keeps going, by the time a human sees the result 3 calls have compounded the error

6. Data integration drift — built against your schema in January, schema changed in February, still calling deprecated endpoints in March

7. Authorization confusion — multi-tenant system, cached context from User A bleeds into User B's session

We've built 50+ test cases across these categories. Most teams test #1 and #3. Almost no one systematically tests #4, #5, and #6 before shipping.

Happy to share the framework. Curious what failure modes you've hit that I haven't listed.
by rramadass | 2026-03-10 16:04:12+00:00
Followup on the above with these two classics;

Retrospective: An Axiomatic Basis For Computer Programming. This was written 30 years after An Axiomatic Basis for Computer Programming to take stock on what was proven right and what was proven wrong - https://cacm.acm.org/opinion/retrospective-an-axiomatic-basi...

How Did Software Get So Reliable Without Proof? More detailed paper on the above theme (pdf) - https://6826.csail.mit.edu/2020/papers/noproof.pdf
by yangro | 2026-03-10 16:04:28+00:00
From the PM side: Claude's value proposition is different than for developers, but equally strong.

For PMs, it's less about code quality and more about persistent context — Claude handles long, structured system prompts (your product knowledge, personas, competitive landscape) in a way that makes every session smarter than the last. ChatGPT context windows are fine for one-offs; Claude feels more like a thought partner you've actually briefed.

The thing that tipped it for me was reusable skills. I can write /prd-generator once with all my product context embedded, and every spec comes out consistent — not just grammatically correct but strategically coherent.

That's what "choosing Claude" looks like when you're not writing code.
by Milpotel | 2026-03-10 16:04:41+00:00
I'm pretty sure that this is not true. I talked to Bud Lawson (the inventor of the pointer) and he claimed that they had implemented special behaviour for null pointers earlier. When I talked to Tony later about it, he said he had never heard of Bud Lawson. So probably both invented them independently, but Bud came first.
by jonathananuma | 2026-03-10 16:05:08+00:00
I put together an analysis comparing the privacy and encryption practices of 15 major AI chat platforms.

The report looks at:
– whether conversations are used for model training by default
– whether platforms provide end-to-end encryption or only TLS in transit
– whether users can export or control their data
– whether systems support multi-model routing

Platforms evaluated include ChatGPT, Claude, Gemini, Grok, Perplexity, DuckDuckGo AI Chat, and others.

One finding: only 7 of the 15 platforms currently offer end-to-end encryption.

Would be curious to hear feedback from people building AI infra or privacy tooling.
by torginus | 2026-03-10 16:05:29+00:00
I'm not familiar with accounting, but I suspect a lot of these cloud infrastructure companies don't throw out hardware for a very long time, just like how AWS sells you their old stuff as whitelabel compute at a markup, behind which I think are mostly old pieces of hardware, I think as long as Anthropic keeps finding uses for the old GPUS provided they dont break, they don't have to write off these assets, which means they don't incur costs using them if they are clever with their books
by patchnull | 2026-03-10 16:05:58+00:00
This lines up with what I have seen doing CKA (centered kernel alignment) analysis on transformer internals. The middle layers in most large models have surprisingly similar representations to their neighbors, so duplicating them is basically giving the model extra compute cycles in a region where it is already doing useful refinement without messing up the input/output encoding stages. Curious whether picking layers by representation similarity instead of just a contiguous block would do even better.
by CoolGuySteve | 2026-03-10 16:05:58+00:00
It didn't seem to make the news but at least in NYC the entire Amazon storefront was broken all afternoon on Friday.

Items weren't displaying prices and it was impossible to add anything to your cart. It lasted from about 2pm to 5pm.

It's especially strange because if a computer glitch brought down a large retail competitor like Walmart I probably would have seen something even though their sales volume is lower.
by baby_souffle | 2026-03-10 16:06:02+00:00
I use Ubnt for layer 1 and 2. Their switches are nice if your needs can be met by their spog. Same with their access points although I haven’t see a good one from then since WiFi 5. Their latest WiFi 7 ap is top dollar but used last generation chip and has a cooling fan :[

Dont use unit for routing, though.
by mr-wendel | 2026-03-10 16:06:17+00:00
My two cents: I've been coding practically my entire life, but a few years back I sustained a pretty significant and lasting injury to my wrists. As such, I have very little tolerance for typing. It's been quite a problem and made full time work impossible.

With the advent of LLMs, AI-autocomplete, and agent-based development workflows, my ability to deliver reliable, high-quality code is restored and (arguably) better. Personally, I love the "hallucinations" as they help me fine-tune my prompts, base instructions, and reinforce intentionality; e.g. is that >really< the right solution/suggestion to accept? It's like peer programming without a battle of ego.

When analyzing problems, I think you have to look at both upsides and downsides. Folks have done well to debate the many, many downsides of AI and this tends to dominate the conversation. Probably thats a good thing.

But, on the flip side, I personally advocate hard for AI from the point-of-view on accessibility. I know (more-or-less) exactly what output I'm aiming for and control that obsessively, but it's AI and my voice at the helm instead of my fingertips.

I also think it incorrect to look at it from a perspective of "does the good outweigh the bad?". Relevant, yes, but utilitarian arguments often lead to counter-intuitive results and end up amplifying the problems they seek to solve.

I'd MUCH rather see a holistic embrace and integration of these tools into our ecosystems. Telling people "no AI!" (even if very well defined on what that means) is toothless against people with little regard for making the world (or just one specific repo) a better place.
by estebank | 2026-03-10 16:06:32+00:00
> these valuable projects that are taking hard-line stances against it are going to find themselves either having to retreat from that position or facing insurmountable difficulties in staying relevant while holding to their stance.

It is the conservative position: it will be easier to walk back the policy and start accepting AI produced code some time down the road when its benefits are clearer than it will be to excise AI produced code from years prior if there's a technical or social reason to do that.

Even if the promise of AI is fulfilled and projects that don't use it are comparatively smaller, that doesn't mean there's no value in that, in the same way that people still make furniture in wood with traditional methods today even if a company can make the same widget cheaper in an almost fully automated way.
by crazygringo | 2026-03-10 16:06:48+00:00
If you run a company or companies on two coasts and have a wife and family on another continent (say she has her own career and can't move), then what exactly are you supposed to do?

I don't know this guy's personal life, but the people I know who fly tons fit into this profile. E.g. the wife can't move because she's a tenured professor at her university, and he's got to be at both offices regularly. He's best qualified to run the company/companies, and he's not going to get divorced to reduce his CO2 emissions.

What exactly is the solution you propose? What personal responsibility do you expect them to take? You think he should get divorced? Only see his wife and kids four times a year? Have his company/companies suffer because he can't be there in person? Quit his jobs?

And let's be clear, there are lots of jobs that require tons of air travel. If you're a highly specialized repair technician for certain equipment, all you do is constantly fly around the world fixing equipment wherever it is. If you're a CEO of a multinational company, you're constantly flying around to different offices. Are you looking for "personal responsibility" here too? How?
by cassonmars | 2026-03-10 16:07:09+00:00
You can if the manufacturer has a track record that refutes the notion, and especially if they have verifiable hardware matching publicly disclosed circuit designs. But this is Intel, with their track record, I wouldn't trust it even if the schematics were public. Intel ME not being disable-able by consumers, while being entirely omitted for certain classes of government buyers tells me everything I need to know.
by doublerabbit | 2026-03-10 16:07:32+00:00
> Most FreeBSD installs still make use of stuff like Wayland and a lot of Linux parts.

No? Wayland is available but Xorg is the default.

FreeBSD doesn't install a display server on default so the choice is yours. There is no GUI installation of FreeBSD so I am not sure what "linux parts" you are referring to.

The installation uses curses is ancient and has existed since the dawn of C, unix which is what FreeBSD is. Is that what your referring to?

pkg is FreeBSD's package manager.
by patchnull | 2026-03-10 16:07:58+00:00
I ended up building something similar but way smaller in scope -- just a cron job that pulls Apple Health exports, bank CSV dumps, and git commit stats into a single SQLite db. The queries that actually proved useful were embarrassingly simple, stuff like correlating sleep hours with commit frequency or spotting that my spending spikes every time I start a new side project. The real 80/20 of personal analytics is just getting your data into one place where you can join across sources. Everything beyond that is diminishing returns.
by wastemaster | 2026-03-10 16:08:05+00:00
Hey HN. I'm the CTO at a startup building AI agents for hotels. Lately, I've been seeing a lot of hype around MCP being the "magic bullet" that will instantly make legacy systems AI-ready. After dealing with 15-year-old SOAP APIs and the reality of how LLMs handle multi-step database writes, I wrote down why I think MCP is great, but fundamentally misunderstood by the market. Happy to answer any questions about bridging LLMs with legacy monoliths.
by thewebguyd | 2026-03-10 16:08:10+00:00
> especially when it was once suggested that frequent use could imply neurodivergence

Well that explains a lot. Interestingly enough, I've found that I naturally write like an LLM, or rather the LLMs write like I did. I wonder how many other patterns we attribute to LLMs are common in neurodivergent writing just as a result of so much of the training data being areas of the internet where I'd imagine neurodivergence is overrepresented vs. the general population.