MurphyA/hacker-news huggingface dataset:
by zurnz | 2026-03-11 03:25:36+00:00
I am a programmer and designer by day. I have been pretty reluctant to use LLMs to do coding.

I Decided to try fully building a project with Claude. I told myself I could not touch the code at all, but in the end I had to make the airplane 3D model in Blender, but I was able to light it in 3js with Claude.

The idea was that you can "upload" your files to your browser and ask AI about your data and have it create queries and graphs for you without ever seeing your actual data.

After some discussion with Claude, we ended up going with DuckDB and OPFS in order to store json and csv as tables directly in the browser. The column names and data type is the only thing that gets sent over to the AI.

DuckDB is a database that can run in your browser with WASM and seems quite performant. It is able to reference files directly in OPFS. OPFS (Origin Private File System) is a browser API that lets us write files directly on disc and reference them.

When you send a question to the AI, it is instructed that it can write duck db queries, Javascript transformations and render a series of different charts with the output.

The chart types are some of the basic chart types from recharts.js as well as a 3js scatter plot and open street maps plotting. Also a tree map, radar map, histogram and heatmap.

The Javascript runs in a web worker with eval, which is a bit scary, so I ended up asking Claude code to implement strict CSPs and removed any remotely fetched assets.

It seems to handle large csv files pretty well. The one I tried was 150mb.

Looking for any data science or accounting people to give it a try with some data and see if it has any use cases at all. You can login with a magic email link and try it with 20 questions.

I asked my girlfriend to try it and she immediately ran into issues, the excel importer didn't work when there were tabs, so I worked those in. Still trying to get her to give the thing a spin, but that may be the hardest part of the whole project.

I initially tried it and found it to be pretty bad, so I switched out the model from Gemini Flash to Claude Sonnet and it seemed much better. I plan to use it when I need to analyze data from a spreadsheet or json files.

If people find it useful I can provide some kind of codes for more credits. Of course the questions are using real $ so I didn't want to open it up too much.
by johng | 2026-03-11 03:25:45+00:00
You can see the lane sort of end, then cones, and then a choice that a human would make easily but that can easily confuse a computer as to "what to do next" -- and the computer chose to pretty much just drive straight. Definitely scary. I have a fear of being launched off these flyovers.... and really tall bridges. I have nightmares that I'm going to be on a bridge that gets steeper and steeper and I have no way to stop or turn around and there is a huge line up of cars behind me.... eventually the bridge gets so steep that I kind of fall backwards off the bridge. It's a recurring nightmare, no idea why :(
by esperent | 2026-03-11 03:26:01+00:00
Someone directly down from you suggested looking up Mike Postock's TDD skill, so I did:

https://github.com/mattpocock/skills/blob/main/tdd%2FSKILL.m...

Everything below quoted from that skill, and serves as a much better rebuttal than I had started writing:

DO NOT write all tests first, then all implementation. This is "horizontal slicing" - treating RED as "write all tests" and GREEN as "write all code."

This produces crap tests:

Tests written in bulk test imagined behavior, not actual behavior
You end up testing the shape of things (data structures, function signatures) rather than user-facing behavior
Tests become insensitive to real changes - they pass when behavior breaks, fail when behavior is fine

You outrun your headlights, committing to test structure before understanding the implementation

Correct approach:

Vertical slices via tracer bullets.

One test → one implementation → repeat. Each test responds to what you learned from the previous cycle. Because you just wrote the code, you know exactly what behavior matters and how to verify it.
by nickcoffee | 2026-03-11 03:26:34+00:00
The supply side problem is what killed this. Asking artists to opt in to something that most of their peers openly oppose is a brutal cold outreach problem before you even get to the product. 1 in 4 artists using the free tier for their own work is actually the most telling stat in here. If the people being compensated don't want to use it themselves, the ethical framing alone isn't enough to drive adoption.

Props for the postmortem.
by jackyli02 | 2026-03-11 03:27:50+00:00
The framing of tests-as-source-code resonates, but I think it extends further than testing specifically. From my experience building with AI coding tools, I spend increasingly more time reviewing and validating code than writing it. You end up acting like an engineering manager running a team of junior devs: scoping tasks tightly, reviewing output critically, deciding whether what came back meets the requirement. Tests are one expression of that, but so is code review - they're both forms of validation. The broader shift is that the developer's primary output is becoming judgment about correctness rather than the code itself.
by nickcoffee | 2026-03-11 03:28:08+00:00
Building Vox Labs, an AI-powered outbound sales platform for founders, small sales teams, and agencies. The idea is you work with it the way you'd direct an SDR: give it guidance and it handles research, enrichment, personalization, and sequencing.

Stack is React frontend, Node.js backend, Claude as the primary AI layer for orchestration.

The most interesting engineering problem has been the orchestration layer. Getting an agent to handle multi-step outbound workflows with real judgment, not just automation, takes a lot of iteration on the prompting and state management side. Also feedback from users on the best workflows to collaborate with agents.
by eschaton | 2026-03-11 03:28:24+00:00
What about IBM i and z/OS, and Stratus VOS, and Burroughs MCP, and Tandem GUARDIAN, and VxWorks and OS-9 and… These all not only still exist but run huge transaction volume (for the mainframe and minicomputer systems) and run a huge amount of embedded systems (for the embedded OSes).
by oefrha | 2026-03-11 03:28:27+00:00
That's not the perfect defense you think it is. Plenty of robots.txts[1] technically allow scraping their main content pages as long as your user-agent isn't explicitly disallowed, but in practice they're behind Cloudflare so they still throw up Cloudflare bot check if you actually attempt to crawl.

And forget about crawling. If you have a less reputable IP (basically every IP in third world countries are less reputable, for instance), you can be CAPTCHA'ed to no end by Cloudflare even as a human user, on the default setting, so plenty of site owners with more reputable home/office IPs don't even know what they subject a subset of their users to.

[1] E.g. https://www.wired.com/robots.txt to pick an example high up on HN front page.
by orf | 2026-03-11 03:28:47+00:00
> what else are you going to lie about?

Nothing? Everything? Does it fucking matter? Assigning trust across a boundary like this is stupid, and that’s my point.

Oh, would you just accept my blatantly, verbatim copied-from-another-codebase-and-relicensed PR just because I said “I solemnly swear this is not blatantly, verbatim copied from another codebase and relicensed”?

That’s on you for stupidly assigning any trust to the author of the change. It’s the internet: nobody knows you’re a dog.
by mindslight | 2026-03-11 03:30:32+00:00
@ratrace:

"My side" is my side because I perceive this movement as fascist, or at least close enough to oppose it on that grounds.

I was much happier both sidesing when digital authoritarianism was centered around theoretically-voluntary digital services, and both the red and blue political teams were pushing bureaucratic authoritarianism.

In my estimation, the red team has switched to autocratic authoritarianism, coopted the digital authoritarian systems of surveillance and control, while engaging in populist rallying with most of the standard tropes of fascists.

As I said, I am open to examining whether it makes sense to apply the term fascist or not. But to do that we need a definition that lays out the general characteristics of fascism, at least as you see it. So far neither of you have supplied one.
by pocksuppet | 2026-03-11 03:31:32+00:00
Was it ever not one? They protect a lot of DDoS-for-hire sites from DDoS by their competitors. In return they increase the quantity of DDoS on the internet. They offer you a service for $150, then months later suddenly demand $150k in 24 hours or they shut down your business. If you use them as a DNS registrar they will hold your domain hostage.
by seer | 2026-03-11 03:31:35+00:00
This seems quite amazing really, thanks for sharing

What is the scope of projects / features you’ve seen this be successful at?

Do you have a step before where an agent verifies that your new feature spec is not contradictory, ambiguous etc. Maybe as reviewed with regards to all the current feature sets?

Do you make this a cycle per step - by breaking down the feature to small implementable and verifiable sub-features and coding them in sequence, or do you tell it to write all the tests first and then have at it with implementation and refactoring?

Why not refactor-red-green-refactor cycle? E.g. a lot of the time it is worth refactoring the existing code first, to make a new implementation easier, is it worth encoding this into the harness?
by ZeroCool2u | 2026-03-11 03:31:38+00:00
If you don't "have the full context on the Persona/Discord story" you should work on getting it.

It's literally the first thing that came to mind when I saw your post and not having a convincing/satisfying answer in direct relation to that catastrophe doesn't bode well for getting people to trust your brand. The rest of your answer is essentially the absolute minimum I'd expect from a business like this, but not sufficiently convincing.
by nunez | 2026-03-11 03:32:12+00:00
McD's burgers are like having Budweiser/Bud Light beer (or Starbucks coffee if you don't drink alcohol). The product is just okay --- sometimes even good --- but it's unbelievably consistent. A Bud Light/Starbucks iced latte in the mountains will taste exactly the same as a Bud Light/Starbucks iced latte on the beach.

I love burgers and have had many all over the US; I wouldn't turn down a McD's burger.
by anon7000 | 2026-03-11 03:32:20+00:00
I don’t understand your view. Reality is that we need some way to encode the rules of the world in a more definitive way. If we want models to be able to make assertive claims about important information and be correct, it’s very fair to theorize they might need a more deterministic approach than just training them more. But it’s just a theory that this will actually solve the problem.

Ultimately, we still have a lot to learn and a lot of experiments to do. It’s frankly unscientific to suggest any approaches are off the table, unless the data & research truly proves that. Why shouldn’t we take this awesome LLM technology and bring in more techniques to make it better?

A really, really basic example is chess. Current top AI models still don’t know how to play it (https://www.software7.com/blog/ai_chess_vs_1983_atari/) The models are surely trained on source material that include chess rules, and even high level chess games. But the models are not learning how to play chess correctly. They don’t have a model to understand how chess actually works — they only have a non-deterministic prediction based on what they’ve seen, even after being trained on more data than any chess novice has ever seen about the topic. And this is probably one of the easiest things for AI to stimulate. Very clear/brief rules, small problem space, no hidden information, but it can’t handle the massive decision space because its prediction isn’t based on the actual rules, but just “things that look similar”

(And yeah, I’m sure someone could build a specific LLM or agent system that can handle chess, but the point is that the powerful general purpose models can’t do it out of the box after training.)

Maybe more training & self-learning can solve this, but it’s clearly still unsolved. So we should definitely be experimenting with more techniques.
by firstdata | 2026-03-11 03:33:52+00:00
The hardest part of running agents autonomously is the data quality problem. When your agent runs unsupervised, every decision is only as good as the data it pulls. Having agents access authoritative structured sources (government APIs, international org datasets) rather than scraping random pages makes a huge difference. The real failure mode is not hallucination - it is the agent confidently acting on unreliable data.
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by veritas9 | 2026-03-11 03:34:26+00:00
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by SilentM68 | 2026-03-11 03:35:02+00:00
Perhaps, but all I've seen in my lifetime is the management of diseases and conditions. For prevention to work, food production needs to change. Doctors, hospitals, colleges used to be free, medicines used to cure (e.g. polio vaccine), doctors experimented on themselves. What changed? In my view, greed, was added to the equation, in all fields, medicine, education (e.g. teachers teach as a means to retire, doctors don't do research). AI is going to fix that for everyone, in my view :|
by 6510 | 2026-03-11 03:35:46+00:00
With google covering only 3% I wonder how much people still care and if they should. Funny: I own and know sites that are by far the best resource on the topic but shouldn't have so many links google says. It's like I ask you for a page about cuban chains then you say you don't have it because they had to many links. Or your greengrocer suddenly doesn't have apples because his supplier now offers more than 5 different kinds so he will never buy there again.
by esseph | 2026-03-11 03:36:16+00:00
So one of the potential use cases mentioned is for secure data transfer.

So here's my question:
Could FHE hardware be used to extremely quickly and reliably secure something like a database connection?

I looked through Gemini, and it says the following:

"Zama are building libraries that use FHE accelerators to allow "Confidential Smart Contracts" or private AI queries. You could send a highly sensitive health query to an AI, and the AI hardware would process it and send the answer back without the AI company ever knowing what you asked."

https://www.zama.org/

Which is why I ask. Because if you have a backdoor into the hardware, as either a corporation or a government, then you can get access to those "very sensitive and fully secured communications".

They also discuss a crypto / smart contracts use case, and advertise " securing L1 or L2".
by ehnto | 2026-03-11 03:36:23+00:00
People bicker about PHP and Javascript, sorry Typescript, like they aren't both mule languages peoppe pick up to get work done. They both matured really well through years of production use.

They are in the same group, similar pedigree. If you were programming purely for the art of it, you would have had time to discover much nicer languages than either, but that's not what most people are doing so it doesn't really matter. They're different but they're about as good as eachother.
by ivan_gammel | 2026-03-11 03:36:29+00:00
>There only needs to not be a reason we need it to survive long enough to reproduce.

Humans had life expectancy even shorter than our fertility period until recently and they developed as social species hundreds of thousands years ago, for which living beyond fertility period is beneficial (grandparents were invented by evolution too).

> And bear in mind that most people don't have a problem surviving colds

That’s modern people with access to antibiotics etc.

> that was probably more true for much of our evolutionary history when we were living much more isolated lives, and not cohabiting with chickens and pigs

For much of our evolutionary history people were eating animals, getting viruses with them.
by AndyKelley | 2026-03-11 03:36:42+00:00
Originally, Zig's type system was less disciplined in terms of the "zero" type (also known as "noreturn").

This was proposed, discussed, and accepted here: https://github.com/ziglang/zig/issues/3257

Later, Matthew Lugg made a follow-up proposal, which was discussed both publicly and in ZSF core team meetings. https://github.com/ziglang/zig/issues/15909

He writes:

> A (fairly uncontroversial) subset of this behavior was implemented in [the changeset we are discussing]. I'll close this for now, though I'll probably end up revisiting these semantics more precisely at some point, in which case I'll open a new issue on Codeberg.

I don't know how evident this is to the casual HN reader, but to me this changeset very obviously moves Zig the language from experimental territory a large degree towards being formally specified, because it makes type resolution a Directed Acyclic Graph. Just look at how many bugs it resolved to get a feel for it. This changeset alone will make the next release of the compiler significantly more robust.

Now, I like talking about its design and development, but all that being said, Zig project does not aim for full transparency. It says right there in the README:

> Zig is Free and Open Source Software. We welcome bug reports and patches from everyone. However, keep in mind that Zig governance is BDFN (Benevolent Dictator For Now) which means that Andrew Kelley has final say on the design and implementation of everything.

It's up to you to decide whether the language and project are in trustworthy hands. I can tell you this much: we (the dev team) have a strong vision and we care deeply about the project, both to fulfill our own dreams as well as those of our esteemed users whom we serve[1]. Furthermore, as a 501(c)(3) non-profit we have no motive to enshittify.

[1]: https://ziglang.org/documentation/master/#Zen

It's been incredible working with Matthew. I hope I can have the pleasure to continue to call him my colleague for many years to come.
by anonnon | 2026-03-11 03:38:30+00:00
It's strange that I don't accept unverified anecdotes on their face, especially when they contradict the best evidence available? Also

> calling this person a liar

"Liar" implies a deliberate attempt to deceive, but I specifically mentioned the possibility that these tools just make you feel much more productive than you actually are, as at least one study found. But I'm sure a lot of these anecdotes are, in fact, lies from liars (bots/shills). The fact that Anthropic has to resort to stuff like this: https://news.ycombinator.com/item?id=47282777

should make everyone suspicious of the extravagant claims being made about Claude.
by tbossanova | 2026-03-11 03:40:14+00:00
Nothing; but PHP, in experienced hands, will be waaay more productive for small-to-medium things. One issue is that experienced hands are increasingly hard to come by. Truly big, complicated things, built by large teams or numbers of teams, teams with a lot of average brains or AIs trained on average brains, will be better off in something like Typescript/React. And everyone wants to work on the big complicated stuff. So the "modern frameworks" will continue to dominate while smaller, more niche shops will wonder why they waste their time.
by arugulum | 2026-03-11 03:40:41+00:00
LoRA? The parameter-efficient fine-tuning method published 2 years before Llama and already actively used by researchers?

RoPE? The position encoding method published 2 years before Llama and already in models such as GPT-J-6B?

DPO, a method whose paper had no experiments with Llama?

QLoRA? The third in a series of quantization works by Tim Dettmers, the first two of which pre-dated Llama?
by AnthonyMouse | 2026-03-11 03:41:36+00:00
STAR is basically fine, it's a variant on score.

There are several problems with IRV, but the most obvious one is that it can often knock the moderate candidate out of the final round.

Suppose you have a district that goes 60% for one party. That party runs two candidates and the other party runs one. With IRV, one of the first party's candidates is the most likely to get knocked out, because they'll each average ~30% of the vote (half of 60%) while the other candidate gets the other 40%. But if the majority party then has a preference for their own extremist, it's their moderate that gets knocked out, and then in a district that goes 60% for that party, the extremist has a decent chance of getting in.

The same dynamic can also cause the minority party candidate to win. 51% of the majority party (i.e. 30.5% of the district's voters) prefer an extremist, but enough of the majority party is afraid of them that in combination with the 40% of the vote from the minority party, the minority party wins the run off. Even though the "winner" would have lost to the majority party's moderate using score voting regardless of whether the extremist was on the ballot and even in a two-candidate election using FPTP.
by timr | 2026-03-11 03:43:15+00:00
> But it works well enough for most use cases.

So does the code produced by any bad engineer.

So either we’re finally admitting that all of that leetcode screening and engineer quality gating was a farce, or it wasn’t, and you’re wrong.

I think the answer is in the middle, but the pendulum has swung too far in the “doesn’t matter” direction.
by bruce511 | 2026-03-11 03:43:18+00:00
By "nobody" I presume you mean you and your friends?
From the article;

>> "More than 60% of the Fortune 500 rely on SUSE to power some of their workloads, according to the company."

This is an Enterprise version of Linux, and unless you are in the enterprise space you're unlikely to come across it.

Also from the article;
>> "The company generates about $800 million in revenue "

So again, this suggests that people are indeed using it.
by dakolli | 2026-03-11 03:45:10+00:00
Yeah, I agree. However, people use llms for the same reason people drive 3 blocks to a store rather than walk. Laziness and convenience. They simply don't care if their leg muscles atrophy. However, I think people aren't taking into account how much more important your thinking "muscles" are and its way more consequential to let those atrophy.

Everyone is vulnerable to the allure of taking shortcuts in life, but I've learned over the years that there is no free lunch. This is going too be quite an expensive trade off for many.
by nemo44x | 2026-03-11 03:45:17+00:00
How do people not understand this? LLMs are goal machines. You need to give them the specific goal if you want good results and continue to reenforce it. So of course this means speccing and design work.

People are so enamored with how fast the 20% part is now and yes it’s amazing. But the 80% part by time (designing, testing, reviewing, refactoring, repairing) still exists if you want coherent systems of non-trivial complexity.

All the old rules still apply.