MurphyA/hacker-news huggingface dataset:
by danpalmer | 2026-03-06 06:13:33+00:00
I recently had a quandary at work. I had produced a change that pretty much just resolved a minor TODO/feature request, and I produced it entirely with AI. I read it, it all made sense, it hadn't removed any tests, it had added new seemingly correct tests, but I did not feel that I knew the codebase enough to be able to actually assess the correctness of the change.

I want to do good engineering, not produce slop, but for 1 min of prompting, 5 mins of tidying, and 30 mins of review, we might save 2 days of eng time. That has to be worth something.

I could see a few ways forward:

- Drop it, submit a feature request instead, include the diff as optional inspiration.

- Send it, but be clear that it came from AI, I don't know if it works, and ask the reviewers to pay special attention to it because of that...

- Or Send it as normal, because it passes tests/linters, and review should be the same regardless of author or provenance.

I posted this to a few chat groups and got quite a range of opinions, including varying approach by how much I like the maintainer. Strong opinions for (1), weak preferences for (2), and a few advocating for (3).

Interestingly, the pro-AI folks almost universally doubled down and said that I should use AI more to gain more confidence – ask how can I test it, how can we verify it, etc – to move my confidence instead of changing how review works.

I thought that was an interesting idea that I hadn't pushed enough, so I spent a further hour or so prompting around ways to gain confidence, throughout which the AI "fixed" so many things to "improve" the code that I completely lost all confidence in the change because there were clearly things that were needed and things that weren't, and disentangling them was going to be way more work than starting from scratch. So I went with option 1, and didn't include a diff.
by nl | 2026-03-06 06:13:50+00:00
This oversimplifies it.

You can't copyright a work that is only generated by a machine: "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"

But human direction of machine processes can be copyright:

"A year later, the Office issued a registration for a comic book incorporating AI-generated material."

and

"In most cases, however, humans will be involved in the creation process, and the work
will be copyrightable to the extent that their contributions qualify as authorship. It is axiomatic
that ideas or facts themselves are not protectible by copyright law and the Supreme Court has
made clear that originality is required, not just time and effort. In Feist Publications, Inc. v. Rural Telephone Service Co., the Court rejected the theory that “sweat of the brow” alone could be
sufficient for copyright protection. “To be sure,” the Court further explained, “the requisite
level of creativity is extremely low; even a slight amount will suffice."

See https://www.copyright.gov/ai/Copyright-and-Artificial-Intell...
by tayo42 | 2026-03-06 06:13:54+00:00
To deal with the problems where there is ambiguity in the problem and the approach to solving it. Not everything is a basic decision tree. Humans aren't deterministic either, the way we woukd approach a problem is probably different. Is one of us right or wrong? We're generally just focused on end results.

Maybe 2 years ago Ai was doing random stuff and we got all those funny screenshots of dumb gemini answers. The indeterminism leading to random stuff isn't really an issue any more.

The way it thinks keeps it on track.
Nuvix – open-source BaaS with a query DSL more expressive than PostgREST
by ravikantsaini | 2026-03-06 06:14:26+00:00
Most BaaS query layers are thin wrappers. Nuvix's filter DSL is composable and typesafe directly in the URL.

# symbolic
filter=_id.eq(9)|Name.like(Air),Stock.gt(0)

# functional — mix freely
filter=or(_id.eq(9),and(Name.like(Air),Stock.gt(0)))

Supports or(), and(), not() with symbolic aliases |, ,, !. Parentheses for explicit grouping when mixing.
Three literal types

'text' → string

42, true → number / bool

"column" → column reference — enables column-to-column comparisons

Inline relation filtering + response shaping

select=*,todos.one{!_id.is(null),$.shape(object)}(text)

Explicit joins, not FK inference
js.join({ table: 'posts', as: 'user_posts' }, (b) => b
.select('title', 'created_at')
.eq('published', true)
)

Joins defined in the query, not inferred from DB schema — so you can join on any condition, alias freely, and stay decoupled from your DB structure.

Full BaaS beyond the query layer — auth, storage, real-time, auto-generated RLS.

GitHub: https://github.com/nuvix-dev/nuvix
by threatofrain | 2026-03-06 06:14:45+00:00
> Anyways, it feels like all sides of the political spectrum are trying to strip away any semblance of anonymity or privacy online both in the US and abroad.

It's not this or that political party, your neighbors simply don't share your values. Maybe you don't agree with their values either — like to what degree we should be ceding privacy in favor of fighting child exploitation on the internet. Child protection arguments work because it is a compass to the true feelings of your neighbors.
by pinkmuffinere | 2026-03-06 06:14:50+00:00
I asked opus 4.6 how to administer an A/B test when data is sparse. My options are to look at conversion rate, look at revenue per customer, or something else. I will get about 10-20k samples, less than that will add to cart, less than that will begin checkout, and even less than that will convert. Opus says I should look at revenue per customers. I don't know the right answer, but I know it is not to look at revenue per customers -- that will have high variance due to outlier customers who put in a large order. To be fair, I do use opus frequently, and it often gives good enough answers. But you do have to be suspicious of its responses for important decisions.

Edit: Ha, and the report claims it's relatively good at business and finance...
by charcircuit | 2026-03-06 06:15:22+00:00
I don't think the argument that children might bypass parental controls therefore devices should not have parental controls.

>Limiting a child’s ability to explore what they can do with a computer limits their future.

Parents don't want to limit their children from writing software. Saying that limiting minors from accessing porn will limit their future is another argument I doing think many will agree with.
by heavyset_go | 2026-03-06 06:18:13+00:00
It's pushed both by those with surveillance agendas and AI companies like Anthropic, who donated millions to PACs and politicians that are pushing online age verification and surveillance laws[1].

The goal for the AI side is that they get to be censors and gatekeepers of all user-generated content on the internet, their models will rank/grade/censor content for age-appropriateness and they will have the pleasure of being paid to train on all new content uploaded to the internet in real-time, in perpetuity.

[1] https://news.ycombinator.com/item?id=47162956
by moomoo11 | 2026-03-06 06:19:00+00:00
always funny af to see all the ugly loser (just because you went to MIT and raised XXX billion doesn't mean you aren't a loser who doesn't care for anything but YOURSELF) dorks and nerds who thought "the Empire were the good guys" finally get placed in the action seat as they help build the Death Star

thankfully, the giga Chads always win against the incel dorks and nerds in the end
by dragonwriter | 2026-03-06 06:19:20+00:00
> > A law compelling providing functionality

> That's forced labor.

Well, that's a 13th Amendment issue not a 1st Amendment one, but, in any case, its not forced if it doesn't direct who does the work to create the functionality, only requires you to have the functionality provided if you are doing some other activity, it is more of an in-kind tax. [0] (Now, if you want to make an argument that when the activity it is conditioned on is expressive that that makes it a 1A violation as a content-based regulation when the condition is tied to the content of the expressive act, that is a better 1A argument, that might actually have some merit against many of the real uses of, say, age verification laws; but “if I am doing this activity, I must either create or acquire and use software that has a specified function” is not, in general, a 1A violation.)

[0] It's not really that other than metaphorically, either, any more than every regulation of any kind is an “in-kind tax”, but its far closer to that than “forced labor”.
by eks391 | 2026-03-06 06:19:43+00:00
Most people are "really dumb" by your standards then. Not only are most people not going to check the URL, but many people don't know how password managers work, and the only reason they use the browser password manager is because it is on by default, and it is saving their collection of 3 reused passwords they manually type at each site when it doesn't auto populate.
by vladdoster | 2026-03-06 06:20:43+00:00
Location: Nashville, TN
Remote: true
Willing to relocate: boston || nashville
Technologies: Python, Django, IaC, Terraform, Golang, CI/CD, Azure/AWS, {ba,z}sh, Lua, Springboot,
Redis, Docker, Open Service Broker, {,non}relational & vector dbs, shared services, SOA
Email: mvdoster@gmail.com

resume: https://linkedin.vdoster.com && http://github.com/vladdoster

sr. cloud software engineer @ Mastercard (5 years)

Designed and built Platform-as-a-Service for microservice workloads running on Azure and AWS
to migrate on-prem workloads to the cloud. The platform operates globally, allowing teams to
deploy to several Azure-backed regions worldwide. Helped plan and create regional landing zones
in Azure for application teams wanting to perform globally. Designed platform components using
Terraform, Go, and Node; developed support tooling using Bash, Python, and Azure CLI. De-risk the
platform by addressing security vulnerabilities and ensuring the platform is PCI compliant.

api/devops software engineer @ Harvard Medical School (2 years)

Worked as part of the DevOps team to manage a 15k core CentOS HPC cluster. Designed and implemented
Python-based security scanning pipeline to securely deploy & run Apptainer images via SLURM.
Coordinated cross-team emergency patching eﬀorts. Collaborated with researchers and engineers across
3-letter institutions to deliver secure research infrastructure. Built CI/CD Pipelines with GitHub, Jenkins,
to automate code builds, testing, and deployments. Wrote Puppet modules to manage configuration of HPC fleet.
Wrote custom Puppet module & Python scripts to automatically remediate Tenable findings.

full-stack software engineer @ Global Prior Art (3 years)

Used Django, Celery, Selenium grid, and AWS API Gateway for IP rotation to scrape USPTO patent PDFs,
apply CV for data extraction, and generate reports with relevant claims, saving employees 4-5 hours daily.
Wrote E2E black-box/fuzzing unit tests. Automated on-premise deployment via Github Actions and Ansible.

maintainer @ Zinit (5 years)

Develop and maintain a zsh plugin manager on Github with 4k stars. Implemented unit tests,
containerization, bug fixes, and new features.
See: https://github.com/zdharma-continuum/zinit

contributor @ Open-source documentation (5 years)

Contributed hundreds of documentation corrections and enhancements to 400+ open source projects.
by harman2607 | 2026-03-06 06:21:17+00:00
Hi HN, I’m one of the authors.

This paper studies how LLMs self-verify candidate solutions when doing test-time scaling (parallel reasoning / Best-of-N style generation).

We found that models are often much better at pairwise comparisons, where the model scores two solutions (A and B) jointly, than at assigning absolute scores to their own solutions independently.

The paper introduces:

• Pairwise self-verification instead of pointwise scoring

• V1-Infer, a ranking algorithm that selects good candidates efficiently

• V1-PairRL, RL training where generation and verification co-evolve to produce stronger self-verifiers

Across coding and reasoning benchmarks, we observe improved verification accuracy and good scaling when increasing verification compute budget.

One motivation is that many recent test-time scaling approaches (for example RSA: https://arxiv.org/abs/2509.26626
) rely on sequential aggregation loops. Pairwise verification enables a more parallel form of selection, which may reduce latency in deep thinking pipelines and scaffolds.

Happy to answer questions.
by tavavex | 2026-03-06 06:21:53+00:00
Ironically, because phone designs have converged so much and innovation had plateaued, a phone with no branding whatsoever represents a brand in and of itself. Think about it, even the worst, cheapest no-name phones built from old parts in some small Chinese factory have something written on them. The absence of a logo is so unique that it performs the same function, because no one else is doing that. The catch is that it only works if people know this gimmick in advance, but this also applies to brands with textless logos.
by the_mitsuhiko | 2026-03-06 06:22:05+00:00
> You can't do that as easily with closed source software. Except, if you can read assembly, every program is open source. I suspect we're not far away from LLMs being able to just disassemble any program and do the same thing.

I have successfully created a partial implementation of p4 by pointing it at the captured network stream and some strace output. It's amazing how good those things are.
by prodigycorp | 2026-03-06 06:22:08+00:00
I've been using it for three hours and it's insanely good. It's almost perfectly (needed a single touchup prompt) completed a full css refactoring that I've wanted to do for months that I've tried to have other models do but nothing worked without heavy babysitting.

Also, in the course of coding, it's actually cleaning up slop and consolidating without being naturally prompted.
by familytreeeasy | 2026-03-06 06:22:32+00:00
Thanks! I’m glad the simplicity resonates with you—fighting against genealogy bloatware was exactly what I wanted to avoid.

As for privacy, everything stays in your browser. There’s no backend database tracking your family history.

The data is stored locally (via LocalStorage), so if you refresh the page or come back later, it’ll still be there—but I never see it.
by apsurd | 2026-03-06 06:24:31+00:00
AI dramatically increases velocity. But is velocity productivity? Productivity relative to which scope: you, the team, the department, the company?

The question is really, velocity _of what_?

I got this from a HN comment. It really hit for me because the default mentality for engineers is to build. The more you build the better. That's not "wrong" but in a business setting it is very much necessary but not sufficient. And so whenever we think about productivity, impact, velocity, whatever measure of output, the real question is _of what_? More code? More product surface area? That was never really the problem. In fact it makes life worse majority of the time.
by BERTmackliin | 2026-03-06 06:24:40+00:00
We built Anchor Engine because LLMs have no persistent memory. Every conversation is a fresh start—yesterday's discussion, last week's project notes, even context from another tab—all gone. Context windows help, but they're ephemeral and expensive.

The STAR algorithm (Semantic Traversal And Retrieval) takes a different approach. Instead of embedding everything into vector space, STAR uses deterministic graph traversal. But before traversal comes atomization—our lightweight process for extracting just enough conceptual structure from text to build a traversable semantic graph.

Atomization, not exhaustive extraction.
Projects like Kanon 2 are doing incredible work extracting every entity, citation, and clause from documents with remarkable precision. That's valuable for document intelligence. Anchor Engine takes a different path: we extract only the core concepts and relationships needed to support semantic memory. The result is a graph that's just rich enough for an LLM to retrieve relevant context, but lightweight enough to run offline in <3GB RAM—even on a Raspberry Pi or in a browser via WASM.

Why graph traversal instead of vector search?

Embeddings drift over time and across models

Similarity scores are opaque and nondeterministic

Vector search often requires GPUs or cloud APIs

You can't inspect why something was retrieved

STAR gives you deterministic, inspectable results. Same graph, same query, same output—every time. And because the graph is built through atomization, it stays small and portable.

Key technical details:

Runs entirely offline in <3GB RAM. No API calls, no GPUs.

Compiled to WASM – embed it anywhere, including browsers.

Recursive architecture – we used Anchor Engine to help write its own code. The dogfooding is real.

AGPL-3.0 – open source, always.

What it's not: It's not a replacement for LLMs or vector databases. It's a memory layer—a deterministic, inspectable substrate that gives LLMs persistent context without cloud dependencies. And it's not a competitor to deep extraction models like Kanon 2; they could even complement each other (Kanon 2 builds the graph, Anchor Engine traverses it for memory).

The whitepaper goes deep on the graph traversal math and includes benchmarks vs. vector search:
https://github.com/RSBalchII/anchor-engine-node/blob/main/do...

We're especially interested in feedback from people who've built RAG systems, experimented with symbolic memory, or worked on graph-based AI. If you've ever wanted to give an LLM long-term memory that fits on a Raspberry Pi, this might be interesting to you.
by eucyclos | 2026-03-06 06:25:49+00:00
Here's an example: https://eudaimonia-project.netlify.app/

I'm happy to sell it to you, though it is also free. I guided Claude to write this in three weeks, after never having written a line of JavaScript or set up a server before. I'm sure a better JavaScript programmer than I could do this in three weeks, but there's no way I could. I just had a cool idea for making advertising a force for good, and now I have a working version in beta.

I'd say it is better software, but better is doing a lot of heavy lifting there. Claude's execution is average and always will be, that's a function of being a prediction engine. But I genuinely think the idea is better than how advertising works today, and this product would not exist at all if I had to write it myself. And I'm someone who has written code before, enough that I was probably a somewhat early adopter to this whole thing. Multiply that by all the people whose ideas get to live now, and I'm sure some ideas will prove to be better even with average execution. Like an llm, that's a function of statistics.
by evanelias | 2026-03-06 06:26:56+00:00
Yeah, similar feelings here. I grew up in the BBS world in the 90s, and love a quality TUI experience, but something about this toolset just gives me the ick. I can't keep any of the naming straight; to me it all reads like "combine Chapstick with Cotton Swab inside of Matcha Latte" and my eyes instantly glaze over.

How does this company make money? Is it all just a ZIRP fever dream?
by selimenes1 | 2026-03-06 06:27:01+00:00
The danpalmer comment really resonates. I've been in similar spots where AI-generated code passes tests and looks fine at first glance, but you don't have the mental model of why it works that way. That missing confidence is real and I think it's the core issue with these low-effort PRs too — the submitter has no skin in the game understanding what the code actually does.

What's interesting is this isn't entirely new. Before AI slop PRs, we had Hacktoberfest spam, drive-by typo-fix PRs that broke things, and copy-paste-from-stackoverflow contributions. The difference now is just volume and the fact that the code looks superficially more competent.

Honestly I think the most practical signal for maintainers is whether the contributor can answer a specific question about the change. Not "explain the PR" but something like "why did you choose X over Y here" or "what happens when Z edge case occurs." A human who wrote or at least deeply understood the code can answer that in seconds. Someone who just prompted and submitted cannot.
by XorNot | 2026-03-06 06:27:42+00:00
They knew the US was at war and they knew it was a government program for military purposes and they knew they were dealing nuclear materials.

A journalist not involved at all figured it out just fine, but at the very least it's not like it wasn't going to be a weapon.

Frankly though I wonder what the various judgemental people in these comments think about say, the tens of thousands of people who at the time were just straight up making artillery ammo.
by Aeglaecia | 2026-03-06 06:28:40+00:00
I really want to be in a world where that's true. in the meantime we live in a zero sum survival of the fittest game where the powerful execute the weak for insubordination. in this world it is often necessary to take roundabout paths to reach the objective.

for example, a constitutional representative in my country attempted to place restrictions on unfettered gambling advertisements. a single day later, photos emerged of that politician having dressed as a nazi for a costume party in his youth. that politician stood up for what was right and then got fired for it, by losing his job and his status in the court of public opinion, effectively achieving no change.

exacting change isnt always such a simple process as embodying the end result.
by mr_november | 2026-03-06 06:29:13+00:00
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We're a 30-person team, spread between Washington, DC, New York City, Philadelphia, and Vancouver, BC. We reached $4MM+ annual revenue before raising any outside capital.

We're looking for a mid to senior full stack software engineer generalist who is eager to manage their own product, likes to build quickly, and is focused on understanding the customer and solving their problems. You will have ownership and autonomy with minimal meetings. We use ruby, rails, javascript, postgres, aws, docker.

I'm Arif - reach out if this sounds interesting arif@digitalhealthstrategies.com.
by wasmainiac | 2026-03-06 06:29:16+00:00
Because its failure rate is too high. Beyond boilerplate code and CRUD apps, if I let AI run freely on the projects I maintain, I spend more time fixing its changes than if I just did it myself. It hallucinates functionally, it designs itself into corners, it does not follow my instructions, it writes too much code for simple features.

It’s fine at replacing what stack overflow did nearly a decade ago, but that isn’t really an improvement from my baseline.
by XorNot | 2026-03-06 06:30:42+00:00
The inability to accurately cite any story about this, and the "friend of a friend" structure is what implies it's garbage.

Not to mention it itself requires a conspiracy theory: "no one would do this work voluntarily" (or "all the smart people have to be tricked because they're so smart they obviously agree with me").

As though people don't just go and work at Boeing or Lockheed Martin.
by ls612 | 2026-03-06 06:31:30+00:00
It is as Aristotle said, the average person is a natural born slave (to their emotions, and thus to the rhetoriticians most skilled in changing them). That is why democracy always fails in the end. Americans just had such good geographic and historic luck to delay this reckoning by a century or two.

If you see politics through this lens then the 'democratic backsliding' that has been universal across the world for the past two decades is entirely unsurprising.

Vae Victus.