MurphyA/hacker-news huggingface dataset:
by throwawaypath | 2026-02-03 17:06:10+00:00
>You were attributing your "knowledge worker 401(k)" growth to Trump though?

You were attributing the "cutting taxes while keeping government spending high AND lowering interest rates" specifically to Trump though?

>No, I'm not on BlueSky or any social media.

Yet you suffer from the same brainrot.

>Is this a poor attempt at some Trumper "own"?

Is this a poor attempt at some pantifa "own"?

>Look at the chart after "liberation day" in April. It's been down since then and stayed there. Not a very good sign for his policies.

Yes, it is still trading in its channel at above historic averages. A regression to the mean, the same drop happened under Biden in 2022. Not a good sign for [Biden's] policies.

>Not sure how electing Trump did much for us other than cause a market crash in April (and subsequent dip buying) and weaken the USD?

Of course you're not sure because you're financially illiterate. We're hitting all time highs again.

>Oh, I know plenty and I'm actually bullish on stocks because there is so much free money going around.

ZIRP is over. You're bullish because the market is hitting ATH under Trump. I've been selling puts all year to gullible "investors" that thought, and still think, the market is going to crash, any time now, under Trump.

>It would be stupid not to have your money in assets with such high inflation on the horizon.

"Inflation is on the horizon!" they screamed for an entire year. Trump's first year of his second term CPI was 2.7%, lower than any Biden year.

>I just find it funny that you're touting bad policy as a win when his whole campaign was about "Biden's inflation" causing high prices and Republicans are supposedly for being fiscally responsible yet the OBBB goes against that.

People find things they don't understand funny, news at 11.
by scratchyone | 2026-02-03 17:07:06+00:00
I actually think AI-human collaboration is quite beneficial. I have a more fundamental issue that it's just bad writing when you use pure LLM generated text. My general feeling is "why should you expect me to spend my time reading something that you didn't care enough to spend your time writing?"

Also, most of the suggestions provided in the AI generated section are just useless. While I think this law is terrible, the suggestions provided completely contradict what the lawmakers are intending. I'll explain what I mean with some of the suggestions provided.

> Narrow the Scope to Intent, Not the Tool

This is essentially a suggestion to throw out the entire law as written. Sure, but this is meaningless advice to lawmakers.

> Drop Mandatory File Scanning

This is the same suggestion as before but rephrased.

> Exempt Open-Source and Offline Toolchains

This is asking them to create a massive loophole in their own law making it useless. Once again, essentially just asking them to throw out the entire law.

> Add safe harbor for sellers and educators who don’t modify equipment or participate in unlawful manufacture.

Two fundamentally different concepts here jammed into one idea. Do you want to add safe harbor for sellers who don't modify equipment or do you want to throw out the entire law and have it not apply to anybody who doesn't participate in unlawful manufacture? These are very different ideas, it makes no sense to treat them as one cohesive concept.

All of these are signals that not much thought went into this. If a human had used AI for ideas and writing assistance, but participated in the writing process as an active contributor, I think they would have caught things like this. I don't think they would have chosen to make multiple bullet points semantically identical. I think they would have chosen to actually cite specific aspects of the law and propose concrete solutions.

Another example, one of their suggestions is to improve the working groups to add specific members. Genuinely a fairly good idea. Having actually read the law, I would have cited the specific passage, which requires that the working group "SHALL INCLUDE EXPERTS IN ADDITIVE MANUFACTURING TECHNOLOGY, ARTIFICIAL INTELLIGENCE AND DIGITAL SECURITY, FIREARMS REGULATION, PUBLIC SAFETY, CONSUMER PRODUCT SAFETY, AND ANY OTHER RELEVANT DISCIPLINES DETERMINED BY THE DIVISION TO BE NECESSARY TO PERFORM THE FUNCTIONS PRESCRIBED HEREIN." I would question, who do they consider to be experts in additive manufacturing? Why does it seem that the working group will be far more heavily weighed towards policy experts as opposed to 3D printing experts? The article suggests that "standards will default to large vendors" yet there is no evidence here that vendors will be included at all.
by pc86 | 2026-02-03 17:07:17+00:00
I don't think it's public (it might not even be saved) but something akin to Reddit's "controversial" would be interesting as well. I've seen my comments go from double-digit positive to double-digit negative, and vice versa, in contentious threads on divisive topics. It'd be interesting to see who has the most votes regardless of +/- but the lowest total karma. Who amongst us is just pissing off everyone?
by dust42 | 2026-02-03 17:07:21+00:00
Unfortunately Qwen3-next is not well supported on Apple silicon, it seems the Qwen team doesn't really care about Apple.

On M1 64GB Q4KM on llama.cpp gives only 20Tok/s while on MLX it is more than twice as fast. However, MLX has problems with kv cache consistency and especially with branching. So while in theory it is twice as fast as llama.cpp it often does the PP all over again which completely trashes performance especially with agentic coding.

So the agony is to decide whether to endure half the possible speed but getting much better kv-caching in return. Or to have twice the speed but then often you have again to sit through prompt processing.

But who knows, maybe Qwen gives them a hand? (hint,hint)
by 9x39 | 2026-02-03 17:08:10+00:00
Their proposal is about getting lines like this ratified:

"No person, firm or corporation shall sell or deliver any three-dimensional printer in the state of New York unless such printer is equipped with blocking technology,"
https://www.nysenate.gov/legislation/bills/2025/S9005

They don't like firearms in the hands of the public.

The goal is to be an indirect ban that's hard to challenge. California has had significant success with strategies such as requiring "microstamping technology" (but it could be anything - it's just a limiting mechanism) in conjunction with an approved handgun roster to limit handgun sales in the state. This is almost certain to be a similar strategy.
by perching_aix | 2026-02-03 17:08:45+00:00
It took me years to notice, but did you catch that the answer actually subtly misinterprets what the question is asking for?

Guy (in my reading) appears to talk about matching an entire HTML document with regex. Indeed, that is not possible due to the grammars involved. But that is not what was being asked.

What was being asked is whether the individual HTML tags can be parsed via regex. And to my understanding those are very much workable, and there's no grammar capability mismatch either.
by james2doyle | 2026-02-03 17:08:52+00:00
Over 60 AI models across 8 categories. The entire benchmark was built and runs inside of n8n and scores models on actual use cases in n8n rather than conversational style or subjective preference.

Smaller models often outperform larger models when it comes to specific tasks. We also found that a model's list price doesn't tell the whole story. One model priced at half the cost of competitors ended up being 10x more expensive in practice because it was so verbose in its outputs.

No single model dominates every category, so use the category filter on the benchmark page to find the best fit for your specific workflow. Whether you're building AI agents, automating data extraction, or generating code, this benchmark helps you make more informed decisions and build more cost-effective solutions.
by pc86 | 2026-02-03 17:09:05+00:00
Your comment is never going to get 75 votes on it if it only gets in front of 25 eyes, regardless of how good, bad, offensive, or insightful it is.

Especially on political threads you'll see the most milquetoast takes imaginable basically locked at the top of the page because they were commented 2 minutes after the post was made, and all the fighting happens underneath.
by nikitau | 2026-02-03 17:09:16+00:00
In (quantitative) genetics literature, heritability is usually defined (simplifying a bit) as the proportion of variance of a trait (lifespan, height, etc), in a population, that can be explained by genetics. The rest, by environmental factors, or error.

If height were a 100% heritability means that all differences in height between individuals would be explainable by genetics.
Show HN: VisiGrid CLI – Git-diff for financial reconciliation
by rdoneill | 2026-02-03 17:09:53+00:00
Most financial reconciliation fails silently.

A stale Excel filter. A CSV export with reordered rows. A tolerance mismatch that looks “close enough” until it isn’t. By the time a number looks wrong, the mistake is already baked into downstream reports.

VisiGrid CLI is a local-first tool that treats reconciliation like Git diff: deterministic, auditable, and safe to run in CI.

The recurring failure mode in financial models isn’t bad math — it’s undetected drift. Spreadsheets rarely fail loudly. This tool is designed to make drift explicit.

The core primitive visigrid-cli diff answers one question reliably:

What matches, what differs, and what’s missing — with numeric tolerance handled explicitly.

Think Git diff, but for datasets:

- Schema-aware row matching (--key)
- Tolerance-aware numeric comparison
- Deterministic output (no hidden state or background recalculation)
- CI-grade exit codes

* 0 = reconciled
* 1 = material drift detected

Example:

textvisigrid-cli diff vendor.csv internal.csv \
--key invoice_id \
--tolerance 0.01 \
--out json

The output is a versioned JSON contract (contract_version = 1) meant for machines, not eyeballing.
Supporting tools

The CLI also includes focused primitives that support reconciliation workflows:

- convert — pipeable XLSX / CSV / TSV / JSON conversion (stdin/stdout)
- calc — evaluate spreadsheet formulas headlessly
- replay — record structural spreadsheet edits as Lua, replay and verify with deterministic fingerprints

Everything runs locally. No accounts. No network.

Why it’s different

Deterministic by design — same inputs, same outputs, every time
Machine-checkable contract — stable schema and explicit exit semantics
Built for automation — CI, cron jobs, and reconciliation checks

If a reconciliation passes today, it will pass tomorrow for the same inputs.

The GUI
VisiGrid started as a native spreadsheet GUI (built with GPUI/Zed). Over time, it became clear the GUI shouldn’t do reconciliation — it should explain it.

When the CLI flags drift, the GUI acts as a debugger: visualize dependencies, trace formulas, and see exactly where a number came from.

CLI finds the problem. GUI explains it.

I’m pre-v1.0 and would love feedback from people dealing with:

- financial reconciliation
- audit trails
- data pipelines where correctness matters more than convenience

GitHub: https://github.com/VisiGrid/VisiGrid

Docs & downloads: https://visigrid.app

Happy to answer questions about diff semantics, tolerance handling, or the Rust internals.
by VladStanimir | 2026-02-03 17:10:04+00:00
I think there is a fundamental misunderstanding here.

A multiuser system is a system where multiple users are logged in at the same time and ussing the computer.

So a multi-user desktop Linux would be a computer where multiple people are logged in each with their own desktop session on the same machine.

That was the way unix was first used, a big computer somewhere with multiple client terminals connected to it all doing their own thing. This is the environment x11 came about as well.

Nowadays even if the computer is shared by multiple people each with their own account only one of them is using it at a time.
by noncentral | 2026-02-03 17:10:41+00:00
Author here.
Quick clarification: RCC is not proposing a new architecture.
It’s a boundary argument — that some LLM failure modes may emerge from the geometric limits of embedded inference rather than from model-specific flaws.

The claim is simple:
if a system lacks (1) full introspective access, (2) visibility into its container manifold, and (3) a stable global reference frame, then hallucination and drift become mathematically natural outcomes.

I’m posting this to ask a narrow question:
if these axioms are wrong, which one — and why?

Not trying to make a grand prediction; just testing whether a boundary-theoretic framing is useful to ML researchers.
by donkeybeer | 2026-02-03 17:10:41+00:00
I also come at it from another direction. Would you accept that other, non-human beings have consciousness. Not just animals, but in principle would you accept a computer program or any other machine that doesn't look like the molecular structure of a human can be conscious? I am of course hoping I am not wrong in assuming you won't disagree that assembling together in the lab or otherwise via means thats not the usual human reproduction, a molecule that is the same as a human would result in a perfectly uncontroversial normal conscious human right.

Since you can say its just a "mimic" and lacks whatever "aphysical" essence. And you can just as well say this about other "humans" than yourself too. So why is this question specially asked for computer programs and not also other people.
by codingdave | 2026-02-03 17:10:47+00:00
It depends on what you mean by "a job in CS". An academic job, actually working with computer science concepts? Doubtful. But that is not what most tech jobs are. Coding and IT roles are certainly related to the academic CS, but you don't need to know the academic side to succeed in the industry. And all the non-tech roles in the tech industry are more about business and communication skills, not CS skills.
by raw_anon_1111 | 2026-02-03 17:10:57+00:00
You are being “tracked” that you got on the plane regardless, even your credit card company gets your complete itinerary where you traveled from and to and can (and does) use that for advertising.

Amex even knows whether you used your card on Delta for instance to buy a ticket or pay ancillary fees/drinkd on board. They use the information to see whether you are eligible for a credit (Amex Platinum)
by kstrauser | 2026-02-03 17:11:10+00:00
No you can’t. You can have an unescaped <hr> inside a script tag, for example. The best you can do is a simple string search for “<hr>” and hope it’s returning what you think it might be returning. Regexps are not powerful enough to determine whether any particular instance of “<hr>” is actually an HTML tag.

Like, it’s not a matter of cleverness, either. You can’t code around it. It’s simply not possible.
by constantius | 2026-02-03 17:11:15+00:00
Immigration is not a left-right issue, there are reasons to want to limit wanted and unwanted immigration, regardless of who one might vote for.

> If we are honest to ourselves about what they want and openly talk about [...] without visible abuses, these people are unhappy.

You seem to be talking about a subset of all people who do not want immigration: caricaturally-extreme racists. You and GP are not talking about the same people, and GP is not carrying water for anyone.
by Soerensen | 2026-02-03 17:11:23+00:00
Definitely possible. I dropped out at 16 and now run a startup after leading growth at Revolut.

The key differentiator for non-degree candidates: demonstrated results over credentials. Build something people can see - a side project, open source contribution, or portfolio piece that shows you can ship.

Three things that worked for me:

1. Start in adjacent roles. My path was athlete → operations → marketing → growth → founder. Each step built skills that compounded.

2. Over-index on learning velocity. Companies hiring non-degree candidates are betting you can learn fast. Show evidence of this - rapid skill acquisition, self-taught domains, etc.

3. Target companies that value output over pedigree. Startups and scale-ups tend to care more about what you can do than where you studied. The larger and more established the company, the more the degree matters as a filtering mechanism.

The current market is tougher than 5 years ago, but the fundamental truth remains: if you can demonstrably solve problems that companies need solved, someone will pay you to do it.
by rdoneill | 2026-02-03 17:11:27+00:00
Hi all — I’m the author.

A quick clarification on intent, since “Git diff for reconciliation” can sound broader than it is:

VisiGrid CLI is deliberately narrow. It’s not a BI tool, not a data warehouse, and not trying to replace Excel or pandas. It’s a deterministic comparison primitive: given two datasets, it answers what matches, what differs, and what’s missing, with explicit numeric tolerance and machine-checkable output.

The main design constraints were:

same inputs → same outputs (no ambient recalculation)

explicit exit codes so it can run in CI

stable JSON output for downstream automation

runs fully local (no accounts, no network)

I’m pre-v1.0 and very interested in feedback on diff semantics, tolerance handling, or edge cases you’ve hit in reconciliation workflows.

Happy to answer questions or go deep on implementation details.
by rbbydotdev | 2026-02-03 17:11:42+00:00
I agree that accountability should always rest with the human submitting the PR. This isn't for deflecting ownership to AI. The goal is transparency, making it visible how code was produced, not who is accountable for it. These signals can help teams align on expectations, review depth, and risk tolerance, especially for beta or proof‑of‑concept code that may be rewritten later. It can also serve as a reminder to the author about which parts of the code were added with less scrutiny, without changing who ultimately owns the outcome.
Show HN: Octosphere, a tool to decentralise scientific publishing
by crimsoneer | 2026-02-03 17:11:42+00:00
Hey HN! I went to an ATProto meetup last week, and as a burnt-out semi-academic who hates academic publishing, I thought there might be a cool opportunity to build on Octopus (https://www.octopus.ac/), so I got a bit excited over the weekend and built Octosphere.

Hopefully some of you find it interesting! Blog post here: https://andreasthinks.me/posts/octosphere/octosphere.html
by reactordev | 2026-02-03 17:11:48+00:00
Fully automatic assault rifles, anti-aircraft guns (that still operate), anti-aircraft missiles (that still operate), land mines over a certain size, or any Comp B. Those are on the naughty list.

There’s a whole community of folks building semi-automatic auto-return triggers that are “technically” semi automatic, but with just a gentle squeeze, fire off another. If you maintain that grip, the return mechanism engages, returning the trigger to firing position, where your pressure causes it to fire again… it’s called a Forced Reset Trigger.
by Soerensen | 2026-02-03 17:12:10+00:00
The positioning shift here is interesting. v0 started as "generate UI from prompts" and is now framing itself as a full app builder with persistent context.

What I find compelling about this direction: the bottleneck in AI-assisted development is not the initial generation, it's the iteration loop. Having a tool that maintains context across changes and understands your codebase holistically is genuinely more useful than one-shot generation.

The real test will be how it handles the messy middle - when you're 70% done and need to refactor, integrate with external APIs, or handle edge cases that weren't in the original prompt. That's where most AI coding tools fall apart in my experience.

Curious how they're handling the economics here. AI-first tools tend to have brutal unit economics at scale - inference costs add up fast when users expect instant iteration. The shift to Claude and "optimized model selection" suggests they're thinking about this carefully.
by Soerensen | 2026-02-03 17:12:55+00:00
Clever application of voice AI. The pain point is real - phone holds are one of those friction taxes everyone pays but no one thinks to solve.

A few questions from someone who would use this:

1. How does it handle identity verification? Many customer service calls require account holder verification (last 4 of SSN, security questions, etc.). Does the user pre-provide these, or does Pamela hand off at that point?

2. What's the latency like in the conversation? I've used some voice AI tools where the delay between human speech and AI response is noticeable enough to confuse the human on the other end.

3. The NYT example is interesting - how does it handle when the rep says no initially? Does it have negotiation logic, or does it just accept the first answer?

The API angle is smart. There's probably a B2B play here for companies that want to automate outbound calls for appointment confirmations, reservation changes, etc. That's where the real volume would be.
by mhalle | 2026-02-03 17:12:55+00:00
Skills are not just documentation. They include computability (programs/scripts), data (assets), and the documentation (resources) to use everything effectively.

Programs and data are the basis of deterministic results that are accessible to the llm.

Embedding an sqlite database with interesting information (bus schedules, dietary info, or a thousand other things) and a python program run by the skill can access it.

For Claude at least, it does it in a VM and can be used from your phone.

Sure, skills are more convention than a standard right now. Skills lack versioning, distribution, updates, unique naming, selective network access. But they are incredibly useful and accessible.
by Ajedi32 | 2026-02-03 17:13:06+00:00
> Starlink has deployed 50x the ISS's solar cap across its entire fleet (admittedly 3 years ago); the author's calcs are 500x the ISS for one datacenter.

So 3 years ago they managed to get to 10% of the power budget of one data center by accident, using satellites not explicitly designed for that purpose, using a partially reusable launch platform with 1/10th the payload capacity of Starship. My point is they've already demonstrated they can do this at the scale that's needed.

> A single Starlink satellite is using power in the order of watts

Then why does each satellite have a 6 kW solar array? Re-read that post I linked; the analysis is pretty thorough.

> Don't their current satellites have like 100Gbps capacity max?

Gen 3 is reportedly up to 1 Tbps ground link capacity, for one satellite.[1] There will be thousands.

> Do you have any idea how many 100Gbps routers go into connecting a single datacenter to the WAN? Or to each other (since intrahall model training is table stakes these days).

Intra-satellite connections use the laser links and would not consume any ground link capacity.

You're also ignoring that this is explicitly being pitched as a solution for compute-heavy workloads (AI training and inference) not bandwidth-heavy workloads.

[1]: https://starlink.com/updates/network-update
by Kotlopou | 2026-02-03 17:13:21+00:00
The percentage of users quoted seems insane: are 10-20% of Americans really on GLP-1??

I found this source: https://www.rand.org/pubs/research_reports/RRA4153-1.html

The numbers across age categories do not correspond to the ones in TFA, but they're also insane, and around 10% average. Combined with 20% vomiting side effects, that would suggest 2% of Americans have been throwing up, which sounds... implausible? Can an American say whether this matches what they're seeing/hearing in their social circles?
by realo | 2026-02-03 17:13:37+00:00
Exactly. Using a slide rule shows how some complex operations in one domain can be made much easier in another.

One you understand slide rules and logarithms, it is easier to understand convolutions in the FFT (frequency) domain...

Almost like programming in APL, where you can solve a problem by expanding it in extra dimensions and getting the answer by re-compacting the complex object using a different view.