MurphyA/hacker-news huggingface dataset:
by infecto | 2026-02-11 14:22:19+00:00
Will be interesting when/if more information is released. I am not sure why folks are so surprised or think it’s shocking. While definitely out of the norm, my mind was immediately thinking 10 days seems like an even number where you are trying to find or do something, not sure how long it’s going to take so you just stick it. Certainly odd that it’s only a few hours but for all I know there is some written government procedure for whoever is doing that sets it at 10 days.
by kxbnb | 2026-02-11 14:22:24+00:00
I built Axiomo because every AI code review tool I tried kept solving the wrong problem. They all want to be a second developer on your PR, catching lint issues and suggesting refactors. That's not what makes review hard.

What makes it hard is context. Who wrote this, have they worked in this area before, what are they actually trying to do, and where should I focus?

Axiomo generates a structured Signal for every PR that answers those questions. Contributor context, intent, risk drivers, CI evidence, focus files.
No auto-generated code suggestions. No comment spam. Just the stuff that helps you actually review instead of skim and approve.

Stack is Next.js on Vercel, Neon for Postgres, Resend for email. GitHub integration. Would love feedback on the approach and what your review workflow looks like.
Show HN: SNKV: 49K vs. 43K ops/SEC (70% Reads), 2MB vs. 20MB RAM vs. RocksDB
by hashmakjsn | 2026-02-11 14:22:35+00:00
I have been working on SNKV, a lightweight SQLite's B-tree based key value store focused on predictable performance and low memory usage. I benchmarked SNKV against RocksDB using 1,000,000 records on the same hardware with minimal tuning on both sides.

Mixed workload 70 percent reads 20 percent writes 10 percent deletes
SNKV 49,119 ops per second
RocksDB 43,394 ops per second

Random reads 50,000 lookups
SNKV 69,174 ops per second
RocksDB 54,029 ops per second

Sequential scan 1,000,000 records
SNKV 1,842,380 ops per second
RocksDB 973,795 ops per second

Sequential writes 1,000,000 inserts
SNKV 122,580 ops per second
RocksDB 438,452 ops per second

Peak process memory usage
SNKV approximately 2 MB
RocksDB approximately 20 MB

These results reflect architectural tradeoffs. RocksDB performs strongly in sustained write heavy scenarios, while SNKV performs well in balanced read write workloads and scan heavy use cases.

SNKV benchmark source
https://github.com/hash-anu/snkv/blob/master/tests/test_benc...

RocksDB benchmark source
https://github.com/hash-anu/rocksdb-benchmark

Feedback on benchmark methodology and configuration fairness is welcome.
by akandilli | 2026-02-11 14:23:15+00:00
I made this metaphor from my understanding of your comment.

Imagine we put a kid in a huge library of book who doesn't know how to write/read and knows nothing about what letter means etc. That kid stayed in the library and had a change for X amount time which will be enough to look over all of them.

what this will do is that not like us but somehow this kid managed to create patterns in the books.

After that X amount of time, we asked this Kid a question. "What is the capital of Germany?"

That kid will just have it is on kind of map/pattern to say "Berlin". Or kid might say "Berlin is the capital of the Germany" or "Capital of Germany is Berlin." The issue here is that we do not have the understanding of how this kid came of with the answer or what kind of "understanding" or "mapping" being used to reach this answer.

The other part basically shows we do not fully understand how LLM works is: Ask a very complex question to an AI. Like "explain me the mechanics of quantum theory like I am 8 years old".

1- Everytime, it will create differnt answer. Main point is the same but the letters/words etc would be different. Like the example I give above.There are unlimited type of answer AI can give you.
2- Can anyone in the Earth - a human - without a technology access for have unlimited amount of book/paper to check whatever info he needs - tell us the exact sentence/words will LLM use? No.

Then we do not have fully understand of LLM.

You can create a linear regression model and give it 100 people data and all these 100 people are blue eyed. Then give 101 person and ask it to predict the eye color. You already know the exact answer. It will be %100.
by ninjin | 2026-02-11 14:23:29+00:00
I think our contexts are all different. But, to share a different experience, as an academic (with plenty of conversations involving people in industry as well each year) I have used interleaved and bottom-posting for decades and it causes confusion maybe once a year at most and mostly because Microsoft's online client is broken and at times does not even render anything below "Dear Foo," in the HTML view (got to give this small start up in Redmond some more time though, we can not expect them to implement standards that have only been around for over 40 years).
by monideas | 2026-02-11 14:23:40+00:00
Communities aren't fungible (in the sense that the actual particular culture and values of a particular group of people living in a particular area do have an extreme impact on that area) but also communities are not defined by particular areas that people live.

People who are from the same community tend to settle in particular areas and I think that is what the author is getting confused by.

Also many people in the developed world don't really have any community that they're a part of at all.

If you're interested in learning more about this you can look up the phrase "covenantal vs majestic community".
by sebstefan | 2026-02-11 14:24:08+00:00
Nope, when corporate overlords sell your data they say it in their terms of use and privacy policies because no one is that stupid. If Discord says they're not selling that data, they're not selling that data. The day they'll start doing it, they'll put it in their policy.

You're making up a reality that doesn't exist in your head and claiming it's the truth.

You have in your head examples like facebook or spotify. Spoiler: They tell you exactly with what sauce you're gonna be eaten
by ErroneousBosh | 2026-02-11 14:24:41+00:00
> But damn, you want to piss off hams? Mention bitrate maximums or encryption. You'll never hear the end from the old gatekeeping idiots.

So much gatekeeping.

Incidentally, I have it Word From On High within Ofcom here in the UK that you literally cannot pay them to take an interest in what happens on the amateur bands.

There's "breaking the law" and there's "being a bit rude", the latter of which might be things like "hey let's do fastscan TV on 70cm and use about half the allocation!" You do have to watch with 70cm in the UK though because amateur radio is a secondary user, with primary users being the armed forces. But it's 10MHz wide and there's space for everyone to play.

Putting the 70cm packet BBS channel 5kHz above where all the car alarm keyfobs work was a bit silly though.

As regards microwave stuff, I've got some scrap 26GHz stuff at work that can apparently be tuned to 24GHz by swapping the cavity tuning screw for one of the slightly longer ODU outer cover screws, and tweaking a setting in the EEPROM in Factory Never Touch This Shit mode. Want to bet they had radio amateurs working for them?
by cbondurant | 2026-02-11 14:25:27+00:00
I feel like the lag-time of communication was an important component of older forms of communication that has been lost. That's not to say that fast communication isn't a boon to society, of course. Only that slower communication gives you more flexibility in how you respond, and more time to think about what your response should be.

When the main form of long distance communication was the postal system, and letters took days to travel from sender to receiver, you could easily wait days, if not weeks, to draft up your reply and mail it out. The recipient on the other end wouldn't even be able to discern the difference between your delay and the delay from the postal network itself. It had some in-built slack.

When the only phones were landlines, if someone called you and you knew you were in a bad mood, the kind of bad mood that would invariably make you say something stupid, you could just not pick up! There were plenty of common, understandable reasons someone wouldn't be available to answer their landline. Then they could leave you a message, and you could call back when you mood improved again. Again, there was slack built into the system.

Now there's this cultural expectation that puts far more attention on your reaction speed. A text message with no immediate response could just be them not seeing it immediately... But actually no! Now we have read receipts too! You can't even pretend to have not seen it yet while you think of your reply. Some platforms even have the little "currently typing" indicator tell them how long you've spent drafting and re-drafting whatever message you ended up sending. A panopticon of communication. Now there's no slack. Any person anywhere in the world could try and get a hold of you with the same expectation of immediacy that a face-to-face conversation would supply.

Now of course, not every single person I might text, call, or send an email to, will have the same expectations for what is an appropriate degree of responsiveness. But, (speaking from my personal experience) I am absolutely miserable at reading that from social clues. I am left having to assume that, in the absence of some clear indicator to the contrary, whoever I am writing to will actually have rather strict expectations, and that allowing myself to be lax may very well give them a terrible opinion of me. (Though, the degree to which their opinion of me actually matters is a different question entirely!)
by pjc50 | 2026-02-11 14:25:41+00:00
Frame sync. In order to reduce latency, these systems tend to be unbuffered, which means that the frames have to arrive at a very specific time, and you can't afford significant jitter or (worse) phase drift. If you have one source at 25.000FPS and one at 25.001FPS eventually you're going to be a frame out between them.
by hiccuphippo | 2026-02-11 14:25:51+00:00
1 and 2, I don't have any of these issues when I use torrents.

3, People already uses torrents for distribution of content unrelated to Zig, the Zig population is way smaller than the torrent population. Heck, people already uses git for unlawful content, that doesn't cause problem for the vast majority of git users.
by Aurornis | 2026-02-11 14:26:03+00:00
> I put in two years worth of payments for 18 years driving the car (9 since my last payment and 9 more after the maintenance) :)

Plus paying for the car itself

You can’t estimate your future repair bills to be $0

I get it that you like the car, but there are some major mental gymnastics happening with your math
Show HN: On-Call Health – Spot signs of overload in incident responders
by jjtang1 | 2026-02-11 14:26:11+00:00
Hi HN,

We built On-Call Health to help teams detect signs of overload in on-call incident responders. Burnout is too common for SREs and other on-call engineers and that’s who we serve at Rootly. We hope to put a dent in this problem with this tool.

Here is the project GitHub repo https://github.com/Rootly-AI-Labs/On-Call-Health, and here is the hosted version https://oncallhealth.ai. The easiest way to try the tool is to log into the hosted version, which has mock data.

The tool uses two types of inputs:
- Observed signals from tools like Rootly, PagerDuty, GitHub, Linear, and Jira (incident volume and severity, after-hours activity, task load…)
- Self-reported check-ins, where responders periodically share how they're feeling

We provide a "risk level", a compound score derived from objective data. The self-reported check-in feature is taking inspiration from the Ecological Momentary Assessment (EMA), a research methodology also used by Apple Health's State of Mind feature.

We provide trend data for all metrics, for both teams and individuals, to help managers spot anomalies that may require investigation. Our tool doesn't provide a diagnosis, nor is it a medical tool; it simply highlights signals.

It can help spot two types of potential issues:

1. Existing high load: when setting up the tool, teams and individuals with a high risk level should be looked at. A high score doesn't always mean there's a problem – for example, some people thrive on high-severity incidents – but it can be a sign that something is already wrong.
2. Growing risk: over time, if risk levels are steeply climbing above a team or individual baseline.

Users can consume the findings via our dashboard, AI-generated summaries, our API, or our MCP server.

The project is fully open source and self-hostable, and the hosted version is available at no cost. We have a ton of ideas to improve the tool to make on-call suck les,s and we are happily accepting PR and welcome feedback on our GitHub repo. You can reach out directly to me at jj@rootly.com
by palmotea | 2026-02-11 14:26:55+00:00
> Even as a software developer affected by it, I don't think it should be banned. Productivity improvements are how we get richer in aggregate over the long term, even if those impacted (like you & me) might feel the brunt of transitional pain.

FYI: software developers can be pretty dumb, and believe (and spread!) the propaganda fed to them even when it's starkly against our interests. I think it has something to do with being smart and wanting to appear clever.

You won't be richer over the long term. AI is a technology for concentrating wealth further, and some cheap crap will be your consolation prize for losing.
by SideQuark | 2026-02-11 14:27:03+00:00
Also a PhD in math, where complex numbers are fundamental, and also part of large swaths of similar structures that are also fundamental. They fit in nicely among a ton of other similar structures and concepts, so they seem about as fundamental as sets or addition or groups or fields (and there it is).

They also seem fundamental to physical reality in a way most math concepts do not: they're required (in structure) for quantum mechanics, in many equations that seem to be part of the universe. The behavior of subatomic particles (and more precisely, QFTs), require the waveforms to evolve as complex valued functions, where the probability of an event is the magnitude of the complex value.

This has been tested between theory and experiment to about 14 decimal digits precision for QED.

I'd guess they should be considered as real as radio waves (which we don't see), as the fact things we think are solid are mostly empty space (which we don't feel), or that time flows at different rates under different situations (which we also don't experience). Yet all those things are more real than stuff our limited senses experiences.

There's some string of research on if/how fundamental complex numbers are to QM, e.g., https://www.scientificamerican.com/article/quantum-physics-f...
by ahhhhnoooo | 2026-02-11 14:27:13+00:00
You think discord is the same order of magnitude as a Microsoft or Google? Both of whom own major operating systems, browsers, ad platforms, office and workspace suites, app stores, cloud providers, search engines, maps products, developer tools suites, robotics, research laboratories, AI products, meeting and chat infrastructure, email products and any a million other things?

The moat there is staggeringly large comparatively.
by PaulHoule | 2026-02-11 14:28:13+00:00
You familiar with the idea of

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

?

This is the standard way of turning a probability-calibrated predictive model into a trading or betting strategy. Even the best AI model you're going to make is going to be making guesses about the direction of the financial market so you have to (1) make it smart about "knowing what it knows" and (2) build it's predictions into a systematic risk-managed trading strategy.
by cucumber3732842 | 2026-02-11 14:28:19+00:00
So basically an ass-covering squabble between bureaucrats?

"can you guarantee shit will be fine?"

"we can't guarantee anything"

"so you're saying it won't be fine"

"no, I'm saying it will"

"so you're guaranteeing it'll be fine then"

"no, I said I can't make any guarantees"

"well if you can't guarantee it'll be fine we have to shut it all down and you'll have to explain that to the boss"

"be my fucking guest"

<shit proceeds to be fine and everyone looks like uncooperative assholes>
by kajolshah_bt | 2026-02-11 14:28:19+00:00
I’ve gone through a similar journey, not in big tech, but in practical business work. We started with quick experiments: generative prompts in internal tooling, a couple of proof-of-concept bots, and integration of recommendations in mobile apps.

What shifted for us was when we stopped experimenting for novelty and started embedding AI where routine work slowed people down. For example, we built an intake assistant for hospitals: guided questions that organize structured history before a doctor sees the patient. At first it felt promising, but adoption only happened when clinic staff saw that it saved them time and didn’t replace their judgment. That forced us to rethink how we framed the feature. It became about support, not replacement.

The real adoption turning point came when non-technical team members began using the tools without hesitation. That’s when it stopped being AI and just became part of workflow.
by m0rtyn | 2026-02-11 14:28:46+00:00
Hi HN — I'm Martyn and I built this.

A few years ago I interviewed with the company behind Stripchat. I would
have taken the offer if I'd passed. At the time I figured the webcam industry
was sleazy but functional. Then someone wrote a comment to my favorite social platform, that laid out what
the industry actually looks like from the inside — contracts that change
after signing, studios making tens of thousands a night while models get
hundreds, performers who looked happy at 25 but had no family and no way
back at 40.

I came up with the domain name first. Then I bought it. Then I used AI
to pull every relevant study I could find — Human Rights Watch reports,
ICIJ investigations, peer-reviewed papers. The research that would have
taken weeks took about an hour. I verified every source by hand.
What you see on onlybots.cam is the result.

The site looks like an AI companion platform. Click any button and it
flips — every fake metric becomes a real statistic. Average creator earns
$180/month while the platform owner took home $1B in dividends. 48% of
sex workers report lifetime suicidality. 7 of 50 models in HRW's Colombia
investigation started before age 18.

Every number links to its primary source. Nothing is made up. I wish some
of it were.

Tech: Astro 5 static site, one React island, Tailwind CSS, GSAP for the
transition (single timeline, 14 animation steps). Zero cookies. Full source
index: https://github.com/m0rtyn/onlybots.cam/blob/main/docs/RESEAR...

This isn't aimed at viewers or the people on camera. It's aimed at the
platforms and studios that take most of the money and look away when things
go wrong.

Happy to answer questions about the data, the tech, or the concept.
by ThrowawayB7 | 2026-02-11 14:29:12+00:00
If I had to guess, the mandate to cram AI in everywhere came down from Nadella and the executive level with each level of management having KPIs for AI in their product all the way down. Much like the "everything has to be .NET even though nobody has any idea what .NET means" when it was first introduced and every MS product suddenly sprouted .NET at the end of their names. When executive management gives stupid non-negotiable orders, they get stupid results.
by bsza | 2026-02-11 14:29:23+00:00
What counts as "large"? I'm pretty sure at some point in my life I'd opened the entirety of Moby Dick in Notepad. Unless you want to look for text in a binary file (which Notepad definitely isn't for) I doubt you'll run into that problem too often.

Also, I hope the irony of you citing Notepad++ [1] as what Notepad should aim to be isn't lost on you. My point being, these kinds of vulnerabilities shouldn't exist in a fucking text editor.

[1] https://notepad-plus-plus.org/news/hijacked-incident-info-up...
by kajolshah_bt | 2026-02-11 14:29:28+00:00
That description matches a lot of what we’ve seen in real products. AI does make some parts of development and workflows easier like summarizing data, generating initial drafts, or auto-completing repetitive patterns. Those wins are real.

The hard part that becomes harder is not the technology. It’s the decision-making around it. When teams rush to integrate a model into core workflows without measuring outcomes or understanding user behavior, they end up with unpredictable results. For instance, we built an AI feature that looked great in demo, but in real usage it created confusion because users didn’t trust the auto-generated responses. The easy part (building it) was straightforward, but the hard part (framing it in a way people trusted and adopted) was surprisingly tough.

In real systems, success with AI comes not from the model itself, but from clear boundaries, human checkpoints, and real measurements of value over time.
by satiated_grue | 2026-02-11 14:29:31+00:00
FL180 is the floor of Class A airspace, "the flight levels", where airliners etc. operate.

Relevant chapter from FAA "Pilot's Handbook of Aeronautical Knowledge":
https://www.faa.gov/sites/faa.gov/files/17_phak_ch15.pdf

In the "Flight Levels", altitudes are referred to not in feet above sea level but as "FLxxx" where xxx is a nominal altitude in 100s of feet.

Altimetry is done using barometric pressure. Since this varies with weather, airplanes at lower altitudes set their altimeters to the local barometric pressure for a reasonably accurate reading. In the flight levels, where planes are typically covering ground quickly and there is very little chance of your path conflicting with the surface of the Earth, every plane sets to an agreed-upon reference of 29.92 inches of mercury as the altimeter setting.
Show HN: Eryx, a fast WASM-based Python sandbox with native extension support
by sd2k | 2026-02-11 14:29:42+00:00
Eryx is an OSS Wasm-based Python sandbox with full CPython support, ~16ms startup, native extension support (numpy, etc.), and bindings for Python, JS, and Rust. There have been a lot of these submitted in the last week or two but I think this one has a few interesting features. Specifically, Eryx:

- uses CPython compiled to WASI, so you have full Python access

- pre-initializes and pre-compiles the Wasm, giving extremely fast startup times (~16ms)

- supports Python packages, both pure-Python and native extensions (such as `numpy` compiled to WASI), by relinking the Wasm at runtime and re-initializing

- implements the `ssl` module so you can make HTTP calls, and `httpx` or `requests` just work

- has full resource limiting (networking, filesystem, timeout, CPU and memory) based on Wasmtime and WASI

- supports mounting host directories, a virtual filesystem, or both

- supports persisting and resuming session state to and from bytes, for distributed execution

- supports 'secret scrubbing', similar to Deno Sandbox, the sandbox can't see secret values

- supports callbacks into the host

- supports streaming stdout/stderr and trace execution (so the host can see the progress of the executed script; useful for showing progress in long or slow scripts)

- has builtin MCP support, in two ways: it can connect to your MCP servers (using your Claude/Codex/Cursor config files) and add the tools as callbacks, and it has an MCP server built-in with a `run_python` tool, where the Python can use those other MCP servers' tools

There's a CLI you can use with `uvx pyeryx`, and bindings for Python, Javascript and Rust. The demo is available at https://demo.eryx.run - give it a try and let me know what you think.