MurphyA/hacker-news huggingface dataset:
by seec | 2026-02-15 12:15:02+00:00
I'm not contesting that AI will become worthwhile on local hardware at some point. With software optimization and hardware costs falling over time, that's pretty much a given.

But I'm arguing that it's not going to be worthwhile doing on Apple hardware.
GPU sharding is already a thing for PCs, and you don't need to stupidly buy multiple full computers for it to work.

Apple has put themselves into a corner with their Apple Silicon strategy. It's good for efficiency and thus quite nice for mobile usage, but it makes no sense for desktops that do not need to be power/space constrained.

Their GPUs are still weak, and their strategy of gluing 2 together gives poor results in general workloads. They are limited by the die size and the RAM bandwidth they can allocate to the whole thing because of physics.

If Apple manages to get good results by aggregating multiple computers, PCs will get even better results by using multiple GPUs in the same box, interconnected by the PCIe bus, which will always be faster than Thunderbolt no matter what, because of physics.
In fact, they could even come up with a new interconnect if need be.

There is just no realistic way for Apple to become a dominant player in AI. They cannot compete properly on the hardware side because they won't get the cash flow/key players NVidia and AMD are getting, and they cannot compete properly on software because it will always be ports of stuff made to run on better/faster hardware. They'll lose AI basically for the same reason they have lost gaming: uncompetitive performance for the price. People who actually want to do stuff care less about how things look and a lot more about how good/fast they run.

And whenever datacenters start offloading older GPUs, their price will fall, making it the cheapest way to do local AI. Apple hardware keeps a stupidly high price even when it's completely obsolete because of the status it confers; it will never be cost competitive.

It's basically a replay of their PPC mistake, where they thought they could compete by going at it alone but in the end fell pretty hard because they couldn't compete against the volume PCs were getting.

Now Apple has money but cannot attract enough talent because they have no vision, and the management style is basically mean girls running the show.

You are arguing about purchasing a solution that would cover 8 years of top-tier AI subscription. Seriously, who in their right mind would do that?
Apple hardware for AI makes no sense; either you have prosumer-level needs that are going to be served just fine with cheaper hardware (like, for example, Ryzen AI) or you have large needs, and investing in a real AI solution is going to be better because it's going to be much faster. Being able to fit large models is useless if they run too slow.
by adrian_b | 2026-02-15 12:15:44+00:00
Only stars significantly bigger than the Sun go through a supernova explosion, and such big stars are a small fraction from the total number of stars.

Moreover, the rate of seeing supernovas depends both on the number of stars that can become supernovas and on the lifetime duration for such stars.

Therefore, even in a hypothetic world where all the stars could become supernovas one might see a very small number of supernovas if the lifetime of stars were great enough.

Thus the frequency of seeing supernovas is not sufficient for any conclusion, without taking in consideration the proportion of stars susceptible to become supernovas and their average lifetime.
by ErroneousBosh | 2026-02-15 12:15:47+00:00
> 0.126 to 0.63%

So, a just-about-measurable increase, if you pick and choose your values carefully?

You are not going to die from cancer caused by getting a PET scan. This will not happen.

You're going to die of heart disease or as a not-too-distant second in a car accident.
by aiven | 2026-02-15 12:16:34+00:00
the system is built in a way that you need to run in order to stay still. you cant just "work less" because you will not have stability this way. new slaves using new shiny tools will work more and more and outcompete you in whatever domain you are working. so you will be forced to work more, to learn more in order just to keep your current lifestyle.

without basic morality and gov regulation capitalism would exploit humans like cattle. 996 should be outlawed everywhere
by throw0101c | 2026-02-15 12:16:48+00:00
Recent NANOG presentation by Doug Madory:

> This talk details how large amounts of IPv4 space, formerly originated by prominent Ukrainian ASes, have moved out of the country, often with the aide of IPv4 brokers, to some of the biggest American cloud and service providers. The result is a dramatic reduction in Ukraine’s footprint in the global routing table — a new consequence of a protracted war in the digital age.

> Lastly, this talk details how a new stricter routing policy by AT&T, put into effect in the past year, has eliminated all of the Ukrainian IPv4 space originated by AS7018, up until recently one of the biggest destinations for this exodus of address space.

* https://www.youtube.com/watch?v=7P_ybLSEQlw
by badpun | 2026-02-15 12:17:36+00:00
Part of the profits come from employment of capital. Almost no company relies solely on labor, they typically rely on both labor and capital (machines, buildings etc.). Owner of capital wants compensation for their investments, and he gets it via profits. Meanwhile, workers want compensation for their work, and they get it via wages. The exact split between compensation for capital and for work is a subject of negotiation (and often laws).
by ngrilly | 2026-02-15 12:17:38+00:00
Hard disagree. We use both in my company. Google Chat is definitely better than Teams for actual collaboration: it's easier to track unread messages in "Home" (it's the "inbox"), and channels (called "spaces") are much better designed (they are conceptually closer to Slack's channels). Also, it's not crashing all the time. What's missing: the message editor doesn't support nested bulleted lists, we can't archive a space/channel.
by EdNutting | 2026-02-15 12:17:58+00:00
Sort of. But SRAM is not all made equal - L1 caches are small because they’re fast, and vice-versa L3 SRAM caches are slow because they’re big.

To address a large amount of SRAM requires an approximately log(N) amount of logic just to do the addressing (gross approximation). That extra logic takes time for a lookup operation to travel through, hence large = slow.

It’s also not one pool of SRAM. It’s thousands of small SRAM groups spread across the chip, with communication pathways in between.

So to have 44GB of SRAM is a very different architecture to 80GB of (unified) HBM (although even then that’s not true as most chips use multiple external memory interfaces).

HBM is high bandwidth. Whether that’s “fast” or not depends on the trade off between bandwidth and latency.

So, what I’m saying is this is way more complicated than it seems. But overall, yeah, Cerebras’ technical strategy is “big SRAM means more fast”, and they’ve not yet proven whether that’s technically true nor whether it makes economic sense.
by charcircuit | 2026-02-15 12:17:59+00:00
Sometimes it doesn't work or it will give up early, but considering these run when I'm not working it is not a big deal. When it does work I would say that it has figured out that hard part of the solution. I may have to do another prompt to clean it up a bit, but it got the hard work out of the way.

>or offer to run it with debug logs.

Enabling it to add its own debug logs and use a debugger can allow it to do these loops itself and understand where it's going wrong with its current approach.
by gjulianm | 2026-02-15 12:18:51+00:00
> it's likely this result in innovations that would drive down costs, improve accuracy, as well as producing a much larger corpus of data with which to guide diagnosis and reduce false positives.

Why is it likely? We already have a lot of MRI data. There are already a lot of incidental findings. It might also be an issue of the MRI not being able to produce enough information to discriminate.

> To use a software analogy, if your downtime detection system kept producing false negatives, would your solution to be just turn it off? You'd get some better night's sleep, but you'd pay for it when the system really went down and you had no idea.

The analogy is rather something like this: your downtime detector is not just a "ping" but a full web browser that tests everything and it sometimes flags things that are not actually issues. So you don't turn it off, but you only use it when you have another signal that indicates that something might be going wrong.
by vict00r99 | 2026-02-15 12:20:24+00:00
You don't map the types, you map the behavior.

Define a behavioral contract (inputs, outputs, edge cases) with test cases, and let each language handle types idiomatically. `dict[str, Any]` becomes `Record<string, unknown>` in TS, `map[string]interface{}` in Go, `HashMap<String, Box<dyn Any>>` in Rust. You don't force one type system onto another.

For dynamic languages, type hints document intent but tests enforce the contract. If the function accepts the specified inputs and produces the specified outputs, the contract is satisfied, regardless of whether types are checked at compile time or runtime.

What do you think?
by rm30 | 2026-02-15 12:20:39+00:00
I think archaeology requires a multidisciplinary approach that has only recently begun to emerge. For too long, especially in past centuries, archaeologists focused on history and languages while neglecting engineering, chemistry and the practical techniques that enabled survival and innovation.
That's why the general public views our ancestors as 'primitive,' when in reality they possessed techniques many of which we've lost or still don't fully understand.
by direwolf20 | 2026-02-15 12:21:18+00:00
The Bell breakup is the only reason we have communication technologies newer than $2/minute telephone calls or (for the same price) Telex.

Bell had one good side, that was Bell Labs. How was it funded? By overcharging the whole country for communications, pocketing 90% of the profit, and using the last 10% to find ways to lower costs to provide the service — cost decreases that would not be passed onto customers.

It was even worse than it is right now with the regional internet monopolies.
by sylware | 2026-02-15 12:21:26+00:00
Allright, the ISA itself is probably protected under some copyrights(+patents), which in the US last for ~one century (cf Disney).

In the end, nobody sane would try its luck, better go for something non "IP-locked".

Aka RISC-V, not to mention that for a modern implementation RISC-V is more friendly.
by adrian17 | 2026-02-15 12:21:59+00:00
Hi, one of Ruffle maintainers here. AFAIK, we do have most of NetConnection API implemented; but direct socket connections are just impossible in browsers. The games should (hopefully) work and connect when run via the desktop player. We also implemented socket emulation in the browser via WebSockets, so they should also start working there if you put a WebSockify proxy on your server (no need to touch the game server code).
by toofy | 2026-02-15 12:22:35+00:00
was it “uk only” or was it the only place that required them to notify users theyre being experimented on?

we know US law allows tech companies to experiment on us without notifying at all. facebook was caught experimenting on users to see if a timeline full of sad posts would cause the users to become depressed.

im guessing his companies will get ahold of discord users data in most other countries. i’d be shocked if he only wants data from a tiny number of UK people.
by coolThingsFirst | 2026-02-15 12:23:02+00:00
Why stop at Slack, we need better software for tons of things. Even a better OS is something which we would do if we had the AI productivity gains that people pretend we do.

Just let the agents spin. But it's not that easy, is it.

Someone that will tackle this will be competing against B dollar companies and extravagant level of features and integration. It's not as simple as a chatroom with people in it.
by adrian_b | 2026-02-15 12:24:02+00:00
HDD cost small dollars only for small amounts of archived data, i.e. up to 100 TB or 200 TB at most.

For greater amounts of data, HDDs become too expensive and this is the main reason to switch to tapes.

Obviously, for someone who is certain of never needing more than a few tens of TB of storage space it would be foolish to use LTO.

On the other hand, for someone storing 500 TB, it is foolish to use HDDs, because tapes are more reliable, more compact, faster for sequential transfers, i.e. the actual backup and restoring, and cheaper.

It is as simple as that. The decision of using HDDs or LTO is strictly determined by the amount of data that must be stored.

The argument that HDDs should be fine for most non-technical people is correct only because those people do not store much data.
by Kim_Bruning | 2026-02-15 12:24:40+00:00
Yeah. I don't want to defend it too hard either. I ultimately canceled my ChatGPT subscription due to the introduction of 4o.

4o was the most famous driver of this kind of behaviour, I think. Other LLMs now have better guardrails.

But looking at the relevant reddit, I can't deny it has helped people function who couldn't otherwise.

It's the same with religion. Some creepy people, you're really glad they've at least <Found Christ>. It's a good thing Christ doesn't have a plug.
by VladCovaci | 2026-02-15 12:24:52+00:00
We built FounderSpace to help founders validate and position their startup idea before writing any code. You explain your idea in simple terms, and our AI evaluates market demand, competition, timing, and early adopters, then creates a clear validation brief with practical next steps. The goal is to save time by avoiding ideas that sound good but don’t have real demand.
check it out: https://www.founderspace.work/
by pfdietz | 2026-02-15 12:25:05+00:00
I much prefer tests with low false positive rates.

I recently had such a cancer-related test. A cousin had a BRCA2 mutation and I was concerned I could have it also. Insurance would not pay for the testing, but one can get a panel of such genetic tests for just $250 now, so I went ahead. And it was negative. This is reassuring not just to me, but also to my children, and (somewhat) my sibling (the relevant parent is no longer alive).

Had this test been positive, the chance of pancreatic cancer would have gone way up, so frequent scans (I think annual MRI and ultrasound?) would have been justified.
by logicprog | 2026-02-15 12:25:40+00:00
Why do you think it's all hallucinated?

You have no evidence of that, and it seems very unlikely unless you're intentionally wildly assuming the craziest possible scenario, as if you're paranoid or insane.

You do realize the user can see the tool calls running and check their real, actual output, during this process, right?

You do realize that there are several sleep masks on Kickstarter that actually have these features, right?

The user has also shared the Claude transcript:

https://gist.github.com/aimihat/a206289b356cac88e2810654adf0...
by gjulianm | 2026-02-15 12:25:47+00:00
> With my change: 95% of people who are shown scans have cancer and are treated earlier. Without my change: many of those 95% die

Why? What happens if the cancer still doesn't respond to treatment even when detected early? Or, to the contrary, if the cancer also responds to treatment when it starts becoming symptomatic?

That's why we have studies to understand if screening is a good practice or not. It's not that clear cut.
by lbreakjai | 2026-02-15 12:26:22+00:00
It's never been the consensus. As far back as I can remember, the wisdom was always to comment why the code does what it does if needed, and to avoid saying what the code does.

Saying that function "getUserByName" fetches a user by name is redundant. Saying that a certain method is called because of a quirk in a legacy system is important.

I regularly implement financial calculations. Not only do I leave comments everywhere, I tend to create a markdown file next to the function, to summarise and explain the context around the calculation. Just plain english, what it's supposed to do, the high level steps, etc.
Show HN: Turn LinkedIn profiles into Markdown for LLM use [open source]
by jonaraphael | 2026-02-15 12:26:47+00:00
If you do AI-assisted writing, founder/investor diligence, sales research, or recruiting, LinkedIn data is high-value but trapped in UI. Copy/paste is slow, messy, and hard to reuse.

I built LinkedMD, a Chrome/Arc extension that exports the LinkedIn profile you’re currently viewing into a clean .md file in one click.

It pulls About plus details pages (Experience, Education, Skills, Publications, Languages, etc.), adds YAML front matter (source, exported_at, title), and filters common LinkedIn UI/ad noise so the output is actually usable in notes, docs, and AI workflows.

Everything runs locally in the browser: no account, no backend, no remote code. It’s for manual exports, not bulk scraping automation.

Repo: https://github.com/jonaraphael/linkedmd

Would love feedback on whether you find this useful!
by philipwhiuk | 2026-02-15 12:27:10+00:00
I've always found it very weird that GitHub's tolerated being completely subverted to become a package manager for ecosystems that can't be bothered to manage it themselves.

NPM doesn't just proxy to GitHub (even though both are owned by Microsoft).

To see maintainers criticise GitHub for not being a perfect package manager is lunacy.

See also https://github.com/CocoaPods/CocoaPods/issues/4989
by villgax | 2026-02-15 12:28:11+00:00
Lol, without any evidence this is just vaporblog, it could just be reudced precision for whatever model either one of them runs & not necessarily a distillation or smaller model to boot or heck even a combo since at this point in time most frontier models are MoEs & getting absurd speeds for 1-20B experts is trivial regardless of batch sizes
by antonvs | 2026-02-15 12:29:05+00:00
FTA:

> “For example, there is no guarantee that the code displayed at https://github.com/example/mod/blob/v1.2.3/exp.go is the actual contents of exp.go from v1.2.3 of module github.com/example/mod: GitHub allows force-pushing git tags and even built its recommended GitHub Actions workflows on top of mutable tags.”

In a sense this is a git issue: the fact that git is mutable instead of append-only. Git wasn’t designed to serve as part of a code integrity system - you’d have to add that at some other level, such as Github.

Or in the case of this example, recognize that tags can’t be secured and implement everything in terms of commit hashes, etc.
by direwolf20 | 2026-02-15 12:30:27+00:00
There's a dead comment here saying that OpenAI doesn't just DDoS the internet because it can. That's true. Any supposed DDoS is a side effect or incompetent scraping, and won't affect anything they can't scrape. Not sure why it's dead — it's important to realise this.

Edit: oh, it's probably dead because of the username
by ktaraszk | 2026-02-15 12:30:30+00:00
I've been watching this thread closely. We built miget (https://miget.com/) specifically because of these pain points. The Heroku-style DX without the eye-watering bills or corporate neglect.

What stood out from the comments: people don't want to leave the simplicity, they just can't justify the cost or trust the direction. That's exactly the gap we're trying to fill. Flat pricing (hobby plans from $5/mo, pro from $22/mo), unlimited apps per plan, and we're actually shipping features.

Not trying to be opportunistic here, but if anyone's evaluating alternatives, happy to help. We have people migrating from Heroku weekly and the feedback has been solid.
by ulfw | 2026-02-15 12:31:27+00:00
Investors still pretend AI is a winner-takes all. The one magic model that will rule all the others forever and ever and gain all the marketshare.

In reality LLMs have proven to be a commodity. Today OpenAI is ahead, tomorrow Anthropic, the next day Gemini and vice versa. Many others, if Qwen or Deepseek are at it's toes and for the majority of people if used unbranded wouldn't even be discernable in difference. Price will dictate who wins. And that is a commodity product.
by johndough | 2026-02-15 12:32:12+00:00
> why are you so defensive?

Because you made stuff up, did not show any proof, and ignored my proof to the contrary.

You made the claim:

> Deepseek lies about costs systematically.

DeepSeek broke down their cost in great detail, yet you simply called it "lies", but did not even mention which specific number of theirs you claim is a lie, so your statement is difficult to falsify. You also ignored my request for clarification.
by gjulianm | 2026-02-15 12:32:21+00:00
> This is much more sensible than just not testing at all and letting people die from cancer.

This is not what happens. You're assuming that if the cancer does not get detected by the screening then it never gets detected. What actually happens is that the test gives information that might actually be redundant and obtainable in less risky way. What the studies are showing is that waiting until there are other, more specific signs and symptoms of the prostate cancer results in the same survival rates.
Show HN: Skill Container – a decent way to run and distribute agent skills
by observerw | 2026-02-15 12:32:43+00:00
Hi HN — I’m trying to figure out a simple/open way to package agent skills with a clear runtime contract and a sane distribution/update story.
What I found frustrating in many existing skill formats is that they’re basically lack of portability:
- unclear how to run (Python shebangs, hidden deps, host-specific assumptions)
- easy to pollute local environments and drift across machines/versions
- little to no safety boundary
And distribution is often hand-wavy too: no standard install/update/uninstall flow, no integrity/pinning story.

I found that simply introducing OCI containers solves pretty much all of this in one move.

So, Skill Container’s approach is: OCI containers + GitHub distribution.
- Each skill is a repo: SKILL.md + Containerfile + a real CLI entrypoint (e.g. cli.py) + deps in pyproject.toml
- Authors publish images to GHCR (ghcr.io/...); users “install” by cloning the repo
- Running is just docker run --rm ... ghcr.io/<owner>/<skill>:<tag> --help with explicit mounts; --help is the discovery surface
- Updates are predictable: git pull for docs + docker pull for runtime (keeping docs and behavior in sync)

Any opinions would be appreciated!
by guerrilla | 2026-02-15 12:32:48+00:00
That's what I remember most about it: how much it lowered expectations on us. We barely ever worked because most of the time we couldn't. It was always broken (not just slow). This became a part of the culture where management didn't even care what we did. They knew eventually things would get done (late) but we'd also have to wait. That was just part of it.
Show HN: 500x faster string matching for Linux Netfilter (O(1) vs. O(N))
by landerrosette | 2026-02-15 12:35:20+00:00
I built a drop-in replacement for the kernel’s xt_string module.

xt_string scales linearly (O(N)), causing massive slowdowns with many rules. Strider uses Aho–Corasick for O(1) matching.

Key Features:

O(1) Algorithmic Complexity: Uses a compact, double-array trie-based Aho–Corasick automaton, sustaining above 1 Gbps when matching 3,000 patterns, while xt_string (KMP) drops below 2 Mbps.

Lockless Datapath: RCU-protected lookups ensure zero locking overhead on the packet processing hot path.

Correctness: Never misses patterns spanning across IP fragments (unlike xt_string’s fast Boyer–Moore mode).
by luoxiaojian | 2026-02-15 12:35:54+00:00
Author here. Thanks for the interest! On the performance side: we've applied optimizations like prefetching, SIMD, and a novel batch distance computation (similar to a GEMV operation) that alone gives ~20% speedup. We're working on a detailed blog post after the Lunar New Year that dives into all the techniques—stay tuned!

And we always welcome independent verification—if you have any questions or want to discuss the results, feel free to reach out via GitHub Issues or our Discord.