MurphyA/hacker-news huggingface dataset:
by pllbnk | 2025-12-03 14:43:38+00:00
It's relevant because it shows models haven't improved as much as the companies delivering them would like you to believe no matter what mode (or code) they work under. Developers are quickly transforming from code writers to code readers and the good ones feel demoralized knowing that they could do it better themselves but are instead forced to read gibberish produced by a machine in the volume of dozens of lines per second. Moreover, when they are reviewing that gibberish and it doesn't make sense, even if they provide arguments, that same gibberish-producing machine can, in a matter of seconds, write counter-arguments that look convincing but lack any kind of substance for those who understand and try to read it.

Edit: I am saying it as a developer who is using LLMs for coding, so I feel that I can constructively criticize them. Also, sometimes the code actually works when I put enough effort to describe what I expect; I guess I could just write the code myself but the problem is that I don't know which way it will result in a quicker delivery.
Show HN: The Journal of AI Slop – an AI peer-review journal for AI "research"
by popidge | 2025-12-03 14:43:44+00:00
What it is: A fully functional academic journal where every paper must be co-authored by an LLM, and peer review is conducted by a rotating panel of 5 LLMs (Claude, Grok, GPT-4o, Gemini, Llama). If 3+ vote "publish," it's published. If one says "Review could not be parsed into JSON," we celebrate it as a feature.

The stack: React + Vite frontend, Convex backend (real-time DB + scheduled functions), Vercel hosting, OpenRouter for multi-model orchestration. Each review costs ~$0.03 and takes 4-8 seconds.

Why I built it: Academic publishing is already slop—LLMs write drafts, LLMs review papers, humans hide AI involvement. This holds a mirror to that, but with radical transparency. Every paper displays its carbon cost, review votes, and parse errors as first-class citizens.

Key features:

- Slop scoring: Papers are evaluated on "academic merit," "unintentional humor," and "Brenda-from-Marketing confusion"

- Eco Mode: Toggle between cost/tokens and CO₂/energy use for peer-review inference

- SLOPBOT™: Our mascot, a confused robot who occasionally co-authors papers

- Parse error celebration: GPT-5-Nano has a 100% rejection rate because it can't output valid JSON. We frame these as "Certified Unparsable" badges.

The data: After 76 submissions, we've observed:

- Average review cost: $0.03/paper

- Parse error rate: 20% (always GPT-5-Nano, expected and celebrated)

- One paper was accepted that was literally Archimedes' work rewritten by ChatGPT

- GPT-5-Nano's reviews are consistently the most creative (even if broken)

Tech details: Full repo at github.com/Popidge/journal_of_ai_slop. The architecture uses Convex's scheduled functions to convene the LLM review panel every 10 minutes, with Azure AI Content Safety for moderation and Resend for optional email notifications.

Try it: Submit your slop at journalofaislop.com. Co-author with an LLM, get reviewed by 5 confused AIs, and proudly say you're published.

Caveat: This is satire, but it's functional satire. The slop is real. The reviews are real. The carbon emissions are tracked. The parse errors are features.
by ToValueFunfetti | 2025-12-03 14:44:01+00:00
I think Anthropic has established that LLMs have at least a rudimentary world model (regions of tensors that represent concepts and relationships between them) and that they modify behavior due to a prediction (putting a word at the end of the second line of a poem based on the rhyme they need for the last). Maybe they come up short on 'analyzing the circumstances'; not really sure how to define that in a way that is not trivial.

This may not be enough to convince you that they do think. It hasn't convinced me either. But I don't think your confident assertions that they don't are borne out by any evidence. We really don't know how these things tick (otherwise we could reimplement their matrices in code and save $$$).

If you put a person in charge of predicting which direction a fish will be facing in 5 minutes, they'll need to produce a mental model of how the fish thinks in order to be any good at it. Even though their output will just be N/E/S/W, they'll need to keep track internally of how hungry or tired the fish is. Or maybe they just memorize a daily routine and repeat it. The open question is what needs to be internalized in order to predict ~all human text with a low error rate. The fact that the task is 'predict next token' doesn't tell us very much at all about the internals. The resulting weights are uninterpretable. We really don't know what they're doing, and there's no fundamental reason it can't be 'thinking', for any definition.
by zuminator | 2025-12-03 14:44:23+00:00
Huh? Are you saying that Pelosi is a worse person because MTG is a worse person? I don't get the logic.

Given the situation (that it's unfortunately legal for them to do this) I don't blame either one for taking money left on the table. Why forgo a financial windfall, who benefits by that? The voters should back candidates who will change the law, and then we can talk about criminality and not selective hypocrisy outrage.
by IanCal | 2025-12-03 14:44:35+00:00
That rather depends on exactly how this is done, although it's a useful upper bound for many tasks either way. You could say the same for images and yet due to the way some work they straight up cannot see in certain ways.

This could describe adding a frame of nonsense into an existing video.

It also could describe finding a semantically useful thing in an actual video, where the exact location is randomised by looking at different time crops of the video. For example, finding a book on a desk in a video that's only there in a panning shot, and you then see if it can find it in a 10s cut, 20s cut, 10 minute cut, etc, and near the start/middle/end.

Here's the paper: https://arxiv.org/pdf/2511.21631

> To evaluate the model’s capability in processing long-context inputs, we construct a video “Needle-ina-Haystack” evaluation on Qwen3-VL-235B-A22B-Instruct. In this task, a semantically salient “needle” frame—containing critical visual evidence—is inserted at varying temporal positions within a long video. The model is then tasked with accurately locating the target frame from the long video and answering the corresponding question. During evaluation, videos are uniformly sampled at 1 FPS, and frame resolution is dynamically adjusted to maintain a constant visual token budget.

This potentially sounds more like the former, but I can't find more accurate information on how this works.

Regardless I'd say again that while not the whole story things like this really are useful to know, and can be very important to test - it's really not a given that models can always find anything in their context window, perhaps even more so for video.
by accrual | 2025-12-03 14:44:50+00:00
In my case phones were just starting to become commonplace, the Razr was the coolest phone to have, we had iPods but not iPhones, etc. Most instructors didn't want to see any phones and would threaten to take them away, so we became skilled at using T9 under the desk or in sweatshirt pockets, etc.
by mothballed | 2025-12-03 14:44:51+00:00
Only if you theorize that power corruption is a step function, and that there isn't survivor bias and influence during the process of getting to the point where you can even win an election.

Running an election campaign as anyone but a filthy rich person means you have to accept a brown bag at the beginning with a lot of implied conditions. Sortition means it's at least possible you get someone who isn't looking for the brown bag.
by GaryNumanVevo | 2025-12-03 14:44:54+00:00
Not to disappoint you, but a career in the games industry will be vastly more difficult than a typical SWE role. You'll be expected to crunch for a game's launch with no guarantee that you won't be laid off after. There's a steep pay decrease for roles with the same YoE.

Skills wise, I would recommend spending a few years learning C++ at the least, try developing an engine from scratch, build an ECS, build some basic multiplayer implementations. I wouldn't expect much of your web experience to transfer, and you would most likely be joining the industry as junior.

Stability wise, if you have any backend experience, applying for infrastructure SWE roles that handle online services for video games is an alright bet, but that job market is quite small.
by embedding-shape | 2025-12-03 14:45:26+00:00
> Starfield claimed to not support HDDs and really ran bad with them.

To be fair, at launch Starfield had pretty shit loading times even with blazing fast SSDs, and the game has a lot of loading screens, so makes sense they'll nip that one in the bud and just say it's unsupported with the slower type of disks.
Show HN: Fresh – A new terminal editor built in Rust
by _sinelaw_ | 2025-12-03 14:45:26+00:00
I built Fresh to challenge the status quo that terminal editing must require a steep learning curve or endless configuration. My goal was to create a fast, resource-efficient TUI editor with the usability and features of a modern GUI editor (like a command palette, mouse support, and LSP integration).

Core Philosophy:

- Ease-of-Use: Fundamentally non-modal. Prioritizes standard keybindings and a minimal learning curve.

- Efficiency: Uses a lazy-loading piece tree to avoid loading huge files into RAM - reads only what's needed for user interactions. Coded in Rust.

- Extensibility: Uses TypeScript (via Deno) for plugins, making it accessible to a large developer base.

The Performance Challenge:

I focused on resource consumption and speed with large file support as a core feature. I did a quick benchmark loading a 2GB log file with ANSI color codes. Here is the comparison against other popular editors:

- Fresh: Load Time: *~600ms* | Memory: *~36 MB* - Neovim: Load Time: ~6.5 seconds | Memory: ~2 GB - Emacs: Load Time: ~10 seconds | Memory: ~2 GB - VS Code: Load Time: ~20 seconds | Memory: OOM Killed (~4.3 GB available)

(Only Fresh rendered the ansi colors.)

Development process:

I embraced Claude Code and made an effort to get good mileage out of it. I gave it strong specific directions, especially in architecture / code structure / UX-sensitive areas. It required constant supervision and re-alignment, especially in the performance critical areas. Added very extensive tests (compared to my normal standards) to keep it aligned as the code grows. Especially, focused on end-to-end testing where I could easily enforce a specific behavior or user flow.

Fresh is an open-source project (GPL-2) seeking early adopters. You're welcome to send feedback, feature requests, and bug reports.

Website: https://sinelaw.github.io/fresh/

GitHub Repository: https://github.com/sinelaw/fresh
by gradientsrneat | 2025-12-03 14:45:56+00:00
W stands for Watts, which means Joules per second.

The energy usage of the human body is measured in kilocalories, aka Calories.

Combustion of gasoline can be approximated by conversion of its chemicals into water and carbon dioxide. You can look up energy costs and energy conversions online.

Some AI usage data is public. TDP of GPUs are also usually public.
by bill3389 | 2025-12-03 14:46:28+00:00
It is because they hired more and trend changed.

The hiring boom happened around 2021-2022. It is due to:

1. Covid hire freeze 2. Company expects online trends continue and more and more people will rely online activities.

However, the 2nd part is not true and the over-hired are laid off.

No need to mention economy is not good and interests rate still high.

all those combined together, cause job opportunities are low right now.

AI is not a factor. Yes, AI is powerful, but AI is not powerful enough to build software from scratch on its own. it did more damage on jobs like art, writers, translators, etc.
by booleandilemma | 2025-12-03 14:46:34+00:00
Politicians shouldn't be allowed to own private property. Only the people that really, really want to see a better future for their country will get involved in politics. This isn't my idea by the way, it's Plato's.

Right now the people entering politics are people who just want more money. They don't care about anything or anyone else. The sooner Americans realize this the better off we'll be.
by fleabitdev | 2025-12-03 14:46:35+00:00
In this case, the bug was 131 GB of wasted disk space after installation. Because the waste came from duplicate files, it should have had little impact on download size (unless there's a separate bug in the installer...)

This is why the cost of the bug was so easy for the studio to ignore. An extra 131 GB of bandwidth per download would have cost Steam several million dollars over the last two years, so they might have asked the game studio to look into it.
by wat10000 | 2025-12-03 14:46:45+00:00
I would modify this a bit. Someone with decent computer architecture knowledge, tools, and time can generally do better than the compiler. But you generally won't, because you have a lot of other things to think about. So I'd state this as, "the compiler is more diligent and consistent than me." It's not so much that it can spot a for loop that's equivalent to a single add, but that it will spot it just about every time, so you don't have to worry about it.
by georgeecollins | 2025-12-03 14:46:54+00:00
Both things are sort of true. Its not sales where size can hurt you but retention, which is why it tended to matter more on phones. When you need space on your device the apps are listed from largest to smallest.

On both phones and PCs storage has just grown so its less of an issue. The one thing I have noticed is that Apple does its price windowing around memory so you pay an absurd amount for an extra 128 gb. The ultra competitive Chinese phone market crams high end phones with a ton of memory and battery. Si some popular Chinese phone games are huge compared to ones made for the iPhone.
Show HN: Remove silences from video/audio for free
by spider853 | 2025-12-03 14:47:04+00:00
Hi HN,

I was editing a 1 hour and 20 minutes video the other day. It had a lot of silent parts (small noises) and I didn't really want to go through and remove everything manually. So, as any engineer does, I built a tool that removes them for me.

It's free, doesn't have a watermark, and works directly in the browser. You can also play with the settings or start from a preset.
by brightball | 2025-12-03 14:47:13+00:00
It's been pretty mainstream since 2012 IIRC.

The article is correct though. Microsoft has never done it just fine. Their reports are constantly interrupted and at one point it lasted years. Microsoft also wasn't following spec on handling DMARC rules and would take failed messages with a reject policy that should have been dropped and then put them in the spam folder, creating massive phishing risk. I believe they have finally started to get their act straight but for a long time they were the biggest problem in the space.

Google has usually been one of the leaders in the DMARC space though. Yahoo too.
by cubefox | 2025-12-03 14:47:25+00:00
> Historically, GPUs have improved in efficiency fast enough that people retired their hardware in way less than 5 years.

This was a time when chip transistor cost was decreasing rapidly. A few years earlier even RAM cost was decreasing quickly. But these times are over now. For example, the PlayStation 5 (where the GPU is the main cost), which launched five years ago, even increased in price! This is historically unprecedented.

Most price/performance progress is nowadays made via better GPU architecture instead, but these architectures are already pretty mature, so the room for improvement is limited.

Given that the price per transistor (which TSMC & Co are charging) has decreased ever more slowly in recent years, I assume it will eventually come almost to a halt.

By the way, this is strictly speaking compatible with Moore's law, as it is only about transistors per chip area, not price. Of course the price per chip area was historically approximately constant, which meant exponentially increasing transistor density implied exponentially decreasing transistor price.
by lordnacho | 2025-12-03 14:47:37+00:00
Largely agree. Anything that is just a multi-file edit, like an interface change, it can do. Maybe not immediately, but you can have it iterate, and it doesn't eat up your attention.

It is without a doubt worth more than the 200 bucks a month I spend on it.

I will go as far as to say it has decent ideas. Vanilla ideas, but it has them. I've actually gotten it to come up with algorithms that I thought were industry secrets. Minor secrets, sure. But things that you don't just come across. I'm in the trading business, so you don't really expect a lot of public information to be in the dataset.
by wavemode | 2025-12-03 14:47:45+00:00
No, async and callbacks in JS are extremely viral. If a function returns a Promise or takes a callback, there is no possible way to execute it synchronously. Hence, coloring.

The reason this coloring isn't a problem for the JS ecosystem, is that it's a single-threaded language by design. So, async/callbacks are the only reasonable way to do anything external to the JS runtime (i.e. reading files, connecting to APIs, etc.)

(notwithstanding that node.js introduced some synchronous external operations in its stdlib - those are mostly unused in practice.)

To put it a different way - yes, JS has function coloring, but it's not a big deal because almost the entire JS ecosystem is colored red anyway.
by wahnfrieden | 2025-12-03 14:47:52+00:00
I found Codex got much better (and with some AGENTS.md context about it) at ignoring unrelated changes from other agents in the same repo. But making worktrees easier to spin up and integrate back in might be a better approach for you.

When the build fails (rather than functional failure), most of the time I like to give the failure to a brand new agent to fix rather than waste context on the original agent resolving it, now that they're good at picking up on those changes. Wastes less precious context on the main task, and makes it easier to not worry about which agent addresses which build failures.

And then for individual agents checking their own work, I rely on them inspecting test or simulator/app results. This works best if agents don't break tests outside the area they're working in. I try to avoid having parallel agents working on similar things in the same tree.

I agree on the Mac target ease. Especially also if you have web views.

Orgs need to adapt to this new world too. The old way of forcing devs generally to work on only one task at a time to completion doesn't make as much sense anymore even from the perspective of the strictest of lean principles. That'll be my challenge to figure out and help educate that transformation if I want to productize this.
by ben_w | 2025-12-03 14:48:37+00:00
> you can help them get those.

A generic "you" might, I personally don't have that skill.

But then, I've never been a manager.

> An LLM will just always be nondeterministic.

This is not relevant, humans are also nondeterministic. At least practically speaking, theoretically doesn't matter so much as we can't duplicate our brains and test us 10 times on the same exact input without each previous input affecting the next one.

> If you're the LLM "worker"'s "boss", there is nothing you can do to help it do better next time.

Yes there is, this is what "prompt engineering" (even if "engineering" isn't the right word) is all about: https://en.wikipedia.org/wiki/Prompt_engineering

> "Recognizing you made a mistake and correcting it" is a common enough pattern in human language—ie, the training corpus—that of course they're going to produce that pattern sometimes.

Yes. This means that anthropomorphising them leads to a useful prediction.

For similar reasons, I use words like "please" and "thank you" with these things, even though I don't actually expect these models to have constructed anything resembling a real human emotional qualia within them — humans do better when praised, therefore I have reason to expect that any machine that has learned to copy human behaviour will likely also do better when praised.
by komali2 | 2025-12-03 14:48:56+00:00
> 't want people in congress as a career. Do it for at most 2 terms and get back to the real world.

How realistic is this though? Getting elected to Congress usually means having some other prior government experience, or your opponents attack you with "why would you give this person responsibility at a congressional level if they've never even worked in local politics?" Not to mention you need name recognition, a history in government that people can use to understand your values as acted, relationships in government to accomplish anything.

As I get older it seems that liberal democracy basically requires career bureaucrats to function. I'm very open to discussing other ways of organizing society, but if we're gonna do liberal democracy, seems this is the way.
by alistairSH | 2025-12-03 14:49:28+00:00
If we don't pay them well (and we arguably already fail to do that[1]), then it becomes really hard for anybody but the independently wealthy to be in Congress.

1 - $174,000 is the current salary. With that, they have to maintain two households (one in DC, one in home district/state). That salary is far from unusual for white collar workers in major metro areas.
by zoezoezoezoe | 2025-12-03 14:49:37+00:00
Personally, I like the LLMs.txt standard. I see AI scrapers hitting a few of my sites all the time, and I honestly am currently ok with my content being scraped (though I still retain my rights over my content, and this does not serve as permission to steal my content should I decide I am not okay with the theft of my content). Anything that can, sometimes quite dramatically, lower the bandwidth and cost of scrapers for both myself and the scrapers sounds like an ultimate good to me. I can understand the... I guess it's a repulsion to it, since you're putting work into something that ultimately leads to your content being stolen. However, we as developers must come to understand that in the current year, developers use and sometimes rely on large language models. I myself use Supermaven and have experimented with various LLM platforms, as well as self-hosted some models. LLMs are a great tool if you can use them correctly, though I am not some AI evangelist, not by any means. I believe LLMs.txt offers a significant benefit to users, operators, and AI providers.
by myrmidon | 2025-12-03 14:49:44+00:00
How can you be certain that policymakers won't make decisions that help their trading instead of their constituents? The incentive is already there, and the whole thing is legal, too.

Don't get me wrong, I think campaign donations or even more explicit bribes are a huge problem, too, but it is already insane to me that you would ban and prosecute "ordinary insider traders" and just let lawmakers do whatever. If presidents have to give up peanut farming, then asking for congressmembers to be utterly decoupled from stock trading decisions is only fair and sensible in my view.
by jmcomets | 2025-12-03 14:49:46+00:00
Obvious caveat: pushing this a bit further it can quickly fallback to the default case. The optimizer is a superpower but you still need to try to write efficient code.

unsigned add_v5(unsigned x, unsigned y) { if (x == y) return 2 * x; return x + y; }

Results in:

add_v5(unsigned int, unsigned int): lsl w8, w0, #1 add w9, w1, w0 cmp w0, w1 csel w0, w8, w9, eq ret

(armv8-a clang 21.1.0 with O3)

If compiler folks can chime in, I'm curious why incrementing in a loop can be unrolled and inspected to optimize to an addition, but doubling the number when both operands are equal can't?