MurphyA/hacker-news huggingface dataset:
by mleonhard | 2025-08-07 20:13:28+00:00
Does it have tooling to help keep Python [0] and TypeScript [1] DTOs in sync?

[0] https://github.com/litestar-org/litestar-fullstack/blob/0996...

[1] https://github.com/litestar-org/litestar-fullstack/blob/0996...
by PaulHoule | 2025-08-07 20:13:50+00:00
AMD keeps serving the needs of x86 users for time being, ARM will get more competitive, maybe an opportunity opens for RISC-V.

Intel's decision that they're only going to make chips that are crazy profitable is a corporate suicide pact -- they won't make enough chips to be relevant.

30 years from now we'll see Intel's trademarks bought up by a nostalgia play like the current Commodore.
by viraptor | 2025-08-07 20:13:51+00:00
Do we really have the data on this? I mean, it does happen on a smaller scale, but where's the 300B version of RWKV? Where's hybrid symbolic/LLM? Where are other experiments? I only see larger companies doing relatively small tweaks to the standard transformers, where the context size still explodes the memory use - they're not even addressing that part.
by thorum | 2025-08-07 20:13:52+00:00
The Information’s report from earlier this month claimed that GPT-5 was only developed in the last 1-2 months, after some sort of breakthrough in training methodology.

> As recently as June, the technical problems meant none of OpenAI’s models under development seemed good enough to be labeled GPT-5, according to a person who has worked on it.

But it could be that this refers to post-training and the base model was developed earlier.

https://www.theinformation.com/articles/inside-openais-rocky...

https://archive.ph/d72B4
by devmor | 2025-08-07 20:13:53+00:00
The bit about single-participant models being unable to generalize is really interesting.

> there are no people who exhibit the ability to generate performant models for other people, nor are there any people for whom other people’s models always perform well

I wonder just how unique an individual's motor neural signaling is? If 100 participants was not enough to expose even a single broad overlap, could it really be so varied as to be near unique among every person? Or does the success of the larger corpus model suggest that the overlap is not being collected and/or exposed well by this methodology?
Show HN: I made a Chrome extension for doing LeetCode with spaced repetition
by mcdrake | 2025-08-07 20:14:21+00:00
Hello! I started preparing for LeetCode interviews last month. Since I'm not regularly implementing Union-Find in my day job, I used Anki to drill problems for common types of solutions/patterns.

The goal wasn't to memorize specific problems, but to be able to code a BFS/Union-Find/whatever without really thinking about it - freeing me up to think about the problem-specific edge cases.

I thought it would be nice to do this directly in the browser, so I made an extension over the weekend. It's free, open-source, with no signups/logins/tracking/etc. Your data is just stored in your browser and you can import/export/reset as you see fit.

I should mention that I used (and highly recommend!) the awesome ts-fsrs library: https://github.com/open-spaced-repetition/ts-fsrs

Thanks for reading!
by mikewarot | 2025-08-07 20:14:32+00:00
My particular interest is in screw cutting lathes, and it appears that the Wikipedia entry[1] (on which this seems to be based) was off by about 25 years (1775 instead of 1800), and thus copied to this work. I've let the folks at Wikipedia know.

[1] https://en.wikipedia.org/wiki/Screw-cutting_lathe
by ascorbic | 2025-08-07 20:15:04+00:00
This looks interesting, but it is quite buggy. A few things I've found:

- error handling is not good. It just dumps the error JSON in the console, and doesn't read and understand it, so keeps retrying even if it's something that it clearly needs to change

- ESC is pretty unreliable at interrupting ongoing activity

- I'd expect to be able to use arrow keys to navigate through history

- is there a way to change the preference order for models?

It succeeded in my standard task of adding support for detecting itself to am-i-vibing, but it got stuck in some API errors before it was able to create a PR. It does now have support though after a little help: https://github.com/ascorbic/am-i-vibing
Show HN: LogSentinelAI: Declarative LLM-Based AI Log Analyzer
by call518 | 2025-08-07 20:15:06+00:00
Hello everyone,

After seeing how brittle and time-consuming rule-based log parsing can be in real-world environments, I set out to build LogSentinelAI, an open-source, LLM-powered log analysis tool. LLM-powered log analysis tool. Whereas traditional solutions force you to normalize logs and write rigid parsing rules, LogSentinelAI works on both structured and unstructured logs: you simply define the fields you care about, and the LLM automatically extracts, semantically analyzes, and understands context across log entries. In our Demo, we even showcase visualization with Elastic Stack.

I’d love feedback from system/security engineers, developers, and ops folks:

How accurate is field extraction on your real-world logs?

Does performance (throughput, latency) and cost feel reasonable?

Are there any security or privacy concerns I haven’t considered?

Your honest critiques and improvement ideas will be hugely valuable. Full details and examples on Medium: https://medium.com/@call518/logsentinelai-practical-llm-base...
by habryka | 2025-08-07 20:16:40+00:00
Makes sense, it certainly is the case that these programs tend to pay people, though I have kind of learned to treat that with a bit of suspicion (having run lots of programs of that kind).

As they say, "if you are not the customer, you are the product", and I really wanted this fellowship to not be the kind of thing where the actual underlying motivation is some kind of recruitment scheme that drives the program objectives, while looking on the surface like a thing that is optimized to help the residents.
by 1718627440 | 2025-08-07 20:16:43+00:00
> I am not required to respect the police.

You are one citizen. The police represent the other million citizens. They have the monopoly on violence, so that all the other citizens can't do that. I think this is way better than every citizen caring weapons and doing self-justice. Even when the police is going crazy, that is still better than civil war. They do have checks and regulation even if they don't always work, the other citizens haven't.

That being said, I can absolutely see, how you distrust the police. I can't imagine what justification the police gave to their superiors in your case. I would expect public outrage if this became public, but maybe your country truly doesn't care.

Of course you shouldn't report such issues to a corrupt militia, which your police seams to be posing as. However that is not what I would call a police and it should be reported to the police if you had such a thing in your region.

When you ignore children with violent intentions this will lead to a stabbing or shooting later on. The police will treat a single claim very differently than recurring threats for violence. Informing the authorities early one makes them able to send therapists the first time. When they only ever hear about problems once things went violent or even only at the time of the amok, they need to send the violent force on the first occurrence, because they can now only try to shield the victims not help the delinquent child.
by achrono | 2025-08-07 20:17:12+00:00
I think this just further demonstrates the truth behind the truly small & scrappy teams culture at OpenAI that an ex-employee recently shared [1].

Even with the way the presenters talk, you can sort of see that OAI prioritizes speed above most other things, and a naive observer might think they are testing things a million different ways before releasing, but actually, they're not.

If we draw up a 2x2 for Danger (High/Low) versus Publicity (High/Low), it seems to me that OpenAI sure has a lot of hits in the Low-Danger High-Publicity quadrant, but probably also a good number in the High-Danger Low-Publicity quadrant -- extrapolating purely from the sheer capability of these models and the continuing ability of researchers like Pliny to crack through it still.

[1] https://calv.info/openai-reflections
Show HN: Remotext.co – a minimalist, text-only remote jobs board
by drdruide | 2025-08-07 20:17:22+00:00
Hi HN,

I built remotext.co – a text-only job board for remote work.

The idea: strip away everything except what matters. Remote jobs, clean text and only 3 proper filters.

No distractions, just the information you need to find your next remote role.

Currently in beta with free job postings.

The name plays on remote + text – exactly what it is.

Feedback welcome.
by techpression | 2025-08-07 20:17:26+00:00
"They claim impressive reductions in hallucinations. In my own usage I’ve not spotted a single hallucination yet, but that’s been true for me for Claude 4 and o3 recently as well—hallucination is so much less of a problem with this year’s models."

This has me so confused, Claude 4 (Sonnet and Opus) hallucinates daily for me, on both simple and hard things. And this is for small isolated questions at that.
by reissbaker | 2025-08-07 20:17:26+00:00
Ah, I actually find it useful to see the error output! But I can add a flag to hide it. Yes, you can change the ordering by going to the menu (via ESC), and going to "Settings" and then "Set default model." (You can also just edit the model order in the config file at ~/.config/octofriend/octofriend.json5).

ESC should reliably interrupt the model, although it doesn't currently interrupt tool calls — I'll add that (although all tool calls have timeouts currently).
by vineyardmike | 2025-08-07 20:17:33+00:00
When I read “simpler” I interpreted that to mean they don’t use their Chat-optimized harness to guess which reasoning level and model to use. The subscription chat service (ChatGPT) and the chat-optimized model on their API seem to have a special harness that changes reasoning based on some heuristics, and will switch between the model sizes without user input.

With the API, you pick a model sizes and reasoning effort. Yes more choices, but also a clear mental model and a simple choice that you control.
by lotyrin | 2025-08-07 20:17:35+00:00
Very nice. Anyone know of projects that aren't tackling the full-song problem but rather instrument parts/loops/stems/acapellas? I'd like something that's more like "infinite AI Loopcloud/Splice" most of these full-song models don't do well to be asked for individual parts in my experience (though I will have to try it with this one).
Ask HN: Wywd with a 256gb/40c 300tb/month server?
by AgentMatrixAI | 2025-08-07 20:17:42+00:00
ended up splurging on a huge server that I intended to run all of my side projects but have not been able to utilize it to its full potential. Basically I just wanted something fixed cost / month to run anything as much as I want.

what would you do with it other than a seedbox? its running proxmox but I ended up spending months learning and configuring it and now afraid to give up on it. it would be great if I could monetize it and keep the server going I've grown quite fond of it.

I might build another one and colocate it
by edwinjones | 2025-08-07 20:17:45+00:00
Sadly, the reason they give is subjectively terrible:

https://x.com/ollama/status/1952783981000446029

No experimental flag option, no "you can use the fork that works fine but we don't have capacity to support this" just a hard "no, we think it's unreliable". I guess they just want you to drop them and use llama.cpp.
by Tadpole9181 | 2025-08-07 20:18:08+00:00
I did a little more research and explain it above. The fundamentals are actually right.

The leading edge pressurizes the air by forcing air up, then the trailing edge opens back up, creating a low pressure zone that sucks air in the leading edge back. As a whole, the air atop the wing accelerates to be much faster than the air below, creating a pressure differential above and below the wing and causing lift.

The AI is still wrong on the actual mechanics at play, of course, but I don't see how this is significantly worse than the way we simplify electricity to lay people. The core "air moving faster on the top makes low pressure" is right.
by colmmacc | 2025-08-07 20:18:10+00:00
I listened to your great podcast and the remark along the lines of "unencrypted police comms let the robbers know when the police are getting close" made me wonder if anyone has built a simple signal intensity detector for the encrypted radios. You don't need to hear the contents to know that the radios are closing in on you. I can't imagine police forces practice RF silence like special forces do.

It really would be better to hide in the noise of 5G.
by instig007 | 2025-08-07 20:18:30+00:00
> but seems to occasionally lambast people about Haskell

If you are to add community notes to my comments, at least add the part that clarifies that I only lambast incompetence and lies.

> risking giving the Haskell community a bad name

as opposed to those that spread FUD, I suppose? It's not the first time I'm asking this question, so what's your take on people who inflate their credibility by telling lies about the tech they clearly don't know?
by dom96 | 2025-08-07 20:18:41+00:00
You can go pretty far without your own DB. Depends on the types of queries you need to make. For my project[1], I was able to use getRecord[2] for a lot of the data that needed fetching on the client-side.

1 - https://scrapboard.org/

2 - https://docs.bsky.app/docs/api/com-atproto-repo-get-record
by morleytj | 2025-08-07 20:18:54+00:00
I'd agree on that.

That's pretty much the key component these approaches have been lacking on, the reliability and consistency on the tasks they already work well on to some extent.

I think there's a lot of visions of what our human lives would look like in that world that I can imagine, but your comment did make me think of one particularly interesting tautological scenario in that commonly defined version of AGI.

If artificial general intelligence is defined as completed "economically valuable tasks" better than human can, it requires one to define "economically valuable." As it currently stands, something holds value in an economy relative to human beings wanting it. Houses get expensive because many people, each of whom have economic utility which they use to purchase things, want to have houses, of which there is a limited supply for a variety of reasons. If human beings are not the most effective producers of value in the system, they lose capability to trade for things, which negates that existing definition of economic value. Doesn't matter how many people would pay $5 dollars for your widget if people have no economic utility relative to AGI, meaning they cannot trade that utility for goods.

In general that sort of definition of AGI being held reveals a bit of a deeper belief, which is that there is some version of economic value detached from the humans consuming it. Some sort of nebulous concept of progress, rather than the acknowledgement that for all of human history, progress and value have both been relative to the people themselves getting some form of value or progress. I suppose it generally points to the idea of an economy without consumers, which is always a pretty bizarre thing to consider, but in that case, wouldn't it just be a definition saying that "AGI is achieved when it can do things that the people who control the AI system think are useful." Since in that case, the economy would eventually largely consist of the people controlling the most economically valuable agents.

I suppose that's the whole point of the various alignment studies, but I do find it kind of interesting to think about the fact that even the concept of something being "economically valuable", which sounds very rigorous and measurable to many people, is so nebulous as to be dependent on our preferences and wants as a society.
by attentive | 2025-08-07 20:19:38+00:00
"Notably, GPT‑5 with minimal reasoning is a different model than the non-reasoning model in ChatGPT, and is better tuned for developers. The non-reasoning model used in ChatGPT is available as gpt-5-chat-latest."

hmm, they should call it gpt-5-chat-nonreasoning or something.
by Ygg2 | 2025-08-07 20:19:58+00:00
> Performance is a deciding factor

There are two things in play: just because it's a deciding factor for you doesn't mean it's a deciding factor for everyone else. Second, even for you, Paint isn't enough. You also got Paint.net. You can reproduce almost any effect in PS or Gimp or Krita in Paint/Imagemagick. Why not just use those two for everything.

It's the same thing as using an IDE vs notepad(++). Anything done in the IDE behemoth can be done in notepad. Albeit at a significant time penalty, and with way more CLI jousting.

> I use Ctrl+Z for non-destructive editing in paint

That's not really non-destructive editing - that's Undo. A non-destructive editing means you can edit, change things, save, close the program. Reopen file after X days amount of time, and change effect or thing you applied.
by wkat4242 | 2025-08-07 20:20:20+00:00
Wow that's not bad. It's strange, for me it is much much slower on a Radeon Pro VII (also 16GB, with a memory bandwidth of 1TB/s!) and a Ryzen 5 5600 with also 64GB. It's basically unworkably slow. Also, I only get 100% CPU when I check ollama ps, the GPU is not being used at all :( It's also counterproductive because the model is just too large for 64GB.

I wonder what makes it work so well on yours! My CPU isn't much slower and my GPU probably faster.
by thewebguyd | 2025-08-07 20:20:27+00:00
Appreciate the other perspective. I'll even admit there's been cases where the demos have been useful and sparked other questions, but in those cases I hadn't heard of the product before or was coming in blind.

Most of my cases now (and I may be an outlier), I'm looking at something because I both have a need and someone I know recommended it or uses it so I'm already familiar, but at that point it's not so much a sales process and more so "I already know I want this, and I already have the budget and approval, let's get this buying process over with as quick as possible."
by reissbaker | 2025-08-07 20:20:29+00:00
They're just Llama 3.1 8b Instruct LoRAs, so yes — you can run them locally! Probably the easiest way is to merge the weights, since AFAIK ollama and llama.cpp don't support LoRAs directly — although llama.cpp has utilities for doing the merge. In the settings menu or the config file you should be able to set up any API base URL + env var credential for the autofix models, just like any other model, which allows you to point to your local server :)

The weights are here:

https://huggingface.co/syntheticlab/diff-apply

https://huggingface.co/syntheticlab/fix-json

And if you're curious about how they're trained (or want to train your own), the entire training pipeline is in the Octofriend repo.
by abeppu | 2025-08-07 20:23:32+00:00
> OpenAI conveniently forgot to include this comparison (ARC-AGI-2) in their livestream recital of benchmark progress, which left the livestream looking like marketing rather than science.

Yeah but it was _supposed_ to be marketing, right? Like, of course a product video isn't science in the same way a "hot take" post also isn't science.
by NitpickLawyer | 2025-08-07 20:23:58+00:00
Yeah, makes sense. Good observations regarding the benchmark vs. vibes in general, and I didn't know / made the connection between the lead of phi models going to oAI and gpt-oss. Could very well be a similar exercise + their "new" prompt level adherence (system > developer > user). In all the traces I've seen of refusals the model "quotes" the policy quite religiously. Similar thing was announced for gpt5.

I think the mention of the "horny people" is warranted, they are an important part of the open models (and first to explore the idea of "identities / personas" for LLMs, AFAIK). Plenty of fine-tuning bits of know-how trickled from there to the "common knowledge".

There's a thing that I would have liked to be explored, perhaps. The idea that companies might actually want what -oss offers. While the local llm communities might want freedom and a horny assistant, businesses absolutely do not want that. And in fact they spend a lot of effort into implementing (sometimes less than ideal) guardrails, to keep the models on track. For very easy usecases like support chatbots and the like, businesses will always prefer something that errs on the side of less than useful but "safe", rather than have the bot start going crazy with sex/slurs/insults/etc.

I do have a problem with this section though:

> Really open weight, not open source, because the weights are freely available but the training data and code is not.

This is factually incorrect. The -oss models are by definition open source. Apache2.0 is open source (I think even the purists agree with this). The requirement of sharing "training data and code" is absolutely not a prerequisite for being open source (and historically it was never required. The craze surrounding LLMs suddenly made this a thing. It's not).

Here's the definition of source in "open source":

> "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.

Well, for LLMs the weights are the "preffered form of making modifications". The labs themselves modify models the same as you are allowed to by the license! They might use more advanced tools, or better datasets, but in the end the definition still holds. And you get all the other stuff, like the right to modify, re-release, etc. I'd really wish people would stop proliferating this open weight nonsense.

Models released under open source licenses are open source. gpt-oss, qwens and mistrals (apache2.0), deepseeks(MIT), etc.

Models released under non open source licenses also exist, and they're not open source because the licenses under which they're released aren't. LLamas, gemmas, etc.
by woodruffw | 2025-08-07 20:24:45+00:00
> Are you proposing that there are people out there making package installers for personal use?

I gave an example in the original comment: there's a lot of random ass tooling out there that treats Python wheels as a mostly opaque archive, and unpacks/repacks them in various ways. The original PEP behind wheels also (implicitly) expects this, since it refers to extraction with a "ZIP client" and not Python's zipfile specifically.

I think security scanners are a simple example, but Linux distros, Homebrew, etc. all also process Python package distributions in ways that mostly just assume a ZIP container, without additionally trying to exactly match how Python's `zipfile` behaves.

> I don't know what you have in mind here.

The security scanner example from the original comment.