MurphyA/hacker-news huggingface dataset:
by reissbaker | 2025-08-07 19:52:33+00:00
It's quite similar to Claude Code. The main advantages are that it's super easy to use with different models when new ones come out (like GPT-5!), and with local LLMs, and we have some optional, custom-trained small models that help auto-fix diff edit failures and minor JSON inaccuracies — they work with any model and especially help with some of the open-source coding models.

We also open-sourced the autofix models:

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

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

They're truly open source, not just open weight BTW: the entire training pipeline is in the Octofriend repo.
by wonderwonder | 2025-08-07 19:52:53+00:00
I was curious if there was any correlation, inverse or other between people with Bi-polar disease treated with lithium and dementia.

Seems like a good real world example that should prove out if lithium works as we know people with BD take it.

Turns out there is a study that says there is.

https://pubmed.ncbi.nlm.nih.gov/31954065/

"Conclusion: Individuals with BD are at higher risk of dementia than both the general population or those with MDD. Lithium appears to reduce the risk of developing dementia in BD."

Overall people with Bi-Polar have a much higher rate of dementia but lithium treatment appears to reduce that.

People treated with valproate instead (a mood stabilizer) do not enjoy the same benefit.

So I'll commit to adding low dose lithium to my daily supplements
by ako | 2025-08-07 19:52:57+00:00
That is easily fixed, ask it to summarize it's learnings, store it somewhere, and make it searchable through vector indexes. An LLM is part of a bigger system that needs not just a model, but context and long term memory. Just like human needs to write things down.

LLMs are actually pretty good at creating knowledge: if you give it a trial and error feedback loop it can figure things out, and then summarize the learnings and store it in long term memory (markdown, RAG, etc).
by mikepurvis | 2025-08-07 19:53:15+00:00
"LLMs tend to regurgitate solutions to solved problems"

People say this, but honestly, it's not really my experience— I've given ChatGPT (and Copilot) genuinely novel coding challenges and they do a very decent job at synthesizing a new thought based on relating it to disparate source examples. Really not that dissimilar to how a human thinks about these things.
by nawgz | 2025-08-07 19:53:35+00:00
I don't really see any relationship between being able to model/simulate the brain and being able to exceed the brain in intelligence, can you explain more about that? Simulations sound like more of a computational and analytic problem with regards to having an accurate model.

Maybe your point is that until we understand our own intelligence, which would be reflected in such a simulation, it would be difficult to improve upon it.
by terminalshort | 2025-08-07 19:53:37+00:00
Knowing how is very rarely the relevant obstacle. In the case of nuclear bombs the obstacles are, in order of easiest to hardest:

1. finding out how to build one

2. actually building the bomb once you have all the parts

3. obtaining (or building) the equipment needed to build it

4. obtaining the necessary quantity of fissionable material

5. not getting caught while doing 3 & 4
by bob1029 | 2025-08-07 19:53:55+00:00
The moment GitHub moved from Rails to React is when the spark went out for me. The web experience doesn't feel nearly as good. Perhaps from the offices in SF it's indistinguishable to the old SSR architecture, but every interaction feels like a trip to a geostationary satellite when using it from TX.

I simply need my tools to not run like shit. There are things that you cannot do with GitHub anymore that you once were able to. For example, reviewing 500 file PRS might be an "anti-pattern" in HN's mind on average, but there are a lot of real-world scenarios at actual businesses where this comes up and there's literally nothing you can do about it other than have tools that don't suck at the job.

I've been working on a self-hosted GitHub alternative in my spare time. The goal is to have a lightweight public instance for discoverability and community over reasonably sized projects and to also offer a freeware download of the same for private and other self-hosting needs. The entire focus is on the simplest possible thing. Absolutely zero energy into AI, LLMs, client-side rendering circuses, etc. All that really matters to me is code/issues/pulls - primarily in B2B SaaS contexts. I'm just trying to run a small team and manage a codebase for 1-100 customers. I don't need to run an entire war and I certainly don't need my tools to entertain me with an endless stream of shiny distractions.
by zahlman | 2025-08-07 19:54:06+00:00
Wind energy ultimately results from solar energy: solar radiation differentially heats the atmosphere, causing atmospheric gases to expand, causing a pressure gradient, etc. Similarly, energy from our Sun enables plants and animals to grow, producing carbon-rich materials (fossil fuels, wood etc.) when they die that can be burned to release energy.

Nuclear power, on the other hand, releases energy stored in fissile heavy elements that were produced by the death of previous stars. There is no natural process using incident light from the Sun to create them.
by BizarroLand | 2025-08-07 19:54:07+00:00
I bet that it will only happen when the ability to process and concrete new information into its training model without retraining the entire model is standard, AND when multiple AIs with slightly different datasets are set to work together to create a consensus response approach.

It's probably never going to work with a single process without consuming the resources of the entire planet to run that process on.
by dang | 2025-08-07 19:54:33+00:00
It's a judgment call of course, so disagreement is legit.

The usual test we use is: are the two links different enough to support substantively different discussion? In this case the answer is no, so in that sense they're the same topic.

Another way of putting it is: how much energy would it take to keep the one thread just about the article and the other thread just about the livestream?
by andix | 2025-08-07 19:55:06+00:00
To me the whole demo [edit: today's openai live stream] didn't feel revolutionary at all.

Especially the code generation part. It feels to me like Claude Web can do those illustration artifacts already for months equally well.

Also the example in Cursor just felt like a regular Claude Code session, just with different UI.

The only part I'm excited about is, that there is no distinction between reasoning and non-reasoning models anymore. I tend to default to reasoning models, because too often I feel like I need to switch mid-conversation to a reasoning model anyway. And reasoning models degraded the user experience drastically, because it often takes them quite some time to start responding.
by drewbeck | 2025-08-07 19:55:08+00:00
You're missing that this isn't an issue of granularity or specificity; "equal time" is just wrong.

If this is "right enough" for you, I'm curious if you tell your bots to "go deeper" on every question you ask. And at what level you expect it to start telling you actual truths and not some oft-repeated lie.
by dudeinhawaii | 2025-08-07 19:55:08+00:00
This is why you need to have your own set of personal benchmarks. I have a few short stories that I have models continue (ones I wrote ages ago in my youth) or refactor. Some models are fantastic at writing but miss key details or enmesh them (Claude). Some are terrible writers at higher reasoning (o3). Some are decent writers but tend to provide very short outputs (gpt-4o). For my personal benchmarks Gemini 2.5 Pro has always generated the most compelling writing that _also_ sticks to the world/script -- and sometimes surprises me by having characters react in ways that I hadn't considered but are consistent with their "worldview" as presented by the context (usually a world guide).

I find coding to be harder to benchmark because there are so many ways to write the same solution. A "correct" solution may be terrible in another context due to loss of speed, security, etc.
by justcallmejm | 2025-08-07 19:55:10+00:00
It is definitively not possible. But the frontier models are no longer “just” LLMs, either. They are neurosymbolic systems (an LLM using tools); they just don’t say it transparently because it’s not a convenient narrative that intelligence comes from something outside the model, rather than from endless scaling.

At Aloe, we are model agnostic and outperforming frontier models. It’s the anrchitecture around the LLM that makes the difference. For instance our system using Gemini can do things that Gemini can’t do on its own. All an LLM will ever do is hallucinate. If you want something with human-like general intelligence, keep looking beyond LLMs.
by nonhaver | 2025-08-07 19:55:15+00:00
i didnt bring examples because i said personal experience. heres my "evidence" - gpt 4 took multiple shots and iterations and couldnt stay coherent with a prompt longer than 20k tokens (in my experience). then when o4 came out it improved on that (in my experience). o1 took 1-2 shots with less iterations (in my experience). o3 zero shots most of the tasks i throw at it and stays coherent with very long prompts (in my experience).

heres something else to think about. try and tell everybody to go back to using gpt-4. then try and tell people to go back to using o1-full. you likely wont find any takers. its almost like the newer models are improved and generally more useful
by olieidel | 2025-08-07 19:56:32+00:00
While rather sarcastic, your comment does hit an interesting point: How much does the infrastructure and society of any given state contribute to the "building" of a company?

I'd argue that, for software companies, not very much; at least if you contrast it with a hardware company. If you're, say, forging steel, you're using roads, trains, a lot of electricity, you've got an industrial plant, worker unions, public accident insurance, etc., etc. - a significant chunk of state-associated infrastructure is a part of your business, and was a part of your business when you built it.

But for software companies? I mean, you need a stable internet connection, good mobile phone coverage (tricky in Germany sometimes), rule of law, efficient bureaucracy (e.g. when hiring people), good banks which don't lose your money, electricity, etc. - none of these "infrastructure factors" feel as big as the ones for a hardware business.

On the contrary, for a software business, one could argue that Germany is actively hostile to you: Founding a company takes weeks / months and is expensive (notary), most processes are still paper-based, hiring people (especially internationally) is a huge pain, mobile internet is spotty, residential internet has outages. Charging customer credit cards via Stripe exposes you to a rabbit hole of VAT bureaucracy - all companies I've met so far rolled their own, broken software stack to somehow match up their Stripe + VAT charges with their internal bookkeeping software (e.g. Datev). A huge mess. It doesn't end there.

But I may be wrong.
by const_cast | 2025-08-07 19:57:03+00:00
If we give it to Americans, then we just also rely on brown people making minimum wage or less - undocumented Americans.

The reality is our entire economy is more or less fake - it's propped up like a dead body by hyper-consumerism. We rely on buying way more shit than we should, and that, in turn, requires warm bodies who can't live off their wages. This is in high contrast to the economy of the early to mid 20th century.

We can rip that bandaid off, but things would suck for a while, maybe a long time. That's a hard sell - especially since we (not underpaid) get nothing.
by 9rx | 2025-08-07 19:57:04+00:00
> If Open AI didn't mention autonomy in their Microsoft agreement

What they mentioned was that their systems have to generate the profit. That requires autonomy. It needn't be explicitly mentioned. It cannot be any other way.

A human swinging a hammer would see the profit attributed to the human, not the hammer. A human "swinging" GPT would see the profit attributed to the human, not GPT. A windmill operates autonomously and thus any profit it generates would be attributed to it. However, a windmill doesn't have broad ability to outperform humans across many tasks.

Coding agents are approaching having the autonomy of a windmill. I expect this is where we will start to see the first semblance of AGI, where you will be able to say "My problem is X" and come back in a few days and have a program written to solve it. However, that is still just a "windmill". It doesn't generalize to a wide range of tasks as your definition and most other definitions of AGI expect.

You are right that details on the agreement are slim enough that we cannot say for sure if Microsoft would accept such a coding agent as being AGI, profits notwithstanding. However, even if they would, it is unlikely a coding agent alone would be able to achieve $100B in profits under any kind of human timescale. The value of software will be effectively nothing when there is no effort involved to create it. And for that reason, we can say with near certainty that the rest of your definition will also be required...

You gave it for a reason.
by zahlman | 2025-08-07 19:57:23+00:00
As a test, I whitelisted JavaScript in NoScript for theatlantic.com and the paywall appeared. I revoked it and it disappeared again. It appears to be purely client-side, not reliant on cookies or anything.

So my conclusion is that an archive indeed shouldn't be necessary; people can just disable JavaScript. It doesn't cause issues with the page formatting or anything.
by tigen | 2025-08-07 19:57:29+00:00
It works great with my kids sometimes. Asking a series of questions about some kid-level science topic for instance. They get to direct it to exactly what they want to know, and you can see they are more actively engaged than watching some youtube video or whatever.

I'm sure it helps that it's not getting outside of well-established facts, and is asking for facts and not novel design tasks.

I'm not sure but it also seems to adopt a more intimate tone of voice as they get deeper into a topic, very cozy. The voice itself is tuned to the conversational context. It probably infers that this is kid stuff too.
by TheoGone | 2025-08-07 19:57:34+00:00
LLMs are good at mimicking human intuition. Still sucks at deep thinking.

LLMs PATTERN MATCH well. Good at "fast" System 1 thinking, instantly generating intuitive, fluent responses.

LLMs are good at mimicking logic, not real reasoning. Simulate "slow," deliberate System 2 thinking when prompted to work step-by-step.

The core of an LLM is not understanding but just predicting the next most word in a sequence.

LLMs are good at both associative brainstorming (System 1) and creating works within a defined structure, like a poem (System 2).

Reasoning is the Achilles heel rn. AN LLM's logic can SEEM plausible, it's based on CORRELATION, NOT deductive reasoning.
by kotaKat | 2025-08-07 19:57:39+00:00
Not like there’s not enough problems with P25… until the day they can deploy LLE (link-layer encryption) across all P25 systems, there will always be a way to gather some kind of intelligence about the system and its radio traffic.

(And the fact that it’s taking so long to implement link layer authorization, barely a scratch in the security dent…)
by grumpyinfosec | 2025-08-07 19:58:02+00:00
i don't really get the point of this? Its a ITX motherboard with a moible chip. I could buy a itx board and a desktop chip for less. Or get a miniPC with the same chip for even less.

They are trying to disrupt building a PC, which was already modular and easy to upgrade, with a 1000+$ motherboard with soldered RAM?
by logifail | 2025-08-07 19:58:21+00:00
> PP was asking to clarify your English, not your French, so that readers could understand...

(This is the first time I've had this on HN) but I have no idea what you mean. Is someone really not understanding? It looks like they're trying to correct what I wrote :/

It's a product produced in quantity, but not “mass-produced”.
by scottLobster | 2025-08-07 19:58:30+00:00
How certain are you that those challenges are "genuinely novel" and simply not accounted for in the training data?

I'm hardly an expert, but it seems intuitive to me that even if a problem isn't explicitly accounted for in publicly available training data, many underlying partial solutions to similar problems may be, and an LLM amalgamating that data could very well produce something that appears to be "synthesizing a new thought".

Essentially instead of regurgitating an existing solution, it regurgitates everything around said solution with a thin conceptual lattice holding it together.
by simonw | 2025-08-07 19:59:13+00:00
You really think so? My goal with this post was to provide the non-hype commentary - hence my focus on model characteristics, pricing and interesting notes from the system card.

I called out the prompt injection section as "pretty weak sauce in my opinion".

I did actually have a negative piece of commentary in there about how you couldn't see the thinking traces in the API... but then I found out I had made a mistake about that and had to mostly remove that section! Here's the original (incorrect) text from that: https://gist.github.com/simonw/eedbee724cb2e66f0cddd2728686f... - and the corrected update: https://simonwillison.net/2025/Aug/7/gpt-5/#thinking-traces-...

The reason there's not much negative commentary in the post is that I genuinely think this model is really good. It's my favorite model right now. The moment that changes (I have high hopes for Claude 5 and Gemini 3) I'll write about it.
by gunnaraasen | 2025-08-07 20:00:25+00:00
I also find current voice interfaces are terrible. I only use voice commands to set timers or play music.

That said, voice is the original social interface for humans. We learn to speak much earlier than we learn to read/write.

Better voice UIs will be built to make new workflows with AI feel natural. I'm thinking along the lines of a conversational companion, like the "Jarvis" AI in the Iron Man movies.

That doesn't exist right now, but it seems inevitable that real-time, voice-directed AI agent interfaces will be perfected in coming years. Companies, like [Eleven Labs](https://elevenlabs.io/), are already working on the building blocks.
by olieidel | 2025-08-07 20:00:35+00:00
True! The purpose of my post was more to zoom in on the very specific case of people with small businesses, and not explain exit tax in general.

I wrote up another post with more generic notes on the exit tax [1] which might be a better post to compare to your link.

The minor benefit of my post is that I don't have an incentive to sell you expensive tax advice, chuckle..

[1] https://eidel.io/notes-and-hacks-on-germanys-exit-tax/
by morleytj | 2025-08-07 20:00:39+00:00
I wouldn't say model development and performance is "leveling off", and in fact didn't write that. I'd say that tons more funding is going into the development of many models, so one would expect performance increases unless the paradigm was completely flawed at it's core, a belief I wouldn't personally profess to. My point was moreso the following: A couple years ago it was easy to find people saying that all we needed was to add in video data, or genetic data, or some other data modality, in the exact same format that the models trained on existing language data were, and we'd see a fast takeoff scenario with no other algorithmic changes. Given that the top labs seem to be increasingly investigating alternate approaches to setting up the models beyond just adding more data sources, and have been for the last couple years(Which, I should clarify, is a good idea in my opinion), then the probability of those statements of just adding more data or more compute taking us straight to AGI being correct seems at the very least slightly lower, right?

Rather than my personal opinion, I was commenting on commonly viewed opinions of people I would believe to have been caught up in hype in the past. But I do feel that although that's a benchmark, it's not necessarily the end-all of benchmarks. I'll reserve my final opinions until I test personally, of course. I will say that increasing the context window probably translates pretty well to longer context task performance, but I'm not entirely convinced it directly translates to individual end-step improvement on every class of task.
by 0x457 | 2025-08-07 20:00:39+00:00
I always find it wild that none of these tools use VCS - completed logical unit of work, make a commit, drop entire context related to that commit, while referencing said commit, continue onto the next stage, rinse and repeat.

Claud always misunderstands how API exported by my service works and every compaction it forgets all over and commits "oh api has changed since last time I've used, let me use different query parameters", my brother Christ nothing has changed, and you are the one who made this API.
by timr | 2025-08-07 20:02:38+00:00
I am a biologist (biochemistry, but close enough). I don’t have a problem with what you wrote.

It’s not the same thing at all, though. We don’t know what “got life started”, and that’s the realm of faith.

This is more like saying that “evolution is due to random mutation”, which is technically wrong, but close enough to get the point across.
by zahlman | 2025-08-07 20:02:43+00:00
> You'd reasonably think, but it's difficult to assert this: a lot of people use third-party tooling (uv, but also a lot of hand-rolled stuff),

I mean, for people (like myself) explicitly attempting to implement alternatives to pip. And to my understanding, pip itself does use `zipfile` as well.

Are you proposing that there are people out there making package installers for personal use?

> and Python packages aren't always processed in a straight-line-from-the-index manner.

I don't know what you have in mind here.
by mnhnthrow34 | 2025-08-07 20:02:53+00:00
I fairly recently got to switch sides on this. I never take sales calls or want to get on demos as a developer ... but I moved roles a bit and needed to join some calls with the reps at my company for a product I now manage. It has no public pricing.

I was surprised by how much the people who show up for demos seemed to like them and have good relationships with their reps. They thank us for saving them a lot of time they would have spent reading docs and marketing materials to learn the specific things that applied to them, or for us talking about roadmap stuff they don't get to see in the public materials.

Sometimes the price is a surprise to them and it needs a bit of context. Customers who are used to buying software this way seem to read between the lines really well and ask suitable questions about discounts or whatever, when they are surprised by pricing. Often we are able to make something work at a different price than the typical quote, or we can connect the dots so that the rationale is more clear, or the value requires some customization to be done.

My reps tell me this sorta thing is difficult over email, that nobody makes $10k+ purchases without talking to somebody, so if we can't get you on a call the deal falls over.

So I dunno. I'm not a big fan of the requirement for calls really, but I can understand why reps don't just throw quotes around without some conversation.
by simonw | 2025-08-07 20:03:03+00:00
It changed quite a bit - we got new model IDs to test every few days. They did tell us when the model was "frozen", and I ran my final tests against those IDs.

OpenAI and Anthropic don't update models without changing their IDs, at least for model IDs with a date in them.

OpenAI do provide some aliases, and their gpt-5-chat-latest and chatgpt-4o-latest model IDs can change without warning, but anything with a date in (like gpt-5-2025-08-07) stays stable.