skysight-inc/HackerNews huggingface dataset:
They released airtags without thinking about stalking, so I'm not that shocked.
No GPT in the name, so maybe not transformer based?
What you're describing is very similar to deep Gaussian processes.
89th percentile on developers performance

This stuff is getting wild
yeah thats probly right, inner parts of forest would be cleaner air wise dust gets trapped on leaves and stuff but sunlight is probly a bigger issue, less of it gets thru all the dust and thats bad for plants and animals alike, especially ones that need lots of it like big herbivores and their predators, theyd struggle to survive on limited sunlight and maybe even worse air quality than outer parts of forest, generalist animals like birds and small mammals would do ok tho, they can eat lots of diff things and dont need as much sunlight
Congrats on launching! Beautiful landing page and the use of wasm is really clever.

Curious. What’s on the Roadmap? I use Dropbox for my paid file storage and sharing, which part does Shuttle do better?
It’s a common LLM riddle. Apparently many fail to give the right answer.
The model performance is driven by chain of thought, but they will not be providing chain of thought responses to the user for various reasons including competitive advantage.

After the release of GPT4 it became very common to fine-tune non-OpenAI models on GPT4 output. I’d say OpenAI is rightly concerned that fine-tuning on chain of thought responses from this model would allow for quicker reproduction of their results. This forces everyone else to reproduce it the hard way. It’s sad news for open weight models but an understandable decision.
LLM are bad at answering that question because inputs are tokenized.
Agreed. There's a place for open, smart tools. Some things want to have serial interfaces and sensors and so on, that will do a whole host of actions automatically.

Other times you just want the equivalent of a drill or toaster. Pull trigger, drill spins. Twist chuck or shift gearbox, it slips or changes speeds. Push toast down, it toasts, twist the dial if you want darker or lighter.

An on/off switch, a potentiometer or 7-segment and some buttons to set temp, and a nice, fast, powerful PID loop to control the temperature (with a 120V AC cable to make 100W all day not a problem) is all I want in a soldering iron. I have a combination soldering/hot air station that's almost 20 years old, it just always works.
Yes, for sure, I feel the same way. It surprises me how many people inject a dependency on stuff like Cloudflare or similar for their local network/services!
Models don't really predict the next word, they predict the next token. Strawberry is made up of multiple tokens, and the model doesn't truely understand the characters in it... so it tends to struggle.
Could rolling dice with matching phrases generate novel research ideas? (ok the metaphor is slightly off, but not a lot)
> So what does unique value proposition does GHC have left? Possibly the GHC runtime system, but it's not as sexy to pitch in a blog post like this.

The point is that programming in a pure language with typed side effects and immutable data dramatically reduces the size of the state space that must be reasoned about. This makes programming significantly easier (especially over the long term).

Of the languages that support this programming style Haskell remains the one with the largest library ecosystem, most comprehensive documentation, and most optimised compiler. I love lean and use it professionally, but it is nowhere near the usability of Haskell when it comes to being a production ready general purpose language.
I never really understand this reasoning of "regex is hard to reason about, so we just use an LLM we custom made instead!" I get it's trendy but reasoning about LLMs is impossible for many devs the idea that this makes it more maintainable is pretty hilarious.
This became something of a meme.

https://community.openai.com/t/incorrect-count-of-r-characte...
Another Extreme Low for Antarctic Sea Ice Signals a Permanent Shift
after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users.
arcade games worked on the same idea (mechanical) and had some pretty next level setups in the arcade-
https://www.youtube.com/watch?v=YedSuvlFElo
No direct indication of what “maximum test time” means, but if I’m reading the obscured language properly, the best scores on standardized tests were generated across a thousand samples with supplemental help provided.

Obviously, I hope everyone takes what any company says about the capabilities of its own software with a huge grain of salt. But it seems particularly called for here.
Honestly, it doesn't matter for the end user if there are more tokens generated between the AI reply and human message. This is like getting rid of AI wrappers for specific tasks. If the jump in accuracy is actual, then for all practical purposes, we have a sufficiently capable AI which has the potential to boost productivity at the largest scale in human history.
I remember back when putting together PCs in the 90s if the cable for the IDE drive was connected incorrectly it would emit smoke - I didn't directly experience it, but any instructions I had read warned about it.
Not really. you need to know the size of the keyboard, know the shape ov peoples eyes, have enough temporal and optical resolution to workout where they are pointing.

Even with optimal conditions (ie dedicated cameras, no eye make up and correct positioning) uncalibrated gaze has at least a 5 degree uncertainty.
Looks really nice on an initial view. Will explore in a bit more depth and check back later
yeah this is kinda cool i guess but 808 elo is still pretty bad for a model that can supposedly code like a human, i mean 11th percentile is like barely scraping by, and what even is the point of simulating codeforces if youre just gonna make a model that can barely compete with a decent amateur, and btw what kind of contest allows 10 submissions, thats not how codeforces works, and what about the time limits and memory limits and all that jazz, did they even simulate those, and btw how did they even get the elo ratings, is it just some arbitrary number they pulled out of their butt, and what about the model that got 1807 elo, is that even a real model or just some cherry picked result, and btw what does it even mean to "perform better than 93% of competitors" when the competition is a bunch of humans who are all over the place in terms of skill, like what even is the baseline for comparison

edit: i got confused with the Codeforce. it is indeed zero shot and O1 is potentially something very new I hope Anthropic and others will follow suit

any type of reasoning capability i'll take it !
oh wow, something you can roughly model as a diy in a base model. so impressive. yawn.

at least NVDA should benefit. i guess.
Journaling helps a lot with that because you catch yourself writing about the same emotions in the same contexts. The predictability of it makes it easier to process rationally.
Congrats to OpenAI for yet another product that has nothing to do with the word "open"
I believe we should let Elon and a few of his friends try.

After all, what have we got to lose?
That's wonderful - I used to solder with a cheap iron until university. A nice iron gets a lot hotter, and it makes everything easier and faster. It may not matter for keyboards, but on a small PCB where everything is a few mm part, the precision of a good tip matters too.
I think they mean that you won’t be able to see the “thinking”/“reasoning” part of the model’s output, even though you pay for it. If you could see that, you might be able to infer better how these models reason and replicate it as a competitor
I'm making a prediction now that the per-hour cost of O1 is higher than paying the salary for a team of senior software engineers.
Sure! Sorry to hear you've had some bad ones. If you have links handy it'd be great to see which ones went wrong so we can try to improve.

But here are some recent predictions that we thought were pretty reasonable:
* https://aipredict.fun/prediction/a0354d22-d44a-4115-a774-1cc...
* https://aipredict.fun/prediction/1518666d-d5f8-40b1-bee9-561...
* https://aipredict.fun/prediction/c13193fa-4155-4b47-b50a-432...
> “Therefore, after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users.”

Trust us, we have your best intention in mind. I’m still impressed by how astonishingly impossible to like and root for OpenAI is for a company with such an innovative product.
Show HN: Void - open source Cursor AI code editor
Hey HN, we're Andrew and Mat and we're building Void, an open source Cursor alternative.

Our editor is an open-source VSCode fork. Our primary goal is to create a fully-featured AI editor where you can keep your data completely private. Going open source is really important because it let us take the logic that would normally live on a server, like prompt building or message handling, and run it on your local machine instead. This way you can do things like self-host Llama 3.1 or Gemma and never have your data leave your computer. You can also just send your prompts directly to foundational models (GPT4o, Claude) without going through a middleman API.

Another exciting part of the project is that we have full control over the IDE, and can build our own native features. Normally if you want to build an AI tool you have to make the same chat UI, history management, filesystem indexing, LSP, VSCode integrations, etc. We think we can standardize all these into a nice native API you can just plug into instead. It would be awesome to create a centralized place that lets people build advanced open source tools like AI edits across many files, and even agents, in an IDE we're all used to.

Let us know what you think of the idea. We've really enjoyed working on it and are super excited to see where it goes :)
You can see an example of the Chain of Thought in the post, it's quite extensive. Presumably they don't want to release this so that it is raw and unfiltered and can better monitor for cases of manipulation or deviation from training. What GP is also referring to is explicitly stated in the post: they also aren't release the CoT for competitive reasons, so that presumably competitors like Anthropic are unable to use the CoT to train their own frontier models.
I find this analogy kind of confusing? Wouldn’t the analogous thing be to say that our DNA doesn’t understand, uh, how we are able to skateboard? But like, we generally don’t regard DNA as understanding anything, so that not unexpected.

Where does “we can’t tell whether a person possesses the skill of ‘skateboarding’?” fit in with, DNA not encoding anything specific to skateboarding? It isn’t as if we designed our genome and therefore if our genome did hard-code skateboarding skill that we would therefore (as designers of our genome) have full understanding of how skateboarding skill works at the neuron level.

I recognize that a metaphor/analogy/whatever does not have to extend to all parts of something, and indeed most metaphors/analogies/whatever fail at some point if pushed too far. But, I don’t understand how the commonalities you are pointing to between [NN architecture : full NN network with the specific weights] and [human genome : the whole behavior of a person’s brain including all the facts, behaviors, etc. that they’ve learned throughout their life] is supposed to apply to the example of _knowing_that_ a person knows how to skateboard?

It is quite possible that I’m being dense.

Could you please elaborate on the analogy / the point you are making with the analogy?
Including the chain of thought would provide competitors with training data.
> I'm definitely not a heavy advocate of GPT or similar... as much as I do like GH Copilot for some things. For almost anything of any complexity, there are often wrong things in the output from ChatGPT and you have to be able to spot them.

While that's true, my problem is something else: by outsourcing the reasoning process to a machine, my brain gets weaker. By doing less programing tasks, I become a worse programmer, even though on the surface I might look like a more productive one.
Efficiency should get you an estimate.
"The big caveat is that users will still need an iPhone with iOS 18 and newer for this new feature to work."
One reason I took interest in Idris (and lately Roc, although it's even less mature) is the promise of a functional but usable to solve problems today language with all the latest thinking on writing good code baked-in already, compiling to a single binary (something I always envied about Go, although unfortunately it's Go). There simply isn't a lot there yet in the space of "pure functional language with only immutable values and compile time type checking that builds a single fast binary (and has some neat developer-friendly features/ideas such as dependent types, Roc's "tags" or pattern-matching with destructuring)" (this rules out OCaml, for example, despite it being mature). You get a lot of that, but not all of it, with other options (OCaml, Elixir/Erlang, Haskell... but those 3 offer a far larger library of ready-to-import software at this point). Haskell did manage to teach everyone who cares about these things that managing side-effects and keeping track of "purity" is important.

But it's frankly still early-days and we're still far from nirvana; Rust is starting to show some warts (despite still being a massive improvement over C from a safety perspective), and people are looking around for what's next.

One barely-touched thing is that there are compiler optimizations made possible by pure functional/pure immutable languages (such as caching a guaranteed result of an operation where those guarantees simply can't be given elsewhere) that have simply been impossible until now. (Roc is trying to go there, from what I can tell, and I'm here for it! Presumably, Rust has already, as long as you stick with its functional constructs, which I hear is hard sometimes)
Kinda disappointed that they're hiding the thought process. Hopefully the open source community will figure out how to effectively match and replicate what OpenAI is doing.

I wonder how far we are from having a model that can correctly solve a word soup search problem directly from just a prompt and input image. It seems like the crossword example is close. For a word search it would require turning the image into an internal grid representation, prepare the list of words, and do a search. I'd be interested in seeing if this model can already solve the word grid search problem if you give it the correct representation as an input.
so o1 seems like it has real measurable edge, crushing it in every single metric, i mean 1673 elo is insane, and 89th percentile is like a whole different league, and it looks like it's not just a one off either, it's consistently performing way better than gpt-4o across all the datasets, even in the ones where gpt-4o was already doing pretty well, like math and mmlu, o1 is just taking it to the next level, and the fact that it's not even showing up in some of the metrics, like mmmu and mathvista, just makes it look even more impressive, i mean what's going on with gpt-4o, is it just a total dud or what, and btw what's the deal with the preview model, is that like a beta version or something, and how does it compare to o1, is it like a stepping stone to o1 or something, and btw has anyone tried to dig into the actual performance of o1, like what's it doing differently, is it just a matter of more training data or is there something more going on, and btw what's the plan for o1, is it going to be released to the public or is it just going to be some internal tool or something
> we are releasing an early version of this model, OpenAI o1-preview, for immediate use in ChatGPT

Awesome!
The other much-argued encounter is that Wittgenstein was a year or two above Herr Schicklgruber in high-school. Obviously Ludwig was Jewish, and far more intelligent than Adolf, so it established a fervent resentment... or not.

https://en.wikipedia.org/wiki/Ludwig_Wittgenstein#Jewish_bac...
It starts to matter if the compute time is 10-100 fold, as the provider needs to bill for it.

Of course, that's assuming it's not priced for market acquisition funded by a huge operational deficit, which is a rarely safe to conclude with AI right now.
This also makes them less useful because I can’t just click stop generation when they make a logical error re: coding.
If there's a way to do something like this with Llama I'd love to hear about it (not being sarcastic)
...just as IronOS does on a Pinecil.
OpenAI – o1-preview and o1-mini
Someone give this model an IQ test stat.
> I like Godot but its open source nature means it does have some rough edges, mostly around GDScript but framework features like tilemap implementation, model imports and physics have always been a bit wonky.

You're attributing a lack in Godot you feel to it being open source. Given the commercial success of other open source projects like Blender, I don't think this is a fair or measured take.
> We have found that the performance of o1 consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-time compute).

Wow. So we can expect scaling to continue after all. Hyperscalers feeling pretty good about their big bets right now. Jensen is smiling.

This is the most important thing. Performance today matters less than the scaling laws. I think everyone has been waiting for the next release just trying to figure out what the future will look like. This is good evidence that we are on the path to AGI.
And Apple's product line this year? Phones. Nothing to do with fruit. Almost 50 years of lying to people. Names should mean something!
Ask HN: What was your favorite piece of technology that you owned?
Schematics are table stakes!

https://www.ifixit.com/Device/iFixit_Soldering
Check out gemini

[1] https://geminiprotocol.net/
Not OP, but much writing advice with which I'm familiar, and my own experience, both suggest these are symptoms of an error made earlier and not yet recognized.

If I can't figure out where the plot could possibly go from here, go back and look for where I sent it off the rails earlier such that there's nowhere to take it now; if I can't find dialogue or action that fits, go back and find where I put the character in a situation they'd never get themselves in, or miswrote them to respond to it in a way they never would. Stuff like that, especially once it's had you stuck too long for inspiration latency still to be tenable as the cause.
I LOVE the long list of contributions. It looks like the credits from a Christoper Nolan film. So many people involved. Nice care to create a nice looking credits page. A practice worth copying.

https://openai.com/openai-o1-contributions/
What's amazing is that given how LLMs receive input data (as tokenized streams, as other commenters have pointed out) it's remarkable that it can ever answer this question correctly.
I am interpreting "immediate use in ChatGPT" the same way advanced voice mode was promised "in the next few weeks."

Probably 1% of users will get access to it, with a 20/message a day rate limit. Until early next year.
On one hand I agree that if everything you have uses PD, your laptop, your phone, your powerbank and a charger like that and it's powerful enough to handle it all then it probably makes sense to also have an iron that works with it and it's all interchangeable.

On the other hand we already have a standard power thing, it's called an outlet. And in practice you need to charge/use things in parallel so you'd need to carry around like four of these.
nurture the model
have patience
and a couple bash scripts
A lot of skepticism here, but these are astonishing results! People should realize we’re reaching the point where LLMs are surpassing humans in any task limited in scope enough to be a “benchmark”. And as anyone who’s spent time using Claude 3.5 Sonnet / GPT-4o can attest, these things really are useful and smart! (And, if these results hold up, O1 is much, much smarter.) This is a nerve-wracking time to be a knowledge worker for sure.
MiniDisc player, then Dreamcast.
That naming scheme...

Will the next model be named "1k", so that the subsequent models will be named "4o1k", and we can all go into retirement?
If they crash they don't make money. I don't live in the USA and I remember that taxi drivers were very fast when I was young. They are among the slowest drivers now.
> from trained medical personnel working in obstetrics and pediatrics

This I would agree is a different situation because of the implicit trust involved. But the solution there is much simpler and easier than reforming facebook trolls: just train medical professionals better. In the same way doctors don't judge you for coming in for a drug overdose or treating you for an STD, don't judge people who don't breastfeed. Medical pros have a code of ethics that they abide by.

I realize that my comment comes off as cold, and I certainly would not talk this way to someone whom I know has had trouble in this area. But part of growing up in the 21st century is learning how to recognize and dismiss worthless comments, and I feel that that skill is not being taught to people. By all means, compassion and love should rule in specific interactions. But part of love is equipping people to be healthy, and sometimes being healthy is being extremely dismissive (towards online comments).
Just wanted to say thanks for bringing this up! So far it is a really interesting tool for me, as someone too lazy to find a proper financial planner
OpenAI O1 Series Reasoning Models
Why not? Isn't that basically what humans do? Sit there and think for a while before answering, going down different branches/chains of thought?
This is a transcription of a literal quote from the article:

> Therefore, after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users
As someone who does not play Magic The Gathering, how exactly is having a board state which depends on a prime conjecture advantageous? And if it is, how does one extend this to Monopoly?
When it was Jailbroken, my OG iPod Touch was just about perfect. Nowadays though, I've been really enjoying homebrew/emulation on the Nintendo Switch.

Edit (rate limit reached): Don't have a Steam Deck because I've been gaming on Linux for the past 4 years :)
But what does that mean? I can't do "pip install nurture" or "pip install patience". I can generate a bunch of answers and take the consensus, but we've been able to do that for years. I can do fine-tuning or DPO, but on what?