skysight-inc/HackerNews huggingface dataset:
Colonising mars will be a lot like colonising the moon or asteroids, but the gravity will be more human-friendly.

Humans must either become a spacefaring civilisation or go extinct along with all life on earth , full stop. Not working on that problem means dying more or less right away from a geological time perspective. (Relatively) soon earth will be outside of a habitable dust from the sun. Maybe we can move it. But im pretty sure that moving the planet would be harder than colonising mars.

I don’t think anyone thinks that colonising mars means living or even going outside of a pressurised artificial habitat . It would be very much like living in a spacecraft…. But it would be relatively easy to make it really, really big.

I have lived in places where people live in one big building for 6 months at a time, with some of them having not left the building for years. It doesn’t seem that far fetched to me that people could adapt to an essentially indoor environment.
People stay at the South Pole and Everest for short times. Nobody lives there permanently.
Yes, it's misleading to share averages for power-law distributions. Median take home pay is probably $20 annually.
Profile data can be restricted based on authorized fetch, just like mastodon.

For messaging, I hadn’t put a much thought into it, but one could establish end to end encryption based on mutual validation signatures. Theoretically. Encryption isn’t my strong suit, but as long as the encoded body is unicode, it’s just as easy to transmit as any other text.

But, like, I also don’t know of any dating site that professes to be encrypting message contents.
If you wanted to squeeze everything out of the network it's not just varying the seeds. It's varying the token vector.
If you take away economic incentive from Apple, the plausible scenario is that Apple is simply not going to develop such superior devices.
It seems OpenAI has decided to keep the CoT results a secret. If they were to allow the model to call out to tools to help fill in the CoT steps, then this might reveal what the model is thinking - something they do not want the outside world to know about.

I could imagine OpenAI might allow their own vetted tools to be used, but perhaps it will be a while (if ever) before developers are allowed to hook up their own tools. The risks here are substantial. A model fine-tuned to run chain-of-thought that can answer graduate level physics problems at an expert level can probably figure out how to scam your grandma out of her savings too.
If we hit that point, it’s then a question of access, cost, learning curve, and vision of individual companies. Some things are technically possible, but done by very few companies.

I’ve seen the videos of Amazon warehouses, where the shelves move around to make popular items more accessible for those fetching stuff. This is possible today, but what percentage of companies do this? At what point is it with the investment for a growing company? For some companies it’s never worth it. Others don’t have the vision to see the light at the end of the tunnel.

A lot of things that we may think of as old or standard practice at this point would be game changing for some smaller companies outside of tech. I hear my friends and family talking about various things they have to do at their job. A day writing a few scripts could solve a significant amount of toil. But they can’t even conceptualize where to begin to change that, they aren’t even thinking about it. Release all the AI the world has to offer and they still won’t. I bet some freelance devs could make a good living bouncing from company to company pair programming with their AI to solve some pretty basic problems for small non-tech companies that would be game changes for them, while being rather trivial to do. Maybe partner with a sales guy to find the companies and sell them on the benefits.
You can port your number to NumberBarn and park it for $2/month. Other services probably exist, but I signed up to NumberBarn ages ago and haven't had any issues the handful of times I've used them.
OpenAI’s answer to this would be, “Okay then, don’t use it.”
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Just the first few notes conjure an image of a slimebag. That kind of person that is only attentive if there is a personal gain to be made.

Sometimes they're astoundingly obvious and oblivious to the obviousness. I wonder why that is?
>The o1-preview model still hallucinates non-existing libraries and functions for me, and is quickly wrong about facts that aren't well-represented on the web. It's the usual string of "You're absolutely correct, and I apologize for the oversight in my previous response. [Let me make another guess.]"

After that you switch to Claude Soñnet and after sometime it also gets stuck.

Problem with LLM is that they are not aware of libraries.

I've fed them library version, using requirements.txt, python version I am using etc...

They still make mistakes and try to use methods which do not exist.

Where to go from here? At this point I manually pull the library version I am using and go to its docs, I generate a page which uses the this library correctly (then I feed that example into LLM)

Using this approach works. Now I just need to automate it so that I don't have to manually find the library, create specific example which uses the methods I need in my code!

Directly feeding the docs isn't working well either.
Sure. Looking at the output would make clear which properties are actually recognized, and it could be commited to version control to avoid confusion
Isn’t there a rule on the internet that says “if you can imagine it, there is porn for it” and “if there isn’t porn for it, somebody is making it”?

I’m pretty sure that applies to this scenario too. I’m 100% sure that there exists a set of customers who would pay good money to get dumped by a realistic AI girlfriend. And once dumped they’ll turn around and pay for the next AI model to dump them only in some other fashion. Maybe the AI model thinks the customers anatomy is the wrong dimensions? Maybe they smell? Maybe they are too short or tall? Perhaps the AI “girlfriend” is a triple tentacled sea monster who wants to return to oceans on Titan? Doesn’t matter. Somebody will pay very good money to experance it.

You want a hot quad breasted space babe who cheats on you with bubble wrap covered little people? Done. Want that with extra bondage? Done.

This is the internet after all. Why pay for a boring “normal” AI girlfriend when the sky is the limit? I say, use your imagination.
When you start mathematically characterizing state spaces it quickly becomes apparent that pure functional languages advantage over imperative ones is more a matter of the poor design of popular imperative languages rather than an intrinsic difference.

That (in)famous goto paper isn't really about spaghetti code, it's about how on Earth do you mathematically define the semantics of any statement in a language with unrestricted goto. If any continuation can follow literally anything then you're pretty much in no man's land. On the other hand imperative code is easy and natural to reason about when it uses a small set of well defined primitives.

If that sounds surprising, consider how mathematical logic itself, especially obviously in the calculational proof style, is essentially a series of assignment statements.
I honestly can’t believe this is the hyped up “strawberry” everyone was claiming is pretty much AGI. Senior employees leaving due to its powers being so extreme

I’m in the “probabilistic token generators aren’t intelligence” camp so I don’t actually believe in AGI, but I’ll be honest the never ending rumors / chatter almost got to me

Remember, this is the model some media outlet reported recently that is so powerful OAI is considering charging $2k/month for
Cutting roku out .... and trying to capture all the value for themselves
Obligatory 2001 reference:
https://youtu.be/TDUxxuNQwEA
"Man will never fly"

Second verse, same as the first.
found no reduction in all-cause mortality by metformin treatment.

Right, but that's not what they were looking for and, even if that's what they wanted, the data they had wouldn't have allowed them to assess this claim.

Roughly speaking, they compared:

- people with diabetes that was treated with Metformin

- their twins who didn't have diabetes and were not taking Metformin

As far as I can tell, they didn't isolate the impact of metformin alone (vs the combined impact of having diabetes and taking metformin).

Have I misunderstood?
It seems pretty close to the multihop QA example in their documentation[1]. I’d imagine you could adapt this to do something similar with more generic constructs.

[1] https://dspy-docs.vercel.app/docs/tutorials/simplified-balee...
Yes, but the whole "combine things and see if they make other thing make sense" seems to me like a procedure that may be reproduced by pattern matching at a very large scale.
Especially here. Expecting good faith in hearing aid regulation - from the FDA?! Remember, Congress authorized OTC hearing aids back in the Trump administration, in August 2017 (https://en.wikipedia.org/wiki/Over-the-Counter_Hearing_Aid_A...). For perspective, GPT-1 didn't even exist yet. But the FDA slow-walked it so long that the Biden administration had to intervene (and is now trying to claim credit for it all, of course), which is part of why the OTC hearing aid explosion has taken so long, been so tentative and slow, and you're only seeing it really taking off the past 2 years or so.
Yeah, I'm sure you "would".
Economical with metal from earth maybe - what about from an asteroid? Time to think outside the box.
> * To convert Linux kernel subsystems into Python so I can quickly understand them (I'm a C programmer but everyone reads Python faster).

Do you have an example chat of this output? Sounds interesting. Do you just dump the C source code into the prompt and ask it to convert to Python?
Haha I wish, although I saw the other one i forgot its name which makes music for you, now you can ask it for a soundtrack and it gives it back to you in your voice or something like that idk interesting times are ahead for sure!
Honestly, it was a very close call.

I had two shopping lists written down, one for Unifi, one for TP-link. It was a combination of a couple of factors. It was actually a really tough decision, because they both seemed very good value for money. I wasn't factoring in cameras, just controller + switch + APs.

It was a combination of a couple of factors (this was in 2022):

- UI and integration

- Number of reviews and write-ups

- What I could find in stock

If I were doing it again today, I couldn't guarantee that my decision would be the same. It'd be a tough one...yet again.
> In this case, we're seeing the tension between "The official docs should be great" and "I've mistaken a community project for the official docs"

What official documentation? The standards? C and C++ are weird languages from a modern perspective, and that includes their cultures: They don't have a single blessed implementation, they have a standards body and a community. The standards body issues standards, the community does everything else. Both C and C++ come from a time when all "serious" languages worked like that. Yes, even BASIC. There's an ANSI standard for BASIC.

It would be great if the FSF and/or the LLVM people wrote documentation for C and C++ and it would be even better if they collaborated on it. But it would be no more "official" than cppreference is, because they don't write the standards.
Yes, this only helps multi-step reasoning. The model still has problems with general knowledge and deep facts.

There's no way you can "reason" a correct answer to "list the tracklisting of some obscure 1991 demo by a band not on Wikipedia." You either know or you don't.

I usually test new models with questions like "what are the levels in [semi-famous PC game from the 90s]?" The release version of GPT-4 could get about 75% correct. o1-preview gets about half correct. o1-mini gets 0% correct.

Fair enough. The GPT-4 line aren't meant to be search engines or encyclopedias. This is still a useful update though.
I mean, considering how many tokens their example prompt consumed, I wouldn't be surprised if it costs ~$2k/month/user to run
It's funny you mention this the same data the Data Colada guys won their motion to dismiss.
Or just buy existing electric van from someone like MB or Rivian.
NASA at a Crossroads: Maintaining Workforce, Infrastructure, & Technology
Amazing. I've tried co-pilot and tabnine extensively and I think it solves one of the major pain points both have: not having an easy way of inserting/modifying code it generates by providing an interactive patch style interface.

That, and the autocomplete is more accurate and faster (I think it has access to recent history and clipboard?)
Show HN: Built an AI Money Tracker as Soloprenuer
Hello everyone, I was always thinking about how I can increase my finances to get out of the rat race. Apart from increasing my income, the other approach I did is to reduce my expenses and hence I started my journey on expense tracking.

I have tried a few apps and most of the time these apps are too complicated and I didn't manage to consistently key in my expense. Though there are some apps that provide link to bank account spending but I think that defeats the purpose of expense tracker since I believe only by manually input your spending in, only you'll be aware of your spending.

This drives me to have a thought about how can I improve this and I decided to create my own app to make this work. I created Rolly: AI Money Tracker - a new AI expense tracker where you can easily record your transactions just by chatting with our bot Rolly and it will automatically record and categorise the transaction into the most suitable category (you can also create any of your own category and it will also take care of it in consideration). Demo video here. More features like budgeting are on the way, stay tuned!

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Anyone that have any feedback or needs for any new feature, do feel free to let me know and I'm happy to discuss and incorporate it. Thanks.
No, this was a sort of AI assistant before AI thing. It was a whole bunch of state machines and decision trees and NLP. It actually worked fairly well.
Not Quite Past
One trick that people are using, when using Cursor and specifically Cursor's compose function, is to dump library docs into a text file in your repo, and then @ that doc file when you're asking it to do something involving that library.

That seems to eliminate a lot of the issues, though it's not a seamless experience, and it adds another step of having to put the library docs in a text file.

Alternatively, cursor can fetch a web page, so if there's a good page of docs you can bring that in by @ the web page.

Eventually, I could imagine LLMs automatically creating library text doc files to include when the LLM is using them to avoid some of these problems.

It could also solve some of the issues of their shaky understanding of newer frameworks like SvelteKit.
No, ChatGPT is way cooler than that. It's already read every line of kernel code ever written. I start with a subsystem: the device mapper is a good recent example. I ask things like "explain the linux device mapper. if it was a class in an object-oriented language, what would its interface look like?" and "give me dm_target as a python class". I get stuff like:

def linear_ctr(target, argc, argv):
print("Constructor called with args:", argc, argv)
# Initialize target-specific data here
return 0

def linear_dtr(target):
print("Destructor called")
# Clean up target-specific data here

def linear_map(target, bio):
print("Mapping I/O request")
# Perform mapping here
return 0

linear_target = DmTarget(name="linear", version=(1, 0, 0), module="dm_mod")
linear_target.set_ctr(linear_ctr)
linear_target.set_dtr(linear_dtr)
linear_target.set_map(linear_map)

info = linear_target.get_info()
print(info)

(A bunch of stuff elided). I don't care at all about the correctness of this code, because I'm just using it as a roadmap for the real Linux kernel code. The example use case code is an example of something GPT 4o provides that I didn't even know I wanted.
At the time (from Australia, having already travelled a good deal mapping and working in global geophysical exploration) my opinion was that OBL executed a carefully thought out and well executed plan with the specific intent of eliciting an over the top batshit crazy response from the USofA.

In this he succeeded without a doubt. (NB. I'm absolutely not endorsing his actions).

He delibrately provoked the US and he was extremely clever about it.

> He wasn't even an official representative of Aghanistan ... Pakistan ..

All immaterial from an OG Al Qaeda PoV - they were intent on starting a mass war between the Arab world and the West based on a dislike of the Saudi Royal Family, their corruption of "true" Arab values (for some intepretation of "true arab values"), the US support of the Saudi's, the US actions in Islamic countries and their support and or acceptance of atrocities committed against Muslims.

Very early on there was a teetering point when the US could have capitalised on a global wave of sympathy that included much of the Islamic world.

Then was the time to form a small group with international partners to target and capture the small Al Qaeda and leverage support from Muslims outraged at OBL's actions.

That opportunity was squandered badly with the Bush versioned War on Terror and sideshow invasion of Iraq, the search for non existent WMD's and the Coalition of the Reluctant Few.

The US went down an avoidable 20 year rabbit hole and squandered reputation and relations.
o1 models might use multiple methods to come up with an idea, only one of them might be correct, that's what they show in ChatGPT. So it just summarises the CoT, does not include the whole reasoning behind it.
[dupe]

More discussion: https://news.ycombinator.com/item?id=41515588
this is why i became skeptical of openai's claims

if they shared the COT the grift wont work

its just RL
It’s definitely way more than a handful; spam makes up the vast majority of my USPS service by quantity and probably the largest share by volume.
The article even talks about using it with an API I’d never heard of: WebSerial, https://hackaday.com/2022/03/21/web-serial-terminal-means-it...

I guess that’s the inexorable trend in tech—add more and more shim layers between problems we’re not allowed or too lazy to fix.
OpenAI's o1 model brings human-like reasoning to AI
The innovation lies in making the whole loop available to an end user immediately, without them being a programmer. My grandma can build games using ChatGPT now.
I've just wasted a few rounds of my weekly o1 ammo by feeding it hard problems I have been working on over the last couple days and for which GPT-4o had failed spectacularly.

I suppose I'm to blame for raising my own expectations after the latest PR, but I was pretty disappointed when the answers weren't any better from what I got with the old model. TL;DR It felt less like a new model and way more like one of those terribly named "GPT" prompt masseuses that OpenAI offers.

Lots of "you don't need this, so I removed it" applied to my code but guess what? I did need the bits you deleted, bro.

It felt as unhelpful and bad at instructions as GPT-4o. "I'm sorry, you're absolutely right". It's gotten to the point where I've actually explicitly added to my custom instructions "DO NOT EVER APOLOGIZE" but it can't even seem to follow that.

Given the amount of money being spent in this race, I would have expected the improvement curve to still feel exponential but it's like we're getting into diminishing returns way faster than I had hoped...

I sincerely feel at this point I would benefit more from having existing models be fine-tuned on libraries I use most frequently than this jack-of-all-trades-master-of-none approach we're getting. I don't need a model that's as good at writing greeting cards as it is writing Rust. Just give me one of the two.
There's some concern that the US is playing with fire at the current ratio.

However, Japan is now over 200% has been over 100% for 23 years. Despite their debt, investors aren't too worried about not getting paid.

There's not really a hard and fast rule at which it becomes a problem, it is up to investors to decide. The total economic situation, monetary policy, and geopolitical situation will matter more than just the debt-to-GDP ratio. That ratio is mostly useful for comparing a country to itself over time.
I configured my Xcode behaviors to play a sound when a build is completed or when the test fails/passes.
How is that a good idea?
In the primary CoT research paper they discuss figuring out how to train models using formal languages instead of just natural ones. I'm guessing this is one piece to the model learning tree-like reasoning.

Based on the quick searching it seems like they are using RL to provide positive/negative feedback on which "paths" to choose when performing CoT.
The Future of Dan.com
The tweet uses "number of companies founded" as a metric, which to me isn't as illustrative as something like revenue/company or similar.
Nice! Would it be possible to transform an error to something else using this syntax?

Or, should we resort to a method of `Either` that transforms its `Left` in that case?
McDonnell Douglas X-36
what game has she made
The shades "reflect" a password that takes you to a honeypot
I'd say the set of people who want an AI girlfriend and the set of people whose defining trait is a lack of empathy is probably a bigger overlap than you think.
What games have people made with ChatGPT? Do you have an example of a live, deployed game?
You don't think they know how to automate downloading the latest Revanced and checking the app ID?
I'm also working on an AI code editor, but why VS code clone tho?

Zero to ones in this space seems more challenging but might be worth it
I think it’s mostly that defense contractors are specialized in selling to the government, which entails jumping through regulatory compliance hoops and placating elected officials by distributing the supply chain across as many congressional districts as possible.
The OP is a report from a committee of leading experts, formed by the National Academies, tasked with reviewing "the critical facilities, workforce, and technology needed to achieve NASA’s long-term strategic goals and mission objectives".

It's interesting not only for the insights into NASA as an organization and the critique, but also for two appendices providing updated reviews and prospects of in-progress long term missions and of R&D into critical tech such as planetary entry, decent and landing; planetary ascent (take-off from other planets); in-space propulsion; habitats outside Earth; and in-situ resource utilization on Mars (i.e., utilzing local materials instead of bringing them).

(And also an interesting section on transitioning from engineer to engineering manager.)