skysight-inc/HackerNews huggingface dataset:
When someone's fear comes from ignorance, what kindness is it to leave them in the dark?
If we're being pedantic then they ARE numbers, even the ones with letters. They're just not in base10.
> He doesn't even have an undergrad degree!

There are a lot of smart people in the tech industry without undergrad degrees.

The problem is, in the current generation of tech workers, there's two kinds of people without degrees:

1) The grinder who has a knack for whatever part of the field that they work in and made their name through hard work and building a portfolio of work through practical experience

and

2) The (usually) guy who went to a college prep school, got into Stanford, and encountered a SV VC with exponentially more money than sense, who then told the 21-year-old that they were not a college student, but, in fact, Jesus Christ, and promised more money than the average person could comprehend to "pursue their dreams".

Altman falls into the latter category. Actually, a lot of the founder set does. I say (usually) guy because Elizabeth Holmes also falls into this category.
The problem is the coverage. I stopped watching a long time ago. The hosts are terrible. The social backstories are insufferable. It's impossible to watch your favorite <insert sport>... I'd tune in to a live streamed event, but I'm sure they'd screw it up somehow.
Our overlords are selecting for risk tolerance in leadership moreso than smarts.
I think a pin is only supposed to be a second line of defense, like entering your zip code with your credit card. People who use 1234 as an ATM pin think their card prolly won;t get stolen, and if it is, the machines all have cameras so you can see the thief picking his nose.
If you want to build a stateless system with transitions, you are limited to regular expressions (finite automata).

I don't think you'd like to write real-life applications using a regular language. It might be interesting as an experiment, but life without recursion or potentially infinite loops sounds bleak... Just imagine the struggle of creating some generic containers like trees / hash-tables.

I don't think it's impossible, and may be even interesting to see how far one can take such a language to make it useful, but enjoyable this is not...
My review of 2024 is a good place to catch up on what's changed in the past 12 months: https://simonwillison.net/2024/Dec/31/llms-in-2024/
You can run it through Openrouter/Fireworks hosted in the US.
How long would it have taken for a human being to evolve? 30 billion years?
SMS-based two-way login would be a better way to do 2FA.

Think of it from the user perspective - now they have to download and use yet another app on their cellphone just to log in.

Yes, I am aware of SMS's vulnerabilities - but the weakest link is always the user.
If it's ok to be so weak that it's trivial to enter like this, why have the lock at all? The cynical (and probably accurate) answer is that it's security theater designed to give customers warm fuzzy feelings cheaply and with low risk of lockout calls and maintainance issues. It does basically nothing to keep someone from taking your stuff.
Because you can't change biometrics. What are you going to do, wear gloves so that you have a different fingerprint?
Another OpenAI Researcher Quits
We already knew that kind of stuff from AlphaZero vs AlphaGo

AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version.[1] By playing games against itself, AlphaGo Zero: surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0; reached the level of AlphaGo Master in 21 days; and exceeded all previous versions in 40 days.[2]

Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills, as expert data is "often expensive, unreliable, or simply unavailable."[3] Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was "no longer constrained by the limits of human knowledge".[4] Furthermore, AlphaGo Zero performed better than standard deep reinforcement learning models (such as Deep Q-Network implementations[5]) due to its integration of Monte Carlo tree search. David Silver, one of the first authors of DeepMind's papers published in Nature on AlphaGo, said that it is possible to have generalized AI algorithms by removing the need to learn from humans.[6]

Google later developed AlphaZero, a generalized version of AlphaGo Zero that could play chess and Shōgi in addition to Go.[7] In December 2017, AlphaZero beat the 3-day version of AlphaGo Zero by winning 60 games to 40, and with 8 hours of training it outperformed AlphaGo Lee on an Elo scale. AlphaZero also defeated a top chess program (Stockfish) and a top Shōgi program (Elmo).[8][9]

Source: https://en.wikipedia.org/wiki/AlphaGo_Zero
It’s CCP‘s decision anyway, not founder‘s.
I believe Simon has full transcripts for some of the projects he’s had LLMs generate the code for. You can see how he steers the LLM for what is desired and how it is course corrected.
> an industry that at best doesn't touch any real problems for humanity

I posted this yesterday, but "AI is failing the Indoor Plumbing Test": https://news.ycombinator.com/item?id=42840785

Real innovations are often boring, but they transform human lives. So far, AI has not cleared that bar. I keep hearing that AI may go rogue and exterminate humanity, but for now I'm not even sure what it will enable me to do that I couldn't do before.
> I worry about junior developers. It will be a while before vocational programming courses retool to teach this new way of writing code, and these are going to be testing times for so many of them.

I don't agree. LLMs work as template engines on steroids. The role of a developer now includes more code reviewing than code typing. You need the exact same core curriculum to be able to parse code, regardless if you're the one writing it, it's a PR, or it's outputted by a chatbot.

> For example, if you're writing web apps, you need to be able to spot say security issues. And various other best practices, depending on what you're making.

You're either overthinking it or overselling it. LLMs generate code, but that's just the starting point. The bulk of developer's work is modifying your code to either fix an issue or implement a feature. You need a developer to guide the approach.
Not really, the two first examples you named are the same meaning.
They are being attacked as well.

https://apnews.com/article/deepseek-ai-artificial-intelligen...
This is my main worry with the entire AI trend too. We're creating a huge gap for those joining the industry right now, with markedly fewer job openings for junior people. Who will inherit the machine?
Whereas, Marvin Minsky promised a PhD from an AI at the end of a decade, he would have done it again, but he passed away. He did this in 1950, 1960, 1970, 1980, 1990, he forgot about it in 2000, but made the claim again in 2010.

Did you miss this principal?
Related:

Boom Supersonic to break sound barrier during historic test flight today

https://news.ycombinator.com/item?id=42852077

XB-1 First Supersonic Flight [video]

https://news.ycombinator.com/item?id=42853633
I may be wrong, but I think right now, from reading stories of people looking at use AI and having poor experiences, AI is useful and effective for some tasks and not for others, and this is an intrinsic property - it won't get better with bigger models. You need a task which fits well with what AI can do, which is basically auto-complete. If you have a task which does not fit well, it's not going to fly.
The Ruby we write is quite strange by the normal reckoning. In particular, it has 100% branch coverage. Being able to do this affordably is one reason I use it.

A fair number of the dependencies we have also have 100% branch coverage, because I copied the practice, starting about ten years ago, from Jeremy Evans, who maintains a huge number of libraries under that principle. That includes "Sequel," the ORM that I've used for many years and originally copied the practice from, around 2015. You can see the libraries he maintains in this way: http://code.jeremyevans.net/ruby.html. He has joined Ubicloud somewhat recently, so I look forward to getting a sense of how he completes the rest of his rather singular & extraordinary maintenance regime.

To have Ubicloud rest at this standard is my objective. My tendentious claim is as follows: this is higher than any other constellation of libraries I have seen in any programming language. If anyone knows of any constellation of libraries that is more capably and rigorously maintained in any language, let me know. The bar as roughly as follows they need to release every month, or something like that (yes really: https://rubygems.org/gems/sequel/versions/), have a wide interface with your program, and break it no more than once every five years.

It's also not a Rails program, and I have never written or maintained a Rails program in any seriousness, which makes me an odd duck among longtime Ruby programmers.
Not really. The 2-ring design was designed so the second ring was a "backup", but as you note, this only increases loads that act in the direction of aiding "Joint Rotation" and hot gas leakage. The third O-Ring was added on the opposite side of the joint from the original O-rings, and the tang was re-designed with an additional clevis socket so that pressure loads are balanced to prevent "Joint Rotation". A comparison of the designs can be seen at https://www.semanticscholar.org/paper/Axisymmetric-shell-ana... . McDonald's book has FEA showing the predicted clevis deflections for both configurations, and the 3 O-ring design significantly reduces the "Joint Rotation." But still, since they were continuing to the same use Viton O-rings, a resistance heater and temperature monitoring was added to address low-temperature performance. (Edit - I think this was McDonald's source for the FEA diagrams in his book - https://web.archive.org/web/20210717050737/https://ntrs.nasa...)

I've never really liked the description of the failure mechanism as "Joint Rotation" anyway. It's only rotating when you look at it in a 2D cross section. In 3D I'd call it "tang outward deflection." It's increasing in diameter, not rotating.

The problem, at it's root cause, was a failure of requirements management. On the night of the launch, no one was able to identify what temperature either the SRBs or the Orbiter had been designed and qualified to. Within the specification for the SRBs, there are several low temperature requirements that conflict with each other, and were misapplied both in design, in testing, and on the night of the launch. Literally no one could say whether it was safe to launch the shuttle that night, because the requirements were not clearly defined.

NB- I've personally designed and specified numerous O-ring installations in both static and dynamic aerospace applications, and done all the required DO-160 environmental testing to demonstrate that they perform as required throughout the thermal envelope. I am literally at a supplier/vendor right now doing a salt fog exposure test per DO-160.
Impressive that it was fixable in the end! Quite brave to keep taking it apart, although I suppose the monitor couldn't get any worse at that point.
I'm not saying it explains the "5 paragraphs of why answering that question is insensitive" but it definitely could explain it no?
Right now my default reaction to phone call from unknown number is to ignore it instead of picking it up. Hopefully this will stay with me as I get older.

(my parents are notably different in this default)
LGTM: Designing a Great Developer Experience
I had the OG pebble, which I only stopped using due to suffering the screen corruption bug that plagued a small percentage of the original models. Other than that it was smooth as silk. It'd last close on a month on a single charge. I literally charged it up before going on a 2 1/2 week vacation, didn't take my charger with me, and had no problems.

Then had an Asus ZenWatch 3. WatchOS was frustrating at the time over the ability to customise what notifications would actually get to the watch, and the battery life of the device was terrible, getting worse over the space of a couple of years. Even had some reliability issues with the messages actually reaching the watch at all.

Wanting to go back to e-ink displays and longer battery life, I've got a Amazfit Blip, who's software was just awful. Even "simple" things like sleep tracking wouldn't work properly, dying out or flat out not being accessible from "Sleep for Android", heart rate monitoring wouldn't sync reliably to anything, notifications would just randomly stop working. It also had no ability to disable certain apps from sending notifications to the watch, even if the notifications are set to silent on the phone.

There was an open source app that I used to have to use alongside their own app that was necessary to actually fix everything wrong with the original software and make the device work with anything approaching reliability.

The recent overhaul of their own app (seems like a ground up rewrite to me) has actually fixed most of the issues that I've had with it. It still occasionally just craps out and requires me to turn the whole bluetooth stack off and on again.
> ai that performs at intern level vs phd vs domain leading expert level.

These are different things:

- Regurgitating advanced text that has been shifted into a shape matching your query

- Understanding intimately the $100M screw to turn
Yep, I paid $33 to to see the New York Red Bulls play (Major League Soccer) and it was just that. It's not the insanity of a Premier League game but it's the closest thing to a European soccer game that you'll see in the states, they even have a seating section to concentrate the die-hard fans right by the opponent's goal. Watching on TV is not the same.
The facts are that AI has been driven by PhD level research. There are vanishingly few cases of people who have not studied the area in depth making serious contributions. The term "AI founder" is usually used to reference the people who have made the technical and mathematical advances, not the person who hired those individuals after the fact to commercialize it.

Altman is a tech founder and a business founder, but he is not an AI founder.
we were talking about self-hosting. the deepseek-r1 is 347-713MB depending on quant. No one is running deepseek-r1 "locally, self hosted".

If people want to argue with me, i wish we'd all stick to what we're talking about, instead of saying "but you technically can if you use someone else's hardware" but that's not self hosted. I self host a deepseek-r1 distill, locally, on my computer.

It is deepseek, it's just been hand-distilled by someone using a different tool. the deepseek-r1 will get chopped down by 1/8th and it won't be called "deepseek-r1 - that's what they call a "foundational model", and then we'll see the 70B and the 30 and the 16 "deepseek deepseek distills"

next to no one who messes with this stuff uses foundational or distilled foundational models. Who's still using llama-3.2? Yeah, it's good, it's fine, but there's mixes and MoE and CoT that use llama as the base model, and they're better.

there is no gguf for running locally, self-hosted. Yes, if you have a DC card you can download the weights and run something but that's different than self-hosting local running with a 30B (for example).
I'm not confused by its definition, I just dislike the use of the term. It leads to significant confusion in laypeople - "they found a habitable planet!" is something I've heard breathlessly repeated multiple times, and "but Earth is so perfectly placed, it can't be by chance!" used as an argument for creationism.
Classic whack-a-mole

IMHO, this can happen with human or robot co-workers.
>So it's more a conceptual belief: Will AI actually make driving cares safer or will the fatalities of AI just be so randomly stochastic that it's more acceptable.

I would argue that we already accept relatively random car fatalities at a huge scale and simply engage in post-hoc rationalization of the why and how of individual accidents that affect us personally. If we can drastically reduce the rate of accidents, the remaining accidents will be post-hoc rationalized the same way we always have rationalized accidents.
Some people around here think Elon Musk is good at public speaking. I wouldn't pay too much attention to the opinions of nerds on topics like charisma.
dissuading the least motivated perpetrators, ability to add a "breaking and entering" charge perhaps?
Indeed. But DeepSeek is the first to release a working repro for LLMs as far as I know.
Then link it unedited.

He's an asshole, I get it. But half of what is said about him is false (pee pee tapes?) that Americans don't give a darn about what is true.
It's popular to dunk on Sam, but I don't think he's wrong here. There's now hundreds of companies that have attempted to train a foundational models and almost every single one of them has failed to build a viable business around it (Even Mistral looks to be in rough shape).

Deepseek has done something remarkable, but they have the resources of a multi billion dollar quant fund at their disposal. Telling startups they have a chance is sending them to near certain death. There's way more promising niches to fill.
would you mind elaborating why you think their assumption doesn't hold?
I have a 6 year old, back then 400EUR (consumer price mind you) GPU that could happily serve 10 "normal" users. sure, we have to add other hardware too, like chassis, power supply, CPU, RAM, ssd etc. so lets 4x that.
Now we're looking at 1600 investment + power cost.
My setup comes down to 0.04 eur per hour of operations, so about ~30 bucks a month (mind you electricity is very likely cheaper wherever else you are). We also need maintenance and operations, eyeballing in large scale at ~2EUR/user/month.

On a 3 year amortisation period we're looking at

investment: 1600

monthly cost to operate: x * 3 + x 2 + 1600/36

monthly revenue: x 20

For x users = 10

monthly cost: 50 + 44.4

monthly revenue: 200

net month result: +105.6

net total result: +3801.6

i know i know thats a napkin calculation but if anything hardware cost will be lower for big providers, which would give them even more profits. just looks like a feasable business model to me.
as long as you can sell those subscriptions
That is exactly how go usually works with error handling though.

func mayfail() error {
if snafu {
return errors.New("oops")
} else { return nil}
}

err := mayfail()
if err != nil { handle }

Same as `mayfail() ? handle : happypath` would behave with lazy evaluation.
Right: LLMs have a "jagged frontier". They are really good at some things and terrible at other things, but figuring out WHAT those things are is extremely unintuitive.

You have to spend a lot of time experimenting with them to develop good intuitions for where they make sense to apply.

I expect the people who think LLMs are useless are people who haven't invested that time yet. This happens a lot, because the AI vendors themselves don't exactly advertise their systems as "they're great at some stuff and terrible at other stuff and here's how to figure that out".
Yeah but LLM and game AIs are very different. An AI can easily tell if it won the game but an LLM can't know by itself if what they said was useful.
Finetunes of smaller models on R1 output are pretty bad, but the main R1 model is very, very far from being "garbage".
First graph tells the story - below a certain model size (500m params), reinforcement learning is close to useless. Above this (task-dependent) model size threshold, reinforcement learning basically works.

I suspect this is what we saw play out with math/coding reasoning models - until recently, the base models were not good enough for ~random output search to hit on a correct path with any reasonable frequency. Below this threshold of base model intelligence, the only efficient way forward was to collect plain supervised data (either through human labeled math problem solutions [1] or meticulous filtering of web text [2].

But as soon the base model (in this case Deepseek V3) breaks through and can actually solve a decent fraction of math problems, then reinforcement learning (plus other simple tricks like chain-of-thought prompting, simple ensemble voting, etc.) can easily juice the results through the following loop:

1) random search through different solution paths

2) identify the correct solution paths based on the final answer

3) train on the correct solution paths

The exciting thing is that not only can RL bump up the performance of the current base model, but it can be used to generate new high-quality reasoning trace data, which was in painfully short-supply for training the initial models. This leads to a new wave of base models with better one-pass intuition, which leads to more efficient reinforcement learning search on harder problems, which leads to better training data...

Note that this was basically impossible for non-LLM models in the past. You could always juice ImageNet classification performance with a simple ensemble of identically trained models, but that path didn't lead anywhere interesting because a juiced model didn't allow the creation of new synthetic data that was superior to the data it was trained on. The key difference is that LLMs not only output the solution but also output a solution path with all the intermediate steps - and these searched-and-filtered solution paths are much more valuable than the vast majority of the model's initial training data.

[1] https://arxiv.org/abs/2305.20050

[2] https://arxiv.org/abs/2402.03300 and https://arxiv.org/abs/2206.14858
makes sense if you think about it - if OpenAI spent, what, $10B on its AI, then it makes sense to say anything less than $1B wont cut it.
Going from English to code via AI feels a lot like going from code to binary via a compiler.

I wonder how long it will be before we eliminate the middle step and just go straight from English to binary, or even just develop an AI interpreter that can execute English directly without having to "compile" it first.
They aren’t though. $5M is the cost of a single training run. $500B includes the cost of operations, data center, a lot more failed runs because they weren’t sure that they’d were on the right path etc.
Good question.
No, these will essentially be black-white "photos". The amount of light is measured. The reason for so many CCDs is so that the field of view would be as large as possible. A larger field of view enables to look at more stars at once. Given that we will be locked into looking at one spot for a whole year, it ups our chances of spotting something cool if we maximise the number of stars we are looking at.

However they won't be photos of planets really. It will be countless photos of the same stars over and over again, it's just that sometimes they will be slightly less bright than other times. Directly imaging exoplanets is incredibly difficult, but humans have managed it: https://en.wikipedia.org/wiki/List_of_directly_imaged_exopla...
> Can you enumerate some of the things that they could do,

Biden had a very long time to do a lot of things. You can get a lot done with a bit of creativity and a willingness to cause a few constitutional crisis'.