skysight-inc/HackerNews huggingface dataset:
I think the charitable interpretation is that they're drawing a distinction between physical hunger and emotional hunger.

I "feel" like I could go for a bacon cheeseburger basically 24s hours today. I feel like I could eat another right after I finished the first. All of that is emotional.

That is pretty different than symptoms true physical hunger. My stomach starts to grumble after 24 hours without food. After about 48 hours I feel lethargic and cognitively disordered.

I think that a majority of Americans confuse the two because it has been years or even decades since they actually experienced the symptoms of insufficient calorie intake
At least they can generate haikus now
"There’s some Soviet science fiction like Tarkovsky, which ended up turning into the movie Stalker."

Tarkovsky was the movie guy. Arkady and Boris Strugatsky were the writer guys.

I really liked it back then.
Latin America cities
"We will have better and better models," wrote OpenAI CEO Sam Altman in a recent Reddit AMA. "But I think the thing that will feel like the next giant breakthrough will be agents."

Is this certain? Are Agents the right direction to AGI?
Why I don’t like ads:

1. Fundamentally propaganda with little real regulatory oversight. Numerous arguments to this point and the negative impact of advertising have been made in the last several decades.

2. Tech companies seem to eventually get into the business of selling data and/or manipulating the user experience to better suit advertisement. I can’t think of a single company that has adopted advertising and not scaled it over time.

3. Security and privacy concerns inherent with letting third parties manipulate page content.
Essentially post-school every job I’ve been hired for was through strong personal connections. And I certainly feel I don’t develop the same kinds of connections remotely. Maybe some folks who grew up in that of remote environment are better adapted.
ARC as a stepping-stone for AGI? For me, ARC has lost all credibility. Your white paper that introduced it claimed that core knowledge priors are needed to solve it, yet all the systems that have any non-zero performance on ARC so far have made no attempt to learn or implement core knowledge priors. You have claimed at different times and in different forms that ARC is protected against memorisation-based Big Data approaches, but the systems that currently perform best on ARC do it by generating thousands of new training examples for some LLM, the quintessential memorisation-based Big Data approach.

I too, believe that ARC will soon be solved: in the same way that the Winograd Schema Challenge was solved. Someone will finally decide to generate a large enough dataset to fine-tune a big, deep, bad LLM and go to town, and I do mean on the private test set. If ARC was really, really a test of intelligence and therefore protected against Big Data approaches, then it wouldn't need to have a super secret hidden test set. Bongard Problems don't and they still stand undefeated (although the ANN community has sidestepped them in a sense, by generating and solving similar, but not identical, sets of problems, then claiming triumph anyway).

ARC will be solved and we won't learn anything at all from it, except that we still don't know how to test for intelligence, let alone artificial intelligence.

The worst outcome of all this is the collateral damage to the reputation of symbolic program synthesis which you have often name-dropped when trying to steer the efforts of the community towards it (other times calling it "discrete program search" etc). Once some big, compensating, LLM solves ARC, any mention of program synthesis will elicit nothing but sneers. "Program synthesis? Isn't that what Chollet thought would solve ARC? Well, we don't need that, LLMs can solve ARC just fine". Talk about sucking out all the air from the room, indeed.
Author here, AMA. And/or especially: correct anything I got wrong!
For what it's worth, I manually compared the original journal article (https://doi.org/10.1111/j.1460-2466.1961.tb00344.x obtained via sci-hub) against the transcribed version in the OP, and the diffs are here: https://www.diffchecker.com/CZRjh0fb/ or https://www.jstoolset.com/diff/3c9ff80979b6e808 (or in case both go down: https://gist.github.com/shreevatsa/0d1aa90e73a7bfbe12e4b4888...)

The he-or-she changes are numerous but insignificant (not consistently done though), but some of the changes to word choices I wouldn't have made (and some are clear errors, e.g. debating -> doubting), and three paragraphs have gone missing (don't know if they got dropped while transcribing into the web page, or were made by the original author at some point).
To be fair, that first part is not just a Kagi hypothesis - they literally quote Brin & Page on this:

"The goals of the advertising business model do not always correspond to providing quality search to users... advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers."

— The Anatomy of a Large-Scale Hypertextual Web Search Engine, Sergey Brin and Lawrence Page, 1998

As far as the model, I think the problem is that once ads are shown to be profitable, they become pervasive in anything mainstream, because, well, why would VCs back a startup that is not willing to take money that's on the table? There's a niche in catering to people who really, really hate the notion of targeted ads and the associated infringement of their privacy, which makes Kagi viable, but I don't see any major ad-free competition in this space. So I doubt that it can work as a cycle. We might get a competitor that works better than Google - it's not exactly a high bar these days! - to replace them eventually, but I'm pretty sure it'll also be ad-ridden. And with LLMs especially, the potential for replacing in-your-face ads with subtle but persistent bias in the desired direction can make it all much worse.
Has anyone used this on a larger codebase yet? I'm curious to see how it handles larger contexts.
AI companies in SF that have raised Series A
To be clear, I'm not pro-censorship.
And, to further be clear, there is zero first amendment infringement when private corporations silence your voice. This is the free market at play.

I'm merely demonstrating why private companies chose to censor. Misinformation during a health crisis isn't free - it costs lives. They don't want to be responsible; they don't want that on their shoulders. So they just silenced the "vaccines cause autism!" crowd and moved on.

The reason conservatives are "targeted" is not because they're conservative, but rather because their platform is being based on lies more and more as time goes on. Science denial, conspiracy theories, a lack of care for human life - these tendencies are rampant in some conservative communities.

This isn't fixed going forward, it's only getting worse. It's gotten so bad that a lot of conservatives won't even listen to Trump. Seriously, they think quoting Trump is misinformation. This abject detachment from reality is very concerning to me. It's absolutely unnerving that you can't even ask conservatives for their own platform and beliefs anymore.
Eh. Titles dont really mean anything. Do we want to start the age old software engineer vs software developer title debate again?

Let someone call themselves whatever they want. If they can do the job they were hired for then... who cares?
having data engineer in my title has led to recruiters calling about DBA type roles. ugh.
I care about the languages you know and how recently you used them and your level of proficiency. Without that trifecta the information is useless

this is not a question just for you, but i wonder what others think about this too.

i get that the number of years of using a language is a useful indicator, but the age? is my experience from 10 years ago no longer valid? also, proficiency in one language does translate to overall proficiency in any language. not 100% obviously, but the more programming experience i have the better i can learn new languages, and the faster i can get productive. same goes for any other tools. old experience is not forgotten. so even the years i have worked with any particular tool is missleading. and if i only list my work from the last 10 years then i'd look like all i have ever done is javascript, and that would limit my options very much.

i have been able to work on python and php projects as a mid-level developer without any formal prior experience in those languages. in the python case the employer knew about it and was still comfortable to hire me. in the php case i was able to cite some php work (which i had done) and i was still producing better results than any of the junior developers on my team. only the team lead was clearly more experienced then me.

how am i going to make that point if i don't list all of the projects i have worked on, no matter how long ago?
That might make sense.
When you train bidirectionally only, you don't get a generative model, that would be the downside. However, you can train on a mixture of causal and bidirectional objectives as some LLM pre-training has done. As far as I am aware, there are no downsides of that, but it is not more common simply because the standard practice has been to train causal only and there just isn't enough funding/attention to go into experimenting on every axis of pre-training (which can be very expensive).
I guarantee you these do not add up to a billion dollars.
Microsoft just locked my brand new Microsoft account
... citing activity in violation of the terms and conditions.

Not saying I had carried out such activity. So... perhaps a bad person tried to log in? But then how could MS know that he/she had given agreement to such T&Cs - without which s/he cannot be in breach of them?

MS has really not thought this through.

Then I find on web MS says "it's because activity associated with your account might violate our Terms of Use."

Now it is only /might/.

What next? Precrime? ;)
Was it? The constitution lays out slander and libel as types of speech that can be censored. The trashy Jones then knowingly and maliciously lied about people for profit - aka libel and slander. Seems reasonable to take the money he made as well as punitive damages.

How is censorship?
> We live in the age of automobiles. If you don't know how to use one you shouldn't be employed where they're necessary. Rather than making a dumbed down workforce we should be building people's skills up.

FTFY ;)

Computers are just another modern convenience. We need to be making them more user friendly and safe. Nobody wants to spend all their time learning, maintaining, and fixing their computers any more than they want to do that with their cars.

They should just work, without surprises.
It's just text interpolation and there already exist template engines in every language ecosystem.

I have even implemented chain of thought in Go's text/template package by providing a custom function to the template system the LLMs could then leverage to essentially "recurse" or "think more"

I don't think a standard will arise. There are already multiple template systems and more generally different programming languages because people have different use cases or preferences
Thanks a ton, I wish good luck and you a happy sane life.
> Also lots of ads prey on people with worse impulse control who bankroll the rest of us who don't click ads.

This reminds me of the Mark Twain adage of "Telling a man he can't have steak just because a baby can't chew it."

I don't want to pay money & subscriptions to every site I visit because some folks don't have impulse control. Similarly, the prevalence of alcoholism in society shouldn't prevent me from having a glass of wine with dinner.
I would allow ads if I could absolutely be assured that malware won't be served to me through them. I'm not talking about something that requires clicking on the ad themselves, because I never click on ads. I'm talking about malicious code being executed as soon as the ad is served. I know that Google is doing everything they can to try and prevent this but I don't trust that this is a solved problem.

https://www.bleepingcomputer.com/news/security/hackers-abuse...

https://support.google.com/adspolicy/answer/6020954?hl=en
I say this as someone who’s worked from home for the last six years of my career pre-Covid

I would never want to work at Amazon or Facebook or whatever while being remote. You’re leaving half of the benefit of being at those places on the table.
Open-Source is in Crisis because Open Source is the corporate spin on Free Software in order to make money, which as many have pointed out, isn't a great fit. It cpuld work, but as an exception.

On the other hand, actual Free Software as in GPL are in no crisis at all. I'll smirk everytime i see such news and it warms my heart. I know, i'm a bad person.
definitely printing fewer lies in the newspaper than Bezos
yeah AI isn't good at uncovering all the foot guns and corner cases, but I think this reflects most of StackOverflow, which (not coincidentally) also misses all of these
> The AGI bubble is bursting a little bit

I'm surprised that any of these companies consider what they are working on to be Artificial General Intelligences. I'm probably wrong, but my impression was AGI meant the AI is self aware like a human. An LLM hardly seems like something that will lead to self-awareness.
I don't think I need to refute the overall glibness here, but in case you're looking for a place in the coming months/years: brokers are often the only ones who take checks these days. The last landlord I had who took a check was 15 years ago, since then it's been cash, bank transfer, or Zelle only.
The niche I've found for LLMs is for implementing individual functions and unit tests. I'll define an interface and a return (or a test name and expectation) and say "this is what I want this to do", and let the LLM take the first crack at it. Limiting the bounds of the problem to be solved does a pretty good job of at least scaffolding something out that I can then take to completion. I almost never end up taking the LLM's autocompletion at face value, but having it written out to review and tweak does save substantial amounts of time.

The other use case is targeted code review/improvement. "Suggest how I could improve this" fills a niche which is currently filled by linters, but can be more flexible and robust. It has its place.

The fundamental problem with LLMs is that they follow patterns, rather than doing any actual reasoning. This is essentially the observation made by the article; AI coding tools do a great job of following examples, but their usefulness is limited to the degree to which the problem to be solved maps to a followable example.
This is not a one-person project ran by someone in their spare time, posted online for fun.

They are going out of their way to advertise so that people use their security-critical software in security-critical applications, and then they neglect the security.

While they aren't under any legal obligation, it's (in my worldview at least) pretty damn unethical.

All they would have to do to not be unethical is make it clear that this software should not be used in any security-critical application because it is not properly/frequently maintained. Put that in a header on the website.
Truly a great piece of satirical writing on The Onion. Just one example:

> With a shrewd mix of delusional paranoia and dubious anti-aging nutrition hacks, they strive to make life both scarier and longer for everyone, a commendable goal.
as as data engieer/data architect , agreed. there are allot of 'tools' experts who are data engineers but can't code a lick.
Ad creatives could be 10x better, but nobody allocates the budget for it.
> Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them.

So you say, but {citation needed}. Stuff like this is simply not known yet.

AI can easily be applied in legacy codebases, like to help with time-consuming refactoring.
The new fad among wireless carriers here in the US is to route what they think are spam calls to a fake voicemail box.

Voicemail that is left in this generic voice mail box never makes it to their customer and the customer is completely unaware that some of their calls have been diverted.
I think it is a good thing for AI that we hit the data ceiling, because the pressure moves toward coming up with better model architectures. And with respect to a decade ago there's a much larger number of capable and smart AI researchers who are looking for one.
But why would that interpretation of 'matter' matter?

There is not much real choice when it comes to WWW browsers. For people that refuse Chrome, Safari and Samsung (distrust is sufficient), the 1-digit-percent remainder is the actual solutions space.

So: either things "matter" in practical terms, e.g. choice, which as said can be pretty scarce, so all plausible options are important, or why should the idea of "matter" be important at all.
I think better advice would be poor people to boldly explore and challenge their bodies limitations, and discover what they are capable, and what works for them.
Sometimes it's difficult to tell the difference between a ghost job and an interview that uses MS Teams on a bad day. Eventually headhunters will catch on and realize that MS Teams gives plausible deniability--"We invited the candidate to an interview but they never showed up".
I’ll save you a click. Just read this from the source. One of the authors joins the comments. https://news.ycombinator.com/item?id=42118399
Jones was sued in civil court.
I agree. To be honest, I don't think I ever used the term EDM before in my life, but I went with the same expression that my sources used.
FirstMate: coding companion that reduces meetings and cognitive load
Python code to match the article:

from statistics import NormalDist

def var(nd):
'Value at risk'
return nd.stdev - nd.mean

safe = NormalDist(5, 3)
bold = NormalDist(9, 10)
shock = NormalDist(-5, 10)

print('Safe: %s\tBold: %s\tBaseline' % (var(safe), var(bold)))
print('Safe: %s\tBold: %s\tConstant shock' % (var(safe - 5), var(bold - 5)))
print('Safe: %s\tBold: %s\tUncorrelated shock' % (var(safe + shock), var(bold + shock)))
print('Safe: %s\tBold: %s\tCorrelated shock' % (var(safe * 2), var(bold *2)))
Overall it should feel faster and simpler. We wanted to build a product that lets you focus on your work and not the tool.

You don't need to organize much like in Notion and it should feel less clunky.

The main differences in terms of features are aggregated tasks across the whole workspace, search across multiple tools (Google Drive, Linear), and side by side view.
> AI makes tech debt more expensive

This isn't AI doing.

It's the doing of adding any new feature to a product with existing tech debt.

And since AI for most companies is a feature, like any feature, it only makes the tech debt worse.
God it's so obvious how they're running out of money with nothing really to show for it, these are gigantic red warning signs of the bubble getting close to bursting, another is how Microsoft released Warcraft 3 reforged 2.0 with literally every asset upressed by AI, they're trying to justify the amount of money thrown into the dumpster to AI by doing these things.
Better yet, route all calls for all disconnected/unassigned numbers in their part of the numbering plan to it. It would probably kill robocalling overnight.
or you just can start with a well maintained boilerplate
For ads to be effective, they need to be targeted. Any ad-supported model incentivizes identifying and tracking users across as many services as possible and data mining to build profiles.
I can't really see a difference between advertising and marketing
not long ago these people would have you believe that a next word predictor trained on reddit posts would somehow lead to artificial general superintelligence
Code that has thorough unit and integration tests, no matter how old and crusty, can be refactored with a good deal of confidence, and AI can help with that.
They're nothing to do with AGI. They're to get people using their LLMs more.
Doesn't seem like you ship to my region, cool product nevertheless
First of all, congrats on getting a new gig! With a few minor differences, your experience is similar to my own. So, clearly, you are not alone! I will note that one other blockage that you likely encountered but maybe didn't note or focus on which has hit me (in addition to other factors you cited): agism. I have been in the workforce for a little over 28 years, and 24 of those years has been in some sort of IT or digital or technical-related team...and most recently i have felt the tinge of agism ridiculously high - moreso while trying to get a job, but also in some (but not all) areas of my current dayjob. Its funny, when applying for jobs, and when speaking with some potential hiring managrers, HR, etc....you get the feeling from their questions...But when working with a team of any age, and you bring value to the team, then agism is not as much of a thing. There are times when one's aged wisdom is highly regarded and valued...and then other times - most especially during hiring processes - where it is considered a potential problem. Again, i'm so glad you got your new gig, but it sure is hell out there! :-)
I think your definition is off from what most people would define AGI as. Generally, it means being able to think and reason at a human level for a multitude/all tasks or jobs.

"Artificial General Intelligence (AGI) refers to a theoretical form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to that of a human being."

Altman says AGI could be here in 2025: https://youtu.be/xXCBz_8hM9w?si=F-vQXJgQvJKZH3fv

But he certainly means an LLM that can perform at/above human level in most tasks rather than a self aware entity.
I had a similar situation/question. I knew an old coworker who was eagerly trying to get me on their team at another company. They said I had to apply and then they'd take it to their manager who was already on board with the idea.

So I did, at 10PM or so at night. And within a few minutes, got a rejection email.

To this day I wonder if someone clicked reject, or the system just autorejected me because of something it didn't like on my resume. Needless to say, I gave up on that idea.