trentmkelly/LessWrong-43k huggingface dataset:
School & Jobs are good SOLELY because people are lazy
by fasf — 2025-05-20T03:15:44.119Z

In majority of cases, obviously ignoring exceptions as "exceptions don't make the rule" + I'm financially speaking

If someone were to tell you that he spends 8 hours a day working on his business a day, you would consider him to be highly agentic

But if someone tells you he spends 8 hours at school or working you would think that to be the bare minimum which billions of people do daily.

The work load is the same, and it's clear that if you can spend the same amount of time people spend on school/work on a business, you'd make substantially more than you would otherwise.

So why does everyone pick the 8 hours at school/work route?

Because the majority of people can't self motivate themselves and need pressure from their schools or their job to be productive.

But if your not like that, and don't need a boss or school to get you to spend 8 hours working, than working or going to school would be quite an idiotic pursuit as anyone with a work ethic like that would make way more money creating a business than they would by:

Chasing a IVY-League admission by taking a ridiculous amount of AP classes, working on obtaining great extracurricular activities that look good on an application, working hard to get a 1550+ on SAT, proceeded by spending 4 years at university, and solving hundreds of Leetcode questions, getting internships at companies, just to make maybe 100K out of university.

Don't you think someone would have a way greater financial outcome if he were to spend that time on creating a business?

Maybe you disagree, maybe you don't. But nevertheless, this isn't necessarily an argument against going to school/work. As I believe that it would take a man with quite high self-will to spend the same effort that he spends on what I listed above on a business as self-motivating is much harder than getting the motivation from their school/job, despite the striking reward difference.

That's why "go to college and get a good job" is actually good advice for the majority, as the majority simply are too lazy to be productive unless otherwise pressured to be productive by school & work.

But if you're not the majority, the optimal move would be to start a business instead of wasting immense time in the highschool>college>job cycle.

To better understand my points look at the reply I made to: https://www.lesswrong.com/posts/SrcG9JQ8SGa4RpivE/school-and-jobs-are-good...
Scroll Snapping
by jefftk — 2025-05-20T02:30:03.557Z

It's been several years since paying attention to the web platform was
a big part of my full time job, and I was curious if I'd missed any
interesting additions. I did some poking around with Claude and ended
up finding something neat, but a bit different than I was looking for:

scroll-snap-type.

Scrolling through long lists of things I've often been mildly
frustrated at how much precision I need. I want to move down a bit,
perhaps to see the next post in a feed, but without going a bit too
far and losing the first few lines. Looking through Claude's output I
saw:

Scroll snap: Control scroll positioning with
scroll-snap-type (2019)

Looking on
MDN it seems to offer just what I was looking for: scrolling that
tries to avoid splitting content.

The main place on my website where I've wanted this are my pictures, and I've
implemented it there. Give it a try!

The code changes were really simple:

html, body {
scroll-snap-type: y proximity;
scroll-padding: 0;
}
figure {
scroll-snap-align: start;
}

While I didn't see other things in Claude's list of newish features
that seemed exciting, is there anything else neat I might have missed?
From dopamine detox to enlightment
by Stefan Scortescu — 2025-05-19T20:27:01.192Z

A few months ago, I realized the thing I’d built my identity on—intelligence—might not survive the AI era. It hit harder than I thought it would. If AI keeps getting smarter, cheaper, faster… what exactly still makes me human?

That question sent me into a quiet existential crisis—and down a rabbit hole. I’d always thought intelligence was what made me—me. But if it’s not, then what is? What does make us human?

The answer I landed on is simple: we die, AI doesn’t. Our attention is sequential, AI’s is parallel. The implications are obvious. Our attention is precious—we can give it to each other in a way no AI can replicate. And our mortality is the ultimate accountability mechanism—it lets us carry risk, accept vulnerability, and build trust. Put those together and you get something only humans can do: feel what’s at stake. Risk is what gives meaning to experience.

New question: If attention is our most precious currency, how was I spending mine? Recklessly—judging by my use of media, tobacco and other hits of stimulation. Restlessly—speeding past the truth that life has a deadline.

In an effort to start living more intentionally, I was looking to reset my dopamine levels. So I went to a Vipassana meditation retreat. What it triggered was anything but simple or obvious. The result was chaotic, beautiful, and enlightening. Almost transcendental—almost, because it’s not over yet.

Here, I synthesize what I’ve learned—through the framework of my body—over the past two months. Everything in this article comes from direct experience. I believe most people can experience the same—for most of my claims, I share supporting peer-reviewed scientific articles.

My Vipassana experience crystallized one insight: consciousness hinges on attention to sensation. Let’s start by defining what sensation is, move on to why it’s important to feel sensation and why we don’t/won’t, and finish with how to do it.

What is sensation?

When I say sensation, I’m not referring just to the inputs from our senses: sight, hearing, touch, smell, taste, balance and proprioception (movement & position). These are just a small slice of the full picture. The “picture” is only complete if we include interoception - what we feel inside our bodies. Here’s an incomplete list to give you an idea: pain, pleasure, temperature, pressure, tension and movement that we feel in our organs, muscles, nerves and bones....
One Year in DC
by tlevin — 2025-05-19T19:46:55.479Z
Link: https://www.greentape.pub/p/one-year-in-dc

(h/t Otis Reid)

I think this post captures a lot of important features of the US policymaking system. Pulling out a few especially relevant/broadly applicable sections:

1. There's No Efficient Market For Policy

There can be a huge problem that nobody is working on; that is not evidence that it's not a huge problem. Conversely, there can be a marginal problem swamped with policy work; that's not evidence it's really all that big of a deal.

On the upside, this means there are never-ending arbitrage opportunities in policy. Pick your workstreams wisely.

2. Personnel Really Is The Most Important Thing

The quality of staffers varies dramatically and can make or break policy efforts. Some Hill staffers are just awesome; if they like your idea, they'll take it and run with it, try to find the right cosponsors, understand where it fits procedurally, etc. Other staffers have absolutely no idea what's going on.

3. The Hill is Not for Wonking

[...]

Optimize for impact, not panel invites. This may seem obvious, but this is not how most (c)(3)s are set up. Many large think tanks conceive of themselves as a "university without students," instead of a policy shop engaging with day-to-day policymaking. Others want to be a convening organization, spending resources on capital-C Conversations between capital-E Experts in Congress. And at an individual level, many think tankers simply do not like taking Hill meetings. There’s nothing wrong with this – but it is not a great strategy for driving policy change.

4. There's Alpha In Doing Boring Stuff

My colleague Lars Schönander has had enormous policy success because he sends hundreds (thousands?) of FOIA requests to state and local governments to track malign foreign influence over critical infrastructure. Much of his success comes from being willing to spend hours every day emailing FOIAs to places like the San Jose Port Authority.

Similarly, my summer and early fall were spent on phone lines with state departments of environmental quality, tracking their use of flexible permits. It was objectively soul-destroying work; it also led to an

executive order

, a

bill being enacted into law

, and several other bills being introduced.

(related EA Forum post)
What if Chinese AI becomes misaligned first?
by Arkhos Winter — 2025-05-19T19:35:59.414Z

This is an alternative scenario I wrote for the AI 2027 bounty.

The date is January 20, 2033.

You are Pete Buttigieg, President of the United States, and it is the first day of your tenure. You feel restless, as you had spent the last week or so reciting your inauguration speech.

As you settle into your seat at the Oval Office, you feel a sense of surrealism, almost as if part of you feels like it’s all a dream. You still can’t believe you’ve just became the most powerful man in the world, despite the knowledge that your landslide victory was essentially guaranteed.

Vance’s approval rating had been polling in the twenties. In all honesty, he shouldn’t have won in the first place.

His victory in 2028 over Alexandria Ocasio-Cortez was essentially a deus ex machina. Trump’s rash economic decisions had caused the US’s credit rating to fall to an AA-, and foreign reception of the US, even by its traditional allies like Canada and the EU, had fallen to an all-time low. In early 2028, the Democrats had a net favorability over the Republicans of around 8 points, and it was looking increasingly likely that Vance would follow suit in Harris’ path.

By June, the Democratic primary had concluded, and you had unfortunately lost to AOC. At this point, AI had already become a “culture war” issue (Republicans pro-AI and Democrats against), and AOC had taken a firm interventionist stance, to the point where regulating AI art to protect artists had become part of her platform. She was popular otherwise too, with her main campaign promise being to raise the federal minimum wage to $15.

In July, however, something totally unexpected occurred. Stargate, now with Google DeepMind and xAI in its ranks (but not Anthropic, as Dario Amodei staunchly opposed Trump’s approach to AI), announced that AGI, both conversational and agentic, had been achieved. They announced a new model, Athena, a chatbot that could double as an agent.

Realistically, Athena wasn’t that much better than o7 or Grok 5#. What set it apart was that it was dirt-cheap, with an API price of only $5 for each million tokens. Free users (if they were an ID-verified citizen of the US or a few other designated countries) got 50 free prompts and 10 free tasks per day, and premium users paying $25 a month got an unlimited number of each. In contrast, the “big four” AI companies charged $250-500 per month for their top models, a pric...
America Makes AI Chip Diffusion Deal with UAE and KSA
by Zvi — 2025-05-19T19:10:04.883Z

Our government, having withdrawn the new diffusion rules, has now announced an agreement to sell massive numbers of highly advanced AI chips to UAE and Saudi Arabia (KSA). This post analyzes that deal and that decision.

It is possible, given sufficiently strong agreement details (which are not yet public and may not be finalized) and private unvoiced considerations, that this deal contains sufficient safeguards and justifications that, absent ability to fix other American policy failures, this decision is superior to the available alternatives. Perhaps these are good deals, with sufficiently strong security arrangements that will actually stick.

Perhaps UAE and KSA are more important markets and general partners than we realize, and the rest of the world really is unable to deploy capital and electrical power the way they can and there is nothing we can do to change this, and perhaps they have other points of strategic importance, so we have to deal with them. Perhaps they are reliable American allies going forward who wouldn’t use this as leverage, for reasons I do not understand. There are potential worlds where this makes sense.

Diplomacy must often be done in private. We should not judge so quickly.

The fact remains that the case being made for this deal, in public, actively makes the situation seem worse. David Sacks in particular is doubling down and extending the rhetoric I pushed back against last week, when I targeted Obvious Nonsense in AI diffusion discourse. Even within the White House, the China hawks are questioning this deal, and Sacks responded by claiming to not even understand their objections and to all but accuse such people of being traitorous decels wearing trench coats.

I stand by my statements last week that even if accept the premise that all we need care about are ‘America wins the AI race’ and how we must ‘beat China,’ our government’s policies, on diffusion and elsewhere, seem determined to lose an AI race against China.

This is all on top of the entire discussion not only dismissing but outright ignoring the very real possibility that if anyone builds superintelligence, everyone dies. Or that everyone might collectively lose control over the future, with other bad outcomes. Once again, in this post, I will do my best to set these concerns aside.

Table of Contents

Choosing Sides In the War on Cancer.

The Central Points From

...
[Beneath Psychology] Introduction Part 2: The Solution and What's to Come
by jimmy — 2025-05-19T18:02:20.695Z

In the opening post we introduced a challenge in which you're sitting in a jacuzzi and you have the opportunity to convince a stranger that she doesn't have to worry about her makeup washing off, so she can go swimming with her friends. It was framed as a rationality problem, where the goal is to convince her by justifying that the idea that she doesn't have to worry is true.

The solution here, as with the solution to any other "psychological problem" that is really a disagreement, relies on not only having the correct answer (e.g. "It doesn't matter what other people think"), but also the justification that allows you to know it to be true -- and that the person will be better rather than worse off for daring to engage with your perspective.

Knowing something to be true, in the face of disagreement, requires understanding your counterparty's perspective well enough to know that your own holds up in face of theirs. Empathy is critical, for the same reason that maps are critical when traveling; if you can't see the territory, you're SOL. I had to correctly infer what her actual concerns were, and show her that the union of my perspective and her perspective was indeed my perspective. If I can't do that, my options are to a) say stuff that predictably won't get her in the pool ("C'mon!"/"You're fine!"), and show myself to be unable to face reality, or b) humble myself, recognize that I actually don't know better than she does, and don't try to change her mind because I don't actually know the right answer.

If I do know what it's like to be an insecure girl, well enough to recognize what her reasons are for her beliefs and where her perspective falls short, then I can demonstrate that. I can show her that I'm not interested in giving advice that fails to take into account her perspective, that I do understand her perspective, and that I have a perspective worth considering. In other words I can earn her security, her respect, and her attention.

Here's how I did it, in this case:

The first thing I did, was to interject with "Yeah, don't do it. Your makeup might wash off, and then people would see what you really look like."

Take a moment to notice how you would anticipate her responding to this. Measure your own anticipation of what kind of response is likely against what actually happened, and notice whether her response is actually surprising, or expected.

She ...
US credit rating downgraded, $1T in Gulf state investments in the US, Kurdistan Workers’ Party disbanded | Sentinel Global Risks Weekly Roundup #20/2025
by NunoSempere — 2025-05-19T17:59:21.690Z
Link: https://blog.sentinel-team.org/p/global-risks-weekly-roundup-202025-df7

Executive summary

Moody’s downgraded the US credit rating as the US budget deficit grows and US borrowing costs rise. House Republicans are advancing a budget that would increase the deficit further. Meanwhile, China is reducing its holdings of US Treasuries and its dependencies on foreign components in its supply chains as it seeks to de-risk its economy from the US and the West.

Google announced a new coding agent, AlphaEvolve, that has creative problem-solving abilities. Gulf states agreed to more than $1T in US investments and joint enterprises during a visit by Trump to the Middle East, including joint AI ventures. Forecasters estimated a 28% chance (range, 25-30%) that the US will pass a 10-year ban on states regulating AI by the end of 2025.

Negotiations between the US and Iran continue. Iran has signaled willingness to limit uranium enrichment and allow inspections in exchange for sanctions being lifted. Forecasters estimated that there is a 46% chance (range, 40%-50%) that the US and Iran will sign an agreement about Iran’s nuclear program by the end of August.

The ceasefire between India and Pakistan is holding. The Kurdistan Workers’ Party (PKK) in Turkey has officially disbanded. Israel has launched a new, major offensive in Gaza. Putin did not attend the first direct ceasefire talks with Ukraine since early in the war.

At the moment we are bottlenecked on distribution, so we’d appreciate it if you, reader, shared our work with your networks. You can also find us on Twitter, and audio narrations of this blog are available on our podcast: Search “Sentinel Minutes” or subscribe via Apple Podcasts, Spotify, RSS, or other platforms.

Economy

Moody's downgraded the US credit rating from a perfect Aaa to Aa1, one notch lower, because of the country’s large deficits and interest costs. Moody’s was the last of the three main credit rating agencies to downgrade the US credit rating. On Monday, Treasury yields rose, and stocks and the dollar fell, following the news.

The Yale Budget Lab estimates that the average effective tariff rate on US imports is 17.8%, the highest since 1934. A month ago, before tariffs were lowered on Chinese goods, the group estimated that the effective tariff rate was 28%, the highest since 1901.

After restricting exports of rare earth metals in early April, China has started to allow some shipments of rare earths. However, if more shipment...
Survey of Multi-agent LLM Evaluations
by Nikola Jurkovic — 2025-05-19T17:42:24.876Z

Disclaimer for LessWrong: This is a project I worked on for a college class. I think it’s useful for people working on evaluations to skim this to get a sense of what types of multi-agent evals exist. Thank you to Xavier Roberts-Gaal for advising me for this project!

Abstract

LLM-based AI agents have the potential to reshape knowledge work and introduce novel risks while amplifying some existing AI risks. In order to adequately measure risks posed by multi-agent systems, bespoke multi-agent evaluations might be needed. We searched the literature and compiled a list of 32 papers about multi-agent evaluations. We found that 26 out of 32 papers measured miscoordination failure modes, while only 5 papers measured collusion failure modes. We also examined various common AI threat models (CBRN, cybersecurity, persuasion, etc.) and failed to find any multi-agent evaluations for most AI threat models. Informed by our findings, we recommend developing evaluations focused on collusion, as well as evaluations grounded in threat models.

Introduction

Advances in the agentic capabilities of large language models (LLMs) have enabled AI systems to perform software engineering tasks (Jimenez et al., 2024) and match the performance of some expert humans on multi-hour self-contained AI research engineering tasks (Wijk et al., 2024). Large numbers of AI agents may be involved in AI research by 2030, possibly completely automating the AI research process (Kokotajlo et al., 2025; Leibowich et al., 2024).

Currently, AI evaluations are mostly focused on evaluating single AI systems, commonly through multiple-choice questions (Hendrycks et al., 2021; Rein et al., 2023) or tasking a single AI agent with performing an isolated software engineering task (Jimenez et al., 2024; Rein et al., 2025; Wijk et al., 2024).

If AI agents start collaborating in large numbers to perform economically useful tasks, novel failure modes which cannot be measured by single-agent evaluations could emerge (Hammond et al., 2025). Therefore, if AI developers continue to rely on single-agent evaluations to measure dangerous capabilities, risks unique to multi-agent systems might remain undetected.

In this paper, we make three contributions:

We identify a set of 32 multi-agent LLM evaluation papers, as well as categorize them by multi-agent failure mode and AI threat mod

...
Semen and Semantics: Understanding Porn with Language Embeddings
by future_detective — 2025-05-19T15:39:55.492Z
Link: https://github.com/dhealy05/semen_and_semantics

Summary

Porn content has gotten more extreme over time. Here's the average title for the first full year of Pornhub's existence, 2008:

"Hot blonde girl gets fucked"

and here's the average title for 2023:

"FAMILYXXX - "I Cant Resist My Stepsis Big Juicy Ass" (Mila Monet)"

Why did this change happen? We can understand porn's progression by converting titles to language embeddings. I downloaded Internet Archive snapshots of "pornhub.com" from 2008 - 2023 and analyzed the embeddings of the titles on the main page.

I found three distinct eras of titling: 2008-2009, 2010-2016, 2017-present. The current trend, since 2017, is characterized mainly by an emphasis on incest and other sexual violence.

Titles are generally representative of actual video content, and provide a reasonable heuristic for measuring actual content change, though some SEO effects exist.

The conclusion is a slightly ominous one: we are close to semantic bedrock with respect to sexual violence. Porn titles cannot become more sexually violent in their descriptions, because we lack the vocabulary.

Data and Methods

Download the repo and run "pip install" to install dependencies.

Pre-downloaded data is located in the "snapshots" folder. Pornhub data goes back to 2007, but analysis begins in 2008, when the format became more consistent. We have a folder for each month of the year, and a roughly weekly cadence of snapshots. For each date, there are two files, e.g.: "20080606.html", the raw HTML file, and "20080606.json", which contains the parsed video titles. The JSON files is an array of dictionaries like so:

{ "title": "Quickie on the car?", "url": "/view_video.php?viewkey=9aeff09be64077906196", "views": "39183", "duration": "7:39\n \t7 hours ago", "embedding": ... }

where the "embedding" field is the "title" value converted by OpenAI's "text-embedding-3-large". The URL format changes slightly over time.

From 4416 available snapshots, we end up with 772 weekly snapshots. Typically, we'll segregate these by year in order to form legible boundaries.

To download more data, run "fetch_snapshots.py" in the "data_retrieval" directory. You can change the website by editing the Python file.

To work with embeddings, you will need an OpenAI API key. Set it with export OPENAI_API_KEY={...}.

Title Accuracy

Do the titles reflect the actual contents of the videos? If the answer is no, analyzing video titles may not tell us much.

In...