WhyOhWhyQ

Fantastic!

NOOOOOO!!!

Let me introduce you to a good time.

Step 1: https://strudel.cc/workshop/getting-started/ . Click play on coastline" @by eddyflux

Step 2: Listen for a while

Step 3: setcps(.75) -> setcps(1.5)

Step 4: Listen :)

That is the extent of my strudel knowledge, but damn this is cool.

Well this is certainly cool! Your first book has been on my reading list. I'm cracking it open tomorrow!

Whoever was insta-killing me in the gemini version over and over.... FUUUU!!!!

I don't see what OpenAI's niche is supposed to be, other than role playing? Google seems like they'll be the AI utility company, and Anthropic seems like the go-to for the AI developer platform of the future.

I don't understand what this comment means. It sounds like you think all programming has ended, which is not the case.

Another anecdote. I've got a personal benchmark that I try out on these systems every time there's a new release. It is an academic math question which could be understood by an undergraduate, and which seems easy enough to solve if I were just to hammer it out over a few weeks. My prompt includes a big list of mistakes it is likely to fall into and which it should avoid. The models haven't ever made any useful progress on this question. They usually spin their wheels for a while and then output one of the errors I said to avoid.

My hit/miss rate with using these models for academic questions is low, but non-trivial. I've definitely learned new math because of using them, but it's really just an indulgence because they make stuff up so frequently.

I predict the way AI will be useful in science from the perspective of mathematics is by figuring out combinatorially complex solutions to problems that would otherwise not be interesting to (or far too complex to be comprehended by) humans. With such capabilities it could be imagined then that the AI will be useful for designing super materials, or doing fancy things with biology / medicine, and generally finding useful patterns in complex systems.

Any recommendations on books / resources to read in order to participate as a beginner (language creator that is..., not beginner with programming games)?

It depends on how you use it. I was running 15 agents at once, 12 hours a day for a month straight because it was more optimal to add more, and that wasn't very enjoyable. Now I'm back to writing code the enjoyable way, with minor LLM assistance here and there.

Seems pretty good for getting a vague idea of what a library can do in a demo project.

I don't think it's fine. Don't lie about your work.

I hope you hold the same contempt for every tech company and their "rules only apply to the poor" attitude about copyright.

Just so I understand who I am talking with here, when you say authorship is a form of slavery, is that because you believe the characters in a written story have a consciousness/sentience/experience just like animals do, or are you just using the word 'slavery' to mean that in traditional literature the characters are static? One of the strengths of traditional literature is that staticness, however, because the best stories from literature are necessarily highly engineered and contrived by the author. Great stories don't happen in the real world (without dramatization of the events) exactly because too many things can happen for a coherent narrative to unfold.

I'm a huge fan of Dwarf Fortress, but the stories aren't Great without imagination from the player selectively ignoring things. Kruggsmash is able to make them compelling because he is a great author

"inhabited by characters ACTUALLY living in the computer"

It's hard to imagine these feeling like characters from literature and not characters in the form of influencers / social media personalities. Characters in literature are in a highly constrained medium, and only have to do their story once. In a generated world the character needs to be constantly doing "story things". I think Jonathan Blow has an interesting talk on why video games are a bad medium for stories, which might be relevant.

It's not clear why your analogy wouldn't have implied the end of white collar work when computers were first invented or when the internet was invented. Both of those should have been massive productivity boosts which meant the workers would have to go elsewhere to feed themselves. Instead Jevon's paradox kicks in every time.

But then it becomes a competitive advantage for another firm to use the same employees to do more work, leading to the jobs not being replaced.

What's a white collar job than can be automated with LLMs plus scaffolding?

You seem to have misread the article (which is not mine by the way), which makes the point that inference costs and revenue seem to scale with each other.

On what basis do you say they're within the range of profitability on inference today? Every source I see paints a different story based on their own bias.

Ed Zitron has a bias and a narrative differing from OpenAI's bias and narrative: https://www.wheresyoured.at/oai_docs/

It just seems hard to imagine that simultaneously running 1,000 instances of claude code will be cheap in the next decade, but maybe running 1,000 instances of claude-like tools is what a corporate LLM subscription will give. And maybe running 1,000,000 or a billion such models is what the government will do once a contract gets awarded.

Just random speculation though.

I think there could be a few directions. Consumer level LLM usage will become free. Corporate-grade LLM use will cost a lot of money. Maybe the highest grade LLM use will be regulated and only done through government labs.

Google can offer their services for free for a lot longer than OpenAI can, and already does to students. DeepSeek offers their competitor product to ChatGPT for free to everyone already.

I don't know what the moneyed insiders think OpenAI is about, but Sam Altman's public facing thoughts (which I consider to be marketing) are definitely oriented toward making it look like they are all-in on AGI:

See:

(1) https://blog.samaltman.com/the-gentle-singularity (June, 2025) - "We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be."

- " It’s hard to even imagine today what we will have discovered by 2035; maybe we will go from solving high-energy physics one year to beginning space colonization the next year; or from a major materials science breakthrough one year to true high-bandwidth brain-computer interfaces the next year."

(2) https://blog.samaltman.com/three-observations (Feb, 2025) - "Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity."

- "In a decade, perhaps everyone on earth will be capable of accomplishing more than the most impactful person can today."

(3) https://blog.samaltman.com/reflections (Jan, 2025) - "We started OpenAI almost nine years ago because we believed that AGI was possible, and that it could be the most impactful technology in human history"

- "We are now confident we know how to build AGI as we have traditionally understood it."

(4) https://ia.samaltman.com/ (Sep, 2024) - "This may turn out to be the most consequential fact about all of history so far. It is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there."

(5) https://blog.samaltman.com/the-merge (Dec, 2017) - "A popular topic in Silicon Valley is talking about what year humans and machines will merge (or, if not, what year humans will get surpassed by rapidly improving AI or a genetically enhanced species). Most guesses seem to be between 2025 and 2075."

(I omitted about as many essays. The hype is strong in this one.)

This guy's kid hits a homerun in the little league game and he bemoans that the kid is wasting his talents not working on backprop.

I've had mixed results with AI on research mathematics. I've gotten it to auto-complete non-trivial arguments, and I've found some domains where it seems hopelessly lost. I think we're still at a point in history where mathematicians will not be replaced by AI and can only benefit by dabbling with it.

Very inspiring!

It just sounds like you're describing almost the ideal outcome for this person as a failure. They're actually incredibly successful if they've got tenure at a good American college.

The average college professor did their PhD at an ivy league school and everyone said exactly what you're saying about them their whole lives. A professor job is extremely competitive.