JT

A possible AGI future.

@damienghader @Lovable Can we sample the goods before requesting the cheat sheet? 😊

FCK it. Here's all the sauce. After shipping 100+ apps with @Lovable — I made the ULTIMATE Design Cheat Sheet. Every prompt. Every design system pattern. Every cloud config + infra setup. Every component standard + best practice we actually use to achieve world-class UI. All in one doc. Follow + comment "Cheat Sheet" and I'll DM it to you.

@dickson_tsai can you please elaborate on this?

I like having a scratch directory that Claude Code instances can write to/reference from. If your team can't agree on adding a scratch directory to gitignore, you can always "gitignore it locally" using the .git/info/exclude file

Just keep clicking until it fits your vibe.

@mackenziechild Yum yum! Delicious, well done, ux and user value is oozing out of this.

@heyandras May your streams come true! 😁

Lets cook.

@mfranz_on Google Streets should have this and then use it to politely name and shame Councils/Districts as to who has the best/worst roads in the country.

This is the power of YOLO, trained on a laptop for ~1 hour, with a Kaggle dataset. Oh, and just ~100 lines of Python. I can make a startup on this and it took me literally a couple of hours.

@abhi1thakur Bravo! Great domain name! 😊

Please try my new product: http://localhost:5173/projects/a0881f07-f194-437f-a442-528c67208c6d

@karpathy I'd say it's more of a genie-ghost 👻

Hah judging by mentions overnight people seem to find the ghost analogy provocative. I swear I don't wake up just trying to come with new memes but to elaborate briefly why I thought it was a fun comparison: 1) It captures the idea that LLMs are purely digital artifacts that don't interact with the physical world (unlike animals, which are very embodied). 2) Ghosts are a kind of "echo" of the living, in this case a statistical distillation of humanity. 3) There is an air of mystery over both ghosts and LLMs, as in we don't fully understand what they are or how they work. 4) The process of training LLMs is a bit like summoning a ghost, i.e. a kind of elaborate computational ritual on a summoning platform of an exotic megastructure (GPU cluster). I've heard earlier references of LLM training as that of "summoning a demon" and it never sounded right because it implies and presupposes evil. Ghosts are a lot more neural entity just like LLMs, and may or may not be evil. For example, one of my favorite cartoons when I was a child was Casper the Friendly Ghost, clearly a friendly and wholesome entity. Same in Harry Potter, e.g. Nearly Headless Nick and such. 5) It is a nod to an earlier reference "ghost in the machine", in the context of Decartes' mind-body dualism, and of course later derived references, "Ghost in the shell" etc. As in the mind (ghost) that animates a body (machine). Probably a few other things in the embedding space. Among the ways the analogy isn't great is that while ghosts may or may not be evil, they are almost always spooky, which feels too unfair. But anyway, I like that while no analogy is perfect, they let you pull in structure laterally from one domain to another as as a way of generating entropy and reaching unique thoughts.

Finally had a chance to listen through this pod with Sutton, which was interesting and amusing. As background, Sutton's "The Bitter Lesson" has become a bit of biblical text in frontier LLM circles. Researchers routinely talk about and ask whether this or that approach or idea is sufficiently "bitter lesson pilled" (meaning arranged so that it benefits from added computation for free) as a proxy for whether it's going to work or worth even pursuing. The underlying assumption being that LLMs are of course highly "bitter lesson pilled" indeed, just look at LLM scaling laws where if you put compute on the x-axis, number go up and to the right. So it's amusing to see that Sutton, the author of the post, is not so sure that LLMs are "bitter lesson pilled" at all. They are trained on giant datasets of fundamentally human data, which is both 1) human generated and 2) finite. What do you do when you run out? How do you prevent a human bias? So there you have it, bitter lesson pilled LLM researchers taken down by the author of the bitter lesson - rough! In some sense, Dwarkesh (who represents the LLM researchers viewpoint in the pod) and Sutton are slightly speaking past each other because Sutton has a very different architecture in mind and LLMs break a lot of its principles. He calls himself a "classicist" and evokes the original concept of Alan Turing of building a "child machine" - a system capable of learning through experience by dynamically interacting with the world. There's no giant pretraining stage of imitating internet webpages. There's also no supervised finetuning, which he points out is absent in the animal kingdom (it's a subtle point but Sutton is right in the strong sense: animals may of course observe demonstrations, but their actions are not directly forced/"teleoperated" by other animals). Another important note he makes is that even if you just treat pretraining as an initialization of a prior before you finetune with reinforcement learning, Sutton sees the approach as tainted with human bias and fundamentally off course, a bit like when AlphaZero (which has never seen human games of Go) beats AlphaGo (which initializes from them). In Sutton's world view, all there is is an interaction with a world via reinforcement learning, where the reward functions are partially environment specific, but also intrinsically motivated, e.g. "fun", "curiosity", and related to the quality of the prediction in your world model. And the agent is always learning at test time by default, it's not trained once and then deployed thereafter. Overall, Sutton is a lot more interested in what we have common with the animal kingdom instead of what differentiates us. "If we understood a squirrel, we'd be almost done". As for my take... First, I should say that I think Sutton was a great guest for the pod and I like that the AI field maintains entropy of thought and that not everyone is exploiting the next local iteration LLMs. AI has gone through too many discrete transitions of the dominant approach to lose that. And I also think that his criticism of LLMs as not bitter lesson pilled is not inadequate. Frontier LLMs are now highly complex artifacts with a lot of humanness involved at all the stages - the foundation (the pretraining data) is all human text, the finetuning data is human and curated, the reinforcement learning environment mixture is tuned by human engineers. We do not in fact have an actual, single, clean, actually bitter lesson pilled, "turn the crank" algorithm that you could unleash upon the world and see it learn automatically from experience alone. Does such an algorithm even exist? Finding it would of course be a huge AI breakthrough. Two "example proofs" are commonly offered to argue that such a thing is possible. The first example is the success of AlphaZero learning to play Go completely from scratch with no human supervision whatsoever. But the game of Go is clearly such a simple, closed, environment that it's difficult to see the analogous formulation in the messiness of reality. I love Go, but algorithmically and categorically, it is essentially a harder version of tic tac toe. The second example is that of animals, like squirrels. And here, personally, I am also quite hesitant whether it's appropriate because animals arise by a very different computational process and via different constraints than what we have practically available to us in the industry. Animal brains are nowhere near the blank slate they appear to be at birth. First, a lot of what is commonly attributed to "learning" is imo a lot more "maturation". And second, even that which clearly is "learning" and not maturation is a lot more "finetuning" on top of something clearly powerful and preexisting. Example. A baby zebra is born and within a few dozen minutes it can run around the savannah and follow its mother. This is a highly complex sensory-motor task and there is no way in my mind that this is achieved from scratch, tabula rasa. The brains of animals and the billions of parameters within have a powerful initialization encoded in the ATCGs of their DNA, trained via the "outer loop" optimization in the course of evolution. If the baby zebra spasmed its muscles around at random as a reinforcement learning policy would have you do at initialization, it wouldn't get very far at all. Similarly, our AIs now also have neural networks with billions of parameters. These parameters need their own rich, high information density supervision signal. We are not going to re-run evolution. But we do have mountains of internet documents. Yes it is basically supervised learning that is ~absent in the animal kingdom. But it is a way to practically gather enough soft constraints over billions of parameters, to try to get to a point where you're not starting from scratch. TLDR: Pretraining is our crappy evolution. It is one candidate solution to the cold start problem, to be followed later by finetuning on tasks that look more correct, e.g. within the reinforcement learning framework, as state of the art frontier LLM labs now do pervasively. I still think it is worth to be inspired by animals. I think there are multiple powerful ideas that LLM agents are algorithmically missing that can still be adapted from animal intelligence. And I still think the bitter lesson is correct, but I see it more as something platonic to pursue, not necessarily to reach, in our real world and practically speaking. And I say both of these with double digit percent uncertainty and cheer the work of those who disagree, especially those a lot more ambitious bitter lesson wise. So that brings us to where we are. Stated plainly, today's frontier LLM research is not about building animals. It is about summoning ghosts. You can think of ghosts as a fundamentally different kind of point in the space of possible intelligences. They are muddled by humanity. Thoroughly engineered by it. They are these imperfect replicas, a kind of statistical distillation of humanity's documents with some sprinkle on top. They are not platonically bitter lesson pilled, but they are perhaps "practically" bitter lesson pilled, at least compared to a lot of what came before. It seems possibly to me that over time, we can further finetune our ghosts more and more in the direction of animals; That it's not so much a fundamental incompatibility but a matter of initialization in the intelligence space. But it's also quite possible that they diverge even further and end up permanently different, un-animal-like, but still incredibly helpful and properly world-altering. It's possible that ghosts:animals :: planes:birds. Anyway, in summary, overall and actionably, I think this pod is solid "real talk" from Sutton to the frontier LLM researchers, who might be gear shifted a little too much in the exploit mode. Probably we are still not sufficiently bitter lesson pilled and there is a very good chance of more powerful ideas and paradigms, other than exhaustive benchbuilding and benchmaxxing. And animals might be a good source of inspiration. Intrinsic motivation, fun, curiosity, empowerment, multi-agent self-play, culture. Use your imagination.

@_adishj this looks like n8n and After Effects had a baby! Brilliant! 😊 👍

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@mirandanover Yum! Where's this?

@wayve_ai are all over the UK!

@chrisfirst @Tesla_Optimus

I created the first ad for the @Tesla_Optimus robot, inspired by classic late-night TV commercials:

@Bob_Zhurunkle @Cloudflare Gold post! 😊 👍

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It's not Sci-fi... Albania has become the first country in the world to have an AI minister politico.eu/article/albani…

@SawyerMerritt @mike_megapack I thought it was a colonscopy video

Me and @mike_megapack just took a Boring Company tunnel ride for the first time. Cool experience!

@claudeai @AnthropicAI Claude Code after 5hrs of back and forth... The Real Issue: Role dropdown saves to database but UI doesn't update immediately (likely 5-10 line fix) What I Broke: Turned simple UI fix into complete auth system rebuild, causing app crashes 😩😩😩😩😩😩

@gregisenberg Tune!!!

pretty crazy that people are vibe coding music now (this is dj_dave) she uses strudel a new live coding app to write music in your browser (open source too) i've never knew this was possible before

@demirdjiantwins can I have a peek pls :)

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