To me, the main advantage of the lecture format is the possibility to have me or others grill the lecturer for questions, benefiting the whole group. From this point of view, a video of a lecture is already a compromise. In both cases, it's always kind of a tedium unless the lecturer is a good and entertaining communicator.
I'm not sure listening to a robot reading me a transcript and chatting with it really gives me those same advantages. What's the upside to this versus just getting slides and/or transcript w/ sources and feeding it to my own LLM?
I briefly worked as a Uni lecturer, and I completely agree.
Universities have made the mistake of competing with YouTube (video lectures), whereas what they can actually offer is direct access to domain experts.
I am skeptical that any other solution has much of a moat. So if the creators of this are after any feedback, I would offer that their best bet is to be competitive on User Experience, rather than underlying technology.
We think videos are special. There is a reason why Khan Academy, Coursera, etc. are essentially video libraries. A lecture video is a recording of a teaching performance, a form of presentation where someone explains while controlling what you see and when you see it.
And in the coming months, we’ll make the answers themselves real-time videos, which is kind of trivial for us at this point. We’ll ask the LLM to answer by writing code, and our software will render that code and show you the response directly as a video.
> To me, the main advantage of the lecture format is the possibility to have me or others grill the lecturer for questions, benefiting the whole group.
I've rarely, if ever, seen this. And if I had, I think most of the class would have just been annoyed, which you probably didn't notice.
I have a hard time believing you've genuinely never had a lecturer take questions. I also genuinely have a hard time believing that last sentence is not, at best, a not-so-subtle attempt at putting my social awareness into question, or at worst, just being insulting for the sake of it.
Thanks for the feedback. Emphasis is definitely important in these videos where there is no human hand to point with. We will improve this more and more in the upcoming days.
It suddenly starts talking about URLs, but it never really sets up the idea that we're using some kind of URL lookup service as an example. The way it introduces the memory limit feels similarly strange. At some point, it just says that we have 8GB of memory, but it does that halfway through the explanation, not as part of the setup of the problem we're actually solving.
> On correctness: LLMs may make mistakes. But these videos are code sitting in our repository, and we can maintain them. Every report and review becomes a fix in the source, and everyone who watches after that gets the corrected lecture. Every recorded lecture on the internet is stuck with its mistakes. Ours will continuously improve.
Models change. Models are unpredictable. The assumption that we can constrain them between prompt bumpers has not been proven and seems unlikely to be provable given how these model works.
Beyond that, how do you know if the reports and reviews are even correct? Are you just gonna rely on the kindness of others to provide that value? Of all things that LLM’s have killed, I’m pretty sure the open sharing of knowledge and understanding are the first on the chopping block. I don’t care whether or not you’re pro or anti LLM, publishing knowledge for reputation is now a dead end for anyone wanting to make a living.
Wrapping others’ knowledge in a black box that may or may not accurately represent that knowledge is basically enshitification on steroids. The reason we hold folks like Lovelace, Sagan, or Feynman in high regard is because they weren’t wrong when they shared their understanding and knowledge.
But the best of luck to everyone involved! I’m sure everything will work out!
> Humans change. Humans are unpredictable. The assumption that we can constrain them between syllabus and tenure review has not been proven and seems unlikely to be provable given how these humans work.
That's such an excellent argument against letting humans teach other humans! Especially unpredictable ones like Feynman. You should start a petition.
Sarcasm off. Now let me stick up for the guy I just made fun of you for arguing with, because his reply to you got flagged to death and most people here can't even see it.
Here's what he said, since it's the only place they actually engage your objection. They're not claiming the LLM is the authority on what's correct. Sina used to make these videos by hand on YouTube and it was slow, so now the LLM drafts and they review: "watch it, review it, correct it, improve the explanation, and put our name behind the result." A user report is a signal to go look at something, not truth. Weeks of production become hours of review. And STEM is a good target because linear algebra isn't going obsolete, so a fix stays fixed, and a lecture can accumulate improvements instead of freezing one teaching performance forever. (Item 49504080, if you have showdead on.)
That last part is the best thing anybody has said for this project, including them. But it doesn't answer what you asked. Who audits the audit? Who writes the bug reports they plan to inspect, now that publishing knowledge for a living is, as you put it, a dead end? "Put our name behind the result" is exactly right and it's the entire argument, and there's no way to check it while the sources and the lecture language are private -- especially with one lecture in this thread starting mid-sentence and ending abruptly, another springing an 8GB memory budget on you halfway through Bloom filters, and the emphasis landing on the wrong words at the crucial moments. Publish the repo and "we reviewed it" stops being a promise.
Somebody with showdead should vouch that comment back. It didn't deserve to die.
We ask Claude Opus 5 to write a specific kind of source code that only our proprietary software understands and can render into a lecture video with computer graphics and TTS.
Without our system, there isn’t really an equivalent target language you can ask the LLM to write. Manim exists, but you won’t get videos like these by simply asking an LLM to generate Manim code.
That's right, just give me the prompt, not the LLM output, and I will prompt my own model! I would rather read the millions of tokens that went into the prompt than watch a video, and I am sure all other students would too.
Just assign years worth of reading homework every week, and I am sure all the students will fulfill their assignments of reading every token that went into each video, no matter how many, rather than watch an LLM generated video.
Students simply need to learn how to read text as fast as an LLM can, and have an enormous token budget to use the LLMs directly themselves.
And while you're at it, increase their tuition by the amount those tokens cost, so each student can pay for feeding the same tokens into their own LLM instead of the school doing it once and paying out of their own pocket immediately. Because the idea of schools producing reusable courseware is unprecedented! That way they can fold the token cost into their student loans.
Sarcasm off. Here is what I actually mean, and I think it makes a better case for this than the authors made for themselves.
The prompt is not "give me a lecture on relativity." That is only the part you type. The rest is the lecture description language, the visual grammar, the worked examples, the pedagogical constraints, and the review loop: a program, not a request. Latent training gives you the physics for free. It does not give you the presentation. Asking why you need this when you have an LLM is like asking why you need a compiler and a library when you already have a CPU.
Authoring cost is paid once and amortized over every viewer. That is what a textbook is. "Just prompt it yourself" is the scriptorium: every student copies their own book, pays their own tokens, and collects their own transcription errors. A school paying once and shipping one reviewed artifact to everybody is not a limitation, it is the entire point of courseware.
Their strongest argument is the one they undersold: the source is the artifact. Every recorded lecture on the internet is a render, frozen and unpatchable, and its mistakes are permanent. If the lecture is source code, a mistake becomes a patch, a translation becomes a recompile, and improvements accumulate instead of being lost every time someone re-records from scratch. And because they render deterministically from source with graphics and TTS instead of generating video with a diffusion model, a fix is a fix and not a re-roll. That is the real correctness argument, and it never got made.
Graphics settled this exact split forty years ago. PostScript is the authoring language: loops, conditionals, a full interpreter. PDF is PostScript with the programming removed, an inert render of flat drawing calls with all the loops unrolled, and Distiller produces one from the other by running the program and recording what it draws. Language in the middle, inert artifact at the edge. A lecture wants the same shape, and "lectures as code" is that observation applied to teaching.
Other arguments available to them: a lecture with a git history is a new kind of object, because you can bisect an explanation and read the changelog of a proof. Errata stop being a sheet nobody reads and become a rebuild. Localization, transcripts, and accessibility fall out of the source instead of being bolted onto a render. One source can emit different levels, and different notation conventions for physicists and mathematicians.
The answer to davisp lives in the same place: sign the source. Feynman is trusted because his lectures are texts we can check, argue with, and cite. Publish the language, publish the sources, publish the diffs, let critics file issues against a named maintainer, and "everyone who watches after the fix gets the corrected lecture" becomes a checkable claim instead of a promise. That's the one thing they're not doing. regnull asked for the language and was told it is the company. Right now they keep the LaTeX and the PostScript and ship only the PDF -- and they're PhD students! arXiv wants their .tex, not just their PDF, because in their own field shipping only the render was never good enough.
Great idea, but buggy. My lecture started mid-sentence and ended abruptly (Kalman filter). Looking forward to when you guys get it working well. Also, I'm curious if the lecture description language is public.
We’re not using video generation models. We ask an LLM to write the lecture as code, then render it deterministically with computer graphics and TTS.
So the main costs are the LLM call, TTS, and some cloud GPU time for rendering. That ends up being much cheaper than generating long form video directly with video models.
I watched the linear regression video. I can see the value behind the idea, but there's a number of flaws that make the videos difficult to watch compared to something like a 3blue1brown video. The flat voice and even word spacing lull you into zoning out, right until a mispronunciation jolts you back.
The idea of using LLMs to write out a script and storyboard for the video is interesting, but I think it needs intermediary work to better instruct the speech and graphics on how to perform.
To me, the main advantage of the lecture format is the possibility to have me or others grill the lecturer for questions, benefiting the whole group. From this point of view, a video of a lecture is already a compromise. In both cases, it's always kind of a tedium unless the lecturer is a good and entertaining communicator.
I'm not sure listening to a robot reading me a transcript and chatting with it really gives me those same advantages. What's the upside to this versus just getting slides and/or transcript w/ sources and feeding it to my own LLM?
I briefly worked as a Uni lecturer, and I completely agree.
Universities have made the mistake of competing with YouTube (video lectures), whereas what they can actually offer is direct access to domain experts.
I am skeptical that any other solution has much of a moat. So if the creators of this are after any feedback, I would offer that their best bet is to be competitive on User Experience, rather than underlying technology.
We think videos are special. There is a reason why Khan Academy, Coursera, etc. are essentially video libraries. A lecture video is a recording of a teaching performance, a form of presentation where someone explains while controlling what you see and when you see it.
Also, you’re not chatting with the transcript; you’re chatting with the video. The AI has much more than the transcript in its context. See this, for example: https://academa.ai/lectures/diffusion-models-learning-to-den...
And in the coming months, we’ll make the answers themselves real-time videos, which is kind of trivial for us at this point. We’ll ask the LLM to answer by writing code, and our software will render that code and show you the response directly as a video.
> To me, the main advantage of the lecture format is the possibility to have me or others grill the lecturer for questions, benefiting the whole group.
I've rarely, if ever, seen this. And if I had, I think most of the class would have just been annoyed, which you probably didn't notice.
I have a hard time believing you've genuinely never had a lecturer take questions. I also genuinely have a hard time believing that last sentence is not, at best, a not-so-subtle attempt at putting my social awareness into question, or at worst, just being insulting for the sake of it.
Fellow PhD student here, always wished for easier ways to update lecture content. This "code to video" idea could be huge for reproducibility.
There are misplaced verbal emphasis and non-emphasis. And unfortunately these happen at the crucial moments. Makes it very hard to follow.
Thanks for the feedback. Emphasis is definitely important in these videos where there is no human hand to point with. We will improve this more and more in the upcoming days.
i find the writing style a bit peculiar at times. Take this lecture on bloom filters for example: https://academa.ai/lectures/bloom-filters
It suddenly starts talking about URLs, but it never really sets up the idea that we're using some kind of URL lookup service as an example. The way it introduces the memory limit feels similarly strange. At some point, it just says that we have 8GB of memory, but it does that halfway through the explanation, not as part of the setup of the problem we're actually solving.
> On correctness: LLMs may make mistakes. But these videos are code sitting in our repository, and we can maintain them. Every report and review becomes a fix in the source, and everyone who watches after that gets the corrected lecture. Every recorded lecture on the internet is stuck with its mistakes. Ours will continuously improve.
Models change. Models are unpredictable. The assumption that we can constrain them between prompt bumpers has not been proven and seems unlikely to be provable given how these model works.
Beyond that, how do you know if the reports and reviews are even correct? Are you just gonna rely on the kindness of others to provide that value? Of all things that LLM’s have killed, I’m pretty sure the open sharing of knowledge and understanding are the first on the chopping block. I don’t care whether or not you’re pro or anti LLM, publishing knowledge for reputation is now a dead end for anyone wanting to make a living.
Wrapping others’ knowledge in a black box that may or may not accurately represent that knowledge is basically enshitification on steroids. The reason we hold folks like Lovelace, Sagan, or Feynman in high regard is because they weren’t wrong when they shared their understanding and knowledge.
But the best of luck to everyone involved! I’m sure everything will work out!
> Wrapping others’ knowledge in a black box that may or may not accurately represent that knowledge...
You wrote it well. This aspect irks me, too.
> Humans change. Humans are unpredictable. The assumption that we can constrain them between syllabus and tenure review has not been proven and seems unlikely to be provable given how these humans work.
That's such an excellent argument against letting humans teach other humans! Especially unpredictable ones like Feynman. You should start a petition.
Sarcasm off. Now let me stick up for the guy I just made fun of you for arguing with, because his reply to you got flagged to death and most people here can't even see it.
Here's what he said, since it's the only place they actually engage your objection. They're not claiming the LLM is the authority on what's correct. Sina used to make these videos by hand on YouTube and it was slow, so now the LLM drafts and they review: "watch it, review it, correct it, improve the explanation, and put our name behind the result." A user report is a signal to go look at something, not truth. Weeks of production become hours of review. And STEM is a good target because linear algebra isn't going obsolete, so a fix stays fixed, and a lecture can accumulate improvements instead of freezing one teaching performance forever. (Item 49504080, if you have showdead on.)
That last part is the best thing anybody has said for this project, including them. But it doesn't answer what you asked. Who audits the audit? Who writes the bug reports they plan to inspect, now that publishing knowledge for a living is, as you put it, a dead end? "Put our name behind the result" is exactly right and it's the entire argument, and there's no way to check it while the sources and the lecture language are private -- especially with one lecture in this thread starting mid-sentence and ending abruptly, another springing an 8GB memory budget on you halfway through Bloom filters, and the emphasis landing on the wrong words at the crucial moments. Publish the repo and "we reviewed it" stops being a promise.
Somebody with showdead should vouch that comment back. It didn't deserve to die.
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Love the 'videos as code' idea! Imagine quickly iterating on explanations or generating variations for different learning styles.
Why do I need to use this? Why can't I just use the LLMs directly to generate lectures if I want?
You can attempt to do it, but you'll see that it won't work. We've built the underlying "lectures as code" technology to make this work.
You just said in another comment that you use Claude Opus 5 to generate the video in a single shot. What about it won't work?
We ask Claude Opus 5 to write a specific kind of source code that only our proprietary software understands and can render into a lecture video with computer graphics and TTS.
Without our system, there isn’t really an equivalent target language you can ask the LLM to write. Manim exists, but you won’t get videos like these by simply asking an LLM to generate Manim code.
That's right, just give me the prompt, not the LLM output, and I will prompt my own model! I would rather read the millions of tokens that went into the prompt than watch a video, and I am sure all other students would too.
Just assign years worth of reading homework every week, and I am sure all the students will fulfill their assignments of reading every token that went into each video, no matter how many, rather than watch an LLM generated video.
Students simply need to learn how to read text as fast as an LLM can, and have an enormous token budget to use the LLMs directly themselves.
And while you're at it, increase their tuition by the amount those tokens cost, so each student can pay for feeding the same tokens into their own LLM instead of the school doing it once and paying out of their own pocket immediately. Because the idea of schools producing reusable courseware is unprecedented! That way they can fold the token cost into their student loans.
Sarcasm off. Here is what I actually mean, and I think it makes a better case for this than the authors made for themselves.
The prompt is not "give me a lecture on relativity." That is only the part you type. The rest is the lecture description language, the visual grammar, the worked examples, the pedagogical constraints, and the review loop: a program, not a request. Latent training gives you the physics for free. It does not give you the presentation. Asking why you need this when you have an LLM is like asking why you need a compiler and a library when you already have a CPU.
Authoring cost is paid once and amortized over every viewer. That is what a textbook is. "Just prompt it yourself" is the scriptorium: every student copies their own book, pays their own tokens, and collects their own transcription errors. A school paying once and shipping one reviewed artifact to everybody is not a limitation, it is the entire point of courseware.
Their strongest argument is the one they undersold: the source is the artifact. Every recorded lecture on the internet is a render, frozen and unpatchable, and its mistakes are permanent. If the lecture is source code, a mistake becomes a patch, a translation becomes a recompile, and improvements accumulate instead of being lost every time someone re-records from scratch. And because they render deterministically from source with graphics and TTS instead of generating video with a diffusion model, a fix is a fix and not a re-roll. That is the real correctness argument, and it never got made.
Graphics settled this exact split forty years ago. PostScript is the authoring language: loops, conditionals, a full interpreter. PDF is PostScript with the programming removed, an inert render of flat drawing calls with all the loops unrolled, and Distiller produces one from the other by running the program and recording what it draws. Language in the middle, inert artifact at the edge. A lecture wants the same shape, and "lectures as code" is that observation applied to teaching.
Other arguments available to them: a lecture with a git history is a new kind of object, because you can bisect an explanation and read the changelog of a proof. Errata stop being a sheet nobody reads and become a rebuild. Localization, transcripts, and accessibility fall out of the source instead of being bolted onto a render. One source can emit different levels, and different notation conventions for physicists and mathematicians.
The answer to davisp lives in the same place: sign the source. Feynman is trusted because his lectures are texts we can check, argue with, and cite. Publish the language, publish the sources, publish the diffs, let critics file issues against a named maintainer, and "everyone who watches after the fix gets the corrected lecture" becomes a checkable claim instead of a promise. That's the one thing they're not doing. regnull asked for the language and was told it is the company. Right now they keep the LaTeX and the PostScript and ship only the PDF -- and they're PhD students! arXiv wants their .tex, not just their PDF, because in their own field shipping only the render was never good enough.
Great idea, but buggy. My lecture started mid-sentence and ended abruptly (Kalman filter). Looking forward to when you guys get it working well. Also, I'm curious if the lecture description language is public.
Thank you very much! That’s strange, we’ll look into it. Really appreciate you reporting it.
The lecture description language isn’t public at the moment. It’s a core part of the technology we’re building the company around.
Also, if you’d like, you can share the video as a public link by clicking the share button!
This is a great idea. I find LLMs to be a good teacher, though I've primarily interacted with them through text.
I'm curious, what are the economics of producing this longer form content?
Thank you!
Actually, the economics are quite good.
We’re not using video generation models. We ask an LLM to write the lecture as code, then render it deterministically with computer graphics and TTS.
So the main costs are the LLM call, TTS, and some cloud GPU time for rendering. That ends up being much cheaper than generating long form video directly with video models.
I watched the linear regression video. I can see the value behind the idea, but there's a number of flaws that make the videos difficult to watch compared to something like a 3blue1brown video. The flat voice and even word spacing lull you into zoning out, right until a mispronunciation jolts you back.
The idea of using LLMs to write out a script and storyboard for the video is interesting, but I think it needs intermediary work to better instruct the speech and graphics on how to perform.
What LLMs are you using?
Claude Opus 5, with high thinking, generates the video in a single shot.
The one that asks questions at the beginning is Gemini Flash 3.7.
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