Someone posts on X, "These are Ilya’s 30 papers", gives no source, doesn't say where he got it from, and isn't connected to either Ilya or Carmack (Ilya gave him the list).
Then someone vibe codes a barely usable website based on that, and it lands on the HN front page? Is this correct?
Hey guys, I really appreciate all of the attention this post has received. I honestly thought it was going to be just a small project to help some of my friends get into reading research papers.
A large number of people complained about how intense some of the backgrounds/animations were (I might have been a bit too focused on making something that looked cool over usability). In response I have added toggles for both the movement on the page and the backgrounds for the papers.
Other people mentioned that they would have liked some more personalised reflections on each paper. I currently have already done some of these for the more popular papers on my X @notmcrowley . I would have no problem adding these to the site if people think it will help. I feel the need to warn that I have not been formally educated on ML or AI so any interpretation will just be mine and may not necessarily be the correct one. (If anyone with more experience would like to contribute to this feel free to reach out).
Please add them on the site for those of us who have never had Twitter and don’t plan to open one ever. Thanks for this compilation, I am — like your friends — trying to get into reading research papers and this is right up my alley right now.
Author here. First year CS student at Trinity College Dublin. I Built this because when I was getting into reading research papers I ended up burning a ton of my Claude usage asking questions other people have probably already asked. The website is just a side project and definitely a WIP. Happy to answer questions or take PRs on GitHub.
The problem is the background is often times doing a wave motion across the screen.
Then the foreground content is doing an in/out undulation on top. So you’re seeing an undulating in/out in every possible direction + the background. And the foreground animations are all at the same time. So it’s not that we’re emphasizing any one thing. We’re emphasizing all of it.
The key with animations is in what they’re trying to draw attention to, the character of the movement, and the timing of it. You usually don’t want everything to equally animate at once.
I would:
• Use background movement that also isn’t a “wave”
• Stagger the timing of foreground animations so the main content is emphasized, followed by a pause, followed by the sidebars
• Change the nature of the animations so they’re not doing the same essentially thing “zoom and pan” - so have the center zoom and pan, but do something different for the sides.
As an aside, I've seen folks mention respecting reduced animation hints and such in the past and was always curious about this because I've never had any negative experiences with animations... until now!
Something about the animations on this site did my brain in while scrolling through the papers, and now I "get it."
Hey thanks for checking out the website. I did not expect this to get as much attention as it did. I was honestly just planning on having it as a small side project for my friends and some others who would like to get into this kind of stuff. I will definitely annotate them in the future if that is something that people would appreciate. I currently have something like this done for a few papers on my X account.
I mean, you don't have to annotate them :) Would I love to read annotations? Sure - but it's a boatload of work for you, and if it's just meant as a repo for you and your friends, no need to do a ton of work just because an Internet rando asked.
But given the sudden wide audience, a quick "here's what this is for" at the top might be helpful.
always a fun day when someone rediscovers these. Lucky 10000
in case folks are interested, i wrote up a ~layman's review of each paper over the course of two years a while back. Several of those reviews ended up doing reasonably well on hn. Full analysis of the ~23 docs that were papers and not massive books
I wish this were organized according to suggested/logical reading order. For example, the paper introducing the attention mechanism probably ought to precede "attention is all you need".
For beginners I'd recommend the Welch Labs Illustrated Guide To AI if your not well versed in reading papers. Its a beautiful book that I've enjoyed going through. I'd recommend going through these papers after reading that to get a deep understanding.
In my opinion, whether it was actually by Ilya or not is not worthy of debate. Many of them are widely recognized for being good pedagogical resources (e.g. annotated transformer, unreasonable effectiveness of RNNs, understanding LSTM networks), and others are landmark papers which anyone interested in the field would benefit from reading:
- Krizhevsky et al. (2012) introduced AlexNet
- Bahdanau et al. (2014) introduced attention
- He et al. (2015) introduced ResNet
- Vaswani et al. (2017) introduced the Transformer
Other papers are more specialized. Of them, I think Kaplan et al. (2020) by OpenAI is probably most important.
I was confused for a minute, I thought this was "top 30 papers by Ilya" and was then wondering why "Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton" is on the list.
> In additition, even though I have read the vast majority of the papers featured on the website, I have not read through each of the website's versions end to end.
Website's versions, as in - the actual text or the "explanations"? Either way this is a big red flag.
After seeing this for the first time, I've build PdfToMp3 to listen to these papers. It has now evolved into ListenDock. Fun fact: PdfToMp3 existed before NotebookLM and I already had "overviews", but I called them teacher explanations.
Here is an example of a "Teacher Explanation" of the paper "Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton"
I haven’t looked at your other comments but the answer is your comment isn’t valuable.
Text to speech summarizing is a dime a dozen. Your audience here prefers reading a blog and is already annoyed by ai vs written by a human content so what you are offering is the opposite of what they want.
Ok, thanks for the viewpoint, that makes sense. I use AI summaries every day and find it very valuable. But I also see the trend e.g. on Reddit, that people are very dismissive of ai content.
Noting the theory papers on Kolmorogov complexity. For those not familiar, Ilya argues that the reason why neural networks generalize -- why they work at all -- is because they are effectively finding a simple description of their training data, converging down onto the limit of the Kolmorogov complexity. [1]
I thought the actual 30 papers have never been disclosed. Do you have a source tying the recommendations back to Ilya, or did you come up with this list?
The list I got was from ex-OpenAI employee Andrew Carr on X. I believe he said in his post however that the list he uploaded is not the full list they were provided at OpenAI however.
So the styling and animation work looks really cool (when isolated), but they distract from the content itself, IMO.
I think it'd work better if you featured the animated background effect toward the top of the page and shifted toward static graphics (or much subtler animations) as the user scrolls.
And I don't think the zoom-out effect on the listing cards has the intended effect; I found myself wanting to get a better look at the papers and was a little disappointed/annoyed when they got smaller and harder to see as I pulled them into view.
The colors/shadows/layout all looks really nice, but I feel like the animations (as-is) ultimately detract from the experience rather than add to it. Thanks for sharing, though!
If you find this interesting, you should look into Solomonoff induction. It combines Kolmogorov complexity with Bayes rule to provide a general framework for inductive inference, and naturally formalizes Occam's razor.
I wouldn't say so about Occam's Razor which is a heuristic.
The relationship between compression and intelligence, while not equal is definitely there. It looks like 3Blue1Brown is going to be doing some videos on this aspect.
The formatting of the articles on this website is bad. I've opened the first one and all the LaTeX formulas are messed up. The subscripts and superscripts are all flattened rendering the math hard to comprehend. Did the author actually try to read any of the articles?
>∏ plocal(x|z) = i p(xi|z,xWindowAround(i))
Images and tables are not rendered at all. What is the point of this? Just keep the links to arxiv and leave it at that, otherwise render the articles properly
Why on earth would you deliberately choose to do whatever the fuck it is you did with the scroll and the animations for each paper when scrolling through the landing page? What are those animations supposed to be? I use firefox but I also visited on chrome, and the page is even more broken there. Scroll doesn't "take" unless I scroll hard enough, otherwise it bounces back. But on chrome, at least, it seems like the animation for each paper is clearer - it's supposed to be animating the scale of the paper as you scroll to it.. but it seems that your background animation is lagging everything so much it just doesn't work.
Then someone vibe codes a barely usable website based on that, and it lands on the HN front page? Is this correct?
Then someone makes a shitty comment. Is that correct?
From my point of view the Jedi are Evil
" rumoured list of papers that Ilya Sutskever gave to John Carmack. "
there is aslo manning book called illya list
https://www.manning.com/books/sutskevers-list
A large number of people complained about how intense some of the backgrounds/animations were (I might have been a bit too focused on making something that looked cool over usability). In response I have added toggles for both the movement on the page and the backgrounds for the papers.
Other people mentioned that they would have liked some more personalised reflections on each paper. I currently have already done some of these for the more popular papers on my X @notmcrowley . I would have no problem adding these to the site if people think it will help. I feel the need to warn that I have not been formally educated on ML or AI so any interpretation will just be mine and may not necessarily be the correct one. (If anyone with more experience would like to contribute to this feel free to reach out).
Then the foreground content is doing an in/out undulation on top. So you’re seeing an undulating in/out in every possible direction + the background. And the foreground animations are all at the same time. So it’s not that we’re emphasizing any one thing. We’re emphasizing all of it.
The key with animations is in what they’re trying to draw attention to, the character of the movement, and the timing of it. You usually don’t want everything to equally animate at once.
I would: • Use background movement that also isn’t a “wave” • Stagger the timing of foreground animations so the main content is emphasized, followed by a pause, followed by the sidebars • Change the nature of the animations so they’re not doing the same essentially thing “zoom and pan” - so have the center zoom and pan, but do something different for the sides.
As an aside, I've seen folks mention respecting reduced animation hints and such in the past and was always curious about this because I've never had any negative experiences with animations... until now!
Something about the animations on this site did my brain in while scrolling through the papers, and now I "get it."
Is it just rehosting the list, plus a reformatted copy of the papers? I was hoping you'd have at least annotated them with what you'd learned?
But given the sudden wide audience, a quick "here's what this is for" at the top might be helpful.
why do you care? this is a disingenuous question.
in case folks are interested, i wrote up a ~layman's review of each paper over the course of two years a while back. Several of those reviews ended up doing reasonably well on hn. Full analysis of the ~23 docs that were papers and not massive books
https://12gramsofcarbon.com/p/ilyas-30-papers-to-carmack-tab...
https://x.com/keshavchan/status/1787861946173186062
In my opinion, whether it was actually by Ilya or not is not worthy of debate. Many of them are widely recognized for being good pedagogical resources (e.g. annotated transformer, unreasonable effectiveness of RNNs, understanding LSTM networks), and others are landmark papers which anyone interested in the field would benefit from reading:
- Krizhevsky et al. (2012) introduced AlexNet
- Bahdanau et al. (2014) introduced attention
- He et al. (2015) introduced ResNet
- Vaswani et al. (2017) introduced the Transformer
Other papers are more specialized. Of them, I think Kaplan et al. (2020) by OpenAI is probably most important.
> In additition, even though I have read the vast majority of the papers featured on the website, I have not read through each of the website's versions end to end.
Website's versions, as in - the actual text or the "explanations"? Either way this is a big red flag.
Here is an example of a "Teacher Explanation" of the paper "Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton"
https://listendock.com/e/quantifying_the_rise_and_fall_of_co...
Text to speech summarizing is a dime a dozen. Your audience here prefers reading a blog and is already annoyed by ai vs written by a human content so what you are offering is the opposite of what they want.
[1] https://www.youtube.com/watch?v=AKMuA_TVz3A
I'd recommend watching a few of his talks/podcasts before during reading these to get the overview and how all the bits in these works tie together.
https://www.dwarkesh.com/p/ilya-sutskever
https://simons.berkeley.edu/talks/ilya-sutskever-openai-2023...
https://www.dwarkesh.com/p/ilya-sutskever-2
CS231n: Convolutional Neural Networks for Visual Recognition - https://cs231n.github.io/
The Unreasonable Effectiveness of Recurrent Neural Networks - https://karpathy.github.io/2015/05/21/rnn-effectiveness/
Understanding LSTM Networks - https://colah.github.io/posts/2015-08-Understanding-LSTMs/
ImageNet Classification with Deep Convolutional Neural Networks - https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436...
Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385
Multi-Scale Context Aggregation by Dilated Convolutions - https://arxiv.org/abs/1511.07122
Identity Mappings in Deep Residual Networks - https://arxiv.org/abs/1603.05027
Recurrent Neural Network Regularization - https://arxiv.org/abs/1409.2329
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin - https://arxiv.org/abs/1512.02595
Order Matters: Sequence to Sequence for Sets - https://arxiv.org/abs/1511.06391
Neural Machine Translation by Jointly Learning to Align and Translate - https://arxiv.org/abs/1409.0473
Pointer Networks - https://arxiv.org/abs/1506.03134
Attention Is All You Need - https://arxiv.org/abs/1706.03762
The Annotated Transformer - https://nlp.seas.harvard.edu/annotated-transformer/
Neural Turing Machines - https://arxiv.org/abs/1410.5401
A Simple Neural Network Module for Relational Reasoning - https://arxiv.org/abs/1706.01427
Relational Recurrent Neural Networks - https://arxiv.org/abs/1806.01822
Neural Message Passing for Quantum Chemistry - https://arxiv.org/abs/1704.01212
Scaling Laws for Neural Language Models - https://arxiv.org/abs/2001.08361
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism - https://arxiv.org/abs/1811.06965
Keeping Neural Networks Simple by Minimizing the Description Length of the Weights - https://www.cs.toronto.edu/~hinton/absps/colt93.pdf
A Tutorial Introduction to the Minimum Description Length Principle - https://arxiv.org/abs/math/0406077
The First Law of Complexodynamics - https://scottaaronson.blog/?p=762
Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton - https://arxiv.org/abs/1405.6903
Kolmogorov Complexity - https://onlinelibrary.wiley.com/doi/book/10.1002/047174882X
Variational Lossy Autoencoder - https://arxiv.org/abs/1611.02731
Machine Super Intelligence - https://www.vetta.org/documents/Machine_Super_Intelligence.p...
It's unknown whether it has anything to do with Ilya Sutskever.
I think it'd work better if you featured the animated background effect toward the top of the page and shifted toward static graphics (or much subtler animations) as the user scrolls.
And I don't think the zoom-out effect on the listing cards has the intended effect; I found myself wanting to get a better look at the papers and was a little disappointed/annoyed when they got smaller and harder to see as I pulled them into view.
The colors/shadows/layout all looks really nice, but I feel like the animations (as-is) ultimately detract from the experience rather than add to it. Thanks for sharing, though!
The relationship between compression and intelligence, while not equal is definitely there. It looks like 3Blue1Brown is going to be doing some videos on this aspect.
https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... https://papers.nips.cc/paper_files/paper/2012/file/c399862d3...
https://arxiv.org/pdf/1512.03385
https://arxiv.org/pdf/1511.07122
https://arxiv.org/pdf/1603.05027
https://arxiv.org/pdf/1409.2329
https://arxiv.org/pdf/1512.02595
https://arxiv.org/pdf/1409.0473
https://arxiv.org/pdf/1506.03134
https://arxiv.org/pdf/1706.03762
https://nlp.seas.harvard.edu/annotated-transformer/
https://arxiv.org/pdf/1410.5401
https://arxiv.org/pdf/1706.01427
https://arxiv.org/pdf/1806.01822
https://arxiv.org/pdf/1704.01212
https://arxiv.org/pdf/2001.08361
https://arxiv.org/pdf/1811.06965
https://www.cs.toronto.edu/~hinton/absps/colt93.pdf
https://arxiv.org/pdf/math/0406077
https://scottaaronson.blog/?p=762
https://arxiv.org/pdf/1405.6903
https://onlinelibrary.wiley.com/doi/10.1002/047174882X.ch14 https://github.com/Bladefidz/information-theory/blob/master/...
https://arxiv.org/pdf/1611.02731
https://www.vetta.org/documents/Machine_Super_Intelligence.p...
https://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://cs231n.github.io/
https://www.zotero.org/
https://www.zotero.org/support/adding_items_to_zotero#add_it...
>∏ plocal(x|z) = i p(xi|z,xWindowAround(i))
Images and tables are not rendered at all. What is the point of this? Just keep the links to arxiv and leave it at that, otherwise render the articles properly
I would request the author to consider something that does not distract us from this educational and informative website ( I have bookmarked it ).