FRESH
Hacker News
Hacker News Hacker NewsA 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).
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...
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...
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://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.
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...
Then someone vibe codes a barely usable website based on that, and it lands on the HN front page? Is this correct?
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 want to improve it I would recommend coming up with a sequential "reading list" including a few classic papers, some intermediate advancements (frequently referenced), and then a few new, cutting edge 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