Show HN: A website to understand and study AI papers
IntuitivePapers.ai provides deep, interactive explainers for seminal AI papers, focusing on intuition before math, with interactive figures and verification against primary sources. Users can browse a curated library, request papers, and upvote to influence the queue.
intuitivepapers.ai · Understand the papers behind modern AI
Understand the papers behind modern AI.
Deep, interactive explainers. Intuition first, then the math, with figures you can play with, and every claim checked against the source.
Browse the libraryRequest a paper
Featured explainer
DiffusionBlocks
Train a diffusion model block-by-block, not end-to-end.
Read the explainer
intuitivepapers.ai/diffusionblocks/
What makes an explainer different
Not a summary. The thing itself.
Intuition before math
One vivid analogy per hard idea, and we say exactly where the analogy breaks.
Figures you can play with
Sliders, play buttons, real interaction. You build the understanding by doing, not by staring at a static diagram.
Verified against the source
Checked against the primary literature. We flag where the paper itself is wrong.
Reads like a human
Written and edited by hand, not generated and dumped. Made to be read for thirty minutes, not skimmed.
The library
The papers worth understanding
DiffusionLive
DiffusionBlocks
Train a diffusion model block-by-block, not end-to-end.
Read the explainer
ArchitectureLive
FlashAttention
Exact attention, with far fewer trips to slow GPU memory.
Read the explainer
RL & alignmentLive
Playing Atari with Deep RL (DQN)
Q-learning plus a convnet learns seven games from raw pixels.
Read the explainer
TrainingLive
Batch Normalization
Re-center and re-scale each layer’s inputs, and deep networks train far faster.
Read the explainer
DiffusionLive
Classifier-Free Diffusion Guidance
Steer a diffusion model by amplifying what the label adds, with no separate classifier.
Read the explainer
DiffusionLive
Latent Diffusion (Stable Diffusion)
Run the diffusion in a compressed latent, not on pixels.
Read the explainer
Browse and search the full library (53)
We're opinionated about what's worth your time
How papers get into the library
01
Hand-picked
Seminal and high-impact papers, chosen deliberately.
02
Requested & upvoted
Readers submit an arXiv link or upvote what they want explained next.
03
Trending on arXiv
We watch what is rising in citations, downloads, and discussion, and explain it while it is current.
The queue
What readers want explained next
Upvote a paper to push it up the queue, or request one of your own below. Click your vote again to take it back.
01
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
arXiv:1712.01815
RL & alignment
02
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
arXiv:1911.08265
RL & alignment
03
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
arXiv:2003.08934
Vision
04
Sequence to Sequence Learning with Neural Networks
arXiv:1409.3215
Architecture
05
Semi-Supervised Classification with Graph Convolutional Networks
arXiv:1609.02907
Architecture
06
Deep reinforcement learning from human preferences
arXiv:1706.03741
RL & alignment
Browse and search all 11 papers
Request a paper
Which paper should we explain next?
Paste an arXiv link and it joins the queue with your vote. We read every request.