Interactive Paper Library
Landmark AI papers as interactive articles — animated architectures, sortable results, three reading levels from beginner to researcher — with every equation and number preserved from the original.
The whole roadmap for understanding LLMs — from the math floor to the frontier — tying together every paper and course here into one guided path.
A whole field explained from zero — for when the papers assume background you do not have yet.
Longer-form, interactive curricula built the same way as the papers.
Runbooks for tools worth understanding properly — architecture first, then the commands, then the failure modes.
Candidates for the next interactive edition — links go to the originals for now.
BERT: Pre-training of Deep Bidirectional Transformers↗
Devlin et al. · 2018 — The masked-pretraining paradigm — the encoder half of the Transformer, weaponized.
Language Models are Few-Shot Learners (GPT-3)↗
Brown et al. · 2020 — In-context learning emerges; the scaling bet pays off.
Training Compute-Optimal LLMs (Chinchilla)↗
Hoffmann et al. · 2022 — Data matters as much as parameters — the scaling-law correction.
FlashAttention↗
Dao et al. · 2022 — Hardware-aware exact attention; why memory bandwidth beats FLOPs.
LoRA: Low-Rank Adaptation↗
Hu et al. · 2021 — The fine-tuning method you'll actually use.
DeepSeek-R1↗
DeepSeek-AI · 2025 — RL-driven reasoning — the model this infrastructure made affordable.