Intel
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From GAN to WGAN
A walk from the original generative adversarial network to Wasserstein GAN.
Why it mattersA rigorous, well-illustrated walkthrough of why vanilla GANs are unstable and how Wasserstein distance fixes the gradient/convergence problems — useful grounding for anyone building or debugging generative models.
articleLilian Weng
Attention Is All You Need
The 2017 paper that dropped recurrence and convolution for self-attention alone, introducing the Transformer — the architecture every large language model still builds on.
Why it mattersEvery model you use descends from this architecture, and reading it is how the rest of the stack stops being magic.
articleAshish Vaswani et al.
An index of the vibe-coding frontier. Corrections welcome.