Dump links

src/fe.md

@@ -11,3 +11,4 @@
 - [Visual design rules you can safely follow every time](https://anthonyhobday.com/sideprojects/saferules/)
 - [CSS Selectors: A Visual Guide](https://fffuel.co/css-selectors/)
 - [Can I use?](https://caniuse.com/)
+- [SVG Tutorial](https://svg-tutorial.com/)

src/from-scratch.md

@@ -46,6 +46,7 @@ Tutorials:
 - [Hypervisor From Scratch](https://rayanfam.com/topics/hypervisor-from-scratch-part-1/)
 - [Writing a Debugger From Scratch](https://www.timdbg.com/posts/writing-a-debugger-from-scratch-part-1/)
 - [Green Threads Explained](http://c9x.me/articles/gthreads/intro.html)
+- [Let’s learn how modern JavaScript frameworks work by building one](https://nolanlawson.com/2023/12/02/lets-learn-how-modern-javascript-frameworks-work-by-building-one/)
 
 Other lists:
 - [Build your own X](https://github.com/codecrafters-io/build-your-own-x)

src/mldl.md

@@ -9,7 +9,7 @@
 - [Dive into Deep Learning](https://d2l.ai/)
 - Manning & Schutze, Foundations of Statistical Natural Language Processing
 - Jurafsky & Martin, [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/)
-- Eisenstein, [Introduction to Natural Language Processing]
+- Eisenstein, [Introduction to Natural Language Processing](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)
 - Molnar, [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)
 - McKay, [Information Theory, Inference and Learning Algorithms](http://www.inference.org.uk/mackay/itprnn/book.html)
 - Hastie, Tibshirani & Friedman, The Elements of Statistical Learning
@@ -20,3 +20,4 @@
 - [ruder.io](https://www.ruder.io/tag/natural-language-processing/)
 - [nlp-library](https://github.com/mihail911/nlp-library)
 - [Neural networks from scratch](https://aegeorge42.github.io/)
+- [LLM Visualization](https://bbycroft.net/llm)