Enjoy To Code

Enjoy To Code is about the art of software development. AI, Machine Learning, Data Science, Cyber Security. insta: @enjoytocode

[Math Cafe @Riazi_Cafe_en]

Structure and Interpretation of Computer Programs
by Harold Abelson and Gerald Jay Sussman with Julie Sussman

PDF: https://t.co/x83sGLaT5O

[FFmpeg @FFmpeg]

FFmpeg was on the @lexfridman podcast!

https://t.co/8D6EqiOvWr

[Swapna Kumar Panda @swapnakpanda]

"Competitive Programmer’s Handbook"

This book of ~300 pages covers 30 important topics. A MUST for tech interviews.

Download for FREE → https://t.co/BdDvKWWdq4

[Tom Dörr @tom_doerr]

Collection of agent use cases

https://t.co/R23XWhB0zd

[Matt Dancho (Business Science) @mdancho84]

This 277-page PDF unlocks the secrets of Large Language Models.

Here's what's inside: 🧵

[Swapna Kumar Panda @swapnakpanda]

Machine Learning from Stanford - by Andrew Ng

Stanford University has kept the complete lecture notes of its CS229 course 💯 FREE.

Download from here → https://t.co/hQqEkI8fHj

[Yash @_Ai_Yash_]

MIT offers 12 Books on AI & ML (FREE TO DOWNLOAD):

1. Foundations of Machine Learning
https://t.co/1jJ3JiO6nO

2. Understanding Deep Learning
https://t.co/D1IlZvwA9l

3. Algorithms for ML
https://t.co/Et5WEnCjgE

4. Reinforcement Learning
https://t.co/1JYGP1sHsP

5. Introduction to Machine Learning Systems
https://t.co/e1J5yHQpp3

6. Deep Learning
https://t.co/Im2J7XobNz

7. Distributional Reinforcement Learning
https://t.co/M9iYfNILNn

8. Multi Agent Reinforcement Learning
https://t.co/stSV649oQj

9. Agents in the Long Game of AI
https://t.co/HwFWKW8fM3

10. Fairness and Machine Learning
https://t.co/sYzltc6cnc

11. Probabilistic Machine Learning

❯ Part 1 : https://t.co/gXdY5Fc077
❯ Part 2 : https://t.co/F7QX3MbKMx

[Math Cafe @Riazi_Cafe_en]

Entertaining Mathematical Puzzles
by Martin Gardner

PDF: https://t.co/Kbrv1DXAWP

[Meeting C++ @meetingcpp]

The real problem of C++ - Klaus Iglberger - Meeting C++ 2025
https://t.co/cykpONWRHB
#cpp
#cplusplus

[Elon Musk @elonmusk]

Carmack is awesome

[FFmpeg @FFmpeg]

Our assembly lessons are trending on @github !
We have nearly 10k stars.

[Enjoy To Code @enjoytocode]

@LetMeCodeee CPP

[Kirk Borne @KirkDBorne]

[Download 698-page PDF eBook]

Everything You Always Wanted To Know About #Mathematics*

(*But didn’t even know to ask)

A Guided Journey Into the World of Abstract Mathematics, Theorems, and the Writing of Proofs: https://t.co/gJ31Zw68Zy

[Swapna Kumar Panda @swapnakpanda]

Stanford's ALL FREE Courses [2024 & 2025]

❯ CS230 - Deep Learning
❯ CS234 - Reinforcement Learning
❯ CS236 - Deep Generative Models
❯ CME295 - Transformers & LLMs
❯ CS336 - Language Model from Scratch
❯ CS224N - NLP with DL

Find links inside:

[Dwarkesh Patel @dwarkesh_sp]

The @karpathy interview

0:00:00 – AGI is still a decade away
0:30:33 – LLM cognitive deficits
0:40:53 – RL is terrible
0:50:26 – How do humans learn?
1:07:13 – AGI will blend into 2% GDP growth
1:18:24 – ASI
1:33:38 – Evolution of intelligence & culture
1:43:43 - Why self driving took so long
1:57:08 - Future of education

Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!

[John Carmack @ID_AA_Carmack]

I recently learned about Cayley transforms.

Similar to how you can parameterize a 3x3 rotation matrix by 3 Euler angles or a 4 element quaternion, Cayley transforms allow you to parameterize an N dimensional rotation matrix with just N*(N-1)/2 unique values in a skew-symmetric matrix, saving more than half the parameters and guaranteeing that the matrix will always be orthogonal.

Unfortunately, the transformation involves a linalg.solv() or pinverse(), so it gets slow with thousands of dimensions.

Still, I am happy to have this in my mental toolbox now!

https://t.co/lWt70e79Dz

[Manish Kumar @manishkhosiya]

Harvard University just released free online courses.

No payment required.

Here are 10 courses you don't want to miss in 2025:

[Mario Nawfal @MarioNawfal]

🚨 BREAKING: 16 BILLION PASSWORDS LEAKED: APPLE, GOOGLE, FACEBOOK USERS EXPOSED

What’s being called the largest password leak in history has just been confirmed: 16 billion login credentials, many from major platforms like Apple, Google, Facebook, GitHub, and more, are now exposed and circulating.

Researchers say the leak comes from 30 separate datasets, most of it new, not recycled.

And it’s not just usernames and passwords... URLs, emails, and full login sequences are included.

This isn’t just a breach. It’s a blueprint for mass phishing, account takeovers, and digital identity theft at global scale.

“This is fresh, weaponizable intelligence at scale,” said researchers.
And it’s live now.

Act fast. Stay ahead. Or get locked out.

Source: Forbes, CyberNews

[Enjoy To Code @enjoytocode]

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[Mike Scully @Mike_Scully_]

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(Must be following)