Ritesh shekhar

#Python | #DataScience| #DeepLearning |#NLP & #Recommendations Engine | #MachineLearning |#Data-Driven Decisions | Loves Traveling and Nature!

[Ritesh shekhar @rss_193]

@hasantoxr Q

[Andrej Karpathy @karpathy]

Software horror: litellm PyPI supply chain attack.

Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords.

LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm.

Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks.

Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages.

Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.

[Avinash Prasad @avionmission]

Watched @kunalstwt's entire "Intro to Linux and Terminal" Crash Course.

Here are my notes and list of all the commands in mentioned in the video. #DevOpswithkunal

Might be helpful to some of you. 🧵👇

[Ritesh shekhar @rss_193]

@VijaySundaram_ Great analogy!

[Shreyas @tech_that_out]

It's finally available and obviously ordered it. Got some discount as a Red Cable Elite member.

[Ritesh shekhar @rss_193]

Not a future I'm ready to accept!!!

[anand mahindra @anandmahindra]

What a nightmare. Not a future I’m ready to accept. When I saw this meme I jumped out of my chair & vowed to monitor & limit the time I spend in a chair in front of a screen every day...

[Kevin Markham @justmarkham]

🤖⚡ scikit-learn tip #20:

New in scikit-learn 0.22: Plot a confusion matrix in one line of code!

Highly customizable, including the colormap, display labels, and value formatting.

See example 👇

#sklearntips #MachineLearning #Python #DataScience

[Emily Annette @EmilyAnnette6]

At the grocery. Wearing my mask. Lady behind me, snarky & loud enough to make sure I heard, “don’t guess she realizes that stupid mask won’t do any good.” Me: “Honey, I’m an off duty nurse, I’m wearing it to protect YOU. But, I can take it off if you’d like.” She practically ran.

[hardmaru @hardmaru]

I just ordered this book for my kids.

[Kirk Borne @KirkDBorne]

Bayesian #MachineLearning —
Part 1: https://t.co/ADIREGWQ7q
Part 2: https://t.co/jH143FeHN8
Part 3: https://t.co/OETWxzpPiM
Part 4: https://t.co/y9JCprMryY
————
#BigData #DataScience #Statistics #Algorithms #abdsc
————
➕See also these books: https://t.co/cIXKTrfb7W

[Jeremy Howard @jeremyphoward]

Want to automatically track *all* hyperparams of your model while it's training, without writing *any* configuration code?

Thanks to @GuggerSylvain, you can in fastai v2, with the new @wandb callback :)

https://t.co/xddpjGgJpf

[Chip Huyen @chipro]

Some people asked about resources/tips on debugging machine learning models. Here you go :-)

1. https://t.co/9Y7kDc1hag
2. https://t.co/6P6GJQIHG3
3. https://t.co/j2lUKTTfqF
4. https://t.co/qJrLtwMBWm
5. https://t.co/qru8lLkILy
6. https://t.co/5KUjp5XD6E

[Ritesh shekhar @rss_193]

What is #ArtificialIntelligence? In 5 minutes!
https://t.co/pIYWAvlCF3
#AI #MachineLearning #ML #DeepLearning #DataScience

[ipfconline @ipfconline1]

[#AI #MachineLearning]
Making a Simple Neural Network (Explained From A To Z! 👍)

https://t.co/kZZSVNKhFH by @_k3no
#NeuralNetworks #DataScience
Cc @evankirstel @KirkDBorne @schmarzo @DeepLearn007 @SpirosMargaris @JimMarous @antgrasso @StephaneNappo

[hardmaru @hardmaru]

Humans are becoming more machine-like at a faster pace than machines becoming more human-like.

[PyCoder’s Weekly @pycoders]

30-seconds-of-python: Collection of Python Snippets That You Can Understand in 30 Seconds or Less https://t.co/5pFsbJTRBB

[Ritesh shekhar @rss_193]

Something interesting... https://t.co/aTKwDsfJmc

[tkasasagi 🐻 @tkasasagi]

Machine Learning, Online Comic from @GoogleAI
https://t.co/IYuO50LyCt

[R-bloggers @Rbloggers]

Bayesian Linear Mixed Models: Random Intercepts, Slopes, and Missing Data {https://t.co/JTcd2gbNGV} #rstats #DataScience

[Ritesh shekhar @rss_193]

Interactive Fourier transform!!!
https://t.co/yCYzo1oU0K

[Ritesh shekhar @rss_193]

https://t.co/ig1dC5iSTm