Found this service by devin.ai that breaks down and lets you talk to a public Github repo. According to the site description its "Deep Research for GitHub".

https://deepwiki.com/

#Github #AI #Repo

From CEO of OpenAi 🤌
https://www.youtube.com/watch?v=5MWT_doo68k&list=PPSV

It's fascinating to hear about the future , safety and security of Ai from CEO of OpenAi. and some gossip about whether DeepSeek have affected them or not. 😁
if you care about Ai , I recommend you to watch it

Today I found this library while trying hand tracking project , check it out for your Ai project
https://ai.google.dev/edge/mediapipe/solutions/guide

https://colab.research.google.com/github/spmallick/learnopencv/blob/master/Introduction-to-MediaPipe/MediaPipe-sample-solutions.ipynb open source code to see how to use

I was training credit card fraud detection model with random forest .
The above image is confusion matrix which is evaluation metrics and can see the model has good performance over test data with small false positive ( classifying fraud data as non ). The data I used is cleaned data you can practice hyper parameter tuning. https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023

ow I forgot to tell in python it is only compatible with python11 and below

By the way, I haven’t posted anything this week because I was catching up on classwork—I was really behind. I've also been becoming more socially active, talking to my classmates and making new friends. Honestly, I like that part. But one thing I regret is messing up my sleep schedule, for real. Aside from that, I’ve only made small progress. What I want to say is that it’s important not to fall behind, catching up is really costly. Good night🚶‍♀‍➡️

https://youtu.be/_LbeAYqCnH4?si=aPCPJ3zUR-Au-KiT I went to the same high school. I am proud that he shines in the game industry.🔥

Check out the xverse library for your machine learning project, it helps you select and prepare the most effective features for modeling.

MLflow is a must-have tool. It helps track experiments, log metrics, and manage model versions all in one place. no need to re-run notebooks 🤌

I was wondering about vs code and I found this article if u r curious... here 🤌 https://www.linkedin.com/pulse/from-origin-optimization-story-visual-studio-code-thirumoorthy-bqtxc/?trackingId=UWxtIt85Te2Q4g4fjrjrog%3D%3D it is short tho

Today, I came across RAG (Retrieval-Augmented Generation) while working on an assignment for 10 Academy.
You know, Large Language Models (LLMs) like GPT or DeepSeek are incredibly powerful, but they do have limitations—especially when it comes to domain-specific expertise or producing accurate responses in specialized contexts.

That’s where RAG comes in. It enhances LLMs by retrieving preprocessed documents from a specific domain (like company data or product manuals), and then integrates this information into the generation process. For example, if you're building a customer service chatbot, RAG can retrieve relevant company documents and use them to generate more accurate, context-aware answers.

Just completed Week 1😊 ... still in development

Spam 😬, you may not even listen to music, but the lyrics in this song are about life. I’ve been listening to it on repeat , it really hits hard. Check it out https://youtu.be/RPpoZYt1QME?si=HzVNItYoxS1W22sV"